PRE-MODULATION: NEURAL ACTIVITY DURING BITING PREPARES A RETRACTOR MUSCLE FOR FORCE GENERATION DURING SWALLOWING IN APLYSIA
by
HUI LU
Submitted in partial fulfillment of the requirements for the
degree of Doctor of Philosophy
Advisor: Hillel J. Chiel, Ph. D.
Department of Biology
CASE WESTERN RESERVE UNIVERSITY
August, 2014
CASE WESTERN RESERVE UNIVERSITY SCHOOL OF GRADUATE STUDIES
We hereby approve the thesis/dissertation of Hui Lu
candidate for the degree of Doctor of Philosophy *.
Committee Chair Robin Snyder
Committee Member Hillel Chiel
Committee Member Roy Ritzmann
Committee Member Jessica Fox
Committee Member Kenneth Gustafson
Date of Defense April 1, 2014
*We also certify that written approval has been obtained for any proprietary material contained therein.
Table of Contents
Chapter 1 Introduction
Mechanisms underlying multifunctionality ………………………… 2 Motivation and research rationale …………………………………... 6 Hypothesis and specific aims ……………………………………… 10 Experimental design ……………………………………………….. 11 Brief introduction to the structure of later chapters ……………….. 13 References …………………………………………………………. 15 Figures and Tables ………………………………………………… 21
Chapter 2 Selective extracellular stimulation of individual neurons in ganglia
Summary …………………………………………………………... 25 Introduction …………………………………. ……………………. 26 Materials and Methods …………………………………………….. 28 Results ……………………………………………………………... 38 Discussion …………………………………………………………. 52 Acknowledgments …………………………………………………. 58 References …………………………………………………………. 60 Figures and Tables ………………………………………………… 64
Chapter 3 Extracellularly identifying motor neurons for a muscle motor pool in Aplysia californica
Summary …………………………………………………………... 92
Introduction …………………………………. ……………………. 93 Materials and Methods …………………………………………….. 94 Representative Results …………………………………………….. 96 Discussion …………………………………………………………. 97 Acknowledgments ………………………………………………... 103 References ………………………………………………………... 104 Figures and Tables ……………………………………………….. 107
Chapter 4 Pre-modulation: Neural activity during biting prepares a retractor muscle for force generation during swallowing in Aplysia
Summary …………………………………………………………. 115 Introduction ………………………………………………………. 117 Materials and Methods …………………………………………… 118 Results ……………………………………………………………. 129 Discussion …………………………………………………………140 Acknowledgments ………………………………………………... 146 References ………………………………………………………... 147 Figures and Tables ……………………………………………….. 151
Chapter 5 Summary and future work
Results summary …………………………………………………. 164 Limitations of this study …………………………………………. 166 Implications of this study ………………………………………… 170 Future work ………………………………………………………. 173
References …………………………………………………………177
Appendix A ...…………………………………………………………………181
B …………………………………………………………………...187
C …………………………………………………………………...203
Bibliography …………………………………………………………… 206
List of Figures
Chapter 1
Figure 1-1: A schematic diagram for extrinsic and intrinsic neuromodulation ………………………………………21
Figure 1-2: A schematic diagram for the anatomy of the buccal mass of Aplysia ……………………………………………...22
Figure 1-3: A schematic diagram for muscle innervation and intrinsic and extrinsic modulation of I1/I3 ……………………..23
Figure 1-4: Schematic diagrams for intrinsic modulatory effects from FMRFamide and SCP on the contraction force amplitudes of I1/I3 ………………………………………………...24
Chapter 2
Figure 2-1: Schematic geometry of stimulating and recording electrodes in the in vitro experiments using Aplysia buccal ganglia ………………………………………...64
Figure 2-2: The experimental setup for measurements of the resistivity of Aplysia saline …………………………...65
Figure 2-3: Morphology and equivalent electrical circuit of the Aplysia NEURON model ……………………………..66
Figure 2-4: The polarization of the axon hillock varies as the extracellular stimulating electrode is placed at different positions ……………………………………………….67
Figure 2-5: The polarization along the neuron by extracellular ganglionic stimulation ………………………………...69
Figure 2-6: Qualitative comparisons between experimental results
and the NEURON model’s predictions: anodic currents activate a neuron by extracellular stimulation on the side of the soma opposite to the axon …………………….70
Figure 2-7: Qualitative comparisons between experimental results and the NEURON model’s predictions: cathodic currents inhibit the neuron by extracellular stimulation near the cell bodies ……………………………………………71
Figure 2-8: Threshold currents for both anodic activation and cathodic inhibition increase with the distance from the stimulating electrode to the soma …………………….72
Figure 2-9: Threshold currents of the same neuron were very close when the sheath was intact and after the sheath was removed ………………………………………………73
Figure 2-10: Anodic currents can selectively activate an individual neuron ………………………………………………..74
Figure 2-11: Cathodic currents can selectively inhibit an individual neuron ………………………………………………..76
Figure 2-12: The spatial specificity for anodic activation predicted by the multiple-cell NEURON model …………………...77
Figure 2-13: The spatial specificity for cathodic inhibition predicted by the multiple-cell NEURON model ………………..78
Figure 2-14: The spatial specificity for a group of neurons with different sizes and geometric configurations predicted by the NEURON model and the analytical model ………79
Figure 2-15: The temporal specificity for anodic activation of an individual neuron demonstrated experimentally ……..81
Figure 2-16: The temporal specificity for cathodic inhibition of an individual neuron demonstrated experimentally ……..83
Figure 2-17: Comparisons of the membrane polarization along a
simulated neuron with different soma and axon diameters ……………………………………………..85
Figure 2-18: Comparisons of the membrane polarization along the simulated vertebrate neuron with different neuronal structures ……………………………………………..87
Table 2-1: The geometric parameters of the NEURON model for an Aplysia buccal neuron ………………………………..89
Table 2-2: The electrical parameters of the NEURON model for an Aplysia buccal neuron …………………………………90
Table 2-3: The electrical parameters of the NEURON model for an Aplysia buccal neuron …………………………………91
Chapter 3
Figure 3-1: Schematic of overall setup and the dish for the force studies ………………………………………………..107
Figure 3-2: Schematic of the buccal ganglia and electrodes setup …………………………………………………108
Figure 3-3: A picture and schematic of the neuron map for extracellular identification of the I1/I3 motor neurons in the Aplysia buccal ganglion ………………………….109
Figure 3-4: Identifying and characterizing the I1/I3 motor neuron B3 ……………………………………………………111
Figure 3-5: Identifying and characterizing the I1/I3 motor neuron B43 …………………………………………………..112
Figure 3-6: The optimized diagnostic tree for identifying some of the I1/I3 motor neurons using extracellular soma and nerve recordings ……………………………………………113
Figure 3-7: Comparison of success rates of neuron identification
during force experiments using either the extracellular technique or the intracellular technique ……………..114
Chapter 4
Figure 4-1: Characterizing the BN2 motor programs during the retraction phase of biting and swallowing …………...151
Figure 4-2: B6, B9 and B3 are more active in swallowing than in biting …………………………………………………153
Figure 4-3: The activity of B6, B9 and B3 is correlated with the overall retraction force in swallowing ……………….155
Figure 4-4: The force/frequency relationships of B6, B9, and B3 in vitro ………………………………………………….157
Figure 4-5: The I1/I3 muscle forces evoked by B6, B9 and B3 at their physiological activity levels are small compared to the overall retraction force exerted during swallowing responses …………………………………………….158
Figure 4-6: B6, B9 and B3 generate no force in biting even after self- modulation, but pre-modulate I1/I3 and prepare it to generate larger forces during the initial swallow ……159
Figure 4-7: Intrinsic modulation from B6, B9 and B3 in swallowing …………………………………………...160
Figure 4-8: The nonlinear summation of the I1/I3 muscle forces evoked by B6, B9 and B3 ……………………………161
Figure 4-9: The I1/I3 muscle forces generated during ingestive-like patterns ………………………………………………162
Figure 4-10: Summary schematics for results ……………………..163
Acknowledgements
I would like thank to my advisor Dr. Hillel Chiel for his unwavering faith, wise advice, and enormous patience and support. I would like to thank the other members of my committee, Dr. Roy Ritzmann, Dr. Jessica Fox and Dr. Kenneth Gustafson for their helpful comments. I would also like to thank my lab colleagues: Jeffrey McManus,
Miranda Cullins, Cindy Chestek, Kendrick Shaw, Catherine Kehl and Jeff Gill for their help during different stages of my Ph.D. work. I would also like to express my special appreciation to my husband, Qing Ran, and parents, who supported me and encouraged me to strive towards my goal.
This work was supported by NIH grant NS047073, EB004018 and T32
GM007250 as well as NSF grant DMS1010434 and IIS1065489.
PRE-MODULATION: NEURAL ACTIVITY DURING BITING PREPARES A RETRACTOR MUSCLE FOR FORCE GENERATION DURING SWALLOWING IN APLYSIA
Abstract
by
HUI LU
The ability to rapidly modify behaviors to meet changing environmental demands is essential to both vertebrates and invertebrates. This flexibility can be achieved by coordinated neuromodulation at different levels (Katz, 1995), which changes the excitability of motor neurons (Trimmer 1994; Schotland et al., 1995) and the responsiveness of a muscle to the same neural activation (Cropper et al., 1994; Brezina et al., 1994). Although neuromodulation has been extensively studied in many systems, less is known about the role of neuromodulation at the individual neuron level under behaviorally relevant conditions (e.g., in vivo). To address this question, I studied biting and swallowing of Aplysia californica, which is robust and experimentally tractable.
Aplysia uses the same feeding apparatus (the buccal mass) to perform three different behaviors, i.e., biting, swallowing and rejection (Kupfermann 1974). Although
biting and swallowing have similar phasing between motor programs, they are functionally distinct and have distinct biomechanical features. In general, biting is associated with strong protraction and weak retraction; swallowing is associated with weak protraction and strong retraction (Neustadter et al., 2007). How do animals rapidly respond to the increased mechanical loads as they switch from biting (no mechanical load) to swallowing (varying mechanical loads)? The primary retractor muscle (I1/I3;
Nagahama and Takata, 1988; Scott et al., 1991) is innervated by a pool of motor neurons
(Church and Lloyd, 1994; Rosen et al., 2000b). These motor neurons release both conventional transmitters and peptide cotransmitters (Church and Lloyd, 1991), which could affect contraction of the I1/I3 muscle via intrinsic neuromodulation (Fox and Lloyd,
1997; Keating and Lloyd, 1999). Thus, both the pool of motor neurons and intrinsic neuromodulation are potential sources for force changes in I1/I3 during retraction phase.
I hypothesized that the primary function of key motor neurons for the retractor muscle in biting is not to generate force for retraction, but to pre-modulate the muscle, so that once Aplysia grasps food, it can generate sufficient retraction force in the initial swallow, allowing an animal to successfully retain food.
To test this hypothesis, I used the extracellular stimulation and recording techniques (Lu et al., 2008) and the simplified diagnostic method (Lu et al., 2013) to reliably identify the I1/I3 motor neurons and to record their activity patterns during biting and swallowing behaviors. I have also demonstrated that (Lu et al., 2014 submitted): (1) without modulation, the key motor neurons generate no I1/I3 force in biting and small forces in swallowing; (2) with intrinsic modulation from the repeated activity of these motor neurons, I1/I3 generates no force in biting, but generates much larger forces in all
but the initial swallows; (3) with intrinsic modulation from the low-frequency activity of these motor neurons in prior bites (pre-modulation), I1/I3 generates larger forces in the initial swallow; (4) the combined activity of multiple motor neurons significantly enhances forces in swallowing, but is still unable to generate any significant force in biting.
1
Chapter 1
Introduction
Animals can use the same periphery and neural controllers to generate qualitatively different behaviors (multifunctionality). For example, both the human hand and tongue are multifunctional peripheral structures (Ye et al., 2006b). The human hand can flexibly modify its motor outputs for multiple tasks, e.g., writing, griping, throwing, punching, and playing instruments. The human tongue is also involved in different functions, e.g., talking, breathing and feeding. What are the mechanisms underlying multifunctionality? To address this question, I studied two similar, but functionally distinct feeding behaviors (i.e., biting and swallowing) of the marine mollusk Aplysia californica. During biting and swallowing, animals use the same peripheral feeding apparatus and the same CPG circuit to perform different phases of both behaviors, i.e., protraction, retraction, opening and closing of the food grasper (Morton and Chiel 1993a, b). The goal of this study is to understand how Aplysia rapidly modifies muscle functions to meet changing mechanical demands as the animal switches from biting to swallowing.
In this chapter, I will first review possible mechanisms underlying multifunctionality. Then I will describe background knowledge of the chosen study system as well as the motivation and research rationale of this study. In addition, I will propose a specific hypothesis and develop a feasible methodology to test it. At the end of this chapter, I will briefly introduce the structure of later chapters of the dissertation.
2
Mechanisms underlying multifunctionality
The flexibility of multifunctionality emerges from interactions between neural control and peripheral biomechanics, which will be briefly described below.
1. Neural reorganization
Neural reorganization is one of the major sources for multifunctionality. Pattern generator circuits can include or exclude different neurons to control pattern generation for multiple behaviors. In a single circuit, groups of neurons can work together to generate independent motor programs (modules) for different phases of behaviors.
Altering the timing and phasing of different modules allows animals to flexibly switch among different behaviors. For example, there are four independent phases, i.e., protraction, retraction, opening, and closing of the grasper, in Aplysia feeding behaviors
(Morton and Chiel, 1993a, b). In biting and swallowing, the grasper opens during protraction and closes during retraction; in rejection, the grasper closes during protraction and opens during retraction. Thus, different relative timings between the protraction/retraction and opening/closing phases form the motor programs for multiple feeding behaviors. This has also been observed in vertebrates. For example, alternating the relative timing between the knee extensor activation and the hip flexion activation produces different hindlimb locomotions (i.e., forward step, forward swim, backpaddle) and scratching (i.e., rostral, pocket, and caudal scratching) in the turtle (Earhart and Stein,
2000).
2. Differential recruitment of motor neurons for a single muscle
3
Besides the alteration of timing and phasing of modules, the motor neurons in those modules can also be differentially activated to modify behavioral intensities. In tasks when a slowly increased force level is needed, such as cat slow walking (Wakeling et al., 2002), motor units are typically recruited in an orderly fashion, called the “size principle” (Henneman and Olson, 1965; Mendell, 2005), according to which the smaller motor neurons are recruited earlier and derecruited later than the larger ones. Smaller motor neurons have less surface areas and thus have larger input resistances than the larger ones. Thus, the same excitatory synaptic inputs would cause larger depolarization in smaller neurons than in the larger neurons. Therefore, smaller neurons would reach threshold potentials at lower synaptic input levels than the larger neurons, and thus be recruited earlier and de-recruited later than the larger neurons. In other cases, when rapid force rise and relaxation rates are required, such as a cat paw shake (Wakeling et al.,
2002) and goat galloping (Lee et al., 2013), faster motor units are preferentially recruited because faster units have faster activation–deactivation rates.
3. Neuromodulation
Neuromodulation is another source for the flexibility of multifunctionality, which can be divided into extrinsic and intrinsic modulation (Katz, 1995). Extrinsic neuromodulation arises from neurons outside the local circuit (Fig. 1-1). Its intensity is independent of the activity levels of the target circuit. A single modulatory neuron can affect multiple circuits. In contrast, intrinsic neuromodulation arises from neurons within a local circuit (Fig. 1-1). Thus, it is always present as the target circuit is active and its intensity is dependent on the activity levels of the target circuit. Both types of
4 neuromodulation can affect a muscle’s function by altering the excitability of motor neurons (Trimmer 1994; Schotland et al., 1995) or the muscle’s responsiveness to the same neural activation (Calabrese, 1989; Cropper et al., 1994; Brezina et al., 1994). For example, Aplysia biting is associated with stronger protractions than swallowing
(Neustadter et al., 2007). However, a kinematic model of the main protractor muscle (I2) shows that it loses mechanical advantage as it shortens during contraction (Sutton et al.,
2004b). Thus, I2 alone cannot generate enough forces for the large amplitude of protraction in biting. Previous studies have shown that I2 can be modulated by myomodulin intrinsically and serotonin extrinsically (Hurwitz et al., 2000). Both types of neuromodulation increase the muscle contraction amplitude and relaxation rate of I2, at least partially mediated by increased cAMP levels (Hurwitz et al., 2000). Therefore, neuromodulation could account for a possible source for generating stronger protractions in biting than in swallowing.
4. Multifunctional motor neurons
A single motor neuron could also have multiple functions. For example, the B8 motor neuron innervates the I4 muscle and mediates the closing phase of the grasper during Aplysia feeding (Morton and Chiel, 1993b; Church and Lloyd, 1994). Ye et al.
(2006a) have shown in smaller-amplitude (type A) swallows, B8 activates the I4 muscle to close on food during retraction phase. In larger-amplitude (type B) swallows, the grasper protracts further forward and thus rotates about the hinge muscle (i.e., the muscle joining the grasper to the buccal mass). Then activating B8 induces not only food grasping (closing) but also pulling food into the buccal cavity (retraction). Therefore,
5 depending on how far the grasper is protracted at the time B8 is activated, the neuron can mediate either closing or both closing and retraction in swallows, and thus is multifunctional.
5. Context-dependent functions
The mechanical context of a muscle could also change its function. For example, the I1/I3 muscle was previously identified as a primary retractor muscle (Howells, 1942) in Aplysia feeding. Stimulation of the nerve innervating this muscle induces retraction movement of the grasper (Morton and Chiel, 1993a). However, several lines of evidence suggest that near the peak protraction of biting, the posterior part of this retractor muscle may also play a role in protraction, assisting the protractor muscle to generate a strong bite (Yu et al., 1999; Sutton et al., 2004a, b; Neustadter et al., 2007). Thus, different positions of I1/I3 relative to the grasper could determine the direction (i.e., protraction or retraction) of the forces exerted by the muscle. The context-dependent functions have also been observed in the claw dactyl closer muscle of the snapping shrimp (Ritzmann,
1974). In cocking, the closer muscle becomes an opener when the closer apodeme is lifted over the pivot point around which the dactyl closes. In snapping, the closer muscle returns to its closing function as the closer apodeme is pulled through the pivot point by contraction of an accessory muscle.
6. Regional differential activation of a single muscle
Different regions of a muscle can also be differentially activated for multifunctionality. For example, a study from our laboratory has recently demonstrated
6 the regional differential activation of the retractor muscle (I1/I3) in Aplysia feeding
(McManus et al., 2014 in press). The motor neuron B38 specifically innervates the anterior region of this muscle (Church and Lloyd, 1994). It is usually activated during the retraction phase of feeding behaviors, acting with other motor neurons to induce the I1/I3 muscle contraction and mediate retraction movements of the grasper. However, B38 is also activated during the protraction phase of swallowing, suggesting that the anterior region of the muscle may have an additional function (i.e., food holding) other than retraction in swallowing (McManus et al., 2014 in press). Regional differential muscle activation has also been observed in vertebrates, e.g., in the human medial gastrocnemius muscles during standing (Vieira et al., 2011) and in cat biceps femoris during walking
(Chanaud et al., 1991).
Motivation and research rationale
The motivation of this study is to explore a possible source for multifunctionality, i.e., the multifunctional roles of motor neurons as both force generators and modulators.
To address this question, I studied feeding behaviors of the marine mollusk Aplysia californica. My rationale for choosing this system is based on the literature that I will review in more detail below: (1) it is a multifunctional system; (2) it is a robust and tractable system allowing analyses of neural control and biomechanics at the individual identified neuron level; (3) it is also a premier system for studying both intrinsic and extrinsic neuromodulation.
Aplysia uses its feeding apparatus, the buccal mass, to perform three different feeding behaviors, biting, swallowing and rejection (Kupfermann, 1974). In biting,
7 animals attempt to grasp food by a strong protraction of the food grasper (the radula/odontophore). If the food is not grasped, a weak retraction is sufficient to return the grasper back to its resting position to prepare it for the next bite (Neustadter et al.,
2007; Sutton et al., 2004a). If the food is grasped, animals immediately switch from biting to swallowing. The grasper closes and retracts strongly to pull food inwards and only protracts weakly to reposition itself on food for a better grasping (Neustadter et al.,
2007; Ye et al., 2006a). If animals take in inedible food, they will reject it by closing the grasper onto the food and pushing it out by a strong protraction of the grasper (Morton and Chiel, 1993a, b; Ye et al., 2006b).
In addition, the Aplysia feeding system is experimentally tractable to the analyses of both neural control and biomechanics, which are the major sources for multifunctionality. Many aspects of the neural control and biomechanics of Aplysia feeding have been intensively studied. First, multiple motor neurons innervating the muscles involved in feeding behaviors have been discovered and characterized (Gardner,
1971; Cohen et al., 1978; Church et al., 1991, Church and Lloyd, 1991, 1994; Evans et al.,
1996; Morton and Chiel, 1993b). The biomechanical properties of those muscles have also been studied (Drushel et al., 1998, 2002; Yu et al., 1999; Neustadter et al., 2007;
Sutton et al., 2004a, b; Ye et al., 2006a, b). In addition, a neural criterion has been established to distinguish ingestion (i.e., biting and swallowing) from egestion (i.e., rejection) based on the relative timing of motor programs corresponding to the important phases, i.e., the protraction/retraction and opening/closing cycles of the grasper (see Fig.
8 in Morton and Chiel 1993a). Many sensory neurons (Rosen et al., 1982, 2000a, b;
Miller et al., 1994; Evans and Cropper, 1998) and interneurons (Hurwitz et al., 1997,
8
Evans and Cropper, 1998; Jing and Weiss, 2001, 2002; Morgan et al., 2002; Jing et al.,
2003) have also been studied for their roles in feeding behaviors.
Aplysia feeding is also a premier system for studying the role of neuromodulation in behavioral contexts. For example, the accessory radular closer (ARC) muscle and its motor neurons, B15 and B16, are one experimentally advantageous neuromuscular preparation of Aplysia feeding. The in vivo firing patterns of B15 and B16 have been characterized based on synaptic currents recorded on the ARC muscle (Cropper et al.,
1990a; this muscle is also known as the I5 muscle). The cotransmitters released by these two neurons have also been identified and their modulatory effects on the ARC muscle contractions have been studied under the in vivo behaviorally relevant conditions
(Cropper et al., 1987, 1990b, 1990c, 1994).
Therefore, Aplysia feeding is an ideal system for this dissertation, which focuses on how neural activity of motor neurons in previous behaviors (biting) pre-modulates the muscle and prepares it for force generation in subsequent behaviors (swallowing) to meet increased mechanical demands. Both biting and swallowing are ingestion behaviors; however, they have different characteristics in both neural control and biomechanics.
Previous magnetic resonance imaging (MRI) data have shown that biting is associated with strong protraction and weak retraction, whereas swallowing is associated with weak protraction and strong retraction (Neustadter et al., 2007).
The buccal mass is the feeding apparatus of Aplysia, consisting of a series of muscles contributing to different phases of feeding behaviors (Fig. 1-2). Unlike vertebrate skeletal muscles that are organized into motor units, Aplysia buccal muscles are innervated by several motor neurons, each of which can innervate all muscle fibers
9 directly or as a result of electrical coupling between these muscle fibers (Cohen et al.,
1978). In addition, Aplysia buccal muscles do not generate action potentials, but produce electrical junction potentials (EJPs) due to motor neurons’ activation, which can be summated both temporarily and spatially (Cohen et al., 1978). There is no muscle contraction responding to initial EJPs until the summated EJPs reach a certain level, above which, the contraction amplitude is linearly correlated with the total EJP integrated over time (see Fig. 21B, Cohen et al., 1978).
In this dissertation, I only focus on the retraction phase, which is the power phase of swallowing. The I1/I3 muscle is the primary retractor muscle (Nagahama and Takata,
1988; Scott et al., 1991), which is innervated by a large pool of motor neurons including
B3, B6, B9, B10, B11, B38, B39, B43, and B82 at different regions (Church and Lloyd,
1994; Rosen et al., 2000b).
In addition, neuromodulation of I1/I3 has also been explored. Previous studies have shown that stimulation of B3 and B9 releases FMRFamide (Fa) and small cardioactive peptide (SCP) as cotransmitters, respectively (Fig. 1-3; Church and Lloyd,
1991), which increases the amplitude of EJPs and contractions in I1/I3 (Fig. 1-4; Fox and
Lloyd, 1997; Keating and Lloyd, 1999). Extrinsically, I1/I3 can also be modulated by serotonin (5-HT) released by the metacerebral cells (MCCs) in the cerebral ganglion (Fig.
1-3; Fox and Lloyd, 1998, 2000). Currently, little is known about the mechanisms of neuromodulation by Fa (Keating and Lloyd, 1999). However, the mechanisms of neuromodulation by SCP and 5-HT have been well studied (Fox and Lloyd, 2000). Both modulators increase the level of cAMP in the muscle. In addition, some part of the
10 extrinsic neuromodulation by 5-HT may be mediated through other second-messenger pathways.
Although intrinsic and extrinsic neuromodulation of I1/I3 have been extensively studied, the behavioral role of neuromodulation has not been addressed under physiologically relevant conditions, which is one of my goals in this dissertation.
Hypothesis and specific aims
In this dissertation, I explore how Aplysia uses intrinsic neuromodulation to rapidly increase force generation of the retractor muscle I1/I3 to adapt to increased mechanical loading as the animal transitions from biting to swallowing. A major source of intrinsic neuromodulation is due to peptide cotransmitters, which have been found widely in both invertebrate and vertebrate neurons (Schwarz et al., 1984; Kuhlman et al.,
1985; Whim and Lloyd, 1989; Donoso et al., 2004; Cifuentes et al., 2013). Unlike conventional transmitters which can be released by a single action potential of a neuron, peptide cotransmitter release usually requires high frequency firing of the neuron (Bartfai et al., 1988; DeCamilli and Jahn, 1990; Whim et al., 1993). Thus, motor neurons usually function solely as force generators when they fire at low frequencies, because only conventional transmitters are released at that time. In contrast, when motor neurons fire at high frequencies, they function as both force generators and modulators because both conventional transmitters and peptide cotransmitters are released.
In this study, I hypothesize a different role for the motor neurons: the primary function of key I1/I3 motor neurons in biting is not to generate force for retraction, but to pre-modulate I1/I3, so that once Aplysia grasps food, it can generate sufficient retraction
11 force in the initial swallow, allowing an animal to successfully retain food. To address the hypothesis, I asked the following questions. What are the key motor neurons for I1/I3 contributing to retraction of biting and swallowing? How much force can these motor neurons generate during biting and swallowing? Is neuromodulation essential for generating effective retraction for biting and swallowing? Are there other sources affecting force generation of I1/I3?
To answer these questions, we pursued four specific aims for the dissertation: (1)
Identify the key motor neurons for I1/I3, and determine their in vivo activity patterns during biting and swallowing. (2) Determine the forces generated by these motor neurons at their physiological activity levels observed in biting and swallowing. (3) Determine whether intrinsic modulation enhances the I1/I3 muscle forces during biting and swallowing. (4) Determine whether combined activity of multiple motor neurons induce stronger muscle forces in biting and swallowing.
Experimental design
To accomplish these specific aims, different preparations and approaches were used for experimental purposes. For Specific Aim 1, I analyzed data from in vivo motor programs of biting and swallowing that were obtained by Cullins and Chiel (2010) via hook electrodes implanted on the key muscle and nerves, i.e., the I2 muscle, radula nerve
(RN), buccal nerves 2 and 3 (BN2 and BN3).
In order to identify the key motor neurons for I1/I3 and determine their activity patterns during behaviors, I needed to selectively stimulate and record from individual motor neurons without damaging them during the muscle movements. Since it was
12 technically difficult to intracellularly stimulate and record from neurons during muscle movements, I developed an extracellular technique to control and monitor the activity of individual motor neurons during behaviors (Lu et al., 2008).
To experimentally validate this extracellular technique and its specificity, I used intracellular soma recordings and extracellular nerve recordings in an in vitro isolated buccal ganglia preparation, in which the sheath of the ganglia was removed to allow intracellular access to the neurons.
Then I used this technique to extracellularly identify individual motor neurons for
I1/I3 based on their soma location and size, nerve projection and muscle innervation (Lu et al., 2013). These experiments were done in an in vitro anchored buccal mass preparation, in which I could measure muscle forces on I1/I3 as I stimulated individual motor neurons extracellularly.
Using the extracellular technique developed by Lu et al. (2008) and the simplified diagnostic method developed by Lu et al. (2013), I reliably identified the motor neurons for I1/I3 and recorded their activity patterns during biting and swallowing responses induced in an in vitro suspended buccal mass preparation (McManus et al., 2012). Biting responses were induced by applying the non-hydrolyzable cholinergic agonist carbachol directly on the cerebral ganglion (Susswein et al., 1996; McManus et al., 2012).
Swallowing responses were obtained by placing a strip of seaweed in the feeding grasper during a bite (McManus et al., 2012). To explore which motor neurons were critical for retraction, I also measured the retraction force (i.e., the inward force pulling on seaweed during retraction) in this preparation.
13
For Specific Aim 2, I first intracellularly stimulated the key motor neurons at different frequencies and measured the I1/I3 muscle forces in the anchored buccal mass preparation to explore the relationship between firing frequency and muscle force. Then I intracellularly stimulated each key motor neuron at its physiological activity levels to mimic its activity during biting and swallowing in vivo. Muscle forces on I1/I3 were measured during the stimulation.
For Specific Aim 3, modulatory effects were induced by repeatedly stimulating these key motor neurons in the anchored buccal mass preparation at the frequencies, durations and inter-pattern intervals observed in vivo. Muscle forces on I1/I3 were measured during the stimulation.
For Specific Aim 4, combined activity of multiple motor neurons was obtained by stimulating two or three key motor neurons at the same time in the anchored buccal mass preparation at their physiological activity levels. Muscle forces on I1/I3 were measured during stimulation and also during carbachol-induced ingestive-like patterns, during which the whole set of I1/I3 motor neurons were active in their physiological sequence.
Brief introduction to the structure of later chapters
My dissertation focuses on how activity of motor neurons in prior bites pre- modulates a retractor muscle, I1/I3, and prepares it for force generation in subsequent swallows.
In Chapter 2, I will describe and analyze a technique that is able to selectively activate and inhibit individual neurons by extracellular stimulation near the somata in ganglia. I used NEURON models to study the mechanism and specificity of this
14 technique and explored whether it is applicable to other ganglia (e.g., vertebrate peripheral ganglia) that have similar geometry as Aplysia buccal ganglia. I also used intracellular soma recordings and extracellular nerve recordings to experimentally demonstrate the possibility and specificity of this technique.
In Chapter 3, I will show how to use the extracellular technique developed above to uniquely identify motor neurons for I1/I3 (Lu et al., 2013). I will also develop a simplified diagnostic method for rapid neuron identification during muscle movements, e.g., in the suspended buccal mass preparation (McManus et al., 2012) or in vivo (Cullins and Chiel, 2010).
In Chapter 4, using the techniques and diagnostic method shown in Chapters 2 and 3, I will accomplish the specific aims and test the hypothesis. First, I will identify the key motor neurons (B6, B9 and B3) for I1/I3 and determined their activity patterns during behaviors. Next, I will determine the forces generated by these key motor neurons without modulation at their physiological activity levels observed in biting and swallowing. Then, I will examine whether neuromodulation is essential for retraction in biting and swallowing. Finally, I will explore whether combined activity of multiple motor neurons can enhance muscle forces in biting and swallowing.
In Chapter 5, I will discuss the significance of these results and propose possible future work following this research.
15
References
Bartfai T, Iverfeldt K, Fisone G (1988) Regulation of the release of coexisting neurotransmitters. Ann Rev Pharmacal Toxicol 28:285-310.
Brezina V, Evans CC, Weiss KR (1994) Enhancement of Ca current in the accessory radula closer muscle of Aplysia californica by neuromodulators that potentiate its contractions. J Neurosci 14:4393-4411.
Calabrese RL (1989) Modulation of muscle and neuromuscular junctions in invertebrates. Seminars in the Neurosciences 1:25-34.
Chanaud CM, Pratt CA, Loeb GE (1991) Functionally complex muscles of the cat hindlimb V. The roles of histochemical fiber-type regionalization and mechanical heterogeneity in differential muscle activation. Exp Brain Res 85:300-313.
Church PJ, Cohen KP, Scott ML, Kirk MD (1991) Peptidergic motoneurons in the buccal ganglia of Aplysia californica: immunocytochemical, morphological, and physiological characterizations. J Comp Physiol A 168:323-336.
Church PJ, Lloyd PE (1991) Expression of diverse neuropeptide cotransmitters by identified motor neurons in Aplysia. J Neurosci 11:618-625.
Church PJ, Lloyd PE (1994) Activity of multiple identified motor neurons recorded intracellularly during evoked feedinglike motor programs in Aplysia. J Neurophysiol 72:1794-1809.
Cifuentes F, Arias ER, Morales MA (2013) Long-term potentiation in mammalian autonomic ganglia: an inclusive proposal of a calcium-dependent, trans-synaptic process. Brain Res Bull 97:32-38.
Cohen JL, Weiss KR, Kupfermann I (1978) Motor control of buccal muscles in Aplysia. J Neurophysiol 41:157-180.
Cropper EC, Brezina V, Vilim FS, Harish O, Price DA, Rosen S, Kupfermann I, Weiss KR (1994) FRF peptides in the ARC neuromuscular system of Aplysia: purification and physiological actions. J Neurophysiol 72:2181-2195.
Cropper EC, Kupfermann I, Weiss KR (1990a) Differential firing patterns of the peptide- containing cholinergic motor neurons B15 and B16 during feeding behavior in Aplysia. Brain Res 522:176-179.
16
Cropper EC, Price D, Tenenbaum R, Kupfermann I, Weiss KR (1990b) Release of peptide cotransmitters from a cholinergic motor neuron under physiological conditions. Proc Natl Acad Sci USA 87:933-937.
Cropper EC, Miller MW, Vilim FS, Tenenbaum R, Kupfermann I, Weiss KR (1990c) Buccalin is present in the cholinergic motor neuron B16 of Aplysia and it depresses accessory radula closer muscle contractions evoked by stimulation of B16. Brain Res. 512(1):175-9.
Cropper EC, Lloyd PE, Reed W, Tenenbaum R, Kupfermann I, Weiss KR (1987) Multiple neuropeptides in cholinergic motor neurons of Aplysia: evidence for modulation intrinsic to the motor circuit. Proc Natl Acad Sci U S A 84:3486-3490.
Cullins MJ, Chiel HJ (2010) Electrode fabrication and implantation in Aplysia californica for multi-channel neural and muscular recordings in intact, freely behaving animals. J Vis Exp 40:e1791, doi:10.3791/1791.
DeCamilli P, Jahn R (1990) Pathways to regulated exocytosis in neurons. Annu Rev Physiol 52:625-645.
Donoso MV, Miranda R, Irarrázaval MJ, Huidobro-Toro JP (2004) Neuropeptide Y is released from human mammary and radial vascular biopsies and is a functional modulator of sympathetic cotransmission. J Vasc Res 41:387-399.
Drushel RF, Neustadter DM, Hurwitz I, Crago PE, Chiel HJ (1998) Kinematic models of the buccal mass of Aplysia californica. J Exp Biol 201:1563-1583.
Drushel RF, Sutton GP, Neustadter DM, Mangan EV, Adams BW, Crago PE, Chiel HJ (2002) Radula-centric and odontophore-centric kinematic models of swallowing in Aplysia californica. J Exp Biol 205:2029-2051.
Earhart GM, Stein PS (2000) Step, swim, and scratch motor patterns in the turtle. J Neurophysiol 84:2181-2190.
Evans CG, Cropper EC (1998) Proprioceptive input to feeding motor programs in Aplysia. J Neurosci 18:8016-8031.
Evans CG, Rosen S, Kupfermann I, Weiss KR, Cropper EC (1996) Characterization of a radula opener neuromuscular system in Aplysia. J Neurophysiol 76:1267-1281.
Gardner D (1971) Bilateral symmetry and interneuronal organization in the buccal ganglia of Aplysia. Science 173:550-553.
Henneman E, Olson CB (1965) Relations between structure and function in the design of skeletal muscles. J Neurophysiol 28:581-598.
17
Howells HH (1942) The structure and function of the alimentary canal of Aplysia Punctata. Quart J Microsc Sci 4:357-397.
Hurwitz I, Cropper EC, Vilim FS, Alexeeva V, Susswein AJ, Kupfermann I, Weiss KR (2000) Serotonergic and peptidergic modulation of the buccal mass protractor muscle (I2) in Aplysia. J Neurophysiol 84:2810-2820.
Hurwitz I, Kupfermann I, Susswein AJ (1997) Different roles of neurons B63 and B34 that are active during the protraction phase of buccal motor programs in Aplysia californica. J Neurophysiol 1997 78:1305-1319.
Jing J, Vilim FS, Wu JS, Park JH, Weiss KR (2003) Concerted GABAergic actions of Aplysia feeding interneurons in motor program specification. J Neurosci 23:5283- 5294.
Jing J, Weiss KR (2001) Neural mechanisms of motor program switching in Aplysia. J Neurosci 21:7349-7362.
Jing J, Weiss KR (2002) Interneuronal basis of the generation of related but distinct motor programs in Aplysia: implications for current neuronal models of vertebrate intralimb coordination. J Neurosci 22:6228-6238.
Katz PS (1995) Intrinsic and extrinsic neuromodulation of motor circuits. Curr Opin Neurobiol 5:799-808.
Katz PS and Frost WN (1996) Intrinsic neuromodulation: altering neuronal circuits from within 19:54-61.
Kuhlman JR, Li C, Calabrese RL (1985) FMRFamide-like substances in the Leech. I. Immunocytological localization. J Neurosci 5:2301-2309.
Kupfermann I (1974) Feeding behavior in Aplysia: A simple system for the study of motivation. Behav Biol 10:1-26.
Lee SSM, de Boef Miara M, Arnold AS, Biewener AA, Wakeling JM (2013) Recruitment of faster motor units is associated with greater rates of fascicle strain and rapid changes in muscle force during locomotion. J Exp Biol 216:198-207.
Lu H, Chestek CA, Shaw KM, Chiel, HJ (2008) Selective extracellular stimulation of individual neurons in ganglia. J Neural Eng 5:287-309.
Lu H, McManus JM, Chiel HJ (2013) Extracellularly identifying motor neurons for a muscle motor pool in Aplysia californica. J Vis Exp 73:e50189, doi:10.3791/50189.
18
Mangan EV, Kingsley DA, Quinn RD, Sutton GP, Mansour JM, Chiel HJ (2005) A biologically inspired gripping device. Industrial Robot: An International Journal 32:49-54.
Mendell LM (2005) The size principle: a rule describing the recruitment of motoneurons. J Neurophysiol 93:3024-3026.
McManus JM, Lu H, Chiel HJ (2012) An in vitro preparation for eliciting and recording feeding motor programs with physiological movements in Aplysia californica. J Vis Exp 70:e4320, doi:10.3791/4320.
McManus JM, Lu H, Cullins MJ, Chiel HJ (2014) Differential activation of an identified motor neuron and neuromodulation provide Aplysia's retractor muscle an additional function. J Neurophysiol, in press.
Miller MW, Rosen SC, Schissel SL, Cropper EC, Kupfermann I, Weiss KR (1994) A population of SCP-containing neurons in the buccal ganglion of Aplysia are radula mechanoafferents and receive excitation of central origin. J Neurosci 14:7008-7023.
Morgan PT, Jing J, Vilim FS, Weiss KR (2002) Interneuronal and peptidergic control of motor pattern switching in Aplysia. J Neurophysiol 87:49-61.
Morton DW, Chiel HJ (1993a) In vivo buccal nerve activity that distinguishes ingestion from rejection can be used to predict behavioral transitions in Aplysia. J Comp Physiol A 172:17-32.
Morton DW, Chiel HJ (1993b) The timing of activity in motor neurons that produce radula movements distinguishes ingestion from rejection in Aplysia. J Comp Physiol A 173:519-536.
Nagahama T, Takata M (1988) Food-induced firing patterns in motoneurons producing jaw movements in Aplysia kurodai. J Comp Physiol A 162:729-738.
Neustadter DM, Herman RL, Drushel RF, Chestek DW, Chiel HJ (2007) The kinematics of multifunctionality: comparisons of biting and swallowing in Aplysia californica. J Exp Biol 210:238-260.
Ritzmann RE (1974) Mechanisms for snapping behavior of two alpheid shrimp, Alpheus californiensis and Alpheus heterochelis. J Comp Physiol 95:217-236.
Rosen SC, Miller MW, Cropper EC, Kupfermann I (2000b) Outputs of radula mechanoafferent neurons in Aplysia are modulated by motor neurons, interneurons, and sensory neurons. J Neurophysiol 83:1621-1636.
19
Rosen SC, Miller MW, Evans CG, Cropper EC, Kupfermann I (2000a) Diverse synaptic connections between peptidergic radula mechanoafferent neurons and neurons in the feeding system of Aplysia. J Neurophysiol 83:1605-1620.
Rosen SC, Weiss KR, Cohen JL, Kupfermann I (1982) Interganglionic cerebral-buccal mechanoafferents of Aplysia: receptive fields and synaptic connections to different classes of neurons involved in feeding behavior. J Neurophysiol 48:271-88.
Schotland J, Shupliakov O, Wikstrom M, Brodin L, Srinivasan M, You Z, Herrera- Marschitz M, Zhang W. Hokielt T, Crlllner S (1995) Control of lamprey locomotor neurons by colocalized monoamine transmitters. Nature 374:266-268.
Schwarz TL, Lee G MH, Siwicki KK, Standaert DG, Kravitz EA (1984) Proctolin in the lobster: the distribution, release, and characterization of a likely neurohormone. J Neurosci 4:1300-1311.
Scott ML, Govind CK, Kirk MD (1991) Neuromuscular organization of the buccal system in Aplysia californica. J Comp Neurol 312:207-222.
Susswein AJ, Rosen SC, Gapon S, Kupfermann I (1996) Characterization of buccal motor programs elicited by a cholinergic agonist applied to the cerebral ganglion of Aplysia californica. J Comp Physiol A 179:509-524.
Sutton GP, Macknin JB, Gartman SS, Sunny GP, Beer RD, Crago PE, Neustadter DM, Chiel HJ (2004a) Passive hinge forces in the feeding apparatus of Aplysia aid retraction during biting but not during swallowing. J Comp Physiol A Neuroethol Sens Neural Behav Physiol 190:501-514.
Sutton GP, Macknin JB, Gartman SS, Sunny GP, Beer RD, Crago PE, Neustadter DM, Chiel HJ (2004b) Neural control exploits changing mechanical advantage and context dependence to generate different feeding responses in Aplysia. Biol Cybern 91:333- 345.
Trimmer BA (1994) Characterization of a muscarinic current that regulates excitability of an identified insect motoneuron. J Neurophysiol 72:1862-1873.
Vieira TM, Loram ID, Muceli S, Merletti R, Farina D (2011) Postural activation of the human medial gastrocnemius muscle: are the muscle units spatially localised? J Physiol 589:431-443.
Wakeling JM, Kaya M, Temple GK, Johnston IA, Herzog W (2002) Determining patterns of motor recruitment during locomotion. J Exp Biol 205:359-369.
Whim MD, Church PJ, Lloyd PE (1993) Functional roles of peptide cotransmitters at neuromuscular synapses in Aplysia. Mol Neurobiol 7:335-347.
20
Whim MD, Lloyd PE (1989) Frequency-dependent release of peptide cotransmitters from identified cholinergic motor neurons in Aplysia. Proc Natl Acad Sci 86:9034-9038.
Ye H, Morton DW, Chiel HJ (2006a) Neuromechanics of coordination during swallowing in Aplysia californica. J Neurosci 26:1470 –1485.
Ye H, Morton DW, Chiel HJ (2006b) Neuromechanics of Multifunctionality during Rejection in Aplysia californica. J Neurosci 26:10743–10755.
Yu SN, Crago PE, Chiel HJ (1999) Biomechanical properties and a kinetic simulation model of the smooth muscle I2 in the buccal mass of Aplysia. Biol Cybern 81:505- 513.
21
Figures and Tables
Figure 1-1: A schematic diagram for extrinsic and intrinsic neuromodulation. This figure was modified based on Fig. 1 from Katz (1995). Extrinsic neuromodulation arises from neurons outside the local circuit. Intrinsic neuromodulation arises from neurons within a local circuit.
22
Figure 1-2: A schematic diagram for the anatomy of the buccal mass of Aplysia, modified based on Fig. 1 in Mangan et al. (2005) and Fig. 1 in Drushel et al. (1998). This is a simplified cutaway view of the buccal mass. The red area represents the cut away view of the the primary retractor muscle, I1/I3 complex, which is innervated by multiple motor neurons (e.g., B3, B6, B9, B10, B38, B39, B43, and B82).
23
Figure 1-3: A schematic diagram for muscle innervation and intrinsic and extrinsic modulation of I1/I3. White circles represent motor neurons that induce intrinsic modulation on I1/I3. The black circle represents the metacerebral cell (MCC) that induces extrinsic modulation on I1/LI3. B3 releases glutamate as conventional neurotransmitter and FMRFamide (Fa) as intrinsic neuromodulator. B6 releases glutamate as conventional neurotransmitter and small cardioactive peptides (SCPs) as intrinsic neuromodulator. B9 releases ACh as conventional neurotransmitter and SCPs as intrinsic neuromodulator. MCC releases serotonin (5-HT) as extrinsic neuromodulator.
24
Figure 1-4: Schematic diagrams for intrinsic modulatory effects from Fa and SCP on the contraction force amplitudes of I1/I3. These results were summarized based on Keating and Lloyd (1999). ASW represents artificial seawater. (A) B3 was stimulated at 15 Hz for 0.9 s. Application of Fa (B3’s cotransmitter) increases the amplitude of contractions in I1/I3. (B) B9 was stimulated at 15 Hz for 0.6 s. Application of SCP (B9’s cotransmitter) also increases the amplitude of contractions in I1/I3. Note that the contraction forces shown in this figure are simply schematics, not real measurements.
25
Chapter 2
Selective extracellular stimulation of individual neurons in ganglia
Summary
Selective control of individual neurons could clarify neural functions and aid disease treatments. To target specific neurons, it may be useful to focus on ganglionic neuron clusters, which are found in the peripheral nervous system in vertebrates. Because neuron cell bodies are found primarily near the surface of invertebrate ganglia, and often found near the surface of vertebrate ganglia, we developed a technique for controlling individual neurons extracellularly using the buccal ganglia of the marine mollusc Aplysia californica as a model system. We experimentally demonstrated that anodic currents can selectively activate an individual neuron and cathodic currents can selectively inhibit an individual neuron using this technique. To define spatial specificity, we studied the minimum currents required for stimulation, and to define temporal specificity, we controlled firing frequencies up to 45 Hz. To understand the mechanisms of spatial and temporal specificity, we created models using the NEURON software package. To broadly predict the spatial specificity of arbitrary neurons in any ganglion sharing similar geometry, we created a steady-state analytical model. A NEURON model based on cat spinal motor neurons showed responses to extracellular stimulation qualitatively similar to those of the Aplysia NEURON model, suggesting that this technique could be widely applicable to vertebrate and human peripheral ganglia having similar geometry.
This chapter is based on a previous publication: Lu H, Chestek CA, Shaw KM, Chiel, HJ (2008) Selective extracellular stimulation of individual neurons in ganglia. J Neural Eng 5:287-309.
26
Introduction
Extracellular stimulation has been extensively used both clinically and experimentally. Clinically, extracellular stimulation has been used to activate neural tissues in prostheses in order to restore function, e.g. visual perception (Mokwa, 2007;
Winter et al., 2007), auditory perception (Spelman, 2006; Lenarz et al., 2006), control of micturition (Gaunt and Prochazka, 2006; Jezernik et al., 2002) and spinal and motor cortical function (Barbeau et al., 1999; Cioni et al., 2007). It has also been used to block neural signals in order to treat movement disorders (Benabid et al., 1996; Anderson and
Lenz, 2006) or suppress pain (De Ridder et al., 2007). Experimentally, extracellular stimulation can be applied to demonstrate the causal role of a single neuron on an animal’s behavior. For example, Ferguson et al. (1986, 1989) successfully induced egg laying in freely behaving Aplysia by selectively stimulating bag cells. More recently, extracellular microstimulation of small groups of neurons has been used to determine the causal relationships between neural circuitry, behavior and cognition in higher vertebrates and humans (Cohen and Newsome, 2004).
In many applications, efficient extracellular stimulation requires selective activation or inhibition of targeted populations, which we will refer to as spatial specificity in this paper. A variety of approaches has been used to improve spatial specificity of stimulation. For example, microelectrodes have been proposed for selective stimulation in the central nervous system (McIntyre and Grill, 2000; McCreery et al.,
2006). In the peripheral nervous system, several electrode devices have also been designed for selective nerve stimulation, including spiral electrodes (Naples et al., 1988), helical electrodes (Tarver et al., 1992), intrafascicular electrodes (McNaughton and
27
Horch, 1996) and flat interface nerve electrodes (Tyler and Durand, 2002; Levanthal et al., 2006). However, it still remains difficult to selectively stimulate individual neurons, particularly smaller ones that are buried deeply and surrounded by large neurons.
Previous work has analyzed the mechanisms by which extracellular stimulation may excite or inhibit individual neurons. In particular, studies have shown that when the electrode is on the side of the soma opposite to the axon, anodic currents can be used to excite the neuron, whereas cathodic currents can be used to inhibit the neuron (Ranck,
1975; Suihko, 1998; Rattay, 1999). To our knowledge, however, no studies have been done in which the target neuron has been recorded intracellularly.
To develop selective stimulation, it may be best to focus on neuron clusters (i.e., ganglia), which are found throughout the peripheral nervous system. In invertebrates, the cell bodies of the excitable neurons are found near the surface of the ganglion, whereas the axonal network is found within the neuropil (Horridge and Bullock, 1965). Many neuron cell bodies are also found near the surface of vertebrate ganglia (e.g., dorsal root ganglia and sympathetic ganglia; Williams et al., 1995). McIntyre and Grill (2002) have demonstrated that a neuron can respond to extracellular stimulation in different ways depending on its position with respect to the electrode. Therefore, if one stimulates near the cell bodies in a ganglion, which we will refer to as extracellular ganglionic stimulation in this paper, it may be possible to selectively stimulate neurons that cannot be controlled by other extracellular stimulation methods. In addition, because the soma diameter of a neuron is usually larger than its axon diameter, the distance between the centers of two cell bodies is usually larger than that between two axons. This could provide better spatial specificity of stimulation.
28
Previous studies have demonstrated selective stimulation and recording of an individual neuron in freely behaving Aplysia (Parson et al., 1983). Parson et al.’s (1983) technique involved attaching fine wire electrodes to the protective sheath above the specific cell bodies and isolating them from the surrounding fluid with glue, which is technically challenging and does not provide a particularly good seal. Therefore, they were forced to use several milliamperes of stimulation current. While the technique improved spatial specificity, the high levels of stimulation current sometimes induced noxious responses in the animals. Warman and Chiel (1995) improved the technique for single cell recording in vivo by attaching a pipette electrode made of glass or plastic to the sheath of the ganglion, and gluing it in place to provide an isolated chamber for electrodes. In this paper, we will extend this technique to stimulation, demonstrating that it is possible to selectively activate or inhibit a single neuron by extracellular stimulation on the side of the soma opposite to the axon.
In addition, to understand the stimulation mechanisms and study their spatial and temporal specificity, we simulate Aplysia buccal neurons using the NEURON software package. We also create a steady-state analytical model to generalize the NEURON model’s predictions of the spatial specificity to arbitrary neurons that have various sizes and geometric configurations.
Materials and Methods
1. Experimental methods
29
Aplysia californica weighing 200-450 g (Marinus Scientific, Garden Grove, CA) were maintained in an aerated aquarium containing artificial seawater (Instant Ocean;
Aquarium Systems, Mentor, OH) kept at 18 ± 1 ◦C.
Extracellular stimulating electrodes were made from single-barrelled capillary glass (catalogue #6150; A-M Systems, Everett, WA), pulled on a Flaming-Brown micropipette puller (model P-80/PC; Sutter Instruments, Novato, CA). Electrodes were backfilled with Aplysia saline (460 mM NaCl, 10 mM KCl, 22 mM MgCl2, 33 mM
MgSO4, 10 mM CaCl2, 10 mM glucose, 10 mM MOPS, pH 7.4-7.5), and placed in the saline above the surface of the ganglion. Their inner diameters were about 40 μm and their resistances were about 0.1 MΩ. Currents were supplied by a stimulus isolator
(model A-360, WPI).
Intracellular recording electrodes were also made from single-barrelled capillary glass (catalogue #6150; AM Systems, Everett, WA) pulled on a Flaming-Brown micropipette puller (model P-80/PC; Sutter Instruments, Novato, CA). Electrodes were backfilled with 3 M potassium acetate, and their resistances were 3-6 MΩ. The bridge was balanced for both stimulation and recording. Intracellular signals were amplified using a dc-coupled amplifier (model 1600; A-M Systems).
Nerve recording electrodes were made from polyethylene tubing (catalogue
#427421; Becton Dickinson, Sparks, MD; outer diameter 1.27 mm, inner diameter 0.86 mm). Electrodes were backfilled with Aplysia saline. Nerve recording signals were amplified using an ac-coupled differential amplifier (model 1700; A-M Systems) and filtered using a 300 Hz high-pass filter and a 1 kHz low-pass filter.
30
To study the responses of neurons to extracellular stimulation near the cell bodies, animals were anaesthetized and the buccal ganglia were dissected out. The buccal ganglia were pinned caudal side up in Aplysia saline. The nerve of interest was suctioned into the tapered end of the nerve recording electrode and recorded simultaneously as the extracellular stimulation was applied (Fig. 2-1, right side schematic). When intracellular recordings were also performed, ganglia were desheathed to expose the cell bodies of the neurons. The cell body of each neuron of interest was impaled by an intracellular electrode for recording, while it was simultaneously stimulated by an extracellular electrode (Fig. 2-1, left side schematic). During extracellular cathodic stimulation, we used the same intracellular recording electrode to induce multiple action potentials at a certain frequency by injecting a depolarizing current into the cell body.
To measure the real membrane potential changes during extracellular stimulation, we subtracted the stimulation artefact from the intracellular recordings. The membrane potential is the difference between the internal and external voltages of the cell. Because the bath is grounded, the external voltage is normally zero, so the recording of the internal voltage of the cell can be used to represent the membrane potential. However, an extracellular current source will create an external voltage gradient, so in this case we cannot assume that the external voltage is zero. Thus, to obtain a more accurate value of the membrane potential during stimulation, we needed to estimate the external voltage as we recorded intracellularly. We first recorded the voltage in the bath near the soma with the intracellular electrode while applying extracellular stimulation at the current levels for every 100 μA in the range of 100-1000 μA. Then we penetrated the cell and measured the internal voltage during identical extracellular stimulation for every current level. Lastly,
31 we subtracted the external voltage from the internal voltage that was recorded when applying the identical extracellular current, which we will refer to as the artefact- subtracted recording. Changes in the intracellular electrode resistance were negligible compared to the impedance of the amplifier and were therefore neglected.
In addition, to study the effects of the sheath on the neurons, we compared the minimum currents (i.e., threshold currents) needed to change the neural activity when the sheath was intact and after the sheath was removed. We placed the extracellular stimulating electrode above the sheath to stimulate the cell bodies underneath (Fig. 2-1, right schematic diagram). Because the sheath blocked the access of intracellular electrodes to the neurons, we instead recorded from the buccal nerve containing the axon of the targeted neuron. We first measured the threshold currents at different stimulating electrode locations when the sheath was intact. Afterwards, we removed the sheath partially or entirely and measured the threshold currents again at different stimulating electrode locations. Because we were not able to directly measure the electrode-to-soma distances with the sheath covering the neuron, we estimated them by adding the thickness of the sheath to the distances from the electrode to the sheath immediately covering the targeted neuron using the reticle of the microscope. We measured the thickness of the sheath that was removed as well as the thickness of the remaining sheath. We added these two values to estimate the original thickness of the sheath. The threshold currents before and after removing the sheath were nearly identical at the electrode locations in the range of 150-300 μm away from the neuron (for details, see Results section). Thus, the sheath of the buccal ganglion has a negligible effect on the threshold currents of neurons for extracellular ganglionic stimulation.
32
To estimate the resistivity of Aplysia saline, we performed a four-wire (Kelvin) resistance measurement (Fig. 2-2; Light, 1997). We applied 100 μA alternating positive and negative 3 ms current pulses at 200 Hz using two stimulus isolators (model A-360,
WPI). We varied the separations of the two recording electrodes in a conductance cell and obtained the slope of the changing voltage versus separation. The resistivity of
Aplysia saline was calculated by dividing the slope by the current amplitude and multiplying by the cross-sectional area of the conductance cell.
2. NEURON model simulations
To understand the mechanisms of extracellular ganglionic stimulation and study its spatial and temporal specificity, we developed models using the NEURON software package.
An unmyelinated neuron was simulated using the NEURON software package
(Hines and Carnevale, 1997), based on the simplified geometry of the Aplysia buccal neurons B4/B5. The parameters describing the geometry of this NEURON model are listed in Table 2-1. Aplysia neurons do not have dendrites surrounding the cell bodies; rather, the dendrites emerge from the axons in the central neuropil (Kreiner et al., 1987).
Thus, we modelled the axon and dendrites as a single cylinder and modelled the soma as a sphere (Fig. 2-3A). The soma diameter was set to 200 μm based on a reticle measurement of B4/B5 using a microscope. The axon diameter was set to 15μm based on the relative size of the soma and axon (Kreiner et al., 1987). The axon length was set to about 20 mm based on a rough estimation of the nerve length. The soma sphere was divided into 100 segments of equal length (Fig. 2-3A). To increase the homogeneity of
33 the electrical field across the soma as the electrode was moved through different angles, the soma segments were automatically rotated according to the stimulating electrode positions to remain perpendicular to the line between the electrode tip and the soma center (Fig. 2-3A, schematic diagrams 1 and 2). As a consequence, the connection location between the soma and the axon changed. When the 100th soma segment was connected to the axon, there was only one closed end at the 1st soma segment. However, when the 100th soma segment was not connected to the axon, there were two closed ends at both the 1st and the 100th soma segments. In order to eliminate this special case, we attached an additional very short soma segment (1/1000 the length of other soma segments) to the 100th segment as a small branch which would always act as the second closed end of the soma. When multiple electrodes were used to stimulate neurons (Fig. 2-
14C1), the soma segments were rotated to point to the weighted average position of the electrodes. Then the axon was divided into 200 segments of equal length except the first axon segment whose length was the mean of one regular soma and axon segments.
Increasing the number of segments by a factor of 10 while maintaining the overall neuronal geometry changed threshold currents by less than 0.1%.
The simulated neuron was represented by an equivalent electrical circuit (Fig. 2-
3B). The midpoint of each segment was chosen to be a representation of the electrical node. The internal potential at the electrical node was represented by . The
axial resistance of the electrical nodes was represented by , which was equal to
( )
Where was the length of the segment, was the diameter of the
segment and was the resistivity of the intracellular fluid of the neuron, which was
34 estimated to be 200 Ω·cm based on McIntyre and Grill (2002) and was in the range from
86 Ω·cm to 410 Ω·cm calculated from Hovey et al. (1972). The electrical parameters of the NEURON model are listed in Table 2-2. The dynamics of the fast sodium, slow potassium and leakage channels in the membrane were a modification of those originally described in the squid giant axon membrane by Hodgkin and Huxley (1952). We did not attempt to match the specific electrical parameters of the single-compartment model of
B4/B5 (Ziv et al., 1994), because we wished to generalize this NEURON model to many other excitable cells. The maximum conductances of fast Na+ and slow K+ channels, and , as well as the conductance of leakage channels, , remained the same in the axon membrane as those in the Hodgkin-Huxley model. The reversal potential of the leakage channels was set to be -65 mV. To simulate the lower densities of Na+ and K+ channels in the soma compared to the axon, and were reduced by a factor of 5 in the soma. The time constants of the Na+ and K+ channels were increased by linear scaling factors based on the ratios of the time constants of the Hodgkin-Huxley model to the time constants of Aplysia sensory neurons (Baxter et al., 1999).
Extracellular stimulation was modelled by a point current source that was applied within an infinite homogeneous saline medium (McIntyre and Grill, 1999), in which the
X-axis was oriented along the axon and the Y-axis was perpendicular to the axon. The stimulating electrode was placed at relative to the center of the soma at (0,
0). We assumed that the presence of the neuron did not affect the extracellular field created by this current source and that the extracellular potentials generated by the membrane currents of the neuron were negligible. We also assumed that the extracellular saline medium was homogeneous, with its resistivity estimated to be 19.3 Ω·cm based on
35 the measurements described above. In addition, we demonstrated that the sheath of the buccal ganglia had negligible effect on the threshold currents of neurons during extracellular stimulation (see previous Methods section and Results section below).
Therefore, we used the same NEURON model to simulate neurons with or without the protective sheath.
Stimulation was modelled with an approach similar to Rattay (1989). The external voltage at any electrical node of the simulated neuron was determined by
Equation 2-2 (Rattay, 1989):
where is the magnitude of the applied extracellular current, is the conductivity of the extracellular medium ( ) and is the distance from the point current source to
the node ( √ ). The ground electrode was assumed to be an infinite distance away. Therefore, these external voltages could be converted to the equivalent intracellular injected currents at each node according to Equation 2-3 (McIntyre and Grill, 1999):
( )
These currents were applied to the neuron in monophasic pulses using the NEURON software package to view cellular voltages, including action potentials, over time. During extracellular cathodic stimulation, we injected additional depolarizing monophasic intracellular current pulses into the soma to induce multiple action potentials.
To investigate the spatial specificity when stimulating multiple cells, we added two more identical neurons to create a multiple-cell NEURON model. The center of the
36 middle neuron was set at (0, 0), and the centers of the neighboring neurons were set at positions (0, -200) and (0, 200), respectively, with their axons parallel and pointing in the same direction. This simulated the geometry of the ganglion in which cell bodies are clustered together near the surface. The X-axis was along the axons and the Y-axis was perpendicular to the axons. The stimulating electrode was again placed at relative to the center of the middle neuron at (0, 0). During cathodic inhibition, we injected depolarizing currents intracellularly into the cell bodies of all three neurons to induce a train of action potentials in each neuron.
To test the applicability of the technique to vertebrate neurons, we reconstructed a previously published model of cat spinal motor neurons (McIntyre and Grill, 2000).
Parameters of the model are presented in Table 2-3. Qualitatively, results are similar to those obtained for the generic model of invertebrate neurons presented here. Results for this model are shown at the end of the paper (Fig. 2-18).
3. Analytical model
To generalize predictions about spatial specificity to arbitrary neurons that have various sizes and geometric configurations, and to provide a way to readily predict the magnitude of the threshold currents needed to stimulate a neuron, we created a steady- state analytical model. We represented the neuron as a semi-infinite axon in this model.
The tip of the new axon was placed at the center of the original soma, based on the work of Lee and Grill (2005) showing that the cytoplasmic potential in an isolated cell stimulated by a point source will approach the potential at its equator. The new axon was capped with a resistor equivalent to the total membrane resistance of the original soma.
37
We then examined the influence of an extracellular electric field on this new axon.
A point current source was placed at a negative position along the X-axis. We assumed that the effects of the neuron on the extracellular electric field were negligible, and used a steady-state form of the cable equation to model the membrane potential in the axon (see
Appendix A for detailed derivation). We will refer to this model as the full analytical model.
While the resulting equation needed to be evaluated numerically, we found that we were able to approximate it with less than 6% error using the following equation when the current source was placed over the range of one to three length constants (or one-fifth to three length constants if , the normalized soma resistance, was greater than 1) away from the tip of the new axon:
( ) √ where is the normalized threshold current (the threshold current divided by
, where is the conductivity of the extracellular medium and is the resting membrane potential), is the normalized distance from the center of the soma to the extracellular stimulating electrode ( , where is the length constant of the axon and is the distance from the center of the soma to the point current source), is the
normalized soma resistance ( ), where is the total resistance across the
membrane of the soma and is the internal resistance per unit length in the axon), is the normalized threshold potential ( , where is the threshold potential) and is the angle between the axon and the line drawn from the current source to the axon (see Appendix A). We will refer to Equation 2-4 as the simplified analytical model.
38
We performed two sets of simulations to determine the constants for the simplified analytical model when comparing it with the NEURON model. First, we determined the length constant by injecting a small intracellular current into the
NEURON model axon and measuring the rate of membrane potential decrease with distance. For example, the length constant was about 514 μm for the simulated neuron with the soma diameter of 200 μm and the axon diameter of 15 μm, and the length constant was 351 μm for the neuron with half the soma and axon diameters. Second, we estimated by measuring the threshold currents when extracellularly stimulating the
NEURON model axon along the axonal axis at distances of 100-300 μm and fitting the results to the simplified analytical model.
Results
In this paper, we will describe and analyze a technique that is able to selectively activate and inhibit individual neurons by extracellular stimulation near the cell bodies in ganglia. For this purpose, we seek to (1) understand how extracellular ganglionic stimulation activates and inhibits neurons, (2) explore an important factor for selective stimulation, i.e. threshold current, (3) experimentally demonstrate whether it is possible to selectively activate and inhibit an individual neuron using this technique, (4) study the characteristics of stimulation specificity, i.e. spatial and temporal specificity and (5) explore whether this technique could be applicable to vertebrate peripheral ganglia that have neuron cell bodies clustering near the surface of the ganglia.
1. Mechanisms of extracellular ganglionic stimulation
39
Previous work demonstrated that a neuron can respond to extracellular stimulation in different ways and in different regions depending on its position relative to the stimulating electrode (McIntyre and Grill, 2002; Iles, 2005; Tehovnik et al., 2006).
Anodic stimulation on the side of the soma opposite to the axon had a lower threshold for exciting the cell than cathodic current (Ranck, 1975; Rattay, 1999), which is the opposite of what is seen in periaxonal stimulation. Similarly, cathodic stimulation from this side could induce inhibition of the cell. To examine quantitatively the voltage changes resulting from either anodic or cathodic extracellular stimulation, we constructed a
NEURON model. We first examined how the same neuronal segment (e.g., the axon hillock) responds differently to extracellular stimulation depending on the electrode position. We measured the membrane potential changes of the axon hillock as we moved the stimulating electrode along the axonal axis from (-500, 200) to (2000, 200). The effects of extracellular electrical fields on axon membrane potentials have been well described (Rattay, 1989). It is well known that anodic currents hyperpolarize the area immediately below the electrode while depolarizing the distal area. Therefore, when the electrode is placed near the axon hillock (Fig. 2-4A, schematic 2), anodic currents strongly hyperpolarize the axon hillock so that the suppression will dominate. When the electrode is placed far away from the axon hillock (Fig. 2-4A, schematic 3), anodic currents weakly depolarize the axon hillock which will not overcome the suppression due to strong hyperpolarization in the axonal areas immediately below the electrode.
Furthermore, anodic currents applied on the side of the soma opposite to the axon (Fig. 2-
4A, schematic 1) weakly depolarize the axon hillock while strongly hyperpolarizing the soma. However, unlike the effects of distal axon stimulation (Fig. 2-4A, schematic 3),
40 this depolarization in the axon hillock will dominate because the axon hillock contains many more voltage-gated sodium channels than does the soma.
The effects of cathodic stimulation on a neuron are the reverse. Cathodic currents strongly depolarize the axon hillock when applied near it (Fig. 2-4B, schematic 2). In contrast, cathodic currents weakly hyperpolarize the axon hillock as the electrode is placed far away from it (Fig. 2-4B, schematics 1 and 3). While stimulating the distal axon, the weak hyperpolarization of the axon hillock cannot suppress the neuron’s excitatory response to the distal axon depolarization. However, when stimulating on the side of the soma opposite to the axon, the hyperpolarization in the axon hillock can inhibit the neuron because of the high density of voltage-gated sodium channels in the axon hillock.
We then examined how various neuronal segments respond differently to extracellular ganglionic stimulation with the same current source. We measured the membrane potential changes along the neuron as we fixed the electrode position at (-150,
0). Anodic stimulation hyperpolarizes the proximal and middle parts of the soma, while depolarizing the distal soma and the proximal axon segments (Fig. 2-5A). Thus, a sufficiently large depolarization could initiate action potentials in the proximal axon segments, which then propagate throughout the rest of the neuron. This is consistent with the earlier modelling studies of Rattay (1999) who noted that spike initiation occurred in the proximal axon, but not at the hillock itself. Because of the low density of voltage- gated sodium channels, the hyperpolarization in the local soma segments will not be able to suppress the neuron’s activity. The effects of cathodic currents on the neuronal segments are the reverse (Fig. 2-5A). Cathodic stimulation depolarizes the proximal and middle parts of the soma, while hyperpolarizing the distal soma and the proximal axon
41 segments. Because the axon contains many more voltage-gated sodium channels than does the soma, the hyperpolarization in the proximal axon would dominate and thus inhibit the neuron. Therefore, we used anodic currents to activate neurons and cathodic currents to inhibit neurons during ganglionic stimulation on the side of the soma opposite to the axon.
2. Qualitative comparisons between experimental results and the NEURON model’s
predictions
To experimentally validate the effects of extracellular currents applied on the side of the soma opposite to the axon, we compared the neural responses obtained from in vitro electrophysiological experiments with those predicted by the NEURON model.
The NEURON model predicted that anodic currents would activate a neuron when applied on the side of the soma opposite to the axon. A 6 ms anodic current pulse was applied to the simulated neuron. Anodic stimulation strongly hyperpolarized the tip of the soma and weakly hyperpolarized the middle of the soma (Fig. 2-6A; three black arrows point to the peaks of action potentials; the two top grey arrows indicate the hyperpolarization). However, the initial part of the axon was depolarized to generate an action potential, which then propagated back to the soma (Fig. 2-6A; the bottom grey arrow indicates the depolarization). Because of the high density of voltage-gated sodium channels in the initial axon segments, the hyperpolarization of the soma could not suppress the neuron’s activation.
To demonstrate anodic activation experimentally, we intracellularly recorded from both the tip and the middle of a soma in a desheathed ganglion as we
42 simultaneously applied a 6 ms anodic current pulse above the soma. The intracellular recordings showed that the neuron was excited to fire an action potential at both sites of the soma (Fig. 2-6B; the two left black arrows point to the peaks of action potentials).
However, the real polarization of the neuron at each soma site was embedded in the stimulation artefact. Thus, we subtracted the artefact from the intracellular recordings to show both the action potentials and the polarizations of the neuron (for details, see
Methods section). We found that the tip of the soma was strongly hyperpolarized while the middle of the soma was only weakly hyperpolarized due to anodic stimulation (Fig.
2-6B; the two grey arrows indicate the hyperpolarization; the two middle black arrows point to the peaks of action potentials). In addition, the action potentials occurred despite the hyperpolarization in both sites of the soma. Therefore, the experimental results qualitatively validate the NEURON model’s prediction that anodic activation is likely to be due to depolarization of the initial axon segments.
The NEURON model also predicted that cathodic currents would inhibit a neuron when applied on the side of the soma opposite to the axon. A 300 ms intracellular current pulse was injected into both the tip and the middle of the simulated neuron to generate multiple action potentials. Then a 100 ms cathodic extracellular current pulse was also applied to the simulated neuron (Fig. 2-7A). Cathodic currents hyperpolarized the initial part of the axon, blocking action potentials, whereas they strongly depolarized the tip of the soma and weakly depolarized the middle of the soma (Fig. 2-7A); note the grey lines, indicating the steady-state membrane potential in response to the intracellular current alone). Thus, cathodic currents inhibit the neuron by directly hyperpolarizing the initial axon segments.
43
To demonstrate cathodic inhibition experimentally, we again recorded intracellularly from both the tip and the middle of the soma. We applied a 3 s intracellular current pulse to both soma sites to induce action potentials, and then applied a 1 s cathodic current pulse immediately above the soma in a desheathed ganglion (Fig. 2-7B).
The intracellular recordings showed that action potentials were completely blocked during cathodic extracellular stimulation at both sites in the soma. In the artefact- subtracted recordings, the tip of the soma was significantly depolarized during extracellular stimulation, whereas the middle of the soma was only slightly depolarized
(Fig. 2-7B; the grey lines correspond to the steady-state membrane potential in response to the intracellular current alone). Thus, the experimental results qualitatively validate the
NEURON model’s prediction that cathodic hyperpolarization of the initial axon segment will inhibit nerve cells.
3. Threshold currents
Stimulation specificity requires that a single neuron can be selectively activated or inhibited among a group of neurons (spatial specificity) or that single spikes can be selectively added to or removed from a firing pattern of an individual neuron (temporal specificity). To obtain stimulation specificity, we need to employ the minimum currents that are necessary for effective stimulation. Therefore, we defined the threshold current for anodic activation to be the minimum current required to activate an individual neuron to generate at least one action potential. The threshold current for cathodic inhibition was defined to be the minimum current needed to totally block all action potentials in an
44 individual neuron driven by a fixed input (e.g., intracellular depolarizing current or synaptic input) during the inhibitory pulse.
To predict threshold currents at various electrode positions, we studied the relationship between threshold currents and the distances from the stimulating electrode to the soma via intracellular recordings (Fig. 2-1, left-side schematic). Consistent with previous studies (Stoney et al., 1968; Nowak and Bullier, 1996), the square root of threshold current for both anodic activation and cathodic inhibition increased as we moved the stimulating electrode away from the soma (Fig. 2-8; n = 3, p < 0.05). The
NEURON model also suggested qualitatively similar linear relationships between the square root of the threshold current and the electrode-to-soma distance (p < 0.05 for the linear fits of the square root transformations of both the anodic and the cathodic model data). We noted that the threshold currents could be relatively low (e.g., 30–60 μA) when the electrode was placed very close to the cell bodies (e.g., at around 5 μm) based on the linear fits from the experimental data.
Do we need more current to stimulate neurons when the protective sheath of the ganglia is intact, which is very important if this technique is to be applied in vivo? To investigate this, we compared the threshold currents for anodic activation of a single neuron when the sheath was intact and after the sheath was removed. We applied anodic stimulation to the soma of the targeted neuron through the sheath and simultaneously recorded from the buccal nerve containing its axon (Fig. 2-1, right-side schematic). We measured threshold currents at various stimulating electrode positions and estimated the electrode-to-soma distances (for details, see Methods section). The threshold currents of the same neuron were very close when the sheath was intact and after the sheath was
45 removed (Fig. 2-9; n = 3). However, we noted that we did need more current to activate a neuron in an intact animal because the stimulating electrode cannot be placed very close to the neuron when the sheath is intact.
4. Selective activation and inhibition of an individual neuron
Can we selectively activate or inhibit an individual neuron by extracellular ganglionic stimulation? As shown in Figs. 2-8 and 2-9, the threshold currents of a neuron increase with the electrode-to-soma distance. As an electrode is moved along the surface of the ganglion, it will be closer to the cell bodies of some neurons and further from others. Thus, if we position the electrode over a certain range, extracellular currents should be able to selectively stimulate a single neuron.
To demonstrate selective activation, we intracellularly recorded simultaneously from the cell bodies of three adjacent neurons as we applied anodic extracellular currents above the soma of the targeted middle neuron in a desheathed ganglion (Fig. 2-10A; n =
8). The intracellular recordings showed that at the threshold current, the middle neuron fired an action potential following the stimulating artefact (Fig. 2-10B; note the black arrow pointing to the action potential). However, this current was not enough to activate the other two neurons. When 386% of the threshold current was applied, both the left and the middle neurons were excited to generate at least one action potential during or following the stimulation (Fig. 2-10C; note the black arrows pointing to the action potentials). Finally, as we increased the current to 471% of the threshold current of the middle neuron, all three neurons were excited and generated at least one action potential during or following the stimulation (Fig. 2-10D; note the black arrows pointing to the
46 action potentials). Therefore, an individual neuron can be selectively activated without activating its neighbors.
To demonstrate selective inhibition of an individual neuron, we again recorded intracellularly from the cell bodies of three adjacent neurons simultaneously. A depolarizing current was injected into each soma to induce a train of action potentials. A cathodic extracellular current was applied above the soma of the targeted middle neuron
(Fig. 2-11A; n = 3). At the threshold current, cathodic stimulation only eliminated the action potentials in the middle neuron (Fig. 2-11B).With 208% of the threshold current of the middle neuron, both the left and the middle neurons were inhibited during the stimulation (Fig. 2-11C). Finally, as we increased the current to 358% of the threshold current of the middle neuron, all three neurons were inhibited during the stimulation (Fig.
2-11D). Therefore, an individual neuron can be inhibited selectively without suppressing the neighboring neurons.
5. Spatial specificity for multiple neurons
The ability to selectively stimulate a single neuron among multiple neurons is one of the most important aspects of our technique, which we refer to as spatial specificity. It requires that the minimum current necessary to stimulate a given neuron is less than that needed to stimulate any other neuron by a reasonably large and reliable margin. In order
to quantify the current window of spatial specificity, we defined it to be �� ,
where was the threshold current for an adjacent neuron and was the threshold current for the neuron of interest. In Figs. 2-10 and 2-11, we show large current
47 windows of spatial specificity, which are 286% for anodic activation and 108% for cathodic inhibition.
To investigate the characteristics of spatial specificity, we created a multiple-cell
NEURON model consisting of three adjacent neurons as described in the Methods section (Figs. 2-12A and 2-13A). We defined the X-axis to be along the axons and the Y- axis to be perpendicular to the axons. The X-coordinate of the stimulating electrode was fixed at 200 μm away from the centers of the cell bodies. The electrode was then moved along the Y-axis from the center of the bottom neuron at = -200 μm to the center of the top neuron at = 200 μm. At each point, the threshold currents of all three neurons were recorded and normalized to be the percentage of the minimum threshold current of the targeted middle neuron. When the electrode was placed between = -99
μm and Yelec = 99 μm, the normalized threshold currents of the middle neuron were lower than those of the other two neurons for both anodic activation and cathodic inhibition
(Figs. 2-12B and 2-13B; note the star symbols and symmetric line). As a consequence, the middle neuron could be activated and inhibited selectively over this range. At =
0 μm, we could obtain the maximum current windows of spatial specificity, which were
88.8% for anodic activation and 113.7% for cathodic inhibition. In addition, the sizes of these maximum current windows fell quickly (approximately as the inverse square of the electrode-to-soma distance) as the stimulating electrode was moved away (Figs. 2-12C and 2-13C).
How general are the spatial specificity results as the neuron geometry varies, in particular, as one neuron is further from the surface than the surrounding neurons? To address this question, we developed a steady-state analytical model (for details, see
48
Methods section and Appendix A) to predict the spatial specificity of arbitrary neurons that have various sizes and geometric configurations. We found that for the distances of interest (about one-third to three length constants from the soma), the simplified analytical model described in Equation 2-4 provides an excellent approximation to the threshold current values from the NEURON model (Figs. 2-14A2, B2 and C2; note the lines for the analytical model and the symbols for the NEURON model).
How does the spatial specificity change if the neurons of interest have different sizes? Since a small neuron has a higher threshold current than a large neuron, it might be difficult to stimulate a small neuron selectively if it is surrounded by large neurons. To explore this problem, we considered the case where a small neuron was surrounded by two large neurons. The parameters of the large neurons were the same as those used in the previous multiple-cell NEURON model containing three identical adjacent neurons
(Fig. 2-12A). The soma and axon diameters of the small neuron were 100 μm and 7.5 μm, respectively, which were both half of those of the large neurons. We first examined the neural responses when all three neurons were aligned at the tips of their cell bodies with their axons parallel to each other (Fig. 2-14A1). We did the same measurements of threshold currents to determine spatial specificity. The targeted small middle neuron could be selectively activated. For example, the maximum current window was 11.2% when the stimulating electrode was 200 μm away along the X-axis from the tips of the cell bodies (Fig. 2-14A2; note the arrow pointing down). Although it is smaller than the maximum current window shown above (Fig. 2-12), it is still sufficient for reliable selective stimulation. Similarly, the maximum current window was largest when the
49 stimulating electrode was close to the neurons along the X-axis, and it fell as the electrode was moved away (data not shown).
How does specificity change if the neurons of interest have both different sizes and geometric configurations? One extreme case would occur if the targeted neuron was both smaller and further away from the surface. For example, the targeted small neuron and its neighbors could be aligned at their hillocks with their axons parallel to each other
(Fig. 2-14B1). In fact, we observed that spatial specificity was now much worse: the targeted small neuron could not be selectively activated (Fig. 2-14B2; note the arrow pointing up). In order to regain the spatial specificity of the targeted small neuron, we had to place the electrode immediately above it and between the two large adjacent neurons.
For example, when the X-coordinate of the electrode was 50 μm from the tip of the small neuron, the maximum specificity window was about 75% of its minimum threshold current (data not shown). Previous work by Tarler and Mortimer (2004) studying axon stimulation showed that if one suppresses the activity in neighboring axons, it was possible to regain spatial specificity. Based on this idea, we were able to restore the spatial specificity of the middle small neuron by adding two cathodic current sources directly above the two large adjacent neurons (Fig. 2-14C).
6. Temporal specificity for an individual neuron
In many applications, it is also crucial to control the firing frequency of a neuron by adding or removing specific spikes at precise times, which we refer to as temporal specificity. At the threshold current for anodic stimulation, one can use a low-frequency pulse train to generate a single action potential during or immediately following each
50 single pulse. This makes it possible to control the firing frequency of the neuron by adding one action potential per pulse (Fig. 2-15). However, a higher current or pulse frequency may induce more than one action potential per pulse, making the neuron’s firing frequency unpredictable. For example, we found that we could reliably drive
B4/B5 to fire at frequencies up to 45 Hz using anodic pulse trains (data not shown), but not at higher frequencies. We were also able to add specific spikes to the firing pattern of
B4/B5 (Fig. 2-15).
Similarly, at the threshold current for cathodic stimulation, we can also use a low- frequency pulse train to block a single action potential during a single pulse. This also allows the control of firing frequency by removing one action potential per pulse (Fig. 2-
16). However, the pulse should be long enough to cover the duration of an action potential so that each pulse can eliminate one action potential. For example, we were able to control the spiking rates of B4/B5 when it was firing at frequencies lower than 45 Hz.
We were also able to remove specific spikes during repetitive firing of B4/B5.
7. Generalization of the technique
To explore whether this technique can be generalized to neurons having varying morphologies in vertebrates, we examined the stimulation mechanisms under different conditions. First, we examined whether the size of the neuron would affect the mechanisms of extracellular ganglionic stimulation by comparing the membrane polarization along the neuron as we varied the soma and axon diameters. We found that in the simulated neuron with the original soma diameter of 200 μm and axon diameters of
15 μm, the maximum membrane polarization occurred at the second axon segment,
51 which would be the initiation site for generating or inhibiting action potentials (Fig. 2-17; black solid lines in both A and B). We fixed the axon diameter and then increased or reduced the soma diameter (Fig. 2-17A; the grey solid line and black dashed line). Note that as we reduced the soma diameter to 41.2 μm, which was the value for a cat spinal motor neuron model developed by McIntyre and Grill (2000), the site of maximum membrane polarization shifted towards the end of the axon by a few axon segments (Fig.
2-17A; the black dashed line). In contrast, as we reduced the axon diameter to 9.6 μm, also obtained from the same model, the site of maximum membrane polarization shifted towards the axon hillock (Fig. 2-17B; the grey solid line). These quantitative changes will affect the predicted threshold currents. However, the overall profile of membrane polarization along the neuron did not vary qualitatively with the soma and axon diameters.
Therefore, the mechanisms of extracellular ganglionic stimulation will still be valid for various sized neurons when it is applied on the side of the soma opposite to the axon.
What are the effects of dendritic trees on the extracellular stimulation technique?
How does the myelination of the axon affect the response to extracellular stimulation applied on the side of the soma opposite to the axon? Since vertebrate neurons have extensive dendritic trees and myelinated axons, this could affect their response. To address these questions, we modified our NEURON model using the geometric parameters of the cat spinal motor neuron from McIntyre and Grill (2000), and replaced the unmyelinated axon with a myelinated axon including nodes of Ranvier and myelinated internodes (Fig. 2-18A; Table 2-3). We measured the membrane polarization along the neuron in this modified model with or without the myelination of the axon as we placed the stimulating electrode both on-axis at (-221, 0) and off-axis at (-71, 50) (Fig.
52
2-18B; grey dashed and grey solid lines. We then added three dendritic trees surrounding the soma (McIntyre and Grill, 2000; Table 2-3). The dendritic trees only had leakage channels in the membrane. We again measured the membrane polarization along the neuron in models that had dendritic trees with or without the myelination of the axon as we placed the stimulating electrode at the identical locations (-221, 0) and (-71, 50) (Fig.
2-18B; black dashed and black solid lines). Lastly, we compared the results under these four conditions. The overall profile of the membrane polarization did not vary qualitatively after adding the dendritic trees on the soma side or after replacing the unmyelinated axon with the myelinated one. However, the site of maximum membrane polarization shifted by one to three axon segments towards the axon hillock. Therefore, the mechanisms of extracellular ganglionic stimulation will still be valid for neurons having various morphologies. As a consequence, this technique could be applicable to vertebrate peripheral ganglia that have cell bodies of neurons clustering near the surface of the ganglia.
Discussion
The objectives of this study were to describe and analyze a technique for selective extracellular stimulation near the cell bodies of ganglia. These results are consistent with earlier modelling studies of the effects of extracellular stimulation on the side of the soma opposite to the axon (Suihko, 1998; Rattay, 1999). They extend these results in several important ways. To our knowledge, we provide the first intracellular recordings to confirm the change in voltage due to these forms of extracellular stimulation. We also describe the spatial and temporal specificity of the technique in greater detail, and define
53 the windows of specificity, strongly suggesting that the technique could find many practical applications. Six main conclusions can be drawn from this study, which we will discuss in turn.
First, the technique uses anodic currents to activate neurons and cathodic currents to inhibit neurons. The NEURON model predicted that anodic stimulation applied near the soma opposite to the axon activates a neuron by direct depolarization of the initial axon segments (Figs. 2-4A and 2-5A). Cathodic stimulation applied near the soma opposite to the axon inhibits a neuron by direct hyperpolarization of the initial axon segments (Figs. 2-4B and 2-5B). In addition, intracellular recordings from two different sites of the soma qualitatively validated the NEURON model’s predictions (Figs. 2-6 and
2-7).
Second, the square root of threshold current increases with the distance from the stimulating electrode to the soma (Figs. 2-8 and 2-9), consistent with previous studies by
Stoney et al. (1968) as well as Nowak and Bullier (1996). This allows us to predict the current required to selectively activate or inhibit a neuron at a fixed electrode-to-soma distance. In addition, nerve recordings demonstrated that the sheath of the ganglion does not affect the threshold currents of a neuron during extracellular ganglionic stimulation
(Fig. 2-10). Thus, this technique can be applied to freely behaving animals.
Third, intracellular recordings from three adjacent neurons demonstrated that an individual neuron can be selectively activated (Fig. 2-9) and inhibited (Fig. 2-11) by extracellular stimulation near the soma opposite to the axon, which would allow for many scientific and clinical applications. In order to minimize the difficulties of positioning the
54 electrodes precisely over the range of interest, it may be important to develop appropriate multi-electrode devices.
Fourth, our models suggest that this technique could be generalized to any ganglion with cell bodies near its surface. The NEURON model was used to explore the characteristics of spatial specificity of stimulation (Figs. 2-12 and 2-13). The simplified analytical model was used to broadly predict the spatial specificity of arbitrary neurons of various sizes and geometric configurations (Fig. 2-14), and provides qualitative insight into the operation of extracellular stimulation that was confirmed by the more quantitative NEURON model.
Fifth, intracellular recordings demonstrated that specific spikes can be added to or removed from the firing pattern of an individual neuron, and thus provide temporal specificity of stimulation (Figs. 2-15 and 2-16). The precise control of spikes will be valuable for clinical applications that minimize side effects, and for scientific experiments.
Finally, modelling studies demonstrate that changing axon or soma diameter, adding dendritic trees or adding myelination do not qualitatively alter the functional actions and mechanism of extracellular stimulation applied on the side of the soma opposite to the axon, although the quantitative effects do change (Figs. 2-17 and 2-18).
Interestingly, adding dendrites appears to be similar to increasing soma diameter, moving the initiation or inhibition zone closer to the soma. Qualitatively, this suggests that decreasing the input resistance of the soma or increasing that of the axon will shift the initiation or inhibition zone closer to the soma, as suggested by the analytical model.
55
1. Limitations of the NEURON and analytical models
The NEURON and analytical models used in this study had three primary limitations. First, the extracellular saline medium used in both the NEURON and the analytical models was assumed to be homogeneous. In the actual ganglion, there are several sources of inhomogeneity: the sheath, the neuropil including the other embedded cells and the extracellular fluid medium within the ganglion. Some direct experimental evidence supports the hypothesis of homogeneity. In particular, the experimental results showed that the presence of the protective sheath only affected the threshold currents slightly (Fig. 2-9). If the assumption of extracellular homogeneity were invalid, the results would be quantitatively different. However, the overall results would still be qualitatively valid.
Second, both the NEURON and analytical models made several simplifying assumptions that may not apply when the electrode is very close to the soma. For example, both models assumed that the extracellular field was not affected by the neuron, which is not likely to be true at sufficiently short distances (Lee and Grill, 2005). In addition, both models did not include the 3D extent of the neuron, which may have different external voltages at different locations surrounding each segment and thus may cause a threshold current error. McIntyre and Grill (1999) showed that the results of the
3D model corresponded closely to the simplified 2D model, with only a 5–10% increase in the relative threshold current value for electrodes positioned over the soma. We have also partially compensated for this potential error by rotating the soma segments to remain perpendicular to the line connecting the stimulating electrode to the center of the soma. Furthermore, in the analytical model, we assumed that any line drawn from the
56 stimulating electrode to any point on the axon met the axon at approximately the same angle. This is unlikely to be true at short distances or when the axon curves. Although all of these effects may be significant at very short electrode-to-neuron distances, they are less relevant at the distances that are likely to be used in experiments.
Finally, the simplified analytical model (Equation 2-4) is a fit of the full analytical model over a relatively limited range of stimulation distances, from about one to three length constants. Above or below this range, Equation 2-4 starts to deviate significantly from the results of the full analytical model. This limitation can be circumvented by using the full analytical model or by fitting the full analytical model over a different range.
2. Potential applications of the technique
The ability to selectively stimulate single neurons could allow for many scientific and medical applications. Experimentally, extracellular ganglionic stimulation could be used to study the causal role of an individual neuron on an animal’s behavior. For example, buccal interneurons B4/B5 are strongly activated during rejection in Aplysia, whereas they are moderately activated during swallowing and weakly activated during biting (Warman and Chiel, 1995; Ye et al., 2006b). By implanting electrodes into freely moving Aplysia, one could stimulate B4/B5 selectively, and thus deduce its causal role in the feeding behavior of Aplysia in vivo.
To minimize the difficulties of positioning the electrodes over the surface of ganglia, in current studies in our laboratory, we are developing a multi-electrode device for specific stimulation of individual neurons in any ganglion with cell bodies near its surface. While multi-electrode array devices have been developed for stimulation in the
57 central nervous system, e.g. cortex (McCreery et al., 2006), spinal cord (Falowski et al.,
2008) and retina (Sekirnjak et al., 2006), as well as peripheral nerves (Kovacs et al.,
1992), no such device for peripheral ganglia (e.g., autonomic ganglia) has been constructed to date.
Previous work by Tarler and Mortimer (2004) suggested that such a multi- electrode device applied to axons could be used to provide better spatial specificity than a single electrode. Our results suggest that similar specificity could be used to target neuron cell bodies. For example, if a targeted neuron is much smaller than its neighbors with their hillocks aligned, anodic stimulation by a single electrode will lose spatial specificity when the electrode is placed far away from the neurons (Fig. 2-14B). However, with a multi-electrode device, one could inject anodic currents via the electrode adjacent to the targeted small neuron while injecting cathodic currents via the electrodes adjacent to the neighboring large neurons, and significantly improve spatial specificity (Fig. 2-14C). In addition, by combining the spatial and temporal specificity of our technique, such a multi-electrode device could be very useful for selective stimulation of multiple neurons and shaping their firing patterns in different ways, thus generating or switching motor patterns.
Clinically, this technique could be applied to peripheral ganglia to control the activity of the neurons whose cell bodies are near the surface of the ganglion. The pseudounipolar geometry of sensory neurons in these ganglia closely resembles the geometry of the unipolar neurons used in this study (Williams et al., 1995). In addition, although postsynaptic sympathetic motor neurons have a more extensive dendritic tree, it is likely that it will be possible to selectively stimulate these neurons if their cell bodies
58 are near the surface and their axons are oriented towards the center of the ganglia (Fig. 2-
18; Williams et al., 1995). This may allow greater stimulation specificity than can be achieved by nerve stimulation (Navarro et al., 2005).
One potential application of the technique described in this paper could be stimulation of the sensory neurons of the vagal ganglia. Vagal nerve stimulation is currently used as a treatment for epilepsy and refractory depression (Shafique and
Dalsing, 2006). However, its mechanism of action is not well understood, and there are a number of side effects of treatment (e.g., hoarseness, coughing or difficulty breathing).
Several of these side effects may be due to the lack of specificity of the stimulation. For example, the current procedure stimulates vagal fibers that branch off to form the recurrent laryngeal nerve. The greater specificity of our technique could allow for equal efficacy with fewer side effects. In addition, it could provide a useful tool for identifying the subpopulation of neurons responsible for the clinical effects of this treatment.
The technique could also be used for many other clinical applications. For example, our extracellular stimulation technique could allow selective inhibition of sensory neurons in dorsal root ganglia for the treatment of chronic pain under conditions such as post-herpetic neuralgia (Holsheimer, 1997). Stimulation of postsynaptic neurons in sympathetic ganglia could also be useful under conditions such as urinary incontinence and diabetic autonomic neuropathy (Pedrini and Magnoni, 2007).
Acknowledgements
This work was supported by NIH grants NS047073, EB004018 and T32
GM007250, as well as Case Western Reserve University Innovation Incentive Fellowship
59 award. We also thank Dr. Cameron McIntyre for helpful discussions at the initiation of the project, and two anonymous reviewers for their comments on an earlier version of the manuscript.
60
References
Anderson WS, Lenz FA (2006) Surgery insight: Deep brain stimulation for movement disorders. Nat Clin Pract Neurol. 2:310-320.
Barbeau H, McCrea DA, O’Donovan MJ, Rossignol S, Grill WM, Lemay MA (1999) Tapping into spinal circuits to restore motor function. Brain Res Rev 30:27–51.
Baxter DA, Canavier CC, Clark JW Jr, Byrne JH (1999) Computational model of serotonergic modulation of sensory neurons in Aplysia. J Neurophysiol 82:2914-2935.
Benabid AL, Pollak P, Gao D, Hoffmann D, Limousin P, Gay E, Payen I, Benazzouz A (1996) Chronic electrical stimulation of the ventralis intermedius nucleus of the thalamus as a treatment of movement disorders. J Neurosurg 84:203-214.
Cioni B, Meglio M, Perotti V, De Bonis P, Montano N (2007) Neurophysiological aspects of chronic motor cortex stimulation. Neurophysiol Clin 37:441-447.
Cohen MR, Newsome WT (2004) What electrical microstimulation has revealed about the neural basis of cognition. Curr Opin Neurobiol 14:169-177.
De Ridder D, De Mulder G, Menovsky T, Sunaert S, Kovacs S (2007) Electrical stimulation of auditory and somatosensory cortices for treatment of tinnitus and pain. Prog Brain Res 166:377-388.
Falowski S, Celii A, Sharan A (2008) Spinal cord stimulation: an update. Neurotherapeutics 5:86-99.
Ferguson GP, Parsons DW, Ter Maat A, Pinsker HM (1986) Spontaneous and elicited bag cell discharges in gonadectomized Aplysia. J Exp Biol 123:159-173.
Ferguson GP, Ter Maat A, Parsons DW, Pinsker HM (1989) Egg laying in Aplysia. I. Behavior patterns and muscle activity of freely behaving animals after selectively elicited bag cell discharges. J Comp Physiol 164:835-847.
Gaunt RA, Prochazka A (2006) Control of urinary bladder function with devices: successes and failures. Prog Brain Res 152:163-194.
Hodgkin AL, Huxley AF (1952) A quantitative description of membrane current and its application to conduction and excitation in nerve. J Physiol 117:500-544.
Holsheimer J (1997) Effectiveness of spinal cord stimulation in the management of chronic pain: analysis of technical drawbacks and solutions. Neurosurgery 40:990- 999.
61
Horridge GA, Bullock TH (1965) Structure and Function in the Nervous System of Invertebrates. ed T H Bullock and G A Horridge (San Francisco and London: W.H. Freeman And Co.) p 52-53.
Hovey MM, Bak AF, Carpenter DO (1972) Low internal conductivity of Aplysia neuron somata. Science 176:1329-1331.
Iles JF (2005) Simple models of stimulation of neurones in the brain by electric fields. Prog Biophys Mol Biol 87:17-31.
Jezernik S, Craggs M, Grill WM, Creasey G, Rijkhoff NJ (2002) Electrical stimulation for the treatment of bladder dysfunction: current status and future possibilities. Neurol Res 24:413-430.
Kovacs GT, Storment CW, Rosen JM (1992) Regeneration microelectrode array for peripheral nerve recording and stimulation. IEEE Trans Biomed Eng 39:893-902.
Kreiner T, Kirk MD, Scheller RH (1987) Cellular and synaptic morphology of a feeding motor circuit in Aplysia californica. J Comp Neurol 264:311-325.
Lee DC, Grill WM (2005) Polarization of a spherical cell in a nonuniform extracellular electric field. Ann Biomed Eng 33:603-615.
Lenarz T, Lim HH, Reuter G, Patrick JF, Lenarz M (2006) The auditory midbrain implant: a new auditory prosthesis for neural deafness – concept and device description. Otol Neurotol 27:838-843.
Levanthal DK, Cohen M, Durand DM (2006) Chronic histological effects of the flat interface nerve electrode. J Neural Eng 3:102-113.
Light TS (1997) Ewing's analytical instrumentation handbook. 2nd edn ed W E Galen (New York: Marcel Dekker, Inc.) p 1104.
McCreery D, Lossinsky A, Pikov V, Liu X (2006) Microelectrode array for chronic deep- brain microstimulation and recording. IEEE Trans Biomed Eng 53:726-737.
McIntyre CC, Grill WM (1999) Excitation of central nervous system neurons by nonuniform electric fields. Biophys J 76:878-888.
McIntyre CC, Grill WM (2000) Selective microstimulation of central nervous system neurons. Ann Biomed Eng 28:219-233.
McIntyre CC, Grill WM (2002) Extracellular stimulation of central neurons: influence of stimulus waveform and frequency on neuronal output. J Neurophysiol 88:1592-1604.
62
McNaughton TG, Horch KW (1996) Metallized polymer fibers as leadwires and intrafascicular microelectrodes. J Neurosci Methods 70:103–110.
Mokwa W (2007) An implantable microsystem as a vision prosthesis. Med Device Technol 18:20, 22-23.
Naples GG, Mortimer JT, Scheiner A, Sweeney JD (1988) A spiral nerve cuff electrode for peripheral nerve stimulation. IEEE Trans Biomed Eng 35:905–916.
Navarro X, Krueger TB, Lago N, Micera S, Stieglitz T, Dario P (2005) A critical review of interfaces with the peripheral nervous system for the control of neuroprostheses and hybrid bionic systems. J Peripher Nerv Syst 10:229-258.
Nowak LG, Bullier J (1996) Spread of stimulating current in the cortical grey matter of rat visual cortex studied on a new in vitro slice preparation. J Neurosci Methods 67:237–247.
Parson DW, Ter Maat A, Pinsker HM (1983) Selective recording and stimulation of individual identified neurons in freely behaving Aplysia. Science 221:1203-1206.
Pedrini L, Magnoni F (2007) Spinal cord stimulation for lower limb ischemic pain treatment Interact. Cardiovasc Thorac Surg 6:495-500.
Rall W, Agmon-Snir H (1998) Methods in Neuronal Modelling – from Ions to Network. 2nd edn ed C Koch and I Segev (Cambridge, MA: The MIT Press) p 27-92.
Ranck JB (1975) Which elements are excited in electrical stimulation of mammalian central nervous system: a review. Brain Res 98:417-440.
Rattay F (1989) Analysis of models for extracellular fiber stimulation. IEEE Trans Biomed Eng 36:676-682.
Rattay F (1999) The basic mechanism for the electrical stimulation of the nervous system. Neurosci 89:335-346.
Sekirnjak C, Hottowy P, Sher A, Dabrowski W, Litke AM, Chichilnisky EJ (2006) Electrical stimulation of mammalian retinal ganglion cells with multielectrode arrays J Neurophysiol 95:3311-3327.
Shafique S, Dalsing MC (2006) Vagus nerve stimulation therapy for treatment of drug- resistant epilepsy and depression. Perspect Vasc Surg Endovasc Ther 18:323-327.
Spelman FA (2006) Cochlear electrode arrays: past, present and future. Audiol Neurootol 11:77-85.
63
Stoney SD, Thompson WD, Asanuma H (1968) Excitation of pyramidal tract cells by intracortical microstimulation: effective extent of stimulating current. J Neurophysiol 31:659-669.
Suihko V (1998) Modeling direct activation of corticospinal axons using transcranial electrical stimulation. Electroencephalogr Clin Neurophysiol/ Electromyogr Motor Cont 109:238-244.
Tarler MD, Mortimer JT (2004) Selective and independent activation of four motor fascicles using a four contact nerve-cuff electrode. IEEE Trans Neural Syst Rehabil Eng 12:251-257.
Tarver WB, George RE, Maschino SE, Holder LK, Wernicke JF (1992) Clinical experience with a helical bipolar stimulating lead. Pacing Clin Electrophysiol 15:1545-1556.
Tehovnik EJ, Tolias AS, Sultan F, Slocum WM, Logothetis NK (2006) Direct and indirect activation of cortical neurons by electrical microstimulation. J Neurophysiol 96:512-521.
Tyler DJ, Durand DM (2002) Functionally selective peripheral nerve stimulation with a flat interface nerve electrode. IEEE Trans Neural Syst Rehabil Eng 10:294-303.
Warman EN, Chiel HJ (1995) A new technique for chronic single-unit extracellular recording in freely behaving animals using pipette electrodes. J Neurosci Methods 57:161-169.
Williams PL, Bannister LH, Berry MM, Collins P, Dyson M, Ferguson MWJ (1995) Grays Anatomy 38th edn ed M M Berry, S M Standring and L H Bannister (Baltimore: Churchill & Livingstone) p 945-946 and p 1259.
Winter JO, Cogan SF, Rizzo JF (2007) Retinal prostheses: current challenges and future outlook. J Biomater Sci Polym Ed 18:1031-1055.
Ye H, Morton DW, Chiel HJ (2006b) Neuromechanics of multifunctionality during rejection in Aplysia californica. J Neurosci 26:10743-10755.
Ziv I, Baxter DA, Byrne JH (1994) Simulator for neural networks and action potentials: description and application. J Neurophysiol 71:294-308.
64
Figures and Tables
Figure 2-1. Schematic geometry of stimulating and recording electrodes in the in vitro experiments using Aplysia buccal ganglia. To illustrate intracellular recording and extracellular stimulation, the left semi-ganglion is shown with its protective sheath removed to expose the cell bodies. An extracellular stimulating electrode is placed directly above the soma of a neuron while an intracellular electrode is used to record from it simultaneously. To illustrate nerve recording and extracellular stimulation, the protective sheath is shown covering the right semi-ganglion. An extracellular stimulating electrode is placed above the sheath covering the soma of a neuron as a suction electrode is simultaneously used to record from the nerve containing its axon.
65
Figure 2-2. The experimental setup for measurements of the resistivity of Aplysia saline. Alternating positive and negative current pulses were applied to Aplysia saline in a conductance cell through two pieces of copper foil. The voltages across the two recording electrodes were measured as the separations between them were varied.
66
Figure 2-3. Morphology and equivalent electrical circuit of the Aplysia NEURON model. (A) Model morphology. The soma sphere is 200 μm in diameter and divided into 100 segments. An additional short soma segment is attached to the 100th soma segment as a small branch. The axon cylinder is 15 μm in diameter and 19951 μm in length, and divided into 200 segments of equal length except the initial axon segment, whose length is 51 μm (mean of the regular soma and axon segment lengths). The center of the soma is set to be (0, 0). The neuron is stimulated by an extracellular point source electrode at , outside the neuron. The soma segments will be automatically rotated to be perpendicular to the line drawn from the stimulating electrode to the center of the soma. See schematic diagrams 1 and 2. (B) Model equivalent electrical circuit.
The midpoint of each segment is a representation of the electrical node. represents the internal potential at the electrical node. represents the external voltage at the
electrical node. is the axial resistance between the and ( electrical nodes.
Each compartment contains an active membrane resistance and a membrane capacitance .
67
Figure 2-4. The polarization of the axon hillock varies as the extracellular stimulating electrode is placed at different positions. The membrane potential changes at the steady state were measured in the NEURON model. Positive membrane potential changes represent depolarization and negative membrane potential changes represent hyperpolarization. (A) A 100 ms current
68
pulse of 10 μA was applied at a variety of locations ( , 200), over the range from = -500
μm to = 2000 μm. Anodic currents depolarize the hillock as the stimulating electrode is placed on the side of the soma opposite to the axon, hyperpolarize the hillock as the electrode is placed near the hillock and depolarize the hillock again as the electrode is placed on the distal axon. (B) A 100 ms current of -10 μA was also applied at a variety of locations ( , 200), over the range from = -500 μm to = 2000 μm. The effects of cathodic currents on the axon hillock are the reverse. The schematics show the stimulating electrode locations, current flows of positive and negative charges and the approximate membrane potential changes along the neuron. For each schematic, the large dashed arrow indicates the membrane potential change at the axon hillock for the electrode location shown in this schematic. The darker color in both figures and schematics represents greater local depolarization.
69
Figure 2-5. The polarization along the neuron by extracellular ganglionic stimulation. The membrane potential changes at the steady state were measured in the NEURON model. Positive membrane potential changes represent depolarization and negative membrane potential changes represent hyperpolarization. (A) A 100 ms current pulse of 10 μA was applied at (-150, 0). Anodic currents hyperpolarize the proximal and middle soma while depolarizing the distal soma and proximal axon. (B) A 100 ms current of -10 μA was also applied at (-150, 0). Cathodic currents depolarize the proximal and middle soma while hyperpolarizing the distal soma and proximal axon. Grey arrows indicate the current flows of positive and negative charges. Darker color in the schematic neurons represents greater local depolarization.
70
Figure 2-6. Qualitative comparisons between experimental results and the NEURON model’s predictions: anodic currents activate a neuron by extracellular stimulation on the side of the soma opposite to the axon. (A) The neural responses due to anodic stimulation in the NEURON model. Anodic currents strongly hyperpolarized the tip of the soma and weakly hyperpolarized the middle of the soma, while depolarizing the initial axon segment to generate an action potential. (B) The intracellular recordings and artefact-subtracted recordings during anodic stimulation (for details, see Methods section). Both the intracellular recordings and artefact-subtracted recordings show an action potential at each site in the soma. The artefact-subtracted recordings also show strong hyperpolarization at the tip of the soma and slight hyperpolarization at the middle of the soma. The bars indicate the timing of extracellular stimulation. The grey lines show the resting membrane potentials. The grey arrows indicate the polarizations of the membrane. Each black arrow points to the peak of the action potential.
71
Figure 2-7. Qualitative comparisons between experimental results and the NEURON model’s predictions: cathodic currents inhibit the neuron by extracellular stimulation near the cell bodies. (A) The neural responses due to cathodic stimulation in the NEURON model. Cathodic currents strongly depolarized the tip of the soma and weakly depolarized the middle of the soma, while hyperpolarizing the initial axon segment and blocking the action potentials. (B) The intracellular recordings and artefact-subtracted recordings during cathodic stimulation (for details, see Methods section). Both the intracellular recordings and artefact-subtracted recordings show that action potentials were completely blocked during extracellular stimulation at both sites in the soma. The artefact-subtracted recordings also show strong depolarization at the tip of the soma and slight depolarization at the middle of the soma. The bars indicate the timing of extracellular stimulation. The grey lines show the resting membrane potentials.
72
Figure 2-8. Threshold currents for both anodic activation and cathodic inhibition increase with the distance from the stimulating electrode to the soma. (A) The square root of the threshold current yields a linear fit over this range of the electrode-to-soma distance: y = 0.193x + 7.04 (R2 = 0.874, p < 0.05). (B) The square root of the threshold current yields a linear fit over this range of the electrode-to- soma distance: y = 0.152x + 4.44 (R2 = 0.954, p < 0.05).
73
Figure 2-9. Threshold currents of the same neuron were very close when the sheath was intact and after the sheath was removed (n = 3; for details, see Methods section). This is one of the typical results of three replications (the sheath was entirely removed in this replication). Black square symbols indicate the threshold currents of the neuron when the sheath was intact. Grey diamond symbols indicate the threshold currents after removing the sheath of the same preparation.
74
Figure 2-10. Anodic currents can selectively activate an individual neuron. (A) Experimental setup of the three intracellular recording electrodes and the extracellular stimulating electrode in a desheathed ganglion (n = 8; for details, see Methods section). (B) Only the middle targeted neuron was activated by the threshold current for anodic stimulation. (C) Both the left and middle neurons were activated by 386% of this threshold current. (D) All three neurons were activated by 471% of this threshold current. In (B)-(D), the grey arrows point to the targeted neuron, the
75 duration of each pulse indicates the timing of stimuli and the black arrows point to the action potentials respectively. We sharpened the image of the lower right electrode in (A), which was present in the original picture but was slightly blurred.
76
Figure 2-11. Cathodic currents can selectively inhibit an individual neuron. (A) Experimental setup of the three intracellular recording electrodes and the extracellular stimulating electrode in a desheathed ganglion (n = 3; for details, see Methods section). (B) Only the middle targeted neuron was inhibited by the threshold current for cathodic stimulation. (C) Both the left and middle neurons were inhibited by 208% of this threshold current. (D) All three neurons were inhibited by 358% of this threshold current. In (B)-(D), the grey arrows point to the targeted neuron.
77
Figure 2-12. The spatial specificity for anodic activation predicted by the multiple-cell NEURON model. (A) Morphology of the NEURON model consisting of three identical adjacent neurons (for details, see Methods section). (B) The normalized threshold currents of the three neurons are predicted by the model as the stimulating electrode is moved along the line between (-200, -200) and (-200, 200). From = -99 μm to = 99 μm, the normalized anodic threshold currents of the middle neuron are lower than those of the other two neurons, so that the middle neuron can be selectively activated (note the star symbols and symmetric line). The maximum current window of spatial specificity occurs at = 0 μm. (C) The maximum current window decreases as the stimulating electrode is moved away from the neurons along the X-axis.
78
Figure 2-13. The spatial specificity for cathodic inhibition predicted by the multiple-cell NEURON model. (A) Morphology of the NEURON model consisting of three identical adjacent neurons (for details, see Methods section). (B) The normalized threshold currents of the three neurons are predicted by the model as the stimulating electrode is moved along the line between
(-200, -200) and (-200, 200). From = -99 μm to = 99 μm, the normalized threshold currents of the middle neuron are lower than those of the other two neurons, so that the middle neuron can be selectively inhibited (note the star symbols and symmetric line). The maximum current window of spatial specificity occurs at Yelec = 0 μm. (C) The maximum current window will decrease quickly as the stimulating electrode is moved away from the neurons along the X- axis.
79
Figure 2-14. The spatial specificity for a group of neurons with different sizes and geometric configurations predicted by the NEURON model and the analytical model. (A1) Morphology of the NEURON model consisting of a small targeted neuron surrounded by two large neighboring neurons, all aligned at their tips. (A2) The normalized threshold currents for anodic stimulation of these three neurons are predicted by both the NEURON model and the analytical model. The arrow pointing down indicates a positive specificity window (11.6%) for the targeted small neuron, so that it can be selectively stimulated. (B1) Morphology of the NEURON model when the targeted small neuron is buried more deeply; the three neurons are aligned at their hillocks. (B2) The normalized threshold currents for anodic stimulation of these three neurons are
80 predicted by both the NEURON model and the analytical model. The arrow pointing up indicates a negative specificity window for the targeted small neuron, so that it cannot be selectively stimulated. (C1) Morphology of the NEURON model with the three neurons aligned at their hillocks and stimulated by three electrodes. Two additional stimulating electrodes are added to apply cathodic currents near the neighboring large neurons. (C2) The normalized threshold currents for multiple-electrode stimulation of these three neurons are predicted by both the NEURON model and the analytical model. The arrow pointing down indicates a restored positive specificity window (8.3%) for the targeted small neuron, so that it can now be selectively stimulated.
81
Figure 2-15. The temporal specificity for anodic activation of an individual neuron demonstrated experimentally (n = 4). (A1) A 40 Hz current pulse train of sub-threshold current was applied to an individual neuron (B4/B5) while it was simultaneously recorded intracellularly. No action potentials were induced by this pulse train. (A2) Expanded intracellular recordings corresponding to the boxed area of (A1). (B1) A 40 Hz current pulse train of super-threshold current was applied to the same neuron. Each pulse induced a single action potential on the top of the stimulation artefact, due to the depolarization of the initial axon segments shown in Fig. 2-6. (B2) Expanded
82 intracellular recordings corresponding to the boxed area of (B1). (B3) Expanded intracellular recordings corresponding to (B2) when the intracellular recordings of (A2) were subtracted from (B2). In (B2) and (B3), the black arrows point to the peaks of action potentials.
83
Figure 2-16. The temporal specificity for cathodic inhibition of an individual neuron demonstrated experimentally (n = 4). A 1 s intracellular monophasic pulse was injected into the soma of a neuron (B4/B5) and generated a train of action potentials during this period. The top traces in both (A) and (B) are the intracellular recordings of the neuron. The bottom traces in both
84
(A) and (B) are the extracellular nerve recordings from buccal nerve 3 (BN3) which contains the axon of B4/B5. The boxed areas on the right are the expanded recordings corresponding to the boxed area on the left. Lines underneath traces represent the time of each cathodic extracellular pulse. Dotted lines show corresponding intracellular and extracellular action potentials. (A) A 100 μA 5 Hz cathodic extracellular pulse train was applied above the soma of the targeted neuron during the 1 s intracellular monophasic pulse. Action potentials appeared during the cathodic pulses in both intracellular and nerve recordings. Thus, this sub-threshold cathodic extracellular pulse train did not suppress action potentials. (B) A 250 μA 5 Hz cathodic extracellular pulse train was then applied above the soma of the targeted neuron during the 1 s intracellular monophasic pulse. Action potentials disappeared during the cathodic pulses in both intracellular and nerve recordings. Thus, this super-threshold cathodic extracellular pulse train suppressed action potentials.
85
Figure 2-17. Comparisons of the membrane polarization along a simulated neuron with different soma and axon diameters. (A) The membrane potential changes at the steady state were measured in the NEURON model as we varied the soma diameter alone. A 100 ms anodic current pulse of 10 μA was applied on the axonal axis 50 μm away from the tip of the soma. The black solid line
86 shows the membrane polarization along the neuron with the original soma diameter of 200 μm and axon diameter of 15 μm. The black dashed line shows the membrane polarization along the neuron as we reduced the soma diameter to 41.2 μm, the value for a cat spinal motor neuron (McIntyre and Grill, 2000). The grey solid line shows the membrane polarization along the neuron as we increased the soma diameter to 312.5 μm. (B) The membrane potential changes at the steady state were measured in the NEURON model as we varied the axon diameter alone. A 100 ms anodic current pulse of 10 μA was also applied on the axonal axis 50 μm away from the tip of the soma. The black solid line shows the membrane polarization along the neuron with the original axon diameter of 15 μm and soma diameter of 200 μm. The grey solid line shows the membrane polarization along the neuron as we reduced the axon diameter to 9.6 μm, obtained from the same model for a cat spinal motor neuron (McIntyre and Grill, 2000). The black dashed line shows the membrane polarization along the neuron with a larger axon diameter of 72.8 μm. Note that the ratios of the soma to the axon diameters are the same for the same style of lines in (A) and (B). Thus, changing the soma and axon diameters in opposite directions will have similar effects on the membrane polarization along the neuron.
87
Figure 2-18. Comparisons of the membrane polarization along the simulated vertebrate neuron with different neuronal structures. (A) Model morphology of the NEURON model of a cat spinal motor neuron using the geometric parameters from McIntyre and Grill (2000). The soma sphere is 41.2 μm in diameter and divided into 100 segments of equal length. The axon is 19951 μm long and consists of 200 node and internode segments. Each node is 9.6 μm in diameter and 1.5 μm in length, and each internode is 12 μm in diameter and 98.5 μm in length. Three dendritic trees are added to the leftmost, topmost and bottommost tips of the soma. Each of them consists of a dendrite root and two dendrite branches. Each dendrite root or branch is 133 μm in length and divided into 25 equal segments. The diameters are 8 μm and 5.04 μm for the dendrite root and branch, respectively. The center of the soma is set to be (0, 0). The neuron is stimulated by an extracellular point source electrode at , outside the neuron. (B1) The membrane potential changes at the steady state were measured under four conditions as we applied a 100 ms
88 anodic current pulse of 10 μA on the axonal axis, at (-221, 0). (B2) The membrane potential changes at the steady state were also measured under four conditions as we applied a 100 ms anodic current pulse of 10 μA off the axonal axis, at (-71, 50). In both (B1) and (B2), the grey dashed lines represent the membrane polarization along the neuron consisting of only the soma and unmyelinated axon. The grey solid lines represent the membrane polarization along the neuron consisting of the soma and myelinated axon. The black dashed lines represent the membrane polarization along the neuron consisting of the soma, the unmyelinated axon and three dendritic trees. The black solid lines represent the membrane polarization along the neuron consisting of the soma, the myelinated axon and three dendritic trees. Thus, for both stimulating electrode locations, the overall profile of the membrane polarization did not vary qualitatively after adding the dendritic trees or after replacing the unmyelinated axon with the myelinated one. However, the site of maximum membrane polarization shifted by one to three axon segments towards the soma after adding dendritic trees and myelinating the axon.
89
Geometric parameter Value Soma Diameter 200 μm Number of segments 101 Length of the 1st to 100th 2 segmentLength of the 101th segment 0.002 Axon Diameter 15 Number of segments 200 Length of the 1st segment 51 Length of the 2nd to 200th 100 segment
Table 2-1. The geometric parameters of the NEURON model for an Aplysia buccal neuron.
90
Electrical parameter Value
Extracellular resistivity of Aplysia saline ( e ) 19.3 Ωcm
Intracellular resistivity ( a ) 200
2 Membrane capacitance ( Cm ) 1 μF/cm Fast Na+ channels 2 Max. conductance ( g Na ) in the soma 0.024 S/cm Max. conductance ( ) in the axon 0.12 S/cm2
1 † Activation term (m ) of m gates (0.15vv 6) exp(-0.1 - 4)) -1
† Inactivation term ( m ) of m gates 6exp( 0.056v -3.61)
1 †† Time constant of ( m ) m gates (mm )*3^(celsius/10 - 0.63) † Activation term (h ) of h gates 0.185exp( 0.05v -3.25)
-1 † Inactivation term ( h ) of h gates 2.65 exp(-0.1v -3.5) 1
1 †† Time constant of ( h ) m gates (hh )*3^(celsius/10 - 0.63)
Reversal potential ( ENa ) 50 mV Slow K+ channels 2 Max. conductance ( g K ) in the soma 0.0072 S/cm Max. conductance ( ) in the axon 0.036 S/cm2
1 † Activation term (n ) -(0.008vv 0.442) exp( 0.1 5.5) 1
† Inactivation term ( n ) 0.1exp(-0.0125v -0.8125)
1 †† Time constant of ( n ) m gates (nn )*3^(celsius/10 - 0.63)
Reversal potential ( EK ) -77 mV Leakage channels 2 Conductance ( gL ) 0.00028 S/cm
Reversal potential ( EL ) -65 mV † v is the membrane potential of a neuronal segment. †† celsuis is the centigrade environmental temperature.
Table 2-2. The electrical parameters of the NEURON model for an Aplysia buccal neuron.
91
Geometric parameter Value Soma Diameter 41.2 μm † Number of segments 100 Length of each segment 0.412 Axon (node) Diameter 9.6 † Number of segments 200 Length of each segment 1.5 Axon (internode) Diameter 12 † Number of segments 200 Length of each segment 98.5 Dendrite tree (dendrite root) Diameter 8 † Total Length 133 † Number of segments 25 Length of each segment 5.32 Dendrite tree (dendrite branch) Diameter 5.04 † Total Length 133 † Number of segments 25 Length of each segment 5.32 † McIntyre and Grill 2000
Table 2-3. The geometric parameters of the model for a cat spinal motorneuron.
92
Chapter 3
Extracellularly identifying motor neurons for a muscle motor pool in Aplysia californica
Summary
To uniquely identify and characterize motor neurons within a motor pool (in particular, for the I1/I3 muscle) during muscle movements, I utilized the extracellular technique described in Chapter 2 to selectively stimulate individual motor neurons and then recorded from them during Aplysia feeding behaviors (Lu et al., 2008). Then I created a simplified diagnostic method for rapid identification of motor neurons in more intact preparations when muscle innervation measurement is not feasible, e.g., in the suspended buccal mass preparation (McManus et al., 2012) or in vivo (Cullins and Chiel,
2010).
This chapter is based on a previous publication: Lu H, McManus JM, Chiel HJ (2013) Extracellularly identifying motor neurons for a muscle motor pool in Aplysia californica. J Vis Exp 73:e50189, doi:10.3791/50189.
93
Introduction
In animals with large identified neurons (e.g. mollusks), analysis of motor pools is done using intracellular techniques (Benjamin and Rose, 1979; McCrohan and Benjamin,
1980; Peters and Altrup, 1984; Church and Lloyd, 1991, 1994). Recently, we developed a technique to extracellularly stimulate individual neurons in Aplysia californica (Lu et al.,
2008). We now describe a protocol for using this technique to uniquely identify and characterize motor neurons within a motor pool.
This extracellular technique has advantages. First, extracellular electrodes can stimulate and record neurons through the sheath (Lu et al., 2008), so it does not need to be removed. Thus, neurons will be healthier in extracellular experiments than in intracellular ones. Second, if ganglia are rotated by appropriate pinning of the sheath, extracellular electrodes can access neurons on both sides of the ganglion, which makes it easier and more efficient to identify multiple neurons in the same preparation. Third, extracellular electrodes do not need to penetrate cells, and thus can be easily moved back and forth among neurons, causing less damage to them. This is especially useful when one tries to record multiple neurons during repeating motor patterns that may only persist for minutes. Fourth, extracellular electrodes are more flexible than intracellular ones during muscle movements. Intracellular electrodes may pull out and damage neurons during muscle contractions. In contrast, since extracellular electrodes are gently pressed onto the sheath above neurons, they usually stay above the same neuron during muscle contractions, and thus can be used in more intact preparations.
94
To uniquely identify motor neurons for a motor pool (in particular, the I1/I3 muscle in Aplysia) using extracellular electrodes, one can use features that do not require intracellular measurements as criteria: soma size and location, axonal projection, and muscle innervation (Church et al., 1991; Church and Lloyd, 1991, 1994). For the particular motor pool used to illustrate the technique, we recorded from buccal nerves 2 and 3 to measure axonal projections, and measured the contraction forces of the I1/I3 muscle to determine the pattern of muscle innervation for the individual motor neurons.
We demonstrate the complete process of first identifying motor neurons using muscle innervation, then characterizing their timing during motor patterns, creating a simplified diagnostic method for rapid identification. The simplified and more rapid diagnostic method is superior for more intact preparations, e.g., in the suspended buccal mass preparation (McManus et al., 2012) or in vivo (Cullins and Chiel, 2010). This process can also be applied in other motor pools (Zhurov et al., 2005b; Hurwitz et al.,
1994; Morton and Chiel, 1993b) in Aplysia or in other animal systems (Iles, 1972;
Benjamin and Rose, 1979; Peters and Altrup, 1984; Westerfield et al., 1986).
Materials and Methods
Note: the original publication includes a very detailed step-by-step protocol here to instruct other researchers to set up a similar preparation and used the extracellular technique to identify motor neurons for a motor pool. For improved readability of the dissertation, the detailed protocol has been moved into Appendix B at the end of this thesis. A summarized protocol is presented here.
95
Aplysia californica weighing 200–350 g (Marinus Scientific, Garden Grove, CA) were maintained in an aerated aquarium containing artificial seawater (Instant Ocean;
Aquarium Systems, Mentor, OH) kept at 16±1 °C. Animals were fed every other day with dried seaweed (Nori). Before experiments, animals were presented with seaweed to measure the intervals between bites. Animals that displayed strong bites at 3-5 s intervals were selected for use.
A specialized dish was made (100 mm in diameter, 15 mm high) for this study
(Fig. 3-1; see Appendix B, section 1). The dish was divided into a black chamber, a middle platform and a front chamber to separate the cerebral ganglion from the buccal ganglia and buccal mass. Extracellular glass electrodes, suction electrodes, hook electrodes, and force transducers were used for this study (see Appendix B, sections 2, 5-
8).
The selected animals were anesthetized by an injection of isotonic MgCl2 (333 mM), whose volume (ml) is about 50% of the animals’ body weights (g). The buccal ganglia, cerebral ganglion and buccal mass were then dissected out and hook electrodes were implanted on the key muscle and nerves, i.e., the I2 muscle, the radular nerve (RN), the buccal nerves 2 and 3 (BN2s and BN3s) for pattern recognition (see Appendix, section 3; Morton and Chiel, 1993a).
Then, the cerebral ganglion and the buccal ganglia and buccal mass were placed in the specialized dish and prepared for extracellular neuron identification and force measurements (see Appendix B, sections 4, 9).
96
Representative Results
Figs. 3-4 and 3-5 show typical results used to identify two I1/I3 motor neurons.
Fig. 3-4 shows the soma recordings of a large motor neuron, B3, during egestive-like and ingestive-like patterns (Fig. 3-4C, D). The one-for-one corresponding spikes on the soma channel and the ipsilateral BN2 channel (Fig. 3-4E) show that the specificity of B3 soma recording was maintained during patterns. B3 fires during the middle-to-late retraction phase of the patterns. From Fig. 3-4 and other results (not shown), we found that the BN2 unit of B3 is always the largest BN2 unit. Thus, it can also be detected directly from BN2 recordings.
Fig. 3-5 shows the soma recordings of a small neuron, B43, during egestive-like and ingestive-like patterns (Fig. 3-5C, D). The one-for-one corresponding spikes on the soma channel and the ipsilateral BN2 channel (Fig. 3-5E) also show that the specificity of
B43 soma recording was maintained during patterns. Neuron B43 bursts at the end of the retraction phase during patterns. Since the BN2 unit of B43 is small, it would be difficult to identify it from the BN2 recordings without the soma recording; however, because it fires most intensely at the end of the BN2 motor pattern, the end of B43’s burst can still be identified from BN2 recordings alone.
Fig. 3-6 shows an optimized diagnostic tree that does not require muscle innervation as a criterion, which makes it much easier to extracellularly identify the I1/I3 motor neurons in the suspended buccal mass preparation or in vivo. The diagnostic tree was developed, however, by using measures of force and EMG, and thus illustrates how the techniques in this protocol can lead to streamlined motor neuron identification.
97
Discussion
In animals with large identified neurons, such as mollusks (for example, Lymnaea,
Helix, and Aplysia), analysis of motor pools is typically done using intracellular recording
(Benjamin and Rose 1979; McCrohan and Benjamin, 1980; Peters and Altrup, 1984;
Church and Lloyd, 1991, 1994). In this protocol, we describe a process for uniquely identifying the motor neurons for a motor pool using an extracellular technique. We used the force measurements as an illustration of this process. One could also use EMG to measure muscle innervations. Briefly, to do so, the protocol needs to be altered to attach hook electrodes to different regions of the I1/I3 muscle for EMG recordings.
The extracellular technique has certain advantages over intracellular techniques, some of which have already been described above. First, the extracellular technique requires less time and effort to prepare ganglia for experiments and will cause less damage to neurons. Usually, it will take 20-30 minutes to prepare the buccal ganglia for extracellular experiments and approximately 1.5 hours to prepare the buccal ganglia that are attached to the buccal mass for intracellular experiments. Since muscles will become less active as the time passes, the time difference between the ganglia preparations for extracellular experiments and intracellular ones might be critical for the success of experiments. Fig. 3-7 shows the comparison of success rates for identifying the motor neurons for the I1/I3 muscle using extracellular or intracellular technique in force studies.
In all 35 extracellular force experiments (100%), we were able to identify at least one motor neuron for the I1/I3 muscle. In 31 out of 35 (89%) extracellular experiments, we
98 were able to identify at least three I1/I3 motor neurons. In 19 out of 35 (54%) extracellular experiments, we were able identify at least five different I1/I3 motor neurons. In contrast, the success rates of the intracellular experiments with the same force transducer setup were lower. In 23 out of 27 (85%) intracellular experiments, we were able to identify at least one I1/I3 motor neuron. In 8 out of 27 (30%) intracellular experiments, we were able to identify at least three I1/I3 motor neurons. In only 1 out of
27 (4%) intracellular experiments, we were able to identify five I1/I3 motor neurons.
Thus, the likelihood of identifying multiple neurons in the same ganglion is higher using extracellular techniques in contrast to intracellular techniques.
In addition, the extracellular technique can access many neurons on both sides of ganglia during the same experiment. Usually, after desheathing, intracellular electrodes can only access neurons on the side of the ganglion that has been desheathed. For example, when one of the two buccal ganglia (e.g., the hemiganglion on the left side) is pinned caudal side up, it will be easy for intracellular electrodes to access the neurons on that side of the ganglion, e.g., B6, B9, B10, B39, and B43, but difficult to access the neurons on the rostral side of the ganglion, such as B4, B5, B8a, B8b, B38 and B82. In contrast, extracellular electrodes can access many neurons on both sides of the same buccal ganglion with appropriate rotation of the ganglion. The degree of rotation is adjustable and reversible. This also increases the likelihood of identifying multiple neurons in the same ganglion.
Since extracellular electrodes are gently pressed onto the sheath covering the neurons, these electrodes will not be pulled out of neurons, which may create large holes in the membrane and cause damage, as occurs with intracellular electrodes during muscle
99 movements. The signal size will vary as the ganglia move during the muscle movements.
Note that sometimes during large muscle movements, the extracellular soma recording signals will be decreased or even lost. However, we can easily move the extracellular electrode back onto the neuron and recover the original signals. This makes it feasible to apply the extracellular technique to the suspended buccal mass preparation for behavioral studies, during which muscles generate large contractions as the preparation generates different behavioral responses. For example, in 47 out of 48 suspended buccal mass experiments (98%), we were able to identify at least one motor neuron for the I1/I3 muscle. In 23 out of 48 (48%) suspended buccal mass experiments, we were able to identify at least three I1/I3 motor neurons. In 11 out of 48 (23%) suspended buccal mass experiments, we were able to identify at least five motor neurons for the I1/I3 muscle and record from them during motor patterns as the buccal mass was performing feeding-like behaviors. The extracellular technique is also applicable to other more complicated semi- intact preparations, such as the isolated head feeding preparations that include the tentacles, lips, jaws, buccal mass, buccal ganglia, and cerebral ganglion (Weiss et al.,
1986; Rose et al., 1991; Morton and Chiel, 1993b; Jing and Weiss, 2005). Since the sensory input is very important for eliciting feeding behaviors in such preparations, the extracellular technique will be particularly useful because of its simplicity and less damaging features. Previous studies also show that it is possible to identify and chronically record B4/B5 in vivo (Warman and Chiel, 1995). In these earlier experiments, the investigators used low current (10-20 µA) BN2-a stimulation to selectively activate
B4/B5 and glued a short polyethylene tube to the sheath above B4/B5 for recording, into which were inserted a pair of twisted stainless steel wires. Thus, it is also possible to
100 identify and record from motor neurons in vivo using the polyethylene tube electrode that is glued onto the sheath covering the ganglia (Chestek and Chiel, unpublished results).
The extracellular technique also has some limitations. First, it will be difficult for extracellular electrodes to stimulate or record neurons that are too small or too deep within the ganglion. Note that it is still possible to activate neurons that are not at the surface via extracellular stimulation. However, our model (Lu et al., 2008) has showed that the stimulation may lose specificity when the target neuron is deeper than the neighboring neurons. When the neuron is deeper, the electrode-to-soma distance will be greater and higher current will be needed to activate this neuron, which may be high enough to activate other surface neurons nearby. Second, if a neuron is stimulated extracellularly with too much current, it may be damaged and no longer respond; much smaller currents are used in intracellular stimulation, though too much current intracellularly can also damage neurons. Sometimes the soma recording will include multiple units from both the target neuron and adjacent neurons, which is less specific than intracellular recording. In addition, it may be more difficult to precisely control and monitor the firing frequency of an individual neuron using the extracellular rather than the intracellular technique, because the extracellular electrode cannot stimulate and record the same neuron simultaneously. Moreover, the extracellular technique will not be able to record the synaptic input from premotor neurons. In addition, it may be difficult to apply neurotransmitters iontophoretically to a specific neuron unless the ganglion is desheathed, although we have shown that it is possible to stimulate a ganglion using carbachol without removing the sheath (Azizi et al., 2010).
101
The limitations of extracellular identification techniques made some neurons in the motor pool difficult to identify. In this particular example, the extracellular technique reliably identified most of the motor neurons for the I1/I3 muscle in Aplysia: B3, B6, B9,
B38, B43, and B82, based on soma size and location, nerve projection, and muscle innervation. However, we have not been able to reliably identify B10 and B39. Previous intracellular work (Church and Lloyd, 1991, 1994) showed that B10 and B39 are two adjacent neurons on the caudal side of the buccal ganglia, between the B4/B5 region and the B6 region. Both neurons project bilaterally onto the BN2s. B10 innervates the middle and posterior region of the I1/I3 muscle, whereas B39 innervates the anterior region of the I1/I3 muscle. Based on the soma location and nerve projection criteria, we found more than two motor neurons that project bilaterally onto the BN2s in four different experiments. Since their soma locations, muscle innervations, and timing of activity during motor patterns were variable from animal to animal, we were not sure if they were the same neurons. Thus, we were not able to reliably identify B10 and B39 using the extracellular technique because of the lack of consistency. To uniquely identify them, we need to do a more thorough survey of neurons in the buccal ganglia, and may need additional criteria, such as the synaptic input from premotor neurons B4/B5, and the responses to transmitters, which require intracellular techniques.
With appropriate modifications, this technique is also applicable to other motor pools, e.g., the I5 muscle (Zhurov et al., 2005b), the I2 muscle (Hurwitz et al., 1994), and the I4 muscle (Morton and Chiel, 1993b) in Aplysia or to other systems, e.g., Lymnaea stagnalis (Benjamin and Rose, 1979), Helix pomatia (Peters and Altrup, 1984), cockroach (Iles, 1972), and zebrafish (Westerfield et al., 1986). For example, if one
102 wants to apply this technique to the motor neurons for the I5 muscle (also known as the accessory radular closer muscle or ARC; Zhurov et al., 2005a, b) in Aplysia, one should keep the BN3s attached to the buccal mass instead of the BN2s, because the I5 motor neurons B15 and B16 project on the ipsilateral BN3 (Church and Lloyd, 1991, 1994).
Then the buccal mass should be prepared to expose the I5 muscle for EMG or force studies. After the neurons have been reliably identified in the reduced preparation, an optimized diagnostic method could also be created for future behavioral studies.
The technique we have described compares favorably with other extracellular techniques such as multi-electrode arrays and voltage-sensitive dyes. The voltage- sensitive dye (Baker et al., 2005) technique is only used for recording, whereas our extracellular electrodes and multi-electrode arrays (Fejtl et al., 2006) can be used for both stimulation and recording. Both the multi-electrode array (Baker et al., 2005) and voltage- sensitive dyes (Fejtl et al., 2006) can record signals from many neurons simultaneously.
Although one single extracellular electrode may only record from one or two neurons depending on its tip size and the electrode location, it is certainly possible to position several on a ganglion simultaneously, and we have done this successfully. The standard in vitro multi-electrode array has 8 x 8 or 6 x 10 electrodes (Baker et al., 2005). Since the electrodes are evenly distributed in the array, it is often challenging to determine the identity of the underlying neurons from which recordings are obtained, since the neurons are not evenly distributed, and significant post-processing of the signals, some of which is still manual, must be done to resolve this ambiguity. In contrast, because the extracellular electrodes are positioned over single somata, the identity of the underlying neuron is clear. Thus, it seems that multi-electrode arrays and voltage-sensitive dyes may
103 be more efficient for multiple simultaneous recordings. However, our extracellular electrode technique may provide better selectivity for both stimulation and recording.
Acknowledgements
This research was supported by NIH grant NS047073 and NSF grant
DMS1010434.
104
References
Azizi F, Lu H, Chiel HJ, Mastrangelo CH (2010) Chemical neurostimulation using pulse code modulation (PCM) microfluidic chips. J Neurosci Methods 192:193-198.
Baker BJ, Kosmidis EK, Vucinic D, Falk CX, Cohen LB, Djurisic M, Zecevic D (2005) Imaging brain activity with voltage- and calcium-sensitive dyes. Cell Mol Neurobiol 25: 245–282.
Benjamin PR, Rose RM (1979) Central generation of bursting in the feeding system of the snail, Lymnaea stagnalis. J Exp Biol 80:93-118.
Church PJ, Cohen KP, Scott ML, Kirk MD (1991) Peptidergic motoneurons in the buccal ganglia of Aplysia californica: immunocytochemical, morphological, and physiological characterizations. J Comp Physiol A 168:323-336.
Church PJ, Lloyd PE (1991) Expression of diverse neuropeptide cotransmitters by identified motor neurons in Aplysia. J Neurosci 11:618-625.
Church PJ, Lloyd PE (1994) Activity of multiple identified motor neurons recorded intracellularly during evoked feedinglike motor programs in Aplysia. J Neurophys 72: 1794-1809.
Cullins MJ, Chiel HJ (2010) Electrode fabrication and implantation in Aplysia californica for multi-channel neural and muscular recordings in intact, freely behaving animals. J Vis Exp 40:e1791, doi:10.3791/1791.
Fejtl M, Stett A, Nisch W, Boven K-H, Möller A (2006) On Micro-Electrode Array Revival. In: Baudry M. and Taketani M. (eds). Advances in Network Electrophysiology Using Multi-Electrode Arrays. New York: Springer Press. p 24-37.
Hurwitz I, Goldstein RS, Susswein AJ (1994) Compartmentalization of pattern-initiation and motor functions in the B31 and B32 neurons of the buccal ganglia of Aplysia californica. J Neurophysiol 71:1514-1527.
Hurwitz I, Neustadter D, Morton DW, Chiel HJ, Susswein AJ (1996) Activity patterns of the B31/B32 pattern initiators innervating the I2 muscle of the buccal mass during normal feeding movements in Aplysia californica. J Neurophys 75:1309-1326.
Iles JF (1972) Structure and synaptic activation of the fast coxal depressor motoneurone of the cockroach, Periplaneta americana. J Exp Biol 56:647-656.
105
Jing J, Weiss KR (2005) Generation of variants of a motor act in a modular and hierarchical motor network. Curr Biol 15:1712-1721.
Kandel ER Behavioral biology of Aplysia. Freeman, San Francisco.
Lu H, Chestek CA, Shaw KM, Chiel, HJ (2008) Selective extracellular stimulation of individual neurons in ganglia. J Neural Eng 5:287-309.
McCrohan CR, Benjamin PR (1980) Synaptic relationships of the cerebral giant cells with motoneurones in the feeding system of Lymnaea stagnalis. J Exp Biol 85:169- 186.
McManus JM, Lu H, Chiel HJ (2012) An in vitro preparation for eliciting and recording feeding motor programs with physiological movements in Aplysia californica. J Vis Exp 70:e4320, doi:10.3791/4320.
Morton DW, Chiel HJ (1993a) In vivo buccal nerve activity that distinguishes ingestion from rejection can be used to predict behavioral transitions in Aplysia. J Comp Physiol A 172:17-32.
Morton DW, Chiel HJ (1993b) The timing of activity in motor neurons that produce radula movements distinguishes ingestion from rejection in Aplysia. J Comp Physiol A 173:519-536.
Nargeot RN, Baxter DA, Byrne JH (1997) Contingent-dependent enhancement of rhythmic motor patterns: an in vitro analog of operant conditioning. J Neurosci 17:8093-8105.
Peters M, Altrup U (1984) Motor organization in pharynx of Helix pomatia. J Neurophysiol 52:389-409.
Rosen SC, Miller MW, Cropper EC, Kupfermann I (2000b) Outputs of radula mechanoafferent neurons in Aplysia are modulated by motor neurons, interneurons, and sensory neurons. J Neurophysiol 83:1621-1636.
Rosen SC, Miller MW, Evans CG, Cropper EC, Kupfermann I (2000a) Diverse synaptic connections between peptidergic radula mechanoafferent neurons and neurons in the feeding system of Aplysia. J Neurophysiol 83:1605-1620.
Rosen SC, Teyke T, Miller MW, Weiss KR, Kupfermann I (1991) Identification and characterization of cerebral-to-buccal interneurons implicated in the control of motor programs associated with feeding in Aplysia. J Neurosci 11:3630-3655.
Scott ML, Govind CK, Kirk MD (1991) Neuromuscular organization of the buccal system in Aplysia californica. J Comp Neurol 312:207-222.
106
Susswein AJ, Rosen SC, Gapon S, Kupfermann I (1996) Characterization of buccal motor programs elicited by a cholinergic agonist applied to the cerebral ganglion of Aplysia californica. J Comp Physiol A 179:509-524.
Warman EN, Chiel HJ (1995) A new technique for chronic single-unit extracellular recording in freely behaving animals using pipette electrodes. J Neurosci Methods 57:161-169.
Weiss KR, Chiel HJ, Koch U, Kupfermann I (1986) Activity of an identified histaminergic neuron, and its possible role in arousal of feeding behavior in semi- intact Aplysia. J Neurosci 6:2403-2415.
Westerfield M, McMurray JV, Eisen JS (1986) Identified motoneurons and their innervation of axial muscles in the zebrafish. J Neurosci 6:2267-2277.
Zhurov Y, Proekt A, Weiss KR, Brezina V (2005a) Changes of internal state are expressed in coherent shifts of neuromuscular activity in Aplysia feeding behavior. J Neurosci. 25:1268-1280.
Zhurov Y, Weiss KR, Brezina V (2005b) Tight or loose coupling between components of the feeding neuromusculature of Aplysia? J Neurophysiol 94:531-549.
107
Figures and Tables
Figure 3-1. Schematic of overall setup and the dish for the force studies. The top image shows a top view. The bottom image shows a side view (corresponding to the dashed line in the middle of the top view). The cerebral ganglion is pinned to Sylgard in the back chamber (area A). The buccal ganglia are pinned to Sylgard on the middle platform (area C). The back chamber and middle platform are separated by an elevated Sylgard wall (area B). The cerebral-buccal connectives (CBCs) pass through a notch in the Sylgard wall, sealed with vacuum grease. The buccal mass is glued to the glass bottom of the front chamber (area D). The buccal nerves 2 (BN2s) are attached to the buccal mass. Two hooks attached to silk sutures are inserted into the anterior and posterior regions of the I1/I3 muscle. The silk sutures are then tied to the force transducer. The figure uses dark gray, light gray, and white to indicate the surfaces of areas A, B, C, and D. The darker the color, the higher the corresponding surface. The figure uses a, b, c, and d to indicate important dimensions of the dish. Length a is 3-4 mm, the width of the notch that connects the back chamber and middle platform. Length b is about 3-5 mm, the height difference between the surfaces of the middle platform (area C) and the Sylgard wall (area B). Length c indicates the length of the notch, which is about 5 mm. Length d shows the width of the middle platform (area C), which is about 5 mm.
108
Figure 3-2. Schematic of the buccal ganglia and electrodes setup. The figure shows the locations of key nerves, including buccal nerves 1, 2, and 3 (BN1, BN2, and BN3), the esophageal nerve (EN), the radular nerve (RN), the I2 nerve and muscle, and the cerebral buccal connective (CBC). Note that the BN2s are attached to the buccal mass (see Fig. 3-1). The CBCs are attached to the cerebral ganglion, passing through the notch of the Sylgard wall and are sealed with vacuum grease (see Fig. 3-1). The RN and the I2 nerve and muscle are pulled above the ganglia and pinned proximal to the buccal mass (front direction). Blue lines indicate the location of pins. Two bent pins (red lines labeled 1) are used to anchor the CBCs. Note that a flap of sheath of the CBC on the left side is folded and pinned down between BN2 and BN3 to rotate the left buccal ganglion (red line labeled 2). In some ganglia, it may be more convenient to pin the sheath down between CBC and BN3. An additional pin is added to the side of the ganglion that is proximal to the EN (red line labeled 3) for further rotation and stabilization. The extracellular glass electrode is placed on top of the sheath above the soma for extracellular stimulation and recording. The hook electrodes are attached to the BN3s and the pins holding those nerves in place should be placed more distally than the attachment points of these hook electrodes. Two suction electrodes are attached to the RN and the I2 nerve and muscle (see inset for a clearer view of the I2 nerve and muscle).
109
110
Figure 3-3. A picture and schematic of the neuron map for extracellular identification of the I1/I3 motor neurons in the Aplysia buccal ganglion. The left picture shows a right side buccal ganglion, pinned caudal side up. To rotate the buccal ganglia, the RN and the I2 nerve/muscle are pulled above the buccal ganglia and pinned proximal to the side of the EN. A flap of the CBC sheath is also folded and pinned for rotation (see Fig. 3-2), so that the neurons at the rostral side or at the caudal/rostral border can be seen. The right schematic is drawn based on the left picture. The picture and schematic together indicate the locations of the I1/I3 motor neurons B3, B6, B9, B10, B38, B39, B436,7 and B8222,23, as well as some other neurons. Neurons B8a and B8b are responsible for the largest unit on the RN, and innervate the muscle I4 controlling the grasper6,17. Neurons B4 and B5 are responsible for the largest unit on the BN318. Although the sizes and locations of the I1/I3 motor neurons are variable from animal to animal, the relative sizes and locations are quite reliable for most neurons: B3, B6, B9, B38, B43, and B82. See Discussion for more details about the I1/I3 motor neurons, especially some of the difficulties of uniquely identifying B10 and B39.
111
Figure 3-4: Identifying and characterizing the I1/I3 motor neuron B3. A) Extracellular stimulation of B3 (at arrow 1) and recording from the B3 soma (starting at arrow 2) as well as from the corresponding nerves and muscle regions. From top to bottom, the channels are recordings from the B3 soma, the contralateral BN2, the ipsilateral BN2, the ipsilateral BN3, the contraction force of the anterior region of the I1/I3 muscle, and the contraction force of the posterior region of the I1/I3 muscle. The blue box highlights the duration of forces in the anterior and posterior regions of the I1/I3 muscle. In this particular case, the posterior force is greater than the anterior force. B) Expanded view of the area outlined by the red box in A1. The one-for-one corresponding action potentials in the B3soma and the iBN2 channels show that B3 only projects on the ipsilateral BN2. C) Extracellular recording from the B3 soma and nerves in an egestive- like motor pattern. D) Extracellular recording from the B3 soma and nerves in an ingestive-like motor pattern. In C and D, from top to bottom, the channels are recordings from the B3 soma, the I2 nerve, the RN, the ipsilateral BN2, and the ipsilateral BN3. The blue boxes indicate the protraction and retraction phases of the patterns. The red bars in the B3soma channel in both C and D highlight the action potentials recorded from the B3 soma. The red bars in the iBN2 channel in both C and D indicate the corresponding timing when B3 is firing in the ipsilateral BN2 during the feeding motor patterns. E) Expanded view of the B3soma and the iBN2 channels marked by the red bars. The dashed lines show the one-for-one relationship between the action potentials in the
B3soma and the iBN2 channels. Note that the BN2 unit of B3 is the largest of all units. Thus, we can also detect the BN2 units of B3 directly from the BN2 recordings without soma recordings.
112
Figure 3-5: Identifying and characterizing the I1/I3 motor neuron, B43. A) Extracellular stimulation of B43 (at arrow 1) and recording from its soma (starting at arrow 2) as well as from the corresponding nerves and muscle regions. From top to bottom, the channels are recordings from the B43 soma, the contralateral BN2, the ipsilateral BN2, the ipsilateral BN3, the contraction force of the anterior region of the I1/I3 muscle, and the contraction force of the posterior region of the I1/I3 muscle. The blue box highlights the force measurements of the I1/I3 muscle during B43 activity. Activating B43 generates a small posterior force, but no anterior force. B) Expanded view of the area outlined by the red box in A. The dashed lines show the one- for-one relationship between the action potentials in the B43soma and the iBN2 channels, which indicates that B43 projects on the ipsilateral BN2 only. C) Extracellular recording from the B43 soma and nerves in an egestive-like motor pattern. D) Extracellular recording from the B43 soma and nerves in an ingestive-like motor pattern. In C and D, from top to bottom, the channels are recordings from the B43 soma, the I2 nerve, the RN, the ipsilateral BN2, and the ipsilateral BN3. The blue boxes indicate the protraction and retraction phases of the patterns. The red bars in the
B43soma channel in both C and D highlight the action potentials recorded from the B43 soma. The red bars in the iBN2 channel in both C and D indicate the corresponding timing when B43 is firing in the ipsilateral BN2 in these patterns. E) Expanded view of the B43soma and the iBN2 channels marked by the red bar in D. The dashed lines show the one-for-one relationship between the action potentials in the B43soma and the iBN2 channels. Note that the BN2 units of B43 are small and very difficult to detect without soma recordings, but fire consistently at the end of the BN2 motor program, providing another way to identify them. Note also that the larger unit shown in the bottom panel in E is a collision of a B43soma unit with another extracellular unit.
113
Figure 3-6: The optimized diagnostic tree for identifying some of the I1/I3 motor neurons using extracellular soma and nerve recordings. This diagnostic method requires the minimal information for identifying the I1/I3 motor neurons, making it much easier to identify motor neurons in the suspended buccal mass preparation or in vivo. B3 has the largest BN2 unit among the identified I1/I3 motor neurons. In the rest of the motor neurons, B6 and B9 are the only two neurons that project on both BN2 and BN3. B9 is more lateral than B6. The rest of the neurons projecting only on BN2 can also be divided into two groups. One group of neurons projects bilaterally through the BN2s, which includes B10 and B39 and some unknown neurons. The other group of neurons projects ipsilaterally on BN2 only, which includes B38, B43, and B82. B38 is near B3 and B9. B82 is near B8 (see Fig. 3-3). B43 is near B6. Its BN2 unit is small and bursts at the end of feeding patterns.
114
Figure 3-7: Comparison of success rates of neuron identification during force experiments using either the extracellular technique or the intracellular technique. With the same force transducer setup, we did 35 experiments using the extracellular technique (small blue dots) and 27 experiments using the conventional intracellular technique (large purple dots) to identify the I1/I3 motor neurons. The x-axis indicates the least number of motor neurons for the I1/I3 muscle that were identified in each type of experiment. The y-axis indicates the percentage success rate of each type of experiment. For example, in 19 out of 35 (54%) of the extracellular experiments, we were able identify at least five different I1/I3 motor neurons. In only 1 out of 27 (4%) of the intracellular experiments, we were able to identify at least five I1/I3 motor neurons. It is clear that the success rate in identifying neurons is much higher for any given number of neurons using the extracellular technique.
115
Chapter 4
Pre-modulation: Neural activity during biting prepares a retractor muscle for force generation during swallowing in Aplysia
Summary
It is essential for animals to rapidly modify behaviors to meet changing environmental demands. The periphery (e.g., muscles) must be prepared to properly respond to neural inputs at the right time (Bernstein, 1967). This can be achieved by properly positioning the muscle (Ye et al., 2006a) or by coordinated neuromodulation.
What is the behavioral role of neuromodulation at the individual motor neuronal level?
How does it prepare muscles for the normal expression of behaviors in vivo? To address these questions, we studied biting and swallowing, two similar but distinct ingestive behaviors of Aplysia, using in vivo nerve and muscle recordings as well as intracellular, extracellular, and force recordings from several in vitro preparations. Biting is an attempt to grasp food, associated with a strong protraction and weak retraction of the feeding grasper (the radula/odontophore). After animals grasp food, the bite rapidly switches to swallows. During swallowing, the radula/odontophore protracts weakly and retracts strongly to transfer food into the esophagus (Neustadter et al., 2007). The I1/I3 complex is the main retractor muscle and is innervated by multiple motor neurons (Church and
Lloyd, 1994; Morton and Chiel, 1993a). We report that (1) B6, B9 and B3 are key motor
This chapter is based on a previously submitted article: Lu H, McManus JM, Cullins MJ, Chiel HJ (2014) Pre- modulation: Neural activity during biting prepares a retractor muscle for force generation during swallowing in Aplysia. J of Neurosci submitted in Feb 2014.
116 neurons for I1/I3 force generation during retraction; (2) the activity of B6, B9 and B3 does not generate force in biting, but pre-modulates I1/I3 for the initial swallow, the most critical response for retaining food; (3) the repeated activity of B6, B9 and B3 in subsequent swallows strongly enhances I1/I3 muscle forces.
117
Introduction
The ability to rapidly modify behaviors to meet changing environmental demands is essential. For this purpose, the periphery (e.g., muscles) must be prepared to generate appropriate responses to neural inputs at the right time (Bernstein, 1967). This can be achieved by properly positioning a muscle to generate different movements in response to neural activity (Ye et al., 2006a). It can also be achieved by coordinated neuromodulation of muscle contractions in response to the same neural activation (Calabrese, 1989; Weiss et al., 1992; Katz, 1995).
To address this question, we studied feeding in Aplysia californica, a robust and tractable system allowing analyses of motor neuronal control, biomechanics and neuromodulation at the level of individual identified motor neurons. Previous studies in
Aplysia have shown the in vivo activity patterns of motor neurons B15 and B16 (Cropper et al., 1990a), and the cotransmitters that are released to modulate closing movements of the feeding grasper (the radula/odontophore) through the actions of the ARC muscle
(Cropper et al., 1990b, 1990c, 1994; Cropper et al., 1987). In this study, we focus on the role of pre-modulation, i.e., the activity of neurons in one behavior prepares a muscle for a subsequent behavior.
Aplysia uses its feeding apparatus, the buccal mass, to perform ingestion and egestion behaviors (Kupfermann, 1974). Biting and swallowing are two similar, but qualitatively distinct ingestion behaviors (Neustadter et al., 2007). In biting, the radula/odontophore needs to protract strongly to reach food. If food is not grasped, a weak retraction returns the radula/odontophore to its resting position before the next bite
118
(Sutton et al., 2004a; Neustadter et al., 2007). As soon as food is grasped, animals switch to swallowing. The radula/odontophore protracts weakly to reposition itself on food, and then retracts strongly to pull food inwards (Sutton et al., 2004a; Neustadter et al., 2007;
Ye et al., 2006a). Thus, the retractor muscle needs to be prepared to properly respond to the activity of motor neurons for a rapid force increase during the retraction phase of swallowing.
The I1/I3 complex is the main retractor muscle, and is innervated by multiple motor neurons (Church and Lloyd, 1991, 1994; Lu et al., 2013). The intrinsic neuromodulation of I1/I3 by these motor neurons has also been explored: stimulation of motor neurons B3, B9 and B38 increases the contraction amplitude and relaxation rate of
I3 evoked by the same neuron (Fox and Lloyd, 1997; Keating and Lloyd, 1999).
We hypothesize that the primary function of key I1/I3 motor neurons in biting is not to generate force for retraction, but to pre-modulate I1/I3, so that once Aplysia grasps food, it can generate sufficient retraction force in the initial swallow, allowing an animal to successfully retain food. In this study, we report that (1) B6, B9 and B3 are key motor neurons for the I1/I3 muscle force generation; (2) the activity of B6, B9 and B3 does not generate force in biting, but pre-modulates I1/I3 for the initial swallow; (3) the repeated activity of B6, B9 and B3 in swallowing strongly modulates I1/I3 for subsequent swallows.
Materials and Methods
1. Animals
119
Aplysia californica weighing 200–450 g (Marinus Scientific, Garden Grove, CA) were maintained in an aerated aquarium containing artificial seawater (Instant Ocean;
Aquarium Systems, Mentor, OH) kept at 16±1 °C. Animals were fed every other day with dried seaweed (Nori). Before experiments, animals were presented with seaweed to measure the intervals between bites. Animals that displayed strong bites at 3-5 s intervals were selected for use (350-450 g for in vivo experiments, 200-350 g for in vitro experiments).
2. Electrodes
Intracellular glass electrodes were pulled from single-barrelled capillary glass
(catalogue #6150; AM Systems, Everett, WA) using a Flaming–Brown micropipette puller (model P-80/PC; Sutter Instruments, Novato, CA). Their resistances were 3-6 MΩ.
Electrodes were backfilled with 3 M potassium acetate before use. The bridge was balanced for both stimulation and recording. Intracellular signals were amplified using a
DC-coupled amplifier (Model 1600; A-M Systems).
Extracellular glass electrodes were also pulled from single-barrelled capillary glass (catalogue #6150; A-M Systems, Everett, WA), using a Flaming–Brown micropipette puller (model P-80/PC; Sutter Instruments, Novato, CA). Their inner diameters were about 40 μm. Electrodes were backfilled with Aplysia saline before use.
Currents were supplied by a stimulus isolator (model A-360, WPI).
120
Extracellular suction electrodes were made from polyethylene tubing (catalogue
#427421; Becton Dickinson, Sparks, MD; outer diameter 1.27 mm, inner diameter 0.86 mm). They were also backfilled with Aplysia saline before use.
Hook electrodes were made from enamel-coated stainless steel wire (California
Fine Wire; 0.001 inch diameter, about 2 feet long) as described in Cullins and Chiel
(2010). All extracellular recording signals (i.e., from extracellular soma electrodes, extracellular suction electrodes, and hook electrodes) were amplified using an AC- coupled differential amplifier (Model 1700; A-M Systems) and filtered using a 300 Hz high-pass filter and a 1 kHz low-pass filter.
3. Experimental preparations
We used an in vivo preparation and two in vitro preparations (i.e., a suspended buccal mass preparation and an anchored buccal mass preparation) in this study. The in vivo preparation was used to record neural and muscular activities as animals performed feeding behaviors. The suspended buccal mass preparation was used to monitor neural activity of individual motor neurons during feeding responses. The anchored buccal mass preparation was used to measure the muscle force on I1/I3 evoked by individual motor neurons or during feeding-like motor programs.
3.1. In vivo preparation
121
In order to record neural and muscular activities as animals perform feeding behaviors, hook electrodes were implanted onto a key muscle and nerves as described by
Cullins and Chiel (2010). Briefly, animals weighing 350-450 g were selected and anesthetized by an injection of isotonic MgCl2 (333 mM). The volume (mL) of the MgCl2 solution was equal to 30% of the animal’s body weight (g). Hook electrodes were implanted on the I2 muscle that initiates the protraction phase of feeding (Hurwitz et al.,
1996), the radula nerve (RN) that carries motor neurons (B8a, b) mediating the closure of the food grasper (Morton and Chiel, 1993a), and the buccal nerves 2 and 3 (BN2 and
BN3) that carry motor neurons mediating the retraction phase of feeding (Church and
Lloyd, 1994; Morton and Chiel, 1993a; Warman and Chiel, 1995). After animals recovered from surgery, recordings from the I2 muscle, RN, BN2 and BN3 were obtained while animals performed feeding behaviors, such as biting, swallowing and rejection. A video camera was simultaneously used to record feeding behaviors.
3.2. In vitro suspended buccal mass preparation
To determine the activity patterns of individual motor neurons during feeding behaviors, an in vitro suspended buccal mass preparation was used to elicit biting and swallowing responses while extracellular soma electrodes were used to record from key motor neurons (Lu et. al., 2013; McManus et. al., 2012).
The protocol has been described in detail by McManus et al. (2012). Briefly, animals weighing 200-350 g were selected and anesthetized with an injection of 50% body weight isotonic MgCl2 (333 mM). The buccal mass was dissected out with the
122 buccal and cerebral ganglia attached. Hook electrodes were applied to the I2 muscle, RN,
BN2 and BN3 for motor program measurements as was done in vivo by Cullins and Chiel
(2010). In most in vitro experiments, BN2s and BN3s on both sides of the buccal ganglia were recorded to help identify motor neurons with ipsilateral and contralateral projections.
The buccal mass and the buccal and cerebral ganglia were placed in a custom- made dish that could isolate the cerebral ganglion from the buccal mass and ganglia
(McManus et al., 2012), so that the cholinergic agonist carbachol could be applied to the cerebral ganglion separately to elicit ingestion responses (Susswein et al., 1996). After pinning out the buccal and cerebral ganglia, the sheaths of the buccal ganglia were thinned, but not completely removed to allow better visualization and access to neurons’ somata with extracellular glass electrodes. To suspend the buccal mass, a silk suture was threaded through the tissue antero-dorsal to the jaws. The suture was then tightened and attached to two balls of modeling clay placed on the side of the dish.
To extracellularly record from a motor neuron, an extracellular glass electrode was gently pressed onto the sheath above the neuron’s soma (Lu et al., 2008). A short anodic current pulse (e.g., 6 ms, 200 µA) was applied through the extracellular glass electrode to activate the neuron (Lu et al., 2008). Once the neuron fired, the extracellular soma channel was then quickly switched from stimulation to recording mode.
Observations of one-for-one spikes on the extracellular soma channel and the corresponding nerve channel(s) were used to help confirm identities of motor neurons
(McManus et al., 2012; Lu et al., 2013). The extracellular glass electrode was left in place to monitor the neuron’s activity during feeding responses. Simplified criteria for extracellularly identifying different motor neurons were established by Lu et al. (2013).
123
Specifically, B6 and B9 are the only two motor neurons that project through BN2s and
BN3s bilaterally and B6 is medial to B9 (see Fig. 4-2 from Church and Lloyd, 1994). B3 only projects through ipsilateral BN2, and its BN2 unit is always the largest. B43 is a small neuron near B6 and only projects through ipsilateral BN2. Its BN2 unit is small and bursts at the end of the retraction phase.
After neurons were located, ingestion responses were induced. Biting responses were elicited by applying the cholinergic agonist carbachol to the cerebral ganglion
(Susswein et al., 1996). Swallowing responses were obtained by placing a strip of seaweed (10 cm long, 0.25 - 1.0 cm wide) in the feeding grasper during a bite.
To further explore the role of identified motor neurons in retraction, we measured the inward force pulling on seaweed during retraction (the retraction force). In those experiments, after biting responses began, we inserted a seaweed strip into the radular halves. The seaweed strip was glued to a string that was attached to a force transducer
(Grass Technologies, West Warwick, RI) for the measurement of the retraction force.
Because the insertion of the seaweed strip into the radular halves would immediately induce swallowing responses, the retraction force could only be measured in swallowing responses, not in biting responses.
3.3. In vitro anchored buccal mass preparation
Since the suspended buccal mass was not stationary during feeding responses, it was not feasible to precisely measure muscle forces in that preparation. Thus, we developed another in vitro anchored buccal mass preparation (Lu et al., 2013), which
124 allowed us to precisely measure the muscle force of I1/I3 in a configuration similar to its physiological state in vivo.
Animals weighting 200-350 g were selected and anesthetized and the buccal mass was dissected out as described above. Since I1/I3 is primarily innervated by BN2 (Scott et al., 1991; BN2 was referred to as nerve 5 in Scott et al.), all nerves except BN2s were severed from the buccal mass, so that only I1/I3 would be innervated during the experiments. The I1/I3 lumen was left intact, so that the muscle would be in a configuration similar to its physiological state. Hook electrodes were attached to BN2s and BN3s bilaterally for motor program measurements.
The buccal mass and the buccal and cerebral ganglia were then placed in a custom-made dish specialized for the in vitro anchored buccal mass preparation (Lu et al.,
2013). To stabilize the buccal mass, it was glued to the bottom of the dish on its ventral surface. After the buccal and cerebral ganglia were pinned to Sylgard, the sheath of the buccal ganglia was either thinned or removed to allow extracellular or intracellular glass electrodes to get access to neurons’ somata, respectively. Two extracellular suction electrodes were attached to the I2 nerve/muscle and RN for motor program measurement
(Lu et al., 2013).
To measure the I1/I3 muscle forces, one or two force transducers were attached to
I1/I3 using hooks and silk sutures. In some experiments, the transducers were attached to the anterior and posterior dorsal regions of I1/I3 to help identify motor neurons because different motor neurons may innervate anterior or posterior or both regions of the muscle
(Church and Lloyd, 1991, 1994; Lu et al., 2013). In other experiments, the transducers
125 were attached to the center of the lateral regions of I1/I3, allowing us to measure the total muscle force generated during feeding-like motor programs and to estimate the total muscle force evoked by stimulating individual motor neurons.
4. Characterizing the BN2 motor programs during the retraction phase
As described by Morton and Chiel (1993a, b) and Lum et al. (2005), the in vivo
BN2 recordings almost always contained three classes of spikes, based on their spike amplitude, labeled as “Small”, “Medium”, and “Large”. To identify the motor neurons corresponding to these different BN2 units and to determine their activity pattern during feeding, we used extracellular soma recordings to record from individual motor neurons during feeding responses in the suspended buccal mass preparation (McManus et al.,
2012).
4.1. Motor neuron identification
The protraction phase of feeding response was defined by the I2 muscle activity
(Fig. 4-1A, white bar). The retraction phase was defined by the BN2 burst that starts at or after the end of the I2 activity (Fig. 4-1A, black bar). We observed that the large BN2 units fired in the middle retraction phase. The medium BN2 units fired in the middle retraction phase as well as during the inter-pattern interval and the protraction phase. The small BN2 units fired throughout the entire retraction phase. There were still some even smaller BN2 units firing within or out of the retraction phase. To distinguish the small
126
BN2 units from these truly small units, we labeled large, medium and small BN2 units as the “1st largest”, “2nd largest”, and “3rd largest” groups of BN2 units.
An amplitude window discriminator was used to detect BN2 units of different spike sizes (Morton and Chiel, 1993a, b). The one-to-one relationships between the extracellular soma and the BN2 recordings show that B6 and B9 corresponded to the 2nd largest group of BN2 units (Fig. 4-1B1, B2; note yellow dashed lines). The latency from the B6 soma spikes to the corresponding BN2 spikes was 9.9 ± 0.6 ms (from 14 pairs of spikes in one experiment). The latency from the B9 soma spikes to the corresponding
BN2 spikes was 11.3 ± 0.4 ms (from 14 pairs of spikes in one experiment). B6 and B9 fired in the middle retraction phase. Without extracellular soma recordings, it was difficult to distinguish B6 from B9. Note that there is one B6 and one B9 in each buccal hemiganglion, and each of these neurons usually projects through both ipsilateral and contralateral BN2s. Thus, the 2nd largest group of units on a single BN2 typically corresponds to at least one of the four B6/B9 neurons, but the other B6/B9 neurons may produce smaller units. We designated these medium units as block 2 (Fig. 4-1A, yellow box).
B3 corresponded to the 1st largest group of BN2 unit (Fig. 4-1B3, note red dashed lines). The latency from the B3 soma spikes to the corresponding BN2 spikes was 7.6 ±
0.1 ms (from 10 pairs of spikes in one experiment). B3 usually started firing after B6/B9 started and ended slightly before or as B6/B9 ended. We also noticed that, occasionally,
B3 started firing early, even before B6/B9 started (unpublished results). Since B3 units are always the largest on BN2, it is easy to detect them directly from BN2 recordings. We designated these large units as block 3 (Fig. 4-1A, red box).
127
Morton and Chiel (1993b) have shown that B10 corresponded to the 3rd largest group of BN2 units and it started firing at the onset of the BN2 motor program and continued firing into the middle retraction phase overlapping the activity of B6, B9 and
B3. Based on our unpublished results, B10, B39 and several other unidentified motor neurons might also fire in the early phase of retraction (Fig. 4-1A, green box). Because of the limitations of the extracellular identification technique, we were not able to reliably identify these neurons and characterize their activity patterns in the suspended buccal mass preparation (Lu et al., 2013). It was also difficult to identify these units directly from the BN2 recordings without soma recordings in vivo. We designated these early small units as block 1 (Fig. 4-1A, green box).
Lu et al. (2013) have shown that B43 corresponded to the 3rd largest group of
BN2 units and it usually burst at the end of the retraction phase at high frequency (Fig. 4-
1A, blue box). This made it easy to detect B43 units directly from BN2 recordings without extracellular soma recordings. However, in swallowing, B43 was more active and usually started much earlier, overlapping the activity of B6, B9 and B3 as well as other small BN2 units, which made it difficult to identify the early B43 units
(unpublished results). We designated these late small units as block 4 (Fig. 4-1A, blue box).
In Fig. 4-1A, we noticed that other than B6 and B9, there were some other medium BN2 units firing in the late retraction phase as well as during the protraction phase and the inter-pattern interval. Our unpublished results have demonstrated that these units were from B38. This was consistent with the results reported by Church and Lloyd
128
(1994), in which B38 was identified as the protraction phase motor neuron (see also
McManus et al., 2014)
4.2. Block definition
Based on unit sizes (Lum et al., 2005), timing and firing features, the BN2 motor programs during the retraction phase (in vivo or in vitro) can be characterized by four blocks (blocks 1, 2, 3 and 4; see Fig. 4-1A, green, yellow, red and blue boxes).
Block 1 contained the early activity of the 3rd largest group of BN2 units (i.e., B10,
B39 and other unidentified units, which started at around the end of I2 activity). The onset of block 1 was defined by the first spike of these small BN2 units after the end of the protraction phase (i.e., after the end of the I2 activity), whose instantaneous firing frequency (IFF) was higher than 2 Hz. Because of their overlap with other small BN2 units in the later retraction phase, we could not determine the end time for these small units, so we focused on their activity before the 2nd largest group of BN2 units. Thus we defined the end of block 1 by the first spike of the 2nd largest group of the BN2 units whose IFF was higher than 2 Hz.
Block 2 was defined by the activity of the 2nd largest group of BN2 units (B6/B9).
The onset of block 2 was the same as the end of block 1. The end of block 2 was defined by the last spike of the 2nd largest group of the BN2 units with IFF higher than 2 Hz.
129
Block 3 was defined by the activity of the 1st largest group of BN2 units (B3). The onset and end of block 3 were defined by the first and last spike of B3 with IFF higher than 2 Hz, respectively.
Block 4 contained the late activity of the 3rd largest group of BN2 units (B43).
Because the earlier activity of this neuron could not be clearly distinguished from other small units during the earlier retraction phase, we used the high-frequency bursting of this neuron at the end of the retraction phase to define block 4. The onset of block 4 was defined by the first spike of B43 with IFF higher than 10 Hz after the end of blocks 2 and
3; the end of block 4 was defined by the last spike of B43 with IFF higher than 5 Hz.
Since B38 was described as the protraction phase neuron (Church and Lloyd, 1994 and our unpublished results; also note B38 units during the protraction phase in Fig. 4-1A), it was excluded from block 4 even if it was firing during block 4.
Results
To determine the role of neuromodulation in preparing muscles for behaviors by individual motor neurons in vivo, it is essential to establish the role of these neurons during particular behaviors (e.g., biting and swallowing of Aplysia), to determine their in vivo patterns of activity, and then to use this information to determine whether neuromodulation is essential to the normal expression of the behavior. In this study, we focused on the retraction phase, during which the radula/odontophore moves backwards and transfers food into the esophagus once food is grasped (Ye et al., 2006a; Neustadter et al., 2007).
130
1. The I1/I3 motor neurons B6, B9 and B3 are more active in swallowing than in biting
During swallowing, motor neurons important for the retraction phase might be differentially recruited to enhance forces for the radula/odontophore to pull food inwards.
Previous anatomical studies suggested that the contraction of the I1/I3 complex mediates retraction (Howells, 1942). I1/I3 is primarily innervated by BN2 (Scott et al., 1991; BN2 was referred to as nerve 5 in Scott et al.); stimulation of BN2 mediates the retraction of the radula/odontophore (Morton and Chiel, 1993a). Thus, the retraction phase motor program is characterized by a burst of BN2 activity.
To characterize the BN2 motor programs during the retraction phase of biting and swallowing, we recorded from BN2 and the I2 muscle and other nerves in vivo as animals were performing feeding (Fig. 4-1A; also see Methods). As described in the Methods section, the BN2 motor programs during the retraction phase can be characterized by four blocks, based on unit size, timing and firing features of the BN2 units (Fig. 4-1A).
To find the motor neurons that are differentially recruited during the retraction phase in swallowing compared to biting, we first analyzed the duration and frequency of the in vivo BN2 motor programs from 162 bites and 182 swallows in 5 animals (Fig. 4-
2A, B, C). The block duration was calculated based on the duration from the first spike to the last spike of the target BN2 unit group in that block. The durations of blocks 2, 3 and
4 were significantly different between biting and swallowing, whereas the duration of block 1 was not significantly different from biting to swallowing (Fig. 4-2B; Mann-
Whitney test, α = 0.05/8 = 0.006, using a Bonferroni correction; p = 0.20 for block 1, p <
131
0.0001 for block 2, p < 0.0001 for block 3, p = 0.001 for block 4). The median durations of block 1 were 0.62 s in biting and 0.60 s in swallowing (decreased by 3% in swallowing compared to biting). The median durations of block 2 were 0.87 s in biting and 1.58 s in swallowing (increased by 82%). The median durations of block 3 were 0 s in biting and
0.70 s in swallowing (increased). The median durations of block 4 were 0.55 s in biting and 0.63 s in swallowing (increased by 15%). Thus, the durations of blocks 2 and 3 are much longer in swallowing than in biting, whereas, the durations of blocks 1 and 4 are either not changed or slightly increased in swallowing.
The average frequency of each block was calculated based on the number of spikes and the duration from the first spike to the last spike of the target BN2 unit group in that block. The average frequencies of the 2nd largest BN2 units in block 2 (i.e., B6/B9) and the 1st largest BN2 units in block 3 (i.e., B3) were significantly different between bites and swallows, whereas the average frequency of the 3rd largest BN2 units in blocks
1 (i.e., B10, B39 and other unidentified neurons) and 4 (i.e., B43) was not significantly different between bites and swallows (Fig. 4-2C; Mann-Whitney Test, α = 0.006; p =
0.012 for block 1, p < 0.0001 for block 2, p < 0.0001 for block 3, p = 0.62 for block 4).
The medians of the average frequencies of block 1 were 14.8 Hz in biting and 12.1 Hz in swallowing (decreased by 18%). The medians of the average frequencies of block 2 were
9.1 Hz in biting and 12.3 Hz in swallowing (increased by 35%). The medians of the average frequencies of block 3 were 0 Hz in biting and 8.1 Hz in swallowing (increased).
The medians of the average frequencies of block 4 were 22.1 Hz in biting and 21.2 Hz in swallowing (decreased by 4%). Thus, the frequencies of B6/B9 in block 2 and B3 in block 3 are much higher in swallowing than in biting.
132
The durations of blocks 2 and 3 represented the durations of B6/B9 and B3, respectively. The frequency of block 3 represented the frequency of B3. However, since we could not distinguish B6 from B9 in vivo, the frequency of block 2 could not be used to represent the frequency of either B6 or B9. Thus, we extracellularly recorded from the somata of B6 and B9 in the suspended buccal mass preparation during biting and swallowing responses. The average frequency of B6 or B9 was calculated based on the number of extracellular soma spikes and the duration from the first spike to the last spike during each behavioral response. We observed that the average frequencies of B6 and B9 were significantly different between biting and swallowing (Fig. 4-2D; Mann-Whitney
Test, α = 0.05/2 = 0.025, using a Bonferroni correction; p < 0.0001 for B6 and p < 0.0001 for B9). The medians of the average frequencies of B6 were 6.31 Hz in biting (from 66 bites in 5 in vitro experiments) and 10.12 Hz in swallowing (from 104 swallows in 5 in vitro experiments) (increased by 60%). The medians of the average frequencies of B9 were 6.65 Hz in biting (from 97 bites in 6 in vitro experiments) and 9.94 Hz in swallowing (from 121 swallows in 6 in vitro experiments) (increased by 49%). Thus, the average frequencies of both B6 and B9 were much higher in swallowing responses than in biting responses. Therefore, the activity of B6, B9 and B3 were selectively enhanced in swallowing relative to biting in both duration and frequency.
2. Activity of B6, B9 and B3 is correlated with the overall retraction force in swallowing
The in vivo and suspended buccal mass data have shown that the I1/I3 motor neurons, B6, B9 and B3, were more active in swallowing compared to biting during the
133 retraction phase. Thus, we needed to determine the behavioral significance of these motor neurons for retraction. Since the animal’s body moves during feeding, it was less feasible to precisely measure the retraction force (i.e., the force pulling on food to transfer it to the esophagus) in vivo. As a consequence, we measured the retraction force in swallowing responses using the suspended buccal mass preparation as we simultaneously recorded from BN2 as well as the I2 protractor muscle and other key nerves (Fig. 4-3A; also see
Methods). The retraction force in biting responses could not be measured using this setup because the seaweed strip had to be grasped by the radula for force measurement, which induced swallowing responses.
We observed that the overall retraction force grew larger as B6/B9 and B3 became more active (Fig. 4-3B). We analyzed the relationship between the activity of B6,
B9 and B3 and the overall retraction force and found that the total number of spikes of
B6, B9 and B3 (blocks 2 and 3) was linearly correlated with the overall retraction force
(Fig. 4-3C, α = 0.05/8 = 0.006, using a Bonferroni correction, p < 0.0001 and R2 = 0.73).
In contrast, the number of spikes of small BN2 units in block 1 and in block 4 was not linearly correlated with the overall retraction force (data not shown; α = 0.006, p = 0.77 for block 1, p = 0.007 for block 4). Thus, the overall retraction force is correlated with the duration of activity of B6, B9 and B3 in swallowing.
3. The I1/I3 muscle forces evoked by B6, B9 and B3 at their physiological activity levels
appear to be too small to be behaviorally effective
134
The results thus far showed that the overall retraction force was strongly correlated with the activity of B6, B9 and B3 in swallowing. We therefore wanted to examine whether the I1/I3 muscle forces evoked by B6, B9 and B3 at their physiological activity levels were comparable to the overall retraction forces exerted during swallowing responses.
First, we needed to determine the force/frequency relationships of B6, B9 and B3 in vitro and compare them with the physiological frequency ranges of B6, B9 and B3. As described in the Methods section, we measured the I1/I3 muscle forces at the center of its lateral regions in the anchored buccal mass preparation (Fig. 4-4A). We observed that the muscle forces evoked by all three neurons were small at frequencies below 10 Hz, and then rose quickly from 15 Hz to 35 Hz, and finally plateaued at 40 Hz or 50 Hz (Fig. 4-
4B, C, D). We also noticed that the median frequencies of B6, B9 and B3 in both biting and swallowing fell into the small force regions (≤ 10 Hz) of their force/frequency curves.
To determine the I1/I3 muscle force evoked by B6, B9 and B3 at their physiological activity levels in biting and swallowing, we intracellularly stimulated these neurons in the anchored buccal mass preparation as we measured the I1/I3 muscle force
(see Methods and Fig. 4-4A). To mimic their activity in a single bite, B6 and B9 were stimulated at around the medians of their physiological frequency ranges in biting (i.e., 5
Hz) for 2 s (i.e., in the upper quartile of their physiological duration ranges in biting, to ensure that maximum forces were observed). In most bites, B3 did not fire at all (Fig. 4-
2B, C); in patterns in which it did fire, the median number of spikes was 4, so we used a
2 Hz stimulation for 2 s to simulate B3’s activity when it did fire in biting. To mimic
135 their activity in a single swallow, B6, B9 and B3 were stimulated to fire at around the medians of their physiological frequency ranges in swallowing (i.e., 10 Hz for B6/B9 and
8 Hz for B3) for 2 s (i.e., in the upper quartile of their physiological duration ranges in swallowing). B6 and B9 innervated both sides of the I1/I3 muscle. The median muscle forces generated by B6 were 0.14 mN on the ipsilateral side and 0.18 mN on the contralateral side of I1/I3 (n = 17). The median muscle forces evoked by B9 were 0.06 mN on the ipsilateral side and 0.28 mN on the contralateral side of I1/I3 (n = 13). B3 only innervated the ipsilateral side of I1/I3 and the median muscle force that it generated was
1.72 mN (n = 12). On the right side of the dashed line, the overall retraction force was measured during swallowing responses in the suspended buccal mass preparation (see
Methods and Fig. 4-3A). The median overall retraction force generated during swallowing responses was 10.5 mN (from 210 patterns in 5 experiments). Due to movement of the whole buccal mass, we could not measure I1/I3 muscle forces during swallowing responses in the suspended buccal mass preparation, so we could not precisely determine the I1/I3 muscle force needed to produce a given retraction force.
However, it was striking that the forces in the main retractor muscle I1/I3 evoked by B6,
B9, and B3 at their physiological activity levels of swallowing were all very small compared to the overall retraction forces exerted during swallowing responses (Fig. 4-5).
In addition, I1/I3 generated no force at the neurons’ physiological activity levels of biting
(Fig. 4-6A, note that no force was elicited by the first stimulation of each neuron).
4. I1/I3 might not be necessary for retraction in biting
136
Although the retraction is weaker in biting than in swallowing (Neustadter et al.,
2007), it is still an important phase of biting. The data above indicated that most of the time, B6, B9 and B3 generated no I1/I3 muscle force in biting (Fig. 4-6A, note the first stimulation). Previous studies have demonstrated that stimulation of B3 and B9 increased the contraction amplitude and relaxation rate of I3 evoked by the same neuron via intrinsic modulation (Fox and Lloyd, 1997; Keating and Lloyd, 1999). Therefore, we predicted that the repeated activity of the same motor neuron (i.e., B6, B9 and B3) might enhance the I1/I3 muscle contraction via intrinsic modulation.
To test the effects of intrinsic modulation of B6, B9 and B3 at their physiological activity levels in biting, we mimicked their activity in a series of bites by repeatedly stimulating B6 and B9 at around the medians of their physiological frequency ranges in biting (i.e., 5 Hz) for 2 s with 3 s inter-stimulus intervals for 10 times in the anchored buccal mass preparation. Since B3 only occasionally fired in biting, we stimulated B3 at
2 Hz for 2 s with 3 s inter-stimulus intervals to simulate B3’s activity when it did fire. We found that I1/I3 still generated no force in biting-like patterns even after intrinsic modulation (Fig. 4-6A). Because the hinge (i.e., the muscle joining the radula/odontophore to the buccal mass) is stretched sufficiently by the stronger protraction during biting, it can mediate a retraction (Sutton et al., 2004a). Thus, I1/I3 may not play a major role during retraction in biting.
5. Pre-modulation: the low-frequency activity of B6, B9 and B3 in prior bites enhances
force generation in I1/I3 for the initial swallow
137
Although the activity of B6, B9 and B3 generated no force in biting, it might prepare I1/I3 to generate large force for the initial swallow via intrinsic modulation (we refer to modulatory effects from the activity during prior behaviors on the subsequent behaviors as pre-modulation).
To test this, we first stimulated B6, B9 and B3 to mimic their activity in a swallowing-like pattern without any prior patterns (Fig. 4-6A, left of gray dashed lines).
After at least a 100 s delay to minimize post-tetanic potentiation and the effects of the endogenous neuromodulators released by these neurons (Fox and Lloyd, 1998; Keating and Lloyd, 1999), we stimulated the neurons again to mimic their activity in a series of prior biting-like patterns and the following initial swallowing-like pattern (Fig. 4-6B, right of gray dashed lines). Since B6 and B9 almost always fired in biting, we tested the effects of pre-modulation of B6 and B9 with 1-10 prior biting-like patterns. Since B3 only occasionally fired in biting, we tested the effects of pre-modulation of B3 with 1-2 prior biting-like patterns.
As demonstrated above, I1/I3 generated no force in these prior biting-like patterns even with intrinsic modulation of B6, B9 and B3 (Fig. 4-6A). In contrast, with pre- modulation of B6, B9 and B3 in prior biting-like patterns, I1/I3 generated much larger muscle forces when the neurons were activated in a swallowing-like pattern (Fig. 4-6B, note red dashed lines). The effects of pre-modulation of B6 and B9 rapidly increased as the number of prior biting-like patterns increased from 1 to 3 and usually plateaued after
5 prior patterns (n = 3; Fig. 4-6B2). In contrast, the effects of pre-modulation of B3 did not change significantly as the number of prior biting-like patterns changed from 1 to 2 (n
= 4; Fig. 4-6B2). Therefore, although the low-frequency activity of B6, B9 and B3 in
138 biting does not generate any I1/I3 muscle force for retraction, their prior activity was crucial for preparing I1/I3 to generate strong force for retraction in the initial swallow.
6. Intrinsic modulation of B6, B9 and B3 strongly enhances I1/I3 muscle contraction in
swallowing
We also tested the effects of intrinsic modulation of B6, B9 and B3 at their physiological activity levels in swallowing. To mimic their activity in a series of swallows, B6, B9 and B3 were repeatedly stimulated at around the medians of their physiological frequency ranges in swallowing (i.e., 10 Hz for B6/B9 and 8 Hz for B3) for
2 s with 3 s inter-stimulus intervals for 10 times. We found that with intrinsic modulation of B6, B9 and B3, the I1/I3 muscle force was greatly enhanced in swallowing-like patterns (Fig. 4-7). The effects of intrinsic modulation in swallowing-like patterns dramatically increased after several repeated stimulations (from the 1st to the 4th stimulation) and then slowly increased until they plateaued (Fig. 4-7A, B). Therefore, intrinsic modulation of B6, B9 and B3 is crucial for I1/I3 to generate large forces to mediate strong retraction for transfer of food to the esophagus during swallowing.
7. The combined activity of B6, B9 and B3 strongly enhances the forces in swallowing,
but not in biting
The results presented so far are all based on individual motor neurons. We observed, both in vivo and in vitro, that B3 activity frequently overlapped the activity of
139
B6/B9 in biting and swallowing. Could the combined activity of different motor neurons also help to create larger muscle forces during retraction? To test this, we selected two different motor neurons from the set B6, B9 and B3, and stimulated them individually and together to mimic their activity in biting-like patterns and swallowing-like patterns.
We observed that, in biting-like patterns, I1/I3 generated no muscle force whether an individual motor neuron or two motor neurons were stimulated (Fig. 4-8A). In contrast, in swallowing-like patterns, I1/I3 generated much larger muscle forces in response to the combined activity of two motor neurons than in response to stimulating the neurons individually (Fig. 4-8B). This nonlinear summation of the motor neuronal activity was very large after intrinsic modulation (Fig. 4-8B). Although the combined activity of any two neurons in the set B6, B9 and B3 did not generate forces in biting-like patterns, combinations of these motor neurons greatly increased the muscle forces that they evoked in swallowing-like patterns, further ensuring that B6, B9 and B3 could generate large forces for effective retraction during swallowing.
8. The combined activity of all I1/I3 motor neurons generates little or no force in biting-
like patterns
The results so far have shown that activation of motor neurons B6, B9 and B3 at frequencies observed during biting, whether the neurons are activated separately or together, do not induce I1/I3 to generate muscle force (Fig. 4-8A). Is it possible that I1/I3 generates forces during biting in response to the combined activity of the entire motor pool?
140
To address this question, we analyzed different ingestive-like BN2 patterns and the corresponding muscle forces using the anchored buccal mass preparation (Fig. 4-9).
We found that some ingestive-like patterns were more like biting patterns (see patterns 1 and 2 in Fig. 4-9); others were more like swallowing patterns (see patterns 4 and 5 in Fig.
4-9). We observed that in those biting-like patterns, I1/I3 generated little or no force even though there was clear activity in blocks 1 (e.g., B10) and 4 (B43), as well as relatively short bursts of activity in blocks 2 (B6/B9) and/or 3 (B3) (Fig. 4-9, patterns 1 and 2). In those swallowing-like patterns, I1/I3 generated much larger forces as more B6/B9 and B3 units were recruited (Fig. 4-9, patterns 4 and 5).
Discussion
The results presented in this paper suggest that neural activity during the retraction phase of biting, despite its generating little or no force in the retractor muscle
(I1/I3), provides pre-modulation of that muscle so that it is ready to generate significant forces during the retraction phase of swallowing. This is consistent with Bernstein’s hypothesis (1967) that the periphery must be properly prepared to appropriately respond to neural inputs. This was previously demonstrated in Aplysia swallowing (Ye et al.,
2006a) and in shrimp snapping (Ritzmann, 1974): properly positioning muscles allowed them to generate functions to neural inputs, which they could not do otherwise. We explored another source for preparation of the periphery, neuromodulation.
Previous studies in Aplysia feeding system (e.g., ARC muscle; Weiss et al., 1992) have demonstrated that the cotransmitters released by motorneurons (e.g., B15 and B16)
141 firing at physiological frequencies modify the relationship between muscle contraction amplitude and relaxation rate and thus maintain the optimal motor output when the intensity and frequency of feeding behavior change. In this study, we focused on the retraction phase of two similar but distinct behaviors, biting and swallowing. First, we identified the key motor neurons for I1/I3. Motor neurons B6, B9 and B3 were differentially recruited in swallowing, and we determined their physiological activity levels (duration and frequency) (Fig. 4-2). Their activity was linearly correlated with the overall retraction force during swallowing (Fig. 4-3). We then examined how much force these neurons could generate during behaviors at physiological activity levels. We found that (a) without modulation, B6, B9 and B3 generated no I1/I3 force in biting and small force for swallowing (Figs. 4-4, 4-5, 4-6A); (b) with intrinsic modulation, I1/I3 generated no force in biting (Figs. 4-6A, 4-10A), but generated much larger forces for all but the initial swallows (Figs. 4-7, 4-10C); (c) with pre-modulation from prior bites, I1/I3 generated larger forces for the initial swallow (Figs. 4-6B, 4-10B); (d) the combined activity of these motor neurons significantly enhanced force during swallowing, but was unable to generate any significant force in biting (Figs. 4-8, 4-9, 4-10).
1. Behavioral significance of intrinsic modulation in biting and swallowing
Preparing I1/I3 for the transition from biting to swallowing is likely to be of great behavioral significance. Once animals grasp food, the bite rapidly transitions to a swallow. Without a strong retraction in the initial swallow, animals may lose contact with the food during the next protraction. The prior activity of B6, B9 and B3 in biting pre-
142 modulates I1/I3 so that it can generate a large force for retraction as soon as animals grasp food (Figs. 4-6B, 4-10B).
In addition, it may be critical for animals not to use high-frequency prior activity of motor neurons to enhance pre-modulation for the initial swallow. In biting, the hinge is stretched during the strong protraction and initiates a weak but rapid retraction to return the radula/odontophore to its resting position (Sutton et al., 2004a). If B6, B9 and B3 were activated at high frequencies in biting, I1/I3 would generate larger force to mediate retraction, which would slow down the retraction phase of biting and reduce behavioral efficiency.
Increasing retraction forces during subsequent swallows may also be behaviorally important for animals to effectively retain food. Once animals begin to ingest seaweed, they may encounter increasing forces of resistance due to the seaweed’s holdfast or tidal surge. As a consequence, being able to generate successively stronger swallows is important, and thus intrinsic modulation may play an important role in allowing animals to generate increasingly powerful retraction forces (Figs. 4-7, 4-10C).
We have observed that the effects of intrinsic modulation of B6, B9 and B3 become weaker as inter-pattern intervals become longer (unpublished results). Normally, inter-pattern intervals are shorter in the early stages of a meal and patterns slow down at later stages (Susswein et al., 1978; Kupfermann and Weiss, 1982). Thus, early in a meal, the effects of intrinsic modulation are stronger and animals can perform stronger retractions to take in more food. Once animals begin to satiate and swallowing patterns slow down, the modulatory effects will be reduced, and swallows will become weaker.
143
How relevant are these results to vertebrate muscles? Previous studies have shown that large potentiation of muscle contraction force due to repeated stimulation is common in vertebrate fast twitch skeletal muscles (Vandenboom et al., 2013).
Specifically, the effect of staircase potentiation seems similar to the modulatory effects observed in our study, although their mechanisms are different (Vandenboom et al.,
2013). Our results suggest that other animals (e.g., cats, Bagust et al., 1974; rats, Abbate et al., 2000) may also use muscle activation in prior behaviors to prepare for a stronger muscle contraction in the subsequent behaviors.
2. Intrinsic vs. extrinsic neuromodulation
The results of this study suggest that intrinsic neuromodulation, i.e., the ability of neurons within a circuit to modulate its function (Katz and Frost, 1996) can be further subdivided into self-modulation and cross-modulation. Self-modulation is due to the activity of the neuron itself, as opposed to cross-modulation, which is due to the activity of other neurons within the circuit. Studies in Tritonia demonstrated that the DSI neurons, which use serotonin both as a transmitter and as a modulator, had widespread cross- modulatory effects on other neurons within the swim circuit, which were critical for normal swim behavior (Katz and Frost, 1995). Our studies suggest that self-modulation of B6, B9 and B3 plays a critical role in force generation for effective swallowing.
Previous studies have shown that B9 expresses SCP as neuromodulator (Church and
Lloyd, 1991), which increases the amplitude of EJPs and contractions, and relaxation rate of I3 evoked by B9 (Keating and Lloyd, 1999); stimulation of B3 expresses Fa, which
144 also increases the amplitude of EJPs and contractions evoked by B3 (Fox and Lloyd,
1997; Keating and Lloyd, 1999). Since B6 and B9 both express SCP (Church and Lloyd,
1991), they may share the same mechanism of intrinsic modulation.
Extrinsic neuromodulation, i.e., the ability of modulators released by neurons outside of a circuit to affect circuit function (Katz and Frost, 1996), has been described in the feeding circuitry of Aplysia. Previous studies have demonstrated that buccal muscles can be modulated extrinsically by serotonin, which is released by the metacerebral cells
(MCCs) located in the cerebral ganglion (e.g., I2, Hurwitz et al., 2000; ARC, Brezina et al., 1994; I3, Fox and Lloyd, 1998). B3-evoked I3 contraction can be modulated both extrinsically and intrinsically (Fox and Lloyd, 1997). It is also known that carbachol activates the MCCs (Susswein et al., 1996), and thus the results we report from in vitro carbachol-induced motor programs (Figs. 4-3, 4-9) used I1/I3 muscles that were subjected to extrinsic neuromodulation. Thus, it is likely that MCC activity also pre- modulates I1/I3 to allow force generation. Our preliminary studies have suggested, however, that the direct effects of the MCCs on B6, B9 and B3 are much smaller than the effects of intrinsic modulation that we have observed for each of the motor neurons in the anchored buccal mass preparation (Fig. 4-7A). Furthermore, even when I1/I3 has been modulated by the MCCs, the BN2 activity during biting-like patterns generates little or no force in I1/I3 (Fig. 4-9). However, the extrinsic modulation by MCCs primes the feeding CPGs for ingestion during the appetitive phase and thus increases the frequency of ingestive motor programs (Morgan et al., 2000). This could also enhance the effects of pre-modulation of biting on the initial swallow because the inter-pattern intervals are shortened.
145
3. Nonlinear summation of the combined activity of multiple motor neurons
We have found that the muscle force produced by simultaneous stimulation of two neurons from the set B6, B9 and B3 is much greater than the linear summation of force generated by each neuron (Fig. 4-8). B6 and B9 both release SCP during stimulation (Church and Lloyd, 1991). SCP modulates I3 contraction evoked by both B9 and B3 (Keating and Lloyd, 1999). Thus, there might be cross-modulation from B6/B9 to
B3 and among B6 and B9, which may contribute to the nonlinear summation (Fig. 4-8).
In addition to cross-modulation, the depolarization of the muscle due to the excitatory junction potentials could summate nonlinearly (McPherson and Blankenship,
1991). Previous studies have reported that the contraction amplitude of the buccal muscle is linearly correlated with the integrated depolarization of the muscle membrane (see Fig.
21B in Cohen et al., 1978). The muscle depolarization can be nonlinearly summated due to facilitation of excitatory junction potentials (EJPs) (McPherson and Blankenship,
1991). Since the muscle regions B6, B9 and B3 innervate may overlap with each other, the combined activity of these neurons could cause both temporary and spatial summation of muscle depolarization and then produce nonlinearly summated muscle force. Nonlinear summation has also been observed in vertebrate muscles. The nonlinear summation could also be due to biomechanical reasons (e.g., cat tibialis posterior muscle,
Powers and Binder, 1991) or geometry of the motor units in the muscle (Zuurbier and
Huijing, 1992). Interestingly, studies in rat medial gastrocnemius muscle have shown
146 nonlinear summation of multiple motor units that could be greater or less than the linear summation (Drzymała-Celichowska et al., 2010).
4. Multifunctionality of motor neurons
Motor neurons can have multiple functions. For example, previous work has shown that whether the B8 motor neurons act to close the grasper on material, or to both close and retract material, depends on the position of the grasper at the time they are activated (Ye et al., 2006a). Similarly, in this paper, the data suggest that motor neurons
B6, B9 and B3 may act purely as modulators of the I1/I3 muscle during biting, similar to the role of the MCCs, and then act as generators of force in addition to modulators during swallowing. More generally, multifunctional peripheries are likely to flexibly utilize both muscles and motor neurons in multiple ways to generate related but distinct behaviors in both vertebrates and invertebrates (Gillis and Biewener, 2000; Gestreau et al., 2005; Ye et al., 2006a, b).
Acknowledgements
Research reported in this paper was supported by NIH grant NS047073 and NSF grants DMS-1010434 and IIS-1065489.
147
References
Abbate F, Sargeant AJ, Verdijk PW, de Haan A (2000) Effects of high-frequency initial pulses and posttetanic potentiation on power output of skeletal muscle. J Appl Physiol 88:35–40.
Bagust J, Lewis DM, Luck JC (1974) Post-tetanic effects in motor units of fast and slow twitch muscle of the cat. J Physiol 237:115–121.
Bernstein N (1967) The coordination and regulation of movements. Oxford: Pergamon.
Brezina V, Evans CC, Weiss KR (1994) Enhancement of Ca current in the accessory radula closer muscle of Aplysia californica by neuromodulators that potentiate its contractions. J Neurosci 14:4393-4411.
Calabrese RL (1989) Modulation of muscle and neuromuscular junctions in invertebrates. Seminars in the Neurosciences 1:25-34.
Church PJ, Cohen KP, Scott ML, Kirk MD (1991) Peptidergic motoneurons in the buccal ganglia of Aplysia californica: immunocytochemical, morphological, and physiological characterizations. J Comp Physiol A 168:323-336.
Church PJ, Lloyd PE (1991) Expression of diverse neuropeptide cotransmitters by identified motor neurons in Aplysia. J Neurosci 11:618-625.
Church PJ, Lloyd PE (1994) Activity of multiple identified motor neurons recorded intracellularly during evoked feedinglike motor programs in Aplysia. J Neurophysiol 72:1794-1809.
Cropper EC, Brezina V, Vilim FS, Harish O, Price DA, Rosen S, Kupfermann I, Weiss KR (1994) FRF peptides in the ARC neuromuscular system of Aplysia: purification and physiological actions. J Neurophysiol 72:2181-2195.
Cropper EC, Kupfermann I, Weiss KR (1990a) Differential firing patterns of the peptide- containing cholinergic motor neurons B15 and B16 during feeding behavior in Aplysia. Brain Res 522:176-179.
Cropper EC, Lloyd PE, Reed W, Tenenbaum R, Kupfermann I, Weiss KR (1987) Multiple neuropeptides in cholinergic motor neurons of Aplysia: evidence for modulation intrinsic to the motor circuit. Proc Natl Acad Sci U S A 84:3486-3490.
Cullins MJ, Chiel HJ (2010) Electrode fabrication and implantation in Aplysia californica for multi-channel neural and muscular recordings in intact, freely behaving animals. J Vis Exp 40:e1791, doi:10.3791/1791.
148
Drzymała-Celichowska H, Krutki P, Celichowski J (2010) Summation of motor unit forces in rat medial gastrocnemius muscle. J Electromyogr Kinesiol 20:599-607.
Fox LE, Lloyd PE (1997) Serotonin and the small cardioactive peptides differentially modulate two motor neurons that innervate the same muscle fibers in Aplysia. J Neurophysiol 17:6064-6074.
Fox LE, Lloyd PE (1998) Serotonergic neurons differentially modulate the efficacy of two motor neurons innervating the same muscle fibers in Aplysia. J Neurophysiol 80:647-655.
Gestreau C, Dutschmann M, Obled S, Bianchi AL (2005) Activation of XII motoneurons and premotor neurons during various oropharyngeal behaviors. Respir Physiol Neurobiol 147:159-176.
Gillis GB, Biewener AA (2000) Hindlimb extensor muscle function during jumping and swimming in the toad (Bufo marinus). J Exp Biol 203:3547-3563.
Howells HH (1942) The structure and function of the alimentary canal of Aplysia Punctata. Quart J Microsc Sci 4:357-397.
Hurwitz I, Cropper EC, Vilim FS, Alexeeva V, Susswein AJ, Kupfermann I, Weiss KR (2000) Serotonergic and peptidergic modulation of the buccal mass protractor muscle (I2) in Aplysia. J Neurophysiol 84:2810-2820.
Hurwitz I, Neustadter D, Morton DW, Chiel HJ, Susswein AJ (1996) Activity patterns of the B31/B32 pattern initiators innervating the I2 muscle of the buccal mass during normal feeding movements in Aplysia californica. J Neurophysiol 75:1309-1326.
Katz PS (1995) Intrinsic and extrinsic neuromodulation of motor circuits. Curr Opin Neurobiol 5:799-808.
Katz PS and Frost WN (1995) Intrinsic neuromodulation in the Tritonia swim CPG: Serotonin mediates both neuromodulation and neurotransmission by the dorsal swim interneurons. J Neurophysiol 74:2281-2294.
Katz PS and Frost WN (1996) Intrinsic neuromodulation: altering neuronal circuits from within 19:54-61.
Keating C, Lloyd PE (1999) Differential modulation of motor neurons that innervate the same muscle but use different excitatory transmitters in Aplysia. J Neurophysiol 82:1759-1767.
Kupfermann I (1974) Feeding behavior in Aplysia: A simple system for the study of motivation. Behav Biol 10:1-26.
149
Kupfermann I, Weiss KR (1982) Activity of an identified serotonergic neuron in free moving Aplysia correlates with behavioral arousal. Brain Res 241:334-337.
Lu H, Chestek CA, Shaw KM, Chiel, HJ (2008) Selective extracellular stimulation of individual neurons in ganglia. J Neural Eng 5:287-309.
Lu H, McManus JM, Chiel HJ (2013) Extracellularly identifying motor neurons for a muscle motor pool in Aplysia californica. J Vis Exp 73:e50189, doi:10.3791/50189.
Lum CS, Zhorov Y, Cropper EC, Weiss KR, Brezina V (2005) Variability of swallowing performance in intact, freely feeding Aplysia. J Neurophysiol 94:2427-2446.
McManus JM, Lu H, Chiel HJ (2012) An in vitro preparation for eliciting and recording feeding motor programs with physiological movements in Aplysia californica. J Vis Exp 70:e4320, doi:10.3791/4320.
McPherson DR, Blankenship JE (1991) Neural control of swimming in Aplysia brasiliana. I Innervation of parapodial muscle by pedal ganglion motorneurons. J Neurophysiol 66:1338-1351.
Morgan PT, Perrins R, Lloyd PE, Weiss KR (2000) Intrinsic and extrinsic modulation of a single central pattern generating circuit. J Neurophysiol. 84:1186-1193.
Morton DW, Chiel HJ (1993a) In vivo buccal nerve activity that distinguishes ingestion from rejection can be used to predict behavioral transitions in Aplysia. J Comp Physiol A 172:17-32.
Morton DW, Chiel HJ (1993b) The timing of activity in motor neurons that produce radula movements distinguishes ingestion from rejection in Aplysia. J Comp Physiol A 173:519-536.
Neustadter DM, Herman RL, Drushel RF, Chestek DW, Chiel HJ (2007) The kinematics of multifunctionality: comparisons of biting and swallowing in Aplysia californica. J Exp Biol 210:238-260.
Powers RK, Binder MD (1991) Summation of motor unit tensions in the tibialis posterior muscle of the cat under isometric and nonisometric conditions. J Neurophysiol 66:1838-1846.
Ritzmann RE (1974) Mechanisms for snapping behavior of two alpheid shrimp, Alpheus californiensis and Alpheus heterochelis. J Comp Physiol 95:217-236.
Scott ML, Govind CK, Kirk MD (1991) Neuromuscular organization of the buccal system in Aplysia californica. J Comp Neurol 312:207-222.
150
Susswein AJ, Rosen SC, Gapon S, Kupfermann I (1996) Characterization of buccal motor programs elicited by a cholinergic agonist applied to the cerebral ganglion of Aplysia californica. J Comp Physiol A 179:509-524.
Susswein AJ, Weiss KR, Kupfermann I (1978) The effects of food arousal on the latency of biting in Aplysia. J Comp Physiol A 123:31-41.
Sutton GP, Macknin JB, Gartman SS, Sunny GP, Beer RD, Crago PE, Neustadter DM, Chiel HJ (2004a) Passive hinge forces in the feeding apparatus of Aplysia aid retraction during biting but not during swallowing. J Comp Physiol A Neuroethol Sens Neural Behav Physiol 190:501-514.
Vandenboom R, Gittings W, Smith IC, Grange RW, Stull JT (2013) Myosin phosphorylation and force potentiation in skeletal muscle: evidence from animal models. J Muscle Res Cell Motil 34:317-332.
Warman EN, Chiel HJ (1995) A new technique for chronic single-unit extracellular recording in freely behaving animals using pipette electrodes. J Neurosci Methods 57:161-169.
Weiss KR, Brezina V, Cropper EC, Hooper SL, Miller MW, Probst WC, Vilim FS, Kupfermann I (1992) Peptidergic co-transmission in Aplysia: functional implications for rhythmic behaviors. Experientia 48:456-463.
Ye H, Morton DW, Chiel HJ (2006a) Neuromechanics of coordination during swallowing in Aplysia californica. J Neurosci 26:1470 –1485.
Ye H, Morton DW, Chiel HJ (2006b) Neuromechanics of Multifunctionality during Rejection in Aplysia californica. J Neurosci 26:10743–10755.
Zuurbier CJ, Huijing PA (1992) Influence of muscle geometry on shortening speed of fibre, aponeurosis and muscle. J Biomech 25:1017-1026.
151
Figures and Tables
152
Figure 4-1. Characterizing the BN2 motor programs during the retraction phase of biting and swallowing. (A) The BN2 motor programs during the retraction phase can be characterized by four blocks (see Methods). The traces shown are in vivo recordings from the I2 muscle and BN2. The white bar indicates the protraction phase, defined by the activity of I2. The black bar indicates the retraction phase, defined by the BN2 burst. The boxes in green, yellow, red, and blue correspond to blocks 1, 2, 3, and 4, respectively. (B1-B3) Identification of motor neurons corresponding to different BN2 units. A suspended buccal mass preparation was used for simultaneous extracellular recordings on the somata of individual motor neurons and the I2 muscle, RN, BN2 and BN3 during biting and swallowing responses (see Methods). Only the extracellular soma recordings and the BN2 motor programs during the retraction phase are shown in this figure. There were one-to-one correspondences (note yellow dashed lines in part B1 and B2, red dashed lines in part B3) between soma spikes of B6, B9 and B3 and the BN2 units. The insets on the right side are expansions of the recordings in the gray boxes on the left side.
153
Figure 4-2. B6, B9 and B3 are more active in swallowing than in biting. (A) The BN2 motor programs during the retraction phase in biting (left) and swallowing (right) in vivo (recordings shown are from the same animal). Both types of motor programs can be characterized by four blocks (see Fig. 4-1 and Methods). The boxes in green, yellow, red, and blue correspond to blocks 1, 2, 3, and 4, respectively. (B) The durations of blocks 2 and 3 are much longer in swallowing than in biting, whereas the durations of blocks 1 and 4 are either not changed or slightly increased in swallowing (Mann-Whitney test, α = 0.05/8 = 0.006, using a Bonferroni correction; p = 0.20 for block 1, p < 0.0001 for block 2, p < 0.0001 for block 3, p = 0.001 for
154 block 4). (C) The average frequencies of B6/B9 in block 2 and B3 in block 3 are much higher in swallowing than in biting, whereas the average frequencies of small units in block 1 and in block 4 are not significantly changed (Mann-Whitney Test, α = 0.006; p = 0.012 for block 1, p < 0.0001 for block 2, p < 0.0001 for block 3, p = 0.62 for block 4). (D) The average frequencies of B6 and B9 are much higher in swallowing responses than in biting responses performed in the suspended buccal mass preparation (Mann-Whitney Test, α = 0.05/2 = 0.025, using a Bonferroni correction; p < 0.0001 for B6 and p < 0.0001 for B9). Therefore, B6, B9 and B3 are more active in swallowing than in biting based on both duration and on frequency.
155
Figure 4-3. The activity of B6, B9 and B3 is correlated with the overall retraction force in swallowing. (A) A schematic diagram of the suspended buccal mass preparation used to measure the overall retraction force in swallowing responses in vitro (see Methods). (B) Two BN2 motor
156 programs (top) and the corresponding force traces (bottom) during the retraction phase of swallowing responses. The boxes in green, yellow, red, and blue correspond to blocks 1, 2, 3, and 4, respectively. (C) The integrated overall retraction force is linearly correlated with the total number of spikes of B6/B9 and B3 (α = 0.05/8 = 0.0006, using a Bonferroni correction; p < 0.0001, R2 = 0.73, y = 2.15x – 69.7). These analyses were obtained from 82 patterns in one experiment. We observed similar correlations between the retraction force and the number of spikes of B6, B9 and B3 in 5 out of 5 experiments. Thus, the overall retraction force is strongly correlated with the activity of B6, B9 and B3 during swallowing.
157
Figure 4-4. The force/frequency relationships of B6, B9, and B3 in vitro. (A) A schematic diagram of the anchored buccal mass preparation used to measure the I1/I3 muscle forces (see Methods). (B-D) B6, B9 and B3 were intracellularly stimulated to fire at a range of different frequencies (i.e., 5 Hz to 50 Hz in increments of 5 Hz) for 2 s (i.e., in the upper quartile of the physiological duration ranges for biting and swallowing to ensure that maximum forces were observed, Fig. 4-2B). The peak amplitudes of the ipsilateral muscle forces evoked by these motor neurons were plotted against the corresponding firing frequencies. To minimize the variance of different preparations (n = 3 for each neuron), we normalized the peak amplitudes of the muscle forces evoked by one neuron at different frequencies by the maximum peak force amplitude evoked by the same neuron in the same experiment, which usually occurred at 45 Hz or 50 Hz. The gray and black arrows point to the medians of the physiological frequency ranges of B6, B9 and B3 in biting and swallowing, respectively (Fig. 4-2C, D). We observed that the median frequencies of all three neurons for both biting and swallowing were in the small force regions of the force/frequency relationships.
158
Figure 4-5. The I1/I3 muscle forces evoked by B6, B9 and B3 at their physiological activity levels are small compared to the overall retraction force exerted during swallowing responses. On the left side of the dashed line, the I1/I3 muscle forces were measured as B6, B9 and B3 were stimulated to fire at the medians of their physiological frequency ranges in swallowing (10 Hz for B6 and B9, 8 Hz for B3) for 2 s (i.e., in the upper quartile of their physiological duration ranges of swallowing) in the anchored buccal mass preparation (see Methods and Fig. 4-2). On the right side of the dashed line, the overall retraction force was measured during swallowing responses in the suspended buccal mass preparation (see Methods and Fig. 4-3A).The I1/I3 muscle force evoked by B6, B9 and B3 at their physiological activity levels appears small compared to the overall retraction force during swallows.
159
Figure 4-6. B6, B9 and B3 generate no force in biting even after intrinsic modulation, but pre- modulate I1/I3 and prepare it to generate larger forces during the initial swallow. (A) I1/I3 generated no force after intrinsic modulation from the repeated activity of B6, B9 and B3 at the medians of their physiological frequency ranges of biting (5 Hz for B6 and B9, 2 Hz for B3; 2 s duration; 3 s inter-stimulus interval; for 10 times). (B1) Activating each neuron in a swallowing- like pattern (10 Hz for B6 and B9, 8 Hz for B3; 2 s duration) generated force, but at a level that might be insufficient for swallowing. After a 100 s rest, each neuron was first activated in a biting-like pattern (5 Hz for B6 and B9, 2 Hz for B3; 2 s duration), and then again stimulated in a swallowing-like pattern with a 3 s inter-stimulus interval. The black bars schematically show the firing patterns of the neurons. With pre-modulation from the low-frequency activity of B6, B9 and B3 in prior biting-like patterns, the motor neurons generated much larger muscle forces in the initial swallowing-like pattern than without pre-modulation (note red dashed lines). (B2) Modulatory effects as a function of number of prior biting-like patterns. With different numbers of prior biting-like patterns, the modulatory effects are enhanced. We measured the modulatory effects by calculating the differences between the unmodulated muscle force and the muscle force pre-modulated by a series of biting-like patterns. To compare the modulatory effects of pre- modulation with different numbers of prior biting-like patterns, we normalized these force differences using the maximum force difference that occurred for the same neuron.
160
Figure 4-7. Intrinsic modulation of B6, B9 and B3 in swallowing. (A) I1/I3 generated much larger forces in swallowing-like patterns after intrinsic modulation of B6, B9 and B3. Each neuron was repeatedly stimulated at the median of its physiological frequency range of swallowing (B6 at 10 Hz, B9 at 10 Hz, and B3 at 8 Hz; 2 s duration; 3 s inter-stimulus interval; for 10 times). Times of stimulations are indicated by black bars. (B) Modulatory effects as a consequence of repeated stimulation. The muscle force generated due to each stimulation was normalized using the maximum muscle force evoked during the whole series of stimulations (10 times). We noticed that the modulatory effects of all three neurons rapidly increased as the stimulation number increased from 1 to 4 and usually plateaued after 5 stimulations (n = 4 for B6, n = 3 for both B9 and B3).
161
Figure 4-8. The nonlinear summation of the I1/I3 muscle forces evoked by B6, B9 and B3. (A) Stimulating two neurons from the set of B6, B9 and B3 together generated no muscle force in biting-like patterns (5 Hz for B6 and B9, 2 Hz for B3; 2 s duration; 3 s inter-stimulus interval; for 10 times). (B1) However, the combined activity of each pair of neurons strongly enhanced the muscle force evoked by these two neurons individually in swallowing-like patterns (10 Hz for B6 and B9, 8 Hz for B3; 2 s duration; 3 s inter-stimulus interval; for 10 times). (B2) The linear summation of the maximum muscle force generated by each motor neuron individually with intrinsic modulation was set to be 100% (note red dashed lines) for normalization. We found that the muscle forces generated by the combined activity of two motor neurons were much larger than the linear summation of the muscle forces evoked by them individually (n = 3 for each neuron).
162
Figure 4-9. The I1/I3 muscle forces generated during ingestive-like patterns. Five ingestive-like BN2 motor programs (top) and the corresponding force traces (bottom) during the retraction phase. The green, yellow, red, and blue boxes indicate blocks 1, 2, 3, and 4. The durations of blocks 2 and 3 were 0.46 s and 0 s in pattern 1 and 1.167 s and 0.006 s in pattern 2, all of which fell into the lower quartiles of the block durations for in vivo biting (see Fig. 4-2B). Thus, patterns 1 and 2 were more like biting patterns. The durations of blocks 2 and 3 were 1.464 s and 0.325 s in pattern 3, both fell into the upper quartiles of the block durations for in vivo biting and both were slightly lower than the medians of the block durations for in vivo swallowing (see Fig. 4-2B). Thus, pattern 3 was between biting and swallowing. The durations of blocks 2 and 3 were 2.94 s and 1.11 s in pattern 4 and 3.58 s and 2.06 s in pattern 5, all of which fell into or were close to the upper quartiles of the block durations for in vivo swallowing (see Fig. 4-2B). Thus, both patterns 4 and 5 were more like swallowing patterns. Note that all of the patterns shown are from the same experiment and are not the initial patterns, so modulation would be already present for each pattern.
163
Figure 4-10. Summary schematics for results. Orange represents the activity and muscle force evoked by B6; yellow represents the activity of B9 and the muscle force generated by B9; red represents the activity and muscle force evoked by B3. Black dashed lines represent the linear summation of forces generated by different motor neurons; black solid lines represent the nonlinear summation of forces generated by these neurons. (A) I1/I3 generates no muscle force by the activity of B6, B9 and B3 in biting, without with intrinsic modulation. The combined activity of multiple motor neurons does not affect the I1/I3 contraction in biting. (B) Without modulation, I1/I3 generates small muscle forces by the activity of B6, B9 and B3; with pre- modulation from the activity of these neurons in biting, I1/I3 generates larger forces for the retraction phase of the initial swallow; the nonlinear summation of the I1/I3 contraction by the combined activity of B6, B9 and B3 strongly enhances muscle force in the initial swallow. (C) With intrinsic modulation of B6, B9 and B3 in swallowing, I1/I3 generates much larger forces for the retraction phase of the subsequent swallows. The nonlinear summation of the I1/I3 contraction by the combined activity of B6, B9 and B3 strongly enhances muscle force in swallowing.
164
Chapter 5
Summary and future work
This dissertation has explored one possible mechanism of multifunctionality: multifunctional roles of motor neurons. Specifically, I studied how Aplysia uses intrinsic neuromodulation to rapidly increase force generation of a retractor muscle I1/I3 as the animal transitions from biting to swallowing. In this chapter, I will summarize the main results, discuss the limitations of this study, and propose several directions for the future studies based on the results presented in this dissertation.
Results summary
1. Identifying motor neurons for I1/I3 and charactering the BN2 motor programs
In this dissertation, I first developed an extracellular technique for selective stimulation of individual neurons on the soma side (see Chapter 2; Fig. 2-1), which has been validated by both NEURON models (Figs. 2-12, 2-13, 2-14) and intracellular experiments (Figs. 2-10, 2-11). Then, I used this extracellular technique to reliably identify individual motor neurons for the retractor muscle I1/I3 (see Chapter 3; Figs. 3-3,
3-4, 3-5) and record them during behaviors in the suspended buccal mass preparation (Fig.
4-1B). These extracellular soma recordings allowed me to identify the BN2 units
165 corresponding to multiple motor neurons and characterize the retraction phase BN2 motor programs by four blocks (Fig. 4-1A).
2. Determining the key I1/I3 motor neurons for retraction
By analyzing the duration and frequency of each block of in vivo biting and swallowing patterns, I have determined the key motor neurons for I1/I3 during retraction,
B6, B9 and B3 (see Chapter 4; Fig. 4-2). These neurons’ activity is also correlated with the retraction force measured during swallowing responses (Fig. 4-3).
3. Role of intrinsic neuromodulation in biting and swallowing
In Chapter 4, I have studied the role of intrinsic neuromodulation in biting and swallowing under behaviorally relevant conditions. I have found that without modulation,
B6, B9 and B3 generate little or no force in biting and swallowing (Figs. 4-4, 4-5). With intrinsic modulation from the repeated stimulation of these neurons, I1/I3 generates no force in biting but much larger forces in swallowing (Figs. 4-6A, 4-7, 4-10C). With pre- modulation from the low-frequency activation of these neurons in prior biting, I1/I3 generates much larger forces in the initial swallow (Figs. 4-6B, 4-10B).
4. Combined activity of multiple motor neurons
166
In Chapter 4, I have also studied the effects of combined activity of multiple motor neurons. I have found that combined activity of two neurons from the set of B6, B9 and B3 strongly enhances the forces generated by I1/I3 during swallowing (Figs. 4-8B, 4-
10B), but still generates no force during biting (Figs. 4-8A, 4-10A). Furthermore, combined activity of the whole set of I1/I3 motor neurons also generates little or no force in biting (Fig. 4-9).
Limitations of this study
1. Necessity of intrinsic neuromodulation in swallowing
This study has demonstrated that intrinsic neuromodulation is essential for effective retraction in swallowing. However, the necessity of intrinsic neuromodulation for effective swallowing has not been tested. To test it, I need to directly compare the total I1/I3 muscle force evoked by key motor neurons at their physiological activity levels with the overall muscle force exerted during swallowing. If the total neuron- evoked force without intrinsic neuromodulation is significantly smaller than the overall behavior-induced force exerted during behaviors, intrinsic neuromodulation can be proved to be necessary for effective swallowing; otherwise, intrinsic neuromodulation is not necessary for effective swallowing.
Since animals are not stationary during feeding behaviors, it is not feasible to measure muscle forces in vivo. In the suspended buccal mass, multiple muscles other than
I1/I3 are innervated and contracting during feeding responses, so it is also not feasible to precisely measure the I1/I3 muscle forces in that preparation. In the anchored buccal
167 mass preparation, I1/I3 is the only muscle that is innervated and contracting; however, because of lack of sensory inputs and other muscles’ contractions, I could only induce ingestive-like motor programs in this preparation, but not actual feeding responses. As described in Chapter 4, I could use the in vivo block duration as a criterion to distinguish between biting-like and swallowing-like patterns (Fig. 4-9), and measure the I1/I3 muscle forces during swallowing-like patterns. Since each side of I1/I3 is innervated by two B6s and two B9s bilaterally and one B3 ipsilaterally, I would need to stimulate all five neurons simultaneously to directly measure the total muscle force evoked by them on one side, which is technically challenging. Therefore, regardless of the experimental difficulties, a direct comparison between the neuron evoked force and the overall behavioral induced force could be made and the necessity of intrinsic neuromodulation could be tested, but it would involve significant technical difficulty.
2. Forces generated by other motor neurons
This dissertation focuses on the key motor neurons B6, B9 and B3, because both duration and frequency of these neurons (blocks 2 and 3) are strongly enhanced in swallowing (Fig. 4-2). The activity of small BN2 units in blocks 1 (e.g., B10 and B39) and 4 (B43) are not significantly changed between biting and swallowing. However, the activity of these small BN2 units in blocks 2 and 3 may also be enhanced in swallowing, which may also contribute to force generation during the retraction phase of swallowing.
Our preliminary data show that the maximum forces generated by B43 are very small. In contrast, B10 and B39 could generate similar forces as B6 and B9. Because of the
168 limitations of the extracellular technique, which has been exclusively discussed in
Chapter 3, I was not able to reliably identify B10 and B39 extracellularly. To identify them, I need to use intracellular techniques and thus would not be able to record them during behaviors. To solve this, I could use the in vitro anchored buccal mass preparation to generate ingestive-like patterns and then use the in vivo block durations as criteria to distinguish biting-like patterns from swallowing-like patterns. Thus, I may be able to determine whether the activity of B10 and B39 is different between biting and swallowing. No matter whether other neurons contribute to retraction or not, the current results presented in this dissertation are still valid and the hypothesis is still supported: the neural activity of B6, B9 and B3 in biting is not primarily to generate force but to pre- modulate I1/I3 for force generation in subsequent swallowing.
3. Combined activity of three or more neurons from the set of B6, B9 and B3
In this study, I have explored the effects of combined activity of two neurons from the set of B6, B9 and B3 in both biting and swallowing (Fig. 4-8); however, I have not extended it to three or more motor neurons. My preliminary results of simultaneously stimulating B6, B9 and B3 have also shown an increased force generated by three neurons together. However, because of the technical difficulties, I could not repeat this experiment sufficiently often for statistical analysis. Thus, I could not quantify and compare the effects of combined activity of two neurons or even more neurons from B6,
B9 and B3. Future experiments could do this.
169
4. The hinge muscle, the other source for retraction in swallowing
Previous studies have shown that swallowing contains two types of responses
(type A and type B), defined by the amplitude of protraction (Ye et al., 2006a). During the smaller-amplitude (type A) swallows, I1/I3 is activated to pull the grasper back to its resting position and pulls food inwards. During the larger-amplitude (type B) swallows, the hinge is stretched by stronger protraction and acts to initiate the retraction of the grasper, and then works in conjunction with I1/I3 to induce the later part of retraction.
Thus, I1/I3 is important for retraction in both type A and type B swallows; the hinge also contributes to retraction in type B swallows.
In this study, I only focused on the main retractor muscle, I1/I3, and did not distinguish between type A and type B swallows. Although the hinge muscle may also assist retraction in some swallows, the results presented so far are still valid and support the central hypothesis.
5. Comparison between intrinsic modulation and extrinsic modulation
My preliminary results have shown that the activity of MCCs in the cerebral ganglion also modulates the muscle contractions evoked by B6, B9 and B3 in an isolated
I1/I3 muscle preparation. The extrinsic modulation of the MCC on the B6/B9-evoked muscle contractions have not been studied before; the results of the effects of the MCC on B3 are consistent with previous studies (Fox and Lloyd, 1997). However, the extrinsic modulation on the neuron-evoked muscle contractions by B6, B9 and B3 are all largely reduced when I tested them in the anchored buccal mass preparation, which is more
170 similar to the physiological configuration of I1/I3 in behaviors. I have found that the extrinsic modulation is much smaller than the intrinsic modulation on I1/I3 when neurons are stimulated at physiological activity levels. A direct comparison between extrinsic and intrinsic modulation could be done in the future.
Implications of this study
Although the motor neuronal control of Aplysia feeding has been well studied
(Gardner, 1971; Hurwitz et al., 1996; Morton and Chiel, 1993a, b; Warman and Chiel,
1995), much less is known about the characteristics of the retraction phase motor program. Previous studies have identified the motor neurons for the retractor muscle I1/I3 and recorded their activity during ingestive-like and egestive-like patterns in a variety of in vitro preparations (Church and Lloyd, 1994). However, it is difficult to distinguish biting-like patterns from swallowing-like patterns using those preparations because both types of patterns are ingestive-like and have similar phasic features of motor programs
(Morton and Chiel, 1993a). In addition, without actual muscle movements, it is difficult to determine whether those activity patterns of motor neurons are relevant to behaviors.
Since the I1/I3 muscles have a complex innervation, it is not possible to identity each motor neuron’s activity through the excitatory junction potentials (EJPs) recorded on the muscle, an approach that was used successfully when studying B15 and B16 in the ARC muscle (Cropper et al., 1990a; this muscle is also known as I5). Also, the BN2 motor program during the retraction phase consists of multiple different sized spike units, which makes it very difficult to determine a specific neuron’s activity pattern during behaviors.
171
To our knowledge, this dissertation is the first attempt to determine the activity patterns of multiple I1/I3 motor neurons during biting and swallowing responses and is crucial for understanding the neural mechanism of different behavioral intensities in Aplysia feeding.
Understanding the neural mechanism for different intensities of retraction allows a future study of the pattern generation circuitry underlying the behavioral switch from biting to swallowing. Although many I1/I3 motor neurons have been identified and characterized (Gardner, 1971; Church and Lloyd, 1991, 1994; Rosen et al., 2000b), it has not been studied whether they have similar functional roles in retraction during biting and swallowing. In this dissertation, I have found that these motor neurons do not equally contribute to the I1/I3 muscle contraction. The activity of small BN2 units during the early and late retraction phases is not significantly changed in biting and swallowing. In contrast, the activity of B6, B9 and B3 is largely enhanced in swallowing than in biting, which is also correlated with the retraction force. Thus, the behavioral intensity difference in biting and swallowing is at least partially due to the differential recruitment of B6, B9 and B3 during retraction. Our preliminary data have also shown that the activity of B6, B9 and B3 could be even more enhanced by increased mechanical loads in swallowing. Thus, sensorimotor feedback may play an important role in determining motor recruitment in Aplysia feeding.
This dissertation also provides insights into the role of neuromodulation under behaviorally relevant conditions. Although neuromodulation has been extensively studied in many systems, e.g., the Tritonia swimming system (Katz et al., 1994), the crab stomatogastric system (Christie et al., 1994), the frog skeletal neuromuscular junction
(Redman and Silinsky, 1994), and the cortex of monkeys (Hasselmo, 1995), less is
172 known about the role of neuromodulation at the individual neuron level within behavioral contexts. For this particular I1/I3 muscle, although intrinsic neuromodulation on the muscle has been well studied for some motor neurons (i.e., B3, B9 and B38; Fox and
Lloyd, 1997; Keating and Lloyd, 1999), none of these results could previously be directly related to natural behaviors in intact, behaving animals. The activity levels (durations and frequencies) of these motor neurons used in the previous studies were obtained from ingestive-like motor patterns, not from natural behavioral responses. This dissertation developed a general methodology for studying neuromodulation at the individual neuron level within behavioral contexts.
In addition, this study took a new approach to understand the behavioral role of intrinsic neuromodulation. Motor neurons can function solely as modulators when they are activated at low frequencies in a behavior that does not need the forces generated by these motor neurons. This allows the same motor neurons to become effective force generators as soon as a subsequent behavior requires them to fire at higher frequencies and generate large amount of force. This finding may also be applicable to other invertebrate systems (e.g., crab, cockroach, and crayfish) that have intrinsic modulation from motor neurons onto muscles (Calabrese, 1989). This finding could also be extended to vertebrate autonomic nervous system that uses cotransmitters as modulators. For example, human blood vessels use cotransmitter neuropeptide Y (NPY) as a modulator to potentiate vasomotor actions evoked by exogenous adenosine 5’ triphosphate (ATP) and noradrenaline (NA) (Donoso et al., 2004). The low-frequency stimulation of the perivascular nerve terminals may not cause vasomotor actions but may release NPY, which could pre-modulate the vascular smooth muscles for stronger contraction as the
173 nerve terminals are stimulated at higher frequencies. For those vertebrate skeletal muscles that do not have cotransmission for intrinsic modulation, repeated stimulation of muscles could also generate large potentiation of contraction forces (e.g., staircase potentiation; see Fig. 1 in Vandenboom et al., 2013), which is similar to the effects of intrinsic modulation by repeated stimulation of motor neurons. Thus, these vertebrate systems
(e.g., cats, Bagust et al., 1974; rats, Abbate et al., 2000) may also use muscle activation in a prior behavior to prepare for a stronger muscle contraction in a subsequent behavior.
Furthermore, my dissertation supports the original hypothesis by Bernstein (1967) that the periphery (e.g., muscles) must be prepared to properly respond to neural inputs at the right time. Ritzmann (1974) and Ye et al. (2006a) have provided direct evidence for this hypothesis. They demonstrated that properly positioning a muscle is essential for normal expression for its functions. In this study, I have demonstrated another mechanism for preparing a muscle: neuromodulation can prepare a muscle for its function generation in subsequent behaviors.
Future work
This study investigated the role of neuromodulation in behavioral contexts and explored how neural activity in prior bites pre-modulates the retractor muscle I1/I3 for force generation in subsequent swallows, which is one important source of behavioral multifunctionality. It would be logical to further investigate or extend this study in these future directions: (1) explore the neural mechanism of enhanced activity of key motor neurons for I1/I3 due to increased mechanical loads during swallowing; (2) explore the
174 neural mechanism that switches feeding behaviors from biting to swallowing as animals grasp food; (3) explore whether the timing of the key motor neurons’ activity would affect behavioral effectiveness of rejection.
1. The neural mechanism of differential recruitment of key motor neurons for I1/I3 due
to increased mechanical loads in swallowing
This study has shown that the activity of B6, B9 and B3 is significantly enhanced due to mechanical loading in swallowing (Fig. 4-2). Our preliminary results have also shown that these neurons’ activity is even further increased as mechanical loads increase
(e.g., thick seaweed strip, holding the seaweed during swallowing). Thus, some mechanoceptors may be involved in the increased recruitment of B6, B9 and B3 in response to increased mechanical loads during swallowing.
Previous studies have shown that a premotor neuron, B51, is important for generating the closing/retraction phase of the grasper during ingestive-like motor programs. Retraction movement is enhanced when B51 is depolarized and is reduced when B51 is hyperpolarized (see Fig. 16 in Evans and Cropper, 1998). In addition, B51 is a sensory neuron that is activated when the grasper rotates backward. The number of centripetal spikes in B51 is increased as the resistance of food increases (see Fig. 8 in
Evans and Cropper, 1998). In addition, B51 makes excitatory fast chemical or electrical connections with B64, a retraction phase interneurons, which excites retraction phase motor neurons, e.g., B6, B9 and B3 (see Fig. 4 in Cropper et al., 2004). Therefore, B51 is a proprioceptor that may be a part of a feedback loop that modifies the retraction phase to
175 meet changing mechanical demands as resistance is encountered in swallowing (Evans and Cropper, 1998). Thus, B51 and B64 may be two neurons that can be studied for the neural mechanisms of differential motor recruitment of the I1/I3 motor neurons in response to changing mechanical loads in swallowing.
2. The neural mechanism of behavioral switch from biting to swallowing
It would be also interesting to study the neural mechanism of the behavioral switch from biting to swallowing. Both biting and swallowing are ingestion behaviors, during which the grasper opens as it protracts and closes as it retracts (Morton and Chiel,
1993a, b). Biting is associated with strong protraction and weak retraction, whereas swallowing is associated with weak protraction and strong retraction (Neustadter et al.,
2007). Once animals grasp food, biting rapidly switches to swallowing, which might involve the activity of radula mechanoafferents (RM) neurons.
B21 is the largest RM neuron, which is activated by tactile stimuli on the surface of radular (grasper) during ingestion (Rosen et al., 2000a). B21 makes excitatory fast chemical and electrical connections with the retraction interneuron, B64. Thus, when animals grasp food during protraction, B21 is activated and excites B64, which inhibits the protraction phase interneurons and motor neurons (Jing and Weiss, 2001). This will eventually end the protraction phase and initiate the retraction phase. Therefore, the protraction phase is shortened and the retraction phase is prolonged by the activation of
B21 due to the touch of food on the radula surface, which initiates a switch from biting to swallowing. Thus, B21 and other RMs (e.g., B22; Rosen et al., 2000a) may be good
176 candidates for studying the neural mechanisms for behavioral switch between biting and swallowing.
3. The timing of activation of B6, B9 and B3 might be essential for behavioral
effectiveness of rejection
In this study, I have demonstrated that B6, B9 and B3 are the key motor neurons for the retractor muscle I1/I3. Their activity is correlated with retraction force (Fig. 4-3) and is significantly enhanced in swallowing due to mechanical loading (Fig. 4-2). During swallowing, the grasper closes as it retracts to pull food inward. Thus, the power stroke of
I1/I3 contraction could assist in closing on food. However, doing rejection, the grasper need to opens during retraction and close during protraction to push inedible material out.
Thus, the power stroke of I1/I3 contraction would prematurely close on the inedible material and pull it back during retraction. Therefore, the activation of B6, B9 and B3 may need to be delayed for an effective rejection.
Previous studies have shown that interneurons B4/B5 inhibit B6, B9 and B3
(Gardner, 1971). In addition, our preliminary results have shown that B4/B5 are more active in rejection than in biting and swallowing. Thus, the activity levels of B4/B5 may be correlated with the delay of the activation of B6, B9 and B3. Direct activation or inhibition of these motor neurons and B4/B5 may affect the behavioral effectiveness of rejection, which could be indicated by the tube (inedible material) outward movement during protraction phase. Future studies should test this hypothesis.
177
References
Abbate F, Sargeant AJ, Verdijk PW, de Haan A (2000) Effects of high-frequency initial pulses and posttetanic potentiation on power output of skeletal muscle. J Appl Physiol 88:35–40.
Azizi F, Lu H, Chiel HJ, Mastrangelo CH (2010) Chemical neurostimulation using pulse code modulation (PCM) microfluidic chips. J Neurosci Methods 192:193-198.
Bagust J, Lewis DM, Luck JC (1974) Post-tetanic effects in motor units of fast and slow twitch muscle of the cat. J Physiol 237:115–121.
Benjamin PR, Rose RM (1979) Central generation of bursting in the feeding system of the snail, Lymnaea stagnalis. J Exp Biol 80:93-118.
Brezina V, Evans CC, Weiss KR (1994) Enhancement of Ca current in the accessory radula closer muscle of Aplysia californica by neuromodulators that potentiate its contractions. J Neurosci 14:4393-4411.
Calabrese RL (1989) Modulation of muscle and neuromuscular junctions in invertebrates. Seminars in the Neurosciences 1:25-34.
Christie AE, Hall C, Oshinsky M, Marder E (1994) Buccalin-like and myomodulin-like peptides in the stomatogastric ganglion of the crab Cancer borealis. Exp Biol 193:337-343.
Church PJ, Lloyd PE (1991) Expression of diverse neuropeptide cotransmitters by identified motor neurons in Aplysia. J Neurosci 11:618-625.
Church PJ, Lloyd PE (1994) Activity of multiple identified motor neurons recorded intracellularly during evoked feedinglike motor programs in Aplysia. J Neurophysiol 72:1794-1809.
Cullins MJ, Chiel HJ (2010) Electrode fabrication and implantation in Aplysia californica for multi-channel neural and muscular recordings in intact, freely behaving animals. J Vis Exp 40:e1791, doi:10.3791/1791.
Cropper EC, Evans CG, Jing J, Klein A, Proekt A, Romero A, Rosen SC (2004) Regulation of afferent transmission in the feeding circuitry of Aplysia. Acta Biol Hung 55:211-220.
Cropper EC, Kupfermann I, Weiss KR (1990a) Differential firing patterns of the peptide- containing cholinergic motor neurons B15 and B16 during feeding behavior in Aplysia. Brain Res 522:176-179.
178
Donoso MV, Miranda R, Irarrázaval MJ, Huidobro-Toro JP (2004) Neuropeptide Y is released from human mammary and radial vascular biopsies and is a functional modulator of sympathetic cotransmission. J Vasc Res 41:387-399.
Drzymała-Celichowska H, Krutki P, Celichowski J (2010) Summation of motor unit forces in rat medial gastrocnemius muscle. J Electromyogr Kinesiol 20:599-607.
Evans CG, Cropper EC (1998) Proprioceptive input to feeding motor programs in Aplysia. J Neurosci 18:8016-8031.
Fox LE, Lloyd PE (1997) Serotonin and the small cardioactive peptides differentially modulate two motor neurons that innervate the same muscle fibers in Aplysia. J Neurophysiol 17:6064-6074.
Fox LE, Lloyd PE (1998) Serotonergic neurons differentially modulate the efficacy of two motor neurons innervating the same muscle fibers in Aplysia. J Neurophysiol 80:647-655.
Gardner D (1971) Bilateral symmetry and interneuronal organization in the buccal ganglia of Aplysia. Science 173:550-553.
Hasselmo ME (1995) Neuromodulation and cortical function: modeling the physiological basis of behavior. Behav Brain Res 67:1-27.
Hurwitz I, Goldstein RS, Susswein AJ (1994) Compartmentalization of pattern-initiation and motor functions in the B31 and B32 neurons of the buccal ganglia of Aplysia californica. J Neurophysiol 71:1514-1527.
Hurwitz I, Cropper EC, Vilim FS, Alexeeva V, Susswein AJ, Kupfermann I, Weiss KR (2000) Serotonergic and peptidergic modulation of the buccal mass protractor muscle (I2) in Aplysia. J Neurophysiol 84:2810-2820.
Iles JF (1972) Structure and synaptic activation of the fast coxal depressor motoneurone of the cockroach, Periplaneta americana. J Exp Biol 56:647-656.
Jing J, Weiss KR (2001) Neural mechanisms of motor program switching in Aplysia. J Neurosci 21:7349-7362.
Katz PS, Getting PA, Frost WN (1994) Dynamic neuromodulation of synaptic strength intrinsic to a central pattern generator circuit. Nature 367:729-731.
Keating C, Lloyd PE (1999) Differential modulation of motor neurons that innervate the same muscle but use different excitatory transmitters in Aplysia. J Neurophysiol 82:1759-1767.
179
McIntyre CC, Grill WM (2000) Selective microstimulation of central nervous system neurons. Ann Biomed Eng 28:219-233.
McPherson DR, Blankenship JE (1991) Neural control of swimming in Aplysia brasiliana. I Innervation of parapodial muscle by pedal ganglion motorneurons. J Neurophysiol 66:1338-1351.
Morgan PT, Jing J, Vilim FS, Weiss KR (2002) Interneuronal and peptidergic control of motor pattern switching in Aplysia. J Neurophysiol 87:49-61.
Morton DW, Chiel HJ (1993a) In vivo buccal nerve activity that distinguishes ingestion from rejection can be used to predict behavioral transitions in Aplysia. J Comp Physiol A 172:17-32.
Morton DW, Chiel HJ (1993b) The timing of activity in motor neurons that produce radula movements distinguishes ingestion from rejection in Aplysia. J Comp Physiol A 173:519-536.
Neustadter DM, Herman RL, Drushel RF, Chestek DW, Chiel HJ (2007) The kinematics of multifunctionality: comparisons of biting and swallowing in Aplysia californica. J Exp Biol 210:238-260.
Peters M, Altrup U (1984) Motor organization in pharynx of Helix pomatia. J Neurophysiol 52:389-409.
Redman RS, Silinsky EM (1994) ATP released together with acetylcholine as the mediator of neuromuscular depression at frog motor nerve endings. Physiol kondi 477:117-127.
Ritzmann RE (1974) Mechanisms for snapping behavior of two alpheid shrimp, Alpheus californiensis and Alpheus heterochelis. J Comp Physiol 95:217-236.
Rosen SC, Miller MW, Evans CG, Cropper EC, Kupfermann I (2000a) Diverse synaptic connections between peptidergic radula mechanoafferent neurons and neurons in the feeding system of Aplysia. J Neurophysiol 83:1605-1620.
Susswein AJ, Rosen SC, Gapon S, Kupfermann I (1996) Characterization of buccal motor programs elicited by a cholinergic agonist applied to the cerebral ganglion of Aplysia californica. J Comp Physiol A 179:509-524.
Sutton GP, Macknin JB, Gartman SS, Sunny GP, Beer RD, Crago PE, Neustadter DM, Chiel HJ (2004a) Passive hinge forces in the feeding apparatus of Aplysia aid retraction during biting but not during swallowing. J Comp Physiol A Neuroethol Sens Neural Behav Physiol 190:501-514.
180
Vandenboom R, Gittings W, Smith IC, Grange RW, Stull JT (2013) Myosin phosphorylation and force potentiation in skeletal muscle: evidence from animal models. J Muscle Res Cell Motil 34:317-332.
Ye H, Morton DW, Chiel HJ (2006a) Neuromechanics of coordination during swallowing in Aplysia californica. J Neurosci 26:1470 –1485.
Zuurbier CJ, Huijing PA (1992) Influence of muscle geometry on shortening speed of fibre, aponeurosis and muscle. J Biomech 25:1017-1026.
181
Appendix A
To understand the mechanism of extracellular stimulation over a range of different neural parameters (e.g., conductances, sizes and morphologies), and to predict the threshold current for stimulation without doing extensive, highly realistic simulations
(which are computationally expensive), we created an analytical model of extracellular stimulation on the side of the soma opposite to the axon. We represented the neuron as a semi-infinite axon with the tip connected to the extracellular medium through a resistance representing the soma. To simplify the model, we decided to focus primarily on steady state phenomena; other researches who focus on high frequency stimuli have used an activation function approach to modelling the responses to extracellular stimulation
(Rattay, 1999). Thus, to model the membrane potential along the axon, we used a steady- state cable equation (Rall and Agmon-Snir, 1998) with a position-dependent extracellular field. In essence, this is simply modelling the axon as a wire with a fixed internal resistance per unit length and a fixed membrane conductance to the external medium per unit length. This gives us Equation A1:
( )
where is the length constant, is the extracellular field, is the distance along the axon, is the membrane potential and is the resting (negative) membrane potential.
At this point, it is convenient to rewrite this equation in terms of dimensionless variables both to simplify its form and to clarify the relationships between parameters. Using the extracellular field of a point source (Equation 2-2) for , Equation A1 can be non- dimensionalized to yield:
182
where is the normalized extracellular current (the extracellular current divided by
, where is the conductivity of the extracellular medium and is the resting membrane potential), is the normalized distance from the center of the soma to the extracellular stimulating electrode ( , where is the length constant of the axon and is the distance from the center of the soma to the point current source), is the normalized distance along the axon and is the normalized membrane potential (
). This is a second order ordinary differential equation, and solving it gives us the following general solution:
( )
where is the exponential integral ( ) and and are ∫ constants of integration. To find a particular solution, we assumed that the membrane potential at infinity was the resting membrane potential (i.e., -1 in dimensionless units) and that the potential at the tip of the axon was the sum of the resting potential and the current flowing into the tip of the axon through the soma multiplied by the resistance of the soma:
(( ) )
183
where is the normalized soma resistance ( , is the total resistance across the
membrane of the soma and is the internal resistance per unit length in the axon). Using these boundary conditions, we found the following general solution for the membrane potential at a position along the axon as a function of the extracellular current :
( )
( )
Note that the membrane potential is a linear function of the extracellular current .
Thus, the point of maximum depolarization or hyperpolarization along the axon is independent of the amount of extracellular current, and the amount of depolarization or hyperpolarization is directly proportional to the current. We can thus, without loss of generality, numerically find the minimum amount of current required to depolarize at least one point on the neuron to zero potential. This can be done by solving Equation A5 for with equal to zero and then adjusting the normalized distance to minimize the value of the normalized current , i.e., minimizing the following equation:
We call the resulting (minimized) value of the normalized threshold current (shown in Fig. A1). Strikingly, even with pulses of current as short as 6 ms, which were similar to those used for all the anodic stimulation in vitro, and for the cathodic inhibition in vitro
184
(Fig. 2-16), the results from the NEURON model were quite close to those predicted by the analytical model, suggesting that it may be of use even for moderate frequency stimulation as well as in steady state.
Figure A1. The surface of threshold currents of arbitrary neurons can be predicted as a function of the electrode distance and soma resistance. is the normalized threshold current, is the natural log of the normalized total resistance of the soma and is the normalized distance to the extracellular current source. See text for further details. The black dots are the normalized threshold currents measured by the NEURON model. The grey dots are the normalized threshold currents predicted by the analytical model.
We found that the rational function in Equation 2-4 (with set to be 0) provided an excellent approximation to this function, with a maximum error of less than 6% if is restricted to one to three length constants (or one-fifth to three length constants if is
185 greater than one), which rose to less than 9% with over a range of two-thirds to four length constants.
To provide a simple correction for angle, the extracellular field can be treated as if it consists of planes of equal potential (as it would be if the source were far away). In this case, an increase in the distance along the axon (e.g., adding an arbitrary constant to ) corresponds to an increase of in the distance to the source, where is the angle between the axon and the line drawn from the current source to the axon (i.e., a vector normal to the isopotential planes). This is equivalent to increasing the conductivity of the
medium, , by a factor of . Since the conductivity of the medium shows up
as a divisor of , this gives us the on the right of Equation 2-4. While the choice of the point from which to measure is arbitrary at large distances (since the angles are all equivalent), at small distances the angle can vary from point to point. Because the source has the greatest effect on the part of the axon near the soma, it makes sense to choose a point in this area. We found a point at half of a length constant down the axon provided a reasonable fit to the NEURON model data.
Over most ranges of interest, the behavior of both the full and the simplified analytical model can be approximated with a quadratic function of as previously described by Stoney et al. (1968) and shown in Fig. A2. This is also suggested by
Equation 2-4, with the quadratic term of clearly dominating as becomes very large or very small. This provides a quick heuristic for approximating the threshold function from any three data points. It does not, however, provide an easy way of predicting the fitting parameters before these first data points are collected, nor does it describe the change in
186 membrane potential along the axon. These can be addressed by using Equations 2-4 and
A5, respectively.
Figure A2. A quadratic fit provides a good approximation for the threshold currents predicted by the analytical model. The solid line shows the predictions of the full analytical model (with = 2), the dashed line shows the prediction of the simplified analytical model (Equation 2-4) and the dotted line shows a quadratic fit of the full model. (A) While both approximations provide a good fit to the full analytical model, the simplified analytical model is noticeably worse than the quadratic fit at larger distances with this value of (which is likely to apply to most values of that are experimentally relevant). (B) At shorter distances (corresponding to the boxed region in A), the quadratic fit deviates more from the full analytical model than the simplified analytical model.
187
Appendix B
The following protocol is from a JoVE manuscript that has been produced for a
video publication (Lu et al., 2013). I present it in the same template as the JoVE
manuscript.
1. Preparation of Recording Dish
1.1. During the force transducer experiments, the buccal ganglia, cerebral ganglion,
and buccal mass are placed in a round Pyrex dish that is specialized for force studies.
1.2. To induce ingestive-like patterns in the experiments, we need to apply the non-
hydrolyzable cholinergic agonist carbachol to the cerebral ganglion (Susswein et al.,
1996). To avoid direct contact from carbachol onto the buccal ganglia and buccal mass,
separate chambers are needed to isolate the cerebral ganglion from the buccal ganglia and
the buccal mass (Fig. 3-1).
1.3. Since the buccal mass is much thicker than the buccal ganglia, they will not be
placed on the same level. Therefore, this dish should have a back chamber for the
cerebral ganglion (area A in Fig. 3-1), a middle platform for the buccal ganglia (area C in
Fig. 3-1), and a much deeper front chamber for the buccal mass (area D in Fig. 3-1).
1.4. To create this dish, begin with a round 100x15 Pyrex dish (15 mm high, 100 mm
in diameter). Construction of the dish will require several pours of Sylgard. Follow the
instructions provided with the Sylgard product. Sylgard must be allowed to set between
different pours.
188
1.5. The first pour is to create the highest level of Sylgard in the dish (area B in Fig. 3-
1), which is the wall between the middle platform and back chamber.
1.6. Use two modeling clay backings to isolate the area for Sylgard wall (area B in Fig.
3-1). Coat the modeling clay backings with plastic wrap where they will contact the
Sylgard for easier removal. Ensure tight seals at the edges, where modeling clay will
contact the dish, to minimize leakage.
1.7. Pour Sylgard into the portion between the two modeling clay backings nearly up
to the top of the dish. Let the Sylgard fully set overnight. Keeping the dish in a warm
place will induce faster setting. Remove the modeling clay backings and clean up any
clay residue on the Sylgard.
1.8. Next, the back chamber (area A in Fig. 3-1) and the middle platform (area C in
Fig. 3-1) should be poured.
1.9. Place a modeling clay backing about 5 mm away from the front surface of the
Sylgard for the section of the middle platform (area C).
1.10. Pour Sylgard into the sections for the back chamber (area A) and middle platform
(area C) up to a height of approximately 3-5 mm below the top level of the first Sylgard
wall (area B). The Sylgard surface of the back chamber should be slightly lower than that
of the middle platform to avoid leakage from the back chamber containing carbachol to
the middle platform. Again, let the Sylgard fully set overnight and then remove the
modeling clay backing.
189
1.11. The final step is to cut a notch in the middle of the Sylgard wall to provide a
channel for the cerebral-buccal connectives (CBCs) to go through between the middle
platform and back chamber. The width of this notch should be approximately 3-4 mm,
which is wide enough for the CBCs. The bottom of the notch should not be lower than
the Sylgard surface of the middle platform to prevent leakage. A scalpel blade can be
used to cut the notch.
2. Electrode Preparation
2.1. Pull extracellular glass electrodes from single-barreled capillary glass using a
Flaming-Brown micropipette puller as described by McManus et al. (2012) in section 3.1.
With the FT345B filament in the puller, our typical program settings are Heat 480, Pull
50, Velocity 13, and Time 20, but note that settings will be different for different
filaments. This program creates the electrodes in a single pull with no fire-polishing stage.
The size of the electrode tip should be smaller than the size of the cell bodies. For the
motor neurons ranging from 50 μm to 400 μm in soma diameter, the inner diameters of
the extracellular glass electrodes should be about 40 μm and their resistances should be
about 0.1 M when they are filled with Aplysia saline.
2.2. Pull suction electrodes from polyethylene tubing using a Bunsen burner. Cut a
piece of polyethylene tubing about 10 cm long. Hold the tubing on both ends and place it
very close to the flame generated by the Bunsen burner while rotating the tube until it
becomes soft from the heat. Stretch the tubing carefully along its length while moving it
190
away from the flame. The middle part of the tubing will elongate and narrow as the
tubing is pulled.
2.3. Cut the tubing in half to form two suction electrodes. Suction electrodes are
generally applied to the cut ends of nerves or muscles, though they can sometimes be
applied en passant.
2.4. Create hook electrodes for nerve recordings following the protocol described by
McManus et al. (2012) in sections 3.2-3.13. These electrodes are especially useful when a
nerve or muscle is not cut.
3. Hook Electrode Attachment
3.1. Dissect the animal and remove the buccal mass following the protocol described
in McManus et al. (2012) section 4.
3.2. For recording and stimulation, hook electrodes can be attached to a number of
different nerves.
3.3. To characterize patterns as was done in vivo by Cullins and Chiel (2010),
recordings must be obtained from the I2 nerve and muscle that indicates the protraction
phase of feeding (Hurwitz et al., 1996), the radular nerve (RN) that indicates the closure
of the food grasper (Morton and Chiel, 1993a), buccal nerve 2 (BN2) and buccal nerve 3
(BN3) that indicate the retraction phase (Morton and Chiel, 1993a; Warman and Chiel,
1995). Attachment of the hook electrodes follows a procedure similar to that described by
McManus et al. (2012), section 5.
191
3.4. The locations of these nerves refer to the schematic of the Aplysia feeding
apparatus shown in Fig. 3-2 of McManus et al. (2012). Note that the BN2 trifurcates into
branches a, b, and c before going beneath the I1 muscle at the lateral groove. Branch a is
the first branch to separate from the main trunk, and is adjacent to the BN3.
3.5. The nomenclature of branches a, b, and c was used by Warman and Chiel18.
Branches a, b, and c correspond to branches 3, 2, and 1, respectively, in the nomenclature
used by Nargeot et al. (1997). Furthermore, the RN, the BN1, the BN2, and the BN3
correspond to nerves 1, 6, 5, and 4, respectively, in the nomenclature used by Kandel
(1979) and Scott et al. (1991).
3.6. To study the muscle innervation of the I1/I3 muscle, all the nerves except buccal
nerves 2 will be severed from the buccal mass during experiments. Thus, we used a hook
electrode to record from the BN2.
3.7. Since the I2 nerve and the RN will not be attached to the buccal mass, and they
are very difficult to access using hook electrodes, it is preferable to apply suction
electrodes to record from them instead. We will describe the application of suction
electrodes in section 7.
3.8. Use either a hook electrode or a suction electrode to record from the BN3,
because it is easy to access using either kind of electrode. We chose to use a hook
electrode for the BN3 recordings to minimize the number of manipulators for holding the
suction electrodes, and to save space for other manipulators or equipment.
3.9. Attach a hook electrode to branch a of BN2 (BN2-a) to initiate rejection-like
patterns during experiments. It is useful to attach an additional hook electrode to the
192
BN2-a on the other side, because some neurons respond differently to the ipsilateral vs.
contralateral BN2-a stimulation.
3.10. To help distinguish neurons with unilateral vs. bilateral projections, it is also
useful to attach hook electrodes to BN2 and the BN3 on the other side of the buccal
ganglia.
4. Ganglia and Muscle Preparation
4.1. The buccal ganglia, cerebral ganglion and buccal mass will be prepared for the
force transducer experiments, in which the cerebral ganglion is attached to the buccal
ganglia via the CBCs and the buccal mass is attached to the buccal ganglia via the BN2s
only.
4.2. After attaching the hook electrodes, cut buccal nerve 1 (BN1) and the esophageal
nerve (EN) bilaterally, cutting at the attachment point to the buccal mass.
4.3. Pull the cerebral ganglion forward to move it out of the way of the I2 muscle.
Make a cut into the I2 muscle over the radular sac, extend the cut laterally and anteriorly
in both directions, and pull the flap of the I2 muscle forward to expose the radular nerve.
Cut the two RN branches and make sure that the branches are long enough for suction
electrode attachment.
4.4. Continue the I2 cut in a wide circle around the buccal ganglia, being careful not to
cut the BN2s or the BN3s, until the buccal ganglia and the attached part of the I2 muscle
193
can be fully separated from the buccal mass. Cut the bilateral BN3s at the attachment
point to the buccal mass, beyond the hook electrode attachment.
4.5. Apply a thin layer of vacuum grease to the notch in the recording dish described
above that connects the back chamber and middle platform, using a pipette tip to pick up
a glob of vacuum grease and spread it over the notch.
4.6. Apply a thin layer of Quick-Gel super glue to the glass bottom of the front
chamber where the buccal mass will be placed, just in front of the Sylgard base of the
middle platform.
4.7. Carefully transfer the cerebral ganglion, buccal ganglia and buccal mass to the
recording dish (Fig. 3-1) described in section 2, making sure that none of the hook
electrodes are pulled tightly, which could damage the nerves.
4.8. Carefully place the buccal mass on the glue in the front chamber of the recording
dish, to ensure that its ventral surface is glued to the bottom of the dish. Be sure to keep
the ganglia and electrodes from touching the glue. Add Aplysia saline (McManus et al.,
2012; 460 mM NaCl, 10 mM KCl, 22 mM MgCl2, 33 mM MgSO4, 10 mM CaCl2, 10
mM glucose, 10 mM MOPS, pH 7.4–7.5) to the dish, which will induce the glue to set.
4.9. If the dish must be transferred to another microscope for preparing the buccal
ganglia for extracellular soma recordings, be very careful with the hook electrodes.
Group the electrodes on one side of the buccal mass together, and also group the
electrodes on the other side of the buccal mass together. Carefully hold the electrodes by
grasping the lab tape that covers the connector pins, again making sure that none of the
electrodes are pulled tightly.
194
4.10. When the dish is positioned under the microscope, the electrodes should be
draped gently over the sides of the dish and rest on the platform next to the dish.
4.11. During breaks and between stages of the experiment, aerate the saline in the
buccal mass chamber using an aquarium airstone.
4.12. Use forceps to grab the sheath of the cerebral ganglion and pull it into the back
chamber, ensuring that the CBCs run through the notch. Pin the cerebral ganglion using
nerves other than the CBCs to avoid damage to the intact CBCs.
4.13. Apply more vacuum grease over the CBCs, and then add more Aplysia saline to
both chambers of the dish, so that the ganglia are completely submerged. Ensure that the
top of the vacuum grease is slightly higher than the Sylgard wall so that no leakage will
occur between the chambers.
4.14. To stabilize the buccal ganglia, first pin the ends of the BN3s, then the BN1s and
the ENs on the Sylgard base of the middle platform (Fig. 3-2). Since the BN3s will be
recorded using hook electrodes, the pins should be placed more distally than the
attachment points of the hook electrodes.
4.15. Use two pins, bent 90 degrees, as hooks to stretch and anchor the CBCs, so that
the CBCs will not be damaged (Fig. 3-2).
4.16. Pin down the RN branches between the back chamber and the buccal ganglia.
Then the I2 muscle will be on top of the RNs. To expose the I2 nerve, use forceps to grab
the I2 muscle and pull it over the buccal ganglia. Pin two corners of the I2 muscle to
avoid damage to the I2 nerve.
195
4.17. Sever the I2 nerve distal to the point at which its two branches merge into the I2
muscle. Make sure the muscle is still innervated to be comparable to the in vivo
recordings. Cut away the rest of the I2 muscle and flip the I2 nerve back and pin it down
between the two RN branches (see Fig. 3-2, inset).
4.18. Adjust the locations of the pins to stretch and add tension if a nerve is too loose,
or to release tension if a nerve is too tight. To further stabilize the buccal ganglia, add
more pins on the sheath between nerves.
4.19. Since the buccal ganglia are placed caudal side up, rotate the buccal ganglia if the
neurons of interest are on the rostral side. To rotate one of the two buccal ganglia, use
fine forceps to grab some excess sheath of the CBC where it is near to the buccal ganglia
and pin it down between the BN2 and the BN3. In some ganglia, it may be more
convenient to pin it down between CBC and BN3.
4.20. Add an additional pin on the sheath of the buccal ganglion on the side close to the
front chamber to minimize the movement of the buccal ganglion.
4.21. To trim the sheath covering the buccal ganglia, use fine forceps to grab the sheath
on the side close to the back chamber, and then cut away the excess sheath with fine
scissors without exposing the cell bodies. In order to minimize damage, only remove the
minimum amount of sheath necessary to see the cell bodies.
4.22. After the sheath of the buccal ganglia is trimmed, pull the I2 nerve and the RNs
over the buccal ganglia and pin them down between the buccal ganglia and front chamber
to further rotate the buccal ganglia (see Fig. 3-2).
196
4.23. To wash out any remaining magnesium chloride8 that was used to anesthetize the
animal before dissection, replace the Aplysia saline in the dish with fresh Aplysia saline.
5. Electrically Connecting Hook Electrodes
5.1. After the ganglia and muscle are prepared, carefully transfer the dish to the
vibration isolation table for the experiments.
5.2. Attach all electrode pins to their sockets on the BNC cables that connect to the
amplifiers (A-M Systems model 1700 amplifier). Again, make sure that the electrodes are
not pulled tightly while doing this. Make sure that the electrodes are correctly attached to
their appropriate cables and that the polarities are correct.
6. Setting up the extracellular glass electrodes for soma recordings
6.1. Fill the electrode with Aplysia saline using a syringe attached to a piece of
polyethylene tubing of around 15-20 cm. Attach the free end of the polyethylene tubing
to the end of the glass electrode. Pull back on the plunger of the syringe to fill up the
electrode with Aplysia saline.
6.2. Place the filled extracellular glass electrode in the notch of the holder on the
manipulator. Use the manipulator to place the electrode tip into the Aplysia saline
containing the buccal ganglia.
6.3. Insert a silver/silver chloride wire soldered to a male gold connector pin into the
electrode to serve as the recording wire. Place another silver/silver chloride wire soldered
197
to a male gold connector pin directly into the Aplysia saline within the section of the
recording dish containing the buccal ganglia to act as the reference wire. Connect both
the recording and reference wires to the BNC cable that connects to the amplifier.
6.4. If there is enough room for more manipulators, additional extracellular glass
electrodes can be added to record multiple neurons simultaneously.
7. Setting up the suction electrodes for the nerve recordings
7.1. Trim the narrow end of the suction electrode tip to match the diameter of the
nerve. The inner diameter of the electrode tip should be similar to or slightly smaller than
the nerve’s diameter to ensure tight suction.
7.2. Since the I2 nerve and the RN are very close to each other, their electrodes can be
held by the same manipulator to save space. Place two electrodes in two notches of the
same holder. Rotate the two electrodes and ensure that their tips are close to one other.
Choose one of them for the I2 nerve recording, the other one for the RN recording.
7.3. Place the electrode tip in the Aplysia saline within the recording dish containing
the buccal ganglia. Attach the free end of the polyethylene tubing on the syringe to the
suction electrode. Use the syringe to fill up the electrode with Aplysia saline. Move the
electrode tip close to the end of the target nerve, i.e., the I2 nerve, and use the syringe to
suck the nerve into the electrode. The length of the nerve within the electrode should be
about 0.5-1.0 mm to ensure a tight seal.
7.4. Repeat the suction for the electrode that will be attached to the RN.
198
7.5. Connect the electrodes to the corresponding BNC cables as described in section
8. Setting up the force transducer to measure the I1/I3 muscle contraction
8.1. To attach the force transducers to the muscle, use silk sutures. Bend the curved
needle of each suture, and tie the suture to the force transducer. Gently grab and lift a
small amount of muscle with forceps and, holding the needle in another set of forceps,
insert the needle through the muscle up to the bent point in the needle (Fig. 3-1).
8.2. Transducers can be attached either dorsally or laterally on the I1/I3 muscle.
Dorsal attachment allows measurement of contractions evoked by activation of either the
left or right side of the muscle. Lateral attachment will show stronger forces for the
majority of neurons, but will only allow measurement of contraction on the side to which
the transducer is attached.
8.3. To help identify neurons that may activate the anterior, posterior, or both regions
of I1/I3, attach a force transducer to the posterior part of the muscle, just anterior to the
pharyngeal tissue, and attach another force transducer to the anterior part of the muscle,
at the jaws (Fig. 3-1; note hooks).
8.4. Lift the force transducers until the sutures are pulled taut, but do not overstretch.
To check this, view the measurement from the force transducer when the suture has some
slack in it, and then lift the transducer until the measurement is slightly above this
baseline level.
199
9. Identifying motor neurons within a motor pool
9.1. This protocol describes a process for extracellularly identifying motor neurons
within a motor pool. We used AxoGraph software to monitor the activity of individual
neurons, multiple nerves, and the muscle (EMG signal or contraction forces). In this
protocol, we used the contraction forces of the muscle as an illustration for the process of
identifying motor neurons; in other experiments, we used EMG as well, and the setup for
such experiments is very similar (see Discussion).
9.2. To locate a candidate neuron, use the manipulator to gently press the tip of the
extracellular glass electrode down onto the sheath over the center of the neuron soma (Fig.
3-3), which is the best location for stimulation and recording selectivity (Lu et al., 2008).
Since the threshold current for activating a neuron increases linearly with electrode-to-
soma distance (Lu et al., 2008), the stimulation selectivity will become worse when the
electrode is moved away from the center of the target neuron toward a neighboring
neuron.
9.3. To identify a motor neuron, first directly stimulate the neuron using the
extracellular glass electrode to ensure that only this neuron is firing, and examine whether
it innervates the muscle. Then, extracellularly record from this neuron to establish a one-
for-one relationship between the extracellular soma recording and the nerve recordings,
which is also crucial for neuron identification.
9.4. Since most extracellular amplifiers do not allow simultaneous stimulation and
recording in a channel, set the channel used to stimulate and record the soma (the soma
channel) to stimulation mode and apply a brief anodic current (e.g., 6 ms for Aplysia
200
neurons; Lu et al., 2008) to the soma (Figs. 3-4A, 3-5A; note arrows 1 in both figures),
starting from a low current (e.g., 200 µA), and gradually increasing the current until the
neuron bursts.
9.5. Once the neuron is activated to burst, one should immediately switch the soma
channel from the stimulation mode to the recording mode (Figs. 3-4A, 3-5A; note arrows
2 in both figures). However, there will still be delays between the soma stimulation and
recording because of human response delays.
9.6. If the neuron fires for a reasonable amount of time, it should be possible to
observe one-for-one corresponding action potentials from the soma recording and on the
nerve(s) through which the neuron projects (Figs. 3-4B, 3-5B; note dashed lines), as well
as the forces generated by the neuron (Figs. 3-4A, 3-5A; note blue boxes). If the neuron
stops firing before the soma recording begins, increase the current to activate it for a
longer time.
9.7. The signal-to-noise ratio of the extracellular recordings depends on the electrode
location and soma size. The extracellular recording will become bigger as the soma size
increase and electrode-to-soma distance decreases. Since the noise only varies in a
narrow range, the signal-to-noise ratio will also increase as the soma size increases and
the electrode-to-soma distance decreases. The most common range of the signal-to-noise
ratio is from 4:1 to 8:1.
9.8. The motor neurons can be identified based on their characteristics, such as soma
location, nerve projection and muscle innervation (Church et al., 1991; Church and Lloyd,
1991, 1994). Since only the two BN2s are attached to the buccal mass, by monitoring the
201
activity on the BN2s, one can ensure that the muscle contraction force is only caused by
the neuron activated by the extracellular stimulation.
9.9. For example, B3 is a large motor neuron for the I1/I3 muscle (300-400 µm in
soma diameter in animals weighing 200 to 350 grams), located on the rostral side of the
buccal ganglion (Fig. 3-3). It only projects through the ipsilateral BN2, and innervates
both the anterior and posterior parts of the I1/I3 muscle. Most of the time, activating it
generates a larger anterior than posterior force (8 out of 9 experiments).
9.10. After a neuron is identified, its activity can be recorded in different feeding-like
behaviors via the extracellular glass electrode (Figs. 3-4 and 3-5), which can be elicited
as described below. The extracellular recording on the soma will be much more specific
than the nerve recordings, which include the activity of many different neurons.
9.11. To induce egestive-like motor programs, stimulate BN2-a with 1-2 minutes of
pulses (2Hz, each pulse is 1 ms; Nargeot et al., 1997). This stimulation reliably generates
egestive patterns in this setting. With sufficient current (e.g., 300 µA), patterns may
persist for the duration of the stimulation. Sometimes there will be one more pattern that
occurs shortly after the stimulation ends.
9.12. To induce ingestive-like motor programs, place a few crystals of solid carbachol
directly onto the sheath of the cerebral ganglion (Susswein et al., 1996). If one wants to
control the level of carbachol exposure, use a solution of 1 to 10 mM carbachol in
Aplysia saline. Higher concentrations are more likely to induce responses. Repetitive
patterns generally begin within five minutes, and may last for approximately ten to fifteen
minutes before beginning to run down.
202
9.13. After washing out carbachol several times and waiting for at least 30 minutes, a
subsequent application of carbachol can be added to the cerebral ganglion to induce more
ingestive-like motor patterns.
9.14. After multiple motor neurons for the particular motor pool have been identified
and characterized during motor programs, one may develop a greatly simplified
diagnostic method that requires minimal information for quickly identifying these
neurons in future work (Fig. 3-6), e.g., in the suspended buccal mass preparations or in
vivo. The criteria may include soma size and location, nerve projection, unit size on the
nerves, and timing of activity during motor patterns.
203
Appendix C
Table of Specific Reagents and Equipment:
Name Company Catalog Number Comments
Sodium chloride Fisher S671 Biological, Certified Scientific
Potassium chloride Fisher P217 Certified ACS Scientific
Magnesium chloride Acros Organics 19753 99% hexahydrate
Magnesium sulfate Fisher M63 Certified ACS heptahydrate Scientific
Calcium chloride Fisher C79 Certified ACS dihydrate Scientifc
Glucose (dextrose) Sigma-Aldrich G7528 BioXtra
MOPS buffer Acros Organics 17263 99%
Carbachol Acros Organics 10824 99%
Sodium hydroxide Fisher SS255 Certified Scientific
Hydrochloric acid Fisher SA49 Certified Scientific
Single-barreled A-M Systems 6150 capillary glass
Flaming-Brown Sutter Filament used: micropipette puller Instruments FT345B model P-80/PC
Enamel coated California Fine 0.001D, coating h stainless steel wire Wire
Household Silicone GE II Glue
204
Duro Quick-Gel Henkel corp. superglue
A-M Systems model A-M Systems Filter settings: 10-500 1700 amplifier Hz for the I2 nerve/muscle; 300-500 Hz for all the other nerves
Pulsemaster Multi- World A300 Channel Stimulator Precision Instruments
Stimulus Isolator World A360 Precision Instruments
AxoGraph X AxoGraph Software for Scientific recordings
Gold Connector Pins Bulgin SA3148/1
Gold Connector Bulgin SA3149/1 Sockets
Sylgard 184 Silicone Dow Corning Elastomer
100 x 15 mm Pyrex Crystalizing Dish
High Vacuum Dow Corning Grease
Pipet Tips Fisher 21-375D Scientific
Minutien Pins Fine Science 26002-10 Tools
Modeling Clay Sargent Art 22-4400
Whisper Air Pump Tetra 77849
205
Aquarium Tubing Eheim 7783 12/16 mm
Elite Airstone Hagen A962
Vannas Spring Fine Science 15000-08 Scissors Tools
Dumont #5 Fine Fine Science 11254-20 Forceps Tools
Kimwipes Kimberly- 34155 Clark
206
Bibliography
Abbate F, Sargeant AJ, Verdijk PW, de Haan A (2000) Effects of high-frequency initial pulses and posttetanic potentiation on power output of skeletal muscle. J Appl Physiol 88:35–40.
Anderson WS, Lenz FA (2006) Surgery insight: Deep brain stimulation for movement disorders. Nat Clin Pract Neurol. 2:310-320.
Azizi F, Lu H, Chiel HJ, Mastrangelo CH (2010) Chemical neurostimulation using pulse code modulation (PCM) microfluidic chips. J Neurosci Methods 192:193-198.
Bagust J, Lewis DM, Luck JC (1974) Post-tetanic effects in motor units of fast and slow twitch muscle of the cat. J Physiol 237:115–121.
Baker BJ, Kosmidis EK, Vucinic D, Falk CX, Cohen LB, Djurisic M, Zecevic D (2005) Imaging brain activity with voltage- and calcium-sensitive dyes. Cell Mol Neurobiol 25: 245–282.
Barbeau H, McCrea DA, O’Donovan MJ, Rossignol S, Grill WM, Lemay MA (1999) Tapping into spinal circuits to restore motor function. Brain Res Rev 30:27–51.
Bartfai T, Iverfeldt K, Fisone G (1988) Regulation of the release of coexisting neurotransmitters. Ann Rev Pharmacal Toxicol 28:285-310.
Baxter DA, Canavier CC, Clark JW Jr, Byrne JH (1999) Computational model of serotonergic modulation of sensory neurons in Aplysia. J Neurophysiol 82:2914-2935.
Benabid AL, Pollak P, Gao D, Hoffmann D, Limousin P, Gay E, Payen I, Benazzouz A (1996) Chronic electrical stimulation of the ventralis intermedius nucleus of the thalamus as a treatment of movement disorders. J Neurosurg 84:203-214.
Benjamin PR, Rose RM (1979) Central generation of bursting in the feeding system of the snail, Lymnaea stagnalis. J Exp Biol 80:93-118.
Bernstein N (1967) The coordination and regulation of movements. Oxford: Pergamon.
Brezina V, Evans CC, Weiss KR (1994) Enhancement of Ca current in the accessory radula closer muscle of Aplysia californica by neuromodulators that potentiate its contractions. J Neurosci 14:4393-4411.
207
Calabrese RL (1989) Modulation of muscle and neuromuscular junctions in invertebrates. Seminars in the Neurosciences 1:25-34.
Chanaud CM, Pratt CA, Loeb GE (1991) Functionally complex muscles of the cat hindlimb V. The roles of histochemical fiber-type regionalization and mechanical heterogeneity in differential muscle activation. Exp Brain Res 85:300-313.
Christie AE, Hall C, Oshinsky M, Marder E (1994) Buccalin-like and myomodulin-like peptides in the stomatogastric ganglion of the crab Cancer borealis. Exp Biol 193:337-343.
Church PJ, Cohen KP, Scott ML, Kirk MD (1991) Peptidergic motoneurons in the buccal ganglia of Aplysia californica: immunocytochemical, morphological, and physiological characterizations. J Comp Physiol A 168:323-336.
Church PJ, Lloyd PE (1991) Expression of diverse neuropeptide cotransmitters by identified motor neurons in Aplysia. J Neurosci 11:618-625.
Church PJ, Lloyd PE (1994) Activity of multiple identified motor neurons recorded intracellularly during evoked feedinglike motor programs in Aplysia. J Neurophysiol 72:1794-1809.
Cifuentes F, Arias ER, Morales MA (2013) Long-term potentiation in mammalian autonomic ganglia: an inclusive proposal of a calcium-dependent, trans-synaptic process. Brain Res Bull 97:32-38.
Cioni B, Meglio M, Perotti V, De Bonis P, Montano N (2007) Neurophysiological aspects of chronic motor cortex stimulation. Neurophysiol Clin 37:441-447.
Cohen JL, Weiss KR, Kupfermann I (1978) Motor control of buccal muscles in Aplysia. J Neurophysiol 41:157-180.
Cohen MR, Newsome WT (2004) What electrical microstimulation has revealed about the neural basis of cognition. Curr Opin Neurobiol 14:169-177.
Cropper EC, Brezina V, Vilim FS, Harish O, Price DA, Rosen S, Kupfermann I, Weiss KR (1994) FRF peptides in the ARC neuromuscular system of Aplysia: purification and physiological actions. J Neurophysiol 72:2181-2195.
Cropper EC, Evans CG, Jing J, Klein A, Proekt A, Romero A, Rosen SC (2004) Regulation of afferent transmission in the feeding circuitry of Aplysia. Acta Biol Hung 55:211-220.
208
Cropper EC, Kupfermann I, Weiss KR (1990a) Differential firing patterns of the peptide- containing cholinergic motor neurons B15 and B16 during feeding behavior in Aplysia. Brain Res 522:176-179.
Cropper EC, Lloyd PE, Reed W, Tenenbaum R, Kupfermann I, Weiss KR (1987) Multiple neuropeptides in cholinergic motor neurons of Aplysia: evidence for modulation intrinsic to the motor circuit. Proc Natl Acad Sci U S A 84:3486-3490.
Cropper EC, Miller MW, Vilim FS, Tenenbaum R, Kupfermann I, Weiss KR (1990c) Buccalin is present in the cholinergic motor neuron B16 of Aplysia and it depresses accessory radula closer muscle contractions evoked by stimulation of B16. Brain Res. 512(1):175-9.
Cropper EC, Price D, Tenenbaum R, Kupfermann I, Weiss KR (1990b) Release of peptide cotransmitters from a cholinergic motor neuron under physiological conditions. Proc Natl Acad Sci USA 87:933-937.
Cullins MJ, Chiel HJ (2010) Electrode fabrication and implantation in Aplysia californica for multi-channel neural and muscular recordings in intact, freely behaving animals. J Vis Exp 40:e1791, doi:10.3791/1791.
DeCamilli P, Jahn R (1990) Pathways to regulated exocytosis in neurons. Annu Rev Physiol 52:625-645.
De Ridder D, De Mulder G, Menovsky T, Sunaert S, Kovacs S (2007) Electrical stimulation of auditory and somatosensory cortices for treatment of tinnitus and pain. Prog Brain Res 166:377-388.
Donoso MV, Miranda R, Irarrázaval MJ, Huidobro-Toro JP (2004) Neuropeptide Y is released from human mammary and radial vascular biopsies and is a functional modulator of sympathetic cotransmission. J Vasc Res 41:387-399.
Drushel RF, Neustadter DM, Hurwitz I, Crago PE, Chiel HJ (1998) Kinematic models of the buccal mass of Aplysia californica. J Exp Biol 201:1563-1583.
Drushel RF, Sutton GP, Neustadter DM, Mangan EV, Adams BW, Crago PE, Chiel HJ (2002) Radula-centric and odontophore-centric kinematic models of swallowing in Aplysia californica. J Exp Biol 205:2029-2051.
Drzymała-Celichowska H, Krutki P, Celichowski J (2010) Summation of motor unit forces in rat medial gastrocnemius muscle. J Electromyogr Kinesiol 20:599-607.
Earhart GM, Stein PS (2000) Step, swim, and scratch motor patterns in the turtle. J Neurophysiol 84:2181-2190.
209
Evans CG, Cropper EC (1998) Proprioceptive input to feeding motor programs in Aplysia. J Neurosci 18:8016-8031.
Evans CG, Rosen S, Kupfermann I, Weiss KR, Cropper EC (1996) Characterization of a radula opener neuromuscular system in Aplysia. J Neurophysiol 76:1267-1281.
Falowski S, Celii A, Sharan A (2008) Spinal cord stimulation: an update. Neurotherapeutics 5:86-99.
Fejtl M, Stett A, Nisch W, Boven K-H, Möller A (2006) On Micro-Electrode Array Revival. In: Baudry M. and Taketani M. (eds). Advances in Network Electrophysiology Using Multi-Electrode Arrays. New York: Springer Press. p 24-37.
Ferguson GP, Parsons DW, Ter Maat A, Pinsker HM (1986) Spontaneous and elicited bag cell discharges in gonadectomized Aplysia. J Exp Biol 123:159-173.
Ferguson GP, Ter Maat A, Parsons DW, Pinsker HM (1989) Egg laying in Aplysia. I. Behavior patterns and muscle activity of freely behaving animals after selectively elicited bag cell discharges. J Comp Physiol 164:835-847.
Fox LE, Lloyd PE (1997) Serotonin and the small cardioactive peptides differentially modulate two motor neurons that innervate the same muscle fibers in Aplysia. J Neurophysiol 17:6064-6074.
Fox LE, Lloyd PE (1998) Serotonergic neurons differentially modulate the efficacy of two motor neurons innervating the same muscle fibers in Aplysia. J Neurophysiol 80:647-655.
Gardner D (1971) Bilateral symmetry and interneuronal organization in the buccal ganglia of Aplysia. Science 173:550-553.
Gaunt RA, Prochazka A (2006) Control of urinary bladder function with devices: successes and failures. Prog Brain Res 152:163-194.
Gestreau C, Dutschmann M, Obled S, Bianchi AL (2005) Activation of XII motoneurons and premotor neurons during various oropharyngeal behaviors. Respir Physiol Neurobiol 147:159-176.
Gillis GB, Biewener AA (2000) Hindlimb extensor muscle function during jumping and swimming in the toad (Bufo marinus). J Exp Biol 203:3547-3563.
Hasselmo ME (1995) Neuromodulation and cortical function: modeling the physiological basis of behavior. Behav Brain Res 67:1-27.
210
Henneman E, Olson CB (1965) Relations between structure and function in the design of skeletal muscles. J Neurophysiol 28:581-598.
Hodgkin AL, Huxley AF (1952) A quantitative description of membrane current and its application to conduction and excitation in nerve. J Physiol 117:500-544.
Holsheimer J (1997) Effectiveness of spinal cord stimulation in the management of chronic pain: analysis of technical drawbacks and solutions. Neurosurgery 40:990- 999.
Horridge GA, Bullock TH (1965) Structure and Function in the Nervous System of Invertebrates. ed T H Bullock and G A Horridge (San Francisco and London: W.H. Freeman And Co.) p 52-53.
Hovey MM, Bak AF, Carpenter DO (1972) Low internal conductivity of Aplysia neuron somata. Science 176:1329-1331.
Howells HH (1942) The structure and function of the alimentary canal of Aplysia Punctata. Quart J Microsc Sci 4:357-397.
Hurwitz I, Cropper EC, Vilim FS, Alexeeva V, Susswein AJ, Kupfermann I, Weiss KR (2000) Serotonergic and peptidergic modulation of the buccal mass protractor muscle (I2) in Aplysia. J Neurophysiol 84:2810-2820.
Hurwitz I, Goldstein RS, Susswein AJ (1994) Compartmentalization of pattern-initiation and motor functions in the B31 and B32 neurons of the buccal ganglia of Aplysia californica. J Neurophysiol 71:1514-1527.
Hurwitz I, Kupfermann I, Susswein AJ (1997) Different roles of neurons B63 and B34 that are active during the protraction phase of buccal motor programs in Aplysia californica. J Neurophysiol 1997 78:1305-1319.
Hurwitz I, Neustadter D, Morton DW, Chiel HJ, Susswein AJ (1996) Activity patterns of the B31/B32 pattern initiators innervating the I2 muscle of the buccal mass during normal feeding movements in Aplysia californica. J Neurophysiol 75:1309-1326.
Iles JF (1972) Structure and synaptic activation of the fast coxal depressor motoneurone of the cockroach, Periplaneta americana. J Exp Biol 56:647-656.
Iles JF (2005) Simple models of stimulation of neurones in the brain by electric fields. Prog Biophys Mol Biol 87:17-31.
Jezernik S, Craggs M, Grill WM, Creasey G, Rijkhoff NJ (2002) Electrical stimulation for the treatment of bladder dysfunction: current status and future possibilities. Neurol Res 24:413-430.
211
Jing J, Vilim FS, Wu JS, Park JH, Weiss KR (2003) Concerted GABAergic actions of Aplysia feeding interneurons in motor program specification. J Neurosci 23:5283- 5294.
Jing J, Weiss KR (2001) Neural mechanisms of motor program switching in Aplysia. J Neurosci 21:7349-7362.
Jing J, Weiss KR (2002) Interneuronal basis of the generation of related but distinct motor programs in Aplysia: implications for current neuronal models of vertebrate intralimb coordination. J Neurosci 22:6228-6238.
Jing J, Weiss KR (2005) Generation of variants of a motor act in a modular and hierarchical motor network. Curr Biol 15:1712-1721.
Kandel ER Behavioral biology of Aplysia. Freeman, San Francisco.
Katz PS (1995) Intrinsic and extrinsic neuromodulation of motor circuits. Curr Opin Neurobiol 5:799-808.
Katz PS and Frost WN (1995) Intrinsic neuromodulation in the Tritonia swim CPG: Serotonin mediates both neuromodulation and neurotransmission by the dorsal swim interneurons. J Neurophysiol 74:2281-2294.
Katz PS and Frost WN (1996) Intrinsic neuromodulation: altering neuronal circuits from within 19:54-61.
Katz PS, Getting PA, Frost WN (1994) Dynamic neuromodulation of synaptic strength intrinsic to a central pattern generator circuit. Nature 367:729-731.
Keating C, Lloyd PE (1999) Differential modulation of motor neurons that innervate the same muscle but use different excitatory transmitters in Aplysia. J Neurophysiol 82:1759-1767.
Kovacs GT, Storment CW, Rosen JM (1992) Regeneration microelectrode array for peripheral nerve recording and stimulation. IEEE Trans Biomed Eng 39:893-902.
Kreiner T, Kirk MD, Scheller RH (1987) Cellular and synaptic morphology of a feeding motor circuit in Aplysia californica. J Comp Neurol 264:311-325.
Kuhlman JR, Li C, Calabrese RL (1985) FMRFamide-like substances in the Leech. I. Immunocytological localization. J Neurosci 5:2301-2309.
Kupfermann I (1974) Feeding behavior in Aplysia: A simple system for the study of motivation. Behav Biol 10:1-26.
212
Kupfermann I, Weiss KR (1982) Activity of an identified serotonergic neuron in free moving Aplysia correlates with behavioral arousal. Brain Res 241:334-337.
Lee DC, Grill WM (2005) Polarization of a spherical cell in a nonuniform extracellular electric field. Ann Biomed Eng 33:603-615.
Lee SSM, de Boef Miara M, Arnold AS, Biewener AA, Wakeling JM (2013) Recruitment of faster motor units is associated with greater rates of fascicle strain and rapid changes in muscle force during locomotion. J Exp Biol 216:198-207.
Lenarz T, Lim HH, Reuter G, Patrick JF, Lenarz M (2006) The auditory midbrain implant: a new auditory prosthesis for neural deafness – concept and device description. Otol Neurotol 27:838-843.
Levanthal DK, Cohen M, Durand DM (2006) Chronic histological effects of the flat interface nerve electrode. J Neural Eng 3:102-113.
Light TS (1997) Ewing's analytical instrumentation handbook. 2nd edn ed W E Galen (New York: Marcel Dekker, Inc.) p 1104.
Lu H, Chestek CA, Shaw KM, Chiel, HJ (2008) Selective extracellular stimulation of individual neurons in ganglia. J Neural Eng 5:287-309.
Lu H, McManus JM, Chiel HJ (2013) Extracellularly identifying motor neurons for a muscle motor pool in Aplysia californica. J Vis Exp 73:e50189, doi:10.3791/50189.
Lum CS, Zhorov Y, Cropper EC, Weiss KR, Brezina V (2005) Variability of swallowing performance in intact, freely feeding Aplysia. J Neurophysiol 94:2427-2446.
Mangan EV, Kingsley DA, Quinn RD, Sutton GP, Mansour JM, Chiel HJ (2005) A biologically inspired gripping device. Industrial Robot: An International Journal 32:49-54.
Mendell LM (2005) The size principle: a rule describing the recruitment of motoneurons. J Neurophysiol 93:3024-3026.
McCreery D, Lossinsky A, Pikov V, Liu X (2006) Microelectrode array for chronic deep- brain microstimulation and recording. IEEE Trans Biomed Eng 53:726-737.
McCrohan CR, Benjamin PR (1980) Synaptic relationships of the cerebral giant cells with motoneurones in the feeding system of Lymnaea stagnalis. J Exp Biol 85:169- 186.
McIntyre CC, Grill WM (1999) Excitation of central nervous system neurons by nonuniform electric fields. Biophys J 76:878-888.
213
McIntyre CC, Grill WM (2000) Selective microstimulation of central nervous system neurons. Ann Biomed Eng 28:219-233.
McIntyre CC, Grill WM (2002) Extracellular stimulation of central neurons: influence of stimulus waveform and frequency on neuronal output. J Neurophysiol 88:1592-1604.
McManus JM, Lu H, Chiel HJ (2012) An in vitro preparation for eliciting and recording feeding motor programs with physiological movements in Aplysia californica. J Vis Exp 70:e4320, doi:10.3791/4320.
McManus JM, Lu H, Cullins MJ, Chiel HJ (2014) Differential activation of an identified motor neuron and neuromodulation provide Aplysia's retractor muscle an additional function. J Neurophysiol, in press.
McNaughton TG, Horch KW (1996) Metallized polymer fibers as leadwires and intrafascicular microelectrodes. J Neurosci Methods 70:103–110.
McPherson DR, Blankenship JE (1991) Neural control of swimming in Aplysia brasiliana. I Innervation of parapodial muscle by pedal ganglion motorneurons. J Neurophysiol 66:1338-1351.
Miller MW, Rosen SC, Schissel SL, Cropper EC, Kupfermann I, Weiss KR (1994) A population of SCP-containing neurons in the buccal ganglion of Aplysia are radula mechanoafferents and receive excitation of central origin. J Neurosci 14:7008-7023.
Mokwa W (2007) An implantable microsystem as a vision prosthesis. Med Device Technol 18:20, 22-23.
Morgan PT, Jing J, Vilim FS, Weiss KR (2002) Interneuronal and peptidergic control of motor pattern switching in Aplysia. J Neurophysiol 87:49-61.
Morgan PT, Perrins R, Lloyd PE, Weiss KR (2000) Intrinsic and extrinsic modulation of a single central pattern generating circuit. J Neurophysiol. 84:1186-1193.
Morton DW, Chiel HJ (1993a) In vivo buccal nerve activity that distinguishes ingestion from rejection can be used to predict behavioral transitions in Aplysia. J Comp Physiol A 172:17-32.
Morton DW, Chiel HJ (1993b) The timing of activity in motor neurons that produce radula movements distinguishes ingestion from rejection in Aplysia. J Comp Physiol A 173:519-536.
Nagahama T, Takata M (1988) Food-induced firing patterns in motoneurons producing jaw movements in Aplysia kurodai. J Comp Physiol A 162:729-738.
214
Naples GG, Mortimer JT, Scheiner A, Sweeney JD (1988) A spiral nerve cuff electrode for peripheral nerve stimulation. IEEE Trans Biomed Eng 35:905–916.
Nargeot RN, Baxter DA, Byrne JH (1997) Contingent-dependent enhancement of rhythmic motor patterns: an in vitro analog of operant conditioning. J Neurosci 17:8093-8105.
Navarro X, Krueger TB, Lago N, Micera S, Stieglitz T, Dario P (2005) A critical review of interfaces with the peripheral nervous system for the control of neuroprostheses and hybrid bionic systems. J Peripher Nerv Syst 10:229-258.
Neustadter DM, Herman RL, Drushel RF, Chestek DW, Chiel HJ (2007) The kinematics of multifunctionality: comparisons of biting and swallowing in Aplysia californica. J Exp Biol 210:238-260.
Nowak LG, Bullier J (1996) Spread of stimulating current in the cortical grey matter of rat visual cortex studied on a new in vitro slice preparation. J Neurosci Methods 67:237–247.
Parson DW, Ter Maat A, Pinsker HM (1983) Selective recording and stimulation of individual identified neurons in freely behaving Aplysia. Science 221:1203-1206.
Pedrini L, Magnoni F (2007) Spinal cord stimulation for lower limb ischemic pain treatment Interact. Cardiovasc Thorac Surg 6:495-500.
Peters M, Altrup U (1984) Motor organization in pharynx of Helix pomatia. J Neurophysiol 52:389-409.
Powers RK, Binder MD (1991) Summation of motor unit tensions in the tibialis posterior muscle of the cat under isometric and nonisometric conditions. J Neurophysiol 66:1838-1846.
Rall W, Agmon-Snir H (1998) Methods in Neuronal Modelling – from Ions to Network. 2nd edn ed C Koch and I Segev (Cambridge, MA: The MIT Press) p 27-92.
Ranck JB (1975) Which elements are excited in electrical stimulation of mammalian central nervous system: a review. Brain Res 98:417-440.
Rattay F (1989) Analysis of models for extracellular fiber stimulation. IEEE Trans Biomed Eng 36:676-682.
Rattay F (1999) The basic mechanism for the electrical stimulation of the nervous system. Neurosci 89:335-346.
215
Redman RS, Silinsky EM (1994) ATP released together with acetylcholine as the mediator of neuromuscular depression at frog motor nerve endings. Physiol kondi 477:117-127.
Ritzmann RE (1974) Mechanisms for snapping behavior of two alpheid shrimp, Alpheus californiensis and Alpheus heterochelis. J Comp Physiol 95:217-236.
Rosen SC, Miller MW, Cropper EC, Kupfermann I (2000b) Outputs of radula mechanoafferent neurons in Aplysia are modulated by motor neurons, interneurons, and sensory neurons. J Neurophysiol 83:1621-1636.
Rosen SC, Miller MW, Evans CG, Cropper EC, Kupfermann I (2000a) Diverse synaptic connections between peptidergic radula mechanoafferent neurons and neurons in the feeding system of Aplysia. J Neurophysiol 83:1605-1620.
Rosen SC, Teyke T, Miller MW, Weiss KR, Kupfermann I (1991) Identification and characterization of cerebral-to-buccal interneurons implicated in the control of motor programs associated with feeding in Aplysia. J Neurosci 11:3630-3655.
Rosen SC, Weiss KR, Cohen JL, Kupfermann I (1982) Interganglionic cerebral-buccal mechanoafferents of Aplysia: receptive fields and synaptic connections to different classes of neurons involved in feeding behavior. J Neurophysiol 48:271-88.
Shafique S, Dalsing MC (2006) Vagus nerve stimulation therapy for treatment of drug- resistant epilepsy and depression. Perspect Vasc Surg Endovasc Ther 18:323-327.
Sekirnjak C, Hottowy P, Sher A, Dabrowski W, Litke AM, Chichilnisky EJ (2006) Electrical stimulation of mammalian retinal ganglion cells with multielectrode arrays J Neurophysiol 95:3311-3327.
Schotland J, Shupliakov O, Wikstrom M, Brodin L, Srinivasan M, You Z, Herrera- Marschitz M, Zhang W. Hokielt T, Crlllner S (1995) Control of lamprey locomotor neurons by colocalized monoamine transmitters. Nature 374:266-268.
Schwarz TL, Lee G MH, Siwicki KK, Standaert DG, Kravitz EA (1984) Proctolin in the lobster: the distribution, release, and characterization of a likely neurohormone. J Neurosci 4:1300-1311.
Scott ML, Govind CK, Kirk MD (1991) Neuromuscular organization of the buccal system in Aplysia californica. J Comp Neurol 312:207-222.
Spelman FA (2006) Cochlear electrode arrays: past, present and future. Audiol Neurootol 11:77-85.
216
Stoney SD, Thompson WD, Asanuma H (1968) Excitation of pyramidal tract cells by intracortical microstimulation: effective extent of stimulating current. J Neurophysiol 31:659-669.
Suihko V (1998) Modeling direct activation of corticospinal axons using transcranial electrical stimulation. Electroencephalogr Clin Neurophysiol/ Electromyogr Motor Cont 109:238-244.
Susswein AJ, Rosen SC, Gapon S, Kupfermann I (1996) Characterization of buccal motor programs elicited by a cholinergic agonist applied to the cerebral ganglion of Aplysia californica. J Comp Physiol A 179:509-524.
Susswein AJ, Weiss KR, Kupfermann I (1978) The effects of food arousal on the latency of biting in Aplysia. J Comp Physiol A 123:31-41.
Sutton GP, Macknin JB, Gartman SS, Sunny GP, Beer RD, Crago PE, Neustadter DM, Chiel HJ (2004a) Passive hinge forces in the feeding apparatus of Aplysia aid retraction during biting but not during swallowing. J Comp Physiol A Neuroethol Sens Neural Behav Physiol 190:501-514.
Sutton GP, Macknin JB, Gartman SS, Sunny GP, Beer RD, Crago PE, Neustadter DM, Chiel HJ (2004b) Neural control exploits changing mechanical advantage and context dependence to generate different feeding responses in Aplysia. Biol Cybern 91:333- 345.
Tarler MD, Mortimer JT (2004) Selective and independent activation of four motor fascicles using a four contact nerve-cuff electrode. IEEE Trans Neural Syst Rehabil Eng 12:251-257.
Tarver WB, George RE, Maschino SE, Holder LK, Wernicke JF (1992) Clinical experience with a helical bipolar stimulating lead. Pacing Clin Electrophysiol 15:1545-1556.
Tehovnik EJ, Tolias AS, Sultan F, Slocum WM, Logothetis NK (2006) Direct and indirect activation of cortical neurons by electrical microstimulation. J Neurophysiol 96:512-521.
Trimmer BA (1994) Characterization of a muscarinic current that regulates excitability of an identified insect motoneuron. J Neurophysiol 72:1862-1873.
Tyler DJ, Durand DM (2002) Functionally selective peripheral nerve stimulation with a flat interface nerve electrode. IEEE Trans Neural Syst Rehabil Eng 10:294-303.
217
Vandenboom R, Gittings W, Smith IC, Grange RW, Stull JT (2013) Myosin phosphorylation and force potentiation in skeletal muscle: evidence from animal models. J Muscle Res Cell Motil 34:317-332.
Vieira TM, Loram ID, Muceli S, Merletti R, Farina D (2011) Postural activation of the human medial gastrocnemius muscle: are the muscle units spatially localised? J Physiol 589:431-443.
Wakeling JM, Kaya M, Temple GK, Johnston IA, Herzog W (2002) Determining patterns of motor recruitment during locomotion. J Exp Biol 205:359-369.
Warman EN, Chiel HJ (1995) A new technique for chronic single-unit extracellular recording in freely behaving animals using pipette electrodes. J Neurosci Methods 57:161-169.
Weiss KR, Brezina V, Cropper EC, Hooper SL, Miller MW, Probst WC, Vilim FS, Kupfermann I (1992) Peptidergic co-transmission in Aplysia: functional implications for rhythmic behaviors. Experientia 48:456-463.
Weiss KR, Chiel HJ, Koch U, Kupfermann I (1986) Activity of an identified histaminergic neuron, and its possible role in arousal of feeding behavior in semi- intact Aplysia. J Neurosci 6:2403-2415.
Westerfield M, McMurray JV, Eisen JS (1986) Identified motoneurons and their innervation of axial muscles in the zebrafish. J Neurosci 6:2267-2277.
Whim MD, Church PJ, Lloyd PE (1993) Functional roles of peptide cotransmitters at neuromuscular synapses in Aplysia. Mol Neurobiol 7:335-347.
Whim MD, Lloyd PE (1989) Frequency-dependent release of peptide cotransmitters from identified cholinergic motor neurons in Aplysia. Proc Natl Acad Sci 86:9034-9038.
Williams PL, Bannister LH, Berry MM, Collins P, Dyson M, Ferguson MWJ (1995) Grays Anatomy 38th edn ed M M Berry, S M Standring and L H Bannister (Baltimore: Churchill & Livingstone) p 945-946 and p 1259.
Winter JO, Cogan SF, Rizzo JF (2007) Retinal prostheses: current challenges and future outlook. J Biomater Sci Polym Ed 18:1031-1055.
Ye H, Morton DW, Chiel HJ (2006a) Neuromechanics of coordination during swallowing in Aplysia californica. J Neurosci 26:1470 –1485.
Ye H, Morton DW, Chiel HJ (2006b) Neuromechanics of Multifunctionality during Rejection in Aplysia californica. J Neurosci 26:10743–10755.
218
Yu SN, Crago PE, Chiel HJ (1999) Biomechanical properties and a kinetic simulation model of the smooth muscle I2 in the buccal mass of Aplysia. Biol Cybern 81:505- 513.
Zhurov Y, Proekt A, Weiss KR, Brezina V (2005a) Changes of internal state are expressed in coherent shifts of neuromuscular activity in Aplysia feeding behavior. J Neurosci. 25:1268-1280.
Zhurov Y, Weiss KR, Brezina V (2005b) Tight or loose coupling between components of the feeding neuromusculature of Aplysia? J Neurophysiol 94:531-549.
Ziv I, Baxter DA, Byrne JH (1994) Simulator for neural networks and action potentials: description and application. J Neurophysiol 71:294-308.
Zuurbier CJ, Huijing PA (1992) Influence of muscle geometry on shortening speed of fibre, aponeurosis and muscle. J Biomech 25:1017-1026.