Role of the human globus pallidus in tremorgenesis

by

Shane Ellis

A thesis submitted in conformity with the requirements for the degree of Master’s of Science

Department of Physiology University of Toronto© Copyright by Shane Ellis 2015

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Role of the human globus pallidus in tremorgenesis

Shane Ellis

Master of Science

Department of Physiology

University of Toronto

2015

Abstract

The GPi is a nucleus that serves as an output of the basal ganglia; a collection of nuclei which

function in selecting movements to be executed. Tremor is defined as an unintentional, rhythmic,

sinusoidal contraction of body parts. Currently, no scientific consensus has been reached as to

where in the brain tremor arises. Using microelectrode recordings in human patients with and

without tremor, we discovered a sub-population of cells that are capable of being induced into a

brief theta oscillation following microstimulation. However, we found that theta burst

stimulation was incapable of inducing visible tremor when microstimulating in the GPi, Vim, or

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

LTP-like response within the GPi. We believe that this work adds strength to the “pallidocentric”

view of tremor initiation which holds that the GPi is responsible for the onset of tremor.

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Acknowledgments

First and foremost, I would like to thank my supervisor, Dr. Hutchison for his mentorship and

guidance through these last two years; I couldn’t have completed this thesis without your help.

Next, I would like to thank my parents and family for their continual support throughout the

highs and lows of this journey called life. Even if you don’t understand what I’m doing, you still

support me whole-heartedly and I couldn’t ask for anything more. To Diellor Basa (I hope I’ve

spelt your name correctly this week) and Luka Srejic, thank you for “showing me the ropes”

inside the lab. Lastly, I would like to thank Dr. Sherri Thiele and Dr. Aman Mann for keeping

me sane and for taking time out of their busy days to help me when I was struggling.

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Table of Contents

Acknowledgments ............................................................................................................................... iii

Table of Contents .................................................................................................................................. iv

List of Tables .......................................................................................................................................... vi

List of Figures ........................................................................................................................................vii

List of Appendices................................................................................................................................. ix

List of Abbreviations ............................................................................................................................ x

Chapter 1 Introduction ........................................................................................................................ 1

Introduction ............................................................................................................................................ 1

1.1 Basal Ganglia Function ............................................................................................................................ 1

1.1.1 The Human Globus Pallidus Internus ........................................................................................................ 1

1.2 Parkinson’s Disease .................................................................................................................................. 4

1.2.1.1 Genetics of Parkinson’s Disease ............................................................................................................... 5

1.2.1.2 Treatments of Parkinson’s Disease ......................................................................................................... 6

1.2.1.2.1 Pharmacological Treatments of PD ..................................................................................................... 6

1.2.1.2.2 Surgical Treatments ................................................................................................................................... 7

1.2.2 Explanatory Models of PD .............................................................................................................................. 8

1.2.2.1 The Rate Model ................................................................................................................................................ 8

1.2.2.2 The Center-Surround Model .................................................................................................................... 11

1.2.2.3 The Oscillatory Network Model .............................................................................................................. 15

1.2.3 Animal Models of Parkinson’s Disease .................................................................................................... 17

1.3 Dystonia ...................................................................................................................................................... 19

1.3.1 Etiology of Dystonia ........................................................................................................................................ 19

1.3.2 Genetics of Dystonia ........................................................................................................................................ 20

1.3.3 Treatment of Dystonia ................................................................................................................................... 20

1.4 Essential Tremor ..................................................................................................................................... 21

1.4.1 Etiology of Essential Tremor ....................................................................................................................... 21

1.4.2 Genetics, Diagnosis and Treatment of Essential Tremor ................................................................. 22

1.5.1 Tremor ..................................................................................................................................................... 23

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1.6 Synaptic Plasticity ................................................................................................................................... 25

1.7 Project Rationale ..................................................................................................................................... 28

1.8 Hypothesis and Aims .............................................................................................................................. 30

Chapter 2- Methods ............................................................................................................................ 31

2.1 General Methods ...................................................................................................................................... 31

2.2 Intraoperative Microelectrode Recordings ................................................................................... 36

2.3 Tremor Entrainment with Theta Burst Stimulation ................................................................... 38

2.3 Effects of Theta Burst Stimulation on Plasticity in GPi .............................................................. 38

3.0 Chapter 3- Results ....................................................................................................................... 42

3.1 Stimulation-Induced Oscillation Characterization...................................................................... 42

3.2 Tremor Entrainment with Theta Burst Stimulation ................................................................... 52

3.2.1 Theta Burst Stimulation in the Globus Pallidus Internus ................................................................ 52

3.2.2 Tremor Entrainment with Theta Burst Stimulation Outside of The GPi ................................... 55

3.3 Effect of Theta Burst Stimulation on Plasticity within the GPi ................................................ 60

4.0 Chapter 4- Discussion ................................................................................................................ 63

4.1 Stimulation-Induced Oscillations ...................................................................................................... 64

4.2 Tremor Modification due to Theta Burst Stimulation in the Basal Ganglia ....................... 67

4.2.1 Theta Burst Stimulation in GPi ................................................................................................................... 67

4.2.2 Theta Burst Stimulation in Motor Thalamus ........................................................................................ 69

4.2.3 Theta Burst Stimulation in Sub Thalamic Nucleus ............................................................................. 70

4.3 Effect of Theta Burst Stimulation on Plasticity within the GPi ................................................ 70

4.4 Future Directions..................................................................................................................................... 73

4.5 Conclusions ................................................................................................................................................ 75

6.0 References ...................................................................................................................................... 76

Appendix ................................................................................................................................................ 85

................................................................................................................................................................... 91

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List of Tables

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

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List of Figures

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

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

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

Figure 4- A diagrammatic representation of the center-surround model of basal ganglia motor

command selection……………………………………………………………………………….26

Figure 5- An illustration demonstrating the dominance of oscillatory power of different

bandwidths as seen in Parkinson patients………………………………………………………..28

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

Figure 7- Sample trajectories taken to reach surgical target during mapping procedures for DBS

implantation surgeries……………………………………………………………………...…….46

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

implantation……………………………………………………………………………………...47

Figure 9- Example traces showing the regularity of neuronal firings and the corresponding burst

index value……………………………………………………………………………………….49

Figure 10- Diagrammatic representations of stimulation protocols used to assess plastic changes

in the GPi………………………………………………………………………………………..53

Figure 11- Group analysis of baseline (pre-stimulation) firing rate and mode burst index

separated by disease and cell type………………………………………………………………………………..55

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

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

Figure 14- Mode burst index ratios and firing rate ratios of PD and dystonia neurons

differentiated by stimulation intensities………………………………………………………..61

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

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

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

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

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

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List of Appendices

Appendix Figure 1- A sample of the read-out produced from the Matlab script MKaneoke from

the neuronal trace seen in Figure 6………………………………………………………………98

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

stimulation.…………………………………………………………………………………….100

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

stimulation………………………………………………………………………………………102

Appendix Figure 3- An example of theta burst stimulation inducing visible tremor in a patients

foot during microelectrode mapping for DBS implantation surgery…………………………...104

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List of Abbreviations

6-OHDA- 6-Hydroxydopamine

BOLD- Blood Oxygenation Level Dependent signal

COMT- Catechol-o-methyl Transferase

cTBS- Continuous Theta Burst

D1/D2- Dopamine Receptor Type 1 and 2

DBS- Deep Brain Stimulation

DYT- Dystonia-related gene

EEG- Electroencephalogram

EPSP- Excitatory Post-Synaptic Potential

EMG- Electromyogram

ET- Essential Tremor

ETM- essenetial tremor genes

FFT- Fast Fourier Transform

fEP- Field Evoked Potential

fMRI- Functional Magnetic Resonance

GABA- Gamma-Aminobutyric Acid

GI- Gastro-intestinal

GPe- Globus Pallidus Externus

GPi- Globus Pallidus Internus

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HFD- High Frequency Discharge

HFS- High Frequency Stimulation

IPSP- Inhibitory Post-Synaptic Potential

LIDs- Levodopa-Induced Dyskinesias

L-DOPA- L-3,4-dihydroxyphenylalanine

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

LRRK- Leucine-Rich Repeat Kinase

LTD- Long Term Depression

LTP- Long Term Potentiation

M1- Primary Motor Cortex

MAO- Monoamine Oxidase

MPP+- 1-methyl-4-phenylpyridinium

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

NMDA- N-Methyl-D-Aspartate

N.S.- Not Significant

PARK- Parkinson Related Gene

PAS- Paired Associative Stimulation

PD- Parkinson’s Disease

S1- Primary Somatosensory Cortex

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SLC1A2- Solute Carrier Family 1 Member 2

SNc- Substantia Nigra Pars Compacta

SNCA- Synuclien, Alpha

SNr- Substantia Nigra Pars Reticulata

STN- Subthalamic Nucleus

Thal- Thalamus

THAP- Trihydroxyacetophenone

TBS- Theta-burst Stimulation

TMS- Transcranial Magnetic Stimulation

Vim- Ventral Intermediate Nucleus of Thalamus

Vop- Ventral Oralis Posterior Nucleus of Thalamus

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Chapter 1 Introduction

Introduction

1.1 Basal Ganglia Function

The basal ganglia are a collection of deep brain nuclei comprised of the caudate

and putamen (collectively known as the striatum), the external and internal segments of

the globus pallidus (GPe/GPi respectively) and substantia nigra pars reticulata (SNr), and

the subthalamic nucleus (STN). These nuclei are involved with various subsystems of the

brain including emotions, cognition, and motor control. The connectivity of the basal

ganglia is shown in Figure 1. With PD, the anatomical connectivity between nuclei

remains the same, but the gain of these connections is altered (see rate model below for

explanation and/or see figure 2). Focusing on movement commands, input to the basal

ganglia comes from the cortex and is received in the striatum and STN. The major

outputs of this circuit are the GPi and the SNr, with the GPi largely controlling movement

commands below the neck while the SNr deals with commands regarding the neck and

head (Mink 1996). Different theories (discussed below) have been put forward to explain

how information is processed within the basal ganglia, but in short, it appears that the

output nuclei (GPi and SNr) of the basal ganglia have been tasked with the final

determination of which motor programs are selected and which are inhibited.

1.1.1 The Human Globus Pallidus Internus

The GPi, along with the SNr are the major output nuclei of the basal ganglia. The

ventroposterior portion of the GPi has been shown to have neurons with kinesthetic

responses and has been identified as the sensorimotor portion of the GPi which is

anatomically separated from the limbic associative areas (Lozano and Hutchison, 2002).

As previously mentioned, the GPi is one of the major GABAergic outputs of the basal

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ganglia, and has extensive connections to the ventral oralis posterior nucleus (Vop) of the

thalamus. It receives extensive inhibitory input from the striatum, as well as from the GPe

and some excitatory input from the STN. Also of particular interest, new evidence has

shown a direct connection between the internal pallidum and the cortex, dubbed the

“super-direct pathway”, which has been shown to be glutamatergic and targets the

peripallidal region of the pallidum (Milardi et al. 2015) (Figure 1). There are two

principle neuron types identified by electrophysiological recordings within the GPi: high

frequency discharge neurons (HFDs), and border cells. Neurons of the GPi are relatively

large and send GABAergic projections to the thalamus and brainstem (Mink 1996).

Although the majority of cells are GABAergic, the border cells are cholinergic and

populate the peri-pallidal region of the nucleus. The border cells recorded from

specifically in this thesis were recorded from the dorsal edge of the GPi (where the

medullary lamina separates the GPe and GPi), and the internal medullary lamina/GPi

borders (figure 7A). These cholinergic border cells are believed to be migrants of the

Nucleus Basalis of Meynert (Mitchell et al. 1987). During microelectrode mapping, the

GPi is identified by the presence of its high frequency discharge, irregular firing neurons,

encapsulated by the lower frequency, regular firing border cells (Hutchison, 1998).

Further, these neurons (both HFDs and border cells) are inhibited by brief electrical

stimulation, believed to be caused by the local release of GABA.

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Figure 1- The connectivity of the nuclei comprising the basal ganglia. Nuclei

encapsulated by the yellow background are part of the indirect pathway, those on the

white background represent the direct pathway, while the red arrow represents the super-

direct pathway. Small black circles indicate inhibitory connections while green circles

indicate excitatory connections. Modified from Purves et al. Neuroscience. Sunderland:

Sinauer, 2008. Print.

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1.2 Parkinson’s Disease

Parkinson’s disease is a debilitating degenerative disease which is characterized

by a triad of cardinal symptoms: bradykinesia, rigidity, and tremor. Its onset occurs later

in life (typically 65 years and older) and its primary cause is due to neuronal death in the

substantia nigra pars compacta- a midbrain, dopaminergic nucleus with extensive

connections to various systems in the brain, including the motor system via the basal

ganglia. The disease is named after Dr. James Parkinson who was the first to characterize

the disease back in 1817, although he initially deemed it “shaking palsy”. It has been

estimated that 1-2% of the general population over 65% contracts PD and that this

incidence increases to 3-5% in the population over 85 years of age (Alves et al, 2008).

PD diagnosis is ascertained post-mortem by the presence of Lewy body inclusions (Alves

et al, 2008) and neuronal death within the substantia nigra pars compacta. These Lewy

bodies are dense cytoplasmic inclusions of ubiquinated α-synuclein protein. Whether

these protein inclusions are cytotoxic or protective remains a debate (Visanji et al. 2013).

It is important to note that Lewy bodies are not specific to Parkinson’s disease and have

been found in other diseases such as Lewy body dementia and multiple systems atrophy

(Dehay et al. 2015), however, their contribution to these different pathologies has yet to

be confirmed.

Although most only think of the motor abnormalities seen in PD, this disease also

effects other areas and processes of the body. Such areas include the olfactory bulbs and

the gastrointestinal tracts, and other processes include cognition, and sleep. In fact,

movement abnormalities usually only arise when ~80% of the neurons of the substantia

nigra pars compacta (SNc) die (Pahuja et al. 2015) and it is believed that non-motor

abnormalities arise before the motor symptoms are seen. These symptoms include a loss

of smell (possibly due to Lewy body formation in olfactory bulbs) (Visanji et al. 2013),

and issues pertaining to the GI tract which can include constipation and delay in gastric

emptying (Fasano et al. 2015).

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1.2.1.1 Genetics of Parkinson’s Disease

Although largely an idiopathic disease, approximately 10% of PD cases are

believed to be inherited (Thomas and Beal, 2007). Genes have been identified in these

cases which, when mutated, can be risk factors or causes of familial PD. It is important to

remember that phenotypes are the result of the interaction between the genotype and the

environment. Thus, having a genetic predisposition towards PD does not guarantee that

one will contract the disease, but is more apt to depend on the environmental stressors

present. There are 18 loci in the human genome that have been designated as PARK

regions due to their involvement with Parkinson’s disease; of which 12 have been

confirmed as Parkinson’s genes while the others may be risk factors or their effects

unsuccessfully replicated (Klein and Westenberger, 2012).

The first gene to be identified as having a role in PD was PARK1

(Polymerpoulous et al. 1996) (also known as SNCA and PARK4). The product of this

mutated gene is responsible for the α-synuclein aggregates that form the aforementioned

Lewy bodies and Lewy neurites. This mutation has a rapid progression, is implicated in

early onset PD and typically causes cognitive decline and dementia. It is also noteworthy

that it has a good initial response to levodopa treatment (Klein and Westenberger, 2012).

Another well-known mutation involved with PD is the LRRK2 (a.k.a. PARK8) gene.

This mutation causes mid-to-late onset PD without dementia and patients show a good

response to levodopa treatment. Patients with either PARK1 or PARK8 mutations have

Lewy body inclusions seen at post mortem. Parkin (PARK2) was the second Parkinson’s

gene identified. This mutation is typically associated with a slowly progressive, early

onset PD which tends to begin around 30-40 years of age, although some cases of

childhood onset have been documented (Klein and Westenberger, 2012). Many other

mutations have been noted in PD, but the last one that will be addressed in this work is

DJ-1. This mutation is of interest as it codes for protein involved in detecting oxidative

stress. Misfolding of this mutated protein renders it useless, and as such can make cells

6

more susceptible to environmental conditions (Klein and Westenberger, 2012). The DJ-1

mutation aids in showing the response between the genotype and environment.

1.2.1.2 Treatments of Parkinson’s Disease

Unfortunately, modern advances in medicine have still been unsuccessful in

developing treatment options to halt the progression of PD or reverse the damage already

done. As such, treatments of PD are currently symptomatic and developed to increase the

quality of life in PD patients (Calabresi et al. 2015).

1.2.1.2.1 Pharmacological Treatments of PD

The gold standard in treating PD is dopamine replacement therapy. This is usually

accomplished through the administration of the dopamine precursor, levodopa (L-DOPA)

that can cross the blood brain barrier and be metabolized into dopamine. An example of

such medication today is Sinemet®, a pill ingested by the patient which contains

levodopa as well as carbidopa. This combination of drugs allows the dopamine precursor

to reach the brain while preventing somatic degradation of dopamine that is designed to

keep the concentration of L-DOPA higher in the central nervous system than if L-DOPA

is delivered solely as a monotherapy (Gilbert et al. 2000). However, with chronic use of

these pharmacological replacement therapies, side effects often occur such as levodopa

induced dyskinesias (LIDs), which are abnormal involuntary movements of the limbs. As

such, investigators such as Pahuja et al. (2015) have begun looking at novel uses of

nanoparticles to deliver dopamine across the blood brain barrier and release this

dopamine in a more controlled manner in hopes of reducing side effects associated with

the treatment.

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As previously mentioned, dopamine depletion is a hallmark of Parkinson’s

disease and hence why some pharmacological techniques seek to increase the level of

dopamine within the brain. Besides administering the metabolic precursor of dopamine to

patients, another way to keep dopamine levels elevated is to prevent their degradation.

The dopamine molecule belongs to a chemical family known as the monoamines

(includes serotonin, adrenaline, and nor adrenaline), which are degraded by a common

enzyme class known as the monoamine oxidases (MAOs). As such, a class of drugs

(monoamine oxidase B inhibitors; MAOBI) have been developed to block the function of

these enzymes (ie. rasagiline and selegiline) (Connolly and Lang 2014). Similarly,

catechol-o-methyl transferase inhibitors (COMTI) are a class of drug that prevent a

different degradation pathway from degrading dopamine and include such drugs as

entacapone and tolcapone. Yet, another strategy is to stimulate dopamine receptors with

dopamine agonists. These chemicals are able to bind to and activate the dopamine

receptor and hence simulate an environment that isn’t deprived of dopamine.

Apomorphine, pramipexole, and ropinirole are examples of this class of drugs.

1.2.1.2.2 Surgical Treatments

Even with a diverse range of drug classes, and an abundance of drugs available

within these classes, pharmacological treatment of PD has yet to be perfected. Due to the

chronic and degenerative nature of PD, patients must take these drugs for extended

periods of time. As such, many develop resistance that causes the drugs to become less

efficacious (decreases the “ON” time- the time where the drugs are maximally effective).

To combat these medically refractive cases, surgical procedures can be implemented.

Two such procedures include the implantation of deep brain stimulating (DBS)

electrodes, as well as strategically placed lesions within the brain. Undoubtedly, the most

common target for DBS electrode implantation is the STN. It is currently unknown how

this stimulation helps in PD; however, it may have an inhibitory effect within the

overactive STN which restores firing rates to a non-pathological level. Another site for

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DBS in PD patients, albeit much less common, is the GPi. The GPi is typically reserved

as the target of choice for patients who suffer from cognitive decline or those with a

primary problem of disabling dyskinesias. Other surgical techniques other than DBS

implantation used to be pallidotomies and thalamotomies (thermo-electrolytic lesions of

the pallidum and thalamus, respectively) (Laitinen 1994). However, due to the advances

in DBS neuromodulation (such as rechargeable batteries and recording capabilities) and

the permanent nature of lesions, these techniques are much less common for the treatment

of PD nowadays.

1.2.2 Explanatory Models of PD

With the advancements in understanding the physiological processes associated

with movement and disease, such as PD, different models have been created over the

years that attempt to explain the abnormalities associated with PD. Currently, no one

model has been successful at explaining all of the symptoms and abnormalities associated

with the disease, but each model contributes its own information towards the

understanding of the pathophysiology of PD.

1.2.2.1 The Rate Model

One of the most well known models involves the balance between the so-called

“direct” and “indirect” pathways of the basal ganglia. In the direct pathway, the striatum

receives glutamatergic input from the cortex and dopaminergic input from the (SNc)

which then projects to the GPi before exiting the basal ganglia and projecting to the

ventral oralis posterior nucleus of the thalamus. In the indirect pathway, the striatum still

receives glutamatergic input from the cortex and dopaminergic input from the SNc, but

projects to the external segment of the globus pallidus as opposed to the internal segment.

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From here, the GPe sends GABAergic projections to the STN which then synapses on the

GPi in an excitatory (glutamatergic) fashion prior to synapsing onto the Vop. Complexity

is added to this system when one considers the activity of each internuclear projection: all

projections are tonic in the presence of sufficient dopamine concentrations in the striatum

(see figure 1). As described by Albin et al (1989), neuronal death in the SNc results in

decreased dopaminergic innervation of the striatum. Within the striatum, there are two

types of dopamine receptors (D1 and D2), which have opposing effects: the D1 receptors

lie in the direct pathway and are activated when dopamine is bound. D2 receptors lie in

the indirect pathway and are inhibited by the binding of dopamine (Gerfen et. al 2003).

The end result of this dopaminergic deprivation is a decrease in activity of the direct

pathway (which promotes movements) and an increase in activity in the indirect pathway

(which inhibits movement). The leads to hyper-activity of the basal ganglia output (the

GPi), which causes increased inhibition of the motor thalamus. This model is capable of

explaining certain symptoms of PD such as akinesia, however, it fails to explain tremor.

This model can be extended to discuss hyperkinetic movements. The rate model

predicts that gain in the direct pathway is enhanced while it is decreased in the indirect

pathway in diseases (such as dystonia, hemiballism, Huntignton’s disease). This leads to

overactivity in the direct pathway (the pathway promoting movement) and underactivity

in the indirect pathway, resulting in an excess of movements, such as chorea, or muscles

contractions like those seen in dystonia.

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Figure 2- Explanation of Parkinson’s disease as explained by the rate model.

Note the increased drive of the indirect pathway (as indicated with the extra thick arrows)

and the reduced drive in the direct pathway (indicated by the skinny arrows), including

the reduced dopaminergic input from the SNc caused by neuronal cell loss. The end

result is increased GABAergic innervation of the thalamus. STN indicates subthalamic

nucleus; GPe, globus pallidus externus; GPi, globus pallidus internus; SNr, substantia

nigra pars compacta; glu, glutamate; D2, type 2 dopamine receptor; D1, type 1 dopamine

receptor. Modified from Hutchison et al., (2004).

SNc Dopamine

D2

D1

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1.2.2.2 The Center-Surround Model

Expanding on the model proposed by Albin et al. (1989), a new model was put

forth, incorporating the new physiology and trains of thought discovered at that time

(Mink 1996). Important to this model is that it doesn’t view the basal ganglia as the

source of movement generation, but rather the cortex generates the movement command

and passes it onto the basal ganglia for execution. This model discusses the competition

between motor programs that occurs during the execution of a movement. As seen in

figure 3, the motor cortex sends multiple overlapping representations of the motor

command to the striatum, and different parts of the striatum receive multiple inputs from

different parts of the cortex with non-functionally connected parts being sent to different

zones of the striatum. Through convergence/divergence within the striatum, a

concentrated signal is propagated to the GPi allowing for this motor command to be

executed, while sending a concentrated inhibitory signal to the GPi to inhibit other

possible motor plans that would compete with the desired movement (Mink 1996)

(Figure 3).

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Figure 3- A representation of the convergence and divergence of different cortical areas

as hypothesized to occur between nuclei of the basal ganglia in the center-surround

model. Taken from Mink (1996).

13

Stated another way, when a motor command is generated by motor pattern

generators in the cortex, inhibitory drive in the basal ganglia is elevated for competing

motor pattern generators (thus inhibiting them), while inhibitiory activity of the

generators of interest are decreased (allowing the motor signal to be propagated). This

model predicts that the dopamine deficiency within the striatum results in increased drive

of the GPi and thereby excessively inhibits motor programs (desired movements and

competing ones) (Figure 4).

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Figure 4- A diagrammatic representation of the centre-surround model of basal ganglia motor

command selection. Line weights indicate relative gain of connections; red lines indicate

inhibitory synapses, black indicates excitatory synapses. STN indicates subthalamic nucleus;

GPi, globus pallidus internus; SNr, substantia nigra pars reticulata. Reconstructed from Mink

(2003).

15

A second aspect of this model is needed to explain the rigidity and postural

disturbances seen with PD, however. As such, it has been further posited that the

competing motor pattern generators are not completely inhibited by the increased tonic

activity seen in the GPi, and this leads to partial activation of these programs that may be

responsible for the rigidity, bradykinesia, and postural instabilities (Mink 1996).

1.2.2.3 The Oscillatory Network Model

The above two models concentrate on single cell phenomenon within different

nuclei of the basal ganglia, but do not address how these cells interact in a network. An

important aspect to consider when determining how a cell’s output may change due to the

progression of PD is how the cell’s firing patterns may change, not just its firing rate. It

has been shown that, on top of firing rate changes, there is an increase in bursting activity

of these cells (Hutchison et al. 1997) and it has been demonstrated that there is an

increase in oscillatory connectivity within and between nuclei of the basal ganglia

(Volkmann et al. 1996, Brown et al. 2001). The increase in oscillatory activity occurs

across the theta, beta, and gamma bandwidths and the increase is believed to be due to the

dopaminergic denervation underlying PD (Brown 2003). Important to this model is that

different frequencies of oscillations are believed to have different effects on movements.

In particular, oscillations below 30Hz are believed to be anitkinetic, while oscillations

above 35Hz are believed to be prokinetic (figure 5).

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Figure 5- An illustration demonstrating the dominance of oscillatory power of

different bandwidths as seen in Parkinson patients off (left side) and on (right side)

dopaminergic medications. Arrow weights indicate gain of connections and arrow heads

indicate directionality of oscillation. Red arrows represent antikinetic oscillations, green

arrows indicate prokinetic oscillations. STN indicates subthalamic nucleus; GPi, globus

pallidus internus. Reconstructed from Brown (2003).

17

Whether these oscillations are purely pathological and what purpose they may

have in the disease course is still up for debate. For example, Little and Brown (2014)

posit that beta oscillations represent an idling rhythm in the motor circuit which promotes

the status quo and that the pathological beta power seen in PD blocks the new motor

commands leading to bradykinsia/akinesia. Beta oscillations have been reported in

healthy humans, however, the power of these oscillations has been positively correlated

with symptom severity indicating a role of the power of the oscillation in the disease

(Kuhn et al., 2006). However, the findings pertaining to beta oscillations are inconsistent.

Recent findings from this lab have found a negative correlation between beta power in

the STN and PD symptoms (Alavi et al., 2013), and reports have shown a decrease in

beta power within M1/S1 of PD patients during tapping movements as measured through

EEG recordings (Stegmoller et al., 2015). As such, the function of beta oscillatory power

has yet to be determined.

1.2.3 Animal Models of Parkinson’s Disease

As in many other diseases being studied, PD has an assortment of animal models

that have been studied to deepen our understanding of the pathology associated with the

disease. Possibly one of the most well known animals used in the study of PD is the 1-

Methyl-4-phenyl-1,2,3,6-tetrahydropiridine (MPTP) monkey. This blood-brain

permeable toxin appears to selectively target mitochondria of the substantia nigra pars

compacta where it leads to neuronal death due to the oxidative stress of its metabolite,

MPP+, as created by astrocytes (Sian et al. 1990). First discovered in synthetic drug

abusers who ingested a merperidine-related drug, this toxin produces many of the motor

abnormalities seen in PD (including the bradykinesia and rigidity) but fails to cause rest

tremor and Lewy bodies as seen in PD (Sian et al. 1990).

18

Another animal model of PD is the 6-OHDA rat model which was first used to

study PD in 1959. This toxin is effective in lesioning the nigrostriatal pathway in mice,

dogs, cats, and monkeys. The 6-OHDA molecule is structurally similar to the dopamine

molecule except 6-OHDA has an added hydroxyl group which is responsible for the

toxicity to dopaminergic neurons. As this toxin is incapable of crossing the blood brain

barrier, direct injection to the ventral tegmental area, striatum, or SNc are required to

cause a lesion (Blesa et al, 2012). Dopaminergic neurons die due to the effect of reactive

oxygen species and quinones produced by 6-OHDA (Cohen, 1984). Although this toxin

is incapable of mimicking all the pathophysiology of PD (such as olfactory deficits and

Lewy body formation), it is capable of producing dopamine depletion, nigral neuronal

death, and neurobehavioural deficits associated with PD. Another advantage of using this

model is each animal serves as it’s own control since the injection only unilaterally

effects the animal leaving an intact, healthy side for comparison sake (Blesa et al. 2012).

The above examples are considered to be excellent models, especially when

examining the effects of Parkinsonian syndromes on movements. However, they are not

particularly useful for studying neuroprotective effects of compounds designed to slow

the progression of PD. For this instance, the rotenone rodent model appears to be quite

useful. Rotenone is a naturally occurring pesticide/insecticide found in tropical plants. It

is commonly chronically injected into rats and appears to mimic many of the hallmarks of

PD, including Lewy body formation. It has been found to work by inhibiting the electron

transport chain in SNc mitochondria leading to neuronal death (Dauer and Przedborski,

2003). A downside to this model is that there is no apparent effect on humans, and as

such, this model may only be capable of explaining disease progression in rodents and

may have difficulties translating to humans.

19

1.3 Dystonia

Dystonia is a class of hyperkinetic movement disorders which includes, but is not

limited to, cervical dystonia (dystonia largely affecting the cervical spine), hand dystonia

(i.e. writers cramp- dystonia specific to the hand), blepharospasm (dystonia of the eyelids

and brow), and general dystonia (non-specific dystonia affecting the majority of the body.

Dystonia is characterized by involuntary activation of muscles leading to abnormal

postures and twisting movements. Important to this thesis, dystonia patients do not have a

dopamine deficiency and thus provide us with a pseudo-control group/comparator group

for our Parkinson’s studies.

1.3.1 Etiology of Dystonia

First identified by Oppenheim in 1911 as “dystonia musculorum deformans”,

dystonia affects people of all ages and can be primary in nature (occurrence is idiopathic

or due to genetics) or secondary (occurs because of previous lesions or disease of the

nervous system) (Albanese et al. 2013). The cause of dystonia is not currently known,

however, work by Prescott et al. (2013) indicates that GABA regulation within the output

nuclei of the basal ganglia (the GPi and SNr) may play a role. This study found that

paired-pulse ratios were decreased in dystonia patients compared to PD patients but could

be normalized with high frequency stimulation (HFS). These findings provide evidence

of abnormalities in synaptic transmission in the basal ganglia which may be underlying

the pathophysiology of dystonia. Other electrophysiological studies have shown a loss of

intracortical inhibition, and increased cortical plasticity indicating that the cortex may

also play a crucial role in this disease (Neuman et al., 2015).

20

1.3.2 Genetics of Dystonia

As mentioned previously, individuals can be genetically predisposed to dystonia.

Researchers have identified a set of genes believed to be involved in different types of

dystonia. For example, early onset torsion dystonia (DYT1) has been traced back to a

glutamic acid deletion in the gene encoding Torsin A. Products of this gene are believed

to be involved with mediating oxidative and endoplasmic reticular stress and so reports

have shown that mutations in this gene may cause flaws in the synaptic terminals which

may underlie the phenotypic expression of DYT1 (Kim et al. 2015). Another gene that

appears to play a role in different types of dystonia is THAP1. Mutations in this gene

have been shown to be related to DYT6, as well as DYT1 through its regulatory role of

Tor1A (Erogullari et al., 2014).

1.3.3 Treatment of Dystonia

Similar to PD, the medicines available to treat dystonias are currently aimed at

improving the patient’s quality of life rather than preventing further development of the

disease or reversing the current progression. One type of treatment available to those with

dystonia is a combination of physical therapy and braces which are designed to help

prevent contractions. Some braces are designed to provide a sensory trick to the patient (a

false sense of sensory input) that helps them avoid a contracted posture (Jankovich 2006).

Another non-pharmacological intervention involves transcranial magnetic stimulation

(TMS). Siebner et al (1999) have shown that transcranial magnetic stimulation at low

frequencies is capable of improving handwriting of patients with writers’ cramp. The

frontline medicinal treatment for dystonias currently is botulinum toxin injections into the

over active muscles (Bruijn et al., 2015). A small subset of patients with childhood onset

dystonia show some improvement in symptoms with dopaminergic drugs (Jankovich,

2006). Antidopaminergic drugs have been used historically, but are currently discouraged

21

due to possible side effects associated with these drugs (including sedation, tardive

dyskinesias, etc.). Other pharmacological strategies include anticholinergic medications,

and muscle relaxants (Jankovich, 2006). As a last resort for those patients who are not

responsive to the medication, surgical procedures can be performed to help ameliorate

symptoms. The most common surgery performed for those with dystonia is bilateral GPi

DBS (Lettieri et al 2014), however, pallidotomies, thalamotomies, and intrathecal

baclofen are also viable options (Marras et al., 2014).

1.4 Essential Tremor

1.4.1 Etiology of Essential Tremor

Essential tremor is a disease that manifests as an intention tremor largely affecting

the upper limbs of patients (Deuschl 1998). It occurs in approximately 0.9% of the

general population and 4.8% of the population over 65. The cerebellum is responsible for

action corrections during movement. This strengthens the theory that it is involved with

ET as tremor in ET really manifests when approaching the target of a goal-oriented

movement: the cerebellum pathologically overcorrects and this manifests as a tremor.

The involvement of the cerebellum is strengthened by the findings that DBS in the

cerebellar-receiving nucleus of the thalamus (the ventral intermediate nucleus of the

thalamus- Vim) has proven extremely successful in treating ET action tremors (Tasker

1998). Also, as discussed below, pharmaceuticals designed to treat ET, and ethanol, tends

to increase GABAergic tone in the cerebellum, further implicating its involvement in ET

(Louis 2015). Four genetic loci have been reported to be risk factors for ET: ETM1-4;

however no gene product has been identified as a causative agent (Schmouth et al. 2014).

22

1.4.2 Genetics, Diagnosis and Treatment of Essential Tremor

The diagnosis for ET is largely based on clinical presentation, as there are no

biological markers currently available to aid in diagnosis. These diagnosis criteria are

exclusionary in nature: by eliminating other diseases (such as PD and dystonia), ET can

be inferred. Other exclusionary conditions, which need to be ruled out as the cause of

tremor, include the abuse of alcohol or drugs, and psychogenic tremors. Although no

causative factor has been identified in ET, considerable work has been done to identify

the gene products that may play a role in the hereditary component of ET. Such genes

involve LINGO1 (which also has been implicated in other diseases such as multiple

sclerosis), and SLC1A2 (a member of the glutamate transporter gene family) (Schmouth

et al. 2014). Morphologically, researchers have reported cellular abnormalities within the

cerebellum of patients with ET that include an increase in the number of “torpedoes”-

which are truncated Purkinje cells with neurofilament accumulations which impair axonal

transport (Liem and Leung, 2003). It has yet to be confirmed whether these “torpedoes”

are a result or cause of ET pathology however, Louis et al. (2014) identified an inverse

relationship between the number of torpedoes and the number of Purkinje cells seen in

the cerebellum of ET patients. This inverse relationship was unique compared to other

diseases such as spinal cerebellar ataxia (no clear relationship seen- similar to controls),

and multiple systems atrophy-C (a strong positive relationship was identified: those with

less Purkinje cells have less torpedoes) indicating that these torpedoes stress the

cerebellar Purkinje cells but do not overwhelm it.

Alluded to earlier, pharmaceuticals designed to treat ET tend to focus on

increasing GABAergic tone in the cerebellum in hopes of improving quality of life for

patients. Interestingly, an antiepileptic drug, primidone has proven successful in reducing

tremor in ET patients (Rincon and Loius, 2005). Primidone is partially metabolized to

phenobarbital, a GABAA receptor agonist which potentiates GABA transmission, which

23

is believed to be its mechanism of action (Charney et al. 2001) Similarly, other

antiepileptic drugs such as benzodiazapines and gabapentin work via similar

mechanisms. Other classes of drugs that appear to be successful with treating symptoms

of ET are drugs targeting the monoamine system, such as β-adrenergic blockers, and α-

adrenergic agonists (Roncon and Louis, 2005). An example of a β-adrenergic blocker is

propranolol which is believed to be efficacious due to its antagonistic effects on the

peripheral nervous system but may also exert its effects centrally (Chung et al. 2013). As

for the previous movement disorders discussed, ET also has surgical interventions

available as treatment options. Vim DBS is the preferred surgical technique employed,

however, thalamotomies within the Vim have shown success in the past (Tasker 1998).

1.5.1 Tremor

Tremor is ubiquitous- it is seen in everyone at some level of expression.

Typically, this is referred to as physiological tremor and is usually benign. However,

some people have tremor that is caused due to pathology of the brain that can be severely

debilitating. Tremor is defined as involuntary, rhythmic, and sinusoidal alternating

movements of the body; and can include limbs, palate, voice, and head (Abdo et al.

2010). Different types of tremor exist which include rest tremor (typically occupying the

4-6 Hz range; characteristic of Parkinson’s disease), intention tremor (typically

occupying 4-12 Hz range and commonly seen in essential tremor), dystonic tremor

(typically 4-10 Hz), and orthostatic tremor (tremor in the legs upon standing typically

occupying 13-18 Hz). Even within these classifications, there are sub-classifications of

tremor which include postural tremor, isometric tremor, and kinetic tremor (Helmich et

al. 2013). Parkinsonian rest tremor is defined as tremor that arises when a body part is at

rest and is fully supported against gravity, and disappears during movement. Action

tremor occurs during voluntary contractions of the tremulous muscles; postural tremor

occurs when the patient tries to maintain a given posture; isometric tremor occurs during

isometric muscular contractions (contracting the muscle without changing the angle of

24

the joint), and intention tremor occurs during voluntary actions such as trying to touch

your finger to your nose (Deuschl et al. 1998). Patients with dystonic tremor tend to

appear in two cohorts. Some patients have dystonic tremor (tremor that is confined to a

dystonic limb), whereas others patients can have tremor associated with dystonia (the

tremor affects body parts unaffected by dystonia). Defazio et al., (2012) found through

clinical assessment and self-report measurements in a large Italian cohort that

approximately 17% of patients with primary adult onset dystonia present with tremor and

that this tremor was found in the head/face, neck, larynx, and upper and lower limbs.

Different brain regions appear to be affected depending on what type of tremor is

present. For instance, action tremor as commonly seen in ET patients is believed to be

due to pathology of the cerebellum. It is believed that the cerebellum is involved with

movement correction, and as such, ET may arise due to mishandling of efferent copies in

the cerebellum (Deuschl et al., 2000). Rest tremor as commonly associated with PD, is

believed to be triggered by pathology within the basal ganglia. Importantly, even though

different types of tremors may have different regions of the brain triggering the

phenotypic expression of tremor, it is possible that tremor is a network phenomenon and

not an entity created by one brain nucleus (Helmich et al., 2012).

As the main focus of this work is on PD tremor, the rest of this literature overview

shall focus solely on PD tremor. PD tremor affects three-quarters of PD patients, but the

cause of this tremor has yet to be determined. Some authors (i.e. Paré et al., 1995; Duval

et al., 2015) hold that tremor is initiated in the motor thalamus. They posit that, through

hysteresis, the thalamus (in monkeys) is capable of converting a 12-15 Hz oscillation

generated elsewhere in the brain into a 4-6 Hz oscillation which leads to the visible

expression of tremor. However, using microelectrode recordings prior to DBS electrode

placement, Hutchison et al., (1997) showed that the human GPi has neurons that fire at

tremor frequency in PD patients. This study did not find any 12-15 Hz oscillatory activity

in the GPi (speculated to be possibly due to species difference), but they found spectral

25

peaks within the 4-6Hz bandwidth. As previously mentioned, using fMRI (via measuring

the BOLD signal), Helmich et al. (2011) showed that activity in the GPi increases prior to

the onset of tremor, indicating the GPi as a candidate in the onset of tremor in PD

patients. Furthermore, the inferior olive has also been hypothesized to play a role in

tremor initiation. Discussed in Sjolund et al. (1977), the inferior olive is a key nucleus

involved in harmaline tremor. Harmaline is a centrally acting tremorgenic indole alkaloid

which has an unknown mechanism of action. It has been speculated that it may exert its

effects by acting at the GABA receptor-ionophore complex, or possibly through

interacting with voltage-dependent sodium channels (Deecher et al., 1992).

These tremorgenic hypotheses above pertain only to the initial onset of tremor and

largely deal with only one nucleus. However, there is evidence to believe that different

aspects of tremor are controlled by different areas of the brain, and thus tremor is

believed to be more of a network phenomena than a single nucleus occurrence. This is

backed by Helmich et al. (2013) who posit that tremor initiation occurs in the GPi but the

cerebellum sets the amplitude. This model of tremorgenesis is known as the Dimmer

Switch Hypothesis and was deduced after EMG-fMRI showed an increase in activity in

the GPi prior to the onset of tremor. Furthermore, they found that tremor amplitude was

correlated with activity in the cerebellothalamic circuit, and not the basal ganglia. This

led these authors to believe that the GPi triggers the initiation of tremor (ie. the switch)

and the cerebellum controls the amplitude of the tremor being expressed (the dimmer).

Clearly, more work needs to be undertaken to ascertain the initial site of tremor initiation

so we can deepen our understanding of this debilitating disorder and develop better

treatments for it.

1.6 Synaptic Plasticity

The brain is comprised of billions of neurons which are organized into many

different circuits which control all aspects of human behaviours. One of the beauties of

26

these circuits is that they are not static- they are constantly undergoing changes in their

connections with other circuits in the brain. This led the McGill psychologist, Donald

Hebb to coin the phrase “Neurons that fire together, wire together” (Hebb, 1949). One of

the most fundamental aspects of neuroscience surrounds the concept of synaptic

plasticity. Synaptic plasticity is extremely important for various functions such as

learning and memory and has been extensively studied in areas such as the hippocampus.

Theta oscillations have been found to naturally occur within the hippocampus and have

been shown to be optimal for inducing synaptic plasticity within this nucleus. In

simplistic terms, when neurons communicate with each other (i.e. through synaptic

transmission), they undergo physiological transformations that make them more prone to

continue to communicate. Importantly, neurons and networks also need to be able to

decrease their gain with other neurons and networks for certain processes to occur. These

two processes, the increase and decrease in gain between neurons and networks have

been coined long term potentiation (LTP) and long term depression (LTD), respectively.

The cellular mechanisms of synaptic plasticity have been a hot topic in the neurosciences

and thus have been extensively studied since its discovery by in 1973 (Bliss and Lomo,

1973; Bliss and Gardner-Medwin, 1973). Different types of neurons undergo LTP in

different manners, but one of the most studied mechanisms is that of LTP in the

hippocampus. Here, the quintessential aspect underlying LTP is the influx of calcium

ions through NMDA receptors which then leads to an intracellular signaling cascade that

terminates with the upregulation and trafficking of receptors and their subsequent

implantation to the cell membrane (Malenka and Bear, 2004). This is believed to increase

the likelihood of excitatory post-synaptic potentials to induce an action potential in the

post-synaptic neuron. Importantly, for this calcium influx to occur the magnesium plug

blocking the NMDA receptor needs to be removed and this requires significant

membrane depolarization. As reviewed in Larson and Munkacsy (2014), theta-burst

stimulation is optimal for inducing maximal depolarization which effectively primes the

synapse LTP in the hippocampus. This is because the feed-forward inhibitory post-

synaptic potential (IPSP) which normally truncates the excitatory post-synaptic potentials

(EPSP) elicited by subsequent bursts arriving within 100-150 ms inhibits itself allowing

maximal depolarization of bursts arriving during 200 ms after the first burst. This

27

enhanced depolarization removes the NMDA receptor magnesium plug and allows

calcium influx that promotes long-term potentiation. It is hypothesized that high

frequency stimulation is less efficient at inducing LTP due to glutamate depletion during

the tonic stimulation train which isn’t seen during the burst paradigm. Theta burst

stimulation in patients with movement disorders has been performed before. Huang et al.

(2005) found that continuous theta-burst (cTBS) with transcranial magnetic stimulation

(TMS) in the motor cortex induced LTD-like effects while intermittent theta-burst

stimulation induces LTP-like effects in human subjects. Recently, Kishore et al. (2014)

used cTBS on the cerebellum combined with paired associative stimulation (PAS) and

found that M1 plasticity can be restored in PD patients. To our knowledge, TBS has

never been examined in the GPi.

Using our dual microelectrode recording unit, we are able to approximate changes

in LTP within a neuron being recorded. Previously, Prescott et al., (2009) have examined

plastic changes within the SNr of PD patients using extracellular HFS protocols and

found that LTP is impaired in PD patients in the OFF condition. To our knowledge, no

one has examined the effect of theta-burst stimulation on plasticity within movement

disorder patients, particularly in the GPi. By stimulating from our distal electrode, we are

able to excite the axon terminals and cause neurotransmitter release. In the case of the

GPi, the striatal projection neuron’s axon terminals release GABA. GABA is an

inhibitory neurotransmitter that allows chloride ions to flow down their concentration

gradients into the neuron. Since these chloride ions are negatively charged, this

hyperpolarizes the cell. Importantly to extracellular electrophysiology recordings, is that

these chloride ions flow down there electrochemical gradient from the extracellular to

intracellular compartments, as predicted by the Nernst equation. This movement of

charge produces a current that can then be detected and measured by our focal electrode

that is recording in the vicinity of a neuron and is displayed as an evoked field potential

(fEP) (figure 19A). Since these are extracellular recordings, we are not recording from

solely one neuron- we are recording the collective activity of many neurons in the area as

chloride ions are flowing into them from extracellular milieu. The more GABA that is

28

released, the more chloride flux there is, and hence the greater the evoked fEP. Since

plasticity deals with the connective strength of neurons, the greater the increase of the

fEP measured, the greater the change in synaptic strength.

1.7 Project Rationale

Models have been put forward to attempt to explain the cause of the

symptoms of PD but to date, no theory is capable of explaining all of the symptoms

collectively. For example, the Firing Rate model is capable of explaining

bradykinesia, and rigidity but suffers when explaining tremor. The Dimmer Switch

Model (Helmich 2012) has strengths when discussing tremor, but doesn’t deal with

bradykinesia and rigidity. This could be due to the fact that tremor still is not very

well understood. Where the site of tremorgenesis lies is currently a debate. Many

different areas of the brain have been examined for their role in tremorgenesis. A

“Thalamocentric” view, which holds that the thalamus is the site where tremor

initiates, has been put forward by Paré et al. (1992). This theory has been supported

by studies indicating that thermo-electric lesioning of the motor thalamus appears

to be the most effective surgical treatment for tremor (Duval et al. 2015). A

“Pallidocentric” view, which posits that tremor onset occurs in the pallidum, has

been put forth by Helmich et al. (2012). Although more related to essential tremor

than PD rest tremor, Deuschl et al. (2000) have collected evidence that the

cerebellum plays a role in tremorgenesis. Another candidate for the site of tremor

generation is the inferior olivary nucleus (Shaikh et al. 2010). These views are

currently hindered by the demonstration of true oscillators (neurons which can

establish the tremor rhythm as opposed to just following an oscillation established

elsewhere in the brain) within respective nuclei. There is obviously heterogeneity in

opinions as to where tremorgenesis occurs within the brain; however, there is no

reason to exclude the possibility that different types of tremor arise due to activity

in different parts of the brain.

29

This project builds on the finding of neurons within the GPi which were induced

into a tremor frequency oscillation following a brief microstimulation in patients lacking

tremor (figure 6). The fact that they lacked tremor is important because these patients

wouldn’t have had the sensory feedback from tremulous limbs which could provide input

to establish the tremor rhythm. The first aim of this project was to characterize the

distribution, incidence, background activity, and firing patterns of these oscillating

neurons. We then attempted to determine whether these neurons are responsible for

tremor initiation, and then examined the effect that these oscillating neurons may have on

the plasticity in the GPi.

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

The top trace shows a baseline recording; bottom trace shows the response of the neuron

following stimulation. A clear inhibitory period is seen before the cell begins to fire

“normally” again, and then it fires in a very prominent 6 Hz oscillation. This spike train

oscillation lasted for approximately 8 seconds. This cell can be identified as a border cell

based on its regular firing pattern (pre-stimulation) and slower firing rate (26 Hz) than

seen in a typical HFD neuron.

30

1.8 Hypothesis and Aims

This thesis has three distinct, yet related sections. The first aim was discovery-

driven and was aimed at characterizing the oscillatory patterns of a newly discovered

theta (tremor) oscillation seen following focal microstimulation of a GPi border cell.

What was interesting about this oscillation was that it was initially found in a dystonic

patient who lacked tremor, and as such, did not have the sensory feedback present to set

the oscillatory rhythm. As such, it is believed that the neuron had intrinsic membrane

mechanisms or network connectivity allowing this oscillatory activity in the absence of

tremulous activity. This could implicate these cell types as the rhythm generators for rest

tremor. Consequently, these oscillations were characterized to determine if they were

significantly different from other firing patterns generated by the GPi.

The second aim of this thesis is aimed at determining if these oscillations serve a

tremorgenic role or not. It was hypothesized that if these oscillations recorded from the

GPi were tremorgenic, then stimulating the GPi (and downstream target- the motor

thalamus) with microelectrode stimulation mimicking the recorded oscillations (i.e. theta

burst stimulation) will show evidence of tremor modification (i.e. tremor resetting, phase

locking of tremor activity to bursting stimulation, tremor induction, etc.)

The third aim of this thesis is aimed at determining the effect of theta burst

stimulation on the plasticity within the GPi. It was hypothesized that theta burst

stimulation would be able to induce long-term potentiation within this nucleus.

31

Chapter 2- Methods

2.1 General Methods

Subjects studied in this work were all patients undergoing DBS implantation

surgeries for movement disorders. 23 subjects were studied in total across all

experiments. All gave informed, written consent according to the UHN Research Ethics

Review Board. PD patients were withheld from dopaminergic medications for twelve

hours prior to surgery. Surgical techniques have previously been described (Lozano et al

1998). In summary, the patient’s scalp was anaesthetized with the local anesthetic

lidocaine/marcaine to block the pain associated with the mounting of the frame to the

skull with screw-pins. One to two burr holes were drilled (depending on whether

unilateral or bilateral electrodes were implanted) 2mm anterior to the coronal suture.

Targets were stereotactically selected following 1.5T or 3T fused MRI (Table 1). See

figure 7 for example trajectories taken to reach the surgical target. All patients were

awake during microelectrode mapping procedures. Two independently driven

microelectrodes were advanced along a track directed at the surgical target. (see figure 8

for illustration of electrode setup).

32

Table 1- Stereotactic coordinates of DBS targets.

Target Coordinates

X

(medial-lateral)

Y

(anterior-posterior)

Z

(inferior-superior)

GPi 20 mm lateral 1 mm posterior to

MCP

5 mm inferior to

AC-PC

STN 12 mm lateral 3 mm posterior to

MCP

3 mm inferior to

AC-PC

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

line)

GPi, globus pallidus internus; STN, subthalamic nucleus; Vim, ventral

intermediate nucleus of thalamus; MCP, mid-commissural point; PC, posterior

commissure; AC-PC, anterior commissure-posterior commissure.

33

c

34

Figure 7- Sample trajectories taken to reach surgical target during mapping

procedures for DBS implantation surgeries.

A) A trajectory taken to reach the Vim target.

B) A trajectory taken to reach the STN target.

C) A trajectory taken to reach the GPi target.

RT, reticular thalamus; Voa, ventral oralis anterior nucleus of thalamus; Vop, ventral

oralis posterior nucleus of thalamus; Vim, ventral intermediate nucleus of thalamus; Vc,

ventral caudal nucleus of thalamus; STN, subthalamic nucleus; ZI, zona incerta; GPi,

globus pallidus internus; GPe, globus pallidus externus; OT, optic tract; AC, anterior

commissure; PC, posterior commissure. Solid, dark black line represents microelectrodes;

dotted black line represents AC-PC line.

35

Figure 8- The microelectrode setup used for microelectrode mapping prior to

DBS implantation.

Two independently driven microelectrodes with tips spaced 0.5 mm apart. The

terms “focal” and “distal” electrodes are relative terms describing the proximity of the

electrode of interest to the unit being recorded.

36

2.2 Intraoperative Microelectrode Recordings

Two independently driven microelectrodes (FHC; Bowdoin, ME) with

impedances ranging from 0.2-0.4 MΩ) were inserted into the brain guided by a Leksell

stereotactic frame (Leksell; Stockholm, Sweden). Electrode advancement proceeded

incrementally until isolated GPi units were identified. Electrode tips were aligned to be at

the same the depths. 10s baseline activity was recorded prior to passing electric current (3

µA, 5 µA, or 7.5µA intensity; 0.3ms biphasic pulse width; 200Hz frequency; 1 second

duration) through the stimulating (focal) electrode while cellular activity was

subsequently recorded through the focal electrode. [NOTE: distal and focal are relative

terms used to discuss the proximity of the electrodes to the unit being recorded. During

these recordings, the focal electrode (the electrode closest to the cell of interest), was the

stimulating and recording electrode.]10s of neuronal activity was collected following

stimulation. Data was recorded using Spike2 software (Cambridge Electronic Design,

UK) and stored for off-line analysis.

Using Spike2 software, trials were excised, band pass filtered (300-3000Hz) and

then template matched using principal component analysis (PCA). In short, the software

analyzes various aspects of the action potential (such as amplitude, waveform shape, and

width) and matches these spikes to other similar spikes allowing any possible spiking

activity from neighboring neurons or noise to be removed so that one cell can be studied.

Trials were segregated into pre- and post-stimulation segments, and then imported into

Matlab for analysis by the Mkaneoke script (Kaneoke and Vitek 1990) (see appendix

figure 1). Values for mode burst index (the mode value of the ratio of intraburst interval:

interburst interval- see figure 9), firing frequency, and tremor frequency signal-to-noise

ratios were collected and compared pre-stimulation against post-stimulation.

37

Figure 9- Example traces showing the regularity of neuronal firings and the

corresponding burst index value.

A) A regular firing neuronal trace with a mode burst index of 1.04 (See appendix

Figure 2 A & B).

B) A slightly more bursty cell with a mode burst index of 3.04.

Both traces are 3 seconds long.

38

2.3 Tremor Entrainment with Theta Burst Stimulation

Patient selection and surgical procedures were performed as outlined in 2.1.

Again, using two independently driven microelectrodes, single neuronal units were

identified and 10s of baseline activity was recorded from the focal electrode. The distal

electrode was connected to a stimulus isolation unit and electrical current (100µA, 4

pulses per burst, 10ms intraburst interval, 200ms interburst interval, 5-10s duration) was

passed into the nucleus. 10s of neuronal activity was recorded following stimulation.

Data was saved for offline analysis. Recordings were excised, and band pass filtered

(300-3000Hz) before being template matched in Spike2 software. Stimulation artifacts

were removed using the Spike2 Artrem script which replaced the stimulation artifacts

with a flat connecting line. Phase histograms were then constructed to look for phase

locking of the tremor peaks with the action potentials and/or theta burst stimulation. Each

phase began at the onset of theta burst stimulation and triggers were placed on the peaks

of the tremor amplitudes as recorded by the accelerometer. These tremor peaks were used

in the phase histogram to examine any realignment of tremor peaks with cellular activity.

2.3 Effects of Theta Burst Stimulation on Plasticity in GPi

Patient selection and surgical procedures were performed as outlined in 2.1.

Single stable units were isolated. Two baseline test pulses were delivered from the distal

electrode (stimulation parameters: 100µA, 10s, 1Hz, 10s rest between sets of test pulses.)

In a non-randomized fashion, theta burst stimulation preceded high frequency

stimulation, allowing the cell to recover before the HFS protocol. Using a stimulus

isolation unit, theta burst stimulation was delivered so that the total energy delivered was

matched in energy to high frequency stimulation (TBS stimulation parameters: 40s, 10ms

intraburst interval, 200ms intraburst interval, 100µA). Test pulses were repeated, except

30s were waited between test pulses instead of 10 (3 to 4 test pulses were

delivered)(Figure 6a). High frequency stimulation was applied for 5-10s (stimulation

parameters: 2s stimulation at 100Hz and 100µA with 8s rest; repeated 4 times) (see figure

39

6b). This was not a balanced study- both times, the HFS occurred after the neuron

recovered from TBS. Offline, field evoked potential amplitudes (fEP) were measured and

compared pre stimulation vs post stimulation.

Statistical Analysis

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

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

GraphPad Software, San Diego California USA) statistical programs.

40

41

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

Stimulation paradigms were matched for energy delivery to the tissue. Each was comprised of 800 pulses at 100µA intensity.

A) The theta burst stimulation used. TBS indicated theta burst stimulation.

B) The high frequency stimulation protocol employed. HFS indicates high frequency stimulation.

42

3.0 Chapter 3- Results

3.1 Stimulation-Induced Oscillation Characterization

95 neurons were sampled from 16 patients- 58 neurons from PD patients, and 37

neurons from the comparison group (dystonia/dystonia-related diseases). 2 neurons were

excluded from analysis as 1 cell was identified as a low frequency bursting cell of the

GPe (identification based on the recording depth in the track, firing rate, burst index, and

visual firing signature), and 1 neuron was lost following electrical stimulation. In

examining the pre-stimulation baseline firing (irrespective of stimulation intensity), it

was found that PD HFDs had a significantly higher mean firing rate (93.46 ± 38.01 Hz

(SD), N= 41 cells) than the PD border cells (47.93 ± 18.44 Hz, N=17 cells) (unpaired-

t83=5.39, p< 0.0001) and the dystonia HFDs (57.95 ± 27.17 Hz, N= 28 cells)

(unpaired-t98= 4.88, p<0.0001). Dystonia HFDs had a significantly higher mean

firing rate than the dystonia border cells (40.17 ± 11.90 Hz, N= 9 dystonia border

cells)(unpaired-t58=2.90 p<0.01), however, there was no significant difference

between the PD and dystonia border cell firing rates (unpaired-t43= 1.67, p=0.10)

(figure 11A). As shown in figure 11 B, the dystonia HFDs (average mode burst

index= 3.32 ± 0.31) were found to have a significantly higher pre-stimulation mode

burst index than the PD HFDs (average mode burst index= 1.85 ± 0.11) (unpaired-

t100=5.20, p<0.0001), and the dystonia border cells (average mode burst index= 1.43

±0.08) (unpaired-t60=4.49, p<0.0001). There was no significant difference found

between the average pre-stimulation mode burst index of PD and dystonia border

cells (unpaired-t45=1.55, p=0.13).

43

PD H

FD

PD B

orde

r Cell

Dys

toni

a HFD

Dys

tonia

Borde

r 0

50

100

150

***

*** **

n.s.

Firin

g F

requency (H

z)

PD H

FD

PD B

orde

r Cell

Dys

toni

a HFD

Dys

tonia

Bor

der C

ell

0

2

4

6

n.s.

***

***

n.s.

Mode B

urs

t In

dex

Figure 11- Group analysis of baseline (pre-stimulation) firing rate and

mode burst index separated by disease and cell type.

A) Group averages of firing rates.

B) Group averages of mode burst indices.

Error bars indicate SD. PD indicates Parkinson’s disease; HFD, high frequency

discharge; n.s., no significance. **p< 0.01, ***P< 0.0001 as determined by ANOVA.

A

B

44

In analyzing cell groups broken down by disease type and stimulation intensity, it

was found that the average pre-stimulation firing rate was significantly higher than the

average post-stimulation firing rate in the PD neurons at 5 µA (pre-stimulation: 79.94 ±

38.33 Hz, post-stimulation: 66.45 ± 39.92; N= 23; unpaired-t48=2.84, p<0.001), 7.5 µA

(pre-stimulation: 84.82 ± 35.76 Hz, post-stimulation: 58.18 ± 34.03 Hz; N= 15; unpaired-

t14= 3.12, p< 0.01), and in the dystonia neurons at 5 µA intensity (pre-stimulation= 49.25

± 21.54 Hz; N= 22; unpaired-t21= 4.78, p< 0.0001). There was no significant difference

seen between the pre-stimulation and post-stimulation firing rates in the PD neurons at 3

µA (pre-stimulation: 73.01 ± 43.28, post-stimulation: 71.80 ± 35.44 Hz; N=23; unpaired-

t22=0.37, p=0.72), or in the dystonia neurons at 3µA (pre-stimulation: 52.93 ± 26.79 Hz,

post-stimulation: 54.09 ± 38.47 Hz; N= 35; unpaired-t34=0.34, p= 0.74) or 7.5 µA (pre-

stimulation: 43.08 ± 13.28 Hz, post-stimulation: 27.42 ± 14.16; N= 5; unpaired-t4= 2.23,

p= 0.09), however, the effect at 7.5 µA may not be significant largely due to the low

sample size used in the analysis (figure 12 A and B). The burst index was seen to

significantly increase following stimulation at 5 µA (pre-stimulation: 1.82 ± 0.83, post-

stimulation 2.46 ± 1.48; unpaired-t48=3.22, p< 0.01) and 7.5 µA (pre-stimulation: 3.08 ±

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

µA (pre-stimulation: 2.57 ± 1.71, post-stimulation: 3.40 ± 3.29; unpaired-t34=2.30, p<

0.05) and 5µA (pre-stimulation: 2.68 ± 1.70, post-stimulation: 3.02 ± 2.05; unpaired-

t21=2.33, p<0.05) in dystonia patients indicating that these neuron traces fired in a more

bursty manner following stimulation. There was no significant difference seen at 3 µA

(pre-stimulation: 2.04 ± 1.20, post-stimulation: 2.01 ± 0.91; unpaired-t22=0.16, p< 0.87)

in the PD neurons or 7.5µA (pre-stimulation: 3.08 ± 3.15, post-stimulation: 3.54 ± 3.65,

unpaired-t4= 1.69, p< 1.67) in the dystonia patients (figure 12 C and D).

45

0

50

100

150 ** *

Firin

g F

req

uen

cy (

Hz)

3 5 7.50

2

4

6

8

** *

Stimulation Intensity (µA)

Mo

de

Burs

t In

dex

0

50

100

150

***

Firin

g F

reque

ncy (

Hz)

3 5 7.50

2

4

6

8 ** *

Stimulation Intensity (µA)

Mode

Burs

t In

dex

A B

C D

nn= 23

np= 5

nn= 36

np=7

nn= 5

np= 3

nn= 23

np=7

nn= 50

np= 8

nn= 15

np= 6

PD Dystonia

Figure 12- Effect of stimulation intensity on neuron populations separated by

disease.

A) and B) The effect of stimulation intensity on mean firing rate in PD and

dystonia neurons, respectively.

C) and D) The effect of stimulation intensity on mean mode burst index in PD and

dystonia neurons, respectively.

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

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

sample; np indicates number of patients analyzed.

46

1

10

Log M

ode

Burs

t In

de

x

n.s.

1

10

Log M

ode

Burs

t In

de

x

**

Pre Stim Post Stim

1

10*

Log M

ode B

urs

t In

dex

1

10

**

1

10

*

Pre Stim Post Stim

1

10

n.s.

Ai)

Aii)

Aiii)

Bi)

Bii)

Biii)

47

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

A) PD neurons.

B) Dystonia neurons.

Each light gray line indicates the change in mode burst index of a single cell; dark black

lined indicate the significant average changes. i= 3 µA; ii= 5 µA; iii= 7.5 µA. *p< 0.05;

**p< 0.005; n.s., no significance.

48

These findings are further supported by figure 13 A i-iii which shows the

individual changes in mode burst index of each PD cell (light grey lines) with the

overlying group average superimposed (dark grey line), and figure 13 B i-iii which shows

the same results but for the dystonia cells.

A one-way ANOVA found that there was no significant difference in the mode

burst index ratio (post-stim/pre-stim) for either PD or dystonia neurons indicating that

cells do not get more bursty with higher stimulation intensities. However, with the PD

neurons, the trend shows an increase in burstiness at 5 µA compared to 3 µA, however

this plateaus at 7.5 µA, possibly indicating a ceiling for how bursty a cell can become.

This trend was not seen in the dystonia neurons (figure 14 A and B). Similarly, there was

no significant difference in the firing rate ratios (post-stimulation/pre-stimulation) of PD

or dystonia neurons as determined by a one-way ANOVA, however, the trend appears to

indicate that there is a greater suppression of firing rates following stimulation (figure 14

C and D), possibly due to a longer inhibitory period post-stimulation, however, this is just

speculation.

49

3 5 7.5 0.0

0.5

1.0

1.5

2.0

Mode B

urs

t In

dex R

atio

(Post/P

re)

3 5 7.5

0.0

0.5

1.0

1.5

Stimulation Intensity (uA)

Firin

g R

ate

Ratio (

Post/P

re)

3 5 7.5

0.0

0.5

1.0

1.5

2.0

3 5 7.5

0.0

0.5

1.0

1.5

Stimulation Intensity (µA)

PD Dystonia

A B

C D

Figure 14- Mode burst index ratios and firing rate ratios of PD and dystonia neurons

differentiated by stimulation intensities.

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

C) and D) Firing rate ratios.

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

50

In analyzing tremor power (via fast-Fourier transforms- FFT) it was seen that

there was a significant decrease in the 3-6 Hz range in the dystonia HFD neurons at 5 µA

only (pre-stimulation mean signal-to-noise ratio: 1.86 ± 0.66 dB, post-stimulation mean

signal-to-noise ratio: 1.53 ± 0.72 dB; unpaired-t12= 3.3, p<0.01). There was no

significant difference seen in any other class of neuron (i.e. PD border cells or HFDs,

or dystonia border cells) at any stimulation intensity (figure 15).

51

0

1

2

3

4

3-6

Hz R

an

ge

Po

we

r (d

B)

3 5 7.50

1

2

3

4

Stimulation Intensity (µA)

3-6

Hz R

an

ge

Po

we

r (d

B)

0

1

2

3

4

3 5 7.50

1

2

3

4

**

Stimulation Intensity (µA)

A B

C D

nn= 23

np=7

nn= 50

np= 8

nn= 15

np= 6

nn= 23

np= 5

nn= 36

np=7

nn= 5

np= 3

PD Dystonia

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

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

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

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

Dark black bars indicate pre-stimulation averages; lighter gray bars are post-stimulation

averages. Error bars indicate 95% confidence intervals. **p< 0.01.

nn indicates the number of neurons sampled; np indicates the number of patients sampled.

52

3.2 Tremor Entrainment with Theta Burst Stimulation

The novel finding of the stimulation-induced neurons described in the above

section are of interest as they may provide new insight into the site of tremor initiation.

However, just because these neurons fire at the tremor frequency in one of the output

nuclei of the basal ganglia, does not mean that they are necessarily involved with tremor.

To test this relationship, neurons in the GPi (the nucleus where these border burst

neurons are found), as well as upstream and downstream targets, were stimulated with a

stimulation paradigm that mimicked the oscillations recorded from border burst cells and

were qualitatively assessed for changes in tremor as well as cellular response to the

stimulation.

3.2.1 Theta Burst Stimulation in the Globus Pallidus Internus

In the GPi, 7 neurons from 4 different patients were stimulated with theta burst

stimulation (TBS). No evidence of tremor modifications as recorded via an accelerometer

were noted in any of these trials. The cellular responses were also analyzed during TBS

(figure 16). 2 neurons appeared to be driven by the stimulation (i.e. their firing rates

increased immediately following burst stimulation). Due to the removal of the artifacts

(and subsequent replacement with filler data), spiking activity during the stimulation

burst could not be analyzed. Of these two neurons that were initially driven by TBS, 1

neuron was only driven for 11 blocks of stimulation near the start of the stimulation train

before being inhibited for the latter blocks of stimulation. The other neuron did not

appear to have this same primacy effect due to the theta burst stimulation but appeared to

be driven throughout the entire stimulation paradigm. These two neurons were recorded

in separate patients. The remaining 5 neurons that were recorded were all at least partially

inhibited (i.e. showed a reduced firing rate compared to the baseline recording) by the

TBS throughout the duration of the stimulation.

53

A

54

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

A) Pre-stimulation: The top trace shows the baseline spiking behavior of the

cell prior to stimulation. The bottom trace shows 3 seconds of the accelerometer

data recorded.

During Stimulation: The neuronal trace during TBS that the phase histogram in B

was produced from. The top trace shows the spiking behavior of the cell during

stimulation with stimulation artifacts largely but not completely removed. The

middle trace shows the onset of the theta burst stimulation phase. The bottom

trace shows the accelerometer data.

Post-Stimulation: The neuronal trace following TBS. The top trace shows the

baseline spiking behavior of the cell following the stimulation protocol. The

bottom trace shows the accelerometer data.

B

55

The top plot is a raster plot of the neuronal firing behavior (indicated by the solid dots)

throughout the TBS train. A phase is defined as the start of TBS to the start of the next

stimulation burst. TBS indicates when theta burst stimulation was applied

3.2.2 Tremor Entrainment with Theta Burst Stimulation Outside of The GPi

As can be seen in figure 1, the connectivity of the basal ganglia is quite complex.

Electrical stimulation of the brain as in GPi DBS can have antidromic as well as

orthodromic effects (Liu et al., 2012) Therefore, we also examined the effect of TBS on

nuclei both upstream (the STN), and downstream (the motor thalamus) of the GPi.

Within the motor thalamus, we stimulated and recorded from 17 neurons across 6

patients. Of these 17 neurons, 9 were initially driven for the first few bursts of theta burst

stimulation before being inhibited later in the train, and of these 9, 2 cells showed

prolonged inhibition even after the stimulation ceased (both neurons from the same

patient). Also of importance is that in 3 of these 9 cells (from 2 different patients)

evidence of tremor reduction (figure 17) was seen during stimulation (no cell that showed

tremor reduction was inhibited after the stimulation was turned off). Also within the

motor thalamus, 2 cells (from 2 patients) were driven, 4 (from 3 patients) were inhibited,

and 2 cells (from 2 different patients) appeared to not have any effect for the duration of

the burst stimulation. Stimulating upstream of the GPi (the STN), 6 neurons were

recorded from 2 different patients. None of these neurons showed tremor reduction in the

accelerometer recording during stimulation (figure 18). 1 neuron was initially driven

before being inhibited towards the end of the stimulation train, 3 neurons were driven

throughout the stimulation train, and 2 appeared to be inhibited throughout the duration

of the stimulation.

56

A

57

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

A) Pre-stimulation: The top trace shows the baseline spiking behavior of the

cell prior to stimulation. The bottom trace shows the accelerometer data.

During Stimulation: The neuronal trace during TBS that the phase histogram in B

was produced from. The top trace shows the spiking behavior of the cell during

stimulation. The middle trace shows the onset of the theta burst stimulation phase.

The bottom trace shows the accelerometer data.

Post-Stimulation: The neuronal trace following TBS. The top trace shows the

baseline spiking behavior of the cell following the stimulation protocol. The

bottom trace shows the accelerometer data.

B) The top plot is a raster plot of the neuronal firing behavior (indicated by the

solid dots) throughout the TBS train. A phase is defined as the start of TBS to the

start of the next stimulation burst. TBS indicates when theta burst stimulation was

applied.

B

58

A

59

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

A) Pre-stimulation: The top trace shows the baseline spiking behavior of the

cell prior to stimulation. The bottom trace shows the accelerometer data.

During Stimulation: The neuronal trace during TBS that the phase histogram in B

was produced from. The top trace shows the spiking behavior of the cell during

stimulation. The middle trace shows the onset of the theta burst stimulation phase.

The bottom trace shows the accelerometer data.

Post-Stimulation: The neuronal trace following TBS. The top trace shows the

baseline spiking behavior of the cell following the stimulation protocol. The

bottom trace shows the accelerometer data.

B) The top plot is a raster plot of the neuronal firing behavior (indicated by the

solid dots) throughout the TBS train. A phase is defined as the start of TBS to the

start of the next stimulation burst. TBS indicates when theta burst stimulation was

applied.

B

60

3.3 Effect of Theta Burst Stimulation on Plasticity within the GPi

Seeing as the tremor frequency lies within the theta bandwidth, we examined the

effect of theta burst stimulation on synaptic plasticity within the GPi of PD and dystonia

patients. We stimulated 6 cells from three patients with theta burst stimulation- 2 neurons

were from patients with PD while 4 neurons were from a patient with dystonia. fEP

amplitudes were measured for up to 90s post stimulation to examine the effect of theta

burst stimulation on plasticity (figure 19 A and B). Data was unavailable for one PD cell

at 60 s and 90 s post stimulation due to recording noise. The other cell showed an initial

65% increase in fEP amplitude compared to baseline at the 30 s time point, and an fEP

peak at 170% of the baseline which was recorded at the 60s post stimulation time point

providing an inverted u-shaped curve. Even at 90s post stimulation, the fEP was still

150% of the baseline. In analyzing the data recorded across the 4 cells from the 1 patient

with dystonia, the peak fEP amplitude across all trials was only 125% of the baseline

measure and occurred at the 90 s stimulation time point (figure 19 C).

61

Individual fEP Amplitudes

TBS 30 60 90 1200

50

100

150

200Cell 1

Cell 2

Cell 3

Cell 4

Cell 5

Cell 6

PD

Dystonia

Baseline

Time Post Stimulation (s)

fEP

Am

plit

ud

e

(% o

f B

ase

line

)

Baseline T1

T2 T3

A

B

62

fEP Amplitude Averages

TBS 30 60 90 80

90

100

110

120

130

TBS

HFS

Baseline

Time Post Stimulation (s)

fEP

Am

plit

ud

e

(% o

f B

ase

line

)

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

A) A sample of individual fEPs indicating a plastic change due to TBS. In the top

left panel, the baseline field evoked potential (fEP) amplitude is shown

(corresponds to the “Test pulses” in figure 6). T1 indicates the fEP amplitude

30 s after the TBS, T2 indicates 60 s post TBS, and T3 indicates 90 s post TBS.

B) Individual cell trials showing the fEP amplitude across time. Theta burst

stimulation ended at time 0. All points measured are expressed as a

percentage of baseline fEPs (baseline is considered 100% as denoted by the

dotted red line). Blue squares indicate cells from PD patients; black circles

indicate cells from dystonia patients.

C) fEPs of individual trials in B averaged together over sampled time points and

expressed as a percentage of baseline fEP amplitude (indicated by the solid

grey line.) Black line indicates the average of all cells averaged together

regardless of disease type. Red line indicated the average of the dystonia only

trials. TBS indicates theta burst stimulation; HFS indicates high frequency

stimulation.

C

63

Another question we set out to answer dealt with how TBS compares to HFS. As

such, we also compared the amplitudes of fEP prior to and following HFS stimulation in

2 neurons from a patient with dystonia at 30 s and 60 s post-stimulation (Figure 19C). No

significance tests have been performed due to the low sample size of this study. However,

in comparing both neurons’ results from TBS and HFS respectively (i.e. neuron 1 TBS to

neuron 1 HFS, and neuron 2 TBS to neuron 2 HFS), it was found that the TBS always

produced a greater increase in fEP than the HFS. In fact, in comparing HFS to TBS

irrespective of aligning trials, it was found that the lowest measured fEP amplitude

recorded from TBS was higher than the highest fEP amplitude recorded following HFS.

The data was then averaged and analyzed as two groups: TBS (PD and dystonia

combined), and dystonia HFS (figure 19 C). Again, no test of significance was performed

due the small sample size; however, although this data is very preliminary, the data is

indicating that TBS is more efficacious at inducing LTP than HFS, as evidenced by the

higher fEP amplitudes measured after the stimulation protocol. This preliminary data

should be taken cautiously, especially due to the PD cell which may be considered an

outlier in this data series.

4.0 Chapter 4- Discussion

The bulk of this thesis has been dedicated to the characterization of the firing

patterns of this new class of border burst cells recently identified in our lab. Smaller side

studies also attempted to determine if this new class of neurons is responsible for

initiating tremor, and also what role these oscillations may play (if any) on plasticity

within the GPi. This work focuses on the role of theta oscillations as pertaining to tremor.

However, to fully discuss theta oscillations, it should be noted that theta oscillations

underlie many other phenomena within the body. Within the hippocampus, theta

oscillations are believed to be intricately linked with learning and memory. Other studies

have shown theta oscillations to be present in the GPi of patients with dystonia (Moll et

al. 2014), and during periods of dyskinesias (Cenci and Lindgren, 2007). As such, even if

64

the induced theta oscillations presented in this thesis are responsible for initiating tremor,

theta oscillations may sub serve other functions, both in and outside of the motor system.

4.1 Stimulation-Induced Oscillations

To date, there is no widespread scientific consensus as to what initiates tremor in

patients with movement disorders. Tremor is a very heterogeneous phenomenon that is

present in a variety of different diseases, and therefore it is unlikely that one “umbrella”

explanation will be capable of explaining all types of tremor. Parkinson’s disease is a

disease commonly affected by tremor as outlined in the introduction. Many theories have

been put forward to explain various aspects of the symptoms of PD, however, no theory

presently available can account for all aspects of the disease collectively. Helmich et al.,

(2011) used a combination of fMRI and SPECT imaging in combination with EMG

recordings and found that activity in the basal ganglia increases prior to the onset of

tremor in PD patients, indicating a role of the GPi in tremor. Specifically, it was found

that activity increased prior to the tremulous EMG activity but then became incoherent

with tremor activity soon after. This led to the formulation of the “Dimmer-Switch

Hypothesis” which states that the GPi initiates the tremor and the cerebellum controls the

amplitude. What is missing from Helmich’s work is what or how tremor actually starts

because imaging studies only indirectly measure the metabolic response to neuronal

activity (i.e. blood flow). In this regard, oscillatory activity in neurons cannot be

measured with the resolution of this technique. We believe to have accumulated evidence

suggesting a pivotal role of the GPi in this capacity. Our data indicates that certain

neurons in patients with and without tremor can be induced into a theta oscillation

(specifically within the tremor frequency) following brief microstimulations ranging from

3-7.5uA. There was a significant increase in burst index in PD patients when stimulated

at 5 and 7.5µA, and in dystonia patients when stimulated with 3 and 5µA indicating that

these neurons are becoming more bursty after their inhibitory period, however, no

significant difference was found in the burst index or firing rate ratios between PD and

65

dystonia patients (i.e. neither group can be deemed to become more bursty than the other

following stimulation.). There was a corresponding significant decrease in firing rate

between pre-stimulation and post-stimulation in PD patients when stimulated with 5 and

7.5µA, and in dystonia patients when stimulated with 5µA. Interestingly, it was found

that dystonia HFD neurons had a significantly lower pre-stimulation firing rate and a

significantly higher pre-stimulation mode burst index compared to the PD HFD neurons.

These findings are in agreement with Vitek et al (1999) who found lower firing rates and

increased burstiness in dystonia patients compared to PD patients. However, these

findings are contrast to those reported by Hutchison et al. (2003) who found no difference

between firing rates. This discrepancy may be due to the anesthetics used during surgery,

If a difference does exist, these findings would add support to the rate model of

movement disorders which imply that hyperkinetic (i.e. dystonia) disorders, such as

dystonia, occur due to decreased neuronal activity in the GPi which leads to decreased

inhibition of the motor thalamus, whereas hypokinetic disorders (i.e. PD) occur due to

increased inhibition of the thalamus from the GPi. Looking at the power spectra, there

was no significant difference in the theta and tremor frequency power spectra between

pre- and post-stimulation, regardless of stimulation intensity, except for tremor power in

dystonia patients at 5µA. Bear in mind that this study utilized microelectrodes, and thus

the amount of energy delivered to the brain is not likely to have been capable of inducing

a significant change in the oscillatory behavior of a network of cells. This finding could

be important as it may answer the “where” question pertaining to tremorgenesis. Another

important aspect to be addressed here is that the MKaneoke script which analyzed the

neuronal activity calculated values (i.e. firing rate, burst index, etc.) based on the whole

sampling window. As all neurons went through a silent period of varying lengths

following stimulation, this may influence (i.e. lower) the values that were attained by this

script. To better characterize these values, a measure such as the silent period (the time

from the end of the stimulation until the first spike) could be used. A 10 s sampling

window was chosen so that the sampling time was small while still having enough spikes

in the spike train during the recovery period.

66

Building on these findings and taking into consideration recent work by Milardi et

al., (2015) and Smith and Wichmann (2015), these border burst cells, if responsible for

the initial instigation of tremor, lie in a critical location within the brain that may endow

them with the potential to start tremor. Milardi and colleagues, using a constrained

spherical deconvolution technique demonstrated the presence of white matter tracts

directly connecting the GPi with the cortex in humans, now deemed the “super-direct”

pathway. In addition to this, Mathai et al. (2012), and Smith et al., (2014) using primates,

determined that this super-direct pathway connects the peripallidal regions of the GPi to

the cortex and uses glutamate as a neurotransmitter. It is in these regions of the GPi

where these border burst cells have been identified. We speculate that these border burst

cells may be initiating the tremor rhythm and the super-direct pathway acts as a short

circuit allowing for the signal to increase in gain to the point of phenotypic expression of

tremor. This discovery is important as it has now been demonstrated that one of the major

outputs of the basal ganglia encompasses neurons that can initiate a theta oscillation that

can be passed on to the downstream Vop of the thalamus and have reverberation directly

from the cortex. From the Vop, the thalamus then projects to the motor cortex; closing

the corticobasal-thalamic loop which is responsible for selecting which movement

commands are executed and which are discarded.

If future work were to confirm that these border burst cells are responsible for the

induction of tremor, more work would have to be undertaken to determine what triggers

these oscillations in the first place. Slice work by Llinas and Yarom (1986) showed that

depending on the membrane polarization state, neurons of the guinea pig inferior olive

could be induced into two different rhythms: if the membrane was depolarized, the cells

preferentially oscillated at 9-12 Hz. However, if the membrane was hyperpolarized, the

neurons would preferentially oscillate at 3-6 Hz. Liu et al., (2012) showed evidence of

decreased GPi firing rates due to stimulation within the GPi with microelectrodes. They

hypothesize that this inhibitory effect is due to the local release of GABA from afferent

67

terminals. In our recordings, all our cells, whether they oscillated or not, experienced a

short inhibitory period (indicating local hyperpolarization) following stimulation. Seeing

as some cells were then induced into a 4-6 Hz oscillatory rhythm following this

inhibition, one could speculate that the same mechanisms seen in the inferior olive

neurons are at play in the pallidal cells as well. Llinas and Yarom (1986) found that the

oscillations were produced due to a subthreshold membrane calcium dependent

oscillation. In short, calcium influx leads to the activation of calcium-dependent

potassium channels that then hyperpolarize the cell. This hyperpolarization leads to a

calcium rebound, which then depolarizes the cell and creates a membrane oscillation.

Cooper and Stanford (2000), using whole-cell recordings, found that the rat globus

pallidus includes neurons characterized by a voltage-dependent inward rectifying and low

threshold calcium currents. However, further work would have to confirm that these

currents are identical to those discussed by Llinas and Yarom, and that they exist within

human subjects.

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

4.2.1 Theta Burst Stimulation in GPi

It is important to remember that just because these border burst cells have been

found to be able to generate their own tremor rhythm following a short microstimulation,

and the fact that they lie within one of the major output nuclei of the basal ganglia does

not necessarily mean that they are intricately involved with initiating tremor. To address

this, we applied theta burst stimulation (a protocol that mimics the induced-oscillations

that were recorded) to the GPi in patients undergoing GPi DBS implantation and looked

for the effect of stimulation on both the cellular activity and tremor activity as recorded

by an accelerometer. We also looked at upstream (STN) and downstream (motor

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thalamus) targets to determine if theta burst stimulation had any effect. Within the GPi

we didn’t see any evidence of tremor modification. Although we did not collect evidence

of tremor modification from stimulating the GPi, we do have one example where

stimulating during a DBS implantation in the thalamus did induce tremor (see appendix

figure 3). However, it is believed that the electrode was lateral to the intended target and

may have been stimulating the corticospinal fibers of passage. We had hypothesized that

if the GPi is a tremorgenic nucleus, then we should see phase locking/resetting of the

tremor during stimulation theta burst stimulation. However, as previously mentioned, the

microelectrode setup that we used for this work isn’t likely to transfer enough energy to

induce a widely dispersed network of cells into a tremor rhythm and hence why we may

not have seen any effect. Plaha et al. (2008) have shown tremor induction through

stimulation of the zona incerta with DBS macroelectrodes. It is believed that if the GPi is

truly the originator of the tremorgenic oscillation that stimulating the GPi with an

electrode that delivers more energy may be capable of entraining the tremor network

neurons responsible for tremor initiation with in the GPi. In further reviewing the

literature, it was found that Constantoyannis et al., (2004) were able to induce postural

and action tremor in two patients by stimulating the ventral posteromedial nucleus of the

thalamus (aka, the Vop) with DBS electrodes. However, in this report, they only used

non-patterned stimulation, and only saw postural and action tremor, but not rest tremor

indicative of PD.

Looking at the cellular response to the theta burst stimulation, the majority (5/7)

of cells appeared to be inhibited by the stimulation protocol. This is expected due to the

local release of GABA due to the microstimulation as mentioned earlier by Liu et al.,

(2012). Seeing as the GPi sends tonically active GABAergic efferents to the motor

thalamus, inhibition of these GPi neurons would promote disinhibition of the thalamic

neurons. I speculate that if this disinhibition occurred rhythmically (say, at tremor

frequency) this could promote a tremor signal to be propagated to the motor cortex and

allow for the phenotypic expression of tremor.

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4.2.2 Theta Burst Stimulation in Motor Thalamus

We also looked to see what effect theta burst stimulation may have on tremor

expression and cellular activity when applied to the Vim in Essential Tremor patients. As

the Vim is a common target of DBS electrode implantation/ thalamotomies for tremor,

we hoped to bypass any divergence of tremor activity that may be passed on from the

basal ganglia. Also, whereas we speculate that the GPi is a tremorgenic center but other

authors hypothesize that the thalamus is the site of tremor initiation, we wondered if theta

burst stimulation in the Vim could induce signs of tremor modification. Initially, I had

predicted that since tremor occurs between 4-6 Hz, that stimulating motor centers of the

brain would initiate tremor or cause a resetting of the tremor rhythm to the stimulation

frequency. Interestingly, in the motor thalamus, the only evidence of tremor modification

that were noted was some cases of tremor reduction. When the cellular activity of the

neurons being stimulated was analyzed, it was found that in three examples where tremor

reduction was seen, the cells were initially driven by the stimulation and then inhibited

later in the stimulation train. This is contrary to what we predicted. If the motor thalamus

is responsible for initiating tremor, and the theta burst stimulation is initially driving the

cellular activity, I would expect that the tremor should have been driven as well, not have

had an amplitude reduction as seen in one isolated case. However, this may be explained

by a recent review by Chiken and Nambu (2015) that discusses that DBS may not exert

its effects through excitation or inhibitory effects on cells/fibers of passage, but instead,

DBS may work via disconnecting the sensory input from the motor output. It is this

interaction of the two signals that is believed to underlie the pathology in many

movement disorders. This may be applicable to what we are seeing when stimulating the

motor thalamus with TBS: the stimulation may be activating the thalamocortical loop

allowing the sensory afferents to be disconnected from the motor efferents, and the

disconnection of these two signals may be reducing the amplitude of the tremor recorded.

Another important result that was seen when analyzing the phase histograms produced

following the theta burst stimulation is that the neuronal activity in the Vim was capable

70

of being entrained to the burst stimulation. This shows that these neurons are capable of

being entrained to a rhythm set elsewhere in the brain. As the GPi is an upstream nucleus

of the motor thalamus, this could strengthen the hypothesis that tremor initiation initially

occurs within the GPi and the tremorgenic signal is capable of entraining neurons in

downstream projections which play an integral part in movement selection.

4.2.3 Theta Burst Stimulation in Sub Thalamic Nucleus

Lastly, we examined the effect of theta burst stimulation on tremor and cellular

activity when the STN was stimulated. As the STN is an upstream target of the GPi, we

were curious to see if a theta burst signal produced here could elicit signs of tremor

modification, presumably through its glutamatergic projections to the GPi. Again, no

evidence of tremor modification was noted in any of the trials. There did not appear to be

any significant phase locking of the neuronal activity either- neuronal activity was diffuse

and sporadic throughout the phase. These findings tend to indicate that a theta rhythm in

the STN does not have the capacity to effect the phenotypic expression of tremor. This

does not necessarily rule out the STN as having a role in tremor but rather that our

experimental protocol was not sufficient in eliciting tremor modifications.

4.3 Effect of Theta Burst Stimulation on Plasticity within the GPi

The last component to this project deals with analyzing the changes in synaptic

plasticity that may be occurring in the GPi due to theta oscillations. As previously stated,

the activity of one cell in a network is not likely to have a large effect on the rest of the

network. Plasticity is important to this project since, for these border burst cells to have a

chance at inducing tremor, they have to be able to entrain other neurons to fire in

71

synchrony with them. We have an example from a patient with PD that shows a 75%

increase in the fEP recorded following TBS compared to before stimulation. Since the

fEP is indicative of the neurotransmitter release from a collection of other local neurons,

and the primary neurotransmitter within the GPi is GABA, this indicates that there is the

ability to induce plastic changes as a result of theta burst stimulation. This outcome was

only seen in one site however, and thus these findings are preliminary. One possible

reason for this enhanced efficacy seen with theta-burst stimulation over high-frequency

stimulation may be due to habituation of the cell during HFS that isn’t seen during TBS.

Since TBS is a burst stimulation paradigm, it has inherent pauses that could allow the cell

to recuperate (i.e. produce more/reuptake expelled neurotransmitter). This is in contrast to

HFS which doesn’t have pauses during the stimulation protocol, and thus

neurotransmitter stores may deplete over the stimulation duration. In looking at the rest of

the data, something else that was found striking was that the change in the fEP amplitude

from the PD neuron appeared to be much greater than that seen in the dystonia neurons.

Although both showed an increase in fEP from baseline conditions, the PD fEP amplitude

reached a maximum of 170% of baseline whereas the average maximum fEP amplitude

of the dystonia neurons only reached 111% of baseline, and the individual peak

maximum only reached 125% of the baseline. Clearly, more work needs to be done in

this area to determine if these results are significant. If found to be significant, the next

question would most likely be: What is responsible for the difference between the two

groups? One explanation could lie in recording variations that naturally occur. Anecdotal

evidence from performing these recordings has shown that the amplitude of the fEP can

vary depending on the proximity of the electrode tip to recording unit. This makes sense

intuitively: the closer the tip is to the cell, the more concentrated the chloride gradient in

the local milieu and this would register as a greater voltage in an electrical recording,

manifested as a larger fEP. It is important to note that these possible confounds could also

explain within group differences along with intergroup differences, and hence why these

results are volatile with small sample sizes. However, these results could also be due to

physiological differences in the neurons of the two disease groups. It is well known that

PD patients have a dopamine deficiency and this is not seen in dystonia groups. Previous

work has shown that there are plasticity abnormalities in PD patients due to this

72

dopamine deficiency. Piccconi et al., (2012) discuss that a lack of dopaminergic

innervation in PD leads to decreased cell excitability, and this has a profound effect on

the susceptibility of neurons to undergo plastic changes. Prescott et al., (2014) showed a

lack of depotentiation (a form of plasticity wherein synapses that have undergone long

term potentiation return back to their baseline gain) in the GPi and SNr of PD patients

taking L-DOPA. Based on these previous findings, one might expect to see atypical

plasticity in PD patients due to dopamine deficiency. However, one may also speculate

that it is not the dopamine levels per se that are responsible for the potential difference in

fEP amplitudes recorded, but the long term administration of L-DOPA commonly

prescribed to those with PD. Studies have posited that chronic L-DOPA therapy can lead

to pathological plastic changes that may underlie levodopa induced dyskinesias (Grace

2008). As such, this L-DOPA-dependent plasticity may be the underlying cause of this

yet-to-be-significantly-determined effect.

Not only did we look at the plastic response of the GPi to theta burst stimulation,

but we also attempted to determine the efficacy of TBS compared to HFS. TBS in the

hippocampus has been shown to be the ideal stimulation frequency as this frequency

allows priming of the synapse (which involves blocking presynaptic GABAB

autoreceptors allowing for maximal post-synaptic excitation leading to the opening of a

maximal number of NMDA receptors (Larson and Munkascy 2014). Zhu et al., (2015)

showed that HFS was also able to elicit LTP in hippocampal neurons, albeit by different

intracellular signaling mechanisms. Although a small sample size was used for this

study, it appeared that TBS may be more efficacious at inducing LTP in GPi neurons that

HFS, irrespective of disease type. However, HFS was only examined in dystonia patients

and therefore, this is not a balanced comparison. Further analysis of this data in light of

the possible plastic abnormalities expected in PD due to dopamine loss appears to show

that HFS and TBS are equally efficacious in eliciting LTP in a dystonia patient. However,

these results are only from one patient with 4 trials of TBS and two of HFS and tests of

significance have yet to be performed.

73

4.4 Future Directions

The work presented here is just the beginning of a possible explanation to

tremorgenesis. Future work needs to be undertaken to confirm these newly identified

border burst cells as the initiators of tremor. We examined the role of theta oscillation in

tremor modification with the use of microelectrodes. An alteration to the tremor

entraining methodology we used in this work could be to record EMG activity as well as

accelerometer data as there may have been tremulous activity occurring in the muscle but

was not detected visibly or by the accelerometer. Another approach to determining if

these oscillations are tremorgenic would be to stimulate the GPi with theta burst

stimulation while a second electrode records in the Vop. This would help determine if the

oscillatory activity in the GPi is transmitted to the motor thalamus; an assumption up to

this point. This experiment would most likely have to be performed in an animal model

due to the surgical restraints of performing this in humans- this protocol would require

two separate trajectories due to the location of the thalamus and GPi within the brain,

which would add to the invasiveness of the surgery.

Also of importance is determining what neuronal properties are responsible for

creating these oscillations. Although not presented in this work, we initially thought that

recurrent inhibitory collaterals within the GPi may have been responsible for setting the

theta oscillation, however, the anatomical connectivity of the GPi does not support this

theory (Bar-Gad et al., 2003). We now speculate that plasma membrane calcium channels

might play a role. Yarom and Llinas (1986) discovered that calcium dependent potassium

channels were responsible for the tremor-frequency oscillatory activity seen in guinea pig

inferior olive neurons (further discussed in the introduction). This may serve as a model

for our neurons.

74

Since the GPi tends to deal with motor commands being distributed to the body,

whereas the SNr distributes motor commands to the head, it would be interesting to see if

neurons in the SNr are capable of initiating their own theta rhythm like the border cells of

the GPi. Although the work presented here deals largely with rest tremor as seen in PD, it

would be unable to explain common tremors associated with dystonia, such as palatal

tremor or blepharospasms.

Lastly, another obvious future direction is to continue the work on the effect of

theta burst stimulation in the GPi to determine the effects of stimulation on plasticity.

With a larger sample size, we could try to determine if any significant difference exists

between PD and dystonia patients, as well as between theta burst stimulation and high

frequency stimulation. This could implicate whether the theta rhythm is capable of

inducing substantial plastic changes within the GPi that may be needed to entrain

networks of cells to fire at the tremor frequency.

75

4.5 Conclusions

This project was not able to successfully indicate the GPi as a tremorgenic nucleus. Data

was collected showing that neurons within the GPi could be induced into a tremor

stimulation following a brief electrical stimulation. However, we were unable to collect

evidence of tremor modification that may have drawn causation between the oscillation

and tremor. We have begun to look at the effect of theta burst stimulation on plasticity

within the GPi but have not collected enough data to draw conclusive results yet.

76

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Appendix

a

b

c

d e

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Appendix Figure 1- A sample of the read-out produced from the Matlab script

MKaneoke from the neuronal trace seen in Figure 6.

A) A raster plot showing discriminated spikes in time (vertical lines). Vertical

lines connected with horizontal lines indicate bursts identified by the script.

Each row represents 1 s with the start of the trace beginning in the bottom

left corner of the plot.

B) A plot showing the instantaneous firing frequency of the cell across time. Red

dots indicate firing frequencies significantly higher than the average firing

rate.

C) A wavelet spectrogram showing the power of the signal across time. Note the

theta-band dominance of the this signal.

D) An autocorelogram showing the periodicity of this oscillation.

E) The power spectra determined by the fast Fourier transform (FFT) showing

power bands throughout the recording. Note the largest power peak occupies

the 4-6 Hz range (part of the theta band).

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

to stimulation.

A) A raster plot showing discriminated spikes in time (vertical lines). Vertical

lines connected with horizontal lines indicate bursts identified by the script.

Each row represents 1 s with the start of the trace beginning in the bottom

left corner of the plot.

B) A plot showing the instantaneous firing frequency of the cell across time. Red

dots indicate firing frequencies significantly higher than the average firing

rate.

C) A wavelet spectrogram showing the power of the signal across time.

D) An autocorelogram showing the periodicity of this oscillation.

E) The power spectra determined by the fast Fourier transform (FFT) showing

power bands throughout the recording.

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90

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

following stimulation.

A) A raster plot showing discriminated spikes in time (vertical lines). Vertical

lines connected with horizontal lines indicate bursts identified by the script.

Each row represents 1 s with the start of the trace beginning in the bottom

left corner of the plot.

B) A plot showing the instantaneous firing frequency of the cell across time. Red

dots indicate firing frequencies significantly higher than the average firing

rate.

C) A wavelet spectrogram showing the power of the signal across time.

D) An autocorelogram showing the periodicity of this oscillation.

E) The power spectra determined by the fast Fourier transform (FFT) showing

power bands throughout the recording.

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Appendix Figure 3- An example of theta burst stimulation inducing visible tremor in a

patients foot during microelectrode mapping for DBS implantation surgery.

Although the target of this surgery was the thalamus, it is believed that the electrodes missed the

target and are stimulating the fibers of passage of the internal capsule.

A) EMG activity recorded from the patient’s left leg.

B) EMG activity from the patient’s right arm.

C) Accelerometer activity recorded from the patient’s right arm.

D) Accelerometer activity recorded from the patient’s right foot.

E) EMG activity recorded from the patient’s right leg,

F) Activity recorded from the distal electrode during theta burst stimulation.

Vertical dotted lined are spaced 1 s apart and aligned with the onset of the TBS. Notice that the

TBS precedes the right leg EMG activity, which precedes the tremor recorded by the

accelerometer attached to the right foot. Also, the tremor amplitude appears to increase as TBS

continues.

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