I SHORT TERM ELECTRICAL STIMULATION for ISOGRAFT PERIPHERAL NERVE REPAIR and FUNCTIONAL RECOVERY a Thesis Presented to the Gradu

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SHORT TERM ELECTRICAL STIMULATION FOR ISOGRAFT PERIPHERAL

NERVE REPAIR AND FUNCTIONAL RECOVERY

A Thesis

Presented to

The Graduate Faculty of The University of Akron

In Partial Fulfillment

Of the Requirements for the Degree

Master of Science in Engineering, Biomedical Concentration

Galina Y. Pylypiv

May, 2018 i

SHORT TERM ELECTRICAL STIMULATION FOR ISOGRAFT PERIPHERAL

NERVE REPAIR AND FUNCTIONAL RECOVERY

Galina Y. Pylypiv

Thesis

Approved: Accepted:

Advisor Dean of the College of Engineering Dr. Rebecca Kuntz Willits Dr. Donald Visco

Committee Member Executive Dean of the Graduate School Dr. Matthew Becker Dr. Chand Midha

Committee Member Date Dr. Ge Zhang

Biomedical Engineering Department Chair Dr. Brian Davis

ABSTRACT

Electrical stimulation (ES) has previously demonstrated promising effects on peripheral nerve repair through enhanced neurite growth in vitro and shortened recovery time in vivo.

In this study, we aimed to evaluate the effect of intraoperative short term ES on a clinically relevant isograft-repair model of a rodent peripheral nerve. In our model, an isograft was used to repair a 13 mm sciatic nerve gap-defect in adult male rats. Intraoperative ES was applied for 10 min at 24 V/m-DC to the experimental group and no stimulation was applied to the control group. We evaluated biweekly functional recovery over 12 weeks for motor function, using the sciatic functional index and external postural thrust. Sensory function was evaluated using a thermal stimulus. Motor nerves are more heavily myelinated and regenerate more quickly, while sensory nerves are less myelinated and have a slower recovery time. Structural repair outcomes were evaluated through histological examination of the sciatic nerves and gastrocnemius muscles at 6 and 12-week time points. The ES group had a significantly better motor recovery than the control group in weeks 4 and 6 after surgery. In addition, the ES group had 7% fewer paw contractures than the control group. Paw contractures form when the flexor muscles are innervated more quickly than extensor muscles, leaving the paw in a chronically curled over position that is representative of a clinical challenge in human nerve recovery. Furthermore, sensory functional testing and histology evaluation confirmed that ES was safe to use, as the ES- treated group had comparable recovery outcomes to the no-ES control group. Our previous

iii study showed that 10 minutes of ES was effective in promoting functional recovery through a collagen scaffold bridging a 10-mm nerve defect. Here, we extend our findings by showing that ES can speed up motor recovery in an isograft-repair model, while slowing contracture formation. Demonstrating the benefits of applying short term intraoperative ES in a clinically relevant nerve injury model, creates an early point of translation to improve the current standard of care for peripheral nerve injuries. Future work is necessary to evaluate the sensory functional recovery response to ES and evaluate the mechanisms of

ES that may lead to a reduction in contracture formation.

DEDICATION

For the other Galina Pylypiv, my mother, from whom I inherited many qualities.

And also for Emmanuel Koh, my fiancé, for his encouragement and shared love for rats.

ACKNOWLEDGEMENTS

First, a huge thank you to Dr. Rebecca Willits for inspiring me to continuously grow as a researcher. You have inspired my curiosity, pushed me to pose quality questions and seek answers for my questions. Furthermore, you have demonstrated a good example for me in maintaining a good work-life balance. I will forever cherish our conversations and the advice I have received from you throughout my career.

Secondly, I would like to thank the Akron General Medical Center – Dr. William

Lanzinger, Dr. Robert Tysklind, and Dr. Carol Fouad for their collaboration and meaningful discussions. Thank you, Carol, for sharing your clinical practice as a surgeon and for the efficient teamwork throughout this project.

Next, I would like to thank my committee members for their advice and constructive feedback. Additionally, I would like to thank Dr. James Keszenheimer and

Stephen Paterson for their help in validating the performance of the electrical stimulation device used in this thesis work. Thank you to the University of Akron Research Vivarium management: Kelly Stevanov, Michelle Evancho-Chapman, and Beth Kanaga. Thank you to Dr. Walter Horne, the attending veterinarian, and to the undergraduate vivarium staff,

Ali Daniliuk and Sofie Cressman.

Thank you to the graduate and undergraduate students I have worked alongside with. I had the privilege to work with past and present members of Willits Materials for

Tissue Engineering Laboratory: Dr. Jessica Stukel, Diana Liz Philip, Elham Malekzadeh,

Nikhil Prasad, Carlisle DeJulius, Kaitlyn Mangus, Jacquie Carpenter, Wafaa Nasir, Abrar

Alniemi, Alena Casella, McKay Cavanaugh, Sean Sulivan, Olivia Detzel, Bailei Hoyng, and Jilian Savage. A special thank you to Jessica for always being encouraging. Also, a special thank you to Diana Liz Philip for becoming like a sister to me, for allowing me to bounce ideas, and for keeping me updated with the latest memes.

Lastly, I would like to acknowledge my long list of immediate family members, who make my life so rich and colorful. Thank you to my wonderful fiancé Emmanuel Koh for being my strongest support! Thank you to my parents Yeugen and Galina, and to my siblings along with their significant others: Illya and Nadiya, Yeugen and Sveta, Andrey in heaven, Jerry and Alana, Nick and Katie, Oksana and Tima, and Aleks. Also, my nieces and nephews: Evaleena, Anita, Jaydon, Benjamin, Daria, Josiah, Solomon, and baby Moses in the belly. And finally, a special shout out to my mom, who remembered every deadline and called me – first, to make sure I was well-fed, and second, to let me know she was always cheering me on!

vii

LIST OF TABLES

Table Page

1. Selection of Circuit Components ...... 19

2. Voltage Variability During Surgery… ...... 21

3. Sensory NR Data…...... 60

viii

LIST OF FIGURES

Figure Page

1. Structure of Nerve ...... 2

2. Setup of Electrical Stimulation Device ...... 17

3. Application of Electrical Stimulation During Surgery… ...... 28

4. Walking Track Testing Setup… ...... 30

5. Paw Measurements for SFI… ...... 31

6. Measurements of Contractured Paws ...... 32

7. Extensor Postural Thrust (EPT) Testing Setup… ...... 33

8. Sensory Testing Setup ...... 35

9. Glass Temperature for Sensory Testing… ...... 36

10. Tissue Cutting Schematic ...... 42

11. Glass Temperature for Sensory Testing… ...... 51

12. EPT Test Evaluation… ...... 54

13. Paw Contractures ...... 56

14. Walking Track Evaluation… ...... 58

15. Sensory Testing Evaluation… ...... 61

16. Muscle Evaluation…...... 62

17. Qualitative Nerve Histology Evaluation… ...... 63

18. Quantitative Nerve Histology Evaluation… ...... 65

TABLE OF CONTENTS

Page

LIST OF TABLES ...... vii LIST OF FIGURES ...... ix

CHAPTER Page I. INTRODUCTION ...... 1 1.1 Significance ...... 1 1.2 Anatomy and Function of Peripheral Nerves ...... 2 1.3 Nerve Response to Damage ...... 3 1.4 Injury Classification… ...... 4 1.5 Clinical Repair Options and their Limitations ...... 5 1.6 Nerve Environment ...... 8 1.7 In Vitro Electrical Stimulation… ...... 9 1.8 In Vivo Electrical Stimulation… ...... 11 1.9 Translational Electrical Stimulation… ...... 14

II. MATERIALS AND DETAILED METHODS ...... 16 2.1 Experimental Design ...... 16 2.2 Fabrication of Electrical Stimulation Device ...... 17 2.3 IACUC Approval and Protocol ...... 22 2.4 Surgical Manipulation ...... 23 2.5 Functional Evaluation After Surgery ...... 29 2.5.1 Walking Track Analysis ...... 29 2.5.2 Extensor Postural Thrust Testing ...... 32 2.5.3 Sensory Testing ...... 34 2.6 Statistical Evaluation of Functional Data ...... 37 2.7 Sacrifice and Tissue Harvest ...... 38 2.7.1 Muscle Harvest ...... 39 2.7.2 Nerve Harvest ...... 40 2.7.3 Preparation of Paraformaldehyde Fixative ...... 41 2.8 Tissue Processing and Analysis ...... 41 2.8.1 Quantification of Nerve Images ...... 42 2.8.2 Qualitative Analysis of Muscle Images ...... 43

III. ELECTRICAL STIMULATION INCREASES SPEED OF MOTOR RECOVERY IN RAT SCIATIC NERVE MODEL ...... 44 3.1 Introduction ...... 44

x 3.2 Methods ...... 46 3.2.1 IACUC Approval and Experimental Groups ...... 46 3.2.2 Surgical Manipulations ...... 47 3.2.3 Complications and Losses ...... 48 3.2.4 Functional Testing Overview ...... 48 3.2.5 Extensor Postural Thrust ...... 49 3.2.6 Walking Track Analysis ...... 49 3.2.7 Sensory Testing… ...... 50 3.2.8 Histology Analysis ...... 51 3.2.9 Statistical Evaluation ...... 53 3.3 Results ...... 53 3.3.1 Extensor Postural Thrust (EPT)… ...... 53 3.3.2 Physical Therapy Effects ...... 55 3.3.3 Walking Track Analysis ...... 56 3.3.4 Sensory Testing… ...... 58 3.3.5 Histology Analysis ...... 61 3.4 Discussion ...... 64 3.5 Conclusion ...... 72

IV. CONCLUSION AND FUTURE DIRECTIONS ...... 73 4.1 Conclusions ...... 73 4.2 Future Directions ...... 74

REFERENCES ...... 75 APPENDIX A. IACUC APPROVAL ...... 80 APPENDIX B. STATISTICAL ANALYSIS ...... 81

CHAPTER I

INTRODUCTION

1.1 Significance

Peripheral nerve injury is a prevalent clinical challenge that is associated with high surgical costs and variable restoration of nerve function. Every year, there are approximately 1 billion cases of peripheral nerve injury around the world, totaling a cost of $150 billion. 1 In the United States, approximately half a million cases of peripheral nerve gap injuries have been reported in 2014, totaling a cost of $1.68 billion. 2 Although the peripheral nervous system is known to spontaneously regenerate, a vast majority of peripheral nerve injuries require surgical intervention. For example, a crush injury is known to repair without surgery while a transection injury requires surgery to bridge a gap in the nerve. 3 Outcomes of surgical repair in a transection nerve injury are limited by incomplete functional recovery due to a slow regeneration process. Current solutions do not address the inherent regeneration potential of the nerve. Targeting the regeneration potential may help speed nerve regeneration and slow target organ degeneration, leading to an improved nerve function. Engineering the nerve environment through a cost effective application of electrical stimulation is one way to address the dual challenge of high health care cost and restoration of nerve function in peripheral nerve repair.

1.2 Anatomy and Function of Peripheral Nerves

Peripheral nerves serve as a network connection to transmit motor and sensory information between all areas of the body and the central nervous system. Information in the nerve is transmitted through action potentials, which are charge-based signals that propagate along myelinated axons. The cross-section of a peripheral nerve reveals a layered structure of outer epineurium, with internal fascicle structures that are sheathed by a perineurium layer (Figure 1). Each fascicle contains a bundle of nerve fibers, which are myelinated axons surrounded by endoneurium. Each axon is a projection from the cell body of a neuron that propagates action potentials through cell membrane depolarization. The myelin sheath surrounding each axon provides insulation for the electrical signals that travel through the peripheral nervous system.

Figure 1. Structure of Nerve Peripheral nerve cross-section with layered structure of outer epineurium, internal fascicles, and nerve fibers of myelinated axons. 4

The peripheral nervous system is primarily supported by Schwann cells. Schwann cells produce myelin, which surrounds myelinated axons and improves the conduction velocity of action potentials propagating along the axon. Conduction velocity is increased

2 with a larger degree of myelination and a thicker myelin sheath around the axon. Schwann cells have a myelinating and non-myelinating phenotype that is expressed in different proportions depending on nerve type. Motor neurons have more Schwann cells of the myelinating phenotype, which makes them more heavily myelinated than sensory neurons.

Therefore, motor nerves have a much faster conduction velocity than sensory nerves. 5

1.3 Nerve Response to Damage

In a damaged peripheral nerve, the nerve tissue undergoes tissue degeneration followed by axonal regeneration to restore the severed nerve connection. Degenerative damage and the subsequent debris clean-up, named Wallerian degeneration, was first described by Augustus Waller. 6 When axons are separated from their cell bodies at the site of injury, the distal nerve segment undergoes degeneration. 7 Distal nerve degeneration creates tissue debris through a breakdown of myelin and other supporting nerve structures.

Schwann cells and macrophages infiltrate the damaged area and utilize their phagocytosis mechanism to degrade myelin debris distal to the injury site. 8

After the debris clean-up process is complete, Schwann cells utilize their biochemical signaling, through their myelinating phenotype, and their interaction with macrophages to create a regeneration-supportive environment. 9 Regenerating neurons have growth cones, which are extensions found at the end of the axon that release neurotransmitters and interact with biochemical signaling of Schwann cells to aid in axon growth. 10 Schwann cells use myelin to form the bands of Bunger, which are tube-like structures that guide axonal growth in the regenerating nerve. Regeneration of the axons starts at the proximal end and is directed towards the distal end of the nerve. Axon growth

3 is estimated to progress ~1 mm/day in humans and ~3 mm/day in the rat. 11 Axonal regeneration is followed by innervation of the end organ, which is the distal target of a peripheral nerve. The viability of the end organ is an essential factor for appropriate functional recovery of the nerve. 12

1.4 Injury Classifications

When a peripheral nerve is damaged, the physical connection between the peripheral nerve and the central nervous system is structurally compromised, resulting in a decreased function of the nerve. A classification of nerve injury has been developed to clearly communicate the extent of structural damage and resulting loss of whole nerve function. These classifications include: neurapraxia, axonotmesis, and neurotmesis, with an increasing magnitude of damage to the nerve. Neurapraxia is identified by demyelination and surrounding tissue damage; axonotmesis is identified by damage to the axons in addition to demyelination while keeping the nerve intact; and neurotmesis is identified by a complete transection of axons as well as surrounding connective tissue, which breaks the structural connections in a nerve. Crush injuries contain neurapraxia and axonotmesis damage to the nerve, whereas transection nerve injuries contain neurotmesis damage to the nerve. As damage in the nerve increases, the conduction velocity of action potentials is significantly reduced, compromised, or completely eliminated, as in the case of a neurotmesis transection nerve injury. 12 Furthermore, increasing nerve damage limits the natural regeneration ability of the peripheral nervous system.

4 1.5 Clinical Repair Options and their Limitations

In a transection injury with a small gap, it is common clinical practice to surgically realign and reconnect the two ends, a repair known as primary neurorrhaphy. Neurorrhaphy is more likely to have successful repair outcomes in nerves that are not mixed in their sensory and motor functions and if the gap size is small enough for achieving a tension- free repair 4. Mixed function nerves are more likely to face complications in reconnecting with their original motor and sensory axons, leading to improper innervation and a loss of function after repair. Tension in a repaired nerve cannot exceed 4% elongation when used in its full range of motion,13 which places a limit on the gap size of a neurorrhaphy repair.

To address elongation challenges and aid neurorrhaphy repair, a synthetic conduit sleeve can be added for support during regeneration. 14 In addition, the injury site needs to have sufficient blood supply and minimal crushing of nerve stumps for an optimal recovery. 4

Although neurorrhaphy often has positive surgical outcomes, it is limited to a very specific and relatively mild injury.

In larger gap sizes, additional surgical interventions are essential for structural regeneration of the nerve and restoration of nerve function. Clinical options for surgically repairing larger gap sizes include autografts, allografts, and a variety of engineered conduits that aim to bridge the gap between severed nerve stumps. Although these options are clinically relevant, have regenerative capacity, and are able to restore some level of nerve function, they have limitations.

The use of autografts is most common clinically and is considered a gold standard for peripheral nerve repair. 4 An autograft procedure involves harvesting a viable nerve from an anatomical donor site on a patient to repair the injury site. The donor nerve

5 provides an optimal guiding structure for axon regeneration through an ideal environment of Schwann cells, neurotrophic factors, and extracellular matrix proteins. 4,13 Sensory nerves are commonly used for autograft harvest because they have the least consequences of lost function at the donor site. Common nerve harvest sites include either the sural nerve from the lateral foot, which provides the longest available length, or nerves from the hand/ forearm, which provide less length and increased donor site morbidity. 15 For successful autograft repair outcomes, graft diameter and vascularization must be evaluated to ensure graft viability through the repair procedure. 4,13,15 Major limitations in the use of autografts include donor site morbidity, limited availability of donor sites, limited length of donor nerves, and surgical complications from an additional surgical site for donor graft harvest.

Alternative autograft options include use of arteries, veins, muscle, or epineurial sheath to provide a tube-like structure of support for the regenerating nerve. 4

Allograft repair addresses some of the autograft limitations, but brings additional complications. Allografts are, primarily, nerve tissue but may also include arteries, veins, muscle, or epineurial sheath harvested from fresh cadaver tissue donors. 4 Nerve grafts offer a longer nerve length, matched specificity of nerve type for motor or sensory nerve repair, and eliminate the need for a second surgery in the patient. In this way, allografts eliminate the complication of donor site morbidity and the mixing of motor and sensory nerves. 4 Additional complications from the use of allografts include immune response due to immunocompatibility mismatches, graft rejection, and limited donor availability. To prevent immune rejection, one study utilized immunosuppressant medication with a combination of allograft and autograft that reduced antigenic load in the repair of a large gap nerve defect. 16 The outcomes were successful with a restoration of sensory function

6 and only one out of seven patients experiencing graft rejection. 16 Although allografts can yield positive results with proper use of immunosuppressant medication, an additional challenge of donor graft availability still remains. To address this challenge, another study considered artificially growing allografts from human dorsal root ganglion (DGR) cells. 17

Further optimizations of immunosuppressant medication, combination treatments, and alternative allograft options are necessary to address the challenges of allograft use in clinical practice.

Engineered conduits utilize either biological, synthetic, or hybrid materials to repair transection gap injuries of up to 2 cm length in humans. Conduits made from processed biological material include cellular and decellularized graft options. Decellularized grafts, which maintain the native extracellular matrix structure, reduce immune response because they eliminate cellular material. 18–20 Synthetic polymeric conduits provide mechanical and chemical support for early regenerating nerves. 4 The benefits of synthetic conduit solutions are their tunable material properties, which can be optimized for the nerve repair environment, and they are readily available. Design components of synthetic conduits include a polymeric tube with topographic cues, mechanical support, and biochemical guidance through peptide functionalization, which collectively provide an adequate environment for glial cell infiltration and support of regenerating axons. 21 Commonly used synthetic conduit materials include polyglycolic acid (PGA), poly-lactic-co-glycolic acid

(PLGA), and polycaprolactone (PCL). A comparison of materials emphasizes the importance of balancing material properties to support soft nerve tissue growth while maintaining clinical relevance. For example, low stiffness is important to support nerve growth, but a synthetic material needs to be stiff enough to maintain suture placement after

7 surgical repair. 22 Commercialized products have been made from synthetic and biological materials, including: Stryker NeuromatrixTM (collagen), Integra NeuraGenTM (collagen),

Synovis NeurotubeTM (PGA), Polyganics NeurolacTM (PCL), and Axogen AvanceTM

(decellularized human extracellular matrix). Further optimizations are necessary to create solutions that address gap injuries exceeding 2 cm in humans.

Further optimizations of current conduit solutions consider a hybrid solution of synthetic and biological components. In humans, a critical size gap is considered 3 cm, where the nerve will not regenerate if unaided. A hybrid conduit solution utilizes a synthetic graft with a cellular component where stem cells are introduced into the conduit space to promote regeneration. 23 The presence of stem cells has been demonstrated to improve the histomorphometric recovery and functional performance in comparison to an acellular conduit repair. 23 Although conduit grafting of nerve injuries is effective in small gap nerve defects, future solutions may allow for bridging of larger gaps.

1.6 Nerve Environment

The various cells that make up peripheral nerves are sensitive to their environmental surroundings and dependent on each other for appropriate whole nerve function. Peripheral nerves are composed of a variety of cells, including neurons, Schwann cells, endothelial cells, and fibroblasts. Environmental surroundings provide support through biochemical, mechanical, and electrical factors. Biochemical support is provided through extracellular matrix proteins such as laminin. 24 Mechanical support is provided through mechanical properties of the nerve environment, such as stiffness of the extracellular matrix. 25–27 Electrical support is provided through positively charged ions in

8 the extracellular environment that depolarize the cell membrane and propagate action potentials.

Nerves develop and perform their physiological functions in an electrically active extracellular environment. An electric field of 100 mV/mm-DC has been observed in a developing nerve. 10 The structure and function of a developed nerve mimic an electric cable through bundles of myelinated axons sheathed in perineurium and epineurium that work together to transmit electrical impulses. Utilizing the electrical environment of a nerve provides insight on how an electrical environment may aid in a regenerating nerve after traumatic damage.

1.7 In vitro electrical stimulation

Electrical stimulation (ES) is a way of probing the developmental electrical environment of a nerve and utilizing it for nerve regeneration after injury. ES affects growth cone activity, neurite growth, neurite alignment, and aids in end-organ viability. In the presence of an electric field, growth cone activity is upregulated and they release more neurotransmitters. 10 Additionally, upregulated growth cone activity aids in neurite growth, where neurites have significantly longer lengths after ES than those of non-ES controls. 28

Prolonged exposure to an electric field guides the orientation of growing neurites and aligns them towards the cathode. 10,29,30 In vivo, the alignment aids directional growth from the proximal end towards the distal end. Furthermore, ES prevents the rate of muscle atrophy and allows for a more viable end organ. 12,31

Electrical stimulation also interacts with the biochemical environment of the nerve through Schwann cell production of neurotrophins and growth factors. In the presence of

9 an electric field, Schwann cells increase their rate of proliferation 32 and change their phenotype to be more nerve supporting through neurotrophin production. 33 In the natural nerve environment, Schwann cells use calcium to produce neurotrophins that interact with the growth cones of regenerating axons and aid in their development. 10,34 After exposure to 8 hr of 50-100 mV/mm-DC, Schwann cells have an upregulated production of neurotrophins and nerve growth factor (NGF) for up to 84 hrs following stimulation. 32,33

An increased level of neurotrophins and NGF have been confirmed to increase neurite growth and overall neurite length. 34,35

Electrical stimulation (ES) for nerve regeneration has been optimized through in vitro studies by evaluating electric field strength and time of applied ES. Considering a potential clinical application, applying prolonged electrical stimulation is not feasible because every minute of applied ES increases procedural cost, where operating room cost averages at $62 per minute. 36 Early studies used prolonged ES times and stronger electric fields. An electric field of 0.1-10 V/cm applied for 6 hours on xenopus neurons showed increased neurite growth and alignment. 30 Another study utilized an electric field of 50- 133 mV/mm for 5 hours to demonstrate a similar neurite growth and alignment. 37

In an effort to reduce ES time, an electric field of 24 V/m-DC was applied for 10 min showed an increased DRG neurite outgrowth on collagen and laminin substrates. 38 These findings were extended to compare with culture media effects, including serum conditions, presence of growth factors, and calcium supplements. Results showed that 10 min of 24

V/m-DC ES increased DRG neurite outgrowth by 40% in comparison to other media modifications.

39 These findings were further confirmed in a 3D collagen gel scaffold with neurotrophins

10 and growth factors to demonstrate that 24 V/m-DC is sufficient to promote a regenerative environment in the nerve. 40

Approaches to repairing peripheral nerve injuries consider the environment necessary to support healthy nerve growth, development, and function as they reinnervate target organs. Electrical stimulation has demonstrated a positive effect on the nerve environment by targeting electrical activity and interacting with biochemical factors that aid in nerve growth. These factors address the inherent growth rate of the nerve, speed up nerve regeneration, and preserve end-organ viability. Therefore, ES may be a simple solution for restoring nerve function more quickly after injury.

1.8 In Vivo Electrical Stimulation

An in vivo model is necessary to extend the findings of in vitro work and show how a full organism responds to ES treatment. A sciatic nerve model in rodents is often used to model peripheral nerve injuries because it is comparable to human nerve injury. 7 Rodents have a similar nerve trunk distribution and a similar regeneration capacity to humans, 11 allowing for a clinically relevant system with both sensory and motor nerve functions. 12

The rodent sciatic nerve provides a sufficiently large working area to create and repair a peripheral nerve injury. Histological appearance of tissues between rodents and humans is similar, allowing for a comparable evaluation of repair. 11 Adult Lewis rats are used because they are an inbred strain and have the lowest occurrence of autophagia after sciatic nerve injury. These Lewis rat characteristics allow for the successful transplant of a donor isograft and for the motor evaluation of rat paws during the weeks of recovery. 41 An in vivo model gives insight on nerve structure and function after ES has been applied.

11 Many in vivo studies have been performed to evaluate the histomorphometric and functional recovery after a gap nerve injury. These studies varied the gap length created in injury, the type of repair mechanism to bridge the gap, and the length of recovery after surgery. Studies with gap lengths of 4mm,28 10mm,42–44 12mm,45 14mm,46 28mm,46 and

30mm47 have been performed. Various mechanisms to bridge the nerve gap include autografts, allografts, decellularized allografts, commercial nerve conduits, collagen conduits, conduits filled with collagen, and conduits with a cellular component for extra support. However, very few studies have evaluated the effect of intraoperative electrical stimulation. Rodents heal much quicker than humans. Therefore, a study with several time points is necessary to observe the steady recovery over time. Recovery times generally range from weeks to months.

Outcomes of in vivo models are evaluated through functional testing, using motor and sensory tests, and histomorphometric parameters, using fiber counts and myelination.

In a 4 mm gap injury, the recovery time is less than 12 weeks, and the key parameter for evaluation is histology.28 In gap sizes larger than 10 mm, recovery time is 12 weeks or more and includes functional testing.42,47 Motor functional tests use the walking track analysis48–50 and the extensor postural thrust (EPT) test.51 The walking track analysis uses paw print measures to calculate a sciatic functional index (SFI), where a value of -100 indicates total impairment and a value around 0 indicates complete recovery. Although the

SFI evaluation produces noisy data with lack of sensitivity between groups, it continues to be used as a motor evaluation.42,46,51,52 The EPT test has been developed as a potential alternative to SFI testing because it has can be performed more quickly and with greater sensitivity, although there is a training period for the researcher performing the test.51

12 Sensory evaluations utilize nociceptive electrical stimulation,45 mechanical Von Frey stimulation,53 or thermal methods such as infrared heat,53 a hot plate54 or hot water bath.47

Sensory response is difficult to probe because these evaluations rely on a measurable motor response to a sensory stimulus.55 Functional testing together with histomorphometric evaluation shows how the nerve recovers in both function and structure.

Many in vivo models with electrical stimulation utilize a short gap defect to study histological details such as reinnervation and molecular pathways. Using a 4 mm nerve gap with 1 hr of 3 V and 20 Hz electrical stimulation of rat femoral nerve demonstrated that the same peripheral nerve repair that occurred in 10 weeks in the group without electrical stimulation was able to take place within 3 weeks after surgery. 28 Furthermore, preferential reinnervation of motor muscle and sensory cutaneous targets was confirmed through

FluoroGold and RubyRed labeling, respectively. 28 Reinnervating end organs for motor and sensory function is a goal in peripheral nerve repair. A 4 mm gap injury model in the rat femoral nerve used DRG Ruby Red labeling to visualize the effect of 1 hr ES on sensory nerve reinnervation. 56 Molecular pathways consider the activity of Schwann cells and trophic factors. Schwann cells produce brain derived neurotrophic factor (BDNF), which binds to trkB receptors on the growing axons to support their growth. After 1 hr of 0.5-5 V and 20 Hz ES in a 3 mm gap defect of the mouse sural nerve, BDNF production is upregulated in Schwann cells. 57 In a 4 mm gap injury model of the rat peripheral nerve, 1 hr ES showed the effects of BNDF, NGF, and calcium. In vivo outcomes were compared with in vitro stimulation of embryonic neurons to show that ES opens voltage-gated channels to allow calcium to flow into the cells. Increased intracellular calcium

13 concentration triggers downstream kinase pathways that increase expression of BNDF and

NGF. 58

To extend the findings of previous studies, a study was performed in our lab to reduce the electrical stimulation time in a larger, more clinically relevant nerve gap injury.

The study used a 10 mm sciatic nerve gap in adult Lewis rats, where the gap was bridged with collagen-filled tubes and compared to an isograft for repair outcomes. Electrical stimulation was applied to the proximal and distal nerve-graft junctions of the collagen grafts for either 10 or 60 minutes to find that the functional outcomes and histological nerve quantifications were similar regardless of stimulation times. 44

1.9 Translational Electrical Stimulation

Although in vitro and in vivo experiments show a benefit of intraoperatively applied electrical stimulation, there are barriers to integration of this technology with clinical practice. Some of these barriers include establishing tunable physiologically relevant electrical stimulation parameters as well as reducing stimulation time to a short duration.

Electrical stimulation has been explored as an in vivo application in a human study utilizing

1 hr of intraoperative electrical stimulation to show the clinical relevance of electrical stimulation-improved recovery. 31In this study, electrical stimulation was applied to the sural nerve coupled with post-operative stimulation of the gastrocnemius muscle. Although the repair outcomes were favorable, translational challenges still remain before this can be implemented into regular clinical practice. 31 Short duration use of electrical stimulation is important for a clinical application because it reduces surgery costs, 36 reduces anesthesia- related surgery risk, 59,60 and potential risks of infection from keeping an open wound for

14 applying electrical stimulation. In a clinical application, maintaining an aseptic surgical environment is critical for healthy recovery. Design of an electrical stimulation device needs to consider the sterile and nonsterile components of the device and how they need to be segregated in a clinical setting to maintain aseptic surgical technique.

In this study, an adult male Lewis rat with a sciatic nerve injury was used as a model for a peripheral nerve injury. An intraoperative electrical stimulation was applied, using parameters established from our lab’s previous in vitro 38–40 and in vivo 44 work, on a critical-sized nerve gap. We aimed to compare the current gold standard of transection nerve injuries with the effect of short term electrical stimulation through motor functional tests, a sensory functional test, and histomorphometric evaluations in the weeks of recovery. The use of an isograft, an equivalent of the human autograft, creates a potential point of translation into surgical practice. We demonstrated that electrical stimulation can supplement clinical autograft repair through the brief application of a low cost therapy at the time of surgery.

CHAPTER II

MATERIALS AND DETAILED METHODS

2.1 Experimental Design

A sciatic nerve injury model in rats was used to evaluate the repair of a peripheral nerve injury. The study had two groups – control and experimental, each group consisted of 20 rats. Both groups underwent the same surgical manipulations, where a sciatic nerve resection injury was created and an isograft was used to repair the injury. An isograft is a native tissue explant from an inbred animal of the same species that was transplanted from a donor rat into a survival rat. The control group of animals received a cellular isograft transplant and the experimental group received the same transplant with an additional short duration electrical stimulation applied after graft transplant. Isografts were obtained from

10 donor animals in each group and implanted into 20 rats within that group, adding up to

30 animals per group and 60 animals total for the study. Sciatic nerves were harvested from both legs of donor animals. Animals within control and experimental groups are referred to as ‘survival animals.’ For each survival animal, the left leg was an internal sham with a surgical site and uninjured nerve and the right leg was the experimental leg with a nerve resection injury. This study had a control group as well as an internal control sham leg for each animal.

16 2.2 Fabrication of Electrical Stimulation Device

Electrical stimulation parameters been developed in our lab and were used in previous studies to demonstrate significant improvements in nerve regeneration both in vitro 38–40 and in vivo. 44 The electrical stimulation device consisted of two resistors and a power source (Figure 2.A). Voltage supplied by the power source was reduced by resistor

R1 and current experienced at the nerve was adjusted by resistor R2. The nerve was integrated into the circuit to act as a parallel resistor to R2. The voltage experienced at the nerve was indirectly measured and the current was calculated using Equation 1. Equation

1 represents the current through the parallel resistors. Parallel resistors experienced the same voltage drop and equally divided current, which is the sum of the incoming current.

To power supply

To Nerve A) B)

C) D)

Figure 2. Setup of Electrical Stimulation Device A) Circuit schematic for B) electrical stimulation device. C) Hooked platinum wire ‘electrode’ in silicone tube. D) Alligator clips used to hold platinum wire electrodes.

"# %& = "# %( + "# *+,-+ Equation 1 𝑖𝑖 𝑖𝑖 𝑖𝑖

= Equation 2

𝑉𝑉 𝐼𝐼𝐼𝐼 Assembly of the device (Figure 2.B) included a trial-and-error selection process to establish appropriate values for input voltage and resistors. Input voltage was delivered by a switching power supply (TekPower TP3010E). Wires were connected by twisting and soldering to create adequate connections between wires and resistors of the circuit.

Fabrication of device considered sterile and nonsterile components of the device to be used during surgery. Alligator clips were used to connect nonsterile circuit resistors to the nonsterile power supply and also to the sterile electrodes that would come in contact with nerves (Figure 2.B). Electrodes consisted of a platinum wire (Alfa Aesar #45093) inside a silicone tube (SMI Manufacturing 0.058” x 0.077” 50D Lot SM09122422) in order to maintain a semi-rigid structure during intraoperative use (Figure 2.C). The wire was curved at one end in order to wrap around and make optimal contact with the nerve and wrapped around the outside of the silicone tube at the other end, allowing for an exposed wire to come in contact by the metal grip on the alligator clip (Figure 2.D). Fabricated electrodes were autoclaved for sterilization prior to use.

Using this setup, the voltage drop at the nerve was indirectly measured by using a multimeter (Fluke 196 NO DM7730503). Voltage drop was measured across both resistors in the circuit and used with known resistance values to calculate the current values at both resistors using Ohm’s law (Equation 2). During device assembly, electrodes were submerged in a beaker of phosphate buffered saline (PBS, VWR #97062-732 Lot

2867C062) to create a simulated nerve environment. Resistor values and input voltage from

18 the power supply were varied, while the voltage drop across both resistors was measured and resulting current was calculated. Chosen values were 1 MΩ for R1, 134 KΩ for R2, and 4 V input voltage from power source, resulting in 312 mV DC voltage and ~1.5 µA, as shown from trial-and-error selection process in Table 1.

Table 1. Selection of Circuit Components R1 and R2 components were verified with a multimeter, voltage values were measured with a multimeter, and current values were calculated using Ohm’s law. Highlighted row shows final values chosen for the electrical stimulation device.

To ensure appropriate stimulation delivery in vivo, the electrical stimulation device was validated prior to intraoperative use. Validation was completed by placing platinum wire electrodes into a simulated nerve environment and confirming that the appropriate electrical stimulation parameters were maintained throughout 10 minutes of stimulation.

After the first use of the device in vivo, one rat death occurred, which led to an additional

19 validation of the device to ensure that intraoperative electrical stimulation was delivered properly. The voltage and current measures were verified to ensure that they were within the established safe parameters of 24 V/m and ~1.5 µA, as previously determined. 44

Variability in voltage measurements existed between different multimeters that were used to measure voltage drop. Two models of multimeters were used: Fluke 196 NO

DM7730503 and Tektronix TX1 True RMS B015348. The voltage measurements variability was ~35 mV and was potentially due to internal resistivity of circuit components. In order to verify proper circuit performance, all wire connections were re- soldered and resistivity of circuit components measured. Internal resistivity of platinum wire inside silicone tube was found to be 0.4-0.8 Ω and resistivity of leads to measure voltage drop were found to be 0.3 Ω with the Tektronix multimeter and 0.2-0.3 Ω with the

Fluke multimeter. During all surgical procedures, the platinum wire was wrapped or

‘hooked’ around the nerve in order to create a nearly circumferential contact between the platinum wire and the nerve tissue. Optimizing the surface area contact reduced any minimal effects from resistivity of the wire itself and allowed for a more accurate environment of electrical stimulation delivery and measurement.

An observed error in voltage delivery occured through electrode placement during the intraoperative electrical stimulation procedure (Table 2). The applied target voltage was 24 mV/mm over a length of 13 mm, resulting in a final voltage measurement of 312 mV. Although the nerve grafts were measured with a ruler each time for surgical manipulations, the placement of the platinum wires was not measured. Due to a lack of electrode placement measurements, it has been estimated that a window of +/- 2 mm placement error may have been present during the application of intraoperative electrical

20 stimulation. In the presence of such error, the distance would potentially vary from 9-17 mm, with a voltage of 217-408 mV. The outcome was a current in a range of 0.278-0.657

µA. In the simulated PBS environment during device development, the current was measured to be ~1.2 µA.

Table 2. Voltage Variability During Surgery * Are the rats that died during procedure. V2 values were measured. The columns with two values for V2 show variation of measured voltage during stimulation time.

A recorded variability in voltage occurred regardless of the assumed placement error, potentially due to the degree of wetness inside the open surgical area when electrical stimulation was applied. If the tissue began to dry out, the delivery of electrical stimulation would be altered. In our previous study, 44 sterile saline-soaked gauze was placed on the open wound to maintain a wet environment during a 60-minute stimulation. However, for

21 this study, the open wound was maintained wet with a syringe application of sterile saline, where the excess was absorbed with sterile gauze after stimulation was completed. This step was performed in this manner to provide a visual assurance that electrodes were in constant contact with the nerve tissue during electrical stimulation.

2.3 IACUC Approval and Protocol

The institutional animal care and use committee (IACUC) approved the protocol for all surgical manipulations and experimental procedures used in this study. All animal care, surgical manipulations, post-operative care, functional testing, and sacrifice were performed in accordance with IACUC-approved protocol 14-11-15-WRD. Male Lewis rats were obtained from Envigo at a weight range of 125-149 g (Envigo #1705M) and 175-199 g (Envigo #1707M), corresponding to an approximate age of 6-8 weeks.

Upon arrival at the animal facility, rats were received and socially housed in pairs by University of Akron Research Vivarium (UARV) staff. Rats were weighed, assigned with identifying numbers for each rat, assigned an identifying cage card for each pair of rats per cage, and handled for comfort. Identifying numbers were assigned numerically, with each pair of rats that were housed together in cages containing an odd number and an even number rat. The odd numbered rat received indelible ink identifying tail marks in order to distinguish between the rats within one cage. Clean cages were provided weekly along with daily food (LabDiet RMH Prolab 3000) and water ad libidum by UARV staff.

Cage bedding consisted of P.J. Murphy’s Sani-Chips and Nepco Shredded Aspen

Shavings, which was refreshed weekly with clean cages. Animal housing room conditions were maintained consistent at 69-79°F ambient temperature, 30-70% humidity, and a 12-

22 hour light/dark (‘daytime’/’night time’) cycle. All testing, handling, and surgical procedures were completed during the rat’s light ‘daytime’ cycle.

On the second and third day after arrival, the rats were acclimated to functional testing. They were brought from the animal housing room into the testing research room, placed on various testing setups, and given time to explore their immediate surroundings.

No data was collected during this acclimation time. On the third day after arrival, the animals were trained on how to perform the functional tests and pre-surgery baseline data was collected. Baseline data was primarily used for training the rats but also used as a day zero control for comparison of post-operative performance.

IACUC protocol defined humane end-points that served as points of animal exclusion from the study. Relief after surgery was judged by absence of criteria for use of analgesia. These criteria include vocalization of pain, anorexia, failure to drink, failure to groom, loss of mobility, or pain upon palpation of areas surrounding surgical site. If intraoperative complications with rat breathing occurred, a bulb aspirator was to be used in order to deliver a flow of oxygen into the septum and lungs for resuscitation. Humane end points for exclusion from the study include loss of 20% of pre-procedural weight, extensive would dehiscence, self-mutilation, distention of isograft, infection, or failure to gain relief from analgesia or other treatments. The attending veterinarian was consulted for all recovery complications with the rats.

2.4 Surgical Manipulations

Drugs used during the procedure included a pre-anesthetic, analgesic, and anesthetic. For all procedures, a mixture of butorphanol (Butorphic, PennVett #VAM4881)

23 and atropine (Vedco PVS120) were used as a light pre-anesthetic agent and isoflurane

(Vedco IsoSol 50989-150-15) as an agent to maintain anesthesia throughout the procedure.

Butorphanol came as a 10 mg/mL stock solution and atropine came as a 0.54 mg/mL stock solution. The mixture was created to administer a dose of 10-12 mg/kg butorphanol and

0.04 mg/kg of atropine. Buprenorphine (PAR Pharmaceuticals 42023-179-05) came as a

0.3 mg/mL stock solution and was diluted to a 1:10 solution and used as a pre-procedural analgesic. Isoflurane was used to maintain anesthesia throughout the procedure and was administered through an isoflurane machine (Highland Medical Equipment 109) as 5% isoflurane gas diluted in 100% oxygen. When the rat was completely anesthesized, isoflurane was reduced to 3% diluted in 100% oxygen.

Rats were used for surgery only after the seventh day from their arrival at the

UARV and after they attained a weight criteria of 225-275 g, corresponding to an approximate age of 8-11 weeks. Rats were weighed within 24 hours of surgery to confirm appropriate surgery weight and also to calculate drug volumes based on the weight of each rat. On surgery days, rats were brought into the animal preparation room and administered a subcutaneous injection of pre-anesthetic butorphanol/ atropine mixture on the back side towards the hind limbs. During injection, rats were either placed into a decapicone

(Braintree Scientific #DC200) or restrained with gloved hands to hold them in place. After approximately ten minutes of allowing the pre-anesthetic to take effect, the rat was brought into the operating room and placed into a clear plastic box with a tube connection that administered isoflurane. After approximately three minutes, the box was turned to check the rat’s responsiveness. If the rat was unresponsive, it was moved out of the box, placed on an absorbent pad (Amazon Basics Pet Training Pads Item TRP100R), and given a

24 nosecone with reduced isoflurane to maintain anesthetized state throughout the procedure.

When the rat was placed on a nosecone, an ophthalmic ointment (Rugby Artificial Tears

0536-1086-91) was applied to both eyes in order to prevent drying of the cornea during the procedure.

All surgical tools were sterilized using an autoclave before surgery. These tools included: scissors, tissue retractors, forceps pickups, needle driver, micro-scissors, surgical towels, surgical drapes, Michel clip tool, Michel clips (Roboz Surgical #RS-9272), glass dish (Pyrex glassware), and sterile water. In between animal procedures, tools were washed in a chlorohexidine (ChlorHex-Q Scrub 50989-576-29) solution, where 3 mL of chlorohexidine was diluted in 500 mL sterile water in a sterile glass dish. Tools were then rinsed in a second sterile glass dish containing only sterile water, sprayed with 70% ethanol, and placed in a heated bead sterilizer (Swissmade Steri 250). Other sterile equipment included: nylon 8-0 sutures (AD Surgical #XXS-N807T6 Lot 15-

14777/010721), alcohol prep pads (Fisher #22-363-750 Lot S20160624), povidone iodine swabsticks (Medline #MDS093901), autoclaved gauze, ½ mL syringe with 27-gauge needle (BD Biosciences #305620), 1 mL syringe with 27-gauge needle (BD Biosciences

#309626), and sterile micro-centrifuge tubes of 0.9% saline solution.

Once anesthetized, the rat was shaved on both hind legs using animal clippers

(Oster Model-A2 small) and transferred to the operating table onto a clean absorbent pad.

On the operating table, the diaper absorbent pad was laying on top of an electric heated pad

(Neco 819 11”x14”) to maintain the rat’s body temperature during the procedure, as rats tend to lose body heat quickly when anesthetized. The rat’s reflexes were checked by pinching the toes, ears, and tail to ensure a response was not elicited. Throughout the

25 procedure, the rat’s respirations were monitored visually and the nosecone maintained in position. The shaved area was wiped with a sterile alcohol pad, marked with a surgical marker (Sandel No. 1041) to indicate incision area, and then sterilized with povidone iodine solution. To identify the surgical incision site, the knee was located and a straight line drawn between the knee and spine. A sterile surgical drape was then placed over the rat, with an open viewing window to expose the surgical area.

Scissors were used to make a small incision based on the marked surgical area, being careful to only cut through the skin and not the underlying muscle tissue. After the muscles were exposed, a distinct white line was visible that separated the upper dorsal thigh muscles along the femur. The muscles were spread with scissors along this white line to create an inter-muscular plane incision extending approximately 1-2 cm below the skin.

The sciatic nerve is located dorsal to the femur bone, and the working area is located approximately 1-2 mm above the knee bifurcation and approximately 2-3 mm below the spine. In donor animals, a 15 mm segment was cut out in order to obtain a 13 mm graft after retraction of the nerve ends. The sciatic nerve tissue had an identifying white, dense, and flexible appearance to confirm proper tissue collection. Nerve grafts obtained from donor animals were stored in sterile 0.9% saline on ice until ready to implant. In survival animals, a 10 mm segment was cut out in order to create a 13 mm gap after retraction of the nerve ends.

Donor animals were sacrificed upon retrieval of the nerve graft from both legs. A fatal injection of 0.5 mL 390 mg/mL sodium pentobarbital (Henry Schein #VPL9373) was injected either via intraperitoneal or intracardiac injection for primary euthanasia. A

26 secondary method of bilateral thoracotomy was performed after signs of respiration were not observed and the rat began to lose color around mouth area, paws, and eyes.

On survival animals, the left leg served as an internal sham control and the right leg served as the experimental leg with a sciatic nerve resection injury. The same surgical procedure was applied to expose the nerve on the sham leg, but no injury was created, thus eliminating any variation due to the surgical procedure. In order to repair the injury on the experimental leg, an isograft from a donor animal was sutured in place to connect the two nerve ends in the gap. For suturing the graft in place, a surgical microscope (Carl Zeiss,

Inc. Universal S3) was used to magnify the repair site. An 8-0 nylon suture was used to create 3 surgeon’s knots at either nerve-graft junction, connecting the epineurium of the graft nerve to the epineurium of the native nerve. Because the surgical site was along an intermuscular plane, there was no need for internal sutures in closing up the site. The muscle plane was manually pulled together and external skin was closed using four Michel clips on the skin. Sham and experimental side leg wounds were closed in the same manner.

Within the group of survival animals, there were two groups – one as a control and the other as an experimental group. In the control group, the wound was closed following implant of the graft. In the experimental group, electrical stimulation was applied at the nerve-graft junction following implant of the graft and prior to wound closure. During electrical stimulation, one sterile platinum electrode was placed at either end of the nerve- graft junction (Figure 3). Proper electrode contact with the tissue was monitored and sterile environment maintained in the open wound. A voltage of 24 V/m was applied for 10 minutes and sterile saline was added via a sterile syringe to the wound as needed to prevent drying of the tissue. After the 10 minutes of electrical stimulation was completed,

27 electrodes were removed and the wound closed with Michel clips. Excess saline from the surgical site was absorbed by sterile gauze during wound closure.

Figure 3. Application of Electrical Stimulation During Surgery. Sterile electrodes placed at either end of the nerve-graft junction for 10 min of electrical stimulation.

Post-operative care of the rats ensured proper recovery of each animal. Anesthetic isoflurane was turned off immediately after the last Michele clip was in place on the rat.

The rat received 100% oxygen through a nosecone for ~1-2 minutes and encouraged to wake up by petting and light ear pinching. Once the rat was awake, it was individually housed in a clean cage with extra bedding and an easily accessible pack of food (DietGel

Recovery 72-01-1062). The day after surgical procedure, rats received two analgesic injections. The first was given in the morning as a subcutaneous injection of 1:10 diluted buprenorphine on one hind leg and the second was given in the afternoon as a subcutaneous injection on the opposite hind leg. Seven to ten days following surgical procedure, the

Michel clips were removed and animals were socially housed.

28 2.5 Functional Evaluation After Surgery

From each group of 20 rats, 12 rats were assigned to a 6-week survival group and

8 rats were assigned to a 12-week survival group. After either 6 or 12 weeks, the rats from their respective groups were euthanized following the same procedure as donor animals during surgery, except without anesthetic. Rats were placed into a decapicone in order to facilitate intraperitoneal or intracardiac fatal injection. The 6-week time point was primarily for the evaluation of histological differences between the groups and the 12-week time point was primarily for the evaluation of functional recovery of the rats.

Throughout the weeks following surgical manipulation, functional data was collected at regular two week intervals, measured from the date of surgery for each rat.

Functional testing consisted of three tests to evaluate both motor and sensory recovery of the rats. Motor ability was evaluated using a walking track test and an extensor postural thrust (EPT) test. Sensory testing consisted of a thermal test based on the Hargreaves method for thermal nociception.

2.5.1 Walking Track Analysis

Evaluation of motor recovery was completed using a walking track analysis that consists of a confined plexiglass walkway with dimensions of 100 cm (length) x 15 cm

(width) x 30 cm (height) (Figure 4). Beneath the walkway is a 45-degree angle mirror to allow for viewing of the rat paws during walking. The plantar surface of both paws of the rat was colored with indelible marker to improve visual contrast in viewing the paws. At the end of the walkway is a dark shelter for the rat to walk towards and into during testing.

The dark shelter, along with a small amount of cage bedding inside, aided the training period, encouraging rats to take continuous steps during data collection.

Figure 4. Walking Track Testing Setup 100cm x 15cm x 30cm (length, width, height) walking track for motor functional evaluation. Rats walked through the track towards the dark shelter while video camera recorded marker-colored paws from the 45-degree angle mirror.

A video camera was used to record the rats walking across the track. 52,61–63 Rats in the 6-week group had videos recorded at weeks 2, 4, and 6. Rats in the 12-week group had videos recorded at weeks 2, 4, 6, 8, 10, and 12. The rats were placed onto the walking track setup and a minimum of 2-3 videos were recorded of the rat walking in both directions on the track – two videos of the rat walking from right to left and two videos of the rat walking from left to right, giving a total of 4-6 good videos per rat at each time point of data collection. A good video recording captured the rat walking comfortably, taking continuous steps without turning around or stopping within the walkway.

30 Recovery over time was quantified using measurements from the rat paws, extracted from videos recorded at each time point. Measurements included print length which is the distance from the heel to the third toe (PL), toe spread which is the distance from the first to the fifth toe (TS), and intermediate toe spread which is the distance from the second to the fourth toe (IT) (Figure 5). These measurements were used in Equation 3

51,61,63 to measure the sciatic functional index (SFI). The SFI is a score that ranges from 0 to -100 and indicates the range of normal function to total impairment. Measurements were compared between the experimental (E) and sham (S) legs in order to normalize and properly quantify repair of the nerve over time. 51

All measurements were made in ImageJ. The walking track had a scale bar taped to it and it was captured in all videos. The scale bar was measured in ImageJ to set a standard length based on pixel size. Paw measurements were made by drawing lines and measuring the distances based on the scale bar from each image.

789:;89 7@A:;@A 7B@:;B@ 38.3 + 109.5 + 13.3 Equation 3 ;89 ;@A ;B@

𝑆𝑆𝑆𝑆𝑆𝑆 = − − 8.8

TS IT

Figure 5. Paw Measurements for SFI Paw measurements for calculating SFI from walking track analysis included print length (PL), toe spread (TS), and intermediate to spread (IT).

31 Over the weeks of recovery, some rats developed paw contractures and did not have a measureable paw print on the walking track because their curled over toes hid the full plantar surface of the paw (Figure 7). For contractured paws, measurements were made as a sum of curled over toes and visible plantar paw surface. 42 Print length (PL) was measured by taking a sum the visible paw print length (PL1) and the curled over toe length (PL2) to obtain the full print length (PL) (Figure 6.A). A similar sum was employed to measure the toe spread (TS), where the sum included the visible toe spread length (TS1) and curled over toe length (TS2) (Figure 6.B).

PL1 TS1 PL2

TS2

A) B)

Figure 6. Measurements of Contractured Paws A) Print length (PL) was taken as the sum of the visible curled over toe (PL2) and the length between the tip of the toe and the heel (PL1) using equation (PL = PL1 + PL2). B) Toe spread (TS) was measured as the visible toe spread length (TS1) and the length of the curled over lateral toe (TS2) using equation (TS = TS1 + TS2).

2.5.2 Extensor Postural Thrust Testing

Extensor postural thrust (EPT) is a motor function parameter used to measure innervated muscle function of the sciatic nerve. Muscle function can be demonstrated by a rat’s ability to push off from a flat surface. In theory, the push-off force will increase as the rat regains ability through re-innervation of the sciatic nerve into the muscle.

32 To simulate this response, each rat was wrapped in a surgical towel with one hind paw extending out from the towel (Figure 7). The paw was placed in contact with the flat surface of a scale for ~20 seconds per trial and the highest observed number on the scale was taken as a measurement of the push response for that paw. Five measurements per paw

(both experimental and sham sides) were recorded prior to surgery and at each 2-week time point measured from the date of surgery. In between each trial, the rat was taken out of the towel, if they did not push themselves out of it, and re-wrapped in the towel to complete data collection on the opposite side paw. Push response was not consistent in between subsequent trials of testing. In order to compensate for this, 5 trials were recorded and the largest 3 values of 5 were taken for statistical analysis. Testing was completed prior to surgery in order to accustom the rats to testing and also establish baseline variability between experimental right and sham left paws during testing.

Figure 7. Extensor Postural Thrust (EPT) Testing Setup Rat wrapped in surgical towel with one hind leg extending out and push-off force measured by digital balance.

Ideally, this test would look at the push response from the digital metatarsus and digits of the paw. However, when the rat underwent sciatic nerve injury there was no response from the digital metatarsus of the paw and the rat had a characteristic foot drop due to a completely transected nerve injury. Recorded measurements showed the response from the digital metatarsus as well as the heel force. Measurements were compared between groups by calculating a % motor deficit value (Equation 4).

% = LM"N:+OP+,QN+*#"R × 100 Equation 4 LM"N

𝑀𝑀𝑀𝑀𝑡𝑡𝑜𝑜𝑜𝑜 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑖𝑖𝑡𝑡

2.5.3 Sensory Testing

Sensory recovery was measured using the rat’s response to heat, based on the

Hargreaves method. 64 To conduct the test, the rats were placed into plexiglass enclosures on a glass surface platform (IITC Life Science Plantar Test Analgesia Meter) (Figure 8).

A thermal lamp was used to focus a light beam onto the plantar surface of the rat’s paw just beneath the toes. The rats were unrestrained within the enclosure during testing. The rat’s response was measured with an electronic timer that started at the same time that the light was turned on. The light was manually turned off by an observer when the rat retracted its paw. Latency value is described as the unaltered response time, and is defined as 5-10 s. Pre-surgery testing established a light intensity of 34%, corresponding to a glass temperature between 50-70 °C, to elicit a consistent latency response time. A cutoff exposure time was defined as 20 s for the thermal lamp, after which it automatically shut off. The glass temperature peaked at 70 °C at the 20 s cutoff. If the rat did not respond

34 during the 20 s window of exposure, the data point was recorded as a ‘No Response’ (NR) value. The recorded time values were translated into a percentage of sensory deficit SD%

(Equation 5).

Figure 8. Sensory Testing Setup Plexiglass enclosures housed rats that rested on a glass platform. A thermal lamp applied a heat stimulus that recorded sensory response with an electronic timer. The timer started when the light turned on and stopped when the light shut off.

% = LM"N:+OP+,QN+*#"R × 100 Equation 5 (WL: LM"N

𝑆𝑆𝑆𝑆𝑛𝑛𝑠𝑠𝑜𝑜𝑜𝑜𝑦𝑦 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑖𝑖𝑡𝑡

On data collection days, the rats were placed in plexiglass enclosures on the glass surface and given sufficient time to relax in their surroundings. Relaxed rats showed signs of grooming and sitting in a calm manner. Because all rats sit on the same glass surface neighboring one another, the glass heats up with each trial of the testing. To compensate for this, a 5-minute wait period was given in between trials to allow for the glass to cool down to ambient temperature. 65 Within each trial, one paw was tested on each rat before testing the second paw. Alternating paws during testing ensured that the rats did not experience unnecessarily high temperatures during testing.

35 80

60 50 40 Glass 30 Room 20 Temperature (C) Safe Glass Temperature 10 0 1 2 3 4 5 6 7 8 9 10 11 12 Time (weeks) A)

80 70

60 50 40 30

Temperature (C) 20 10 0 0 10 20 30 40 50 60 70 80 90 100 Time (seconds) B)

Figure 9. Glass Temperature for Sensory Testing A) Average temperatures of glass throughout the weeks of data collection, including the temperature of the testing room each week. All temperatures past the first week were below the safe glass temperature threshold of 70°C. B) Characteristic temperature testing plot, where a 20°C baseline was established in the first 10 s of testing, an increase in temperature between 10-30 s peaked at 70°C, and a decrease back down to baseline after the light was shut off at 30 s.

Glass temperature testing was conducted on a weekly basis to ensure consistent temperature performance of the thermal lamp and also to maintain safe temperature exposures below 70°C 54 (Figure 9.A). To perform the test, a thermocouple (Surface temperature sensor STS-BTA) was taped onto the glass surface with thermal tape, ensuring

36 a sufficient seal. A LabQuest 2 Model LQ2-LE temperature device was used to record glass surface temperature from the thermocouple. During the first 10 seconds, baseline glass temperature was recorded around 20°C and then the light was turned on for 20 seconds to observe the increase in temperature, which peaked at 70°C. After the light shut off, recording continued until glass temperature returned to baseline temperature of 20°C

(Figure 9.B).

2.6 Statistical Evaluation of Functional Data

Statistical evaluation was performed in IBM SPSS Statistics V24 for normalized functional data. One-way ANOVA was used to compare ES and control groups at each time point over 12 weeks. Post-hoc tests were not performed because there were only two groups for comparison. A repeated measures ANOVA was used to evaluate differences in one group over the 12 weeks. The repeated measures ANOVA did not consider unequal sample size. This analysis only included the first N=8 samples from each group. A p-value of p<0.05 was considered statistically significant.

Another way of evaluating the normalized functional data was to nullify the sham response, performed by plotting all the sham values in a histogram and taking the mean value. The mean value was then used to calculate all MD% values for EPT testing and all

SD% values for sensory testing. One-way ANOVA was used to compare the experimental groups over time and a repeated measures ANOVA was used to consider the changes in one group over time. A p-value of p<0.05 was considered statistically significant.

An alternative analysis was performed on only the experimental leg data from the

EPT and sensory tests. A one-way ANCOVA analysis was performed on all experimental

37 leg values, using the sham leg values as a covariate variable. This method evaluates experimental leg means that were adjusted for the sham leg covariate in order to control for the sham leg while observing effects on the experimental leg. A p-value of p<0.05 was considered statistically significant.

Quantitative histology statistical testing utilized a two-way ANOVA, where the main effects of time and experimental groups were evaluated along with the interaction between the two. Main effect analysis compared the difference in combined means between time points and also between experimental groups, ES and control. The interaction evaluation evaluated the difference between ES and control groups at each time point using a Tukey post hoc test. A p-value of p<0.05 was considered statistically significant.

2.7 Sacrifice and Tissue Harvest

At the end of each rat’s 6- or 12-week time point, the rat was euthanized and tissue was harvested for analysis. The day of sacrifice was the last data collection time point (i.e.

12-week time point functional measures were taken the same day as 12-week time point sacrifice). The rat weight was recorded prior to sacrifice. For each rat weighing less than

500 g, a lethal dose of 0.5 mL Sodium Pentabarbitol (Fatal Plus) was administered either through intraperitoneal injection or intracardiac injection. Rats were awake and placed into a decapicone for administration of euthanasia. Rats were placed back in their cage and observed for signs of death including loss of color in the eyes, ears, and toes, lack of respirations, and undetectable heartbeat. A secondary euthanasia as performed either by removal of the heart or bilateral thoracotomy.

38 Following death, rats were prepared in a similar way to surgery preparation without the aseptic methods to harvest the gastrocnemius muscle and the sciatic nerve graft. The hind legs were shaved, rat was placed ventral side down on an absorbant pad, and similar tools were used for making an incision. Hind limbs were stretched out for dissection and taped to the absorbant pad for stability.

2.7.1 Muscle Harvest

An incision was made through the skin, starting at the heel and working towards the proximal end of the leg while being careful to only cut through the skin and not the underlying muscle. Skin and muscle were separated using a spreading motion with the back of the scissors to create a wider working area for dissection. Scissors were distally inserted through the biceps femoris, a thin muscle layer covering the gastrocnemius muscle, and separated from the underlying gastrocnemius muscle with a spreading motion using the back of the scissors. A clear distinction between the biceps femoris and the gastrocnemius muscle was made by paying attention to the direction of muscle fibers, where the gastrocnemius muscle had a visible longitudinal unidirectional fiber alignment. The gastrocnemius muscle inserts into the Achilles heel near the foot at the distal end and into the knee at the proximal end. After the biceps femoris has been separated from the gastrocnemius muscle, further spreading was done with the scissors to free the gastrocnemius muscle prior to harvesting it by cutting the proximal and distal insertion points as close to the insertion as possible.

The full gastrocnemius muscle is wide in appearance and covers the dorsal side of the leg. The soleus muscle, located directly underneath the gastrocnemius muscle, is a thin

39 strip of muscle connected to the gastrocnemius at common insertion points. When harvesting the gastrocnemius muscle, it is important to remove the soleus muscle and harvest only the gastrocnemius. Upon harvest, muscle tissue weight was recorded in milligrams, placed into appropriately labeled tissue cassette (Global Scientific #1094w), and submerged into freshly prepared cooled 4% paraformaldehyde (PFA) in a specimen container (VWR #25384-144). The gastrocnemius muscle was harvested from both experimental and sham legs.

2.7.2 Nerve Harvest

Following muscle harvest, the sciatic nerve graft was harvested by using a similar incision to the surgical manipulations. Starting from the muscle harvest incision, the scissors were used to spread the tissue apart further moving towards the proximal end of the leg to expose the proximal biceps femoris and gluteus superficialis muscles. The distinguishing white line between the proximal biceps femoris and gluteus superficialis muscles marks the approximate location of the sciatic nerve. The intermuscular plane was spread open using scissors to expose the sciatic nerve below. The nerve was cut approximately 2 mm proximal and 2 mm distal to the graft nerve junction on either end of the sutured graft. After harvesting the nerve, a suture was placed in the proximal end of the nerve tissue to maintain orientation for analysis. Nerve tissue was also placed into an appropriately labeled tissue cassette and submerged in freshly prepared cooled 4% paraformaldehyde in a specimen container. Sciatic nerve graft tissue samples were harvested only from the experimental side leg of each rat.

40 2.7.3 Preparation of Paraformaldehyde Fixative

Fixative was prepared either the evening prior to harvest or the morning preceding harvest. Each tissue specimen bottle contained 60 mL of paraformaldehyde fixative and was large enough to store 6 tissue cassettes. Paraformaldehyde (PFA) was prepared at a

4% concentration by adding 2.4 g of PFA powder (Fisher Scientific #04042-500 Lot

163883) and 0.6 mL of 1M NaOH (J.T. Packer 3722-01 Lot H46K51) to 30 mL of ultrapure water. The solution was stirred at 60°C until all powder was dissolved. The solution was then cooled to room temperature, 6 mL 10X PBS was added, and then 0.6 mL 1M HCl was added to bring pH to an approximate 7.4. Solution brought up to final volume by adding

22.8 mL ultrapure water (Millipore Sigma DIRECT Q 3UV Serial No. F1AA21624E).

Final PFA solution was kept refrigerated until ready for use.

2.8 Tissue Processing and Analysis

Specimen bottles containing tissue cassettes were stored in 4°C for approximately

48 hours for tissue fixation. After fixation, cassettes were rinsed two times with sterile 1X

PBS with 5 min for each rinse followed by a final rinse and storage in sterile 1X PBS with

0.02% sodium azide (VWR 07064-646 Lot 1685C271). Specimen bottles refrigerated until histology processing.

Histology processing was performed by the Cleveland Clinic Lerner Research

Institute Histology and Imaging Core facilities. Upon receipt of the tissue samples, gross images were taken of the whole tissue. Tissue was further fixed in 2.5% glutaraldehyde for

48 hours. Nerve tissue samples were stained with osmium tetroxide and then embedded into paraffin blocks. Muscle tissue samples were stained with hematoxylin and eosin

41 (H&E) and also embedded into paraffin blocks. All paraffin blocks were sectioned at 4 µm thickness and mounted onto slides for imaging. A slide scanner was used to bulk image all the tissue. Nerve sections were taken 2 mm away from the proximal nerve-graft junction, at the approximate midline of the graft, and 2 mm away from the distal nerve-graft junction

(Figure 10). Muscle sections were taken as a cross-sectional cut of the estimated muscle midline.

Figure 10. Tissue Cutting Schematic Nerve sections taken 2 mm proximal and 2 mm distal to the nerve graft junction. An estimated midline section was taken from the nerve and muscle samples for histology evaluation.

2.8.1 Quantification of Nerve Images

To quantify images obtained from nerve cross sections, a random sampling was employed to eliminate bias in evaluation. Each image had 3 randomly selected regions of interest (ROI) that were used for analysis. Sampling was random, while excluding visible damage in the sample, blurry areas of the image, and oversaturated osmium staining.

Within each ROI, the number of nerve fibers were counted using software.

Measurements were used to obtain calculated quantifications of nerve histology based on previous work. 44 The fiber density was calculated as a percentage of nerve fiber count to total ROI area. Percent nerve tissue, which gives an estimate of the quality of the regenerating nerve, was calculated based on fiber area as a ratio of ROI area. Mean fiber

42 width, which is an indirect measure of myelination and potentially mature nerve fibers, was measured. The number of blood vessels serves as an indicator of nutrients present in the regenerating nerve environment.

2.8.2 Qualitative Analysis of Muscle Images

Muscle images were evaluated and representative regions of interest (ROI) were captured. Within each ROI, the muscle was observed for visible fiber density, fiber size, number of nuclei, and presence of blood vessels.

CHAPTER III

ELECTRICAL STIMULATION INCREASES SPEED OF MOTOR RECOVERY IN

RAT SCIATIC NERVE MODEL

3.1 Introduction

Peripheral nerve injury is a prevalent clinical challenge associated with high surgical costs. Current repair options yield unpredictable functional recovery and variable restoration of the injured nerve. Nerve trauma in the extremities affects nearly 1 billion patients worldwide, totaling an estimated cost of $150 billion in annual healthcare spending.1 In 2014, it was documented that 558,862 nerve lacerations were surgically repaired in the United States alone, totaling $1.68 billion in healthcare spending.2

Traumatic nerve injuries range in complexity from mild crushing to severe transection with a gap defect.12 Although mild nerve defects have the potential to heal through the inherent repair mechanism of Wallerian degeneration,6,8 larger defects need surgical intervention in order to re-establish a connection between nerve ends. In addition, Wallerian degeneration has the limitation of being a slow process with challenges such as target muscle degeneration, misguided muscle innervation, incomplete nerve regeneration, and incomplete restoration of nerve function. Currently available clinical options include autografts, allografts, or engineered conduits that are used to bridge nerve gap defects. The clinical gold standard for transection injuries with a gap too large for spontaneous

44 regeneration is to repair with an autograft harvested from a donor site of the patient.12

Common nerves used for grafting include either the sural nerve from the lateral foot, which provides the longest available length, or nerves from the hand/forearm, which provides less available length and increased morbidity of the donor site.15 Autograft repair provides the optimal native environment for a repaired nerve.

Nerves are sensitive to their environment, where electrical impulses are a natural component of peripheral nerve development and regeneration. An electric field (EF) of up to 100 V/m has been reported in the developing nervous system.10 Electric impulses, in the form of action potentials, are transmitted through the developed nervous system to relay motor and sensory information. An exogenous EF can target the natural electrical environment to improve regeneration of damaged peripheral nerves. Through in vitro studies, a pulsed EF of 0.1-10 V/m on chick dorsal root ganglion (DRG) cells demonstrated cell alignment, extension of neurite length, and directional growth after 5-6 hours of exposure.30,35,37 Our previous work showed enhanced neurite growth after a reduced in vitro stimulation time of 10 min using 24 V/m direct current (DC) on chick DRG cells.38,39

Furthermore, electric field exposure was found to have an impact on the biochemical environment by upregulating production of neurotransmitters and growth factors to support neuron growth in vitro.33,34 Therefore, developing strategies that target the electrical environment of peripheral nerves may translate to improved in vivo nerve repair.

Intraoperative electrical stimulation (ES) times ranging from one hour to two weeks have been applied for nerve repair and the application more quickly improved the structural repair of peripheral nerves. A quicker structural repair of a nerve in vivo is due to the combined effects of biochemical and electrical mechanisms observed through in vitro

45 studies.28,66 For example, a 1 hr ES on a sural nerve has been applied clinically in humans with increased nerve outgrowth leading to a quicker structural repair.31 Although effective, an intraoperative ES time of one hour is not feasible for full clinical use because it increases surgical costs, where every added minute increases both procedural costs and the potential for surgical complications and infection.36 Our previous in vivo work utilized a 10 mm gap defect bridged by collagen-filled conduits and showed that 10 minutes of 24 V/m-DC intraoperative ES yielded similar functional repair outcomes to those of 60 minutes ES.44

However, as an autograft is the gold standard in the operating room, we questioned if the effects of ES could be used to boost the functional outcomes.

As a rodent isograft models the current standard of care in humans, which is repair with an autograft, we used this model to study the impact of ES on functional recovery. In this study, we aimed to investigate the synergistic effects of autograft repair with short duration electrical stimulation to highlight the in vivo effects of electrical stimulation.

Through the use of motor and sensory functional tests, we demonstrated that short-duration electrical stimulation did not damage the peripheral nerve and promoted faster motor functional recovery in an autograft repair model.

3.2 Methods

3.2.1 IACUC Approval and Experimental Groups

The institutional animal care and use committee (IACUC) approved the protocol for all surgical procedures and experimental evaluations used in this study. A total of 60 adult male Lewis Rats were placed into two experimental groups of 30 rats in each group, where 20 rats were randomly selected as the survival animals and 10 were randomly

46 selected as the donor animals. Survival animals were subject to surgical isograft nerve repair and subsequent functional testing during the weeks of recovery. Donor animals were used for obtaining isograft nerves. Within each group of 20 survival rats, 12 rats were maintained for 6 weeks following surgery and 8 rats were maintained for 12 weeks following surgery. Animals were socially housed in a room with a consistent environment of 69°F-79°F ambient temperature, 30-70% humidity, and a 12-hour light/dark

(‘daytime’/’night time’) cycle. All surgical procedures and experimental evaluations were completed during the light ‘daytime’ cycle. Clean cages were provided weekly along with ad libitum food and water.

3.2.2 Surgical Manipulations

Our peripheral nerve injury model utilized aseptic surgical manipulations of the rat sciatic nerve. In this model, a critical-sized gap was created in the sciatic nerve of survival animals and repaired with an isograft nerve from donor animals. A 15 mm segment of the sciatic nerve was excised from both hind legs of a donor rat to obtain a 13 mm segment after retraction of the nerve ends. In survival animals, a 10 mm segment was excised in the experimental leg of the rat to create a 13 mm gap defect after retraction of the nerve ends.

The gap defect was repaired using 3 epineurial sutures of the donor isograft. An internal sham was created on the contralateral leg for each survival rat by exposing the sciatic nerve without creating an injury. The surgical site was closed with Michel clips in both legs. In the control group, the surgical site on the experimental leg was closed immediately, but in the experimental group, the sham side was closed and the experimental side was maintained open for a 10-min electrical stimulation procedure. Sterile platinum electrodes

47 were placed on both sides of the nerve-graft junction and 24 V/m-DC was applied. Sterile saline was applied with a syringe as needed during stimulation time to prevent drying of the surgical site. The group with experimental surgical manipulations is referred to as the electrical stimulation (ES) group.

3.2.3 Complications and losses

Surgical complications have resulted in the death of two rats with an additional rat failing to recover from surgery. For this reason, an additional set of three rats were obtained to replace the two rats that died during surgery, but the rat that failed to recover after surgery was eventually euthanized after losing more than 20% of its pre-procedural weight.

The attending veterinarian performed a necropsy on the euthanized rat and found some signs of ileus but did not identify the cause of severe health decline that led to exclusion of this rat from the study. Final group samples included N=19 for the control group and N=20 for the ES group.

3.2.4 Functional Testing Overview

After surgical manipulations, rats were subject to biweekly functional testing for

12 weeks. Time points were measured from the date of surgery for each rat and included weeks 2, 4, 6, 8, 10, and 12 after surgery for recording functional data. Functional tests included two motor tests and one sensory test. Motor tests utilized extensor postural thrust

(EPT) test and the walking track analysis. Sensory testing utilized a heat stimulus to elicit a motor response from the rat. Functional data was collected before surgery for animal training and after surgery to observe functional recovery over time.

48 3.2.5 Extensor Postural Thrust

Extensor postural thrust is a widely used analysis in quantifying motor recovery.46,47,51 To perform this test, the rat was wrapped into a surgical towel with one hind leg extending out. The toe-digit metatarsals were placed in contact with the surface of a digital balance to elicit a push-off response. The largest value from the scale was recorded over a 20 s time period. The test was repeated five times and the three largest values were used to calculate the percent motor deficit (MD%) values in Equation 1.

LM"N:+OP+,QN+*#"R % = × 100 Equation 1 LM"N

𝑀𝑀𝐷𝐷

3.2.6 Walking Track Analysis

A walking track analysis is widely used to quantify motor recovery from the sciatic functional index (SFI).42,47,49,51,61,67 To perform this test, rats were trained to walk continuously through an enclosed plexiglass walkway towards a dark shelter. Their paws were colored to enhance visual contrast as they were viewed on an angled mirror beneath the walkway. We recorded a minimum of three videos per rat and obtained three mid-gait screen captures for each paw for a total of six screen captures for each rat at each time point. Screen captures were evaluated in ImageJ to measure print length (PL), toe spread

(TS), and the intermediate toe spread (IT) on the experimental (E) and sham (S) paws

(Figure 14a).67 The SFI value was calculated using Equation 2. An SFI value of zero indicates complete recovery and an SFI value of -100 indicates complete impairment.

789:A89 7@A:A@A 7B@:AB@ 38.3 + 109.5 + 13.3 Equation 2 A89 A@A AB@ 𝑆𝑆𝑆𝑆𝑆𝑆 = − − 8.8

3.2.7 Sensory Testing

Sensory recovery was measured as a response to heat stimulus through a modified

Hargreaves method.53,54,64 To conduct this test, the rats were placed into plexiglass enclosures on a glass platform. A thermal lamp, placed under the glass platform, was used to focus a light beam at the center of the plantar paw surface. Paw withdrawal time was measured with an electronic timer that was synchronized with the thermal light, where starting the timer also turned on the light. Response time was recorded as exposure time to the thermal lamp before the rat retracted its paw. Latency time is the unaltered response time observed in an uninjured rat, and is defined as 5-10 s. Pre-surgery testing tuned the light intensity to 34% to elicit a consistent response time within the latency response window. A cutoff exposure time was defined as 20 s for the thermal lamp, where the light and timer automatically shut off if the rat did not respond. Glass temperature testing was conducted on a weekly basis to ensure consistent temperature performance of the thermal lamp and also to maintain safe temperature below 70°C.54 A 34% light intensity corresponded with a glass temperature of 50-70°C, where the glass temperature did not exceed 70°C at the 20 s cutoff (Figure 11).The test was performed four times on each rat, with a wait period of 5 minutes in between tests to allow glass temperature to cool down to ambient temperature.65 If the rat did not respond during the 20 s window of exposure, the data point was recorded as a ‘No Response’ (NR) value. From the four collected values, the three lowest values were chosen for analysis and used to calculate the sensory deficit

(SD%) in Equation 3. Lowest values were chosen because they indicate a more recovered response and a lower sensory deficit.

70 C) °

( 60 50 40 Glass 30 Room 20 Safe Glass Temperature

Temperature Temperature 10 0 1 2 3 4 5 6 7 8 9 10 11 12 Time (weeks)

Figure 11. Glass Temperature for Sensory Testing Average temperatures of glass throughout the weeks of data collection, including the temperature of the testing room each week. All temperatures past the first week were below the safe glass temperature threshold of 70°C.

LM"N:+OP+,QN+*#"R % = × 100 Equation 3 (WL: LM"N

𝑆𝑆𝑆𝑆

3.2.8 Histology Analysis

Sciatic nerve grafts and gastrocnemius muscles were evaluated at weeks 6 and 12 after surgery. The gastrocnemius muscle was harvested from both experimental and sham legs for each rat and the sciatic nerve including graft and proximal/ distal ends were harvested only from the experimental leg. Muscle weights were recorded immediately after dissection. Percent of muscle weight was calculated using Equation 4.

% = LM"N:+OP+,QN+*#"R × 100 Equation 4 LM"N 𝑀𝑀𝑢𝑢𝑢𝑢𝑐𝑐𝑙𝑙𝑒𝑒 𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊ℎ𝑡𝑡

51 All tissue were fixed in 4% paraformaldehyde for 48 hours and then rinsed and stored in phosphate buffered saline (PBS) with 0.02% sodium azide at 4°C. Histological processing was completed by the Cleveland Clinic Lerner Research Institute, where all tissue samples were post-fixed in 4% glutaraldehyde for 48 hours. Muscle samples were embedded into paraffin blocks, sectioned at the estimated midline into 4 µm sections and stained with hematoxylin and eosin (H&E). Nerve grafts were stained with osmium tetroxide and embedded into paraffin blocks. Blocks were sectioned in a cross-sectional manner 2 mm proximal of the nerve-graft junction, at the estimated midline, and 2 mm distal of the nerve-graft junction. A slide scanner was used to image all tissue slides at 20X.

( # / = ;cNd+, ef "Oe*L Equation 5 %gB ",+" (NNi )

𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹 𝐷𝐷𝐷𝐷𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑦𝑦 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑠𝑠 𝑚𝑚𝑚𝑚

i) % = mOe* ",+" LcN (µN × 100 Equation 6 %gB ",+" (µ Ni )

𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑒𝑒

Muscle images were qualitatively assessed for visible muscle atrophy through fiber size and apparent density of muscle cell nuclei. To quantify images obtained from nerve cross sections, a random sampling was employed to obtain 3 regions of interest (ROIs) for analysis, where each ROI had an area of 300 x 300 pixels and 0.1488 mm2. Nerve ROI analysis included the total number of axons, axon area and axon diameter. Using these measures and the known ROI area, the fiber density (Equation 5) and percent nerve

(Equation 6) were calculated. Quantitative measures were compared between control and

ES groups and over time points of 6 and 12 weeks.

52 3.2.9 Statistical Evaluation

Statistical evaluation was performed in IBM SPSS Statistics V24. Weeks 2 through

6 had sample sizes of N=19 for control and N=20 for ES and weeks 8 through 12 had N=8 for both groups. At every time point, a histogram was plotted for each group. Because the data was approximately normally distributed, parametric statistical evaluation was used. A one-way ANOVA was used to compare the SD%, SFI, and MD% between experimental groups at each time point using the full data set. A repeated measures ANOVA evaluated the difference over time for each experimental group individually. The repeated measures

ANOVA requires equal sample sizes at each week and was performed separately on all 12- week rats and all 6-week rats. Muscle weights of rats per group and per time point were compared with a one-way ANOVA. Quantitative histology statistical testing utilized a two- way ANOVA, where the main effects of time and experimental groups were evaluated along with the interaction between the two. Main effect analysis compared the difference in combined means between time points and also between experimental groups, ES and control. The interaction evaluation evaluated the difference between ES and control groups at each time point using a Tukey post hoc test. For all statistical evaluations, p<0.05 was considered significant.

3.3 Results

3.3.1 Extensor Postural Thrust (EPT)

EPT testing was performed biweekly during the weeks of recovery for every rat, where the push-off force was recorded from the hind legs. On every testing day, the test was repeated five times and the three largest values were used for calculating MD%

53 (Equation 1). Largest values were chosen to filter the data for poor responsiveness of the rat to EPT testing because the rats were more likely to be limp than to demonstrate their ability to push off of the surface. Using a histogram plot of all sham values, one outlier was found in the ES group at week 10, where a sham value of 914 g was recorded. This value was excluded from analysis because it was nearly 5-fold larger than the average sham value of 184 g.

* *

Figure 12. EPT Test Evaluation Motor deficit (MD%) for control and electrical stimulation (ES) groups at every time point. Differences between groups at each time point were evaluated using a one-way ANOVA. The ES group had a statistically lower MD% value than the control group at weeks 4 (p=0.04) and 6 (p=0.02). Recovery over time was evaluated using a repeated measures ANOVA on 12-week rats. Both groups showed significant MD% decrease over weeks 4 and 10 (control p=0.02, ES p=0.05), 4 and 12 (control p=0.03, ES p=0.02), and 6 and 10 (control p=0.013, ES p=0.008). Control group showed MD% decrease over weeks 4 and 8 (p=0.09), 6 and 8 (p=0.035), and 6 and 12 (p=0.044). ES group showed MD% decrease over weeks 2 and 12 (p<0.001). No statistically significant improvements were found in the 6-week rats using repeated measures ANOVA. For all statistical tests, p<0.05 was considered a significant value.

54 A decreasing MD% trend over the weeks of recovery showed a gradual recovery of motor function (Figure 12). At week 2, the MD% values were between 50-60% and decreased below 40% by week 12. The ES group had a significantly lower MD% value than the control group at weeks 4 (p=0.04) and 6 (p=0.02), suggesting a more recovered motor response at these time points. Both groups, control and ES, showed a statistical decrease in MD% for the 12-week rats over weeks 4 and 10 (control p=0.02, ES p=0.05),

4 and 12 (control p=0.03, ES p=0.02), and 6 and 10 (control p=0.013, ES p=0.008). In addition, the control group showed a statistical decrease in MD% over weeks 4 and 8

(p=0.09), 6 and 8 (p=0.035), and 6 and 12 (p=0.044). The ES group showed a statistical decrease in MD% over weeks 2 and 12 (p<0.001). No statistically significant trends were identified from evaluating the ES and control groups of the 6-week rats over their weeks of recovery.

3.3.2 Physical Therapy Effects

Contractured rat paws are a prevalent complication encountered in peripheral nerve injury models. Contractures form when flexor muscles are innervated before extensor muscles, leading to a curled over paw appearance that hides the full plantar paw surface.

42,46 To avoid this complication, weekly physical therapy was applied through a consistent paw massaging protocol. Although it was beneficial in preventing early contractures, it did not completely eliminate them. Contractures first began to form at week 6 and were sustained through 12 weeks with increasing severity of contracture (Figure 13a-c). At week

6, the control group had 7 out of 19 rats with contractures, where 3 of the rats with contractures were sacrificed in accordance with our study protocol. In the ES group, 5 out

55 of 20 rats developed contractures at week 6, where 3 of the rats with contractures were

sacrificed. At week 8, 3 new contractures formed in the control group and led to a sustained

7 out of 8 rats with contractures during weeks 8 through 12. In the ES group, 2 new

contractures formed at week 8 and were sustained through week 12, and 2 new contractures

formed at week 12. Overall, the ES group had a consistently lower contracture count than

the control group during weeks 6 through 12 (Figure 13d). In the control group, 52% of the

rats developed contractures and in the ES group, 45% of the rats developed contractures.

A B C

Figure 13. Paw Contractures A) Slight and B) moderate and C) severely contractured paws. D) Table showing total contracture count throughout the study.

3.3.3 Walking Track Analysis

Walking track analysis was performed biweekly during the weeks of recovery,

using video recording to capture rat paw prints for SFI evaluation. We recorded a minimum

of three videos for each rat at every biweekly time point. Videos were used to obtain three

56 mid-gait screen captures of each paw for a total of six screen captures for every rat at every time point. Screen captures were evaluated in ImageJ to measure print length (PL), toe spread (TS), and the intermediate toe spread (IT) on the experimental (E) and sham (S) paws (Figure 14a). The SFI value was calculated using Equation 2. Starting at week 6, some rats developed paw contractures and did not have a measureable paw print (Figure

14a-b). We proceeded with analysis on these contractured paws, as previously noted in literature,42 by taking the sum the visible paw print length (PL1) and the curled over toe length (PL2) to obtain the full print length (PL) (Figure 14b). A similar sum was employed to measure the toe spread (TS) (Figure 14c), where the sum included the visible toe spread length (TS1) and curled over toe length (TS2).

Overall, SFI values from control and ES groups had an increasing trend towards zero over the weeks of repair (Figure 14d). At week 2, SFI values were between -80 and -

100% and improved to approach -60% by the 12th week. High variations in the data indicated a varied recovery in motor function measured by SFI and potential variation due to variability in the measurement methods. There were no statistically significant differences between the SFI values of the control and ES groups at any of the time points.

However, the ES group did show statistically significant improvement in mean SFI values between weeks 2 and 12 in 12-week rats (Figure 14d). No statistically significant trends were found from evaluating the ES and control groups of the 6-week rats over their weeks of recovery.

A B C

Figure 14. Walking Track Evaluation A) Paw measurements for calculating SFI from walking track analysis included print length (PL), toe spread (TS), and intermediate to spread (IT). Contractured paws were measured using B) PL = PL1 + PL2 for print length and C) TS = TS1 + TS2 for toe spread. D) Sciatic functional index (SFI) was plotted for each time point in the weeks of recovery. The ES group had a statistically significant improvement between weeks 2 and 12 (p=0.004), evaluated by a repeated measures ANOVA with p<0.05 significant value. No significant differences were found between groups using a one-way ANOVA at every time point.

3.3.4 Sensory Testing

Sensory testing was performed biweekly to evaluate response time after exposure to a thermal stimulus. On every testing day, the test was completed four times and the three

58 lowest values were chosen for analysis. If a rat did not respond during the 20 s window of exposure, the time was recorded as ‘No Response’ (NR). In the data set used for analysis,

NR data points were replaced with numerical values of 20. NR values were only observed during the first 6 weeks (Table 3a). After filtering for the three lowest values, all 6-week

NR values were eliminated and a few NR values remained in weeks 2 and 4. Overall, the

ES group had a higher number of NR values than the control group (Table 3b). Response times recorded from sensory testing were used to calculate SD% (Equation 3). One outlier was identified in the control group at week 6, where the right and left paw values were reversed and not recorded properly. The SD% value changed from -138.95% to 58.2% after the values for right and left paws were corrected.

Immediately after injury, SD% averages were between 50-80%, indicating a loss of sensory function two weeks after injury (Figure 15). Over the 12 weeks of repair, the

SD% values decreased to 0-30%, indicating a steady recovery of sensory function (Figure

15). Although no significant differences were found between the control and electrical stimulation groups at any time point, both showed an overall decreasing trend in SD%, with decreasing variation over time. Both groups, control and ES, showed a statistically significant decrease in SD% in the 6-week rats over weeks 2 and 4 (control p=0.021, ES p=0.037) and weeks 2 and 6 (control p=0.045, ES p=0.002). Although there were no statistically significant differences over the weeks of recovery for the 12-week rats, the control group had a statistically lower SD% average than the ES group at week 4 (p=0.009).

59 Table 3. Sensory NR Data A) Number of no response (NR) values from sensory testing at each time point. B) Number of NR values included in sensory testing data analysis.

A Number of rats per with NR:

Group Week 2 Week 4 Week 6

Control 12 6 5

ES 15 8 2

B Number of NR included in filtered data:

Group Week 2 Week 4 Week 6

Control 4 1 0

ES 8 1 0

Overall, the sensory data contained high variations, as observed through the large minimum and maximum bars on the boxplot (Figure 15). These variations may be attributed to rat paws not being completely flat on the glass surface and also to the presence of a contractured paw, which led to error in positional placement of the thermal light. Large minimum value bars in the boxplots of early time points indicate the presence of negative

SD% values. A negative SD% value suggested that the injured leg was responding more quickly than the uninjured leg. Because more negative values were recorded in the early weeks prior to the formation of paw contractures, the negative values may also indicate a hypersensitive response to the heat stimulus, where a quicker paw response was recorded.

Figure 15. Sensory Testing Evaluation Sensory deficit (SD%) plotted for control and electrical stimulation (ES) groups at each time point in the weeks of repair. No statistically significant differences were found when evaluating the complete data set. When evaluating only the 12-week rats, the control group had a statistically lower SD% average (p=0.043) than the ES group at week 4 using a one-way ANOVA test. When evaluating only the 6-week rats, the control group had a statistically significant decrease in SD% values between weeks 2 and 4 (p=0.025) and between weeks 2 and 6 (p=0.001) using a repeated measures ANOVA test. Using the same test for 6-week rats, the ES group had a statistically significant decrease in SD% values over weeks 2 and 4 (p=0.02) and over weeks 2 and 6 (p=0.001). For all statistical tests, p<0.05 was considered a significant value.

3.3.5 Histology Analysis

Muscles were weighed immediately after dissection and the weights were grossly compared between control and ES groups (Equation 4). Muscles were harvested at weeks

6 and 12, based on the randomized rat groups. No statistical differences between the control and ES groups were noted at either 6 weeks or 12 weeks (Figure 16a). Qualitative evaluation of muscle histology showed a visible degeneration in the 6-week experimental leg when compared to the 6-week sham. By 12 weeks, the experimental leg muscles had

61 an appearance of regeneration more similar to morphological appearance to the sham leg.

Muscle fibers appeared more organized and larger, signs of regeneration (Figure 16b).

Figure 16. Muscle Evaluation A) Muscle weight of the injured leg normalized to the sham leg for each group at each time point. No significant differences were found in % muscle weight between the control and ES groups. B) Qualitative assessment of muscle histology found no differences between appearance of control and ES group muscles at both time points. Between 6 and 12 weeks, there is a visible increase in fiber diameter and overall tissue architecture, indicating recovery.

Qualitative nerve histology yielded no significant differences between ES and

control groups. In the 6-week time point, degeneration of the nerve was visible at the distal

ends through a visibly lower amount of black osmium-stained axons and more connective

62 tissue than the proximal nerve (Figure 17a). In the 12-week time point, the number of axons in the distal nerve increased compared to the 6-week time point (Figure 17b). Similar visual trends can be observed in the midline and proximal segments.

Figure 17. Qualitative Nerve Histology Evaluation At a) 6 weeks, there is visible degeneration through lower black axon staining and increased connective tissue than the proximal nerve. b) 12-week nerve histology is similar, but with less connective tissue.

63 Quantitative analysis of the nerve confirmed qualitative analysis by showing lower averages in fiber density at the distal end, which were below 10,000 fibers/mm2, than the proximal end, which were above 10,000 fibers/mm2 (Figure 18a). A similar outcome is visible in the percent nerve, where the distal segment has a lower percent nerve than the proximal segment. A higher fiber density, along with a higher value for percent nerve, together indicate a more developed nerve. The average values of fiber density and percent nerve were statistically higher at week 12 and week 6 in the proximal (p<0.001), midline

(p<0.001), and distal (p<0.001) segments (Figure 18a-b). Furthermore, the proximal percent nerve was statistically higher in the control group (p=0.036) at week 12 (Figure

18b). Overall, the mean fiber width was between 1.4 µm and 1.7 µm for every segment at each time point. However, the proximal fiber width average was statistically higher in the control group at 12 weeks (p=0.002) (Figure 18c).

3.4 Discussion

Limited knowledge exists on how the application of intraoperative ES on an isograft repair model affects functional nerve recovery. In our gap injury model, we used

ES on an isograft repair and we evaluated motor and sensory functional outcomes over 12 weeks. Our findings suggested that short-duration ES promoted a faster motor recovery in the early weeks of repair without inducing nerve damage. A faster motor recovery addresses the challenge of preserving end organ viability and slowing muscle degeneration after nerve damage. In addition, we demonstrated that sensory recovery is not inhibited through application of ES. Furthermore, we demonstrated that ES may reduce the

64 occurrence of contracture formation, another clinical challenge with peripheral nerve

injury.

Figure 18. Quantitative Nerve Histology Evaluation A) The calculated fiber density at the proximal, midline, and distal nerve show a significant increase between time points through a two-way ANOVA. (p<0.001) B) The calculated percent nerve at the proximal, midline, and distal nerve show a significant increase between time points (p<0.001) and a significantly higher percent nerve in the control group at week 12 (p=0.036) through a two-way ANOVA. C) The mean fiber width at the proximal, midline, and distal nerve showed a significant increase between time points (p<0.001) and a significantly higher mean fiber width in the control group at week 12 (p=0.002) through a two- way ANOVA.

Electrical stimulation mimics the developmental peripheral nerve environment and has been used to safely direct in vitro and in vivo nerve regeneration. The use of ES in vitro has previously enhanced nerve regeneration through increased neurite length, directional alignment of growing neurites, and the upregulation of biochemical factors to support growing neurites.29,30,33–35 The use of ES in vivo has been previously shown to decrease recovery time,28 promote preferential reinnervation of motor and sensory nerves,28,56 and enhance the regenerative from trophic factors.57 Our study was motivated by these known in vitro and in vivo factors that enhance nerve repair through the application of ES. In our in vivo model, we demonstrated that the intraoperative application of ES did not induce further damage on the peripheral nerve. As functional and histological evaluations demonstrated, the group with applied ES was either as good as the group without ES or slightly better.

A key component of in vivo models is the size of the nerve gap injury, where a critical gap is a nerve defect that will not regenerate if unaided. One study showed that a 4 mm nerve defect in a rodent model will fully regenerate, but the regeneration time is significantly reduced through the application of ES.28 Other in vivo work utilizing gap defects of 2 mm,62 4 mm,66 10 mm,42 and 12 mm45 demonstrated that complete nerve regeneration occurs in 2 mm and 4 mm defects, but does not occur in defects exceeding 10 mm in a rodent model. Even though a peripheral nerve can regenerate over short gap lengths, the functional recovery is often insufficient. Others have demonstrated that the functional recovery is incomplete in non-critical gap defects over extended time periods.

In studies utilizing nerve transection injuries, 40% of motor function was restored through

66 EPT and SFI evaluations over 36 weeks,51 and 10% of motor function was restored through

SFI evaluations over 52 weeks.63 Our study used a gap size of 13 mm to model a critical sized nerve defect bridged with an isograft repair. We aimed to investigate the effect of ES on an isograft repair of a rat sciatic nerve through functional recovery over 12 weeks and histomorphometric evaluations at 6 and 12 weeks.

Our findings suggested that the application of ES speeds motor functional recovery.

Through extensor postural thrust (EPT) motor evaluation, we showed that motor recovery was consistently lower in the ES group when compared to the control group that did not receive ES. In weeks 4 and 6 of recovery, the motor deficit (MD%) was significantly lower in the ES group than in the control group. At the end of 12 weeks, both groups showed

MD% values below 40% through EPT testing. Others have reported that the application of

ES improved motor function because it increases the rate of axonal growth.28 Furthermore,

ES helps growing axons establish functional connections with motor muscles.68 In the process of axonal regeneration, developing axons communicate with Schwann cells to promote a regenerative environment. This communication has been shown to be more efficient after the application of ES.69 In our model, the isograft bridged the nerve defect with viable nerve tissue that contained healthy axons. We suggest that applied ES allowed the isograft axons to develop functional connections with proximal and distal axons of the native nerve stumps to preserve motor function in muscles innervated by the sciatic nerve.

Furthermore, histological evaluations illustrated that the number of axons were similar at the proximal end, midline, and distal ends, suggesting that axonal connections were made throughout the nerve. Through the walking track evaluation, we demonstrated that the SFI scores started around -80 to -100 immediately after injury at week 2 and increased to -60

67 at the end of the 12 weeks. Although there were no differences between the groups at any time point, the ES group showed a statistically significant increase in SFI over weeks 2 through 12. These SFI outcomes suggest that motor recovery was detected in later weeks, as reported by others. 42,43,47,51

The sensory test is difficult to probe because it relies on a motor response to a sensory stimulus as an indirect measure of sensory function. Functional assays used to probe sensory recovery include using a thermal stimulus,54,64 a hot water bath,47 mechanical

Von Frey filaments,53 or nociceptive response to electrical stimuli.45 The mechanical Von

Frey filament test has been used to evaluate the effects of ES and supported the findings of a thermal sensory stimulus,69 where a point-source stimulus is used to elicit a sensory response. However, the hot water bath and nociceptive response to electrical stimuli have not been used to evaluate the effects of ES on peripheral nerve recovery because they are less specific and expose the entire paw to a sensory stimulus.55 In our evaluation, we utilized a sensory stimulus to probe a specific sensory response from the center of the plantar surface of the rat paw.

Sensory testing is of particular interest in human hand surgery due to the importance of sensory awareness in the hands. An early study that aimed to probe functional sensory recovery after the application of ES,66 showed that intraoperative ES significantly increased the number of sensory neurons. We demonstrated that the average sensory deficit started around 50-80% in both the ES and control groups at week 2 and dropped down to

0-30% by the end of 12 weeks. Although no significant differences were found to demonstrate the benefits of ES, the control group SD% averages were lower than the ES group in the first 4 weeks and then higher in the remaining 8 weeks. This result was

68 consistent with previous findings,69 where ES effects were first detected at week 6 in sensory functional evaluations. Sensory neuron connections form more slowly than motor connections, which provided some rationale why the sensory recovery was detected in later weeks.66,69 Furthermore, our data had higher variations in the early weeks of recovery due to negative SD% values and quick response times to the thermal stimulus. As a quick response to a heat stimulus immediately after injury cannot be attributed to nerve regeneration, it may be suggestive of a hypersensitive response to treatment, as previously noted in literature for thermal sensory testing.53

Paw contractures are a challenge for functional testing, but also resemble the challenges for human recovery after peripheral nerve injury. In a rodent model, contractures may reduce responsiveness to a thermal stimulus by obstructing the accurate delivery of the thermal stimulus and conceal the full plantar surface of the paw in the walking track evaluations. In the presence of a paw contracture, where the paw does not lay flat on the glass surface, thermal heat conduction is interfered, which may lead to a slower response to thermal stimuli. Furthermore, accurate placement of heat stimulus in the middle of the paw is a key component of a reproducible sensory response. In a contractured paw, the middle portion of the paw is hidden and alters the placement of heat stimulus. If the heat stimulus is applied to the dorsum of the toes instead of the center of the plantar paw surface, the response is quicker than expected. Our data shows that in the early weeks of recovery, if the rats did not keep their paws flat on the glass during testing, their noted response time was quicker than anticipated for their level of recovery after injury. Such behavior was noted in 2 rats at week 2 and 4 rats in week 4, prior to the formation of contractures. Although the presence of contractures did not hinder our

69 analysis in the motor evaluations,42 it may have contributed to the high variability in our sensory testing data.

Histomorphometric measures of a peripheral nerve reveal the structure of the regenerating nerve and indicate the level of nerve repair through a quantitative analysis.

Fiber density, a measure of average number of fibers over a specified cross-sectional nerve area, has been reported as 11,882 fibers/mm2 in a healthy rat peripheral nerve.70 The fiber density average was increased after injury and has been reported as 25,382 fibers/mm2 in a neurorraphy nerve repair after 1 month of recovery and 28,934 fibers/mm2 after 3 months of recovery.70 In gap injury repair models, the midline of the bridged gap has been demonstrated as more mature in fiber density than the distal end, which undergoes

Wallerian degeneration.71 Others have shown fiber densities around 26,000 fibers/mm2 in the distal segment after a 10mm gap injury repaired with an autograft after 20 weeks of recovery.72 Our previous study, with a 10 mm nerve defect, showed 30,000 fibers/mm2 at the midline and 25,000 fibers/mm2 at the distal end after 12 weeks of isograft repair.44 In our results, both the ES and control groups were above the healthy average of 11,882 fibers/mm2 at the 6-week time point in the proximal and midline segments, but below the healthy average in the distal end. After 12 weeks, our fiber density values were between

22,000 fibers/mm2 and 29,000 fibers/mm2 in all three locations on the nerve, which are close to the recovery level of a neurorraphy repair during 1-3 months of recovery. However,

ES had a lower fiber density than the control group at the proximal and midline segments, suggesting the repair was closer to a healthy uninjured rat after 12 weeks of recovery. This result was not reflected in the distal end, where the ES group had a higher fiber density than the control group. The application of ES in an isograft yielded fiber density values

70 comparable to neurorraphy repair, showed expected distal degeneration, and showed signs of slightly improved nerve recovery through lower fiber density values after 12 weeks.

Percent nerve and mean fiber width are additional histomorphometric parameters that indicate the quality and maturity of regenerating nerve fibers. Others have reported a percent nerve value of 9% in the distal segment and 17% in the midline segment after 6 weeks of isograft recovery,71 which is higher than our reported percent nerve. Our previous study reported 33% in the distal and midline segments after 12 weeks of isograft recovery.44

Fiber width values found in literature are approximately 2.8 µm71 or 3.7 µm44 for isograft repair, which is higher than our reported 1.4-1.7 µm range of mean fiber width values.

Although our percent nerve and mean fiber width values are below what is reported in literature, both of these histomorphometric parameters are similar between the ES and control groups in the midline and distal segments of the nerve. This outcome suggests that

ES does not have a negative effect on the quality and maturity of the regenerating nerve.

Electrical stimulation is a helpful tool that could enhance clinical repair outcomes of peripheral nerve injuries. In our study, functional tests were used to evaluate motor and sensory recovery of a damaged sciatic nerve repaired with an isograft. Because an isograft models the standard of care for peripheral nerve injury, we anticipated full recovery in both groups and slight differences between ES and control groups. We used two time points to show a partial recovery at 6 weeks and a more complete recovery at 12 weeks through functional evaluations and histomorphometric comparisons. We demonstrated that the motor recovery was enhanced through the application of ES and that the sensory recovery was not inhibited. The sciatic nerve is a mixed motor and sensory nerve, where distinguishing the responses of sensory and motor recovery may pose a challenge for future

71 studies. Furthermore, we demonstrated that there is an enhanced histomorphometric recovery in fiber density without negatively affecting the quality and maturity of regenerating fibers. Reducing paw contractures and improving sensitivity of sensory functional evaluations could aid in further highlighting the effects of electrical stimulation on functional recovery of an isograft-repaired peripheral nerve defect.

3.5 Conclusion

Our study utilized a rodent model to evaluate the effects of ES on an isograft repair of a critical nerve defect. We found that ES promoted a faster motor recovery and did not induce further damage on the nerve. Furthermore, we found that ES may reduce the occurrence of contracture formation in the weeks of recovery and positively impact the fiber density histomorphometric parameters. Future work needs to be done to better evaluate the sensory functional recovery in response to ES and evaluate the mechanisms of ES that may lead to a reduction in contracture formation.

CHAPTER IV

CONCLUSIONS AND FUTURE WORK

4.1 Conclusions

In this study, we evaluated the effects of intraoperative electrical stimulation (ES) on functional outcomes through motor and sensory tests and histomorphometric parameters. Our model utilized a 13 mm critical gap defect of the rat sciatic nerve, which was repaired by an isograft. Recovery was evaluated over a 12-week time period for functional and histological evaluations, with an additional 6-week time point for histological evaluations.

Aim 1: Evaluate Functional Recovery

Motor functional outcomes were evaluated using the extensor postural thrust (EPT) and sciatic functional index (SFI) tests. The application of intraoperative ES showed a significantly improved motor function in weeks 4 and 6, when compared with the control group. Through the SFI results, we demonstrated that the ES group had significant improvement between weeks 2 and 12 of recovery. Furthermore, the ES group had 7% less paw contractures than the control group. Sensory functional evaluation utilized a thermal stimulus and found that ES slightly affects 12-week sensory function. The application of intraoperative ES speeds motor recovery without negatively affecting sensory recovery.

73 Aim 2: Evaluate Histological Differences

Histomorphometric examination was performed on the sciatic nerve grafts and the gastrocnemius muscle, which is innervated by the sciatic nerve. Histomorphometric parameters for the sciatic nerve included fiber density, percent nerve, and mean fiber width.

The application of ES had a positive effect on the fiber density without negatively affecting percent nerve and mean fiber width. Nerve structure showed signs of repair and regeneration through qualitative and quantitative evaluations. Muscle histology showed an improved muscle regeneration appearance between weeks 6 and 12.

4.2 Future Work

Further work is necessary to distinguish the responses between motor and sensory nerve recovery after the application of intraoperative ES. Many peripheral nerve models use a mixed motor and sensory nerve, such as the sciatic nerve. Although previous studies have demonstrated preferential innervation of motor and sensory nerves through the application of ES, this has not been confirmed through functional evaluations. The reduction in paw contracture formation was an interesting observation in our work. Further work is necessary to investigate the mechanisms of ES that may lead to a reduction in contracture formation. Lastly, further work is necessary to highlight the effects of ES on sensory recover and to increase the sensitivity of such functional assays. ES is a promising clinical therapy to enhance the outcomes of peripheral nerve repair and through further evaluation, may become more clinically translational.

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APPENDIX A. IACUC APPROVAL

APPENDIX B. STATISTICAL ANALYSIS

Extensor Postural Thrust – One-Way ANOVA Analysis

ONEWAY Week2 Week4 Week6 Week8 Week10 Week12 BY Group /STATISTICS DESCRIPTIVES /MISSING ANALYSIS /POSTHOC=TUKEY ALPHA(0.05).

Descriptive Statistics 95% Confidence

Interval for Mean Std. Lower Upper Minimum Maximum N Mean Deviation Std. Error Bound Bound

Week2 1.00 19 50.2084 7.13317 1.63646 46.7703 53.6465 32.00 58.75 2.00 20 48.0645 7.71659 1.72548 44.4530 51.6760 31.25 59.55 Total 39 49.1090 7.41981 1.18812 46.7038 51.5142 31.25 59.55

Week4 1.00 19 51.8958 7.84211 1.79910 48.1160 55.6756 38.07 67.56 2.00 20 46.6825 7.43274 1.66201 43.2039 50.1611 29.18 62.12 Total 39 49.2223 7.98264 1.27825 46.6346 51.8100 29.18 67.56

Week6 1.00 19 52.5979 8.03372 1.84306 48.7258 56.4700 29.60 63.65 2.00 20 43.1785 9.38733 2.09907 38.7851 47.5719 23.85 61.80 Total 39 47.7674 9.86828 1.58019 44.5685 50.9664 23.85 63.65

Week8 1.00 8 42.7850 9.97904 3.52812 34.4423 51.1277 25.83 57.14 2.00 8 38.6875 4.20887 1.48806 35.1688 42.2062 32.51 44.31 Total 16 40.7363 7.69515 1.92379 36.6358 44.8367 25.83 57.14

Week10 1.00 8 40.3800 4.50542 1.59291 36.6134 44.1466 34.76 47.02 2.00 8 32.7025 10.48538 3.70714 23.9365 41.4685 10.46 41.14 Total 16 36.5413 8.74631 2.18658 31.8807 41.2018 10.46 47.02

Week12 1.00 8 36.8000 8.25386 2.91818 29.8996 43.7004 23.78 50.98 2.00 8 31.1975 6.60792 2.33625 25.6731 36.7219 21.40 42.82 Total 16 33.9988 7.78069 1.94517 29.8527 38.1448 21.40 50.98

ANOVA Sum of Squares df Mean Square F Sig.

Week2 Between Groups 44.785 1 44.785 .809 .374

Within Groups 2047.248 37 55.331

Total 2092.034 38

Week4 Between Groups 264.815 1 264.815 4.543 .040

Within Groups 2156.643 37 58.288

Total 2421.458 38

Week6 Between Groups 864.500 1 864.500 11.279 .002

Within Groups 2836.049 37 76.650

Total 3700.549 38

Week8 Between Groups 67.158 1 67.158 1.145 .303

Within Groups 821.071 14 58.648

Total 888.229 15

Week10 Between Groups 235.776 1 235.776 3.621 .078

Within Groups 911.693 14 65.121

Total 1147.469 15

Week12 Between Groups 125.552 1 125.552 2.246 .156

Within Groups 782.535 14 55.895

Total 908.087 15

85 Extensor Postural Thrust – Control Group Repeated Measures ANOVA for 6-week Rats

GLM Cntrl_2 Cntrl_4 Cntrl_6 /WSFACTOR=time 3 Polynomial /MEASURE=SD /METHOD=SSTYPE(3) /EMMEANS=TABLES(time) COMPARE ADJ(BONFERRONI) /PRINT=DESCRIPTIVE /CRITERIA=ALPHA(.05) /WSDESIGN=time.

Within-Subjects Factors Measure: SD Dependent time Variable

1 Cntrl_2

2 Cntrl_4 3 Cntrl_6

Descriptive Statistics Mean Std. Deviation N Cntrl_2 50.2084 7.13317 19 Cntrl_4 51.8958 7.84211 19

Cntrl_6 52.5979 8.03372 19

Tests of Within-Subjects Effects Measure: SD Type III Sum of

Source Squares df Mean Square F Sig.

time Sphericity Assumed 57.315 2 28.658 .605 .552 Greenhouse-Geisser 57.315 1.716 33.409 .605 .528 Huynh-Feldt 57.315 1.879 30.506 .605 .542 Lower-bound 57.315 1.000 57.315 .605 .447

Error(time) Sphericity Assumed 1705.381 36 47.372

Greenhouse-Geisser 1705.381 30.880 55.226

Huynh-Feldt 1705.381 33.819 50.427

Lower-bound 1705.381 18.000 94.743

Estimated Marginal Means time Estimates Measure: SD 95% Confidence Interval

time Mean Std. Error Lower Bound Upper Bound 1 50.208 1.636 46.770 53.646 2 51.896 1.799 48.116 55.676 3 52.598 1.843 48.726 56.470

Pairwise Comparisons Measure: SD 95% Confidence Interval for

a Mean Difference Difference (I) time (J) time (I-J) Std. Error Sig.a Lower Bound Upper Bound

1 2 -1.687 2.567 1.000 -8.463 5.088 3 -2.389 1.763 .576 -7.041 2.262

2 1 1.687 2.567 1.000 -5.088 8.463 3 -.702 2.294 1.000 -6.756 5.352

3 1 2.389 1.763 .576 -2.262 7.041 2 .702 2.294 1.000 -5.352 6.756

Based on estimated marginal means a. Adjustment for multiple comparisons: Bonferroni.

87 Extensor Postural Thrust – ES Group Repeated Measures ANOVA for 6-week Rats

GLM ES_2 ES_4 ES_6 /WSFACTOR=time 3 Polynomial /MEASURE=SD /METHOD=SSTYPE(3) /EMMEANS=TABLES(time) COMPARE ADJ(BONFERRONI) /PRINT=DESCRIPTIVE /CRITERIA=ALPHA(.05) /WSDESIGN=time.

Within-Subjects Factors Measure: SD Dependent time Variable 1 ES_2 2 ES_4 3 ES_6

Descriptive Statistics Mean Std. Deviation N ES_2 48.0645 7.71659 20

ES_4 46.6825 7.43274 20 ES_6 43.1785 9.38733 20

Tests of Within-Subjects Effects Measure: SD Type III Sum of

Source Squares df Mean Square F Sig.

time Sphericity Assumed 253.740 2 126.870 3.200 .052 Greenhouse-Geisser 253.740 1.706 148.721 3.200 .061 Huynh-Feldt 253.740 1.857 136.605 3.200 .056 Lower-bound 253.740 1.000 253.740 3.200 .090

Error(time) Sphericity Assumed 1506.768 38 39.652

Greenhouse-Geisser 1506.768 32.417 46.481

Huynh-Feldt 1506.768 35.292 42.694

Lower-bound 1506.768 19.000 79.304

Estimated Marginal Means time Estimates Measure: SD 95% Confidence Interval

time Mean Std. Error Lower Bound Upper Bound 1 48.065 1.725 44.453 51.676 2 46.683 1.662 43.204 50.161 3 43.179 2.099 38.785 47.572

Pairwise Comparisons Measure: SD 95% Confidence Interval for

a Mean Difference Difference (I) time (J) time (I-J) Std. Error Sig.a Lower Bound Upper Bound

1 2 1.382 1.584 1.000 -2.777 5.541 3 4.886 2.315 .145 -1.191 10.963

2 1 -1.382 1.584 1.000 -5.541 2.777 3 3.504 2.007 .291 -1.764 8.772

3 1 -4.886 2.315 .145 -10.963 1.191 2 -3.504 2.007 .291 -8.772 1.764

Based on estimated marginal means a. Adjustment for multiple comparisons: Bonferroni.

89 Extensor Postural Thrust – Control Group Repeated Measures ANOVA for 12-week Rats

GLM Cntrl_2 Cntrl_4 Cntrl_6 Cntrl_8 Cntrl_10 Cntrl_12 /WSFACTOR=time 6 Polynomial /MEASURE=SD /METHOD=SSTYPE(3) /EMMEANS=TABLES(time) COMPARE ADJ(BONFERRONI) /PRINT=DESCRIPTIVE /CRITERIA=ALPHA(.05) /WSDESIGN=time.

Within-Subjects Factors Measure: SD Dependent time Variable 1 Cntrl_2 2 Cntrl_4 3 Cntrl_6

4 Cntrl_8 5 Cntrl_10 6 Cntrl_12

Descriptive Statistics Mean Std. Deviation N

Cntrl_2 49.9425 5.72279 8 Cntrl_4 55.5250 7.46857 8

Cntrl_6 53.0888 6.12780 8 Cntrl_8 42.7850 9.97904 8 Cntrl_10 40.3800 4.50542 8 Cntrl_12 36.8000 8.25386 8

Tests of Within-Subjects Effects

Measure: SD Type III Sum of

Source Squares df Mean Square F Sig.

time Sphericity Assumed 2256.164 5 451.233 11.011 .000 Greenhouse-Geisser 2256.164 2.351 959.662 11.011 .001 Huynh-Feldt 2256.164 3.615 624.043 11.011 .000 Lower-bound 2256.164 1.000 2256.164 11.011 .013

Error(time) Sphericity Assumed 1434.335 35 40.981

Greenhouse-Geisser 1434.335 16.457 87.157

Huynh-Feldt 1434.335 25.308 56.676

Lower-bound 1434.335 7.000 204.905

Estimated Marginal Means time

Estimates Measure: SD 95% Confidence Interval

time Mean Std. Error Lower Bound Upper Bound 1 49.943 2.023 45.158 54.727

2 55.525 2.641 49.281 61.769 3 53.089 2.167 47.966 58.212 4 42.785 3.528 34.442 51.128 5 40.380 1.593 36.613 44.147

6 36.800 2.918 29.900 43.700

Pairwise Comparisons

Measure: SD 95% Confidence Interval for

b Mean Difference Difference (I) time (J) time (I-J) Std. Error Sig.b Lower Bound Upper Bound

1 2 -5.583 4.399 1.000 -24.743 13.578 3 -3.146 3.255 1.000 -17.321 11.028 4 7.158 4.998 1.000 -14.612 28.927 5 9.563 2.974 .221 -3.389 22.514 6 13.143 3.797 .158 -3.394 29.679

2 1 5.583 4.399 1.000 -13.578 24.743 3 2.436 2.484 1.000 -8.382 13.255 4 12.740* 2.165 .009 3.310 22.170 5 15.145* 2.069 .002 6.134 24.156 6 18.725* 2.649 .003 7.188 30.262

3 1 3.146 3.255 1.000 -11.028 17.321 2 -2.436 2.484 1.000 -13.255 8.382 4 10.304* 2.213 .035 .667 19.940 5 12.709* 2.303 .013 2.680 22.738

6 16.289* 3.659 .044 .353 32.225

4 1 -7.158 4.998 1.000 -28.927 14.612 2 -12.740* 2.165 .009 -22.170 -3.310

3 -10.304* 2.213 .035 -19.940 -.667 5 2.405 3.189 1.000 -11.484 16.294 6 5.985 3.739 1.000 -10.301 22.271

5 1 -9.563 2.974 .221 -22.514 3.389 2 -15.145* 2.069 .002 -24.156 -6.134 3 -12.709* 2.303 .013 -22.738 -2.680 4 -2.405 3.189 1.000 -16.294 11.484 6 3.580 2.365 1.000 -6.722 13.882

6 1 -13.143 3.797 .158 -29.679 3.394 2 -18.725* 2.649 .003 -30.262 -7.188 3 -16.289* 3.659 .044 -32.225 -.353 4 -5.985 3.739 1.000 -22.271 10.301 5 -3.580 2.365 1.000 -13.882 6.722

92 Extensor Postural Thrust – ES Group Repeated Measures ANOVA for 12-week Rats

GLM ES_2 ES_4 ES_6 ES_8 ES_10 ES_12 /WSFACTOR=time 6 Polynomial /MEASURE=SD /METHOD=SSTYPE(3) /EMMEANS=TABLES(time) COMPARE ADJ(BONFERRONI) /PRINT=DESCRIPTIVE /CRITERIA=ALPHA(.05) /WSDESIGN=time.

Within-Subjects Factors Measure: SD Dependent time Variable 1 ES_2 2 ES_4 3 ES_6

4 ES_8 5 ES_10 6 ES_12

Descriptive Statistics Mean Std. Deviation N

ES_2 50.2475 8.57601 8 ES_4 49.4225 7.63489 8

ES_6 42.9850 7.49051 8 ES_8 38.6875 4.20887 8 ES_10 32.7025 10.48538 8 ES_12 31.1975 6.60792 8

Tests of Within-Subjects Effects

Measure: SD Type III Sum of

Source Squares df Mean Square F Sig.

time Sphericity Assumed 2644.676 5 528.935 13.684 .000 Greenhouse-Geisser 2644.676 2.543 1039.920 13.684 .000 Huynh-Feldt 2644.676 4.116 642.506 13.684 .000 Lower-bound 2644.676 1.000 2644.676 13.684 .008

Error(time) Sphericity Assumed 1352.910 35 38.655

Greenhouse-Geisser 1352.910 17.802 75.997

Huynh-Feldt 1352.910 28.813 46.954

Lower-bound 1352.910 7.000 193.273

Estimated Marginal Means

Time

Estimates

Measure: SD 95% Confidence Interval

time Mean Std. Error Lower Bound Upper Bound 1 50.248 3.032 43.078 57.417

2 49.423 2.699 43.040 55.805 3 42.985 2.648 36.723 49.247

4 38.688 1.488 35.169 42.206 5 32.703 3.707 23.937 41.468 6 31.198 2.336 25.673 36.722

Pairwise Comparisons

Measure: SD 95% Confidence Interval for

b Mean Difference Difference (I) time (J) time (I-J) Std. Error Sig.b Lower Bound Upper Bound

1 2 .825 3.057 1.000 -12.491 14.141 3 7.263 3.432 1.000 -7.687 22.212 4 11.560 3.101 .111 -1.947 25.067 5 17.545 4.055 .052 -.115 35.205 6 19.050* 2.025 .000 10.232 27.868

2 1 -.825 3.057 1.000 -14.141 12.491 3 6.438 2.426 .491 -4.127 17.002 4 10.735 2.966 .128 -2.184 23.654 5 16.720 3.845 .050 -.025 33.465 6 18.225* 2.388 .002 7.826 28.624

3 1 -7.263 3.432 1.000 -22.212 7.687 2 -6.438 2.426 .491 -17.002 4.127 4 4.297 2.838 1.000 -8.065 16.660 5 10.282* 1.712 .008 2.828 17.737

6 11.787 3.239 .124 -2.320 25.895

4 1 -11.560 3.101 .111 -25.067 1.947 2 -10.735 2.966 .128 -23.654 2.184

3 -4.297 2.838 1.000 -16.660 8.065 5 5.985 3.933 1.000 -11.143 23.113 6 7.490 2.056 .124 -1.462 16.442

5 1 -17.545 4.055 .052 -35.205 .115 2 -16.720 3.845 .050 -33.465 .025 3 -10.282* 1.712 .008 -17.737 -2.828 4 -5.985 3.933 1.000 -23.113 11.143 6 1.505 4.163 1.000 -16.625 19.635

6 1 -19.050* 2.025 .000 -27.868 -10.232 2 -18.225* 2.388 .002 -28.624 -7.826 3 -11.787 3.239 .124 -25.895 2.320 4 -7.490 2.056 .124 -16.442 1.462 5 -1.505 4.163 1.000 -19.635 16.625

95 Sensory Test – One-Way ANOVA Analysis

ONEWAY Week2 Week4 Week6 Week8 Week10 Week12 BY Group /STATISTICS DESCRIPTIVES /MISSING ANALYSIS /POSTHOC=TUKEY ALPHA(0.05).

Descriptives 95% Confidence

Interval for Mean Std. Std. Lower Upper Minimum Maximum N Mean Deviation Error Bound Bound

Week2 1.00 19 39.4463 26.88858 6.16866 26.4864 52.4062 -17.76 79.10 2.00 20 57.1885 33.02267 7.38409 41.7334 72.6436 -25.62 100.00 Total 39 48.5449 31.11968 4.98314 38.4570 58.6327 -25.62 100.00

Week4 1.00 19 19.2389 24.06222 5.52025 7.6413 30.8366 -38.74 51.90 2.00 20 31.9380 16.64386 3.72168 24.1484 39.7276 4.62 62.36 Total 39 25.7513 21.31004 3.41234 18.8434 32.6592 -38.74 62.36

Week6 1.00 19 16.1368 17.57638 4.03230 7.6653 24.6084 -30.34 40.14 2.00 20 21.2255 20.79027 4.64885 11.4954 30.9556 -3.12 76.06 Total 39 18.7464 19.21177 3.07634 12.5187 24.9741 -30.34 76.06

Week8 1.00 8 21.6600 15.29590 5.40792 8.8723 34.4477 -6.85 38.28 2.00 8 14.7575 15.74041 5.56508 1.5982 27.9168 -2.90 37.01 Total 16 18.2088 15.41137 3.85284 9.9966 26.4209 -6.85 38.28

Week10 1.00 8 16.2875 10.45033 3.69475 7.5508 25.0242 -1.97 32.74 2.00 8 14.3275 7.71633 2.72814 7.8765 20.7785 .46 25.08

Total 16 15.3075 8.93169 2.23292 10.5481 20.0669 -1.97 32.74

Week12 1.00 8 9.2975 12.98991 4.59263 -1.5623 20.1573 -17.22 28.69 2.00 8 9.3700 10.97371 3.87979 .1957 18.5443 -7.98 22.57 Total 16 9.3337 11.61649 2.90412 3.1438 15.5237 -17.22 28.69

ANOVA Sum of Squares df Mean Square F Sig.

Week2 Between Groups 3067.137 1 3067.137 3.364 .075

Within Groups 33733.368 37 911.713

Total 36800.505 38

Week4 Between Groups 1571.309 1 1571.309 3.707 .062

Within Groups 15685.171 37 423.924

Total 17256.480 38

Week6 Between Groups 252.305 1 252.305 .678 .416

Within Groups 13773.193 37 372.248

Total 14025.497 38

Week8 Between Groups 190.578 1 190.578 .791 .389

Within Groups 3372.075 14 240.863

Total 3562.653 15

Week10 Between Groups 15.366 1 15.366 .182 .676

Within Groups 1181.259 14 84.376

Total 1196.625 15

Week12 Between Groups .021 1 .021 .000 .991

Within Groups 2024.120 14 144.580

Total 2024.141 15

97 Sensory Test – Control Group Repeated Measures ANOVA for 6-week Rats

GLM Cntrl_2 Cntrl_4 Cntrl_6 /WSFACTOR=time 3 Polynomial /MEASURE=SD /METHOD=SSTYPE(3) /EMMEANS=TABLES(time) COMPARE ADJ(BONFERRONI) /PRINT=DESCRIPTIVE /CRITERIA=ALPHA(.05) /WSDESIGN=time.

Within-Subjects Factors Measure: SD Dependent time Variable 1 Cntrl_2 2 Cntrl_4 3 Cntrl_6

Descriptive Statistics Mean Std. Deviation N Cntrl_2 39.4463 26.88858 19

Cntrl_4 19.2389 24.06222 19 Cntrl_6 16.1368 17.57638 19

Tests of Within-Subjects Effects Measure: SD Type III Sum of

Source Squares df Mean Square F Sig.

time Sphericity Assumed 6088.185 2 3044.092 8.108 .001 Greenhouse-Geisser 6088.185 1.952 3119.487 8.108 .001 Huynh-Feldt 6088.185 2.000 3044.092 8.108 .001 Lower-bound 6088.185 1.000 6088.185 8.108 .011

Error(time) Sphericity Assumed 13515.423 36 375.428

Greenhouse-Geisser 13515.423 35.130 384.727

Huynh-Feldt 13515.423 36.000 375.428

Lower-bound 13515.423 18.000 750.857

Estimated Marginal Means time

Estimates Measure: SD 95% Confidence Interval

time Mean Std. Error Lower Bound Upper Bound 1 39.446 6.169 26.486 52.406 2 19.239 5.520 7.641 30.837 3 16.137 4.032 7.665 24.608

Pairwise Comparisons Measure: SD 95% Confidence Interval for

b Mean Difference Difference (I) time (J) time (I-J) Std. Error Sig.b Lower Bound Upper Bound

1 2 20.207* 6.663 .021 2.621 37.793 3 23.309* 6.357 .005 6.532 40.087

2 1 -20.207* 6.663 .021 -37.793 -2.621 3 3.102 5.809 1.000 -12.228 18.432

3 1 -23.309* 6.357 .005 -40.087 -6.532 2 -3.102 5.809 1.000 -18.432 12.228

Based on estimated marginal means *. The mean difference is significant at the .05 level. b. Adjustment for multiple comparisons: Bonferroni.

99 Sensory Test – ES Group Repeated Measures ANOVA for 6-week Rats

GLM ES_2 ES_4 ES_6 /WSFACTOR=time 3 Polynomial /MEASURE=SD /METHOD=SSTYPE(3) /EMMEANS=TABLES(time) COMPARE ADJ(BONFERRONI) /PRINT=DESCRIPTIVE /CRITERIA=ALPHA(.05) /WSDESIGN=time.

Within-Subjects Factors Measure: SD Dependent time Variable

1 ES_2

2 ES_4 3 ES_6

Descriptive Statistics Mean Std. Deviation N ES_2 57.1885 33.02267 20 ES_4 31.9380 16.64386 20

ES_6 21.2255 20.79027 20

Tests of Within-Subjects Effects Measure: SD Type III Sum of

Source Squares df Mean Square F Sig.

time Sphericity Assumed 13637.885 2 6818.943 9.986 .000 Greenhouse-Geisser 13637.885 1.759 7753.357 9.986 .001 Huynh-Feldt 13637.885 1.924 7086.684 9.986 .000 Lower-bound 13637.885 1.000 13637.885 9.986 .005

Error(time) Sphericity Assumed 25949.355 38 682.878

Greenhouse-Geisser 25949.355 33.420 776.454

Huynh-Feldt 25949.355 36.564 709.690

Lower-bound 25949.355 19.000 1365.756

100 Estimated Marginal Means time

Estimates Measure: SD 95% Confidence Interval

time Mean Std. Error Lower Bound Upper Bound 1 57.189 7.384 41.733 72.644 2 31.938 3.722 24.148 39.728 3 21.226 4.649 11.495 30.956

Pairwise Comparisons Measure: SD 95% Confidence Interval for

b Mean Difference Difference (I) time (J) time (I-J) Std. Error Sig.b Lower Bound Upper Bound

1 2 25.251* 9.151 .037 1.228 49.273 3 35.963* 8.825 .002 12.796 59.130

2 1 -25.251* 9.151 .037 -49.273 -1.228 3 10.713 6.575 .359 -6.549 27.974

3 1 -35.963* 8.825 .002 -59.130 -12.796 2 -10.713 6.575 .359 -27.974 6.549

Based on estimated marginal means *. The mean difference is significant at the .05 level. b. Adjustment for multiple comparisons: Bonferroni.

101 Sensory Test – Control Group Repeated Measures ANOVA for 12-week Rats

Within-Subjects Factors Measure: SD Dependent time Variable 1 Cntrl_2

2 Cntrl_4 3 Cntrl_6 4 Cntrl_8 5 Cntrl_10

6 Cntrl_12

Descriptive Statistics Mean Std. Deviation N

Cntrl_2 32.1900 32.13863 8 Cntrl_4 2.6500 20.01507 8 Cntrl_6 8.5988 22.55661 8 Cntrl_8 21.6600 15.29590 8 Cntrl_10 16.2875 10.45033 8 Cntrl_12 9.2975 12.98991 8

102

Tests of Within-Subjects Effects

Measure: SD Type III Sum of

Source Squares df Mean Square F Sig.

time Sphericity Assumed 4539.588 5 907.918 2.055 .095 Greenhouse-Geisser 4539.588 2.452 1851.693 2.055 .152 Huynh-Feldt 4539.588 3.872 1172.331 2.055 .116 Lower-bound 4539.588 1.000 4539.588 2.055 .195

Error(time) Sphericity Assumed 15464.017 35 441.829

Greenhouse-Geisser 15464.017 17.161 901.108

Huynh-Feldt 15464.017 27.106 570.503

Lower-bound 15464.017 7.000 2209.145

Estimated Marginal Means time

Estimates Measure: SD 95% Confidence Interval

time Mean Std. Error Lower Bound Upper Bound

1 32.190 11.363 5.321 59.059 2 2.650 7.076 -14.083 19.383 3 8.599 7.975 -10.259 27.457

4 21.660 5.408 8.872 34.448 5 16.288 3.695 7.551 25.024 6 9.298 4.593 -1.562 20.157

103

Pairwise Comparisons

Measure: SD 95% Confidence Interval for

a Mean Difference Difference (I) time (J) time (I-J) Std. Error Sig.a Lower Bound Upper Bound

1 2 29.540 9.286 .232 -10.903 69.983 3 23.591 13.231 1.000 -34.034 81.217 4 10.530 14.649 1.000 -53.272 74.332 5 15.902 14.073 1.000 -45.388 77.193 6 22.893 13.613 1.000 -36.395 82.180

2 1 -29.540 9.286 .232 -69.983 10.903 3 -5.949 10.573 1.000 -51.998 40.100 4 -19.010 11.379 1.000 -68.567 30.547 5 -13.638 10.227 1.000 -58.180 30.905 6 -6.647 9.987 1.000 -50.143 36.848

3 1 -23.591 13.231 1.000 -81.217 34.034 2 5.949 10.573 1.000 -40.100 51.998 4 -13.061 10.944 1.000 -60.727 34.605 5 -7.689 9.178 1.000 -47.660 32.282

6 -.699 8.110 1.000 -36.020 34.622

4 1 -10.530 14.649 1.000 -74.332 53.272 2 19.010 11.379 1.000 -30.547 68.567

3 13.061 10.944 1.000 -34.605 60.727 5 5.372 5.678 1.000 -19.358 30.103 6 12.362 3.644 .173 -3.507 28.232

5 1 -15.902 14.073 1.000 -77.193 45.388 2 13.638 10.227 1.000 -30.905 58.180 3 7.689 9.178 1.000 -32.282 47.660 4 -5.372 5.678 1.000 -30.103 19.358 6 6.990 6.059 1.000 -19.398 33.378

6 1 -22.893 13.613 1.000 -82.180 36.395 2 6.647 9.987 1.000 -36.848 50.143 3 .699 8.110 1.000 -34.622 36.020 4 -12.362 3.644 .173 -28.232 3.507 5 -6.990 6.059 1.000 -33.378 19.398

104 Sensory Test – ES Group Repeated Measures ANOVA for 12-week Rats

GLM ES_2 ES_4 ES_6 ES_8 ES_10 ES_12 /WSFACTOR=time 6 Polynomial /MEASURE=SD /METHOD=SSTYPE(3) /EMMEANS=TABLES(time) COMPARE ADJ(BONFERRONI) /PRINT=DESCRIPTIVE /CRITERIA=ALPHA(.05) /WSDESIGN=time.

Within-Subjects Factors Measure: SD Dependent time Variable 1 ES_2

2 ES_4 3 ES_6 4 ES_8 5 ES_10

6 ES_12

Descriptive Statistics Mean Std. Deviation N

ES_2 41.6550 36.55494 8 ES_4 30.2050 16.24700 8 ES_6 25.6525 23.09673 8 ES_8 14.7575 15.74041 8 ES_10 14.3275 7.71633 8 ES_12 9.3700 10.97371 8

105

Tests of Within-Subjects Effects

Measure: SD Type III Sum of

Source Squares df Mean Square F Sig.

time Sphericity Assumed 5881.570 5 1176.314 2.572 .044 Greenhouse-Geisser 5881.570 1.933 3042.560 2.572 .114 Huynh-Feldt 5881.570 2.657 2213.278 2.572 .091 Lower-bound 5881.570 1.000 5881.570 2.572 .153

Error(time) Sphericity Assumed 16005.706 35 457.306

Greenhouse-Geisser 16005.706 13.532 1182.831

Huynh-Feldt 16005.706 18.602 860.438

Lower-bound 16005.706 7.000 2286.529

Estimated Marginal Means time Estimates Measure: SD 95% Confidence Interval

time Mean Std. Error Lower Bound Upper Bound 1 41.655 12.924 11.094 72.216

2 30.205 5.744 16.622 43.788 3 25.653 8.166 6.343 44.962 4 14.758 5.565 1.598 27.917

5 14.328 2.728 7.876 20.779 6 9.370 3.880 .196 18.544

106

Pairwise Comparisons

Measure: SD 95% Confidence Interval for

a Mean Difference Difference (I) time (J) time (I-J) Std. Error Sig.a Lower Bound Upper Bound

1 2 11.450 15.890 1.000 -57.757 80.657 3 16.003 16.488 1.000 -55.806 87.811 4 26.898 16.783 1.000 -46.195 99.990 5 27.328 14.550 1.000 -36.041 90.696 6 32.285 13.888 .795 -28.199 92.769

2 1 -11.450 15.890 1.000 -80.657 57.757 3 4.552 12.153 1.000 -48.376 57.481 4 15.447 8.004 1.000 -19.410 50.305 5 15.877 6.204 .564 -11.142 42.897 6 20.835 8.387 .629 -15.691 57.361

3 1 -16.003 16.488 1.000 -87.811 55.806 2 -4.552 12.153 1.000 -57.481 48.376 4 10.895 6.120 1.000 -15.761 37.551 5 11.325 6.495 1.000 -16.962 39.612

6 16.283 6.463 .598 -11.865 44.430

4 1 -26.898 16.783 1.000 -99.990 46.195 2 -15.447 8.004 1.000 -50.305 19.410

3 -10.895 6.120 1.000 -37.551 15.761 5 .430 3.926 1.000 -16.670 17.530 6 5.388 6.066 1.000 -21.033 31.808

5 1 -27.328 14.550 1.000 -90.696 36.041 2 -15.877 6.204 .564 -42.897 11.142 3 -11.325 6.495 1.000 -39.612 16.962 4 -.430 3.926 1.000 -17.530 16.670 6 4.958 3.183 1.000 -8.905 18.820

6 1 -32.285 13.888 .795 -92.769 28.199 2 -20.835 8.387 .629 -57.361 15.691 3 -16.283 6.463 .598 -44.430 11.865 4 -5.388 6.066 1.000 -31.808 21.033 5 -4.958 3.183 1.000 -18.820 8.905

107 SFI – one-way ANOVA Analysis

ONEWAY Week2 BY cntrl1_ES2 /STATISTICS DESCRIPTIVES /MISSING ANALYSIS /POSTHOC=TUKEY ALPHA(0.05). Descriptives Week2 95% Confidence

Interval for Mean Std. Lower Upper N Mean Deviation Std. Error Bound Bound Minimum Maximum 1.00 19 -82.1457 6.19293 1.42075 -85.1306 -79.1608 -92.77 -70.54 2.00 20 -82.4566 4.70610 1.05232 -84.6592 -80.2541 -88.89 -71.41 Total 39 -82.3051 5.40975 .86625 -84.0588 -80.5515 -92.77 -70.54

ANOVA

Week2 Sum of Squares df Mean Square F Sig.

Between Groups .942 1 .942 .031 .860

Within Groups 1111.143 37 30.031

Total 1112.085 38

ONEWAY Week4 BY cntrl1_ES2 /STATISTICS DESCRIPTIVES /MISSING ANALYSIS /POSTHOC=TUKEY ALPHA(0.05). Descriptives Week4 95% Confidence

Interval for Mean Std. Lower Upper N Mean Deviation Std. Error Bound Bound Minimum Maximum 1.00 19 -82.5745 4.20230 .96407 -84.5999 -80.5491 -90.51 -76.22 2.00 20 -81.2184 4.55137 1.01772 -83.3485 -79.0883 -89.30 -72.96 Total 39 -81.8791 4.38109 .70154 -83.2993 -80.4589 -90.51 -72.96

108 ANOVA

Week4 Sum of Squares df Mean Square F Sig. Between Groups 17.918 1 17.918 .932 .341

Within Groups 711.453 37 19.228

Total 729.371 38

ONEWAY Week6 BY cntrl1_ES2 /STATISTICS DESCRIPTIVES /MISSING ANALYSIS /POSTHOC=TUKEY ALPHA(0.05). Descriptives Week6 95% Confidence

Interval for Mean Std. Lower Upper N Mean Deviation Std. Error Bound Bound Minimum Maximum 1.00 19 -75.4440 6.00457 1.37754 -78.3381 -72.5498 -86.13 -63.04 2.00 20 -78.0454 6.56676 1.46837 -81.1188 -74.9721 -89.98 -69.27

Total 39 -76.7780 6.35414 1.01748 -78.8378 -74.7183 -89.98 -63.04

ANOVA

Week6 Sum of Squares df Mean Square F Sig. Between Groups 65.942 1 65.942 1.662 .205

Within Groups 1468.313 37 39.684

Total 1534.254 38

109 ONEWAY Week8 BY cntrl1ES2 /STATISTICS DESCRIPTIVES /MISSING ANALYSIS /POSTHOC=TUKEY ALPHA(0.05).

Descriptives

Week8 95% Confidence

Interval for Mean Std. Lower Upper N Mean Deviation Std. Error Bound Bound Minimum Maximum 1.00 8 -77.1862 10.65798 3.76816 -86.0965 -68.2759 -99.43 -66.91

2.00 8 -72.7674 10.66703 3.77136 -81.6853 -63.8496 -87.84 -60.55 Total 16 -74.9768 10.55067 2.63767 -80.5989 -69.3548 -99.43 -60.55

ANOVA

Week8 Sum of Squares df Mean Square F Sig. Between Groups 78.104 1 78.104 .687 .421

Within Groups 1591.646 14 113.689

Total 1669.750 15

ONEWAY Week10 BY cntrl1ES2 /STATISTICS DESCRIPTIVES /MISSING ANALYSIS /POSTHOC=TUKEY ALPHA(0.05). Descriptives Week10 95% Confidence

Interval for Mean Std. Lower Upper N Mean Deviation Std. Error Bound Bound Minimum Maximum 1.00 8 -74.8645 17.42999 6.16243 -89.4364 -60.2927 -104.69 -59.75 2.00 8 -73.4128 9.03618 3.19477 -80.9672 -65.8584 -85.63 -58.09 Total 16 -74.1387 13.43287 3.35822 -81.2965 -66.9808 -104.69 -58.09

110 ANOVA

Week10 Sum of Squares df Mean Square F Sig. Between Groups 8.430 1 8.430 .044 .837

Within Groups 2698.199 14 192.729

Total 2706.629 15

ONEWAY Week12 BY cntrl1ES2 /STATISTICS DESCRIPTIVES /MISSING ANALYSIS /POSTHOC=TUKEY ALPHA(0.05). Descriptives Week12 95% Confidence Interval

for Mean Std. Lower Upper N Mean Deviation Std. Error Bound Bound Minimum Maximum 1.00 8 -72.3859 16.04361 5.67227 -85.7987 -58.9731 -100.43 -58.56 2.00 8 -72.2750 4.60315 1.62746 -76.1233 -68.4266 -78.60 -64.84

Total 16 -72.3304 11.40220 2.85055 -78.4063 -66.2546 -100.43 -58.56

ANOVA

Week12 Sum of Squares df Mean Square F Sig.

Between Groups .049 1 .049 .000 .985

Within Groups 1950.104 14 139.293

Total 1950.154 15

111 SFI – Control Group Repeated Measures ANOVA for 12-week Rats

GLM SFIcn_2wk SFIcn_4wk SFIcn_6wk SFIcn_8wk SFIcn_10wk SFIcn_12wk /WSFACTOR=Time 6 Polynomial /MEASURE=SFI /METHOD=SSTYPE(3) /EMMEANS=TABLES(Time) COMPARE ADJ(BONFERRONI) /PRINT=DESCRIPTIVE ETASQ /CRITERIA=ALPHA(.05) /WSDESIGN=Time.

Within-Subjects Factors Measure: SFI Dependent Time Variable 1 SFIcn_2wk 2 SFIcn_4wk 3 SFIcn_6wk

4 SFIcn_8wk 5 SFIcn_10wk 6 SFIcn_12wk

Descriptive Statistics Mean Std. Deviation N SFIcn_2wk -80.2897 4.31754 8 SFIcn_4wk -80.9799 3.58169 8 SFIcn_6wk -73.5255 6.89598 8

SFIcn_8wk -77.1862 10.65798 8 SFIcn_10wk -74.8645 17.42999 8 SFIcn_12wk -72.3859 16.04361 8

112 Tests of Within-Subjects Effects

Measure: SFI Type III Sum of Mean

Source Squares df Square F Sig. Time Sphericity Assumed 506.728 5 101.346 1.170 .343 Greenhouse-Geisser 506.728 1.488 340.550 1.170 .331

Huynh-Feldt 506.728 1.797 282.024 1.170 .336 Lower-bound 506.728 1.000 506.728 1.170 .315

Error(Time) Sphericity Assumed 3032.030 35 86.629

Greenhouse-Geisser 3032.030 10.416 291.099

Huynh-Feldt 3032.030 12.577 241.072

Lower-bound 3032.030 7.000 433.147

Estimated Marginal Means

Time Estimates Measure: SFI 95% Confidence Interval

Time Mean Std. Error Lower Bound Upper Bound 1 -80.290 1.526 -83.899 -76.680

2 -80.980 1.266 -83.974 -77.985 3 -73.526 2.438 -79.291 -67.760 4 -77.186 3.768 -86.097 -68.276

5 -74.865 6.162 -89.436 -60.293 6 -72.386 5.672 -85.799 -58.973

110 Pairwise Comparisons

Measure: SFI 95% Confidence Interval for

a Mean Difference Difference (I) Time (J) Time (I-J) Std. Error Sig.a Lower Bound Upper Bound

1 2 .690 2.041 1.000 -8.199 9.579 3 -6.764 2.730 .635 -18.653 5.124 4 -3.103 4.609 1.000 -23.177 16.971 5 -5.425 6.797 1.000 -35.030 24.179 6 -7.904 6.007 1.000 -34.065 18.258

2 1 -.690 2.041 1.000 -9.579 8.199 3 -7.454 2.899 .554 -20.079 5.170 4 -3.794 3.244 1.000 -17.924 10.337 5 -6.115 5.963 1.000 -32.087 19.857 6 -8.594 5.503 1.000 -32.561 15.373

3 1 6.764 2.730 .635 -5.124 18.653 2 7.454 2.899 .554 -5.170 20.079 4 3.661 4.769 1.000 -17.110 24.431 5 1.339 6.513 1.000 -27.029 29.707

6 -1.140 6.095 1.000 -27.683 25.404

4 1 3.103 4.609 1.000 -16.971 23.177 2 3.794 3.244 1.000 -10.337 17.924 3 -3.661 4.769 1.000 -24.431 17.110

5 -2.322 3.195 1.000 -16.236 11.593 6 -4.800 3.127 1.000 -18.420 8.819

5 1 5.425 6.797 1.000 -24.179 35.030 2 6.115 5.963 1.000 -19.857 32.087 3 -1.339 6.513 1.000 -29.707 27.029 4 2.322 3.195 1.000 -11.593 16.236 6 -2.479 1.626 1.000 -9.559 4.602

6 1 7.904 6.007 1.000 -18.258 34.065 2 8.594 5.503 1.000 -15.373 32.561 3 1.140 6.095 1.000 -25.404 27.683 4 4.800 3.127 1.000 -8.819 18.420 5 2.479 1.626 1.000 -4.602 9.559

111 SFI – ES Group Repeated Measures ANOVA for 12-week Rats

GLM SFIes_2 SFIes_4 SFIes_6 SFIes_8 SFIes_10 SFIes_12 /WSFACTOR=Time 6 Polynomial /MEASURE=SFI /METHOD=SSTYPE(3) /EMMEANS=TABLES(Time) COMPARE ADJ(BONFERRONI) /PRINT=DESCRIPTIVE ETASQ /CRITERIA=ALPHA(.05) /WSDESIGN=Time.

Within-Subjects Factors Measure: SFI Dependent Time Variable 1 SFIes_2

2 SFIes_4 3 SFIes_6 4 SFIes_8

5 SFIes_10

6 SFIes_12

Descriptive Statistics Mean Std. Deviation N SFIes_2 -81.2145 3.65052 8

SFIes_4 -79.0049 3.58489 8 SFIes_6 -74.3762 5.26833 8 SFIes_8 -72.7674 10.66703 8 SFIes_10 -73.4128 9.03618 8 SFIes_12 -72.2750 4.60315 8

112 Tests of Within-Subjects Effects

Measure: SFI Type III Sum of

Source Squares df Mean Square F Sig.

Time Sphericity Assumed 547.411 5 109.482 3.551 .011

Greenhouse-Geisser 547.411 1.836 298.172 3.551 .062

Huynh-Feldt 547.411 2.457 222.816 3.551 .043

Lower-bound 547.411 1.000 547.411 3.551 .102

Error(Time) Sphericity Assumed 1079.143 35 30.833

Greenhouse-Geisser 1079.143 12.851 83.972

Huynh-Feldt 1079.143 17.198 62.750

Lower-bound 1079.143 7.000 154.163

Estimated Marginal Means

Time Estimates Measure: SFI 95% Confidence Interval

Time Mean Std. Error Lower Bound Upper Bound 1 -81.214 1.291 -84.266 -78.163 2 -79.005 1.267 -82.002 -76.008 3 -74.376 1.863 -78.781 -69.972

4 -72.767 3.771 -81.685 -63.850 5 -73.413 3.195 -80.967 -65.858

6 -72.275 1.627 -76.123 -68.427

113 Pairwise Comparisons

Measure: SFI 95% Confidence Interval for

b Mean Difference Difference (I) Time (J) Time (I-J) Std. Error Sig.b Lower Bound Upper Bound

1 2 -2.210 1.364 1.000 -8.150 3.731 3 -6.838 2.526 .455 -17.841 4.165 4 -8.447 4.149 1.000 -26.519 9.625 5 -7.802 3.795 1.000 -24.332 8.728 6 -8.940* 1.300 .004 -14.599 -3.280

2 1 2.210 1.364 1.000 -3.731 8.150 3 -4.629 2.487 1.000 -15.462 6.205 4 -6.237 3.916 1.000 -23.291 10.816 5 -5.592 3.000 1.000 -18.657 7.473 6 -6.730 1.697 .081 -14.122 .662

3 1 6.838 2.526 .455 -4.165 17.841 2 4.629 2.487 1.000 -6.205 15.462 4 -1.609 2.354 1.000 -11.862 8.644 5 -.963 2.538 1.000 -12.018 10.091

6 -2.101 1.884 1.000 -10.306 6.103

4 1 8.447 4.149 1.000 -9.625 26.519 2 6.237 3.916 1.000 -10.816 23.291 3 1.609 2.354 1.000 -8.644 11.862

5 .645 2.199 1.000 -8.932 10.222 6 -.492 3.204 1.000 -14.447 13.462

5 1 7.802 3.795 1.000 -8.728 24.332 2 5.592 3.000 1.000 -7.473 18.657 3 .963 2.538 1.000 -10.091 12.018 4 -.645 2.199 1.000 -10.222 8.932 6 -1.138 3.168 1.000 -14.935 12.659

6 1 8.940* 1.300 .004 3.280 14.599 2 6.730 1.697 .081 -.662 14.122 3 2.101 1.884 1.000 -6.103 10.306 4 .492 3.204 1.000 -13.462 14.447 5 1.138 3.168 1.000 -12.659 14.935

114 Distal Fiber Density – two-way ANOVA Analysis

UNIANOVA Fiber_Density BY Time Exp_Grp /METHOD=SSTYPE(3) /INTERCEPT=INCLUDE /POSTHOC=Time Exp_Grp(TUKEY) /PLOT=PROFILE(Time*Exp_Grp) /EMMEANS=TABLES(Time) COMPARE ADJ(LSD) /EMMEANS=TABLES(Exp_Grp) COMPARE ADJ(LSD) /EMMEANS=TABLES(Time*Exp_Grp) COMPARE(Exp_Grp) /PRINT=ETASQ DESCRIPTIVE HOMOGENEITY /CRITERIA=ALPHA(.05) /DESIGN=Time Exp_Grp Time*Exp_Grp.

Between-Subjects Factors N Time 6 53 12 33

Exp_Grp 1.00 41 2.00 45

Descriptive Statistics Dependent Variable: Fiber_Density Time Exp_Grp Mean Std. Deviation N

6 1.00 6592.0780 4325.49711 25 2.00 8890.7078 4182.08992 28 Total 7806.4484 4365.62086 53

12 1.00 19373.7030 6193.58622 16 2.00 22387.2279 9949.22153 17 Total 20926.1249 8355.48471 33

Total 1.00 11580.0292 8090.57926 41 2.00 13989.3932 9514.13432 45 Total 12840.7429 8895.24806 86

115 Tests of Between-Subjects Effects Dependent Variable: Fiber_Density Type III Sum of Partial Eta

Source Squares df Mean Square F Sig. Squared Corrected Model 3645197581.387 3 1215065860.462 32.344 .000 .542 a

Intercept 16630528971.67 1 16630528971.678 442.694 .000 .844 8 Time 3504611094.318 1 3504611094.318 93.291 .000 .532 Exp_Grp 143216104.558 1 143216104.558 3.812 .054 .044 Time * Exp_Grp 2593791.723 1 2593791.723 .069 .793 .001

Error 3080464657.795 82 37566642.168

Total 20905744552.16 86 9

Corrected Total 6725662239.182 85

Estimated Marginal Means 1. Time Estimates Dependent Variable: Fiber_Density 95% Confidence Interval

Time Mean Std. Error Lower Bound Upper Bound

6 7741.393 843.257 6063.885 9418.900 12 20880.465 1067.441 18756.986 23003.945

Pairwise Comparisons

Dependent Variable: Fiber_Density 95% Confidence Interval for

b Mean Difference Difference (I) Time (J) Time (I-J) Std. Error Sig.b Lower Bound Upper Bound 6 12 -13139.073* 1360.335 .000 -15845.213 -10432.932 12 6 13139.073* 1360.335 .000 10432.932 15845.213

Based on estimated marginal means *. The mean difference is significant at the .05 level. b. Adjustment for multiple comparisons: Least Significant Difference (equivalent to no adjustments).

116 2. Exp_Grp

Estimates Dependent Variable: Fiber_Density 95% Confidence Interval

Exp_Grp Mean Std. Error Lower Bound Upper Bound 1.00 12982.890 981.145 11031.081 14934.700

2.00 15638.968 942.267 13764.499 17513.437

Pairwise Comparisons Dependent Variable: Fiber_Density 95% Confidence Interval for

a Mean Difference Difference (I) Exp_Grp (J) Exp_Grp (I-J) Std. Error Sig.a Lower Bound Upper Bound

1.00 2.00 -2656.077 1360.335 .054 -5362.218 50.063 2.00 1.00 2656.077 1360.335 .054 -50.063 5362.218

3. Time * Exp_Grp Estimates Dependent Variable: Fiber_Density 95% Confidence Interval

Time Exp_Grp Mean Std. Error Lower Bound Upper Bound

6 1.00 6592.078 1225.833 4153.506 9030.650 2.00 8890.708 1158.303 6586.474 11194.941

12 1.00 19373.703 1532.291 16325.488 22421.918 2.00 22387.228 1486.540 19430.025 25344.430

Pairwise Comparisons Dependent Variable: Fiber_Density 95% Confidence 95% Confidence

Interval for Interval for

(I) (J) Mean Differencea Difference

Time Exp_Grp Exp_Grp Difference (I-J) Std. Error Sig.a Lower Bound Upper Bound

6 1.00 2.00 -2298.630 1686.515 .177 -5653.645 1056.385 2.00 1.00 2298.630 1686.515 .177 -1056.385 5653.645

12 1.00 2.00 -3013.525 2134.881 .162 -7260.484 1233.434 2.00 1.00 3013.525 2134.881 .162 -1233.434 7260.484

117 Distal Fiber Width – two-way ANOVA Analysis UNIANOVA Fiber_Width BY Time Exp_Grp /METHOD=SSTYPE(3) /INTERCEPT=INCLUDE /POSTHOC=Time Exp_Grp(TUKEY) /PLOT=PROFILE(Time*Exp_Grp) /EMMEANS=TABLES(Time) COMPARE ADJ(LSD) /EMMEANS=TABLES(Exp_Grp) COMPARE ADJ(LSD) /EMMEANS=TABLES(Time*Exp_Grp) COMPARE(Exp_Grp) /PRINT=ETASQ DESCRIPTIVE HOMOGENEITY /CRITERIA=ALPHA(.05) /DESIGN=Time Exp_Grp Time*Exp_Grp.

Between-Subjects Factors N

Time 6 53 12 33

Exp_Grp 1.00 41 2.00 45

Descriptive Statistics Dependent Variable: Fiber_Width Time Exp_Grp Mean Std. Deviation N

6 1.00 1.5375 .14234 25 2.00 1.4841 .08395 28 Total 1.5093 .11720 53

12 1.00 1.5476 .08527 16 2.00 1.5332 .06801 17 Total 1.5402 .07599 33

Total 1.00 1.5415 .12210 41 2.00 1.5027 .08115 45 Total 1.5212 .10395 86

118 Tests of Between-Subjects Effects

Dependent Variable: Fiber_Width Type III Sum of Partial Eta

Source Squares df Mean Square F Sig. Squared Corrected Model .059a 3 .020 1.869 .141 .064 Intercept 189.002 1 189.002 18029.067 .000 .995

Time .018 1 .018 1.695 .197 .020 Exp_Grp .023 1 .023 2.228 .139 .026 Time * Exp_Grp .008 1 .008 .736 .393 .009

Error .860 82 .010

Total 199.919 86

Corrected Total .918 85

Estimated Marginal Means 1. Time Estimates Dependent Variable: Fiber_Width 95% Confidence Interval

Time Mean Std. Error Lower Bound Upper Bound

6 1.511 .014 1.483 1.539 12 1.540 .018 1.505 1.576

Pairwise Comparisons Dependent Variable: Fiber_Width 95% Confidence Interval for

a Mean Difference Difference (I) Time (J) Time (I-J) Std. Error Sig.a Lower Bound Upper Bound 6 12 -.030 .023 .197 -.075 .016 12 6 .030 .023 .197 -.016 .075

119 2. Exp_Grp Estimates Dependent Variable: Fiber_Width 95% Confidence Interval

Exp_Grp Mean Std. Error Lower Bound Upper Bound 1.00 1.543 .016 1.510 1.575 2.00 1.509 .016 1.477 1.540

Pairwise Comparisons Dependent Variable: Fiber_Width 95% Confidence Interval for

a Mean Difference Difference (I) Exp_Grp (J) Exp_Grp (I-J) Std. Error Sig.a Lower Bound Upper Bound

1.00 2.00 .034 .023 .139 -.011 .079 2.00 1.00 -.034 .023 .139 -.079 .011

3. Time * Exp_Grp Estimates Dependent Variable: Fiber_Width 95% Confidence Interval

Time Exp_Grp Mean Std. Error Lower Bound Upper Bound

6 1.00 1.538 .020 1.497 1.578 2.00 1.484 .019 1.446 1.523

12 1.00 1.548 .026 1.497 1.599 2.00 1.533 .025 1.484 1.583

Pairwise Comparisons Dependent Variable: Fiber_Width 95% Confidence 95% Confidence

Interval for Interval for

(I) (J) Mean Differencea Difference

Time Exp_Grp Exp_Grp Difference (I-J) Std. Error Sig.a Lower Bound Upper Bound 6 1.00 2.00 .053 .028 .061 -.003 .109 2.00 1.00 -.053 .028 .061 -.109 .003

12 1.00 2.00 .014 .036 .687 -.057 .085 2.00 1.00 -.014 .036 .687 -.085 .057

120 Distal Percent Nerve – two-way ANOVA Analysis

UNIANOVA Percent_Nerve BY Time Exp_Grp /METHOD=SSTYPE(3) /INTERCEPT=INCLUDE /POSTHOC=Time Exp_Grp(TUKEY) /PLOT=PROFILE(Time*Exp_Grp) /EMMEANS=TABLES(Time) COMPARE ADJ(LSD) /EMMEANS=TABLES(Exp_Grp) COMPARE ADJ(LSD) /EMMEANS=TABLES(Time*Exp_Grp) COMPARE(Exp_Grp) /PRINT=ETASQ DESCRIPTIVE HOMOGENEITY /CRITERIA=ALPHA(.05) /DESIGN=Time Exp_Grp Time*Exp_Grp.

Between-Subjects Factors N Time 6 53 12 33

Exp_Grp 1.00 41 2.00 45

Descriptive Statistics Dependent Variable: Percent_Nerve Time Exp_Grp Mean Std. Deviation N

6 1.00 1.4838 1.36279 25 2.00 1.7008 .81788 28 Total 1.5984 1.10293 53

12 1.00 4.0641 1.33797 16 2.00 4.6475 2.25819 17 Total 4.3647 1.86454 33

Total 1.00 2.4908 1.84649 41 2.00 2.8140 2.08622 45 Total 2.6599 1.97074 86

121 Tests of Between-Subjects Effects Dependent Variable: Percent_Nerve Type III Sum of Partial Eta

Source Squares df Mean Square F Sig. Squared Corrected Model 159.047a 3 53.016 25.411 .000 .482 Intercept 718.244 1 718.244 344.267 .000 .808

Time 155.038 1 155.038 74.312 .000 .475 Exp_Grp 3.251 1 3.251 1.558 .215 .019 Time * Exp_Grp .682 1 .682 .327 .569 .004

Error 171.077 82 2.086

Total 938.582 86

Corrected Total 330.124 85

Estimated Marginal Means 1. Time Estimates Dependent Variable: Percent_Nerve 95% Confidence Interval

Time Mean Std. Error Lower Bound Upper Bound

6 1.592 .199 1.197 1.988 12 4.356 .252 3.855 4.856

Pairwise Comparisons

Dependent Variable: Percent_Nerve 95% Confidence Interval for

b Mean Difference Difference (I) Time (J) Time (I-J) Std. Error Sig.b Lower Bound Upper Bound

6 12 -2.764* .321 .000 -3.401 -2.126 12 6 2.764* .321 .000 2.126 3.401

122 2. Exp_Grp Estimates Dependent Variable: Percent_Nerve 95% Confidence Interval

Exp_Grp Mean Std. Error Lower Bound Upper Bound 1.00 2.774 .231 2.314 3.234 2.00 3.174 .222 2.732 3.616

Pairwise Comparisons Dependent Variable: Percent_Nerve 95% Confidence Interval for

a Mean Difference Difference (I) Exp_Grp (J) Exp_Grp (I-J) Std. Error Sig.a Lower Bound Upper Bound

1.00 2.00 -.400 .321 .215 -1.038 .238 2.00 1.00 .400 .321 .215 -.238 1.038

3. Time * Exp_Grp Estimates Dependent Variable: Percent_Nerve 95% Confidence Interval

Time Exp_Grp Mean Std. Error Lower Bound Upper Bound

6 1.00 1.484 .289 .909 2.059 2.00 1.701 .273 1.158 2.244

12 1.00 4.064 .361 3.346 4.782 2.00 4.648 .350 3.951 5.344

Pairwise Comparisons Dependent Variable: Percent_Nerve 95% Confidence 95% Confidence

Interval for Interval for

(I) (J) Mean Differencea Difference

Time Exp_Grp Exp_Grp Difference (I-J) Std. Error Sig.a Lower Bound Upper Bound

6 1.00 2.00 -.217 .397 .587 -1.008 .574 2.00 1.00 .217 .397 .587 -.574 1.008

12 1.00 2.00 -.583 .503 .250 -1.584 .417 2.00 1.00 .583 .503 .250 -.417 1.584

123 Midline Fiber Density – two-way ANOVA Analysis

Between-Subjects Factors N Time 6 56 12 43

Exp_Grp 1.00 45 2.00 54

Descriptive Statistics Dependent Variable: Fiber_Density Time Exp_Grp Mean Std. Deviation N

6 1.00 13729.1347 6392.38214 25 2.00 14068.3348 3407.71744 31 Total 13916.9062 4918.73900 56

12 1.00 29209.1491 5502.98043 20 2.00 25972.0135 6560.73347 23 Total 27477.6580 6238.18012 43

Total 1.00 20609.1411 9791.73960 45 2.00 19138.4202 7729.21407 54 Total 19806.9297 8711.93838 99

124 Tests of Between-Subjects Effects Dependent Variable: Fiber_Density Type III Sum of Partial Eta

Source Squares df Mean Square F Sig. Squared Corrected Model 4586589967.189 3 1528863322.396 50.937 .000 .617 a

Intercept 41544624508.79 1 41544624508.798 1384.14 .000 .936 8 0 Time 4524455632.345 1 4524455632.345 150.741 .000 .613 Exp_Grp 50671028.863 1 50671028.863 1.688 .197 .017 Time * Exp_Grp 77171839.840 1 77171839.840 2.571 .112 .026

Error 2851401331.489 95 30014750.858

Total 46277123303.67 99 4

Corrected Total 7437991298.677 98

Estimated Marginal Means 1. Time Estimates Dependent Variable: Fiber_Density 95% Confidence Interval

Time Mean Std. Error Lower Bound Upper Bound

6 13898.735 736.344 12436.908 15360.562 12 27590.581 837.515 25927.903 29253.259

Pairwise Comparisons

Dependent Variable: Fiber_Density 95% Confidence Interval for

b Mean Difference Difference (I) Time (J) Time (I-J) Std. Error Sig.b Lower Bound Upper Bound 6 12 -13691.847* 1115.183 .000 -15905.765 -11477.928 12 6 13691.847* 1115.183 .000 11477.928 15905.765

125 2. Exp_Grp Estimates Dependent Variable: Fiber_Density 95% Confidence Interval

Exp_Grp Mean Std. Error Lower Bound Upper Bound 1.00 21469.142 821.786 19837.691 23100.593 2.00 20020.174 753.858 18523.577 21516.771

Pairwise Comparisons Dependent Variable: Fiber_Density 95% Confidence Interval for

a Mean Difference Difference (I) Exp_Grp (J) Exp_Grp (I-J) Std. Error Sig.a Lower Bound Upper Bound

1.00 2.00 1448.968 1115.183 .197 -764.951 3662.886 2.00 1.00 -1448.968 1115.183 .197 -3662.886 764.951

3. Time * Exp_Grp Estimates Dependent Variable: Fiber_Density 95% Confidence Interval

Time Exp_Grp Mean Std. Error Lower Bound Upper Bound

6 1.00 13729.135 1095.714 11553.867 15904.403 2.00 14068.335 983.981 12114.886 16021.783

12 1.00 29209.149 1225.046 26777.125 31641.173 2.00 25972.014 1142.361 23704.140 28239.887

Pairwise Comparisons Dependent Variable: Fiber_Density 95%

95% Confidence Confidence

Interval for Interval for

a (I) (J) Mean Difference Difference Time Exp_Grp Exp_Grp Difference (I-J) Std. Error Sig.a Lower Bound Upper Bound

6 1.00 2.00 -339.200 1472.687 .818 -3262.854 2584.454 2.00 1.00 339.200 1472.687 .818 -2584.454 3262.854

12 1.00 2.00 3237.136 1675.030 .056 -88.220 6562.491 2.00 1.00 -3237.136 1675.030 .056 -6562.491 88.220

126 Midline Fiber Width – two-way ANOVA Analysis

UNIANOVA Fiber_Width BY Time Exp_Grp /METHOD=SSTYPE(3) /INTERCEPT=INCLUDE /POSTHOC=Time Exp_Grp(TUKEY) /PLOT=PROFILE(Time*Exp_Grp) /EMMEANS=TABLES(Time) COMPARE ADJ(LSD) /EMMEANS=TABLES(Exp_Grp) COMPARE ADJ(LSD) /EMMEANS=TABLES(Time*Exp_Grp) COMPARE(Exp_Grp) /PRINT=ETASQ DESCRIPTIVE HOMOGENEITY /CRITERIA=ALPHA(.05) /DESIGN=Time Exp_Grp Time*Exp_Grp.

Between-Subjects Factors N Time 6 56 12 43

Exp_Grp 1.00 45 2.00 54

Descriptive Statistics Dependent Variable: Fiber_Width Time Exp_Grp Mean Std. Deviation N

6 1.00 1.5184 .07963 25 2.00 1.4527 .08082 31 Total 1.4821 .08611 56

12 1.00 1.5581 .11525 20 2.00 1.5532 .09380 23 Total 1.5555 .10308 43

Total 1.00 1.5361 .09794 45 2.00 1.4955 .09932 54 Total 1.5140 .10026 99

127 Tests of Between-Subjects Effects Dependent Variable: Fiber_Width Type III Sum of Partial Eta

Source Squares df Mean Square F Sig. Squared Corrected Model .191a 3 .064 7.620 .000 .194 Intercept 223.226 1 223.226 26704.668 .000 .996

Time .119 1 .119 14.180 .000 .130 Exp_Grp .030 1 .030 3.595 .061 .036 Time * Exp_Grp .022 1 .022 2.665 .106 .027

Error .794 95 .008

Total 227.899 99

Corrected Total .985 98

Estimated Marginal Means 1. Time Estimates Dependent Variable: Fiber_Width 95% Confidence Interval

Time Mean Std. Error Lower Bound Upper Bound

6 1.486 .012 1.461 1.510 12 1.556 .014 1.528 1.583

Pairwise Comparisons Dependent Variable: Fiber_Width 95% Confidence Interval for

b Mean Difference Difference (I) Time (J) Time (I-J) Std. Error Sig.b Lower Bound Upper Bound 6 12 -.070* .019 .000 -.107 -.033 12 6 .070* .019 .000 .033 .107

128 2. Exp_Grp Estimates Dependent Variable: Fiber_Width 95% Confidence Interval

Exp_Grp Mean Std. Error Lower Bound Upper Bound 1.00 1.538 .014 1.511 1.565 2.00 1.503 .013 1.478 1.528

Pairwise Comparisons Dependent Variable: Fiber_Width 95% Confidence Interval for

a Mean Difference Difference (I) Exp_Grp (J) Exp_Grp (I-J) Std. Error Sig.a Lower Bound Upper Bound

1.00 2.00 .035 .019 .061 -.002 .072 2.00 1.00 -.035 .019 .061 -.072 .002

3. Time * Exp_Grp Estimates Dependent Variable: Fiber_Width 95% Confidence Interval

Time Exp_Grp Mean Std. Error Lower Bound Upper Bound

6 1.00 1.518 .018 1.482 1.555 2.00 1.453 .016 1.420 1.485

12 1.00 1.558 .020 1.518 1.599 2.00 1.553 .019 1.515 1.591

Pairwise Comparisons Dependent Variable: Fiber_Width 95%

95% Confidence Confidence

Interval for Interval for

(I) (J) Mean Differenceb Difference

Time Exp_Grp Exp_Grp Difference (I-J) Std. Error Sig.b Lower Bound Upper Bound 6 1.00 2.00 .066* .025 .009 .017 .114 2.00 1.00 -.066* .025 .009 -.114 -.017

12 1.00 2.00 .005 .028 .861 -.051 .060 2.00 1.00 -.005 .028 .861 -.060 .051

129 Midline Percent Nerve – two-way ANOVA Analysis

Between-Subjects Factors N Time 6 56 12 43

Exp_Grp 1.00 45 2.00 54

Descriptive Statistics Dependent Variable: Percent_Nerve Time Exp_Grp Mean Std. Deviation N

6 1.00 2.7724 1.40926 25 2.00 2.6078 .73059 31 Total 2.6813 1.07916 56

12 1.00 6.2482 1.68201 20 2.00 5.4422 1.56326 23 Total 5.8171 1.65088 43

Total 1.00 4.3172 2.31423 45 2.00 3.8150 1.82152 54 Total 4.0433 2.06449 99

130 Tests of Between-Subjects Effects Dependent Variable: Percent_Nerve Type III Sum of Partial Eta

Source Squares df Mean Square F Sig. Squared Corrected Model 246.495a 3 82.165 45.595 .000 .590 Intercept 1758.245 1 1758.245 975.692 .000 .911

Time 240.246 1 240.246 133.318 .000 .584 Exp_Grp 5.684 1 5.684 3.154 .079 .032 Time * Exp_Grp 2.482 1 2.482 1.377 .243 .014

Error 171.195 95 1.802

Total 2036.166 99

Corrected Total 417.689 98

Estimated Marginal Means 1. Time Estimates Dependent Variable: Percent_Nerve 95% Confidence Interval

Time Mean Std. Error Lower Bound Upper Bound

6 2.690 .180 2.332 3.048 12 5.845 .205 5.438 6.253

Pairwise Comparisons Dependent Variable: Percent_Nerve 95% Confidence Interval for

b Mean Difference Difference (I) Time (J) Time (I-J) Std. Error Sig.b Lower Bound Upper Bound 6 12 -3.155* .273 .000 -3.698 -2.613 12 6 3.155* .273 .000 2.613 3.698

131 2. Exp_Grp Estimates Dependent Variable: Percent_Nerve 95% Confidence Interval

Exp_Grp Mean Std. Error Lower Bound Upper Bound 1.00 4.510 .201 4.111 4.910 2.00 4.025 .185 3.658 4.392

Pairwise Comparisons Dependent Variable: Percent_Nerve 95% Confidence Interval for

a Mean Difference Difference (I) Exp_Grp (J) Exp_Grp (I-J) Std. Error Sig.a Lower Bound Upper Bound

1.00 2.00 .485 .273 .079 -.057 1.028 2.00 1.00 -.485 .273 .079 -1.028 .057

3. Time * Exp_Grp Estimates Dependent Variable: Percent_Nerve 95% Confidence Interval

Time Exp_Grp Mean Std. Error Lower Bound Upper Bound

6 1.00 2.772 .268 2.239 3.305 2.00 2.608 .241 2.129 3.086

12 1.00 6.248 .300 5.652 6.844 2.00 5.442 .280 4.886 5.998

Pairwise Comparisons Dependent Variable: Percent_Nerve 95%

95% Confidence Confidence

Interval for Interval for

(I) (J) Mean Differencea Difference

Time Exp_Grp Exp_Grp Difference (I-J) Std. Error Sig.a Lower Bound Upper Bound 6 1.00 2.00 .165 .361 .649 -.552 .881 2.00 1.00 -.165 .361 .649 -.881 .552

12 1.00 2.00 .806 .410 .052 -.009 1.621 2.00 1.00 -.806 .410 .052 -1.621 .009

132 Proximal Fiber Density – two-way ANOVA Analysis

Between-Subjects Factors N Time 6 62 12 48

Exp_Grp 1.00 51 2.00 59

Descriptive Statistics Dependent Variable: Fiber_Density Time Exp_Grp Mean Std. Deviation N

6 1.00 12194.8184 3579.24215 29 2.00 14556.8288 3745.63969 33 Total 13452.0175 3827.80469 62

12 1.00 22753.9311 7786.99366 22 2.00 22249.9462 7772.77748 26 Total 22480.9393 7700.25042 48

Total 1.00 16749.7298 7780.57684 51 2.00 17947.0161 6972.89393 59 Total 17391.9106 7348.54031 110

133 Tests of Between-Subjects Effects Dependent Variable: Fiber_Density Type III Sum of Partial Eta

Source Squares df Mean Square F Sig. Squared Corrected Model 2294668045.114 3 764889348.371 22.575 .000 .390 a

Intercept 34625356051.09 1 34625356051.092 1021.953 .000 .906 2 Time 2240347733.855 1 2240347733.855 66.123 .000 .384 Exp_Grp 23215973.577 1 23215973.577 .685 .410 .006 Time * Exp_Grp 55237560.595 1 55237560.595 1.630 .204 .015

Error 3591445833.231 106 33881564.464

Total 39158754945.92 110 8

Corrected Total 5886113878.345 109

Estimated Marginal Means 1. Time Estimates Dependent Variable: Fiber_Density 95% Confidence Interval

Time Mean Std. Error Lower Bound Upper Bound

6 13375.824 740.784 11907.147 14844.500 12 22501.939 843.091 20830.429 24173.448

Pairwise Comparisons

Dependent Variable: Fiber_Density 95% Confidence Interval for

b Mean Difference Difference (I) Time (J) Time (I-J) Std. Error Sig.b Lower Bound Upper Bound 6 12 -9126.115* 1122.303 .000 -11351.189 -6901.041 12 6 9126.115* 1122.303 .000 6901.041 11351.189

134 2. Exp_Grp Estimates Dependent Variable: Fiber_Density 95% Confidence Interval

Exp_Grp Mean Std. Error Lower Bound Upper Bound 1.00 17474.375 822.861 15842.973 19105.777 2.00 18403.387 763.193 16890.284 19916.491

Pairwise Comparisons Dependent Variable: Fiber_Density 95% Confidence Interval for

a Mean Difference Difference (I) Exp_Grp (J) Exp_Grp (I-J) Std. Error Sig.a Lower Bound Upper Bound

1.00 2.00 -929.013 1122.303 .410 -3154.087 1296.061 2.00 1.00 929.013 1122.303 .410 -1296.061 3154.087

3. Time * Exp_Grp Estimates Dependent Variable: Fiber_Density 95% Confidence Interval

Time Exp_Grp Mean Std. Error Lower Bound Upper Bound

6 1.00 12194.818 1080.893 10051.843 14337.794 2.00 14556.829 1013.269 12547.924 16565.733

12 1.00 22753.931 1240.996 20293.536 25214.326 2.00 22249.946 1141.550 19986.712 24513.181

Pairwise Comparisons Dependent Variable: Fiber_Density 95% 95%

Confidence Confidence

Interval for Interval for

(I) (J) Mean Differencea Difference

Time Exp_Grp Exp_Grp Difference (I-J) Std. Error Sig.a Lower Bound Upper Bound 6 1.00 2.00 -2362.010 1481.568 .114 -5299.363 575.342

2.00 1.00 2362.010 1481.568 .114 -575.342 5299.363

12 1.00 2.00 503.985 1686.182 .766 -2839.034 3847.004 2.00 1.00 -503.985 1686.182 .766 -3847.004 2839.034

135 Proximal Fiber Width – two-way ANOVA Analysis

Between-Subjects Factors N Time 6 62 12 48

Exp_Grp 1.00 51 2.00 59

Descriptive Statistics Dependent Variable: Fiber_Width Time Exp_Grp Mean Std. Deviation N

6 1.00 1.6380 .15854 29 2.00 1.5834 .11234 33 Total 1.6089 .13753 62

12 1.00 1.7490 .13617 22 2.00 1.6260 .13122 26 Total 1.6824 .14589 48

Total 1.00 1.6859 .15796 51 2.00 1.6021 .12182 59 Total 1.6410 .14527 110

136 Tests of Between-Subjects Effects Dependent Variable: Fiber_Width Type III Sum of Partial Eta

Source Squares df Mean Square F Sig. Squared Corrected Model .373a 3 .124 6.830 .000 .162 Intercept 292.613 1 292.613 16091.530 .000 .993

Time .159 1 .159 8.740 .004 .076 Exp_Grp .212 1 .212 11.672 .001 .099 Time * Exp_Grp .032 1 .032 1.732 .191 .016

Error 1.928 106 .018

Total 298.507 110

Corrected Total 2.300 109

Estimated Marginal Means 1. Time Estimates Dependent Variable: Fiber_Width 95% Confidence Interval

Time Mean Std. Error Lower Bound Upper Bound

6 1.611 .017 1.577 1.645 12 1.688 .020 1.649 1.726

Pairwise Comparisons Dependent Variable: Fiber_Width 95% Confidence Interval for

b Mean Difference Difference (I) Time (J) Time (I-J) Std. Error Sig.b Lower Bound Upper Bound 6 12 -.077* .026 .004 -.128 -.025 12 6 .077* .026 .004 .025 .128

137 2. Exp_Grp

Estimates Dependent Variable: Fiber_Width 95% Confidence Interval

Exp_Grp Mean Std. Error Lower Bound Upper Bound 1.00 1.694 .019 1.656 1.731

2.00 1.605 .018 1.570 1.640

Pairwise Comparisons Dependent Variable: Fiber_Width 95% Confidence Interval for

b Mean Difference Difference (I) Exp_Grp (J) Exp_Grp (I-J) Std. Error Sig.b Lower Bound Upper Bound

1.00 2.00 .089* .026 .001 .037 .140 2.00 1.00 -.089* .026 .001 -.140 -.037

3. Time * Exp_Grp Estimates Dependent Variable: Fiber_Width 95% Confidence Interval

Time Exp_Grp Mean Std. Error Lower Bound Upper Bound

6 1.00 1.638 .025 1.588 1.688 2.00 1.583 .023 1.537 1.630

12 1.00 1.749 .029 1.692 1.806 2.00 1.626 .026 1.574 1.678

Pairwise Comparisons Dependent Variable: Fiber_Width 95% Confidence 95% Confidence

Interval for Interval for

b (I) (J) Mean Difference Difference Time Exp_Grp Exp_Grp Difference (I-J) Std. Error Sig.b Lower Bound Upper Bound

6 1.00 2.00 .055 .034 .115 -.013 .123 2.00 1.00 -.055 .034 .115 -.123 .013

12 1.00 2.00 .123* .039 .002 .046 .200 2.00 1.00 -.123* .039 .002 -.200 -.046

138 Proximal Percent Nerve – two-way ANOVA Analysis

Between-Subjects Factors N Time 6 62 12 48

Exp_Grp 1.00 51 2.00 59

Descriptive Statistics Dependent Variable: Percent_Nerve Time Exp_Grp Mean Std. Deviation N

6 1.00 2.8886 .97619 29 2.00 3.1919 .97559 33 Total 3.0500 .97979 62

12 1.00 6.2220 2.54306 22 2.00 5.1827 2.10228 26 Total 5.6590 2.34825 48

Total 1.00 4.3265 2.45559 51 2.00 4.0692 1.85036 59 Total 4.1885 2.14581 110

139 Tests of Between-Subjects Effects Dependent Variable: Percent_Nerve Type III Sum of Partial Eta

Source Squares df Mean Square F Sig. Squared Corrected Model 198.451a 3 66.150 23.108 .000 .395 Intercept 2055.974 1 2055.974 718.212 .000 .871

Time 190.631 1 190.631 66.593 .000 .386 Exp_Grp 3.643 1 3.643 1.273 .262 .012

Time * Exp_Grp 12.122 1 12.122 4.234 .042 .038

Error 303.439 106 2.863

Total 2431.655 110

Corrected Total 501.890 109

Estimated Marginal Means 1. Time Estimates Dependent Variable: Percent_Nerve 95% Confidence Interval

Time Mean Std. Error Lower Bound Upper Bound

6 3.040 .215 2.613 3.467 12 5.702 .245 5.216 6.188

Pairwise Comparisons Dependent Variable: Percent_Nerve 95% Confidence Interval for

b Mean Difference Difference (I) Time (J) Time (I-J) Std. Error Sig.b Lower Bound Upper Bound 6 12 -2.662* .326 .000 -3.309 -2.015 12 6 2.662* .326 .000 2.015 3.309

140 2. Exp_Grp Estimates Dependent Variable: Percent_Nerve 95% Confidence Interval

Exp_Grp Mean Std. Error Lower Bound Upper Bound 1.00 4.555 .239 4.081 5.029 2.00 4.187 .222 3.747 4.627

Pairwise Comparisons Dependent Variable: Percent_Nerve 95% Confidence Interval for

a Mean Difference Difference (I) Exp_Grp (J) Exp_Grp (I-J) Std. Error Sig.a Lower Bound Upper Bound

1.00 2.00 .368 .326 .262 -.279 1.015 2.00 1.00 -.368 .326 .262 -1.015 .279

3. Time * Exp_Grp Estimates Dependent Variable: Percent_Nerve 95% Confidence Interval

Time Exp_Grp Mean Std. Error Lower Bound Upper Bound

6 1.00 2.889 .314 2.266 3.511 2.00 3.192 .295 2.608 3.776

12 1.00 6.222 .361 5.507 6.937 2.00 5.183 .332 4.525 5.841

Pairwise Comparisons Dependent Variable: Percent_Nerve 95% Confidence 95% Confidence

Interval for Interval for

(I) (J) Mean b Difference Difference Time Exp_Grp Exp_Grp Difference (I-J) Std. Error Sig.b Lower Bound Upper Bound

6 1.00 2.00 -.303 .431 .483 -1.157 .551 2.00 1.00 .303 .431 .483 -.551 1.157

12 1.00 2.00 1.039* .490 .036 .068 2.011 2.00 1.00 -1.039* .490 .036 -2.011 -.068

141