Biomechanical Assessment of Normal and Parkinsonian Gait In

BIOMECHANICAL ASSESSMENT OF NORMAL AND PARKINSONIAN GAIT IN

THE NON-HUMAN PRIMATE DURING TREADMILL LOCOMOTION

ANIL KUMAR THOTA

Submitted in partial fulfillment of the requirements

For the degree of Master of Science

Theses Adviser: Dr. Jay L. Alberts

Department of Biomedical Engineering

CASE WESTERN RESERVE UNIVERSITY

August 2012

CASE WESTERN RESERVE UNIVERSITY

SCHOOL OF GRADUATE STUDIES

We hereby approve the thesis of

Anil Kumar Thota

candidate for the Master of Science degree*.

(signed) Dr. Jay L Alberts (Chair of the committee)

Dr. Robert F. Kirsch

Dr. Dawn Taylor

(date) July 7, 2011

*We also certify that written approval has been obtained for any proprietary material contained therein.

TABLE OF CONTENTS

LIST OF TABLES ...... III

LIST OF FIGURES ...... IV

CHAPTER 1 : INTRODUCTION ...... 1

1.1 ETIOLOGY ...... 1

1.2 PD SYMPTOMS ...... 4

1.2.1 Motor symptoms ...... 4

1.2.2 Non-Motor symptoms ...... 5

1.3 PATHOPHYSIOLOGY ...... 6

1.4 DIAGNOSIS ...... 9

1.5 TREATMENTS ...... 10

1.5.1 Pharmacotherapy: ...... 10

1.5.2 Surgical treatments: ...... 11

1.6 OBJECTIVE AND RATIONALE ...... 13

1.7 REFERENCES ...... 16

CHAPTER 2 : BIOMECHANICAL ASSESSMENT OF NORMAL AND

PARKINSONIAN GAIT IN THE NON-HUMAN PRIMATE DURING TREADMILL

LOCOMOTION 18

2.1 INTRODUCTION ...... 18

2.2 METHODS...... 21

2.2.1 Primate training ...... 21

2.2.2 Marker placement ...... 22

i 2.2.3 Data collection ...... 24

2.2.4 Data processing ...... 25

2.2.5 Statistical Analysis ...... 27

2.3 RESULTS ...... 28

2.4 DISCUSSION ...... 43

2.5 CONCLUSIONS ...... 48

2.6 REFERENCES ...... 49

CHAPTER 3 : BIBLIOGRAPHY ...... 54

ii LIST OF TABLES

Table 1-1: List of Non-motor symptoms of Parkinson’s disease adapted from Chaudhuri et al (Chaudhuri, Yates et al. 2005)...... 6

iii LIST OF FIGURES

Figure 1-1: Motor loops of basal ganglia and other structures in normal persons (A) and in Parkinson’s disease patients (B). Green and red lines indicate excitatory and inhibitory neuronal connections. In Parkinson’s disease circuit block, thicker lines indicate increased activation and thinner lines indicate decreased activation, Neurotransmitters associated with neuronal connections are provided adjacent to the connection line. DA: Dopamine; Glut: Glutamate; GABA: Gamma Amino Butyric Acid. Neuronal structures are represented as blocks. SNc: Substantia Nigra pars compacta; SNr: Substantia Nigra pars reticulata; Gpi: Global pallidus internal; GPe: Global pallidus externa...... 7

Figure 1-1: Complex motor loops of basal ganglia and other structures. Green lines indicate excitatory connections and the red includes inhibitory connections. The neurotransmitters associated with the connections are provided adjacent to the connection line. Abbreviations: DA – Dopamine; Glut – Glutamate; GABA – Gamma Amino Butyric Acid; SNc – Substantia Nigra pars compacta; ; SNr - Substantia Nigra pars reticulata; Gpi – Global pallidus internal GPe – Global pallidus externa; PPN – Pedunculo Pontine Nucleus...... 14

Figure 2-2: Body segments of normal NHP are illustrated as stick figures. (A) Right hindlimb showing (top to bottom), hip to knee (thigh), knee to ankle (shank) and ankle to toe (foot) segments (B) right forelimb showing (top to bottom) shoulder to elbow (arm) and elbow to wrist (forearm) and wrist to finger (hand) segments. Body segments at touch down and at lift off are represented by thick solid and thick broken lines. For clarity body segments at mid-stance (thick line) and mid-swing (thick broken lines) are shown only in one cycle and marked with arrows. Thin short and long arrows show swing and stance phase’s direction and duration respectively. Thick arrow shows the direction of treadmill motion. (C) Foot fall patterns shows instant time at which individual foot lift-off and touch downs from the treadmill floor. Stance phases: white filled rectangles; swing phases: black filled rectangles for right hindlimb (HLR) and right forelimb (FLR) and, grey filled rectangles for left hindlimb (HLL) and left forelimb (FLL). HLLFLRHLRFLL footfall pattern indicates symmetric diagonal gait sequence...... 28

iv side of the body. Lift-off (dotted vertical line) and touchdown (solid vertical line) event markers for the right hindlimb are shown...... 30

Figure 2-4: Average +SD values of (A) step cycle duration and (B) % stance phase within each cycle for hindlimb (black) and forelimb (grey). Step cycle duration is not significantly different when compared between FL and HL in all NHPs. Percentage stance phase is significantly higher in FL when compared to HL in less affected limbs of M1 and M2. Step cycle duration is significantly lower in normal. Percentage stance phase is not significantly different when compared between normal and mild parkinsonian most affected side (M1-MA), but significantly higher in moderate parkinsonian NHP (M2) and in mild parkinsonian less affected side (M1-LA)...... 32

Figure 2-5: Comparison of angle trajectories. The trajectories illustrate the average (solid line) +/- 1SD (dashed line) angle trajectory of (5-17) consecutive gait cycles in one rat from touch-down to touch-down (See text for further details). (A) Hip, (B) Knee, (C) Ankle (D) Shoulder, and (E) Elbow angle trajectories. Vertical dotted line: Lift-off event marker...... 34

Figure 2-7: Joint angle range of motion during swing and stance phases of forelimb step cycle. Bar plots represents averages maximum, minimum and range angles during swing (Row A) and during stance (Row B) for shoulder and elbow joints of normal (black filled bar), M1 (dark grey filled bars) less affected followed by more affected and M2 (grey filled bars) less affected followed by more affected...... 36

Figure 2-9: Hindlimb - forelimb interlimb joint-angle coordination. Angle-angle (A) hip vs. shoulder and (B) Knee vs. Elbow plots illustrate the coordination between the joints of different ipsilateral limbs during treadmill walking. Data (5-15 cycles) (average (solid line) +/- 1SD (dashed line) shows the maximum, minimum and range of excursion of each joint and the typical patterns observed...... 39

Figure 2-10: Intralimb and interlimb phases. Bar plots represents intralimb joint phases (Row A) and interlimb footfall phases (Row B) for normal (black filled

v bar), M1 (dark grey filled bars) less affected followed by more affected and M2 (grey filled bars) less affected followed by more affected. HL: Hindlimb; FL: Forelimb; subscripts R and L for right and left...... 40

vi Biomechanical Assessment of Normal and Parkinsonian Gait in the Non-Human Primate during Treadmill Locomotion

Abstract

ANIL KUMAR THOTA

Parkinson's disease (PD) is a progressive neurodegenerative disorder caused by death of dopamine producing cells in Substantia Nigra. Clinical symptoms of PD include both motor and non-motor symptoms. The motor symptoms are akinesia, bradykinesia, tremor, rigidity and, postural instability and gait disorder (PIGD). In advanced PD patients, PIGD may be refractory to medical treatment and leads to increased risk of fall injuries. A major limitation in studying the underlying mechanisms of PIGD is the lack of a quantitative method to objectively assess gait abnormality and postural instability. The primary aim of this thesis is to quantify the gait pattern abnormality in non-human primate (NHP) model of PD, a

1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) treated NHP. Gait pattern was assessed by capturing and analyzing 3D kinematic data while NHP was walking on a modified treadmill. The primary biomechanical variables used to distinguish normal and MPTP treated gait patterns were: step cycle duration, percentage of stance phase and angular range. Angle-angle plots were analyzed to assess inter- and intra-limb co-ordination and balance.

vii Chapter 1 : Introduction

In 1817, a physician from London, James Parkinson published investigative articles and essays chronicling the disease, which caused resting tremor, posture and gait abnormality, and dystrophy. Dr. Parkinson referred this disease as “Shaking Palsy” or “Paralysis Agitans”. Between 1868 and 1881,

Jean-Martin Charcot, the father of neurology of France, made further additions and clarifications after extensively studying investigative articles and essays written by Dr. Parkinson. In his publications, Dr. Charcot referred the disease as

"maladie de Parkinson". Thus, the modern name "Parkinson disease (PD)" was coined in honor of James Parkinson for his contribution to the western medical field (Parkinson 2002; Kempster, Hurwitz et al. 2007; Lees 2007). However, recent literature claims that the existence of PD and its therapy, in the east, was known as early as 3000 BCE (Gourie-Devi, Ramu et al. 1991; Ellenberg,

Langston et al. 1995; Dhanasekaran, Tharakan et al. 2008).

1.1 Etiology Early clinical investigators suggested that dysfunction of midbrain or part of it is the probable cause of PD. After reviewing a case report of the patient who developed Parkinsonism symptoms from contra-lateral tuberculoma of the midbrain (Ellenberg, Langston et al. 1995), in 1885, Dr. Brissaud reported that

PD was probably caused by the impairment of Substantia Nigra. About a decade later, Tretiakoff (Tretiakoff 1919) proved that significant cellular damage occurred in the Substantia Nigra of patients with the encephalitic form of PD. The studies done by Swedish and Viennese neuroscientists in 1950s led to the hypothesis

1 that reduction of dopamine transmitter in striatum of the brain was the probable cause of PD (Chapter 8 in (Factor and Weiner 2008)). Finally the exemplary work by Hornykiewicz in 1970 (Hornykiewicz 1970) proved that the Parkinson’s disease caused by the reduction of dopaminergic neurons in Substantia Nigra of the brain.

PD is classified as one of the progressive neurological disorders attributing to its early slow onset of the symptoms and gradual deterioration of the symptoms as the disease progresses. PD is considered as motor disorder primarily because of the presence of three cardinal motor symptoms (tremor, rigidity and bradykinesia). However, recent literature (Langston 2006) suggests that PD not only exhibits motor symptoms but also exhibits several non-motor symptoms and thus suggests that PD is multifaceted and complex disorder. For better management of symptoms, PD researchers suggest that Parkinson’s disease should be termed as Parkinson’s disorder.

Although, PD is caused by reduction of dopamine neurons in Substantia

Nigra, the mechanisms for reduction in dopamine neurons is still a mystery despite extensive work done by the eminent scientists. Thus, PD is termed as idiopathic PD. However, some of the risk factors that are believed to be causing reduction of dopamine neurons are given below:

Environmental factors: Epidemiological studies (Olanow and Tatton

1999) suggest that there is a high incidence of PD among rural and farming dwellers. This is probably because of the constant consumption of water and food contaminated with pesticides and herbicides. These studies also report that

2 more people diagnosed with PD in a group who had occupational exposure to manganese, copper-lead composites and other industrial toxins.

Genetic mutations: Idiopathic PD is considered as non-genetic disorder.

However, in small percentage of PD population, the disease is caused by genetic mutations. Some of the mutated genes found (Olanow and Tatton 1999) in familial type PD are 1) α- Synuclein (PARK1), 2) Parkin (PARK2) 3) PTEN induced putative kinase 1 (PINK1) (PARK6) 4) DJ-1 (PARK7) 5) Dardarin

(PARK8) 6) Ubiquiton Carboxy terminal Hydrolase L1 (UCH-L1). These mutated genes cause reduction of dopamine by inducing abnormalities in associated protein synthesis or degeneration.

Neurotoxins: Symptoms similar to PD were caused by 1-methyl-4phenyl,

1-2-3-6-tetrahydropyridine (MPTP), a compound found in synthesized recreational drugs. MPTP induced Parkinsonism symptoms are similar to idiopathic PD except for onset of the disease (Langston and Ballard 1984). The onset of PD by MPTP is rapid when compared to idiopathic PD, which is slow and progressive. The chemistry of MPTP and its metabolism was studied extensively to create an animal model of PD. The animal model PD paved a way to research interventional drugs, surgical and other rehabilitative treatments for

PD.

Demographical factors: Age - The occurrence of PD in patient population aged between 50 and 80 years is high. The risk of getting PD increases as the age increases. Gender - Incidence of PD in male population is

1.2 to 1.5 times than that of female population. The possible reasons include the

3 presence of male sex hormones, effect of environmental toxicity, exposure to occupations toxins and other habits such as drug usage etc. Race - The incidence of PD in African American population is less when compared to

Caucasian population. This difference is attributed to the pigmentation of skin

(melanin), which hinders the passage of neurotoxins through the skin and thus reduces the entry of toxin into the body. Hereditary - A study (Factor and Weiner

2008) indicates that there are about 11% of PD patients had an affected relative.

This may be due to genetic factor or their similar life style such as sharing the same occupation or living in farming or rural area.

1.2 PD symptoms PD symptoms include several motor and non-motor symptoms. A partial list of most common symptoms is listed below. Among the symptoms mentioned below, only one or two symptoms manifest as primary parkinsonian symptoms and other symptoms exhibited are considered as secondary symptoms.

1.2.1 Motor symptoms Rest Tremor is characterized by involuntary movements in hands, legs and neck, or entire body at frequencies between 3Hz and 6Hz. Resting tremor in

PD is unique and is apparent when the muscles are relaxed. It is also referred to as “pill rolling” for its rhythmic back-and-forth motion of the thumb and forefinger.

Rigidity cause undesired resistance to the movement due to muscle stiffness and increased muscle tone. This symptom produces “Cog Wheeling” effect, i.e. stop-go, stop-go, ratchet-like short and jerky movements. Rigidity

4 decreases the momentum in task resulting in decreased range of motion and also inflicts pain and cramps at the muscle insertion site.

Bradykinesia is characterized by the slowness of voluntary movement and difficulty in initiating the movement. It reduces the movement of facial muscle leading to mask-like expression and cause abnormality in speech and swallowing. Akinesia, an aggravated form of bradykinesia cause freezes in action.

Postural instability and gait disturbance/difficulty/disorder (PIGD) is most debilitating symptom of all which reduces the quality of life significantly.

PIGD cause reduced swinging of hands, reduced swaying of hips, reduced stride length and intermittent shuffle during walking. The paucity of movements leads to freezing of gait and inability to initiate the movements or reduction in reaction time lead to frequent falling.

1.2.2 Non-Motor symptoms Non-motor symptoms (NMS) are often ignored during neurological consultation. However, these non-motor symptoms are considered disabling by reducing the quality of life and eventually cause institutional care instead of home care. Chaudhuri et al (Chaudhuri, Healy et al. 2006) reported that sleep disturbance, memory failure and dribbling of saliva were among the most disabling symptoms when compared to bradykinesia and tremor. It is also reported that one third of 149 PD patients recruited survived and among the survived 84% suffered with debilitating effects cognitive failure, 48% with dementia, 50% with hallucinations and depression, 50% with choking, 41% with

5 incontinence and 35% with postural hypotension.

Table 1-1: List of Non-motor symptoms of Parkinson’s disease adapted from Chaudhuri et al (Chaudhuri, Yates et al. 2005).

Neuropsychiatric symptoms Sleep disorders Autonomic symptoms Depression, apathy, anxiety Restless legs Bladder disturbances Anhedonia Periodic limb movements Urgency Attention deficit REM behavior disorder Nocturia Hallucinations Excessive daytime somnolence Frequency Delusions Vivid dreaming Sweating Non–REM sleep-related Dementia Orthostatic hypotension movement disorders Falls related to orthostatic Obsessional behavior (pounding) Insomnia hypertension Coat hanger pain Sexual dysfunction Hypersexuality Erectile impotence Hypotestosterone state

Gastrointestinal symptoms Sensory symptoms Other symptoms Dribbling of saliva Pain Fatigue Ageusia Paraesthesia Diplopia Dysphagia/choking Olfactory disturbance Blurred vision Reflux Seborrhoea Vomiting Weight loss Nausea Constipation Unsatisfactory voiding of bowel Fecal incontinence

A full list of NMS adapted from Chaudhuri et. al. (Chaudhuri, Yates et al.

2005) is given in the table 1. Of these NMS: constipation, olfactory deficit, REM sleep behavior disorder and depression are proved to occur before the parkinsonian motors symptoms appear (Chaudhuri, Yates et al. 2005; Chaudhuri,

Healy et al. 2006; Langston 2006)

1.3 Pathophysiology PD symptoms develop due to disruptions or malfunction in basal ganglia circuitry. A schematic of the neural circuitry between the basal ganglia and the external inputs and outputs is shown in Figure 1-1A (Modified from chapter 22 in

6 A Normal B Parkinson’s Disease

Cortex Cortex

Striatum Glut Striatum Glut

D2 D2 GABA-enk GABA-enk DA DA Thalamus Thalamus Gpe SNc Gpe SNc

D1 D1 GABA-dyn GABA-dyn GABA GABA Glut Glut STN Gpi/SNr STN Gpi/SNr

Brainstem/ Brainstem/ Spinal Cord Spinal Cord

(Factor and Weiner 2008)). These complex motor loops in basal ganglia play a significant role in the regulation and control of muscle tone and motor function.

This complex motor loops follow two separate pathways: direct pathway and indirect pathway.

The direct pathway motor loop starts from the inputs to the striatum from cortex. Striatum in turn connects to the Gpi-SNr complex nuclei. Gpi-SNr completes the motor loop by connecting to cortex via thalamus. Cortex then connects to motor center in the brain stem. Neurons in direct pathway

7 (striatonigral GABAergic) express substance P, dynorphin (Dyn) and D1 dopamine receptor. Stimulation of striatum in the direct pathway inhibits the

GPi/SNr. This in turn reduces inhibition to the ventrolateral thalamus to increase the excitation to primary motor cortex inducing movement. Therefore, activation of the direct pathway increases motor function.

The indirect pathway motor loop also starts from the inputs to the striatum from cortex. Striatum in turn connects to the intermediary oscillator formed by input nuclei Gpe and output nuclei STN. Intermediary oscillator connects to Gpi-

SNr complex. Gpi-SNr completes the motor loop by connecting to cortex via thalamus. Cortex then connects to motor center in the brain stem. Neurons in indirect pathway (striatopallidal GABAergic) express enkephalin (enk) and D2 dopamine receptor. Stimulation of striatum in the indirect pathway excites the

GPi/SNr. This in turn increases inhibition to the ventrolateral thalamus to decrease the excitation to primary motor cortex. Therefore, activation of the indirect pathway decreases motor function.

Normal motor function is attributed to balanced activity between hyperkinetic direct pathway [Cortex (Excitation (↑)) → Striatum (Inhibition (↓)) →

"SNr-GPi" complex (less ↓ of thalamus) → Thalamus (↑) → Cortex (↑) →

Motor output] and hypokinetic indirect pathway [Cortex (↑) → Striatum (↓) →

GPe (less ↓ of STN) → STN (↑) → "SNr-GPi" complex (↓) → Thalamus (less ↑)

→ Cortex (less ↑) → Motor output]. PD motor symptoms emanate due to the loss of nigrostriatal dopamine causing imbalance in direct and indirect pathway signals (Figure 1-1B). The pathophysiology of PD includes (1) degeneration of

8 pigmented (monoaminergic) neurons of the mesencephalon and brainstem (2) degeneration of the dopaminergic nigrostriatal pathway and (3) loss of pigmented neurons in the locus coeruleus (norepinephrine) and dorsal motor nucleus of the vagus nerve.

1.4 Diagnosis PD is primarily characterized as symptomatic motor disorder and therefore there are no specific tests to identify PD. In order to rule other type of disorders from PD, PD patients are subjected to series of motor tests in combination with radiotracer imaging such as PET/SPECT or fMRI. Motor tests are performed to investigate the presence of cardinal symptoms: tremor, rigidity and bradykinesia of PD. Resting tremor is observed when patient rest hands on his or her lap. PD resting tremor is characterized by pill rolling. Bradykinesia is assessed by observing the frequency and amplitude of finger tapping and foot tapping tasks and rigidity is tested by feeling the resistance in hand/wrist while flexion and extension. Loss of balance is tested by pulling the patient backwards gently while the patient is standing. Gait is observed for number of steps and step lengths while the patient is walking. Olfactory test is performed to assess the sensitivity to smell. To diagnose PD, all the above tests should be positive and most importantly the patient should respond to Levodopa therapy. Finally, the presence of Lewy bodies in the brain stem after autopsy confirms PD. Lewy bodies are mainly composed of alpha synuculein protein and aggregates into microscopic spherical masses that displaces the intracellular content in affected neurons.

9 1.5 Treatments Currently, there is no cure for PD except for treating to alleviate the symptoms and thus improving the activities of daily life and quality of daily life.

1.5.1 Pharmacotherapy: Treating PD with drugs is the first choice. Since reduction of dopamine in

CNS is the main responsible for the PD, drugs that synthesize dopamine or drugs that inhibit the destruction of dopamine are used for treating PD symptoms.

Dopamine itself could not be used as a drug since it neither can cross the blood brain barrier (BBB) nor can absorb into the gut. Therefore, L-DOPA, which is precursor to dopamine, is used as a drug for the treatment of PD. Aromatic amino acid decarboxylase (AADC), which converts L-DOPA to dopamine is not only present in the CNS system but also in peripheral body. As a result, high dose of L-DOPA is given to elicit clinical improvement. In addition, dopamine is available in higher quantity than needed in the periphery causing nausea and vomiting. Thus, carbidopa, DOPA decarboxyalse inhibitor that cannot cross BBB, is given in combination with L-DOPA to reduce the conversion of dopamine in periphery.

L-DOPA and carbidopa combinational therapy has very short half-life because catechol-O-methyltransferase (COMT) enzyme disintegrates dopamine into 3-methoxy – tyramine. Therefore, in order to increase half-life of the drug,

COMT inhibitor is also added to the drug combination. Other drugs such as

Symmetrel, which activates the release of dopamine from the storage sites and blocks the re-uptake of dopamine, and dopamine antagonist drugs that activate

10 dopamine receptor directly are also used as secondary drug to boost the effect of

L-DOPA.

1.5.2 Surgical treatments: However, in advanced stages of PD, the symptoms get worse and do not respond to drug therapy. In many cases, the side effect dyskinesia, a short burst of uncontrolled movements, including other side effects from long-term usage of drugs elicit more disability than the disease itself. In such cases, surgical techniques are utilized to treat PD.

Ablation: In ablation surgery, small portion of the brain is surgically removed from the patient to alleviate the symptoms. Different parts of the brain were ablated for different PD symptoms. If the patient’s main symptom was tremor then thalamotomy was performed and if patient exhibits tremor and rigidity then pallidotomy was performed. Though ablation surgeries alleviated debilitating

PD symptoms, the procedure is highly invasive, requires destroying parts of the brain permanently and has serious side effects. Ablation surgery technique to treat PD was common before the advent of L-DOPA but after discovering deep brain stimulation therapy, which is reversible and adjustable, ablation were rarely performed.

Deep Brain Stimulation: High frequency Deep brain stimulation (DBS) is considered as a treatment of choice for not only medication-refractory

Parkinson’s disease (PD) (Benabid, Pollak et al. 1987; Benabid, Pollak et al.

1991; Gross and Lozano 2000; Benabid, Chabardes et al. 2006) but also for many neuro-motor and psycho-pathological conditions. The popularity of the

11 DBS can be contributed to its clinical benefits similar to the lesion/ablation surgery but without the need of lesions. The stimulation parameters can be further adjusted postoperatively to ameliorate the symptoms of the disease.

Though the mechanism of DBS (McIntyre and Thakor 2002; Vitek 2002; Liu,

Postupna et al. 2006) is not understood completely, it is hypothesized that DBS disrupts the flow of information or processing in basal ganglion circuitry

(Anderson, Postupna et al. 2003; Maurice, Thierry et al. 2003) by injecting electric charge via implanted electrodes.

Subthalamic Nucleus (STN) or Globus Pallidum Externus (GPe) is the neuronal target of choice (Volkmann, Allert et al. 2001) for DBS electrode implantation in PD. For maximizing the clinical benefit of DBS, surgical team follows three important steps. 1) Selection of the DBS recipient using the guidelines and recommendations provided by Core Assessment Program for

Surgical Interventional Therapies in Parkinson’s Disease. 2) Implantation of the electrode(s) precisely within proximity to the target area and 3) programming the

DBS stimulus generator to adjust the quantity of current injection and the spread of current density. The precise placement of the electrodes is achieved (Pinto, Le

Bas et al. 2007) with highest success rate using CT/MRI image guiders, by analyzing the patterns of neuronal firing during intra-operative microelectrode recording (MER) and confirming the neuronal target by assessing the effect of microstimulation on cardinal symptoms PD and by analyzing side effects. The stimulation parameters (amplitude, pulse width and frequency of voltage pulses) are programmed (Volkmann, Herzog et al. 2002; Volkmann, Moro et al. 2006) to

12 adjust the current injection and spread by using a handheld radio telemetry device during an initial programming session, four to six weeks after DBS implantation surgery

Other therapies include stem cell, cell regeneration and gene therapy.

Several phase 0, 1 and 2 clinical trails are going on using all the above- mentioned therapies.

1.6 Objective and Rationale PD patients exhibit several motor, non-motor symptoms but one of the symptoms is usually prominent i.e. primary symptom, and others are secondary symptoms. This phenomenon prompted researchers to group the patients in different PD subtypes. Jankovic et al (Jankovic, McDermott et al. 1990) analyzed

UPDRS data from 800 PD patients and proposed two major subtypes: tremor- dominant and postural-instability-gait-difficulty (PIGD). Van Rooden et. al. (van

Rooden, Visser et al. 2009) identified four major subtypes from a cohort of 399

PD patients. These include tremor-dominant, bradykinetic-rigid, axial subtype1

(rise, gait, postural instability) and axial subtype2 (freezing, speech, and swallowing). Axial subtype PD patients typically suffer with gait disorders characterized by short shuffling steps, freezing, and difficulties in initiation. These axial instabilities results in increased falls, immobilization and social isolation thus influencing the patients’ quality of life. STN-DBS and GPi-DBS have been shown to improve tremor, bradykinesia, rigidity and levodopa (L-dopa) induced dyskinesia but ineffective in treating axial instabilities (Giladi, McDermott et al.

2001).

13 Normal

Cortex

Striatum

Glut D2 GABA-enk DA Thalamus Gpe SNc

D1 GABA-dyn GABA Glut STN Gpi/SNr

Brainstem/ PPN Spinal Cord

Figure 1-2: Complex motor loops of basal ganglia and other structures. Green lines indicate excitatory connections and the red includes inhibitory connections. The neurotransmitters associated with the connections are provided adjacent to the connection line. Abbreviations: DA – Dopamine; Glut – Glutamate; GABA – Gamma Amino Butyric Acid; SNc – Substantia Nigra pars compacta; ; SNr - Substantia Nigra pars reticulata; Gpi – Global pallidus internal GPe – Global pallidus externa; PPN – Pedunculo Pontine Nucleus.

A major limitation in the study of the axial instabilities of PD is the lack of techniques to objectively assess gait and a fundamental lack of understanding the mechanisms underlying these pathological conditions. Biomechanical analyses have often been used successfully to characterize the gait dysfunctions in human patients, but the underlying mechanisms are not understood well due to the limitation of interventional studies in humans. Non-human primate parkinsonian models are being used to study and understand physiological and biomechanical mechanisms of Parkinson’s disease. Currently, gait in primates is typically assessed through activity counts while the monkey is moving in his/her

14 home cage; this method provides only a crude estimate of locomotor behavior with little regard to the quality of movement patterns. A fundamental limitation of animal gait studies has been the reliance on subjective measures of gait dysfunction rather than utilizing objective biomechanical measures to precisely characterize gait mechanics on therapeutic interventions. The subjective rating score approaches are limited in their ability to capture the specifics of gait dysfunction and treatment-derived benefits or how these changes may relate to an improvement in the patients’ quality of life.

In this theses, we have developed quantitative locomotor measures to characterize the gait of normal and 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine

(MPTP) treated primate by capturing 3-D kinematic data while the amimals walk on a modified treadmill. The outcome measures from 3D kinematic data provide important insights into physiological mechanisms of postural instability and gait dysfunction. Development of this method is especially important for development of DBS in pedunculopontine nucleus (PPN), a new potentially promising target, which has connections to basal ganglia and cortical spinal circuits and plays an important role in the control of locomotion and thus for treating gait dysfunctions in PD (Figure 1-2).

15 1.7 References

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16 the Pathogenesis of Parkinsons-Disease." Canadian Journal of Neurological Sciences 11(1): 160-165. Lees, A. J. (2007). "Unresolved issues relating to the shaking palsy on the celebration of James Parkinson's 250th birthday." Mov Disord 22 Suppl 17: S327-334. Liu, Y., N. Postupna, et al. (2006). "High frequency deep brain stimulation: What are the therapeutic mechanisms?" Neurosci Biobehav Rev. Maurice, N., A. M. Thierry, et al. (2003). "Spontaneous and evoked activity of substantia nigra pars reticulata neurons during high-frequency stimulation of the subthalamic nucleus." J Neurosci 23(30): 9929-9936. McIntyre, C. C. and N. V. Thakor (2002). "Uncovering the mechanisms of deep brain stimulation for Parkinson's disease through functional imaging, neural recording, and neural modeling." Crit Rev Biomed Eng 30(4-6): 249-281. Olanow, C. W. and W. G. Tatton (1999). "Etiology and pathogenesis of Parkinson's disease." Annu Rev Neurosci 22: 123-144. Parkinson, J. (2002). "An essay on the shaking palsy. 1817." J Neuropsychiatry Clin Neurosci 14(2): 223-236; discussion 222. Pinto, S., J. F. Le Bas, et al. (2007). "Comparison of two techniques to postoperatively localize the electrode contacts used for subthalamic nucleus stimulation." Neurosurgery 60(4): 285-292. Tretiakoff, C. (1919). Contribution a l‘etude de l‘anatomie pathologique du locus niger de Soemmering avec quelques deductions relatives a la pathogenie des troubles du tonus musculaires et de la maladie de Parkinson., University of Paris. van Rooden, S. M., M. Visser, et al. (2009). "Motor patterns in Parkinson's disease: a data-driven approach." Mov Disord 24(7): 1042-1047. Vitek, J. L. (2002). "Mechanisms of deep brain stimulation: excitation or inhibition." Mov Disord 17 Suppl 3: S69-72. Volkmann, J., N. Allert, et al. (2001). "Safety and efficacy of pallidal or subthalamic nucleus stimulation in advanced PD." Neurology 56(4): 548- 551. Volkmann, J., J. Herzog, et al. (2002). "Introduction to the programming of deep brain stimulators." Mov Disord 17 Suppl 3: S181-187. Volkmann, J., E. Moro, et al. (2006). "Basic algorithms for the programming of deep brain stimulation in Parkinson's disease." Movement Disorders 21: S284-S289.

17 Chapter 2 : Biomechanical Assessment of Normal and Parkinsonian Gait in the Non-Human Primate during Treadmill Locomotion

2.1 Introduction Parkinson's disease (PD) is a chronic, progressive neurodegenerative disease caused by loss of dopamine producing neurons in basal ganglia

(Tretiakoff 1919; Hornykiewicz 1970; Ellenberg, Langston et al. 1995). Etiology of

PD is unknown but environmental factors (Olanow and Tatton 1999), hereditary and genetic mutations (Gasser 2007; Factor and Weiner 2008), neurotoxins

(Langston and Ballard 1984), and trauma (Bower, Maraganore et al. 2002) have been attributed for the cause of PD. The clinical symptoms are primarily motor symptoms involved in regulating and/or controlling motor functions. However, recent studies suggest that the basal ganglia circuitry is also involved in cognitive and behavioral functions and exhibits various behavioral and psychiatric symptoms (Parent and Hazrati 1995; Bevan, Atherton et al. 2006; Frankemolle,

Wu et al. 2010)}. The cardinal motor symptoms of PD include: akinesia – paucity of movement; tremor - involuntary movements observed in hands, legs and neck or entire body at frequencies between 3Hz and 6 Hz; rigidity - muscle stiffness and increased muscle tone which provides unwanted resistance to the movement; bradykinesia - slowness of voluntary movement; postural instability – loss of balance and gait dysfunction – unable to modulate the gait. Gait and balance disorders can become a limiting factor in the patients’ quality of life and a potential source of severe injury from frequent falls. Falls are the main cause of

18 disability and dependence, as annually approximately 70% of individuals with PD fall and 13% fall more than once a week (Adkin, Frank et al. 2003; Robinson,

Dennison et al. 2005).

Currently, there is no cure for PD. However, pharmacological approaches that synthesize dopamine (e.g.: L-DOPA) and/or that inhibit the destruction of dopamine are the prime choices for symptomatic treatment of PD.

Pharmacological therapy is effective in controlling the motor symptoms in early stages of PD but after prolonged use it often results in dyskinesias or uncontrolled spontaneous movements of limbs. Patients also experience response fluctuations as the disease progresses, namely, an unstable reaction characterized by either increased or decreased sensitivity to the drug, delusions and hallucinations (Chaudhuri, Healy et al. 2006). Long-term usage of the drugs increases orthostatic hypotension that increases risk of fall injuries and dyskinesias (Senard, Rai et al. 1997; Goldstein 2003). As PD progresses the dosage of the medications needs to be increased. Eventually, PD symptoms becomes drug refractory in which case the next course of action is the surgical intervention deep brain stimulation (DBS).

The subthalamic nucleus (STN) and the globus pallidus pars internus

(GPi) are two DBS targets that have been shown to improve tremor, bradykinesia, rigidity and levodopa (L-dopa) induced dyskinesia. As the disease progresses, DBS therapy continues to relieve other motor symptoms but either becomes ineffective or cause axial instabilities such as akinesia, postural instability, and gait dysfunction (Davis, Lyons et al. 2006; Ferraye, Debu et al.

19 2008). Gait disorders in PD patients typically are characterized by short shuffling steps, freezing, difficulties in initiation, and bradykinesia resulting in increased falls, immobilization and social isolation thus impacting the patients’ quality of life.

It is reported that drug resistant gait and posture instabilities are the most debilitating symptoms in approximately 10% of PD patients (Giladi, McDermott et al. 2001).

A major limitation in the study of the axial instabilities of PD is the lack of techniques to objectively assess gait and a fundamental lack of understanding the mechanisms underlying these pathological conditions. In human studies, gait function is typically assessed utilizing subjective rating scales such as United

Parkinson’s Disease Rating Scale (UPDRS) and Hoehn and Yahr Staging of

Parkinson's Disease and occasionally using biomechanical techniques such as kinematics (Sofuwa, Nieuwboer et al. 2005; Svehlik, Zwick et al. 2009; Delval,

Snijders et al. ; Kurz and Hou 2010; Roiz Rde, Cacho et al.) and kinetics (Ueno,

Yanagisawa et al. 1993; Sofuwa, Nieuwboer et al. 2005; Svehlik, Zwick et al.

2009). Biomechanical analyses have often been used successfully to characterize the gait dysfunctions in human patients, but the underlying mechanisms are not understood well due to the limitation of interventional studies in humans. Non-human primate parkinsonian models are being used to study and understand physiological and biomechanical mechanisms of

Parkinson’s disease. Currently, gait in primates is typically assessed through activity counts while the monkey is moving in his/her home cage; this method provides only a crude estimate of locomotor behavior with little regard to the

20 quality of movement patterns. A fundamental limitation of animal gait studies has been the reliance on subjective measures of gait dysfunction rather than utilizing objective biomechanical measures to precisely characterize gait mechanics on therapeutic interventions. The subjective rating score approaches are limited in their ability to capture the specifics of gait dysfunction and treatment-derived benefits or how these changes may relate to an improvement in the patients’ quality of life.

In this project, we have developed quantitative locomotor measures to characterize the gait of normal and 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine

2.2 Methods

2.2.1 Primate training Data were collected from three female rhesus (Macacca mulatta) non-human primates (NHP). Normal (N), aged 14, was non-MPTP treated NHP. M1, aged 9, was treated with right side intracarotid injections of MPTP had developed mild

21 hemi-parkinsons symptoms. M2 aged 10 was treated with left-side intracarotid injections and systemic injections of MPTP had developed mild bradykinesia/akinesia on her left side and mild rigidity on her right side. The study was in compliance with The National Institutes of Health Guide for the Care and Use of Laboratory Animals (1996) and approved by the Cleveland Clinic

IACUC. All monkeys were placed on a modified regular treadmill (vision fitness) enclosed with a custom built enclosure manufactured using Lucite and 8020 material for an hour daily during a period of 5-10 days for acclimatization. During the acclimatization process, we observed the monkey for number of instances of defecation and urination, the time spent for exploration and response to their favorite treats given. The acclimatization period was concluded after noting minimal bodily excretions, less exploration and accepting and eating their favorite treats. Monkeys were then trained to walk on the treadmill for a period of 5-10 days. The maximum speed was determined on individual capability of the monkeys.

2.2.2 Marker placement After the training phase is completed, monkeys were anesthetized with acepromazine (0.5 – 1.0 mg/kg i.m.) and placed in upright position mimicking walking stance on an elevated Styrofoam pedestal to mark the joint positions as accurately as possible. The bony processes of the following body landmarks on both right hindlimb (HLR) and left hindlimb (HLL) sides: the head of the greater trochanter (GT), the lateral head of femoral condyle (FC), the lateral malleolus for

(LM), the fifth metatarsal (MT), and the outside tip of the fifth digit/toe (TO); on

22 both right forelimb (RFL) and left forelimb (LFL): the head of the humerus (HU),

Figure 2-1: 3D kinematic video data acquisition system. A modified regular treadmill enclosed with a custom built enclosure and a four camera video system is used for 3D kinematic data. Infrared lights were attached under each camera along axis of sight of the camera to illuminate the retro-reflective paint markers placed on primate bony landmarks. the lateral epicondyle (LE), the distal head of the ulna (DU), the metacarpo- phalangeal (MCP) joint, and the outside tip of the third digit/finger (FI) were marked by black permanent marker. After a day of rest, reflective material

(mixture of 80% by volume reflective powder (3MTM - 8010 Gray) and 20% by volume of petroleum jelly) was applied on top of the markings to collect 3D kinematic data.

23 2.2.3 Data collection The 3D kinematic data were collected using VICON Motus® motion analysis video capture (100Hz) system consisted of four black and white, CCD, genlocked digital cameras (two on each side of the boby) placed at 2-3 feet from the treadmill, such that any given reflective marker on the monkey was visible in two of the cameras (Figure 2-1). An infrared light was attached under each camera along axis of sight of the camera to illuminate the retro-reflective paint markers. After each data collection session, the reflective material was removed by wiping with dry and wet paper towels and the prior markers made by sharpie were retouched for subsequent data collection session. These sharpie markers improved accuracy and repeatability of reflective marker placement for longitudinal studies and reduced inter-investigator variability. Data from left and right side was collected simultaneously in N using four cameras while walking at

1.5 miles/hour (0.675 meters/second) Data from M1 and M2 was collected while walking at 0.8 miles/hour (0.36 meters/second) using two cameras and collected the data for less affected (LA) and more affected (MA) in two different sessions.

Prior to data collection, calibration of 3D space was performed using a customized rectangular calibration cube. The calibration cube consists of 50 points distributed among 10 rods and spans the 3D space in which the monkey walks on the treadmill. Static digitization volume errors in x, y and z directions were calculated within VICON and calibration was accepted only when average static digitization volume errors in all three spatial directions were less than 0.5%.

Further to validate the system for accuracy during dynamic motion, dynamic angular and distance measurement errors were calculated by placing reflective

24 markers three corners (both sides) of set square triangle. The set square triangle with markers facing cameras was moved back and forth in the calibrated volume while collecting the data simultaneously in all four cameras. The average and standard deviation of values calculated from digitized data for actual angles of

90, 30 and 60 degrees are 89.73 ± 0.34, 29.76 ± 0.16 and 60.51 ± 0.21 degrees respectively and for other side are 89.91 ± 0.58, 29.41 ± 0.26 and 60.67 ± 0.38 degrees. RMS errors for angles are within the range of 0.28 and 0.77. The average and standard deviation of values calculated from digitized data for actual lengths of 19.3, 22.2 and 11.3 are 19.1 ± 0.05, 21.94 ± 0.05 and 10.89 ± 0.04 centimeters respectively. For other side, the actual lengths are 18.3, 21 and 9.9 and corresponding values calculated from digitized data are 18.2 ± 0.07, 20.87 ±

0.06 and 10.25 ± 0.06 centimeters. RMS errors for distances are within the range of 0.11 and 0.35. Thus, dynamic angular error is less than 0.7 degree and dynamic distance error is less than 0.35cm. 3D kinematic data were collected in

1-2 minute long sessions where the NHP was able to walk/run comfortably at a constant pace without sliding on the treadmill at the speed levels reported above.

2.2.4 Data processing From the video captured, the reflective markers placed on primate body landmarks were digitized offline for each of the four camera views for 5-17 locomotion cycles. The digitized spacial co-ordinates (x, y, z) were used to model the primate body by the rigid interconnected body segments. Digitized markers at

GT, FC, LM, MT and TO were used to form body segments thigh, shank and foot. These body segments were used to model hindlimb. Digitized markers at

25 HU, LE, DU, MCP and FI were used to form arm, forearm, hand and finger segments. These segments were used to model forelimb. Markers from ride side were used to model FLR and HLR and markers from left side were used to model

FLL and HLL. Additionally, left and right body segments were connected by connecting GT and HU from both left and right side.

Joint angles were calculated using two intersecting limb segments: trunk and thigh segments for hip joint; thigh and shank for knee joint; shank and foot for ankle joint; foot and fifth digit for MT joint; trunk and arm for shoulder joint; arm and forearm for elbow joint; forearm and hand for wrist joint; hand and finger for MCP joint. The video frame, in which the monkey’s right hindlimb toe touched the treadmill belt, was marked as a right hindlimb touch-down (TD) event. A right hindlimb lift-off (LO) event was marked when the monkey’s right hindlimb toe lifted off the treadmill belt. Similarly, touch-down and lift-off events were marked for left hindlimb, right forelimb and left forelimb. For each limb, the swing duration

(LO to TD), the stance duration (TD to LO), and step cycle duration (TD to TD) were calculated on a cycle-by-cycle basis. The swing and stance durations were used to reproduce footfall diagrams to determine gait sequences. Additionally,

HL mid-stance events were determined when the hip joint and ankle joints are perpendicular to the treadmill during the stance phase. And for FL: when the shoulder joint and elbow joints are perpendicular to the treadmill phase. Mid- swing events were also determined similar to mid-stance events but during swing phase (Larney and Larson 2004).

26 For each monkey, 201 point spline interpolation method was used to normalize each gait cycle and an average joint angle trajectory was obtained by averaging 5- 17 normalized gait cycles. Joint angle-angle plots were generated by plotting one joint angle vs. another (hip vs. knee, knee vs. ankle, shoulder vs. elbow, and elbow vs. wrist) and were used for graphical qualitative assessment of intralimb coordination (Winstein and Garfinkel 1989; Brustein and Rossignol

1998). Relative times of occurrence of maximum flexions during the swing phase of one reference joint with respect to another were used as quantitative intralimb coordination measure (Thota, Watson et al. 2005). For HL; hip joint angle was used as reference and for FL; shoulder joint angle was used as reference.

Similarly, relative times of occurrence of TD of one reference limb to another were used as interlimb coordination measure (Thota, Watson et al. 2005). The coordination measures were calculated for each step cycle of the limb and averaged for the entire trial. Yield angle was calculated as the difference of angles at TD and mid-stance (Larney and Larson 2004).

2.2.5 Statistical Analysis 3D kinematic variables for hip, knee, ankle, shoulder and elbow angles are averaged on a cycle-by-cycle basis and average ± standard deviation (SD) values are reported. A two factor ANOVA was conducted to assess the effects of mild or moderate Parkinson on step cycle duration and percentage stance phase in forelimb compared to in hindlimb. A one-way ANOVA was conducted to assess the effects of Parkinson disease on maximum flexion, maximum

2.3 Results Reconstructed body segments of primate while walking on the treadmill using digitized markers are illustrated by stick figures to show limb segments in space

28 and in time (Figure 2-2). Stick figures representation of HLR: thigh, shank and foot segments (top to bottom) and stick figures representation of FLR: arm, forearm and hand segments (top to bottom) for four consecutive cycles are illustrated in Figure 2-2A and Figure 2-2B respectively. Stick figures are plotted along the time axis and spaced 50 ms apart for clarity and to reduce cluttering.

Limb segments at TD is indicated by thick line and segments at LO is indicated by thick broken line. Mid-stance and mid-swing events are also represented by thick line and thick broken line respectively. For clarity, mid-events are shown in only one gait cycle. Limb segments move in the same direction as that of treadmill motion (thick arrow) during the stance phase (thin long arrow) and opposite direction during swing phase (thin short arrow). Footfall patterns of all four limbs indicating times at which each individual foot lift-off from the treadmill floor and touchdown on the treadmill floor occurred are illustrated in Figure 2-2C.

Footfall patterns also show the duration of stance (white filled rectangles) and swing phases (black filled rectangles for HLR and FLR and, grey filled rectangles for HLL and FLL). Footfall pattern also show gait sequence and Figure 2-2C indicates the primate is walking in symmetric diagonal gait sequence

(HLLFLRHLRFLL) (Hildebra.M 1967).

Joint angle trajectories calculated using two intersecting limb segments are plotted in Figure 2-3. Four gait cycles of hindlimb: (A) hip, (B) knee and (C) ankle and, of forelimb: (D) shoulder and (E) elbow joint angle trajectories along with lift-off (dotted vertical line) and touchdown (solid vertical line) event markers are shown. Angles from right are shown in black trajectories and angles from left

Figure 2-3: Joint angle trajectories of hindlimb (Hip (A), knee (B) and ankle (C)) and for forelimb (shoulder (D) and elbow (E)) for normal NHP. Black line graph indicate angles from right side of the body and grey line graphs indicate from left side of the body. Lift-off (dotted vertical line) and touchdown (solid vertical line) event markers for the right hindlimb are shown. are shown in grey trajectories. Right and left trajectories show 180 degrees out of 30 phase. Decreasing joint angle is defined as flexion and increasing joint angle is defined as extension. Maximum flexion angle is minimum joint angle value and maximum extension angle is maximum joint angle value. Turning points (TP) are defined as points at which joint angle changes from increasing angle to decreasing angle which also indicate the start of flexion (F) subphase or decreasing angle to increasing angle which also indicate the start of extension

(E) subphase. Hip and shoulder joint angles show one F and E subphases for each gait cycle while knee, ankle and elbow show two F (F1 and F2) and two E

(E1 and E2) subphases. At HL TD, hip is fully flexed, knee and ankle fully extend.

At HL LO, hip is fully extended. Thus, hip is actively extending during the stance phase and flexing during swing phase. In contrast, both knee and ankle flexes during stance, knee slower than the ankle. Around HL LO, both knee and ankle extends and fully flexed around mid-swing phase. Shoulder exhibits 180 degrees out of phase with respect to hip. Elbow pattern is similar to ankle trajectory. All angles of right sides show 180 degrees out of phase with respect to left side angles.

Step cycle duration and percentage of stance for each cycle for each limb from normal (N), M1 less affected (M1-LA), M1 more affected (M1-MA), M2 less affected (M2-LA) and M2 more affected (M2-MA) are presented in Figure 2-4.

Step cycle duration of N, M1-LA, M1-MA, M2-LA and M2-MA for HL are 873.5 ±

29.14, 1219 ± 61.73, 1210 ± 10, 1256.7 ± 119.30 and 1462.0 ± 57.62 msecs respectively and for FL are 872.8 ± 34.09, 1196.7 ± 40.33, 1186.7 ± 40.41,

1206.7 ± 164.42 and 1452.0 ± 44.94 msecs respectively. The percentage of

Figure 2-4: Average +SD values of (A) step cycle duration and (B) % stance phase within each cycle for hindlimb (black) and forelimb (grey). Step cycle duration is not significantly different when compared between FL and HL in all NHPs. Percentage stance phase is significantly higher in FL when compared to HL in less affected limbs of M1 and M2. Step cycle duration is significantly lower in normal. Percentage stance phase is not significantly different when compared between normal and mild parkinsonian most affected side (M1- MA), but significantly higher in moderate parkinsonian NHP (M2) and in mild parkinsonian less affected side (M1-LA). stance in N, M1-LA, M1-MA, M2-LA and M2-MA for HL are 58.0 ± 2.73, 64.0 ±

32 1.88, 58.2 ± 2.05, 63.1 ± 3.36 and 84.1 ± 1.07 respectively and for FL are 60.3 ±

2.56, 77.7 ± 1.63, 57.9 ± 0.80, 75.4 ± 2.34 and 87.3 ± 2.47. Step cycle duration and percentage stance phases are significantly different between the groups with

F(9,62)=128.2 and F(9,62)=143.1 respectively. Step cycle duration is not significantly different when compared between FL and HL in all NHPs. However, percentage stance phase is significantly higher in FL when compared to HL in less affected limbs of M1 and M2. Step cycle duration is significantly lower in normal when compared to M1 and M2. Mild parkinsonian NHP’s (M1) step cycle duration for both less affected and most affected limbs are significantly lower when compared with moderate parkinsonian NHP’s (M2) most affected limb but not with less affected limb. Percentage stance phase is not significantly different when compared between normal and mild parkinsonian most affected side (M1-MA), but significantly higher in moderate parkinsonian NHP (M2) and in mild parkinsonian less affected side (M1-LA).

For comparison, Figure 2-5 to Figure 2-11, the data from N, M1-LA, M1-

MA, M2-LA and M2-MA are presented either line graphs arranged in columns next to each other or bar plots next to each other. Average data from normalized individual gait cycles (5-17) data ± 1SD. Hip, knee, ankle, shoulder and elbow joint angle data are shown in rows Figure 2-5A-E. The average LO marker

(vertical dashed line) with ± SD is also shown. Hip and shoulder angle trajectories retain general shape of E and F subphases but the duration changed in M1 and M2 NHPs when compared to normal NHP. In less affected M1 and

M2, the amplitude of E1 in knee joint angle is decreased and in more affected the

33 Normal M1 - LA M1 - MA M2 - LA M2 - MA 110  65 Hip 20 150  105

Knee 60 130  95

Ankle 60

 110 60

Shoulder 10 160  115

Elbow 70 0 50 100 0 50 100 0 50 100 0 50 100 0 50 100

E1 phase is lost. In less affected M1 and M2, the amplitude of E1 in ankle angle is decreased and in more affected, the amplitude of E1 phase is increased. In only, M2-MA the amplitude of F1 is decreased. In M1-LA, M2-MA and M2-MA, the F1 is lost.

Average ± SD values of maximum flexion (minimum angle value), maximum extension (maximum angle value) and range (height of each bar) angles are shown as bar plots in Figure 2-6 for hindlimb angles and in Figure 2-7

Figure 2-6: Joint angle range of motion during swing and stance phases of hindlimb step cycle. Bar plots represents averages maximum, minimum and range angles during swing (Row A) and during stance (Row B) for Hip, Knee and ankle joints of normal (black filled bar), M1 (dark grey filled bars) less affected followed by more affected and M2 (grey filled bars) less affected followed by more affected. * p<0.5. for forelimb angles. Figure 2-6: Row A shows angles of hip, knee and ankle during swing and Row B during stance phase of the step cycle. Black filled bars, dark grey filled bars and grey filled bars represent data from normal, M1 and M2

NHPs respectively. Two same colored bars denote the data from less affected side followed by more affected. The does not show any particular trend, but in

35 general, the hip is more flexed in M1 and M2. The angular range is smaller in M2 for all the joint angles. Degrees of freedom for between groups and within groups

36 for the F statistics values reported below are 4 and 32 respectively. The data show significant different maximum flexion, maximum extension and range in both swing and stance phases with F statistics values for hip angle: 99.3, 173.5,

144.6, 49.7, 113.1 and 50.2 respectively; for knee angle: 23.7, 211, 84.4, 74.8,

141.7 and 73.7; and for ankle angle: 34.1, 28.5, 26.6, 31.4, 14.2 and 6.2 respectively. Post-hoc multiple comparisons are performed for maximum flexion, maximum extension and angular ranges for swing and stance phases for hip, knee, ankle, shoulder and elbow angles. For clarity only angular range comparisons between normal, most affected and less affected limbs are reported. In both M1 and M2, angular range of hip show significant increase in more affected limb when compared to less affected limb. In contrast to hip, knee angular range is significantly increased in less affected limb in mild Parkinson

(M1) NHP only. In contrast with hip and knee, ankle angular range is significantly increased in more affected limb in moderate Parkinson (M2) NHP only.

Figure 2-7: Row A shows angles of shoulder and elbow during swing and

Row B during stance phase of the step cycle. Similar to hindlimb angles, the data does not distinct trend but in general, shoulder is more flexed in M1 and M2 when compared with normal NHP. The data show significant different maximum flexion, maximum extension and range in both swing and stance phases with F(4,

30) statistics values for shoulder angle: 28.1, 160.6, 216.2, 21.7, 100.9 and 185 respectively and for elbow angle: 86.5, 104.6, 52.1, 237.7, 15.8 and 183.5 respectively. Post-hoc comparisons show that shoulder angular range is significantly higher in most affected limb when compared to the same in less

37 Normal M1 - LA M1 - MA M2 - LA M2 - MA

 150

100 vs. Knee  50 Hip 20 65 110 20 65 110 20 65 110 20 65 110 20 65 110

 140

100 vs. Ankle  60

Knee 50 100 150 50 100 150 50 100 150 50 100 150 50 100 150

 110

vs. Hip 65 

20 Ankle 60 100 140 60 100 140 60 100 140 60 100 140 60 100 140

 170

vs. Elbow 120 

70 10 65 120 10 65 120 10 65 120 10 65 120 10 65 120 Shoulder

Figure 2-8: Intralimb joint-angle coordination. Angle-angle plots illustrate the coordination between the joints of the same limb during treadmill walking. Data (5-15 cycles) (average (solid line) +/- 1SD (dashed line) shows the maximum, minimum and range of excursion of each joint. (A) Hip vs. Knee, (B) Knee vs. Ankle, (C) Ankle vs. Hip and (D) Shoulder vs. Elbow. affected limb of mild parkinsonian NHP (M1) during both stance and swing phases. In contrast, shoulder angular range is significantly higher in less affected limb when compared to shoulder angular range in less affected limb of moderate parkinsonian NHP (M2). Elbow angular range in M1-LA is significantly higher when compared to angular range in M1-MA during both stance and swing phases. In contrast, the elbow angular range in M2-LA is significantly lower when compared to angular range in M2-MA.

38 Intralimb coordination patterns are derived by plotting one joint angle against another joint angle of the same limb. Intralimb coordination patterns for hindlimb and forelimb are shown Figure 2-8. Intra-hindlimb coordination patterns for hip vs. knee, knee vs. ankle and ankle vs. hip are shown in rows A to C.

Intra-forelimb coordination patterns for shoulder vs. elbow is shown in row D.

Coordination patterns for hip vs. knee and shoulder vs. elbow are crescent shaped pattern while for knee vs. ankle and ankle vs. hip are ‘distorted figure 8’ pattern caused by prominent two F and two E phases in ankle. General shapes are preserved in M1 and M2 but the orientation and size of the shapes are different for each of M1-LA, M1-MA, M2-LA and M2-MA when compared to normal.

Figure 2-9: Hindlimb - forelimb interlimb joint-angle coordination. Angle- angle (A) hip vs. shoulder and (B) Knee vs. Elbow plots illustrate the coordination between the joints of different ipsilateral limbs during treadmill walking. Data (5-15 cycles) (average (solid line) +/- 1SD (dashed line) shows the maximum, minimum and range of excursion of each joint and the typical patterns observed.

39 Interlimb coordination patterns are derived by plotting one joint angle from one limb to another joint angle from different. Interlimb coordination patterns for hip vs. shoulder, and knee vs. elbow are shown in Figure 2-9 rows A and B. The patterns of coordination are preserved only in M1-MA.

Figure 2-10: Intralimb and interlimb phases. Bar plots represents intralimb joint phases (Row A) and interlimb footfall phases (Row B) for normal (black filled bar), M1 (dark grey filled bars) less affected followed by more affected and M2 (grey filled bars) less affected followed by more affected. HL: Hindlimb; FL: Forelimb; subscripts R and L for right and left.

Average ± SD values of quantitative assessments of the intra- and interlimb phase relationship values calculated as described in the methods section are shown in Figure 2-10 row A and row B respectively, as bar graphs

40 and with similar data organization as other bar graphs show above. During the swing, in HL, all NHP’s showed that the maximum flexion in knee occurs first and then ankle followed by hip. In FL, the maximum flexion in shoulder occurs first followed by elbow. All the intra- and interlimb phase relationships are significantly different when compared among N, M1-LA, M1-MA, M2-LA, and M2-MA. Post-

Figure 2-11: Weight bearing performance. Bar plots represents yield angle (Row A) calculated in knee for hindlimb (right column) and in elbow for forelimb (left column). Percentage mid-stance occurrence (Row B) for normal (black filled bar), M1 (dark grey filled bars) less affected followed by more affected and M2 (grey filled bars) less affected followed by more affected. hoc comparisons show that maximum flexion in knee occurring significantly earlier in M1-MA when compared to M1-LA while it is opposite in M2; maximum knee flexion in M2-MA occurs significantly later and more proximity to ankle

41 when compared to M2-LA. There is no significant difference in occurrence of ankle angle in M1 but it occurs significantly earlier in M2-LA when compared to

M2-MA and also close proximity to knee flexion of M2-LA. In M1, the maximum flexion of elbow during swing is occurs significantly later in less affected limb when compared to most affected limb. In contrast, in M2, the maximum flexion of elbow during swing is occurs significantly earlier in less affected limb when compared to most affected limb. Interlimb phase plot shows the footfall or gait pattern of N is diagonal symmetrical gait (HLR  FLL  HLL  FLR); M1 is lateral symmetrical gait (HLR  FLR  HLL  FLL) and M2 is not a standard gait pattern

(HLR  FLL  FLR  HLL).

The flexion phase of knee and elbow joints after touchdown is defined as the yield angle because the joints yields under body weight as joint flexes despite the activation of extensor muscles (Kuhtz-Buschbeck, Johnk et al. 1999; Larney and Larson 2004; Lieber 2010). Yield angle measure can be used as the weight bearing accepting or performance of the limbs, higher the yield angle higher the weight bearing accepting or performance and shown in row A of Figure 2-11.

Weight bearing performance of less affected side of mild parkinsonian NHP (M1-

LA) is significantly (Not done the stats yet) higher when compared to normal and

M1-MA. Weight bearing performance of less affected side of moderate parkinsonian NHP (M2-LA) is significantly (Not done the stats yet) higher when compared to normal, M1-LA and M2-MA. Weight bearing performance in forelimb exhibits opposite trend as that of hindlimb. The percentage occurrence of

42 midstance event is shown in row B of Figure 2-11 and exhibits similar trend as that of yield angle.

2.4 Discussion We have successfully developed the first, to our knowledge, qualitative and quantitative locomotor measures to characterize the gait of MPTP treated primate by capturing 3-D kinematic data during locomotion on a modified treadmill. There are several established technologies available to perform joint angle kinematics in human (Ueno, Yanagisawa et al. 1993; Sofuwa, Nieuwboer et al. 2005; Svehlik, Zwick et al. 2009; Delval, Snijders et al. 2010; Kurz and Hou

2010; Roiz Rde, Cacho et al. 2010). However, using these technologies in primate is challenging because of non-cooperation of primate and thus relied on tedious and time consuming video data analysis. For this reason, most of the studies are either 2D kinematics using only one camera and performed unilaterally. Xiang et al (Xiang, John et al. 2007) used active markers, which were sewn into a customized garment, which was worn by primate, to measure the 3D spatial locations of wrist and ankles. Utilizing similar methodology to obtain joint angle kinematics will be difficult because of additional sliding of markers on the skin along with skin slippage. In this study, only temporal characteristics of gait were analyzed. Polk et al (Polk 2002; Polk 2004) utilized passive retro-reflective markers on shaved joint bony landmarks to calculate the limb segments. This methodology is effective if the markers stay on the primate body or until the primate removes the markers. Courtine and colleagues

(Courtine, Roy et al. 2005; Courtine, Roy et al. 2005) used nontoxic whiteout

43 paint to mark the joints. In this methodology, automatic identification of markers was not feasible because of lower marker contrast and required to digitize the markers manually. In our method, we developed the usage of retro-reflective paint as alternative markers to whiteout and along with infrared light source provided better contrast to automatically identify the joints. This novel usage of marker placement reduced the manual analyzing by more than 70%.

The basic motor pattern of stepping, the locomotor movements, are elicited autonomously by spinal central pattern generator (CPG) (Calancie,

Needhamshropshire et al. 1994; Bussel, RobyBrami et al. 1996; Bussel,

RobyBrami et al. 1996; MacKay-Lyons 2002), however higher motor center’s commands are needed to initiate, modulate and adapt the rhythmic locomotor movements. The motor cortex and cerebellum commands are primarily involved in the adaptation in gait pattern and/or the position of the limbs (Kandel,

Schwartz et al. 2000). Pedunculoponitne neucleus (PPN) a major part of mesencephalic locomotor region (MLR) (Jordan 1998; Pahapill and Lozano

2000; Stefurak, Mikulis et al. 2003), is primarily involves initiation and modulation of the gait via the medial reticular formation. The studies in the primates showed the afferent connections from the GPi and the SNr and in the rat studies, showed afferent connections from STN too (Pahapill and Lozano 2000; Stefurak, Mikulis et al. 2003). Thus the pathways arising from PPN plays a key role in the modulation of gait and the postural control. Further the evidence of PPN role in modulation of gait is showed in post-mortem studies (Jellinger 1988). The tonic

(non rhythmic) signal from the MLR is imperative for the spinal CPG to elicit the

44 symmetric rhythmic left and right limbs locomotion pattern. The dependency of the descending signals on the spinal CPG and the various rhythm patterns are shown using the mathematical modeling (Abe, Asai et al. 2003; Asai, Nomura et al. 2003). Recently pedunculopontine nucleus, which acts as an interface neuron between the limbic system, basal ganglia nucleii and brainstem reticular formation and which modulates temporal characteristics of locomotion (Lee,

Rinne et al. 2000), has been explored as a novel DBS target to treat gait disorders in PD patients after the failure of dopamine replacement pharmacotherapy, STN and GPi DBS treatment in advanced stages of PD

(Davis, Lyons et al. 2006; Ferraye, Debu et al. 2008). Clinical studies have shown that the outcome measures of PPN DBS are variable, some studies

(Plaha and Gill 2005; Stefani, Lozano et al. 2007; Ferraye, Debu et al. 2010) showed promising results while others (Mazzone, Lozano et al. 2005; Moro,

Hamani et al. 2010) showed partial improvement. The PPN DBS studies in non- human primate PD models (Nandi, Aziz et al. 2002; Jenkinson, Nandi et al. 2004;

Jenkinson, Nandi et al. 2006) also showed promising results. However, the results in these studies were measured using gross motor activity levels through the analyses of videotaped movements in the primate cage and using Primate

Parkinsonism Motor Rating Scale or other subjective scales. Thus there is a lack of techniques to objectively assess gait and understanding the mechanisms underlying the pathological conditions. Kinematic measures, in contrast to subjective assessments, obtained using our methodology provides detailed analysis of primate gait mechanics and will be instrumental in future studies with

45 non-human primates exploring the effectiveness of PPN stimulation or other novel therapeutic approaches to the treatment of gait disorders.

The gait pattern of normal NHP is diagonal symmetrical (DS) gait (HLR 

FLL  HLL  FLR), mild parkinsonian NHP exhibits lateral symmetrical (LS) gait

(HLR  FLR  HLL  FLL) (Vilensky and Larson 1989; Cartmill, Lemelin et al.

2002) and moderate parkinsonian NHP exhibits non-standard gait pattern (HLR

 FLL  FLR  HLL). The functional stability superiority of DS over LS gait pattern is contentious (Stevens 2006; Wallace and Demes 2008) but typically, in

DS gait, limbs on the tested substrate lie approximately underneath the primate’s center of mass while contra-lateral forelimb touches the untested substrate and in LS gait, hindlimb on tested substrate is substantially behind the center of mass when the forelimb touches the untested substrate. Thus, DS gait enables the primate to maintain the stability even if the untested substrate is unfavorable.

Several researchers reported that primate’s preferred gait pattern is DS gait

(Hildebra.M 1967; Vilensky and Larson 1989; Cartmill, Lemelin et al. 2002;

Schmitt and Lemelin 2002; Courtine, Roy et al. 2005) while most mammals preferred gait is LS (Vilensky 1989; Vilensky and Larson 1989). Few studies

(Eidelberg, Walden et al. 1981; Fedirchuk, Nielsen et al. 1998; Vilensky and

O'connor 1998) reported that the supra-spinal input is either pre-requisite or heavily involved in control of locomotion in primates than other mammals. Thus, evolving of more sophisticated DS gait in primates from less sophisticated LS gait in other mammals is attributed to the presence of supra-spinal input.

Nyakatura et al (Nyakatura, Fischer et al. 2008; Nyakatura and Heymann 2010)

46 reported that primate shifts to LS gait from DS gait when walking down the ramp possibly to provide more braking force. In our study, mild parkinsonian NHP (M1) walked in LS gait pattern may be because of diminished supra-spinal input or may be to compensate for rigidity similar to the braking force required during walking down the ramp.

During the gait cycle, flexors are active primarily in swing phase and extensors are primarily active in stance phase. It has been shown that speed of locomotion is modulated primarily varying the stance phase duration by changing the extensor drive as opposed to swing phase (Grillner, Halbertsma et al. 1979;

Halbertsma 1983; Courtine, Roy et al. 2005, Goslow, 1973 #140). The physiological basis for this extensor/stance phase mediated modulation of locomotion is still unresolved and could be attributed to change in CPG itself or change in the drive from supra spinal centers (Frigon and Gossard 2009; Hayes,

Chang et al. 2009). Courtine et al (Courtine, Roy et al. 2005) showed that in normal primate HL percentage stance phase is higher than FL percentage stance phase at low speed treadmill walking. In contrast, our current study shows

(Figure 2-4B) that percentage stance phase is significantly higher in FL when compared to HL in less affected limbs of both mild and moderate parkinsonian

NHPs. This change in gait further results in variation in angular excursions of both hindlimb and forelimb joints (Figure 2-5) and as well as during both stance and swing phases for both hindlimb (Figure 2-6) and forelimb (Figure 2-7) joints.

The asymmetry in stance phases and in cycle duration between the most affected and less affected limbs probably the reason why PD patients could not

47 walk in straight line (Mohr, Bracha et al. 2003). The higher degree of asymmetry in hemi-parkinsonian patients (Mohr, Bracha et al. 2003) and in hemi- parkinsonian NHP may also cause turning behavior (Pycock 1980)

Primates use hindlimbs more than their forelimbs for weight support during walking (Larson and Stern 2009). This is also evident from our data (Figure

2-11), yield angle, which is the marker for weight performance, is higher in hindlimb than in forelimb for normal and in less affected limbs of both M1 and M2

NHPs. Conversely, in more affected limbs of both M1 and M2, weight performance higher in forelimb than in hindlimb. This suggests the compensation mechanism is employed in most affected limbs but not in less affected limbs and caused further asymmetries among the limbs. These asymmetries in combination with other asymmetries caused by stance duration further cause variations in angular excursions (Figure 2-5, Figure 2-6 and Figure 2-7), intralimb coordination

(Figure 2-8) and interlimb coordination (Figure 2-9 and Figure 2-10).

2.5 Conclusions Quantitative locomotor measures that developed here to characterize the gait of MPTP) treated primate will play important role in development of PPN

DBS for treating in postural instability in advanced PD. In addition to the measures provided here, velocity profiles of limb movements could provide more objective information on bradykinesia and rigidity. This method of characterization of gait can also be used in other motor disease primate models such as Huntington disease and dystonia.

48 2.6 References

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53 Chapter 3 : Bibliography

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