Role of the Human Globus Pallidus in Tremorgenesis

Role of the human globus pallidus in tremorgenesis

Shane Ellis

A thesis submitted in conformity with the requirements for the degree of Master’s of Science Department of Physiology University of Toronto© Copyright by Shane Ellis 2015

Role of the human globus pallidus in tremorgenesis

Shane Ellis

Master of Science

Department of Physiology University of Toronto

2015 Abstract

The GPi is a nucleus that serves as an output of the basal ganglia; a collection of nuclei which function in selecting movements to be executed. Tremor is defined as an unintentional, rhythmic, sinusoidal contraction of body parts. Currently, no scientific consensus has been reached as to where in the brain tremor arises. Using microelectrode recordings in human patients with and without tremor, we discovered a sub-population of cells that are capable of being induced into a brief theta oscillation following microstimulation. However, we found that theta burst stimulation was incapable of inducing visible tremor when microstimulating in the GPi, Vim, or

STN. We also found preliminary evidence that this theta oscillation is capable of producing an

LTP-like response within the GPi. We believe that this work adds strength to the “pallidocentric” view of tremor initiation which holds that the GPi is responsible for the onset of tremor.

Acknowledgments

First and foremost, I would like to thank my supervisor, Dr. Hutchison for his mentorship and guidance through these last two years; I couldn’t have completed this thesis without your help. Next, I would like to thank my parents and family for their continual support throughout the highs and lows of this journey called life. Even if you don’t understand what I’m doing, you still support me whole-heartedly and I couldn’t ask for anything more. To Diellor Basa (I hope I’ve spelt your name correctly this week) and Luka Srejic, thank you for “showing me the ropes” inside the lab. Lastly, I would like to thank Dr. Sherri Thiele and Dr. Aman Mann for keeping me sane and for taking time out of their busy days to help me when I was struggling.

iii

Table of Contents Acknowledgments ...... iii

Table of Contents ...... iv

List of Tables ...... vi

List of Figures ...... vii

List of Appendices...... ix

List of Abbreviations ...... x

Chapter 1 Introduction ...... 1

Introduction ...... 1 1.1 Basal Ganglia Function ...... 1 1.1.1 The Human Globus Pallidus Internus ...... 1 1.2 Parkinson’s Disease ...... 4 1.2.1.1 Genetics of Parkinson’s Disease ...... 5 1.2.1.2 Treatments of Parkinson’s Disease ...... 6 1.2.1.2.1 Pharmacological Treatments of PD ...... 6 1.2.1.2.2 Surgical Treatments ...... 7 1.2.2 Explanatory Models of PD ...... 8 1.2.2.1 The Rate Model ...... 8 1.2.2.2 The Center-Surround Model ...... 11 1.2.2.3 The Oscillatory Network Model ...... 15 1.2.3 Animal Models of Parkinson’s Disease ...... 17 1.3 Dystonia ...... 19 1.3.1 Etiology of Dystonia ...... 19 1.3.2 Genetics of Dystonia ...... 20 1.3.3 Treatment of Dystonia ...... 20 1.4 Essential Tremor ...... 21 1.4.1 Etiology of Essential Tremor ...... 21 1.4.2 Genetics, Diagnosis and Treatment of Essential Tremor ...... 22 1.5.1 Tremor ...... 23 iv

1.6 Synaptic Plasticity ...... 25 1.7 Project Rationale ...... 28 1.8 Hypothesis and Aims ...... 30

Chapter 2- Methods ...... 31 2.1 General Methods ...... 31 2.2 Intraoperative Microelectrode Recordings ...... 36 2.3 Tremor Entrainment with Theta Burst Stimulation ...... 38 2.3 Effects of Theta Burst Stimulation on Plasticity in GPi ...... 38

3.0 Chapter 3- Results ...... 42 3.1 Stimulation-Induced Oscillation Characterization...... 42 3.2 Tremor Entrainment with Theta Burst Stimulation ...... 52 3.2.1 Theta Burst Stimulation in the Globus Pallidus Internus ...... 52 3.2.2 Tremor Entrainment with Theta Burst Stimulation Outside of The GPi ...... 55 3.3 Effect of Theta Burst Stimulation on Plasticity within the GPi ...... 60

4.0 Chapter 4- Discussion ...... 63 4.1 Stimulation-Induced Oscillations ...... 64 4.2 Tremor Modification due to Theta Burst Stimulation in the Basal Ganglia ...... 67 4.2.1 Theta Burst Stimulation in GPi ...... 67 4.2.2 Theta Burst Stimulation in Motor Thalamus ...... 69 4.2.3 Theta Burst Stimulation in Sub Thalamic Nucleus ...... 70 4.3 Effect of Theta Burst Stimulation on Plasticity within the GPi ...... 70 4.4 Future Directions...... 73 4.5 Conclusions ...... 75

6.0 References ...... 76

Appendix ...... 85

...... 91

List of Tables

Table 1- Stereotactic coordinates of DBS targets ……………………………………………….44

List of Figures

Figure 1- The connectivity of the nuclei comprising the basal ganglia…………………………15

Figure 2- Explanation of Parkinson’s disease by the rate model…………………………….….22

Figure 3 - A representation of convergence and divergence in the center surround model……...24

Figure 4- A diagrammatic representation of the center-surround model of basal ganglia motor command selection……………………………………………………………………………….26

Figure 5- An illustration demonstrating the dominance of oscillatory power of different bandwidths as seen in Parkinson patients………………………………………………………..28

Figure 6- An example of a neuronal trace recorded from patient with dystonia………………..41

Figure 7- Sample trajectories taken to reach surgical target during mapping procedures for DBS implantation surgeries……………………………………………………………………...…….46

Figure 8 - The microelectrode setup used for microelectrode mapping prior to DBS implantation……………………………………………………………………………………...47

Figure 9- Example traces showing the regularity of neuronal firings and the corresponding burst index value……………………………………………………………………………………….49

Figure 10- Diagrammatic representations of stimulation protocols used to assess plastic changes in the GPi………………………………………………………………………………………..53

Figure 11 - Group analysis of baseline (pre-stimulation) firing rate and mode burst index separated by disease and cell type………………………………………………………………………………..55

Figure 12- Effect of stimulation intensity on neuron populations separated by disease……….57

Figure 13- Individual changes in mode burst index across stimulation intensities…………….59

Figure 14- Mode burst index ratios and firing rate ratios of PD and dystonia neurons differentiated by stimulation intensities………………………………………………………..61

vii

Figure 15- The effect of stimulation intensity on tremor FFT frequency bands……………………...63

Figure 16- The effect of TBS in the GPi on tremor and neuronal activity…………………………..66

Figure 17- The effect of TBS in the Vim on tremor and neuronal activity…………………………. 69

Figure 18- The effect of TBS in the STN on tremor and neuronal activity………………………….71

Figure 19- fEP amplitudes measured prior to and after TBS………………………………………...74

viii

List of Appendices

Appendix Figure 1- A sample of the read-out produced from the Matlab script MKaneoke from the neuronal trace seen in Figure 6………………………………………………………………98

Appendix Figure 2A- The MKaneoke produced from the top trace in Figure 9 prior to stimulation.…………………………………………………………………………………….100

Appendix Figure 2B- The MKaneoke produced from the lower trace in Figure 9 following stimulation………………………………………………………………………………………102

Appendix Figure 3 - An example of theta burst stimulation inducing visible tremor in a patients foot during microelectrode mapping for DBS implantation surgery…………………………...104

List of Abbreviations

6-OHDA- 6-Hydroxydopamine

BOLD- Blood Oxygenation Level Dependent signal

COMT- Catechol-o-methyl Transferase cTBS- Continuous Theta Burst

D1/D 2- Dopamine Receptor Type 1 and 2

DBS- Deep Brain Stimulation

DYT- Dystonia-related gene

EEG- Electroencephalogram

EPSP- Excitatory Post-Synaptic Potential

EMG- Electromyogram

ET- Essential Tremor

ETM- essenetial tremor genes

FFT- Fast Fourier Transform fEP- Field Evoked Potential fMRI- Functional Magnetic Resonance

GABA- Gamma-Aminobutyric Acid

GI- Gastro-intestinal

GPe- Globus Pallidus Externus

GPi- Globus Pallidus Internus

HFD- High Frequency Discharge

HFS- High Frequency Stimulation

IPSP- Inhibitory Post-Synaptic Potential

LIDs- Levodopa-Induced Dyskinesias

L-DOPA- L-3,4-dihydroxyphenylalanine

LINGO- Leucine Rich Repeat and Ig Domain Containing, NoGo recptor interacting protein

LRRK- Leucine-Rich Repeat Kinase

LTD- Long Term Depression

LTP- Long Term Potentiation

M1- Primary Motor Cortex

MAO- Monoamine Oxidase

MPP +- 1-methyl-4-phenylpyridinium

MPTP- 1-Methyl-4-phenyl-1,2,3,6-tetrahydropyridine

NMDA- N-Methyl-D-Aspartate

N.S.- Not Significant

PARK- Parkinson Related Gene

PAS- Paired Associative Stimulation

PD- Parkinson’s Disease

S1- Primary Somatosensory Cortex

SLC1A2- Solute Carrier Family 1 Member 2

SNc- Substantia Nigra Pars Compacta

SNCA- Synuclien, Alpha

SNr- Substantia Nigra Pars Reticulata

STN- Subthalamic Nucleus

Thal- Thalamus

THAP- Trihydroxyacetophenone

TBS- Theta-burst Stimulation

TMS- Transcranial Magnetic Stimulation

Vim- Ventral Intermediate Nucleus of Thalamus

Vop- Ventral Oralis Posterior Nucleus of Thalamus

xii

Chapter 1 Introduction Introduction 1.1 Basal Ganglia Function

The basal ganglia are a collection of deep brain nuclei comprised of the caudate and putamen (collectively known as the striatum), the external and internal segments of the globus pallidus (GPe/GPi respectively) and substantia nigra pars reticulata (SNr), and the subthalamic nucleus (STN). These nuclei are involved with various subsystems of the brain including emotions, cognition, and motor control. The connectivity of the basal ganglia is shown in Figure 1. With PD, the anatomical connectivity between nuclei remains the same, but the gain of these connections is altered (see rate model below for explanation and/or see figure 2). Focusing on movement commands, input to the basal ganglia comes from the cortex and is received in the striatum and STN. The major outputs of this circuit are the GPi and the SNr, with the GPi largely controlling movement commands below the neck while the SNr deals with commands regarding the neck and head (Mink 1996). Different theories (discussed below) have been put forward to explain how information is processed within the basal ganglia, but in short, it appears that the output nuclei (GPi and SNr) of the basal ganglia have been tasked with the final determination of which motor programs are selected and which are inhibited.

1.1.1 The Human Globus Pallidus Internus

The GPi, along with the SNr are the major output nuclei of the basal ganglia. The ventroposterior portion of the GPi has been shown to have neurons with kinesthetic responses and has been identified as the sensorimotor portion of the GPi which is anatomically separated from the limbic associative areas (Lozano and Hutchison, 2002). As previously mentioned, the GPi is one of the major GABAergic outputs of the basal

ganglia, and has extensive connections to the ventral oralis posterior nucleus (Vop) of the thalamus. It receives extensive inhibitory input from the striatum, as well as from the GPe and some excitatory input from the STN. Also of particular interest, new evidence has shown a direct connection between the internal pallidum and the cortex, dubbed the “super-direct pathway”, which has been shown to be glutamatergic and targets the peripallidal region of the pallidum (Milardi et al. 2015) (Figure 1). There are two principle neuron types identified by electrophysiological recordings within the GPi: high frequency discharge neurons (HFDs), and border cells. Neurons of the GPi are relatively large and send GABAergic projections to the thalamus and brainstem (Mink 1996). Although the majority of cells are GABAergic, the border cells are cholinergic and populate the peri-pallidal region of the nucleus. The border cells recorded from specifically in this thesis were recorded from the dorsal edge of the GPi (where the medullary lamina separates the GPe and GPi), and the internal medullary lamina/GPi borders (figure 7A). These cholinergic border cells are believed to be migrants of the Nucleus Basalis of Meynert (Mitchell et al. 1987). During microelectrode mapping, the GPi is identified by the presence of its high frequency discharge, irregular firing neurons, encapsulated by the lower frequency, regular firing border cells (Hutchison, 1998). Further, these neurons (both HFDs and border cells) are inhibited by brief electrical stimulation, believed to be caused by the local release of GABA.

Figure 1- The connectivity of the nuclei comprising the basal ganglia. Nuclei encapsulated by the yellow background are part of the indirect pathway, those on the white background represent the direct pathway, while the red arrow represents the super- direct pathway. Small black circles indicate inhibitory connections while green circles indicate excitatory connections. Modified from Purves et al. Neuroscience. Sunderland: Sinauer, 2008. Print.

1.2 Parkinson’s Disease

Parkinson’s disease is a debilitating degenerative disease which is characterized by a triad of cardinal symptoms: bradykinesia, rigidity, and tremor. Its onset occurs later in life (typically 65 years and older) and its primary cause is due to neuronal death in the substantia nigra pars compacta- a midbrain, dopaminergic nucleus with extensive connections to various systems in the brain, including the motor system via the basal ganglia. The disease is named after Dr. James Parkinson who was the first to characterize the disease back in 1817, although he initially deemed it “shaking palsy”. It has been estimated that 1-2% of the general population over 65% contracts PD and that this incidence increases to 3-5% in the population over 85 years of age (Alves et al, 2008). PD diagnosis is ascertained post-mortem by the presence of Lewy body inclusions (Alves et al, 2008) and neuronal death within the substantia nigra pars compacta. These Lewy bodies are dense cytoplasmic inclusions of ubiquinated α-synuclein protein. Whether these protein inclusions are cytotoxic or protective remains a debate (Visanji et al. 2013). It is important to note that Lewy bodies are not specific to Parkinson’s disease and have been found in other diseases such as Lewy body dementia and multiple systems atrophy (Dehay et al. 2015), however, their contribution to these different pathologies has yet to be confirmed.

Although most only think of the motor abnormalities seen in PD, this disease also effects other areas and processes of the body. Such areas include the olfactory bulbs and the gastrointestinal tracts, and other processes include cognition, and sleep. In fact, movement abnormalities usually only arise when ~80% of the neurons of the substantia nigra pars compacta (SNc) die (Pahuja et al. 2015) and it is believed that non-motor abnormalities arise before the motor symptoms are seen. These symptoms include a loss of smell (possibly due to Lewy body formation in olfactory bulbs) (Visanji et al. 2013), and issues pertaining to the GI tract which can include constipation and delay in gastric emptying (Fasano et al. 2015).

1.2.1.1 Genetics of Parkinson’s Disease

Although largely an idiopathic disease, approximately 10% of PD cases are believed to be inherited (Thomas and Beal, 2007). Genes have been identified in these cases which, when mutated, can be risk factors or causes of familial PD. It is important to remember that phenotypes are the result of the interaction between the genotype and the environment. Thus, having a genetic predisposition towards PD does not guarantee that one will contract the disease, but is more apt to depend on the environmental stressors present. There are 18 loci in the human genome that have been designated as PARK regions due to their involvement with Parkinson’s disease; of which 12 have been confirmed as Parkinson’s genes while the others may be risk factors or their effects unsuccessfully replicated (Klein and Westenberger, 2012).

The first gene to be identified as having a role in PD was PARK1 (Polymerpoulous et al. 1996) (also known as SNCA and PARK4). The product of this mutated gene is responsible for the α-synuclein aggregates that form the aforementioned Lewy bodies and Lewy neurites. This mutation has a rapid progression, is implicated in early onset PD and typically causes cognitive decline and dementia. It is also noteworthy that it has a good initial response to levodopa treatment (Klein and Westenberger, 2012). Another well-known mutation involved with PD is the LRRK2 (a.k.a. PARK8) gene. This mutation causes mid-to-late onset PD without dementia and patients show a good response to levodopa treatment. Patients with either PARK1 or PARK8 mutations have Lewy body inclusions seen at post mortem. Parkin (PARK2) was the second Parkinson’s gene identified. This mutation is typically associated with a slowly progressive, early onset PD which tends to begin around 30-40 years of age, although some cases of childhood onset have been documented (Klein and Westenberger, 2012). Many other mutations have been noted in PD, but the last one that will be addressed in this work is DJ-1. This mutation is of interest as it codes for protein involved in detecting oxidative stress. Misfolding of this mutated protein renders it useless, and as such can make cells

more susceptible to environmental conditions (Klein and Westenberger, 2012). The DJ-1 mutation aids in showing the response between the genotype and environment.

1.2.1.2 Treatments of Parkinson’s Disease

Unfortunately, modern advances in medicine have still been unsuccessful in developing treatment options to halt the progression of PD or reverse the damage already done. As such, treatments of PD are currently symptomatic and developed to increase the quality of life in PD patients (Calabresi et al. 2015).

1.2.1.2.1 Pharmacological Treatments of PD

The gold standard in treating PD is dopamine replacement therapy. This is usually accomplished through the administration of the dopamine precursor, levodopa (L-DOPA) that can cross the blood brain barrier and be metabolized into dopamine. An example of such medication today is Sinemet ®, a pill ingested by the patient which contains levodopa as well as carbidopa. This combination of drugs allows the dopamine precursor to reach the brain while preventing somatic degradation of dopamine that is designed to keep the concentration of L-DOPA higher in the central nervous system than if L-DOPA is delivered solely as a monotherapy (Gilbert et al. 2000). However, with chronic use of these pharmacological replacement therapies, side effects often occur such as levodopa induced dyskinesias (LIDs), which are abnormal involuntary movements of the limbs. As such, investigators such as Pahuja et al. (2015) have begun looking at novel uses of nanoparticles to deliver dopamine across the blood brain barrier and release this dopamine in a more controlled manner in hopes of reducing side effects associated with the treatment.

As previously mentioned, dopamine depletion is a hallmark of Parkinson’s disease and hence why some pharmacological techniques seek to increase the level of dopamine within the brain. Besides administering the metabolic precursor of dopamine to patients, another way to keep dopamine levels elevated is to prevent their degradation. The dopamine molecule belongs to a chemical family known as the monoamines (includes serotonin, adrenaline, and nor adrenaline), which are degraded by a common enzyme class known as the monoamine oxidases (MAOs). As such, a class of drugs (monoamine oxidase B inhibitors; MAOBI) have been developed to block the function of these enzymes (ie. rasagiline and selegiline) (Connolly and Lang 2014). Similarly, catechol-o-methyl transferase inhibitors (COMTI) are a class of drug that prevent a different degradation pathway from degrading dopamine and include such drugs as entacapone and tolcapone. Yet, another strategy is to stimulate dopamine receptors with dopamine agonists. These chemicals are able to bind to and activate the dopamine receptor and hence simulate an environment that isn’t deprived of dopamine. Apomorphine, pramipexole, and ropinirole are examples of this class of drugs.

1.2.1.2.2 Surgical Treatments

Even with a diverse range of drug classes, and an abundance of drugs available within these classes, pharmacological treatment of PD has yet to be perfected. Due to the chronic and degenerative nature of PD, patients must take these drugs for extended periods of time. As such, many develop resistance that causes the drugs to become less efficacious (decreases the “ON” time- the time where the drugs are maximally effective). To combat these medically refractive cases, surgical procedures can be implemented. Two such procedures include the implantation of deep brain stimulating (DBS) electrodes, as well as strategically placed lesions within the brain. Undoubtedly, the most common target for DBS electrode implantation is the STN. It is currently unknown how this stimulation helps in PD; however, it may have an inhibitory effect within the overactive STN which restores firing rates to a non-pathological level. Another site for

DBS in PD patients, albeit much less common, is the GPi. The GPi is typically reserved as the target of choice for patients who suffer from cognitive decline or those with a primary problem of disabling dyskinesias. Other surgical techniques other than DBS implantation used to be pallidotomies and thalamotomies (thermo-electrolytic lesions of the pallidum and thalamus, respectively) (Laitinen 1994). However, due to the advances in DBS neuromodulation (such as rechargeable batteries and recording capabilities) and the permanent nature of lesions, these techniques are much less common for the treatment of PD nowadays.

1.2.2 Explanatory Models of PD

With the advancements in understanding the physiological processes associated with movement and disease, such as PD, different models have been created over the years that attempt to explain the abnormalities associated with PD. Currently, no one model has been successful at explaining all of the symptoms and abnormalities associated with the disease, but each model contributes its own information towards the understanding of the pathophysiology of PD.

1.2.2.1 The Rate Model

One of the most well known models involves the balance between the so-called “direct” and “indirect” pathways of the basal ganglia. In the direct pathway, the striatum receives glutamatergic input from the cortex and dopaminergic input from the (SNc) which then projects to the GPi before exiting the basal ganglia and projecting to the ventral oralis posterior nucleus of the thalamus. In the indirect pathway, the striatum still receives glutamatergic input from the cortex and dopaminergic input from the SNc, but projects to the external segment of the globus pallidus as opposed to the internal segment.

From here, the GPe sends GABAergic projections to the STN which then synapses on the GPi in an excitatory (glutamatergic) fashion prior to synapsing onto the Vop. Complexity is added to this system when one considers the activity of each internuclear projection: all projections are tonic in the presence of sufficient dopamine concentrations in the striatum (see figure 1). As described by Albin et al (1989), neuronal death in the SNc results in decreased dopaminergic innervation of the striatum. Within the striatum, there are two types of dopamine receptors (D 1 and D 2), which have opposing effects: the D 1 receptors lie in the direct pathway and are activated when dopamine is bound. D 2 receptors lie in the indirect pathway and are inhibited by the binding of dopamine (Gerfen et. al 2003). The end result of this dopaminergic deprivation is a decrease in activity of the direct pathway (which promotes movements) and an increase in activity in the indirect pathway (which inhibits movement). The leads to hyper-activity of the basal ganglia output (the GPi), which causes increased inhibition of the motor thalamus. This model is capable of explaining certain symptoms of PD such as akinesia, however, it fails to explain tremor.

This model can be extended to discuss hyperkinetic movements. The rate model predicts that gain in the direct pathway is enhanced while it is decreased in the indirect pathway in diseases (such as dystonia, hemiballism, Huntignton’s disease). This leads to overactivity in the direct pathway (the pathway promoting movement) and underactivity in the indirect pathway, resulting in an excess of movements, such as chorea, or muscles contractions like those seen in dystonia.

Dopamine SNc

Figure 2- Explanation of Parkinson’s disease as explained by the rate model. Note the increased drive of the indirect pathway (as indicated with the extra thick arrows) and the reduced drive in the direct pathway (indicated by the skinny arrows), including the reduced dopaminergic input from the SNc caused by neuronal cell loss. The end result is increased GABAergic innervation of the thalamus. STN indicates subthalamic nucleus; GPe, globus pallidus externus; GPi, globus pallidus internus; SNr, substantia nigra pars compacta; glu, glutamate; D 2, type 2 dopamine receptor; D 1, type 1 dopamine receptor. Modified from Hutchison et al., (2004).

1.2.2.2 The Center-Surround Model

Expanding on the model proposed by Albin et al. (1989), a new model was put forth, incorporating the new physiology and trains of thought discovered at that time (Mink 1996). Important to this model is that it doesn’t view the basal ganglia as the source of movement generation, but rather the cortex generates the movement command and passes it onto the basal ganglia for execution. This model discusses the competition between motor programs that occurs during the execution of a movement. As seen in figure 3, the motor cortex sends multiple overlapping representations of the motor command to the striatum, and different parts of the striatum receive multiple inputs from different parts of the cortex with non-functionally connected parts being sent to different zones of the striatum. Through convergence/divergence within the striatum, a concentrated signal is propagated to the GPi allowing for this motor command to be executed, while sending a concentrated inhibitory signal to the GPi to inhibit other possible motor plans that would compete with the desired movement (Mink 1996) (Figure 3).

Figure 3 - A representation of the convergence and divergence of different cortical areas as hypothesized to occur between nuclei of the basal ganglia in the center-surround model. Taken from Mink (1996).

Stated another way, when a motor command is generated by motor pattern generators in the cortex, inhibitory drive in the basal ganglia is elevated for competing motor pattern generators (thus inhibiting them), while inhibitiory activity of the generators of interest are decreased (allowing the motor signal to be propagated). This model predicts that the dopamine deficiency within the striatum results in increased drive of the GPi and thereby excessively inhibits motor programs (desired movements and competing ones) (Figure 4).

Figure 4- A diagrammatic representation of the centre-surround model of basal ganglia motor command selection. Line weights indicate relative gain of connections; red lines indicate inhibitory synapses, black indicates excitatory synapses. STN indicates subthalamic nucleus; GPi, globus pallidus internus; SNr, substantia nigra pars reticulata. Reconstructed from Mink (2003).

A second aspect of this model is needed to explain the rigidity and postural disturbances seen with PD, however. As such, it has been further posited that the competing motor pattern generators are not completely inhibited by the increased tonic activity seen in the GPi, and this leads to partial activation of these programs that may be responsible for the rigidity, bradykinesia, and postural instabilities (Mink 1996).

1.2.2.3 The Oscillatory Network Model

The above two models concentrate on single cell phenomenon within different nuclei of the basal ganglia, but do not address how these cells interact in a network. An important aspect to consider when determining how a cell’s output may change due to the progression of PD is how the cell’s firing patterns may change, not just its firing rate. It has been shown that, on top of firing rate changes, there is an increase in bursting activity of these cells (Hutchison et al. 1997) and it has been demonstrated that there is an increase in oscillatory connectivity within and between nuclei of the basal ganglia (Volkmann et al. 1996, Brown et al. 2001). The increase in oscillatory activity occurs across the theta, beta, and gamma bandwidths and the increase is believed to be due to the dopaminergic denervation underlying PD (Brown 2003). Important to this model is that different frequencies of oscillations are believed to have different effects on movements. In particular, oscillations below 30Hz are believed to be anitkinetic, while oscillations above 35Hz are believed to be prokinetic (figure 5).

Figure 5- An illustration demonstrating the dominance of oscillatory power of different bandwidths as seen in Parkinson patients off (left side) and on (right side) dopaminergic medications. Arrow weights indicate gain of connections and arrow heads indicate directionality of oscillation. Red arrows represent antikinetic oscillations, green arrows indicate prokinetic oscillations. STN indicates subthalamic nucleus; GPi, globus pallidus internus. Reconstructed from Brown (2003).

Whether these oscillations are purely pathological and what purpose they may have in the disease course is still up for debate. For example, Little and Brown (2014) posit that beta oscillations represent an idling rhythm in the motor circuit which promotes the status quo and that the pathological beta power seen in PD blocks the new motor commands leading to bradykinsia/akinesia. Beta oscillations have been reported in healthy humans, however, the power of these oscillations has been positively correlated with symptom severity indicating a role of the power of the oscillation in the disease (Kuhn et al., 2006). However, the findings pertaining to beta oscillations are inconsistent. Recent findings from this lab have found a negative correlation between beta power in the STN and PD symptoms (Alavi et al., 2013), and reports have shown a decrease in beta power within M1/S1 of PD patients during tapping movements as measured through EEG recordings (Stegmoller et al., 2015). As such, the function of beta oscillatory power has yet to be determined.

1.2.3 Animal Models of Parkinson’s Disease

As in many other diseases being studied, PD has an assortment of animal models that have been studied to deepen our understanding of the pathology associated with the disease. Possibly one of the most well known animals used in the study of PD is the 1- Methyl-4-phenyl-1,2,3,6-tetrahydropiridine (MPTP) monkey. This blood-brain permeable toxin appears to selectively target mitochondria of the substantia nigra pars compacta where it leads to neuronal death due to the oxidative stress of its metabolite, MPP +, as created by astrocytes (Sian et al. 1990). First discovered in synthetic drug abusers who ingested a merperidine-related drug, this toxin produces many of the motor abnormalities seen in PD (including the bradykinesia and rigidity) but fails to cause rest tremor and Lewy bodies as seen in PD (Sian et al. 1990).

Another animal model of PD is the 6-OHDA rat model which was first used to study PD in 1959. This toxin is effective in lesioning the nigrostriatal pathway in mice, dogs, cats, and monkeys. The 6-OHDA molecule is structurally similar to the dopamine molecule except 6-OHDA has an added hydroxyl group which is responsible for the toxicity to dopaminergic neurons. As this toxin is incapable of crossing the blood brain barrier, direct injection to the ventral tegmental area, striatum, or SNc are required to cause a lesion (Blesa et al, 2012). Dopaminergic neurons die due to the effect of reactive oxygen species and quinones produced by 6-OHDA (Cohen, 1984). Although this toxin is incapable of mimicking all the pathophysiology of PD (such as olfactory deficits and Lewy body formation), it is capable of producing dopamine depletion, nigral neuronal death, and neurobehavioural deficits associated with PD. Another advantage of using this model is each animal serves as it’s own control since the injection only unilaterally effects the animal leaving an intact, healthy side for comparison sake (Blesa et al. 2012).

The above examples are considered to be excellent models, especially when examining the effects of Parkinsonian syndromes on movements. However, they are not particularly useful for studying neuroprotective effects of compounds designed to slow the progression of PD. For this instance, the rotenone rodent model appears to be quite useful. Rotenone is a naturally occurring pesticide/insecticide found in tropical plants. It is commonly chronically injected into rats and appears to mimic many of the hallmarks of PD, including Lewy body formation. It has been found to work by inhibiting the electron transport chain in SNc mitochondria leading to neuronal death (Dauer and Przedborski, 2003). A downside to this model is that there is no apparent effect on humans, and as such, this model may only be capable of explaining disease progression in rodents and may have difficulties translating to humans.

1.3 Dystonia

Dystonia is a class of hyperkinetic movement disorders which includes, but is not limited to, cervical dystonia (dystonia largely affecting the cervical spine), hand dystonia (i.e. writers cramp- dystonia specific to the hand), blepharospasm (dystonia of the eyelids and brow), and general dystonia (non-specific dystonia affecting the majority of the body. Dystonia is characterized by involuntary activation of muscles leading to abnormal postures and twisting movements. Important to this thesis, dystonia patients do not have a dopamine deficiency and thus provide us with a pseudo-control group/comparator group for our Parkinson’s studies.

1.3.1 Etiology of Dystonia

First identified by Oppenheim in 1911 as “dystonia musculorum deformans”, dystonia affects people of all ages and can be primary in nature (occurrence is idiopathic or due to genetics) or secondary (occurs because of previous lesions or disease of the nervous system) (Albanese et al. 2013). The cause of dystonia is not currently known, however, work by Prescott et al. (2013) indicates that GABA regulation within the output nuclei of the basal ganglia (the GPi and SNr) may play a role. This study found that paired-pulse ratios were decreased in dystonia patients compared to PD patients but could be normalized with high frequency stimulation (HFS). These findings provide evidence of abnormalities in synaptic transmission in the basal ganglia which may be underlying the pathophysiology of dystonia. Other electrophysiological studies have shown a loss of intracortical inhibition, and increased cortical plasticity indicating that the cortex may also play a crucial role in this disease (Neuman et al., 2015).

1.3.2 Genetics of Dystonia

As mentioned previously, individuals can be genetically predisposed to dystonia. Researchers have identified a set of genes believed to be involved in different types of dystonia. For example, early onset torsion dystonia (DYT1) has been traced back to a glutamic acid deletion in the gene encoding Torsin A. Products of this gene are believed to be involved with mediating oxidative and endoplasmic reticular stress and so reports have shown that mutations in this gene may cause flaws in the synaptic terminals which may underlie the phenotypic expression of DYT1 (Kim et al. 2015). Another gene that appears to play a role in different types of dystonia is THAP1. Mutations in this gene have been shown to be related to DYT6, as well as DYT1 through its regulatory role of Tor1A (Erogullari et al., 2014).

1.3.3 Treatment of Dystonia

Similar to PD, the medicines available to treat dystonias are currently aimed at improving the patient’s quality of life rather than preventing further development of the disease or reversing the current progression. One type of treatment available to those with dystonia is a combination of physical therapy and braces which are designed to help prevent contractions. Some braces are designed to provide a sensory trick to the patient (a false sense of sensory input) that helps them avoid a contracted posture (Jankovich 2006). Another non-pharmacological intervention involves transcranial magnetic stimulation (TMS). Siebner et al (1999) have shown that transcranial magnetic stimulation at low frequencies is capable of improving handwriting of patients with writers’ cramp. The frontline medicinal treatment for dystonias currently is botulinum toxin injections into the over active muscles (Bruijn et al., 2015). A small subset of patients with childhood onset dystonia show some improvement in symptoms with dopaminergic drugs (Jankovich, 2006). Antidopaminergic drugs have been used historically, but are currently discouraged

due to possible side effects associated with these drugs (including sedation, tardive dyskinesias, etc.). Other pharmacological strategies include anticholinergic medications, and muscle relaxants (Jankovich, 2006). As a last resort for those patients who are not responsive to the medication, surgical procedures can be performed to help ameliorate symptoms. The most common surgery performed for those with dystonia is bilateral GPi DBS (Lettieri et al 2014), however, pallidotomies, thalamotomies, and intrathecal baclofen are also viable options (Marras et al., 2014).

1.4 Essential Tremor

1.4.1 Etiology of Essential Tremor

Essential tremor is a disease that manifests as an intention tremor largely affecting the upper limbs of patients (Deuschl 1998). It occurs in approximately 0.9% of the general population and 4.8% of the population over 65. The cerebellum is responsible for action corrections during movement. This strengthens the theory that it is involved with ET as tremor in ET really manifests when approaching the target of a goal-oriented movement: the cerebellum pathologically overcorrects and this manifests as a tremor. The involvement of the cerebellum is strengthened by the findings that DBS in the cerebellar-receiving nucleus of the thalamus (the ventral intermediate nucleus of the thalamus- Vim) has proven extremely successful in treating ET action tremors (Tasker 1998). Also, as discussed below, pharmaceuticals designed to treat ET, and ethanol, tends to increase GABAergic tone in the cerebellum, further implicating its involvement in ET (Louis 2015). Four genetic loci have been reported to be risk factors for ET: ETM1-4; however no gene product has been identified as a causative agent (Schmouth et al. 2014).

1.4.2 Genetics, Diagnosis and Treatment of Essential Tremor

The diagnosis for ET is largely based on clinical presentation, as there are no biological markers currently available to aid in diagnosis. These diagnosis criteria are exclusionary in nature: by eliminating other diseases (such as PD and dystonia), ET can be inferred. Other exclusionary conditions, which need to be ruled out as the cause of tremor, include the abuse of alcohol or drugs, and psychogenic tremors. Although no causative factor has been identified in ET, considerable work has been done to identify the gene products that may play a role in the hereditary component of ET. Such genes involve LINGO1 (which also has been implicated in other diseases such as multiple sclerosis), and SLC1A2 (a member of the glutamate transporter gene family) (Schmouth et al. 2014). Morphologically, researchers have reported cellular abnormalities within the cerebellum of patients with ET that include an increase in the number of “torpedoes”- which are truncated Purkinje cells with neurofilament accumulations which impair axonal transport (Liem and Leung, 2003). It has yet to be confirmed whether these “torpedoes” are a result or cause of ET pathology however, Louis et al. (2014) identified an inverse relationship between the number of torpedoes and the number of Purkinje cells seen in the cerebellum of ET patients. This inverse relationship was unique compared to other diseases such as spinal cerebellar ataxia (no clear relationship seen- similar to controls), and multiple systems atrophy-C (a strong positive relationship was identified: those with less Purkinje cells have less torpedoes) indicating that these torpedoes stress the cerebellar Purkinje cells but do not overwhelm it.

Alluded to earlier, pharmaceuticals designed to treat ET tend to focus on increasing GABAergic tone in the cerebellum in hopes of improving quality of life for patients. Interestingly, an antiepileptic drug, primidone has proven successful in reducing tremor in ET patients (Rincon and Loius, 2005). Primidone is partially metabolized to phenobarbital, a GABA A receptor agonist which potentiates GABA transmission, which

is believed to be its mechanism of action (Charney et al. 2001) Similarly, other antiepileptic drugs such as benzodiazapines and gabapentin work via similar mechanisms. Other classes of drugs that appear to be successful with treating symptoms of ET are drugs targeting the monoamine system, such as β-adrenergic blockers, and α- adrenergic agonists (Roncon and Louis, 2005). An example of a β-adrenergic blocker is propranolol which is believed to be efficacious due to its antagonistic effects on the peripheral nervous system but may also exert its effects centrally (Chung et al. 2013). As for the previous movement disorders discussed, ET also has surgical interventions available as treatment options. Vim DBS is the preferred surgical technique employed, however, thalamotomies within the Vim have shown success in the past (Tasker 1998).

1.5.1 Tremor

Tremor is ubiquitous- it is seen in everyone at some level of expression. Typically, this is referred to as physiological tremor and is usually benign. However, some people have tremor that is caused due to pathology of the brain that can be severely debilitating. Tremor is defined as involuntary, rhythmic, and sinusoidal alternating movements of the body; and can include limbs, palate, voice, and head (Abdo et al. 2010). Different types of tremor exist which include rest tremor (typically occupying the 4-6 Hz range; characteristic of Parkinson’s disease), intention tremor (typically occupying 4-12 Hz range and commonly seen in essential tremor), dystonic tremor (typically 4-10 Hz), and orthostatic tremor (tremor in the legs upon standing typically occupying 13-18 Hz). Even within these classifications, there are sub-classifications of tremor which include postural tremor, isometric tremor, and kinetic tremor (Helmich et al. 2013). Parkinsonian rest tremor is defined as tremor that arises when a body part is at rest and is fully supported against gravity, and disappears during movement. Action tremor occurs during voluntary contractions of the tremulous muscles; postural tremor occurs when the patient tries to maintain a given posture; isometric tremor occurs during isometric muscular contractions (contracting the muscle without changing the angle of

the joint), and intention tremor occurs during voluntary actions such as trying to touch your finger to your nose (Deuschl et al. 1998). Patients with dystonic tremor tend to appear in two cohorts. Some patients have dystonic tremor (tremor that is confined to a dystonic limb), whereas others patients can have tremor associated with dystonia (the tremor affects body parts unaffected by dystonia). Defazio et al., (2012) found through clinical assessment and self-report measurements in a large Italian cohort that approximately 17% of patients with primary adult onset dystonia present with tremor and that this tremor was found in the head/face, neck, larynx, and upper and lower limbs.

Different brain regions appear to be affected depending on what type of tremor is present. For instance, action tremor as commonly seen in ET patients is believed to be due to pathology of the cerebellum. It is believed that the cerebellum is involved with movement correction, and as such, ET may arise due to mishandling of efferent copies in the cerebellum (Deuschl et al., 2000). Rest tremor as commonly associated with PD, is believed to be triggered by pathology within the basal ganglia. Importantly, even though different types of tremors may have different regions of the brain triggering the phenotypic expression of tremor, it is possible that tremor is a network phenomenon and not an entity created by one brain nucleus (Helmich et al., 2012).

As the main focus of this work is on PD tremor, the rest of this literature overview shall focus solely on PD tremor. PD tremor affects three-quarters of PD patients, but the cause of this tremor has yet to be determined. Some authors (i.e. Paré et al., 1995; Duval et al., 2015) hold that tremor is initiated in the motor thalamus. They posit that, through hysteresis, the thalamus (in monkeys) is capable of converting a 12-15 Hz oscillation generated elsewhere in the brain into a 4-6 Hz oscillation which leads to the visible expression of tremor. However, using microelectrode recordings prior to DBS electrode placement, Hutchison et al., (1997) showed that the human GPi has neurons that fire at tremor frequency in PD patients. This study did not find any 12-15 Hz oscillatory activity in the GPi (speculated to be possibly due to species difference), but they found spectral

peaks within the 4-6Hz bandwidth. As previously mentioned, using fMRI (via measuring the BOLD signal), Helmich et al. (2011) showed that activity in the GPi increases prior to the onset of tremor, indicating the GPi as a candidate in the onset of tremor in PD patients. Furthermore, the inferior olive has also been hypothesized to play a role in tremor initiation. Discussed in Sjolund et al. (1977), the inferior olive is a key nucleus involved in harmaline tremor. Harmaline is a centrally acting tremorgenic indole alkaloid which has an unknown mechanism of action. It has been speculated that it may exert its effects by acting at the GABA receptor-ionophore complex, or possibly through interacting with voltage-dependent sodium channels (Deecher et al., 1992).

These tremorgenic hypotheses above pertain only to the initial onset of tremor and largely deal with only one nucleus. However, there is evidence to believe that different aspects of tremor are controlled by different areas of the brain, and thus tremor is believed to be more of a network phenomena than a single nucleus occurrence. This is backed by Helmich et al. (2013) who posit that tremor initiation occurs in the GPi but the cerebellum sets the amplitude. This model of tremorgenesis is known as the Dimmer Switch Hypothesis and was deduced after EMG-fMRI showed an increase in activity in the GPi prior to the onset of tremor. Furthermore, they found that tremor amplitude was correlated with activity in the cerebellothalamic circuit, and not the basal ganglia. This led these authors to believe that the GPi triggers the initiation of tremor (ie. the switch) and the cerebellum controls the amplitude of the tremor being expressed (the dimmer). Clearly, more work needs to be undertaken to ascertain the initial site of tremor initiation so we can deepen our understanding of this debilitating disorder and develop better treatments for it.

1.6 Synaptic Plasticity

The brain is comprised of billions of neurons which are organized into many different circuits which control all aspects of human behaviours. One of the beauties of

these circuits is that they are not static- they are constantly undergoing changes in their connections with other circuits in the brain. This led the McGill psychologist, Donald Hebb to coin the phrase “Neurons that fire together, wire together” (Hebb, 1949). One of the most fundamental aspects of neuroscience surrounds the concept of synaptic plasticity. Synaptic plasticity is extremely important for various functions such as learning and memory and has been extensively studied in areas such as the hippocampus. Theta oscillations have been found to naturally occur within the hippocampus and have been shown to be optimal for inducing synaptic plasticity within this nucleus. In simplistic terms, when neurons communicate with each other (i.e. through synaptic transmission), they undergo physiological transformations that make them more prone to continue to communicate. Importantly, neurons and networks also need to be able to decrease their gain with other neurons and networks for certain processes to occur. These two processes, the increase and decrease in gain between neurons and networks have been coined long term potentiation (LTP) and long term depression (LTD), respectively.

The cellular mechanisms of synaptic plasticity have been a hot topic in the neurosciences and thus have been extensively studied since its discovery by in 1973 (Bliss and Lomo, 1973; Bliss and Gardner-Medwin, 1973). Different types of neurons undergo LTP in different manners, but one of the most studied mechanisms is that of LTP in the hippocampus. Here, the quintessential aspect underlying LTP is the influx of calcium ions through NMDA receptors which then leads to an intracellular signaling cascade that terminates with the upregulation and trafficking of receptors and their subsequent implantation to the cell membrane (Malenka and Bear, 2004). This is believed to increase the likelihood of excitatory post-synaptic potentials to induce an action potential in the post-synaptic neuron. Importantly, for this calcium influx to occur the magnesium plug blocking the NMDA receptor needs to be removed and this requires significant membrane depolarization. As reviewed in Larson and Munkacsy (2014), theta-burst stimulation is optimal for inducing maximal depolarization which effectively primes the synapse LTP in the hippocampus. This is because the feed-forward inhibitory post- synaptic potential (IPSP) which normally truncates the excitatory post-synaptic potentials (EPSP) elicited by subsequent bursts arriving within 100-150 ms inhibits itself allowing maximal depolarization of bursts arriving during 200 ms after the first burst. This

enhanced depolarization removes the NMDA receptor magnesium plug and allows calcium influx that promotes long-term potentiation. It is hypothesized that high frequency stimulation is less efficient at inducing LTP due to glutamate depletion during the tonic stimulation train which isn’t seen during the burst paradigm. Theta burst stimulation in patients with movement disorders has been performed before. Huang et al. (2005) found that continuous theta-burst (cTBS) with transcranial magnetic stimulation (TMS) in the motor cortex induced LTD-like effects while intermittent theta-burst stimulation induces LTP-like effects in human subjects. Recently, Kishore et al. (2014) used cTBS on the cerebellum combined with paired associative stimulation (PAS) and found that M1 plasticity can be restored in PD patients. To our knowledge, TBS has never been examined in the GPi.

Using our dual microelectrode recording unit, we are able to approximate changes in LTP within a neuron being recorded. Previously, Prescott et al., (2009) have examined plastic changes within the SNr of PD patients using extracellular HFS protocols and found that LTP is impaired in PD patients in the OFF condition. To our knowledge, no one has examined the effect of theta-burst stimulation on plasticity within movement disorder patients, particularly in the GPi. By stimulating from our distal electrode, we are able to excite the axon terminals and cause neurotransmitter release. In the case of the GPi, the striatal projection neuron’s axon terminals release GABA. GABA is an inhibitory neurotransmitter that allows chloride ions to flow down their concentration gradients into the neuron. Since these chloride ions are negatively charged, this hyperpolarizes the cell. Importantly to extracellular electrophysiology recordings, is that these chloride ions flow down there electrochemical gradient from the extracellular to intracellular compartments, as predicted by the Nernst equation. This movement of charge produces a current that can then be detected and measured by our focal electrode that is recording in the vicinity of a neuron and is displayed as an evoked field potential (fEP) (figure 19A). Since these are extracellular recordings, we are not recording from solely one neuron- we are recording the collective activity of many neurons in the area as chloride ions are flowing into them from extracellular milieu. The more GABA that is

released, the more chloride flux there is, and hence the greater the evoked fEP. Since plasticity deals with the connective strength of neurons, the greater the increase of the fEP measured, the greater the change in synaptic strength.

1.7 Project Rationale

Models have been put forward to attempt to explain the cause of the symptoms of PD but to date, no theory is capable of explaining all of the symptoms collectively. For example, the Firing Rate model is capable of explaining bradykinesia, and rigidity but suffers when explaining tremor. The Dimmer Switch Model (Helmich 2012) has strengths when discussing tremor, but doesn’t deal with bradykinesia and rigidity. This could be due to the fact that tremor still is not very well understood. Where the site of tremorgenesis lies is currently a debate. Many different areas of the brain have been examined for their role in tremorgenesis. A “Thalamocentric” view, which holds that the thalamus is the site where tremor initiates, has been put forward by Paré et al. (1992). This theory has been supported by studies indicating that thermo-electric lesioning of the motor thalamus appears to be the most effective surgical treatment for tremor (Duval et al. 2015). A “Pallidocentric” view, which posits that tremor onset occurs in the pallidum, has been put forth by Helmich et al. (2012). Although more related to essential tremor than PD rest tremor, Deuschl et al. (2000) have collected evidence that the cerebellum plays a role in tremorgenesis. Another candidate for the site of tremor generation is the inferior olivary nucleus (Shaikh et al. 2010). These views are currently hindered by the demonstration of true oscillators (neurons which can establish the tremor rhythm as opposed to just following an oscillation established elsewhere in the brain) within respective nuclei. There is obviously heterogeneity in opinions as to where tremorgenesis occurs within the brain; however, there is no reason to exclude the possibility that different types of tremor arise due to activity in different parts of the brain.

This project builds on the finding of neurons within the GPi which were induced into a tremor frequency oscillation following a brief microstimulation in patients lacking tremor (figure 6). The fact that they lacked tremor is important because these patients wouldn’t have had the sensory feedback from tremulous limbs which could provide input to establish the tremor rhythm. The first aim of this project was to characterize the distribution, incidence, background activity, and firing patterns of these oscillating neurons. We then attempted to determine whether these neurons are responsible for tremor initiation, and then examined the effect that these oscillating neurons may have on the plasticity in the GPi.

Figure 6- An example of a neuronal trace recorded from patient with dystonia. The top trace shows a baseline recording; bottom trace shows the response of the neuron following stimulation. A clear inhibitory period is seen before the cell begins to fire “normally” again, and then it fires in a very prominent 6 Hz oscillation. This spike train oscillation lasted for approximately 8 seconds. This cell can be identified as a border cell based on its regular firing pattern (pre-stimulation) and slower firing rate (26 Hz) than seen in a typical HFD neuron.

1.8 Hypothesis and Aims

This thesis has three distinct, yet related sections. The first aim was discovery- driven and was aimed at characterizing the oscillatory patterns of a newly discovered theta (tremor) oscillation seen following focal microstimulation of a GPi border cell. What was interesting about this oscillation was that it was initially found in a dystonic patient who lacked tremor, and as such, did not have the sensory feedback present to set the oscillatory rhythm. As such, it is believed that the neuron had intrinsic membrane mechanisms or network connectivity allowing this oscillatory activity in the absence of tremulous activity. This could implicate these cell types as the rhythm generators for rest tremor. Consequently, these oscillations were characterized to determine if they were significantly different from other firing patterns generated by the GPi.

The second aim of this thesis is aimed at determining if these oscillations serve a tremorgenic role or not. It was hypothesized that if these oscillations recorded from the GPi were tremorgenic, then stimulating the GPi (and downstream target- the motor thalamus) with microelectrode stimulation mimicking the recorded oscillations (i.e. theta burst stimulation) will show evidence of tremor modification (i.e. tremor resetting, phase locking of tremor activity to bursting stimulation, tremor induction, etc.)

The third aim of this thesis is aimed at determining the effect of theta burst stimulation on the plasticity within the GPi. It was hypothesized that theta burst stimulation would be able to induce long-term potentiation within this nucleus.

Chapter 2- Methods

2.1 General Methods

Subjects studied in this work were all patients undergoing DBS implantation surgeries for movement disorders. 23 subjects were studied in total across all experiments. All gave informed, written consent according to the UHN Research Ethics Review Board. PD patients were withheld from dopaminergic medications for twelve hours prior to surgery. Surgical techniques have previously been described (Lozano et al 1998). In summary, the patient’s scalp was anaesthetized with the local anesthetic lidocaine/marcaine to block the pain associated with the mounting of the frame to the skull with screw-pins. One to two burr holes were drilled (depending on whether unilateral or bilateral electrodes were implanted) 2mm anterior to the coronal suture. Targets were stereotactically selected following 1.5T or 3T fused MRI (Table 1). See figure 7 for example trajectories taken to reach the surgical target. All patients were awake during microelectrode mapping procedures. Two independently driven microelectrodes were advanced along a track directed at the surgical target. (see figure 8 for illustration of electrode setup).

Table 1- Stereotactic coordinates of DBS targets.

Target Coordinates

X Y Z

(medial-lateral) (anterior-posterior) (inferior-superior)

GPi 20 mm lateral 1 mm posterior to 5 mm inferior to MCP AC-PC

STN 12 mm lateral 3 mm posterior to 3 mm inferior to MCP AC-PC

Vim 14 mm lateral 6 mm anterior to PC O mm (on AC-PC line)

GPi, globus pallidus internus; STN, subthalamic nucleus; Vim, ventral intermediate nucleus of thalamus; MCP, mid-commissural point; PC, posterior commissure; AC-PC, anterior commissure-posterior commissure.

Figure 7- Sample trajectories taken to reach surgical target during mapping procedures for DBS implantation surgeries. A) A trajectory taken to reach the Vim target. B) A trajectory taken to reach the STN target. C) A trajectory taken to reach the GPi target.

RT, reticular thalamus; Voa, ventral oralis anterior nucleus of thalamus; Vop, ventral oralis posterior nucleus of thalamus; Vim, ventral intermediate nucleus of thalamus; Vc, ventral caudal nucleus of thalamus; STN, subthalamic nucleus; ZI, zona incerta; GPi, globus pallidus internus; GPe, globus pallidus externus; OT, optic tract; AC, anterior commissure; PC, posterior commissure. Solid, dark black line represents microelectrodes; dotted black line represents AC-PC line.

Figure 8- The microelectrode setup used for microelectrode mapping prior to DBS implantation.

Two independently driven microelectrodes with tips spaced 0.5 mm apart. The terms “focal” and “distal” electrodes are relative terms describing the proximity of the electrode of interest to the unit being recorded.

2.2 Intraoperative Microelectrode Recordings

Two independently driven microelectrodes (FHC; Bowdoin, ME) with impedances ranging from 0.2-0.4 M W ) were inserted into the brain guided by a Leksell stereotactic frame (Leksell; Stockholm, Sweden). Electrode advancement proceeded incrementally until isolated GPi units were identified. Electrode tips were aligned to be at the same the depths. 10s baseline activity was recorded prior to passing electric current (3 µA, 5 µA, or 7.5µA intensity; 0.3ms biphasic pulse width; 200Hz frequency; 1 second duration) through the stimulating (focal) electrode while cellular activity was subsequently recorded through the focal electrode. [ NOTE: distal and focal are relative terms used to discuss the proximity of the electrodes to the unit being recorded. During these recordings, the focal electrode (the electrode closest to the cell of interest), was the stimulating and recording electrode.]10s of neuronal activity was collected following stimulation. Data was recorded using Spike2 software (Cambridge Electronic Design, UK) and stored for off-line analysis.

Using Spike2 software, trials were excised, band pass filtered (300-3000Hz) and then template matched using principal component analysis (PCA). In short, the software analyzes various aspects of the action potential (such as amplitude, waveform shape, and width) and matches these spikes to other similar spikes allowing any possible spiking activity from neighboring neurons or noise to be removed so that one cell can be studied. Trials were segregated into pre- and post-stimulation segments, and then imported into Matlab for analysis by the Mkaneoke script (Kaneoke and Vitek 1990) (see appendix figure 1). Values for mode burst index (the mode value of the ratio of intraburst interval: interburst interval- see figure 9), firing frequency, and tremor frequency signal-to-noise ratios were collected and compared pre-stimulation against post-stimulation.

Figure 9- Example traces showing the regularity of neuronal firings and the corresponding burst index value. A) A regular firing neuronal trace with a mode burst index of 1.04 (See appendix Figure 2 A & B). B) A slightly more bursty cell with a mode burst index of 3.04.

Both traces are 3 seconds long.

2.3 Tremor Entrainment with Theta Burst Stimulation

Patient selection and surgical procedures were performed as outlined in 2.1. Again, using two independently driven microelectrodes, single neuronal units were identified and 10s of baseline activity was recorded from the focal electrode. The distal electrode was connected to a stimulus isolation unit and electrical current (100µA, 4 pulses per burst, 10ms intraburst interval, 200ms interburst interval, 5-10s duration) was passed into the nucleus. 10s of neuronal activity was recorded following stimulation. Data was saved for offline analysis. Recordings were excised, and band pass filtered (300-3000Hz) before being template matched in Spike2 software. Stimulation artifacts were removed using the Spike2 Artrem script which replaced the stimulation artifacts with a flat connecting line. Phase histograms were then constructed to look for phase locking of the tremor peaks with the action potentials and/or theta burst stimulation. Each phase began at the onset of theta burst stimulation and triggers were placed on the peaks of the tremor amplitudes as recorded by the accelerometer. These tremor peaks were used in the phase histogram to examine any realignment of tremor peaks with cellular activity.

2.3 Effects of Theta Burst Stimulation on Plasticity in GPi

Patient selection and surgical procedures were performed as outlined in 2.1. Single stable units were isolated. Two baseline test pulses were delivered from the distal electrode (stimulation parameters: 100µA, 10s, 1Hz, 10s rest between sets of test pulses.) In a non-randomized fashion, theta burst stimulation preceded high frequency stimulation, allowing the cell to recover before the HFS protocol. Using a stimulus isolation unit, theta burst stimulation was delivered so that the total energy delivered was matched in energy to high frequency stimulation (TBS stimulation parameters: 40s, 10ms intraburst interval, 200ms intraburst interval, 100µA). Test pulses were repeated, except 30s were waited between test pulses instead of 10 (3 to 4 test pulses were delivered)(Figure 6a). High frequency stimulation was applied for 5-10s (stimulation parameters: 2s stimulation at 100Hz and 100µA with 8s rest; repeated 4 times) (see figure

6b). This was not a balanced study- both times, the HFS occurred after the neuron recovered from TBS. Offline, field evoked potential amplitudes (fEP) were measured and compared pre stimulation vs post stimulation.

Statistical Analysis

ANOVAs were performed with SPSS (v.22, IBM Corp, Armonk, New York

USA) and T-tests were carried out using GraphPad Prism (version 5 for Mac OS X,

GraphPad Software, San Diego California USA) statistical programs.

Figure 10- Diagrammatic representations of stimulation protocols used to assess plastic changes in the GPi.

Stimulation paradigms were matched for energy delivery to the tissue. Each was comprised of 800 pulses at 100µA intensity. A) The theta burst stimulation used. TBS indicated theta burst stimulation. B) The high frequency stimulation protocol employed. HFS indicates high frequency stimulation.

3.0 Chapter 3- Results

3.1 Stimulation-Induced Oscillation Characterization

95 neurons were sampled from 16 patients- 58 neurons from PD patients, and 37 neurons from the comparison group (dystonia/dystonia-related diseases). 2 neurons were excluded from analysis as 1 cell was identified as a low frequency bursting cell of the GPe (identification based on the recording depth in the track, firing rate, burst index, and visual firing signature), and 1 neuron was lost following electrical stimulation. In examining the pre-stimulation baseline firing (irrespective of stimulation intensity), it was found that PD HFDs had a significantly higher mean firing rate (93.46 ± 38.01 Hz (SD), N= 41 cells) than the PD border cells (47.93 ± 18.44 Hz, N=17 cells) (unpaired- t83 =5.39, p< 0.0001) and the dystonia HFDs (57.95 ± 27.17 Hz, N= 28 cells)

(unpaired-t98 = 4.88, p<0.0001). Dystonia HFDs had a significantly higher mean firing rate than the dystonia border cells (40.17 ± 11.90 Hz, N= 9 dystonia border cells)(unpaired-t58 =2.90 p<0.01), however, there was no significant difference between the PD and dystonia border cell firing rates (unpaired-t43 = 1.67, p=0.10) (figure 11A). As shown in figure 11 B, the dystonia HFDs (average mode burst index= 3.32 ± 0.31) were found to have a significantly higher pre-stimulation mode burst index than the PD HFDs (average mode burst index= 1.85 ± 0.11) (unpaired- t100 =5.20, p<0.0001), and the dystonia border cells (average mode burst index= 1.43

±0.08) (unpaired-t60 =4.49, p<0.0001). There was no significant difference found between the average pre-stimulation mode burst index of PD and dystonia border cells (unpaired-t45 =1.55, p=0.13).

*** A 150 *** **

n.s. 100

Firing Frequency (Hz) Frequency Firing 0 D ll r F e D e H C F rd r H o D e ia B P rd n o o ia B t n s to D y s P D y D

*** B *** 6 n.s.

4 n.s.

2 Mode Burst Index Burst Mode 0 ll ll D e D e F C F C H r H r D e ia e P rd n rd o to o B s B D y ia P D n to ys D

Figure 11- Group analysis of baseline (pre-stimulation) firing rate and mode burst index separated by disease and cell type. A) Group averages of firing rates. B) Group averages of mode burst indices.

Error bars indicate SD. PD indicates Parkinson’s disease; HFD, high frequency discharge; n.s., no significance. ** p< 0.01, *** P< 0.0001 as determined by ANOVA.

In analyzing cell groups broken down by disease type and stimulation intensity, it was found that the average pre-stimulation firing rate was significantly higher than the average post-stimulation firing rate in the PD neurons at 5 µA (pre-stimulation: 79.94 ±

38.33 Hz, post-stimulation: 66.45 ± 39.92; N= 23; unpaired-t48 =2.84, p<0.001), 7.5 µA (pre-stimulation: 84.82 ± 35.76 Hz, post-stimulation: 58.18 ± 34.03 Hz; N= 15; unpaired- t14= 3.12, p< 0.01), and in the dystonia neurons at 5 µA intensity (pre-stimulation= 49.25

± 21.54 Hz; N= 22; unpaired-t21 = 4.78, p< 0.0001). There was no significant difference seen between the pre-stimulation and post-stimulation firing rates in the PD neurons at 3 µA (pre-stimulation: 73.01 ± 43.28, post-stimulation: 71.80 ± 35.44 Hz; N=23; unpaired- t22 =0.37, p=0.72), or in the dystonia neurons at 3µA (pre-stimulation: 52.93 ± 26.79 Hz, post-stimulation: 54.09 ± 38.47 Hz; N= 35; unpaired-t34 =0.34, p= 0.74) or 7.5 µA (pre- stimulation: 43.08 ± 13.28 Hz, post-stimulation: 27.42 ± 14.16; N= 5; unpaired-t4= 2.23, p= 0.09), however, the effect at 7.5 µA may not be significant largely due to the low sample size used in the analysis (figure 12 A and B). The burst index was seen to significantly increase following stimulation at 5 µA (pre-stimulation: 1.82 ± 0.83, post- stimulation 2.46 ± 1.48; unpaired-t48 =3.22, p< 0.01) and 7.5 µA (pre-stimulation: 3.08 ±

3.15, post-stimulation: 3.54 ± 3.65; unpaired-t14 = 2.51, p< 0.05) in PD neurons, and at 3

µA (pre-stimulation: 2.57 ± 1.71, post-stimulation: 3.40 ± 3.29; unpaired-t34 =2.30, p< 0.05) and 5µA (pre-stimulation: 2.68 ± 1.70, post-stimulation: 3.02 ± 2.05; unpaired- t21 =2.33, p<0.05) in dystonia patients indicating that these neuron traces fired in a more bursty manner following stimulation. There was no significant difference seen at 3 µA

(pre-stimulation: 2.04 ± 1.20, post-stimulation: 2.01 ± 0.91; unpaired-t22 =0.16, p< 0.87) in the PD neurons or 7.5µA (pre-stimulation: 3.08 ± 3.15, post-stimulation: 3.54 ± 3.65, unpaired-t4= 1.69, p< 1.67) in the dystonia patients (figure 12 C and D).

PD Dystonia A B

nn= 23 nn= 50 nn= 15 nn= 36 nn= 23 nn= 5 np=7 np= 8 np= 6 np=7 np= 5 np= 3

150 ** * 150 *** 100 100

50 50 Firing Frequency (Hz) Frequency Firing Firing Frequency (Hz) Frequency Firing

0 0

C D

8 ** * 8 ** * x

e 6

d 6 n I

t s r

u 4

B 4

e d o

M 2 2 Mode Burst Index Burst Mode

0 0 3 5 7.5 3 5 7.5

Stimulation Intensity (µA) Stimulation Intensity (µA)

Figure 12- Effect of stimulation intensity on neuron populations separated by disease. A) and B) The effect of stimulation intensity on mean firing rate in PD and dystonia neurons, respectively.

C) and D) The effect of stimulation intensity on mean mode burst index in PD and dystonia neurons, respectively.

Black bars indicate pre-stimulation, grey bars indicate post-stimulation. Error bars

indicate SD. * P< 0.05; ** p<0.005; p< 0.0001. n n indicates number of neurons included in

sample; n p indicates number of patients analyzed.

Ai) Bi) **

10 n.s . 10 Log Mode Burst Index Burst Mode Log

1 1

Aii) Bii ) * ** 10 10 Log Mode Burst Index Burst Mode Log

1 1

Aiii ) Biii ) n.s.

10 * 10 Log Mode Burst Index Burst Mode Log

1 1

Pre Stim Post Stim Pre Stim Post Stim

Figure 13- Individual changes in mode burst index across stimulation intensities. A) PD neurons. B) Dystonia neurons.

Each light gray line indicates the change in mode burst index of a single cell; dark black lined indicate the significant average changes. i= 3 µA; ii = 5 µA; iii = 7.5 µA. * p< 0.05; ** p< 0.005; n.s., no significance.

These findings are further supported by figure 13 A i-iii which shows the individual changes in mode burst index of each PD cell (light grey lines) with the overlying group average superimposed (dark grey line), and figure 13 B i-iii which shows the same results but for the dystonia cells.

A one-way ANOVA found that there was no significant difference in the mode burst index ratio (post-stim/pre-stim) for either PD or dystonia neurons indicating that cells do not get more bursty with higher stimulation intensities. However, with the PD neurons, the trend shows an increase in burstiness at 5 µA compared to 3 µA, however this plateaus at 7.5 µA, possibly indicating a ceiling for how bursty a cell can become. This trend was not seen in the dystonia neurons (figure 14 A and B). Similarly, there was no significant difference in the firing rate ratios (post-stimulation/pre-stimulation) of PD or dystonia neurons as determined by a one-way ANOVA, however, the trend appears to indicate that there is a greater suppression of firing rates following stimulation (figure 14 C and D), possibly due to a longer inhibitory period post-stimulation, however, this is just speculation.

PD Dystonia A B

2.0

2.0 o i t a

R 1.5 1.5 x e d n

I 1.0

t 1.0 s r u (Post/Pre) B 0.5 0.5 e d o M 0.0 0.0 3 5 7.5 3 5 7.5

C D 1.5 ) e r 1.5 P / t

s 1.0 o P (

1.0 o i t a 0.5 R

e 0.5 t a R

g 0.0 n

i 0.0 3 5 7.5 r

i 3 5 7.5

F Stimulation Intensity (µA) Stimulation Intensity (uA)

Figure 14- Mode burst index ratios and firing rate ratios of PD and dystonia neurons differentiated by stimulation intensities.

A) and B) Mode burst index ratios (post-stim/prestim)

C) and D) Firing rate ratios.

No significant differences were found with a 1-way ANOVA.

In analyzing tremor power (via fast-Fourier transforms- FFT) it was seen that there was a significant decrease in the 3-6 Hz range in the dystonia HFD neurons at 5 µA only (pre-stimulation mean signal-to-noise ratio: 1.86 ± 0.66 dB, post-stimulation mean signal-to-noise ratio: 1.53 ± 0.72 dB; unpaired-t12 = 3.3, p<0.01). There was no significant difference seen in any other class of neuron (i.e. PD border cells or HFDs, or dystonia border cells) at any stimulation intensity (figure 15).

A PD B Dystonia

4 4 ) n = 23 n = 50 n = 15 n = 36 n = 23 n = 5

B n n n n n n d (

n =7 n = 8 n = 6 n =7 n = 5 n = 3 r p p p p p p

e 3 3 w o P

e 2 2 g n a R

z 1 1 H 6 - 3 0 0

C D 4 4 ) B d (

** r

e 3 3 w o P

e 2 2 g n a R

z 1 1 H 6 - 3 0 0 3 5 7.5 3 5 7.5 Stimulation Intensity (µA) Stimulation Intensity (µA)

Figure 15- The effect of stimulation intensity on tremor FFT frequency bands.

PD neurons are shown on the left side while dystonia neurons are on the right.

A) and B) are the border cell populations of each disease group.

C) and D) are the HFD cell populations of each disease group.

Dark black bars indicate pre-stimulation averages; lighter gray bars are post-stimulation averages. Error bars indicate 95% confidence intervals. ** p< 0.01.

nn indicates the number of neurons sampled; n p indicates the number of patients sampled.

3.2 Tremor Entrainment with Theta Burst Stimulation

The novel finding of the stimulation-induced neurons described in the above section are of interest as they may provide new insight into the site of tremor initiation. However, just because these neurons fire at the tremor frequency in one of the output nuclei of the basal ganglia, does not mean that they are necessarily involved with tremor. To test this relationship, neurons in the GPi (the nucleus where these border burst neurons are found), as well as upstream and downstream targets, were stimulated with a stimulation paradigm that mimicked the oscillations recorded from border burst cells and were qualitatively assessed for changes in tremor as well as cellular response to the stimulation.

3.2.1 Theta Burst Stimulation in the Globus Pallidus Internus

In the GPi, 7 neurons from 4 different patients were stimulated with theta burst stimulation (TBS). No evidence of tremor modifications as recorded via an accelerometer were noted in any of these trials. The cellular responses were also analyzed during TBS (figure 16). 2 neurons appeared to be driven by the stimulation (i.e. their firing rates increased immediately following burst stimulation). Due to the removal of the artifacts (and subsequent replacement with filler data), spiking activity during the stimulation burst could not be analyzed. Of these two neurons that were initially driven by TBS, 1 neuron was only driven for 11 blocks of stimulation near the start of the stimulation train before being inhibited for the latter blocks of stimulation. The other neuron did not appear to have this same primacy effect due to the theta burst stimulation but appeared to be driven throughout the entire stimulation paradigm. These two neurons were recorded in separate patients. The remaining 5 neurons that were recorded were all at least partially inhibited (i.e. showed a reduced firing rate compared to the baseline recording) by the TBS throughout the duration of the stimulation.

Figure 16- The effect of TBS in the GPi on tremor and neuronal activity. A) Pre-stimulation : The top trace shows the baseline spiking behavior of the cell prior to stimulation. The bottom trace shows 3 seconds of the accelerometer data recorded. During Stimulation : The neuronal trace during TBS that the phase histogram in B was produced from. The top trace shows the spiking behavior of the cell during stimulation with stimulation artifacts largely but not completely removed. The middle trace shows the onset of the theta burst stimulation phase. The bottom trace shows the accelerometer data. Post-Stimulation : The neuronal trace following TBS. The top trace shows the baseline spiking behavior of the cell following the stimulation protocol. The bottom trace shows the accelerometer data.

The top plot is a raster plot of the neuronal firing behavior (indicated by the solid dots) throughout the TBS train. A phase is defined as the start of TBS to the start of the next stimulation burst. TBS indicates when theta burst stimulation was applied

3.2.2 Tremor Entrainment with Theta Burst Stimulation Outside of The GPi

As can be seen in figure 1, the connectivity of the basal ganglia is quite complex. Electrical stimulation of the brain as in GPi DBS can have antidromic as well as orthodromic effects (Liu et al., 2012) Therefore, we also examined the effect of TBS on nuclei both upstream (the STN), and downstream (the motor thalamus) of the GPi.

Within the motor thalamus, we stimulated and recorded from 17 neurons across 6 patients. Of these 17 neurons, 9 were initially driven for the first few bursts of theta burst stimulation before being inhibited later in the train, and of these 9, 2 cells showed prolonged inhibition even after the stimulation ceased (both neurons from the same patient). Also of importance is that in 3 of these 9 cells (from 2 different patients) evidence of tremor reduction (figure 17) was seen during stimulation (no cell that showed tremor reduction was inhibited after the stimulation was turned off). Also within the motor thalamus, 2 cells (from 2 patients) were driven, 4 (from 3 patients) were inhibited, and 2 cells (from 2 different patients) appeared to not have any effect for the duration of the burst stimulation. Stimulating upstream of the GPi (the STN), 6 neurons were recorded from 2 different patients. None of these neurons showed tremor reduction in the accelerometer recording during stimulation (figure 18). 1 neuron was initially driven before being inhibited towards the end of the stimulation train, 3 neurons were driven throughout the stimulation train, and 2 appeared to be inhibited throughout the duration of the stimulation.

Figure 17- The effect of TBS in the Vim on tremor and neuronal activity. A) Pre-stimulation : The top trace shows the baseline spiking behavior of the cell prior to stimulation. The bottom trace shows the accelerometer data. During Stimulation : The neuronal trace during TBS that the phase histogram in B was produced from. The top trace shows the spiking behavior of the cell during stimulation. The middle trace shows the onset of the theta burst stimulation phase. The bottom trace shows the accelerometer data. Post-Stimulation : The neuronal trace following TBS. The top trace shows the baseline spiking behavior of the cell following the stimulation protocol. The bottom trace shows the accelerometer data. B) The top plot is a raster plot of the neuronal firing behavior (indicated by the solid dots) throughout the TBS train. A phase is defined as the start of TBS to the start of the next stimulation burst. TBS indicates when theta burst stimulation was applied.

Figure 18- The effect of TBS in the STN on tremor and neuronal activity. A) Pre-stimulation : The top trace shows the baseline spiking behavior of the cell prior to stimulation. The bottom trace shows the accelerometer data. During Stimulation : The neuronal trace during TBS that the phase histogram in B was produced from. The top trace shows the spiking behavior of the cell during stimulation. The middle trace shows the onset of the theta burst stimulation phase. The bottom trace shows the accelerometer data. Post-Stimulation : The neuronal trace following TBS. The top trace shows the baseline spiking behavior of the cell following the stimulation protocol. The bottom trace shows the accelerometer data. B) The top plot is a raster plot of the neuronal firing behavior (indicated by the solid dots) throughout the TBS train. A phase is defined as the start of TBS to the start of the next stimulation burst. TBS indicates when theta burst stimulation was applied.

3.3 Effect of Theta Burst Stimulation on Plasticity within the GPi

Seeing as the tremor frequency lies within the theta bandwidth, we examined the effect of theta burst stimulation on synaptic plasticity within the GPi of PD and dystonia patients. We stimulated 6 cells from three patients with theta burst stimulation- 2 neurons were from patients with PD while 4 neurons were from a patient with dystonia. fEP amplitudes were measured for up to 90s post stimulation to examine the effect of theta burst stimulation on plasticity (figure 19 A and B). Data was unavailable for one PD cell at 60 s and 90 s post stimulation due to recording noise. The other cell showed an initial 65% increase in fEP amplitude compared to baseline at the 30 s time point, and an fEP peak at 170% of the baseline which was recorded at the 60s post stimulation time point providing an inverted u-shaped curve. Even at 90s post stimulation, the fEP was still 150% of the baseline. In analyzing the data recorded across the 4 cells from the 1 patient with dystonia, the peak fEP amplitude across all trials was only 125% of the baseline measure and occurred at the 90 s stimulation time point (figure 19 C).

Baseline T1

T2 T3

B In dividual fE P A mplitu des Baseline 200 Cell 1 Cell 2

e 150

d Cell 3 u t i l Cell 4 p 100 m Cell 5 A

P Cell 6 E f (% of Baseline) of (% 50 PD Dystonia 0 TBS 30 60 90 120 Time Post Stimulation (s)

C fE P A mplitu de A verages

130

TBS 120 e

d HFS u t i l 110 Baseline p m A 100 P E f (% of Baseline) of (% 90

80 TBS 30 60 90 Time Post Stimulation (s)

Figure 19- fEP amplitudes measured prior to and after TBS. A) A sample of individual fEPs indicating a plastic change due to TBS. In the top left panel, the baseline field evoked potential (fEP) amplitude is shown

(corresponds to the “Test pulses” in figure 6). T 1 indicates the fEP amplitude

30 s after the TBS, T 2 indicates 60 s post TBS, and T 3 indicates 90 s post TBS. B) Individual cell trials showing the fEP amplitude across time. Theta burst stimulation ended at time 0. All points measured are expressed as a percentage of baseline fEPs (baseline is considered 100% as denoted by the dotted red line). Blue squares indicate cells from PD patients; black circles indicate cells from dystonia patients. C) fEPs of individual trials in B averaged together over sampled time points and expressed as a percentage of baseline fEP amplitude (indicated by the solid grey line.) Black line indicates the average of all cells averaged together regardless of disease type. Red line indicated the average of the dystonia only trials. TBS indicates theta burst stimulation; HFS indicates high frequency stimulation.

Another question we set out to answer dealt with how TBS compares to HFS. As such, we also compared the amplitudes of fEP prior to and following HFS stimulation in 2 neurons from a patient with dystonia at 30 s and 60 s post-stimulation (Figure 19C). No significance tests have been performed due to the low sample size of this study. However, in comparing both neurons’ results from TBS and HFS respectively (i.e. neuron 1 TBS to neuron 1 HFS, and neuron 2 TBS to neuron 2 HFS), it was found that the TBS always produced a greater increase in fEP than the HFS. In fact, in comparing HFS to TBS irrespective of aligning trials, it was found that the lowest measured fEP amplitude recorded from TBS was higher than the highest fEP amplitude recorded following HFS. The data was then averaged and analyzed as two groups: TBS (PD and dystonia combined), and dystonia HFS (figure 19 C). Again, no test of significance was performed due the small sample size; however, although this data is very preliminary, the data is indicating that TBS is more efficacious at inducing LTP than HFS, as evidenced by the higher fEP amplitudes measured after the stimulation protocol. This preliminary data should be taken cautiously, especially due to the PD cell which may be considered an outlier in this data series.

4.0 Chapter 4- Discussion

The bulk of this thesis has been dedicated to the characterization of the firing patterns of this new class of border burst cells recently identified in our lab. Smaller side studies also attempted to determine if this new class of neurons is responsible for initiating tremor, and also what role these oscillations may play (if any) on plasticity within the GPi. This work focuses on the role of theta oscillations as pertaining to tremor. However, to fully discuss theta oscillations, it should be noted that theta oscillations underlie many other phenomena within the body. Within the hippocampus, theta oscillations are believed to be intricately linked with learning and memory. Other studies have shown theta oscillations to be present in the GPi of patients with dystonia (Moll et al. 2014), and during periods of dyskinesias (Cenci and Lindgren, 2007). As such, even if

the induced theta oscillations presented in this thesis are responsible for initiating tremor, theta oscillations may sub serve other functions, both in and outside of the motor system.

4.1 Stimulation-Induced Oscillations

To date, there is no widespread scientific consensus as to what initiates tremor in patients with movement disorders. Tremor is a very heterogeneous phenomenon that is present in a variety of different diseases, and therefore it is unlikely that one “umbrella” explanation will be capable of explaining all types of tremor. Parkinson’s disease is a disease commonly affected by tremor as outlined in the introduction. Many theories have been put forward to explain various aspects of the symptoms of PD, however, no theory presently available can account for all aspects of the disease collectively. Helmich et al., (2011) used a combination of fMRI and SPECT imaging in combination with EMG recordings and found that activity in the basal ganglia increases prior to the onset of tremor in PD patients, indicating a role of the GPi in tremor. Specifically, it was found that activity increased prior to the tremulous EMG activity but then became incoherent with tremor activity soon after. This led to the formulation of the “Dimmer-Switch Hypothesis” which states that the GPi initiates the tremor and the cerebellum controls the amplitude. What is missing from Helmich’s work is what or how tremor actually starts because imaging studies only indirectly measure the metabolic response to neuronal activity (i.e. blood flow). In this regard, oscillatory activity in neurons cannot be measured with the resolution of this technique. We believe to have accumulated evidence suggesting a pivotal role of the GPi in this capacity. Our data indicates that certain neurons in patients with and without tremor can be induced into a theta oscillation (specifically within the tremor frequency) following brief microstimulations ranging from 3-7.5uA. There was a significant increase in burst index in PD patients when stimulated at 5 and 7.5µA, and in dystonia patients when stimulated with 3 and 5µA indicating that these neurons are becoming more bursty after their inhibitory period, however, no significant difference was found in the burst index or firing rate ratios between PD and

dystonia patients (i.e. neither group can be deemed to become more bursty than the other following stimulation.). There was a corresponding significant decrease in firing rate between pre-stimulation and post-stimulation in PD patients when stimulated with 5 and 7.5µA, and in dystonia patients when stimulated with 5µA. Interestingly, it was found that dystonia HFD neurons had a significantly lower pre-stimulation firing rate and a significantly higher pre-stimulation mode burst index compared to the PD HFD neurons. These findings are in agreement with Vitek et al (1999) who found lower firing rates and increased burstiness in dystonia patients compared to PD patients. However, these findings are contrast to those reported by Hutchison et al. (2003) who found no difference between firing rates. This discrepancy may be due to the anesthetics used during surgery, If a difference does exist, these findings would add support to the rate model of movement disorders which imply that hyperkinetic (i.e. dystonia) disorders, such as dystonia, occur due to decreased neuronal activity in the GPi which leads to decreased inhibition of the motor thalamus, whereas hypokinetic disorders (i.e. PD) occur due to increased inhibition of the thalamus from the GPi. Looking at the power spectra, there was no significant difference in the theta and tremor frequency power spectra between pre- and post-stimulation, regardless of stimulation intensity, except for tremor power in dystonia patients at 5µA. Bear in mind that this study utilized microelectrodes, and thus the amount of energy delivered to the brain is not likely to have been capable of inducing a significant change in the oscillatory behavior of a network of cells. This finding could be important as it may answer the “where” question pertaining to tremorgenesis. Another important aspect to be addressed here is that the MKaneoke script which analyzed the neuronal activity calculated values (i.e. firing rate, burst index, etc.) based on the whole sampling window. As all neurons went through a silent period of varying lengths following stimulation, this may influence (i.e. lower) the values that were attained by this script. To better characterize these values, a measure such as the silent period (the time from the end of the stimulation until the first spike) could be used. A 10 s sampling window was chosen so that the sampling time was small while still having enough spikes in the spike train during the recovery period.

Building on these findings and taking into consideration recent work by Milardi et al., (2015) and Smith and Wichmann (2015), these border burst cells, if responsible for the initial instigation of tremor, lie in a critical location within the brain that may endow them with the potential to start tremor. Milardi and colleagues, using a constrained spherical deconvolution technique demonstrated the presence of white matter tracts directly connecting the GPi with the cortex in humans, now deemed the “super-direct” pathway. In addition to this, Mathai et al. (2012), and Smith et al., (2014) using primates, determined that this super-direct pathway connects the peripallidal regions of the GPi to the cortex and uses glutamate as a neurotransmitter. It is in these regions of the GPi where these border burst cells have been identified. We speculate that these border burst cells may be initiating the tremor rhythm and the super-direct pathway acts as a short circuit allowing for the signal to increase in gain to the point of phenotypic expression of tremor. This discovery is important as it has now been demonstrated that one of the major outputs of the basal ganglia encompasses neurons that can initiate a theta oscillation that can be passed on to the downstream Vop of the thalamus and have reverberation directly from the cortex. From the Vop, the thalamus then projects to the motor cortex; closing the corticobasal-thalamic loop which is responsible for selecting which movement commands are executed and which are discarded.

If future work were to confirm that these border burst cells are responsible for the induction of tremor, more work would have to be undertaken to determine what triggers these oscillations in the first place. Slice work by Llinas and Yarom (1986) showed that depending on the membrane polarization state, neurons of the guinea pig inferior olive could be induced into two different rhythms: if the membrane was depolarized, the cells preferentially oscillated at 9-12 Hz. However, if the membrane was hyperpolarized, the neurons would preferentially oscillate at 3-6 Hz. Liu et al., (2012) showed evidence of decreased GPi firing rates due to stimulation within the GPi with microelectrodes. They hypothesize that this inhibitory effect is due to the local release of GABA from afferent

terminals. In our recordings, all our cells, whether they oscillated or not, experienced a short inhibitory period (indicating local hyperpolarization) following stimulation. Seeing as some cells were then induced into a 4-6 Hz oscillatory rhythm following this inhibition, one could speculate that the same mechanisms seen in the inferior olive neurons are at play in the pallidal cells as well. Llinas and Yarom (1986) found that the oscillations were produced due to a subthreshold membrane calcium dependent oscillation. In short, calcium influx leads to the activation of calcium-dependent potassium channels that then hyperpolarize the cell. This hyperpolarization leads to a calcium rebound, which then depolarizes the cell and creates a membrane oscillation. Cooper and Stanford (2000), using whole-cell recordings, found that the rat globus pallidus includes neurons characterized by a voltage-dependent inward rectifying and low threshold calcium currents. However, further work would have to confirm that these currents are identical to those discussed by Llinas and Yarom, and that they exist within human subjects.

4.2 Tremor Modification due to Theta Burst Stimulation in the Basal Ganglia

4.2.1 Theta Burst Stimulation in GPi

It is important to remember that just because these border burst cells have been found to be able to generate their own tremor rhythm following a short microstimulation, and the fact that they lie within one of the major output nuclei of the basal ganglia does not necessarily mean that they are intricately involved with initiating tremor. To address this, we applied theta burst stimulation (a protocol that mimics the induced-oscillations that were recorded) to the GPi in patients undergoing GPi DBS implantation and looked for the effect of stimulation on both the cellular activity and tremor activity as recorded by an accelerometer. We also looked at upstream (STN) and downstream (motor

thalamus) targets to determine if theta burst stimulation had any effect. Within the GPi we didn’t see any evidence of tremor modification. Although we did not collect evidence of tremor modification from stimulating the GPi, we do have one example where stimulating during a DBS implantation in the thalamus did induce tremor (see appendix figure 3). However, it is believed that the electrode was lateral to the intended target and may have been stimulating the corticospinal fibers of passage. We had hypothesized that if the GPi is a tremorgenic nucleus, then we should see phase locking/resetting of the tremor during stimulation theta burst stimulation. However, as previously mentioned, the microelectrode setup that we used for this work isn’t likely to transfer enough energy to induce a widely dispersed network of cells into a tremor rhythm and hence why we may not have seen any effect. Plaha et al. (2008) have shown tremor induction through stimulation of the zona incerta with DBS macroelectrodes. It is believed that if the GPi is truly the originator of the tremorgenic oscillation that stimulating the GPi with an electrode that delivers more energy may be capable of entraining the tremor network neurons responsible for tremor initiation with in the GPi. In further reviewing the literature, it was found that Constantoyannis et al., (2004) were able to induce postural and action tremor in two patients by stimulating the ventral posteromedial nucleus of the thalamus (aka, the Vop) with DBS electrodes. However, in this report, they only used non-patterned stimulation, and only saw postural and action tremor, but not rest tremor indicative of PD.

Looking at the cellular response to the theta burst stimulation, the majority (5/7) of cells appeared to be inhibited by the stimulation protocol. This is expected due to the local release of GABA due to the microstimulation as mentioned earlier by Liu et al., (2012). Seeing as the GPi sends tonically active GABAergic efferents to the motor thalamus, inhibition of these GPi neurons would promote disinhibition of the thalamic neurons. I speculate that if this disinhibition occurred rhythmically (say, at tremor frequency) this could promote a tremor signal to be propagated to the motor cortex and allow for the phenotypic expression of tremor.

4.2.2 Theta Burst Stimulation in Motor Thalamus

We also looked to see what effect theta burst stimulation may have on tremor expression and cellular activity when applied to the Vim in Essential Tremor patients. As the Vim is a common target of DBS electrode implantation/ thalamotomies for tremor, we hoped to bypass any divergence of tremor activity that may be passed on from the basal ganglia. Also, whereas we speculate that the GPi is a tremorgenic center but other authors hypothesize that the thalamus is the site of tremor initiation, we wondered if theta burst stimulation in the Vim could induce signs of tremor modification. Initially, I had predicted that since tremor occurs between 4-6 Hz, that stimulating motor centers of the brain would initiate tremor or cause a resetting of the tremor rhythm to the stimulation frequency. Interestingly, in the motor thalamus, the only evidence of tremor modification that were noted was some cases of tremor reduction. When the cellular activity of the neurons being stimulated was analyzed, it was found that in three examples where tremor reduction was seen, the cells were initially driven by the stimulation and then inhibited later in the stimulation train. This is contrary to what we predicted. If the motor thalamus is responsible for initiating tremor, and the theta burst stimulation is initially driving the cellular activity, I would expect that the tremor should have been driven as well, not have had an amplitude reduction as seen in one isolated case. However, this may be explained by a recent review by Chiken and Nambu (2015) that discusses that DBS may not exert its effects through excitation or inhibitory effects on cells/fibers of passage, but instead, DBS may work via disconnecting the sensory input from the motor output. It is this interaction of the two signals that is believed to underlie the pathology in many movement disorders. This may be applicable to what we are seeing when stimulating the motor thalamus with TBS: the stimulation may be activating the thalamocortical loop allowing the sensory afferents to be disconnected from the motor efferents, and the disconnection of these two signals may be reducing the amplitude of the tremor recorded. Another important result that was seen when analyzing the phase histograms produced following the theta burst stimulation is that the neuronal activity in the Vim was capable

of being entrained to the burst stimulation. This shows that these neurons are capable of being entrained to a rhythm set elsewhere in the brain. As the GPi is an upstream nucleus of the motor thalamus, this could strengthen the hypothesis that tremor initiation initially occurs within the GPi and the tremorgenic signal is capable of entraining neurons in downstream projections which play an integral part in movement selection.

4.2.3 Theta Burst Stimulation in Sub Thalamic Nucleus

Lastly, we examined the effect of theta burst stimulation on tremor and cellular activity when the STN was stimulated. As the STN is an upstream target of the GPi, we were curious to see if a theta burst signal produced here could elicit signs of tremor modification, presumably through its glutamatergic projections to the GPi. Again, no evidence of tremor modification was noted in any of the trials. There did not appear to be any significant phase locking of the neuronal activity either- neuronal activity was diffuse and sporadic throughout the phase. These findings tend to indicate that a theta rhythm in the STN does not have the capacity to effect the phenotypic expression of tremor. This does not necessarily rule out the STN as having a role in tremor but rather that our experimental protocol was not sufficient in eliciting tremor modifications.

4.3 Effect of Theta Burst Stimulation on Plasticity within the GPi

The last component to this project deals with analyzing the changes in synaptic plasticity that may be occurring in the GPi due to theta oscillations. As previously stated, the activity of one cell in a network is not likely to have a large effect on the rest of the network. Plasticity is important to this project since, for these border burst cells to have a chance at inducing tremor, they have to be able to entrain other neurons to fire in

synchrony with them. We have an example from a patient with PD that shows a 75% increase in the fEP recorded following TBS compared to before stimulation. Since the fEP is indicative of the neurotransmitter release from a collection of other local neurons, and the primary neurotransmitter within the GPi is GABA, this indicates that there is the ability to induce plastic changes as a result of theta burst stimulation. This outcome was only seen in one site however, and thus these findings are preliminary. One possible reason for this enhanced efficacy seen with theta-burst stimulation over high-frequency stimulation may be due to habituation of the cell during HFS that isn’t seen during TBS. Since TBS is a burst stimulation paradigm, it has inherent pauses that could allow the cell to recuperate (i.e. produce more/reuptake expelled neurotransmitter). This is in contrast to HFS which doesn’t have pauses during the stimulation protocol, and thus neurotransmitter stores may deplete over the stimulation duration. In looking at the rest of the data, something else that was found striking was that the change in the fEP amplitude from the PD neuron appeared to be much greater than that seen in the dystonia neurons. Although both showed an increase in fEP from baseline conditions, the PD fEP amplitude reached a maximum of 170% of baseline whereas the average maximum fEP amplitude of the dystonia neurons only reached 111% of baseline, and the individual peak maximum only reached 125% of the baseline. Clearly, more work needs to be done in this area to determine if these results are significant. If found to be significant, the next question would most likely be: What is responsible for the difference between the two groups? One explanation could lie in recording variations that naturally occur. Anecdotal evidence from performing these recordings has shown that the amplitude of the fEP can vary depending on the proximity of the electrode tip to recording unit. This makes sense intuitively: the closer the tip is to the cell, the more concentrated the chloride gradient in the local milieu and this would register as a greater voltage in an electrical recording, manifested as a larger fEP. It is important to note that these possible confounds could also explain within group differences along with intergroup differences, and hence why these results are volatile with small sample sizes. However, these results could also be due to physiological differences in the neurons of the two disease groups. It is well known that PD patients have a dopamine deficiency and this is not seen in dystonia groups. Previous work has shown that there are plasticity abnormalities in PD patients due to this

dopamine deficiency. Piccconi et al., (2012) discuss that a lack of dopaminergic innervation in PD leads to decreased cell excitability, and this has a profound effect on the susceptibility of neurons to undergo plastic changes. Prescott et al., (2014) showed a lack of depotentiation (a form of plasticity wherein synapses that have undergone long term potentiation return back to their baseline gain) in the GPi and SNr of PD patients taking L-DOPA. Based on these previous findings, one might expect to see atypical plasticity in PD patients due to dopamine deficiency. However, one may also speculate that it is not the dopamine levels per se that are responsible for the potential difference in fEP amplitudes recorded, but the long term administration of L-DOPA commonly prescribed to those with PD. Studies have posited that chronic L-DOPA therapy can lead to pathological plastic changes that may underlie levodopa induced dyskinesias (Grace 2008). As such, this L-DOPA-dependent plasticity may be the underlying cause of this yet-to-be-significantly-determined effect.

Not only did we look at the plastic response of the GPi to theta burst stimulation, but we also attempted to determine the efficacy of TBS compared to HFS. TBS in the hippocampus has been shown to be the ideal stimulation frequency as this frequency allows priming of the synapse (which involves blocking presynaptic GABA B autoreceptors allowing for maximal post-synaptic excitation leading to the opening of a maximal number of NMDA receptors (Larson and Munkascy 2014). Zhu et al., (2015) showed that HFS was also able to elicit LTP in hippocampal neurons, albeit by different intracellular signaling mechanisms. Although a small sample size was used for this study, it appeared that TBS may be more efficacious at inducing LTP in GPi neurons that HFS, irrespective of disease type. However, HFS was only examined in dystonia patients and therefore, this is not a balanced comparison. Further analysis of this data in light of the possible plastic abnormalities expected in PD due to dopamine loss appears to show that HFS and TBS are equally efficacious in eliciting LTP in a dystonia patient. However, these results are only from one patient with 4 trials of TBS and two of HFS and tests of significance have yet to be performed.

4.4 Future Directions

The work presented here is just the beginning of a possible explanation to tremorgenesis. Future work needs to be undertaken to confirm these newly identified border burst cells as the initiators of tremor. We examined the role of theta oscillation in tremor modification with the use of microelectrodes. An alteration to the tremor entraining methodology we used in this work could be to record EMG activity as well as accelerometer data as there may have been tremulous activity occurring in the muscle but was not detected visibly or by the accelerometer. Another approach to determining if these oscillations are tremorgenic would be to stimulate the GPi with theta burst stimulation while a second electrode records in the Vop. This would help determine if the oscillatory activity in the GPi is transmitted to the motor thalamus; an assumption up to this point. This experiment would most likely have to be performed in an animal model due to the surgical restraints of performing this in humans- this protocol would require two separate trajectories due to the location of the thalamus and GPi within the brain, which would add to the invasiveness of the surgery.

Also of importance is determining what neuronal properties are responsible for creating these oscillations. Although not presented in this work, we initially thought that recurrent inhibitory collaterals within the GPi may have been responsible for setting the theta oscillation, however, the anatomical connectivity of the GPi does not support this theory (Bar-Gad et al., 2003). We now speculate that plasma membrane calcium channels might play a role. Yarom and Llinas (1986) discovered that calcium dependent potassium channels were responsible for the tremor-frequency oscillatory activity seen in guinea pig inferior olive neurons (further discussed in the introduction). This may serve as a model for our neurons.

Since the GPi tends to deal with motor commands being distributed to the body, whereas the SNr distributes motor commands to the head, it would be interesting to see if neurons in the SNr are capable of initiating their own theta rhythm like the border cells of the GPi. Although the work presented here deals largely with rest tremor as seen in PD, it would be unable to explain common tremors associated with dystonia, such as palatal tremor or blepharospasms.

Lastly, another obvious future direction is to continue the work on the effect of theta burst stimulation in the GPi to determine the effects of stimulation on plasticity. With a larger sample size, we could try to determine if any significant difference exists between PD and dystonia patients, as well as between theta burst stimulation and high frequency stimulation. This could implicate whether the theta rhythm is capable of inducing substantial plastic changes within the GPi that may be needed to entrain networks of cells to fire at the tremor frequency.

4.5 Conclusions

This project was not able to successfully indicate the GPi as a tremorgenic nucleus. Data was collected showing that neurons within the GPi could be induced into a tremor stimulation following a brief electrical stimulation. However, we were unable to collect evidence of tremor modification that may have drawn causation between the oscillation and tremor. We have begun to look at the effect of theta burst stimulation on plasticity within the GPi but have not collected enough data to draw conclusive results yet.

6.0 References

Abdo, W. F., van de Warrenburg, B. P. C., Burn, D. J., Quinn, N. P., & Bloem, B. R. (2010). The clinical approach to movement disorders. Nature Reviews. Neurology , 6(1), 29–37. http://doi.org/10.1038/nrneurol.2009.196

Alavi, M., Dostrovsky, J. O., Hodaie, M., Lozano, A. M., & Hutchison, W. D. (2013). Spatial extent of β oscillatory activity in and between the subthalamic nucleus and substantia nigra pars reticulata of Parkinson's disease patients. Experimental Neurology , 245 , 60–71. http://doi.org/10.1016/j.expneurol.2012.09.021

Albanese, A., Bhatia, K., Bressman, S. B., Delong, M. R., Fahn, S., Fung, V. S. C., et al. (2013). Phenomenology and classification of dystonia: a consensus update. Movement Disorders , 28 (7), 863–873. http://doi.org/10.1002/mds.25475

Alves, G., Forsaa, E. B., Pedersen, K. F., Dreetz Gjerstad, M., & Larsen, J. P. (2008). Epidemiology of Parkinson's disease. Journal of Neurology , 255 Suppl 5 (S5), 18–32. http://doi.org/10.1007/s00415-008-5004-3

Balint, B., & Bhatia, K. P. (2014). Dystonia: an update on phenomenology, classification, pathogenesis and treatment. Current Opinion in Neurology , 27 (4), 468–476. http://doi.org/10.1097/WCO.0000000000000114

Bar-Gad, I., Heimer, G., Ritov, Y., & Bergman, H. (2003). Functional correlations between neighboring neurons in the primate globus pallidus are weak or nonexistent. The Journal of Neuroscience : the Official Journal of the Society for Neuroscience , 23 (10), 4012–4016.

Blesa, J., Phani, S., Jackson-Lewis, V., & Przedborski, S. (2012). Classic and New Animal Models of Parkinson's Disease. Journal of Biomedicine and Biotechnology , 2012 (3), 1–10. http://doi.org/10.1155/2012/845618

Bliss, T. V., & Gardner-Medwin, A. R. (1973). Long-lasting potentiation of synaptic transmission in the dentate area of the unanaestetized rabbit following stimulation of the perforant path. The Journal of Physiology , 232 (2), 357–374.

Bliss, T. V., & Lomo, T. (1973). Long-lasting potentiation of synaptic transmission in the dentate area of the anaesthetized rabbit following stimulation of the perforant path. The Journal of Physiology , 232 (2), 331–356. http://doi.org/10.1111/(ISSN)1469-7793

Brown, P. (2003). Oscillatory nature of human basal ganglia activity: relationship to the pathophysiology of Parkinson's disease. Movement Disorders , 18 (4), 357–363. http://doi.org/10.1002/mds.10358

Brown, P., Oliviero, A., Mazzone, P., Insola, A., Tonali, P., & Di Lazzaro, V. (2001). Dopamine dependency of oscillations between subthalamic nucleus and pallidum in Parkinson's disease. The Journal of Neuroscience : the Official Journal of the Society for Neuroscience , 21 (3), 1033–1038.

Clark, L. N., & Louis, E. D. (2015). Challenges in essential tremor genetics. Revue Neurologique , 171 (6-7), 466–474. http://doi.org/10.1016/j.neurol.2015.02.015

Cohen, G. (1984) “Oxy-radical toxicity in catecholamine neurons,” NeuroToxicology , vol. 5, no. 1, pp. 77–82

Cooper, A. J., & Stanford, I. M. (2000). Electrophysiological and morphological characteristics of three subtypes of rat globus pallidus neurone in vitro. The Journal of Physiology , 527 Pt 2 (2), 291–304. http://doi.org/10.1111/j.1469-7793.2000.t01-1- 00291.x

Connolly, B. S., & Lang, A. E. (2014). Pharmacological treatment of Parkinson disease: a review. Jama , 311 (16), 1670–1683. http://doi.org/10.1001/jama.2014.3654

Connolly, A. T., Jensen, A. L., Bello, E. M., Netoff, T. I., Baker, K. B., Johnson, M. D., & Vitek, J. L. (2015). Modulations in oscillatory frequency and coupling in globus pallidus with increasing parkinsonian severity. The Journal of Neuroscience : the Official Journal of the Society for Neuroscience , 35 (15), 6231–6240. http://doi.org/10.1523/JNEUROSCI.4137-14.2015

Chung, S. J., Kwon, H., Lee, D.-K., Hong, J. Y., Sunwoo, M.-K., Sohn, Y. H., et al. (2013). Neuroanatomical heterogeneity of essential tremor according to propranolol response. PloS One , 8(12), e84054. http://doi.org/10.1371/journal.pone.0084054

Cenci, M. A., & Lindgren, H. S. (2007). Advances in understanding L-DOPA-induced dyskinesia. Current Opinion in Neurobiology , 17 (6), 665–671. http://doi.org/10.1016/j.conb.2008.01.004

Dauer, W., & Przedborski, S. (2003). Parkinson's disease: mechanisms and models. Neuron , 39 (6), 889–909.

Deecher, D. C., Teitler, M., Soderlund, D. M., Bornmann, W. G., Kuehne, M. E., & Glick, S. D. (1992). Mechanisms of action of ibogaine and harmaline congeners based on radioligand binding studies. Brain Research , 571 (2), 242–247.

Deuschl, G., Bain, P., & Brin, M. (1998). Consensus statement of the Movement Disorder Society on Tremor. Ad Hoc Scientific Committee. Movement Disorders , 13 Suppl 3 , 2–23.

Defazio, G., Gigante, A. F., Abbruzzese, G., Bentivoglio, A. R., Colosimo, C., Esposito, M., et al. (2013). Tremor in primary adult-onset dystonia: prevalence and associated clinical features. Journal of Neurology, Neurosurgery, and Psychiatry , 84 (4), 404–408. http://doi.org/10.1136/jnnp-2012-303782

Duval C. & Daneault J-F. (2015) A novel brain network model explaining tremor in Parkinson’s disease. Manuscript Revision.

De Bruijn, E., Nijmeijer, S. W. R., Forbes, P. A., Koelman, J. H. T. M., van der Helm, F. C. T., Tijssen, M. A. J., & Happee, R. (2015). Improved identification of dystonic

cervical muscles via abnormal muscle activity during isometric contractions. Journal of the Neurological Sciences , 354 (1-2), 10–16. http://doi.org/10.1016/j.jns.2015.03.047

Dehay, B., Bourdenx, M., Gorry, P., Przedborski, S., Vila, M., Hunot, S., et al. (2015). Targeting α-synuclein for treatment of Parkinson's disease: mechanistic and therapeutic considerations. The Lancet Neurology . http://doi.org/10.1016/S1474-4422(15)00006-X

Erogullari, A., Hollstein, R., Seibler, P., Braunholz, D., Koschmidder, E., Depping, R., et al. (2014). THAP1, the gene mutated in DYT6 dystonia, autoregulates its own expression. Biochimica Et Biophysica Acta , 1839 (11), 1196–1204. http://doi.org/10.1016/j.bbagrm.2014.07.019

Fasano, A., Visanji, N. P., Liu, L. W. C., Lang, A. E., & Pfeiffer, R. F. (2015). Gastrointestinal dysfunction in Parkinson's disease. The Lancet Neurology , 14 (6), 625– 639. http://doi.org/10.1016/S1474-4422(15)00007-1

Fujioka, S., Strongosky, A. J., Hassan, A., Rademakers, R., Dickson, D. W., & Wszolek, Z. K. (2015). Clinical presentation of a patient with SLC20A2 and THAP1 deletions: differential diagnosis of oromandibular dystonia. Parkinsonism & Related Disorders , 21 (3), 329–331. http://doi.org/10.1016/j.parkreldis.2014.12.024

Gerfen, C. R. (2003). D1 dopamine receptor supersensitivity in the dopamine-depleted striatum animal model of Parkinson's disease. The Neuroscientist : a Review Journal Bringing Neurobiology, Neurology and Psychiatry , 9(6), 455–462. http://doi.org/10.1177/1073858403255839

Gilbert, J. A., Frederick, L. M., & Ames, M. M. (2000). The aromatic-L-amino acid decarboxylase inhibitor carbidopa is selectively cytotoxic to human pulmonary carcinoid and small cell lung carcinoma cells. Clinical Cancer Research : an Official Journal of the American Association for Cancer Research , 6(11), 4365–4372.

Huang, Y.-Z., Edwards, M. J., Rounis, E., Bhatia, K. P., & Rothwell, J. C. (2005). Theta burst stimulation of the human motor cortex. Neuron , 45 (2), 201–206. http://doi.org/10.1016/j.neuron.2004.12.033

Hutchison, W.; Dostrosvky J.O.; Walters J.R.; Coutemanche, R.; Boraud, T.; Goldberg, J.; Brown, P. (2004) Neuronal Oscillations in the Basal Ganglia and Movement Disorders: Evidence from Whole Animal and Human Recordings. The Journal of Neuroscience, 24(42 ): 9240-9243

Jinnah, H. A., & Factor, S. A. (2015). Diagnosis and treatment of dystonia. Neurologic Clinics , 33 (1), 77–100. http://doi.org/10.1016/j.ncl.2014.09.002

Kim, A.-Y., Seo, J. B., Kim, W.-T., Choi, H. J., Kim, S.-Y., Morrow, G., et al. (2015). The pathogenic human Torsin A in Drosophila activates the unfolded protein response and increases susceptibility to oxidative stress. BMC Genomics , 16 (1), 338. http://doi.org/10.1186/s12864-015-1518-0

Kishore, A., Popa, T., Balachandran, A., Chandran, S., Pradeep, S., Backer, F., et al. (2014). Cerebellar sensory processing alterations impact motor cortical plasticity in Parkinson's disease: clues from dyskinetic patients. Cerebral Cortex (New York, N.Y. : 1991) , 24 (8), 2055–2067. http://doi.org/10.1093/cercor/bht058

Klein, C., Breakefield, X. O., & Ozelius, L. J. (1999). Genetics of primary dystonia. Seminars in Neurology , 19 (3), 271–280. http://doi.org/10.1055/s-2008-1040843

Kühn, A. A., Kupsch, A., Schneider, G.-H., & Brown, P. (2006). Reduction in subthalamic 8-35 Hz oscillatory activity correlates with clinical improvement in Parkinson's disease. The European Journal of Neuroscience , 23 (7), 1956–1960. http://doi.org/10.1111/j.1460-9568.2006.04717.x

D.O. Hebb The Organization of Behavior , Wiley (1949)

Iacono, R. P., Lonser, R. R., Ulloth, J. E., & Shima, F. (1995). Postero-ventral pallidotomy in Parkinson's disease. Journal of Clinical Neuroscience : Official Journal of the Neurosurgical Society of Australasia , 2(2), 140–145.

Laitinen, L. V. (1994). Ventroposterolateral pallidotomy. Stereotactic and Functional Neurosurgery , 62 (1-4), 41–52.

Larson, J., & Munkácsy, E. (2014). Theta-burst LTP. Brain Research . http://doi.org/10.1016/j.brainres.2014.10.034

Lettieri, C., Rinaldo, S., Devigili, G., Pisa, F., Mucchiut, M., Belgrado, E., et al. (2015). Clinical outcome of deep brain stimulation for dystonia: constant-current or constant- voltage stimulation? A non-randomized study. European Journal of Neurology : the Official Journal of the European Federation of Neurological Societies , 22 (6), 919–926. http://doi.org/10.1111/ene.12515

Little, S., & Brown, P. (2014). The functional role of beta oscillations in Parkinson's disease. Parkinsonism & Related Disorders , 20 Suppl 1 , S44–8. http://doi.org/10.1016/S1353-8020(13)70013-0

Neuronal intermediate filament overexpression and neurodegeneration in transgenic mice. (2003). Neuronal intermediate filament overexpression and neurodegeneration in transgenic mice., 184 (1), 3–8.

Larson, J., & Munkácsy, E. (2014). Theta-burst LTP. Brain Research . http://doi.org/10.1016/j.brainres.2014.10.034

Louis, E. D., Lee, M., Babij, R., Ma, K., Cortés, E., Vonsattel, J.-P. G., & Faust, P. L. (2014). Reduced Purkinje cell dendritic arborization and loss of dendritic spines in essential tremor. Brain , 137 (Pt 12), 3142–3148. http://doi.org/10.1093/brain/awu314

Louis, E. D., Kuo, S.-H., Vonsattel, J.-P. G., & Faust, P. L. (2014). Torpedo formation and Purkinje cell loss: modeling their relationship in cerebellar disease. Cerebellum (London, England) , 13 (4), 433–439. http://doi.org/10.1007/s12311-014-0556-5

Louis, E. D. (2015). Essential Tremor: A Common Disorder of Purkinje Neurons? The Neuroscientist : a Review Journal Bringing Neurobiology, Neurology and Psychiatry , 1073858415590351. http://doi.org/10.1177/1073858415590351

Liu LD, Prescott IA, Dostrovsky JO, Hodaie M, Lozano AM, Hutchison WD. (2012). Frequency-dependent effects of electrical stimulation in the globus pallidus of dystonia patients., 108 (1), 5–17. http://doi.org/10.1152/jn.00527.2011

Mathai A, Pare JF, Jenkins S, Smith Y. (2012) A vGluT1-positive projection to the primate globus pallidus: evidence for a cortico-pallidal system in primates? Soc Neurosci Abstr; 790:06

Martinez-Martin, P., Rodriguez-Blazquez, C., Forjaz, M. J., & Kurtis, M. M. (2015). Impact of Pharmacotherapy on Quality of Life in Patients with Parkinson's Disease. CNS Drugs , 1–17. http://doi.org/10.1007/s40263-015-0247-x

Milardi D, Gaeta S, et al, (2015) Basal ganglia network by constrained spherical deconvolution: a possible cortico-pallidal pathway? Mov Disord, 30 (3):342-349

Mink, J. W. (1996). The basal ganglia: focused selection and inhibition of competing motor programs. Progress in Neurobiology , 50 (4), 381–425.

Mink, J. W. (2003). The Basal Ganglia and involuntary movements: impaired inhibition of competing motor patterns. Archives of Neurology , 60 (10), 1365–1368. http://doi.org/10.1001/archneur.60.10.1365

Moll, C. K. E., Galindo-Leon, E., Sharott, A., Gulberti, A., Buhmann, C., Koeppen, J. A., et al. (2014). Asymmetric pallidal neuronal activity in patients with cervical dystonia. Frontiers in Systems Neuroscience , 8, 15. http://doi.org/10.3389/fnsys.2014.00015

Ni, Z., Gunraj, C., Kailey, P., Cash, R. F. H., & Chen, R. (2014). Heterosynaptic modulation of motor cortical plasticity in human. The Journal of Neuroscience : the Official Journal of the Society for Neuroscience , 34 (21), 7314–7321. http://doi.org/10.1523/JNEUROSCI.4714-13.2014

Pahuja, R., Seth, K., Shukla, A., Shukla, R. K., Bhatnagar, P., Chauhan, L. K. S., et al. (2015). Trans-blood brain barrier delivery of dopamine-loaded nanoparticles reverses functional deficits in parkinsonian rats. ACS Nano , 9(5), 4850–4871. http://doi.org/10.1021/nn506408v

Picconi, B., Piccoli, G., & Calabresi, P. (2012). Synaptic dysfunction in Parkinson's disease. Advances in Experimental Medicine and Biology , 970 , 553–572. http://doi.org/10.1007/978-3-7091-0932-8_24

Prescott, I. A., Dostrovsky, J. O., Moro, E., Hodaie, M., Lozano, A. M., & Hutchison, W. D. (2009). Levodopa enhances synaptic plasticity in the substantia nigra pars reticulata of Parkinson's disease patients. Brain , 132 (Pt 2), 309–318. http://doi.org/10.1093/brain/awn322

Prescott, I. A., Dostrovsky, J. O., Moro, E., Hodaie, M., Lozano, A. M., & Hutchison, W. D. (2013). Reduced paired pulse depression in the basal ganglia of dystonia patients. Neurobiology of Disease , 51 , 214–221. http://doi.org/10.1016/j.nbd.2012.11.012

Rincon, F., & Louis, E. D. (2005). Benefits and risks of pharmacological and surgical treatments for essential tremor: disease mechanisms and current management. Expert Opinion on Drug Safety , 4(5), 899–913. http://doi.org/10.1517/14740338.4.5.899

Schmouth, J.-F., Dion, P. A., & Rouleau, G. A. (2014). Genetics of essential tremor: from phenotype to genes, insights from both human and mouse studies. Progress in Neurobiology , 119-120 , 1–19. http://doi.org/10.1016/j.pneurobio.2014.05.001

Sian J, Youdin MBH, Reiderer P, Gerlach M. MPTP-induced parkinsonian syndrome (1999) Basic Neurochemistry: Molecular, cellular, and medical aspects. 6 th edition. Philadelphia: Lippincott- Raven

Siebner H. R., Tormos J. M., Ceballos-Baumann A. O., Auer, C, Catala, M. D., Conrad, B., Pascual-Leone, A. (1999) Low- frequency repetitive transcranial magnetic stimulation of the motor cortex in writer’s cramp. Neurology, 52 : 529–37.

Smith Y, Mathai A, Pare JF, Moot RC. (2014) A putative vGluT1-Positive glutamatergic cortico-pallidal projection: Differential organization between the internal and external globua pallidus. Soc Neurosci Abstr ; 248:10

Smith, Y., and Wichmann, T. (2015) The cortico-pallidal projection: An additional route for cortical regulation of the basal ganglia circuitry. Mov Disord 30 (3): 293-295

Stegemöller, E. L., Allen, D. P., Simuni, T., & MacKinnon, C. D. (2015). Motor cortical oscillations are abnormally suppressed during repetitive movement in patients with Parkinson's disease. Clinical Neurophysiology : Official Journal of the International

Federation of Clinical Neurophysiology . http://doi.org/10.1016/j.clinph.2015.05.014

Tasker, R. R. (1998). Deep brain stimulation is preferable to thalamotomy for tremor suppression. Surgical Neurology , 49 (2), 145–53– discussion 153–4.

Thomas, B., & Beal, M. F. (2007). Parkinson's disease. Human Molecular Genetics , 16 Spec No. 2 (R2), R183–94. http://doi.org/10.1093/hmg/ddm159

Volkmann, J., Joliot, M., Mogilner, A., Ioannides, A. A., Lado, F., Fazzini, E., et al. (1996). Central motor loop oscillations in parkinsonian resting tremor revealed by magnetoencephalography. Neurology , 46 (5), 1359–1370.

Yelnik, J., Percheron, G., & François, C. (1984). A Golgi analysis of the primate globus pallidus. II. Quantitative morphology and spatial orientation of dendritic arborizations. The Journal of Comparative Neurology , 227 (2), 200–213. http://doi.org/10.1002/cne.902270206

Zhu, G., Liu, Y., Wang, Y., Bi, X., & Baudry, M. (2015). Different patterns of electrical activity lead to long-term potentiation by activating different intracellular pathways. The Journal of Neuroscience : the Official Journal of the Society for Neuroscience , 35 (2), 621–633. http://doi.org/10.1523/JNEUROSCI.2193-14.2015

Appendix

d e

Appendix Figure 1- A sample of the read-out produced from the Matlab script MKaneoke from the neuronal trace seen in Figure 6.

A) A raster plot showing discriminated spikes in time (vertical lines). Vertical lines connected with horizontal lines indicate bursts identified by the script. Each row represents 1 s with the start of the trace beginning in the bottom left corner of the plot. B) A plot showing the instantaneous firing frequency of the cell across time. Red dots indicate firing frequencies significantly higher than the average firing rate. C) A wavelet spectrogram showing the power of the signal across time. Note the theta-band dominance of the this signal. D) An autocorelogram showing the periodicity of this oscillation. E) The power spectra determined by the fast Fourier transform (FFT) showing power bands throughout the recording. Note the largest power peak occupies the 4-6 Hz range (part of the theta band).

Appendix Figure 2A- The MKaneoke produced from the top trace in Figure 9 prior to stimulation.

A) A raster plot showing discriminated spikes in time (vertical lines). Vertical lines connected with horizontal lines indicate bursts identified by the script. Each row represents 1 s with the start of the trace beginning in the bottom left corner of the plot. B) A plot showing the instantaneous firing frequency of the cell across time. Red dots indicate firing frequencies significantly higher than the average firing rate. C) A wavelet spectrogram showing the power of the signal across time. D) An autocorelogram showing the periodicity of this oscillation. E) The power spectra determined by the fast Fourier transform (FFT) showing power bands throughout the recording.

Appendix Figure 2B- The MKaneoke produced from the lower trace in Figure 9 following stimulation.

A) A raster plot showing discriminated spikes in time (vertical lines). Vertical lines connected with horizontal lines indicate bursts identified by the script. Each row represents 1 s with the start of the trace beginning in the bottom left corner of the plot. B) A plot showing the instantaneous firing frequency of the cell across time. Red dots indicate firing frequencies significantly higher than the average firing rate. C) A wavelet spectrogram showing the power of the signal across time. D) An autocorelogram showing the periodicity of this oscillation. E) The power spectra determined by the fast Fourier transform (FFT) showing power bands throughout the recording.

91 104

Appendix Figure 3- An example of theta burst stimulation inducing visible tremor in a patients foot during microelectrode mapping for DBS implantation surgery.

Although the target of this surgery was the thalamus, it is believed that the electrodes missed the target and are stimulating the fibers of passage of the internal capsule. A) EMG activity recorded from the patient’s left leg. B) EMG activity from the patient’s right arm. C) Accelerometer activity recorded from the patient’s right arm. D) Accelerometer activity recorded from the patient’s right foot. E) EMG activity recorded from the patient’s right leg, F) Activity recorded from the distal electrode during theta burst stimulation.

Vertical dotted lined are spaced 1 s apart and aligned with the onset of the TBS. Notice that the TBS precedes the right leg EMG activity, which precedes the tremor recorded by the accelerometer attached to the right foot. Also, the tremor amplitude appears to increase as TBS continues.

204