Mahan Alavi. A thesis submitted in conformity with the requirements. for the Degree of Master of Science. Graduate Department of Physiology

Transcription

1 SPATIAL EXTENT OF BETA OSCILLATORY ACTIVITY IN AND BETWEEN THE SUBTHALAMIC NUCLEUS AND SUBSTANTIA NIGRA PARS RETICULATA OF PARKINSON S DISEASE PATIENTS by Mahan Alavi A thesis submitted in conformity with the requirements for the Degree of Master of Science Graduate Department of Physiology University of Toronto Copyright by Mahan Alavi 2012

2 Spatial extent of Beta oscillatory activity in and between the Subthalamic Nucleus and Substantia Nigra Pars Reticulata of Parkinson s disease patients Mahan Alavi Master of Science Department of Physiology University of Toronto 2012 ABSTRACT Parkinson s disease (PD) is accompanied by a significant amount of beta β-band (11Hz-30Hz) neuronal and local field potential (LFP) oscillatory activity in the subthalamic nucleus (STN). The aim of this study was to measure the spatial extent of β coherent activity in the STN and coherence between STN-SNr in PD patients OFF levodopa by systematically varying the vertical distance between two microelectrodes. We found significant β-lfp coherence across the dorsoventral extent of STN. Spatially extended beta LFP was positively correlated with the mupdrs scores of the PD patients in the OFF state. Additionally, a significant coherence was found between β-lfps in dorsal STN and dorsal SNr. These data suggest that the whole STN may be entrained within the β band in PD patients OFF meds. The finding of coherence between STN and SNr suggests that β oscillations synchronize both the input and output nuclei of the basal ganglia. ii

3 ACKNOWLEDGEMENTS I am very grateful to have Dr. William Hutchison as my supervisor. It was only through his guidance, patience and support that this work became possible. I am also indebted to my advisors Dr. Jonathan Dostrovsky and Dr. Robert Chen for their invaluable guidance that shaped this work during the past two years. In addition, I would like to thank Dr. Akihiro Yugetta and Ian Prescott for their tremendous help with data collection. I would also like to thank Dr. Martin J Steinbach and the Vision Science Research Program for providing me with a grant to finish this work. This work was not possible without contributions of several other members of the research group: Drs. A. Lozano, M. Hodaie, E. Moro, and N. Mahant. Special thanks to the patients who participated in this study and Y. Y. Poon for her help with obtaining the clinical data. I would also like to thank the following individuals for their precious friendship, advice and encouragement: Ian Prescott, Dr. Akihiro Yugetta, Nicholas Howell, Luka Srejic, Massieh Moayedi, Dielor Basa, Dave Liu, Takashi Yoshida, Mina Rafiee, Dr. Phillipe Huot, Nina Bahl, Dr. Nicolas Phielip, Dr. Ron Levy, Danielle DeSouza, Aaron Kucyi, and Sonia Sugumar. This work is dedicated to my parents, Ozra and Mahmoud, and my sister, Mahnam, for their constant love and support. iii

4 TABLE OF CONTENTS ABSTRACT... ii ACKNOWLEDGEMENTS... iii LIST OF TABLES... viii LIST OF FIGURES... viii ABBREVIATIONS...x CHAPTER 1 GENERAL INTRODUCTION Parkinson`s disease Etiology Clinical motor features of PD Dopamine therapy of PD Deep brain stimulation of STN The Basal Ganglia Subthalamic Nucleus a - Intrinsic organization of the STN b - Subthalamic nucleus afferents c - Subthalamic nucleus efferents d - Physiological properties of STN neurons Substantia Nigra Pars Reticulata a - Intrinsic organization of the SNr b - Functional properties of the SNr c - SNr afferents d - Physiological properties of SNr neurons e - SNr efferents Models of basal ganglia function and pathophysiology of Parkinson s disease The rate model...24 iv

5 The center-surround model The oscillatory model Spatial extent of Beta oscillatory activity in the STN of PD patients Aim of the present study Hypotheses: CHAPTER 2 - GENERAL METHODS Patients and consent Intraoperative neuronal recordings Operative procedures Physiological targeting a - Subthalamic Nucleus b - Substantia nigra pars reticulata Microelectrode setup Single unit and local field potential recordings Experimental design Data analysis Spectral analysis of neuronal discharges and local field potentials a - Power spectrum b - Coherence and cross-correlation Statistical comparison...48 CHAPTER 3 - RESULTS Beta LFP power progressively declines over the full extent of STN Beta oscillation are widely distributed and synchronized within and between STN and SNr Spatial extent of LFP beta oscillatory activity in STN is a better predictor of motor symptoms of PD in the OFF state Comparison of neuronal and LFP beta oscillatory activity in STN and SNr Ratios of beta LFP powers in STN and SNr v

6 3.6 - Phase differences between oscillatory activity of pairs of neurons...53 CHAPTER 4 - DISCUSSION Beta LFP coherence declines dorsoventrally, but remains significant throughout the full extent of STN Are Beta LFPs recorded in ventral STN and in SNr due to volume conduction from dorsal STN? Spatial extent of LFP beta oscillatory activity in STN is a better predictor of motor symptoms of PD in the OFF state Beta LFPS synchronize both input and output of basal ganglia Beta LFPs recorded in SNr are result of physiological coupling with STN Summary and significance...74 APPENDIX - OSCILLATORY ACTIVITY IN THE GLOBUS PALLIDUS INTERNUS: COMPARISON BETWEEN PARKINSON S DISEASE AND DYSTONIA A.1 - Introduction A.2 - Methods...79 A Patients...79 A Recordings A Data analysis A.3 - Results A Firing rates of GPi neurons A Coherence between neuronal firing and oscillatory LFPs in PD patients...87 A Distribution of coherent neurons in dorsal and ventral regions of the GPi in PD A Neurons are less correlated to oscillatory LFPs in dystonia patients A Changes in GPi oscillatory activity during levodopa-induced dyskinesia in PD...94 A.4 - Discussion...95 A GPi firing rates...95 vi

7 A GPi LFP oscillatory activity and its relationship to neuronal discharges in PD A Modulation of GPi oscillatory activity during levodopa-induced dyskinesia A GPi oscillatory activity in dystonia and its comparison to PD A Methodological constraints A.5 - Conclusion vii

8 LIST OF TABLES Table 2.T1 Demographic and clinical characteristics of the PD patients...37 Table 3.T1 Number and percent significance of recording pairs in STN and SNr of PD patients Table 3.T2 Characteristics of Oscillatory/Coherent with LFP Cells in STN/SNr of PD Patients...63 Table A.T1 Demographic and clinical characteristics of the patients.80 Table A.T2 Distribution of Oscillatory/Coherent with LFP Cells in Dorsal/Ventral GPi of PD Patients. 91 LIST OF FIGURES Figure 1.F1 Schematic representation of the basal ganglia connections... 8 Figure 1.F2 Schematic representation of the intrinsic organization of the subthalamic nucleus. 11 Figure 1.F3 Illustration of the the ordered representation of the cortico-striatal functional mosaic within the SNr Figure 1.F4 Illustration of the centre-surround model of the basal ganglia function Figure 1.F5 Illustration of the oscillatory model of basal ganglia function Figure 2.F1 Head stage assembly, microelectrodes, and cannulas Figure 2.F2 Reconstruction of microelectrode recording track through the subthalamic nucleus and substantia nigra pars reticulata Figure 3.F1 Location of the electrode tracks and power spectrum analysis comparison at different distances within STN Figure 3.F2 Progressive attenuation of beta power and the change in LFP/LFP and Cell/Cell coherence over the dorsoventral extent of STN Figure 3.F3 Example of coherence between a SNr cell and a STN cell Figure 3.F4 Change in cross-spectral beta band LFP coherence and positive correlations between these measures of spatially extended beta LFP and motor impairments of PD Figure 3.F5 Scatterplots showing negative correlations between beta LFP power and UPDRS motor scores Figure 3.F6 Comparison of β power in STN and SNr of each PD patient viii

9 Figure 3.F7 Comparison of β peak frequency (Hz) in STN and SNr of each PD patient Figure 3.F8 Scatterplot showing positive correlation between beta LFP power in STN and SNr. 61 Figure 3.F9 An example of a phase delay between STN and SNr. 62 Figure 3.F10 Normalized β spectral power modulation before/during active flexion of wrist in STN and SNr Figure 3.F11 Normalized β spectral power modulation before/during isometric contraction of fist in STN and SNr Figure A.F1 Example of synchronized neuronal and local field potential beta oscillatory activity recorded from the globus pallidus internus of a PD patient Figure A.F2 Reconstruction of the microelectrode tracks through the GPi in PD patients and box plots of the relative LFP beta power in the dorsal and ventral GPi...88 Figure A.F3 Examples of the coherence between simultaneously recorded LFPs that were obtained from the GPi in each of the six dystonia patients...92 Figure A.F4 Example of synchronized neuronal and LFP activity recorded from a dystonia patient Figure A.F5 Examples of synchronized LFP activity recorded form the GPi of a Parkinson s disease patients Figure A.F6 Example of synchronized neuronal and LFP oscillatory 10 Hz activity recorded from the GPi of a Parkinson s disease patient during levodopa-induced dyskinesias ix

10 ABBREVIATIONS Anterior commissure Basal ganglia Deep brain stimulation Electromyography Gamma-aminobutyric acid Globus pallidus externus Globus pallidus internus Local field potentials Medium spiny neurons 1-methyly-4-phenyl-1,2,3,6-tetrahydropyridine N-methly-d-aspartate Nucleus ventralis intermedius Posterior commissure Parkinson s disease Pedunculopontine nucleus Superior colliculus Substantia nigra pars compacta Substantia nigra pars reticulata Subthalamic nucleus Supplementary motor area Unified Parkinson s disease Rating Scale AC BG DBS EMG GABA GPe GPi LFP MSN MPTP NMDA Vim PC PD PPN SC SNc SNr STN SMA UPDRS x

11 CHAPTER 1 GENERAL INTRODUCTION Parkinson`s disease Parkinson s disease is a progressive neurodegenerative movement disorder that was first described by James Parkinson in 1817 (Parkinson, 1817). It affects multiple brain systems and consists of a triad of primary motor symptoms and many other secondary non-motor characteristics (Cummings, 1999; Park and Stacy, 2009). It is estimated that approximately 0.3% of the world population and 3% of the people over the age of 65 are affected by PD (Zhang and Roman, 1993). In North America alone, over one million people have been diagnosed with PD. Its prevalence increases with age (Bennett et al., 1996) and its mean age of onset is 60 years of age (Hughes et al., 1993). PD is associated with significant disability, decreased quality of life, and two to five fold increase in the risk of mortality (Louis et al., 1997) Etiology The hallmark of PD is a substantial loss (~ 90%) of dopaminergic neurons of the substantia nigra pars compacta (SNc) in the basal ganglia (BG) (Lang and Lozano, 1998a). The exact cause of PD pathogenesis is unknown, but current evidence points to diverse etiologies that might result in dopaminergic neuronal degeneration in SNc and manifest PD phenotype. One of such factors is the formation of eosinophilic hyaline inclusions (Lewy bodies) in the vulnerable neuronal population in the Parkinsonian brain (Lang and Lozano, 1998a). However, Lewy bodies are not specific to PD and are found in non-pd brains as well (Gibb and Lees, 1988; Vekrellis et al., 2011). Excitotoxic mechanisms are also implicated in the pathogenesis of PD. It has been suggested that excessive N-methyl-D-aspartate (NMDA) receptor activation results in high intracellular calcium concentration, mitochondrial DNA damage, and death of SNc dopaminergic 1

12 2 neurons (Dawson and Dawson, 2004). The reported hyperactivity of STN, which projects excitatory glutamatergic efferents to SNc, is in line with this hypothesis (Lang and Lozano, 1998a). The neurotoxin 1-methly-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) is also capable of selectively killing nigral dopaminergic neurons (Marsden and Jenner, 1987) leading to its wide spread use to reproduce parkinsonian symptoms in nonhuman primate models of PD. It is a lipophilic compound capable of crossing the blood brain barrier. Within serotonergic neurons and astrocytes, it is converted into the neurotoxin 1-methyl-4-phenylpyridinium (MPP+) and subsequently released into extracellular space and is then taken up by dopaminergic neurons. It accumulates in the mitochondrial matrix and inhibits complex 1 of the electron transport chain (Beal, 2003). Consistent with this hypothesis, a 30-40% decrease in complex 1 activity has been reported in PD patients (Mann et al., 1992). The role of genetic factors has also been implicated in PD as studies of families with inherited forms of PD pointed to α-synuclein, parkin, DJ-1 and PINK genes related to PD pathogenesis. The α-synuclein gene and mutations in the parkin gene lead to aggregation and accumulation of damaged or misfolded proteins, which might be precursors for Lewy body formation (Cookson, 2010; Vekrellis et al., 2011). Products of DJ-1 and PINK genes are involved in protection against mitochondrial damage and their loss of function as a result of mutations might lead to neurodegeneration and the PD phenotype (Dodson and Guo, 2007; Bekris et al., 2010). Mutation in the gene for LRRK2 is yet another cause for dominant PD (Vekrellis et al., 2011). Finally, epidemiological studies have reported that living in rural areas, farming and exposure to pesticides are environmental factors that increase the risk of developing PD (Priyadarshi et al., 2001; Wirdefeldt et al., 2011). It is important to note that the most common genetic factor (LRRK2) only accounts for 5% of PD cases, suggesting that a combination of genetic and environmental factors might lead to neurodegenraration and the PD phenotype (Iraola-Guzmán et al., 2011; Wirdefeldt et al., 2011). Recently, Braak proposed a

13 3 dual-hit hypothesis about the pathogenesis of idiopathic Parkinson's disease. According to their hypothesis, an unknown pathogen akin to a slow-virus may enter the nervous system through both the nasal and intestinal mucosae, eventually causing a cascade of neurodegenerative events in the brain that lead to Parkinson s disease (Braak, 2007; Hawkes et al., 2009) Clinical motor features of PD Bradykinesia (slowness of movement) and akinesia (lack of spontaneous movement) are the two cardinal motor features of PD. It has been suggested that akinesia is the result of inability to integrate cognitive and motor processes (Brown and Jahanshahi, 1996). Bradykinesia is the most characteristic clinical feature of PD (Marsden, 1984), and is believed to be the result of an inability in generating sufficient amount of muscle activity as measured by electromyography (EMG). It is also measured by testing for reaction time or movement speed as PD patients have significantly delayed reaction times, which are worsened with increasing task complexity (Brown and Marsden, 1998). Interestingly, external cues improve bradykinesia supporting the hypothesis that externally-cued and internally-generated movements are regulated by different brain structures and that bradykinesia is a central problem involving cognition (Kelley et al., 2002). Another cardinal symptom of PD is rigidity, which is manifested in increased resistance to passive movement. Surgical interventions, such as deep brain stimulation of central structures (e.g. basal ganglia) are effective in ameliorating rigidity, and therefore it was suggested that it may involve central mechanisms (Lang et al., 1999). Presence of a resting tremor of about 4-6 Hz frequency is another principal symptom of PD that occurs in approximately 75% of PD patients. It was suggested that the pathophysiology of resting tremor is distinct from that of bradykinesia and rigidity (Jellinger, 1999; Pavese et al., 2006). In line with this hypothesis and in

14 4 contrast to bradykinesia and rigidity, tremor does not necessarily increase with age and it also does not correlate with degree of dopamine deficiency in the striatum (Stebbins et al., 1999; Deuschl et al., 2000). Advanced PD patients also suffer from postural instability (Jankovic, 2008) that contributes significantly to risk of falls and hip fractures (Williams et al., 2006). Impairment in modulation of postural reflexes in lower extremities that are required for the body to correct sudden changes in position is one possible cause of this symptom (Beckley et al., 1993) Dopamine therapy of PD PD is associated with significant loss of dopaminergic neurons in SNc, which by the time of diagnosis reaches the approximate level of 90 percent. This feature of PD has led to the development of the dopamine precursor levodopa in 1960s (Cotszias et al., 1967), which since its discovery remains one of the most potent drugs for alleviation of the PD symptoms. Levodopa is administered orally and is capable of crossing the blood brain barrier, where it is taken up by the striatal terminals of dopaminergic neurons and converted into dopamine (Lang and Lozano, 1998b). To inhibit the conversion of levodopa to dopamine in the periphery, it is routinely administered with carbidopa or benserazide (decarboxylase inhibitors), which results in an increase of dopamine availability in the central nervous system. Levodopa is especially effective at alleviating bradykinesia, which in comparison to other symptoms of PD correlates most with the degree of nigrostriatal dopamine deficiency (Vingerhoets et al., 1997). It also markedly improves rigidity and tremor; however it has no significant effect on postural instability suggesting that symptoms might involve nondopaminergic mechanisms (Bloem et al., 1996). Levodopa has its own side effects as well with chronic levodopa therapy leading to levodopa induced dyskinesias (LID) (Miyawaki et al., 1997). These are unwanted movements that are

15 5 present in 50% of patients after five years and 70% of patients after fifteen years of levodopa intake (Miyawaki, et al., 1997). Dopamine receptor hypersensitivity as a result of discontinuous stimulation of dopamine receptors has been suggested as a possible cause of LID (Bezard et al., 2001). However, the exact underlying mechanism of LID is yet unknown Deep brain stimulation of STN Deep brain stimulation (DBS) surgery involves implantation of an electrode in a target region of the brain and stimulating that area with a programmable pulse generator that is implanted above the clavicle (Lemaire et al., 2007). DBS of central structures in BG began in 1987 (Benabid et al., 1987) with stimulation of the ventralis intermedius (Vim) nucleus of the thalamus and since then it has been used on other structures like the subthalamic nucleus (STN) and the internal segment of globus pallidus (GPi) as a treatment for most motor symptoms of PD. DBS of STN is now a standard procedure that is recommended to levodopa-responsive patients who suffer from LID (Moro et al., 1999). It is especially preferred over DBS of other structures like GPi as it allows for reduction in levodopa intake leading to marked decline in LID (Lozano and Mahant, 2004). Despite its therapeutic benefit, the underlying mechanism of DBS action remains poorly understood. Synaptic inhibition of neuronal firing during GPi and STN high frequency stimulation (HFS) was suggested in early studies as a possible mediator of DBS action (Dostrovsky et al., 2000; Filali et al., 2004). However, other evidence suggests that HFS induces excitation (Anderson et al., 2003) or even no significant change in the mean firing rate of neurons within the stimulated nucleus (McCairn and Turner, 2009). This has led researchers to study the effects of HFS on the downstream targets of the stimulated nuclei. Indeed, time-locked changes in the firing of target nuclei were reported during HFS and were attributed to activation

16 6 of efferent axons (Hashimoto et al., 2003; Bar-Gad et al., 2004; Maltete et al., 2007; McCairn and Turner, 2009). Liu et al. (2012) used both microelectrode and macrroelectrode stimulation within GPi and showed that in addition to reduction of neuronal firing in GPi during DBS, excitation of GPi axon fibers and neurons, and enhancement of inhibitory synaptic transmission by high frequency GPi DBS may underlie the clinical benefit of DBS in dystonia. It has also been suggested that DBS exerts its therapeutic effect via suppressing or overriding pathologically synchronous oscillatory activity that is mainly centered around 20 Hz; the so called β frequency band (Brown et al., 2004; Kuhn et al., 2008; Bronte-Stewart et al., 2009, Eusebio et al., 2010). However, there are other studies that failed to find any consistent suppression in beta activity after the cessation of DBS (Priori et al., 2006; Foffani et al., 2006; Rossi et al., 2008; Giannicola et al., 2010). Recently, Rosin et al. (2011) demonstrated the superiority of closed loop stimulation compared to standard DBS in both alleviating the main motor symptom of experimental Parkinsonism (MPTP treated monkeys) and disrupting the oscillatory discharge patterns of the Parkinsonian cortico-basal ganglia loops The Basal Ganglia The basal ganglia (BG) are a collection of subcortical nuclei that are involved in control and regulation of motor and cognitive functions. They are comprised of caudate and putamen, that are collectively known as striatum, the external (GPe) and internal (GPi) globus pallidus, the subthalamic nucleus (STN), and substantia nigra pars compacta (SNc) and reticulata (SNr) (see Figure 1.F1). The striatum is considered as the input structure of the BG as it receives projections from several regions of cerebral cortex, while GPi/SNr send projections to thalamus, brainstem and superior colliculus (SC) and hence are considered the output structures of BG. It is believed

17 7 that BG serve to reciprocally interconnect different regions of the cortex, cerebellum and brainstem through five circuits (i.e. the motor, the oculomotor, the orbitofrontal, the lateral orbitofrontal, and the dorsolateral prefrontal and cingulate) (Kelly and Strick, 2004; Wichmann et al., 2011). It has been suggested that under normal conditions these circuits remain segregated and the functional activities of the BG pathways are parallel in nature (Hoover and Strick, 1993). It is proposed that the basal ganglia function via two parallel pathways known as the direct and indirect pathways (Albin et al., 1989; DeLong, 1990). In the direct pathway, the striatum sends inhibitory GABAergic projections to GPi/SNr, monosynaptically inhibiting the outputs of BG. In the indirect pathway, striatal GABAergic projections inhibit GPe. The predominant projection neurons of GPe oppositely influence the activity of GPi/SNr neurons directly via GABAergic efferents and indirectly by sending inhibitory GABAergic projections to STN, which projects excitatory glutamatergic projections to GPi/SNr. In contrast to the direct pathway, which when active would inhibit the output structures of BG, the indirect pathway excites them indicating that the two pathways are antagonistic. Striatal neurons giving rise to each of these pathways express neurochemically distinct dopamine receptors (i.e. D 1 and D 2 ). It was suggested that the balance between the direct and indirect pathways are regulated by differential action of dopamine on these receptors. For instance, it has been proposed that DA-dependent plasticity was selectively expressed in MSNs of the indirect pathway, while MSNs of the direct pathway did not express this form of synaptic plasticity (Kreitzer and Malenka, 2007). However, in a recent study, Bagetta et al. (2011) used combined BAC technology and receptor immunohistochemistry demonstrating that in physiological conditions, DA-dependent plasticity is expressed in both pathways attributing the lack of synaptic plasticity found in D 1 egfp mice to behavioral deficits. These results suggest that not all synaptic dysfunctions in PD can be explained with the striatal

18 8 segregation hypothesis. It has also been suggested that some striatal neurons project to both pathways and that the two are not completely separate (Parent et al.,1995; Levesque and Parent, 2005). Another pathway also exists that involves direct cortical glutamatergic projections to STN. It is referred to as hyperdirect pathway and its activation excites GPi/SNr neurons. Figure 1.F1. Schematic representation of the basal ganglia connections. Excitatory projections are indicated by green arrows, and inhibitory projections are indicated by red arrows. GPe: globus pallidus externa; GPi: globus pallidus interna; SNc: substantia nigra pars compacta; SNr: substantia nigra pars reticulata; STN: subthalamic nucleus; SC: superior colliculus; PPN: pedunculopontine nucleus.

19 Subthalamic Nucleus The subthalamic nucleus has an important role in BG circuitry as it receives input from, and projects to a diverse group of nuclei (Alexander and Crutcher, 1990). It is a biconvex- shaped structure that is highly packed (especially dorsal) with glutamatergic projection neurons and is surrounded by myelinated fibers (Yelnik and Percheron, 1979). Dorsally, STN is adjacent to the zona incerta and a portion of the fasciculus lenticularis. Laterally, it is separated from the globus pallidus by the fibers of the internal capsule. Rostromedially, it is surrounded by the nucleus of the Fields of Forel and the posterior lateral hypothalamic area. Posteromedially, it is limited by the red nucleus, and ventromedially, it abuts the cerebral peduncle. Finally, it is adjacent to the substantia nigra at its ventrolateral aspect (Hamani et al., 2004) a - Intrinsic organization of the STN The primate STN is functionally subdivided into motor, limbic and associative units (Hamani et al., 2004). The functional anatomy of STN is illustrated in figure 1.F2. Briefly, the nucleus is subdivided into rostral two thirds, and a caudal third. The two rostral thirds are further subdivided into medial (medial third) and lateral portions. Functionally, it is thought that the limbic and part of associative regions of STN are represented by the medial portion of the rostral two-thirds, while the additional portions of the associative territories are composed by the ventral aspect of the lateral portion of the rostral two-thirds. The motor territories of STN are represented by the dorsal aspect of the lateral portion of the rostral two-thirds and the caudal third (Parent and Hazrati, 1995a, Hamani et al., 2004). However, a more simplified description

20 10 of these functional sub territories is reflected in the general division of STN into a dorsolateral sensorimotor portion and ventromedial associative portion. The majority of neurons in the dorsolateral region of the STN respond to passive and/or active movements of single contralateral joints (DeLong et al., 1985; Bergman et al., 1994; Wichmann et al., 1994a).The sensorimotor portion of STN was suggested to have a course somatotopic organization in primates with neurons in the lateral fraction responding to arm movements and neurons in the medial fraction responding to leg movement (DeLong et al., 1985; Wichmann et al., 1994a; Rodriguez-Oroz et al., 2001). However, the ventromedial part of STN is involved in oculomotor and associative aspect of motor behavior, with neurons in this region responding to visuosensory and oculomotor tasks (Matsumura et al., 1992).

21 11 Figure 1.F2. Schematic representation of the intrinsic organization of the subthalamic nucleus (STN) according to the tripartite functional subdivision of the basal ganglia. Modified from Hamani et al., Brain, b - Subthalamic nucleus afferents The primate subthalamic nucleus receives projections from cortex, thalamus, globus pallidus externus, and brain stem. Cortical afferents to STN mostly originate in the primary motor cortex, supplementary motor area (SMA), pre-sma, and the dorsal and ventral cortices (Nambu et al., 1996, 1997, 2002, Hamani et al., 2004). In cats and monkeys, these pathways are of excitatory glutamatergic type (Romansky et al., 1979; Moriizumi et al., 1987), and as a part of the motor

22 12 loop, innervate mostly the distal dendrites of dorsal STN in a somatotopically organized manner (Rodriguez-Oroz et al., 2001). The term hyperdirect pathway has been proposed for this pathway in rat studies (Ryan and Clark, 1992a; Ryan et al., 1992b; Ryan and Sanders, 1993). Projections from primary motor cortex related to arm, leg, and face are represented mediolaterally in the lateral portion of STN. However, the supplementary motor area and premotor cortex innervate the medial portion of STN in an inverse somatotopic distribution with leg, arm and face being represented from its medial to lateral aspect in non-human primates (Nambu et al., 1996; Nambu et al., 1997; Nambu et al., 2002). In accordance to its role in eye movement, the ventromedial portion of STN receives afferents from the ipsilateral frontal eye field and the supplementary frontal eye fields (Monakow et al., 1978). The external pallidum provides one of the major sources of GABAergic inhibitory afferents to the STN. In rodents, lateral STN receives projections from the lateral aspect of pallidum, while medial STN receives input from the ventral and medial pallidum (Parent and Hazrati, 1995a). The topographic distribution of these fibers is quite complex in primates. Briefly, motor and limbic portions of STN are innervated by their corresponding counterparts in GPe (Parent and Cicchetti, 1998). These afferent fibers innervate mostly the proximal dendrites and cell bodies of STN neurons (Parent and Hazrati, 1995a) and are of GABAergic type. Electrophysiological studies demonstrated that the activity of STN neurons is modulated following the injection of muscimol (a GABA receptor agonist, inhibition) and bicuculline (a GABA receptor antagonist, excitation) into STN of monkey (Wichmann et al., 1994b). The thalamus is another source of glutamatergic projections to STN that mainly originate from its CM and Pf nuclei (Sugimoto et al., 1983; Sadikot et al., 1992a). In primates, Pf is the predominant source of thalamic afferent fibers to STN (Sadikot et al., 1992b). These fibers

23 13 mostly innervate the medial third of rostral STN, which comprises the associative and limbic territories of the nuclei. CM nucleus on the other hand projects to dorsolateral motor territories of STN in primates (Parent and Hazrati, 1995a). Afferent projections from brainstem are also an important source of input to STN, the majority of which arise from substantia nigra compacta (SNc), pedunculopontine nucleus (PPN), and dorsal raphe nucleus. The STN receives important dopaminergic afferents from SNc known to modulate the activity of cortical glutamatergic and pallidal GABAergic afferents to this nucleus (Brown et al., 1979; Lavoie et al., 1989; Francois et al., 2000). These afferents mainly contact the neck of dendritic spines. The PPN and laterodorsal tegmental nuclei send both cholinergic and noncholinergic input to STN in rodents (Gerfen et al., 1982; Jackson and Crossman, 1983; Scarnati et al., 1987; Lee et al., 1988; Lavoie and Parent, 1994). The dorsal raphe nucleus is a source of afferent serotoninergic projections to STN and is believed to modulate the activity of STN neurons (Woolf and Butcher, 1986; Canteras et al., 1990) c - Subthalamic nucleus efferents The STN has an important role as the modulator of BG output as it is the only nucleus within BG that projects excitatory glutamatergic afferents to its target nuclei. It uniformly innervates both segments of the globus pallidus (GPe and GPi/entopeduncular nucleus) in rats affecting an extensive number of cells (Hazrati and Parent, 1992; Parent and Hazrati, 1995a). The electrophysiological manifestation of these excitatory inputs has been indicated in studies where STN lesions with excitotoxic acid has led to reduced firing rate of pallidal neurons (Hamada and DeLong, 1992).

24 14 The STN also sends excitatory efferents to SNr and SNc with most of the fibers innervating SNr. The remaining few fibers that reach the SNc are thought to be involved in mechanisms that regulate dopamine release (Smith et al., 1990b; Parent and Hazrati, 1995a). These excitatory fibers mostly originate in the ventromedial portion of STN and enter through the ventromedial region of SNr spreading laterally in the rostrocaudal direction (Parent and Hazrati, 1995a). Once in SNr, the axons of STN arborize forming several local collaterals innervating mainly dendritic shafts of SNr cells in rodents (Parent and Hazrati, 1995a; Rinvik and Ottersen, 1993). The striatum also receives projections from STN in rodents and non-human primates (Parent and Hazrati, 1995a). The dorsolateral motor portion of the STN innervates mostly the putamen, while excitatory efferents from the ventromedial limbic and associative regions of the STN innervate mostly the caudate (Parent and Hazrati, 1995a). The STN also sends projections to PPN and ventral tegmental area in rodents and non-human primates (Parent and Hazrati, 1995a), and these pathways are thought to modulate motor activity via the spinal cord (Pahaphill and Lozano, 2000) d - Physiological properties of STN neurons In vitro, depending on its initial membrane potential, a single STN neuron can demonstrate regular, irregular or bursting firing activity. It is also capable of undergoing rhythmic firing at 5-15 Hz in vitro in the absence of GABAergic and glutamatergic synaptic inputs (Bevan and Wilson, 1999). In vivo, it is observed that the majority of STN neurons fire irregularly at Hz in normal rats and Hz in non-human primates (Georgopoulos et al., 1983; DeLong et al., 1985; Bergman et al., 1994; Wichmann et al., 1994a; Urbain et al., 2000). A neuronal oscillatory cycle is composed of a slow depolarization, an action potential followed by an after-

25 15 hyperpolarization. In the STN, the slow depolarization is evoked by the voltage dependent sodium currents, cationic currents, or low threshold calcium currents (in the case of bursting activity) that are evoked when the resting membrane potential becomes more negative than the usual resting state. Upon entry of sodium and calcium into the cell, an action potential may be generated. Thereafter, beside the usual hyperpolarization mechanisms (voltage dependent potassium current), oscillatory STN cells utilize calcium-dependent potassium channels, which upon activation lead to a more hyperpolarized state or the so-called after-hyperpolarized potentials (AFH) (Hamani et al., 2004). These channels have an important role in developing broad plateau potentials, which are necessary for generating bursting activity (Beurrier et al., 1999). The negative potential resulting from activation of calcium-dependent potassium channels also promotes activation of a slow depolarization current and a subsequent new oscillatory cycle (Hamani et al., 2004). All STN cells in rodents and non-human primates respond to cortical stimulation usually with characteristic triphasic response (positive, negative, and positive) followed by a long hyperpolarization (Fujimoto and Kita, 1993; Nambu et al., 2000). It has been suggested that the first peak (~ 2ms) latency is due to the activation of cortico-subthalamic pathways (Nambu et al., 2000). The subsequent inhibitory phase is generated following the orthodromic and/or antidromic activation of GPe neurons. The second excitatory peak seems to be related to the excitation of cortico-striatal and striatal-gpe pathways that occurs concomitant with the excitation of the cortico-stn pathway (Feger et al., 1997, Nambu et al., 2000). It is noted already that some of the cortical excitatory connections bypass the striatum and directly innervate the STN and pallidum (hyperdirect pathway ) (Nambu et al., 1996; Nambu et al., 2002). Parafascicular thalamic nucleus is another source of excitatory afferents to STN, the

26 16 stimulation of which yields triphasic response with similar underlying mechanism (Feger et al., 1997, Mouroux et al., 1997). Pallidal afferents from GPe are an important modulator of the pattern of neuronal activity in STN. Inhibitory post synaptic potentials (IPSPs) generated by these afferents depending on their magnitude can culminate in synchronization or desynchronization of the circuit (Bevan et al., 2002a, b). Multiple IPSPs can promote rebound bursting activity by bringing the membrane potential closer to the equilibrium potential of GABA. Once the membrane potential is hyperpolarized to -50 to -75 mv for short time, STN cells submit to depolarizing currents and develop plateau potentials that lead to bursting activity (Bevan et al., 2002b) Substantia Nigra Pars Reticulata Along with GPi, the SNr provides a major output of basal ganglia. It likely plays an important role in the circuitry of BG as it is the site of final information processing within this system. It is characterized by low neuronal density and mainly composed of GABAergic projection neurons. The SNr receives input from striatum, external globus pallidus, and subthalamic nucleus, elaborates the message and projects to extrinsic structures like thalamus, superior colliculus and the PPN (Deniau, et al., 2007) a - Intrinsic organization of the SNr In contrast to the overlying cell-rich SNc, the SNr is characterized by a loose structure that is mainly composed of projection neurons. The SNr neurons have soma of varying shapes from triangular, fusiform, and ovoid to polygonal with their size ranging from medium to large. Their

27 17 sparsely branched dendrites start off smooth, but then carry an increasing number of appendages at their distal ends (Domesick et al., 1983; Poirier et al., 1983). The SNr is mainly composed of GABAergic neurons, but it also possesses dopaminergic (DA) (Nelson et al., 1996; Richards et al., 1997; Gonzalez-Hernandez and Rodriguez, 2000) and cholinergic neurons (Gould and Butcher, 1986; Martinez-Murillo et al., 1989). In rodents, DA neurons are mainly distributed in caudal and ventral parts of the SNr, where they aggregate and form distinct clusters. Cholinergic neurons of the SNr are located in the caudal aspect of the nuclei. Besides these projection neurons, in rodents and non-human primates, the SNr also possesses a few local circuit neurons that are characterized by a small soma size, a lack of axon and short dendrites (Francois et al., 1979). However, their functional role is still unknown. Previously, the existence of these nigral interneurons was hypothesized based on the local synaptic interaction between the SNr and SNc (Grace and Bunney, 1985), however, it is likely that intranuclear axon collaterals of projection neurons support such interactions indicating that the SNr neurons perform the dual function of both projection neurons and interneurons (Deniau et al., 1982; Grofova et al., 1982; Tepper et al., 1995; Maillly et al., 2003) b - Functional properties of the SNr The SNr receives information from the cortex through striato-nigral projections (Parent and Hazrati, 1995b; Deniau and Thierry, 1997; Smith et al., 1998). Similar to striatum where the cortico-striatal projections are topographically arranged, the dendritic arborizations of nigral efferent neurons are spatially organized to maintain the segregation of corticostriatal inputs in their outflow. Studies in rats have shown that the SNr neurons and their striato-nigral afferents are arranged in longitudinal and curved laminae in an onion-like manner that conforms to the

28 18 geometry of striatal projections (Gerfen, 1985; Deniau and Chevalier, 1992; Deniau et al., 1996; Maily et al., 2001, 2003). For instance, in the lateral aspect of the SNr where somatosensory input from cortex is processed, neurons are longitudinally arranged in the core of the nigral onion in the same orientation of striatal projections. Figure 1.F3 illustrates the midrostrocaudal portion of the SNr and the ordered representation of its functional mosaic. However, in the more peripheral regions, flat dendritic fields of the SNr neurons curve around the central core (Deniau, et al., 2007). This onion-like lamellar architecture also applies to nigral output neurons representing the topographical arrangements of the three main nigral efferent systems (i.e., nigrothalamic, nigrocollicular and nigrotegmental) (Deniau and Chevalier, 1992; Mana and Chevalier, 2001). Previously, functional compartmentalization of the SNr efferents was based on the segregation of the cells that innervate the superior colliculus and the thalamus (Faull and Mehler, 1978). According to this scheme, the nigrothalamic subnucleus provides the efferents that connect the sensorimotor striatum to the motor thalamus, whereas the efferents of nigrocollicular subnucleus are involved in the visuo-motor circuit, channeling striatal inputs to the superior colliculus. However, more recent data show that nigrothalamic neurons are only partially segregated from the nigrocollicular neurons and the two are distributed throughout the nucleus. Since the SNr neurons possess highly collateralized axons directed to thalamus and superior colliculus, a new model has been proposed that favors an organization of SNr that is based on the pattern of axonal branching, which is specific to each region of the SNr, promoting a laminar architecture of the nucleus (Deniau, et al., 2007). This laminar organization provides a channeling mechanism that allows for the corticostriatal inputs to be directed to specific and functionally associated sites in the targets of the SNr (i.e., thalamus, superior colliculus and tegmentum).

29 19 The functional implication of the onion-like lamellar architecture is manifested in the inputoutput circuits formed by this arrangement. These circuits can be generally subdivided into two parts involved in sensorimotor and associative functions. The lamellar organization in the sensorimotor cortex allows for the formation of links that lead to integration of information required for completion of a behavior. For instance, neurons in the dorsolateral core of the SNr are well positioned to integrate inputs from orofacial sensorimotor and gustatory cortical areas, and then project to specific regions of the thalamus that innervate orofacial sensorimotor of the cortex and areas of superior colliculus that process and support orofacial and head orienting behaviors. It is therefore expected that during feeding behavior, such integration of information and selection of programs supporting this behavior are promoted by neurons in the dorsolateral SNr and their lamellar arrangement (Deniau, et al., 2007). Nigral lamination in the associative subdivision of the SNr forms input-output circuits that support integration and selection of programs involved in cognitive or affective/motivational aspects of behavior. The three dimensional organization of the SNr and its functional compartmentalization is best known in rats, however, there is evidence that the laminar architecture of the SNr and its pattern of axonal branching is preserved in cats (Tokuno et al., 1990) and non-human primates (Parent et al., 1983), allowing for defined corticostriatal inputs to be directed to functionally associated sites in the targets of SNr. However, a more precise model of the intrinsic organization of the SNr is yet to be elucidated in monkey brain.

30 20 Figure 1.F3. Illustration of the the ordered representation of the cortico-striatal functional mosaic within the SNr. Modified from Deniau et al., Prog Brain Res, c - SNr afferents The SNr receives its major GABAergic input from the medium sized spiny neurons (MSNs) of the striatum (Parent and Hazrati, 1995a; Smith et al., 1998; Bolam et al., 2000). The precise source of these striatonigral inputs is a subpopulation of spiny neurons that express substance P and are located in the matrix compartment of the striatum (Parent et al., 1995).

31 21 Electrophysiological studies have indicated a series of short duration inhibitory synaptic events following the stimulation of the striatum that are due to the activation of GABA A receptors with chloride ions as charge carriers (Yoshida et al., 1981; Wallmichrath and Szabo, 2002). The striatonigral pathway serves the function of inducing a transient interruption of the tonic firing of the SNr neurons. In contrast to the repetitive firing of the SNr cells, striatonigral neurons are maintained in the quasi-silent state, and once their glutamatergic cortical afferents undergo low levels of synchronization, they discharge and inhibit the SNr cells (Chevalier and Deniau, 1990). Silencing of the SNr neurons leads to increased excitability in the target nuclei of the SNr. This disinhibitory process is the basic mechanism by which BG promotes action and activates associative and motor circuits (Hikosaka, 2007). It has been shown that prior to rapid eye movement, SNr neurons display a pause in activity causing a transient disinhibition of superior colliculus neurons and their burst of activity, which leads to an eye movement command (Hikosaka et al., 2000). It has also been suggested that SNr is involved in selecting appropriate reward-dependent saccade (Hikosaka, 2007). The SNr also receives GABAergic input from the lateral part of the GPe (Smith and Bolam, 1989; Bevan et al., 1996). Due to the proximity of the pallidonigral terminals, the GPe may have a more significant impact on discharges of the SNr neuron. In fact, GPe stimulation evokes IPSPs of larger amplitude compared to striatal stimulation in rat slice preparations (Kita, 2001). Pallidonigral neurons are also innervated by striatum. Projection to both the GPe and SNr allows striatum to exert its influence on the output of BG via both direct (inhibitory) and indirect (disinhibitory) pathways. Functionally, this arrangement enables shaping of the behavioral output by organizing the spatio-temporal pattern of inhibitory (direct pathway) and excitatory (indirect pathway) signals (Deniau et al., 1996; Smith et al., 1998). The striatonigral and pallidonigral

32 22 pathways are both topographically organized and converge onto the same SNr neuron (Smith et al., 1998; Francois et al., 2004). The STN is the only source of excitatory glutamatergic input to the SNr. It has been suggested that subthalamonigral afferents originate mainly from the ventromedial and lateral parts of the STN (Parent and Hazrati, 1995a; Smith et al., 1998) and innervate the proximal and distal dendrites of the SNr projection neurons and rarely their cell bodies (Bevan et al., 1994). According to electrophysiological studies, STN stimulation elicits monosynaptic EPSPs leading to the generation of action potential discharges, in line with the excitatory nature of the STN-SNr interaction (Hammond et al., 1978). There is evidence suggesting that the subthalamonigral afferents along with the striatonigral and the pallidonigral terminals converge on the same SNr neurons (Bevan et al., 1994; Nambu, 2004). It is possible that through this convergence, the STN contributes to the calibration of the disinhibitory signals transmitted by the striato-nigral pathway (Nambu, 2004). Similar to the striatum, the STN also receives direct cerebral projections mainly from the frontal and prefrontal cortical areas, providing another pathway through which cortical information is transmitted to the output of the BG. Moreover, the STN is involved in the indirect striato-pallidosubthalamo-nigral circuit, where a striatal discharge inhibits the GABAergic projections to the STN, disinibiting the STN neurons (Smith et al., 1998). Following cortical stimulation, an early excitation is evoked in the SNr that is followed by a possible inhibition and a late excitation. The early excitation is believed to be the result of the cortico-subthalamo-nigral (hyperdirect) pathway, the inhibition is due to the striato-nigral (direct pathway), and the late excitation is elicited by the striato-pallido-subthalamo-nigral (indirect) pathway (Kita and Kitai, 1994; Nambu, 2004).

33 d - Physiological properties of SNr neurons GABAergic projection neurons of the SNr are characterized by short duration action potentials and a spontaneous repetitive firing of Hz in rats (Deniau et al., 1978) and brain slices (Wilson et al., 1977; Atherton and Bevan, 2005). This is achieved by a strong voltage dependent K + conductance that prevents the membrane potential from reaching Na + inactivation level even in the case of strong depolarization (Nakanishi et al., 1987). Repetitive firing of the SNr neurons exerts a tonic inhibition on their target structures, and the BG shape behavioral outputs via modulation of this tonic inhibitory influence. The repetitive firing of the SNr cells is autonomous in nature and is preserved even in the absence of fast synaptic transmission in brain slices (Atherton and Bevan, 2005). This pacemaker property of the SNr cells is driven by slow inactivating voltage-dependant Na + currents that depolarize SNr cells up to the action potential threshold. In addition, SNr cells possess calcium activated potassium channels that are activated upon the entry of Ca 2+ during the action potential discharge and control the precision of autonomous activity via post-spike hyperpolarization. Cationic conductance is another feature of the SNr cells that enables generation of slow calcium spikes (Atherton and Bevan, 2005; Deniau et al., 2007). In MPTP treated monkeys, an increase in firing rate and bursting pattern of SNr neurons has been observed (Wichmann et al., 2001). However, no evidence of synchronized oscillations has been detected in the SNr of normal monkey (Wichmann and DeLong, 1999). This may be due to the local interaction of SNr neurons innervating the same target. The spatial arrangement of the axon collateral network of the SNr projection neurons supports inhibitory interactions between neurons sharing the same input. This may contribute to the desynchronization of the SNr discharge reinforcing its tonic nature (Mailly et al., 2003).

34 e - SNr efferents The SNr projects to the thalamus, SC and the PPN. Anatomical studies in monkey have described the population and the intranigral distribution of nigro-thalamic, nigro-tectal, and nigrotegmental neurons (Parent et al., 1983). The nigrothalamic cells are the most abundant in SNr, distributed mainly in the rostral half of the mediolateral extent of the nucleus. The nigrotegmental cells are the second largest cell population in the SNr that abound particularly in the caudal half of the mediolateral extent of the SNr. The least numerous of these are the nigrotectal cells that are exclusively confined to the lateral margin of the rostral half of the SNr (Parent and Hazrati, 1995b). SNr neurons with collaterals that innervate both thalamus and PPN have been also reported (Beckstead, 1983). Nigral efferents vary in their pattern of innervation of thalamus and SC (Warton et al., 1983). Whereas nigral terminals contact singly small mediumsized dendrites of the caudal two thirds of SC, their terminals in the ventral lateral thalamic nucleus cluster at the soma and bases of primary dendrites of thalamic projection neurons, suggesting a highly specific influence of the SNr neurons on the thalamocortical relay neurons as opposed to their diffuse influence on SC projection neurons (Parent and Hazrati, 1995b) Models of basal ganglia function and pathophysiology of Parkinson s disease Several functional models of the basal ganglia have been proposed to better understand the pathophysiology of PD. Three will be discussed here: the classic rate model, a centre-surround model and a relatively recent oscillatory model The rate model The rate model of PD was first proposed in 1989 (Albin et al., 1989; Delong, 1990). This model focuses on firing rates in the nuclei involved in the direct and indirect pathways. With the

35 25 assumption that the direct pathway facilitates movement and the indirect pathway inhibits movement, the rate model predicted hyperactivity of the indirect pathway as the pathological change in the BG activity of PD patients. Activation of the indirect pathway disinhibits STN leading to increase in the activity of GPi/SNr. Since GPi/SNr also inhibit thalamocortical relay neurons, their overactivity results in suppression of movement. Indeed, hyperactivity of the indirect pathway has been reported in the PD patients (Hutchison et al., 1994; Hutchison et al., 1998) and in MPTP-treated monkeys (Bergman et al., 1994). Hyperactivity in the direct pathway has also been implicated in the pathophysiology of hyperkinetic disorders like Huntington disease (Alexander, 1994). However, the rate model fails to explain why pallidotomy improves symptoms of both hypokinetic and hyperkinetic movement disorders, a phenomenon that is referred to as the paradox of the pallidotomy (Marsden and Obeso, 1994). Pallidotomy destroys projection neurons of GPi diminishing their inhibitory influence on the thalamic projection neurons and facilitating movement. Hence, it is expected that pallidotomy would not improve symptoms of hyperkinetic disorders like dystonia. Moreover, anatomical studies indicated that the direct and indirect pathways are not completely segregated and the rate model does not take into consideration several other interconnections among the nuclei of the BG including the direct cortical projections to the STN. In addition, several studies that recorded firing rate of GPi neurons of patients with various movement disorders failed to confirm predictions of the rate model (Hutchison et al., 2003; Tang et al., 2005). Despite these limitations and several others, the rate model has shed light on the basal ganglia function and has guided research for over 20 years.

36 The center-surround model The centre-surround model of PD was proposed by Mink in This model was developed after anatomical studies showed that striatal afferents exert a restricted and focused inhibition on a subset of GPi/SNr neurons, while STN projections broadly arborize these neurons and excite a large number of them (Hazrati and Parent, 1992). From these observations it was concluded that the differential arborization pattern of striatal and STN inputs to the GPi/SNr (see Figure 1.F.4) allows for a focused facilitation and surround inhibition of motor programs in the thalamus, brainstem and the cortex. Hence, in contrast to the push-pull scheme of the rate model, the center-surround model considers inhibitory striatal neurons as the designators of the desired motor programs, while excitatory afferents from the STN prevent the activation of concurrent motor programs (Mink, 1996). Based on this model, PD arises when alterations of these pathways not only result in an inability to remove inhibition from the desired motor circuits, but also debilitate the full inhibition of competing motor programs. This explains the PD symptoms of abnormal posture, co-contraction rigidity and inability to suppress unwanted postural reflexes (Mink, 1996). However, several studies have failed to confirm physiological predictions of the center-surround model. According to this model, it is expected that a negative correlation exists between pallidal neurons that participate in a specific action and those that participate in competing actions, and this differential pattern of activation has not been observed by several studies (Nini et al., 1995; Raz et al., 2000; Bar-Gad et al., 2003).

37 27 Figure1.F4. Illustration of the centre-surround model of the basal ganglia function. Red arrows indicate inhibitory projections; green arrows, excitatory projections. Line thickness represents the relative magnitude of activity. GPi indicates globus pallidus pars interna; STN, subthalamic nucleus. Modified from Mink, Prog Neurobiol, The oscillatory model Oscillatory activity in the basal ganglia has stimulated intellectual debate and attracted much attention over the past decade. With the failure of the anatomically constrained rate model in explaining the paradox of pallidotomy, and the efficacy of functional neurosurgery in PD, attentions were shifted from the altered discharge rates, to altered discharge patterns and

38 28 excessive synchronization of the neuronal activities as possible mediators of PD pathophysiology (Brown, 2003). Studies in the MPTP primate model of PD were the first line of evidence that PD is associated with increased oscillatory firing and synchrony within the basal ganglia (Bergman et al., 1994; Nini et al., 1995; Raz et al., 1996). Functional neurosurgical techniques allowed for direct recording of BG in humans. Single unit recordings and local field potentials (LFPs) can be made intra-operatively using microelectrodes. In addition, LFPs can be recorded few days after the surgery directly from the DBS-electrode, while the DBS-electrode leads are externalised. Using these techniques, oscillatory activities were consistently detected both in single unit and LFP recordings from the GPi and the STN of PD patients that are withdrawn from their antiparkinsonian medication (Brown et al., 2001; Marsden et al., 2001; Levy et al., 2002a; Kuhn et al., 2004; Brown and Williams, 2005; Weinberger et al., 2006). These were mostly prominent in the Hz, the beta-band, however, LFP activities at alpha (< 8 Hz), and gamma (> 35 Hz) are also present in BG (Brown, 2003). These oscillatory LFPs are thought to reflect summation of electrical fields caused by synchronization of a population of local neurons (Galvan and Wichmann, 2008). Indeed, intraoperative microelectrode recordings have shown time-locked coupling of beta LFPs to the discharges of local neurons in the STN of PD patients (Kuhn et al., 2005, Weinberger et al., 2006). However, beta oscillatory LFPs are also coherent between STN and GPi (Brown et al., 2001) and between these and the cortical EEG (Marsden et al., 2001; Williams et al., 2002), indicating that these oscillations might reflect synchronization of the whole basal ganglia-cortical loop. One of the most robust findings with regard to beta oscillatory activity is its modulation prior to and during self and externally paced movements (Cassidy et al., 2002; William et al., 2003;

39 29 Kuhn et al., 2004; Joundi et al., 2012). Interestingly, the degree of modulation in beta activity following a go' signal precedes and positively correlates with the reaction time (Kuhn et al., 2004; Williams et al., 2005), while in the non-predictive trials where a nogo' signal followed the warning cue, beta suppression reversed into an early phase of beta increase (Kuhn et al., 2004), indicating that beta activity might also be involved in suppression of movement. Moreover, the oscillatory beta LFP activity of PD patients is suppressed by treatment with dopaminergic drugs in tandem with clinical improvement (Brown et al., 2001; Williams et al., 2002; Levy et al., 2002b). These data along with the relative lack of beta oscillatory activity in the GPi and the STN of healthy monkeys (Nini et al., 1995) has led to the thinking that beta activity might have a mechanistic role in the pathophysiology of PD (Brown, 2007). In the oscillatory model of PD proposed by Brown, oscillatory activities in the BG are divided into three major frequency bands: low frequency (<10 Hz), beta band (11-30 Hz), and high gamma band (> 60 Hz). In this model (see Figure 1.F5), beta activity is assumed to have an antikinetic role and originate from cortex, while gamma activity has a prokinetic characteristic and also originates from the cortex (Brown, 2003). The pro-kinetic nature of gamma activity is implicated by its enhancement following dopaminergic medication and also before and during movement (Cassidy et al., 2002). In addition, short-term administration of STN DBS at gamma frequencies is as effective as high frequency stimulation in reducing parkinsonian motor symptoms (Tsang et al., 2012) A unidirectional coupling from cortex to the BG was suggested by Brown according to the phase relationship between the two structures (Brown, 2003). Later studies does not confirm the bidirectional pattern of cortico-basal ganglia communication with cortex driving STN at frequencies below 60 Hz, yet between Hz, the pattern of communication was found to be bidirectional + symmetrical (Lalo et al., 2008).

40 30 Figure 1.F5: Illustration of the oscillatory model of basal ganglia function. The arrows show the dominating direction of connectivity in each frequency band, antikinetic (red), pro-kinetic (green). Line thickness represents the relative magnitude of connectivity. STN, subthalamic nucleus; GP, globus pallidus. Modified from Brown, Mov Disord, According to the oscillatory model, excessive beta synchrony might contribute to pathophysiology of PD by limiting the information coding capacity of neurons involved in the processing of movement (Brown, 2007; Hammond et al., 2007). Information encoded by neurons of a correlated network is smaller than the sum of information encoded by each individual neuron. Therefore, it is hypothesized that once locked in the beta rhythm, neurons might less effectively engage in a dynamic assembly formation and be less able to change timing rates and therefore be less able to assist in movement generation (Brown, 2007; Hammond et al., 2007).

41 31 Indeed, studies of synchrony at the level of cortex, have reported this phenomenon (Salinas and Sejnowski 2000; Svirskis and Rinzel, 2000; Mazurek and Shadlen, 2002). Similarly, neuronal recordings from the STN of PD patient has shown that their firing has reduced response variability compared to those of neurons in the brain of a normal monkey (Gale et al., 2009) attributing it to the disruptive effect of synchrony at the level of the BG. In contrast, it was suggested that gamma activity facilitates information transfer. Indeed, it was shown that elevations of STN gamma LFP influence spike timing and information coding capacity of the STN neurons (Trottenberg et al., 2006; Pogosyan et al., 2006). Based on this model, excessive beta oscillations disrupt the information coding process by restricting neurons from engaging in other rhythms (gamma) that facilitate such phenomenon. Therefore, pallidotomy works by removing abnormally high beta and gamma activity at the output of basal ganglia and hence relieving symptoms of both hyper-kinetic and hypo-kinetic movement disorders as suggested by this model. Even though the oscillatory model resolved the paradox of pallidotomy, it has raised many questions. First and foremost, beta activity and its suppression are not unique to the parkinsonian state as they also have been observed in the striatum of healthy monkeys (Courtemanche et al., 2003), and in the healthy human putamen (Sochurkova and Rektor, 2003) and cortex (Doyle et al., 2005). Ironically, cortical beta is restored following treatment with levodopa or deep brain stimulation (DBS) and is diminished in untreated PD patients (Brown, 2007). These contradictory findings resulted in the proposed idea of functional polymorphism of cortical beta (Brown, 207), but they also raised the question of how beta activity in other structures of the brain might be related to PD.

42 32 Moreover, the question of if and how beta mediates parkinsonian motor symptoms is still under debate (Weinberger et al., 2009; Eusebio and Brown, 2009). The most causal link comes from DBS studies in which, STN of the PD patients were temporarily stimulated with the implanted electrodes at beta frequency. It was shown that stimulation at 5 25 Hz exacerbates bradykinesia while stimulation at higher frequencies (30-50 Hz) did not produce the same effect (Fogelson et al., 2005; Chen et al., 2007; Chen et al., 2011). However, the effect of stimulation at beta frequencies is weak (about 5-20% slowing in movement) and one other study have failed to produce such effects (Tsang et al., 2012). In addition, beta activity in the MPTP treated monkey occurs at lower frequencies and is delayed beyond the appearance of the very first motor deficits (Leblois et al., 2007) indicating that a causative relationship between beta and parkinsonian motor symptoms of PD remains to be demonstrated. Correlative studies of elevated beta synchrony and negative motor symptoms of PD (akinesia, bradykinesia and rigidity) also remain speculative as to whether the beta activity has a mechanistic role in the emergence of such symptoms. It has been shown that beta activity may be suppressed following the administration of levodopa and high frequency stimulation of STN. Further, the degree of suppression is positively correlated with clinical improvement as evaluated by the third section of the Unified Parkinson s Disease Rating Scale (motor score) (mupdrs), suggesting a possible relationship between local oscillatory activity in the beta band and motor impairments in PD (Kühn et al., 2006, 2008; Ray et al., 2008; Weinberger et al., 2006). For instance, a positive correlation between the number of neurons exhibiting oscillatory firing and the patients benefit from dopaminergic medication, but not with their baseline motor deficit has been observed (Weinberger et al., 2006). Further, a study by Kühn et al. (2009) found that the decrease in LFP power can account for 38% of the variance in levodopa-induced improvement in combined rigidity and bradykinesia scores. However, at least five studies have failed to

43 33 demonstrate a correlation between beta band synchrony and motor symptoms of PD in the untreated state (Kühn et al., 2006, 2009; Ray et al., 2008; Weinberger et al., 2006, Zaidel et al., 2010). More recent findings, however, pointed to the spatial extent of beta oscillatory activity and stability of beta band LFP over time as possible new parameters in the link between pathological synchronization and motor symptoms of PD (Zaidel et al., 2010; Pogosyan et al., 2010; Little at al., 2012) Spatial extent of Beta oscillatory activity in the STN of PD patients The origin of the beta LFP activity is still poorly understood (Weinberger and Dostrovsky, 2011). Although focal LFP oscillations are locally generated in the dorsal STN largely due to the summation of dendritic synaptic currents, they may also reflect oscillations of the whole basal ganglia-cortical loop. It has been hypothesized that in the absence of dopamine, the cortico-basal ganglia network is more tightly coupled in the beta oscillatory range, giving rise to increased beta oscillatory activity which interferes with information processing at all levels of the circuit (Brown and Williams 2005; Hammond et al., 2007). Several studies of the distribution of beta activity across the STN have yielded interesting results. Simultaneous recordings of neuronal and LFP activity in STN of PD patients at rest have shown significant neuronal oscillatory activity in the beta frequency band with the majority of these being coherent with simultaneously recorded LFPs (Weinberger et al., 2006). Moreover, the incidence of neuronal beta oscillatory activity was significantly higher in the dorsal STN and corresponds to significantly increased beta power in this region (Weinberger et al., 2006). In a recent study by Zaidel et al. (2010), the length of the dorsolateral oscillatory region as measured by microelectrode recording of oscillatory spiking activity was demonstrated to be a positive predictor of the therapeutic effect of medication and STN DBS. However, no correlation

44 34 was found between the length of the dorsolateral oscillatory region and the PD motor symptoms in the OFF state, although the finding is important in that it points to the spatial extent of oscillatory activity as a possible new parameter in the link between pathological synchronization and motor symptoms of PD. In another recent study, Pogosyan et al. (2010) used phase coherence in the beta frequency band as a measure of spatial extent of beta synchronization mainly in Zona incerta and dorsal STN, and demonstrated that it indeed correlates with severity of Parkinsonian bradykinesia and rigidity in the off state. Another question of interest is to explore beta oscillatory activity in other regions of the brain, describe their relationships (same frequency, phase relationship and amplitude fluctuations) and determine their role in motor features of PD. In this study, we carry out microelectrode recordings in substantia nigra pars reticulata (SNr), and describe beta LFP activity in this output nucleus of the basal ganglia. Although coherence between STN and GPi in the beta frequency range has been described, the existence of β-lfps in SNr, is largely unknown in human. In one recent study, an increase in the beta activity in SNr was observed in the rodent model of PD (Avila et al., 2010). Since DBS stimulation of SNr has been shown to have comparable effects to stimulation of STN on the improvement of gait and balance (Chastan et al., 2009), it is possible that disruption of beta activity generated independently in SNr was producing this therapeutic benefit. Indeed rat brain slice experiments suggested that bursting oscillatory activity can be generated in tonic SNr neurons by altering synaptic inputs in a manner consistent with loss of striatal dopamine (Ibáñez-sandoval et al., 2007). If this were the case, then it might be expected that the frequency and amplitude of fluctuations would be different from STN. Therefore, any evidence as to the existence of beta activity in SNr and whether it is coherent with that in STN can shed more light on the relationship between β-lfps and motor symptoms of PD.

45 Aim of the present study The aim of this thesis is to investigate the spatial extent of beta oscillatory activity in the STN of the PD patients in the OFF levodopa state. Many of the previous attempts at describing beta activity in STN utilized large DBS contacts, which lacked spatial resolution. In addition, previous work by our group referenced above used dual recordings of LFP made at parallel sites as both microelectrodes were advanced from dorsal to ventral STN. However, the coherence observed between the two closely spaced electrodes only reflects local relationships. Moreover, if the baseline beta activity changed during the course of the measurements due to further wearing off of medications, or a subthalamotomy effect to decrease the patient s symptoms due to microelectrode penetrations, this might have confounded the results. Therefore, in this study, we have set up an experimental paradigm to record single unit and LFP beta activity while systematically varying the distance between the two microelectrodes. Using this approach, we measured beta activity over the full extent of STN and between STN and SNr. Moreover, we described LFP beta activity in the SNr and explored its relationship to that of the STN Hypotheses: 1. STN beta oscillations decrease in amplitude from dorsal to ventral but are coherent throughout the entire extent of the nucleus 2. Since STN projects to SNr, it is hypothesized that if beta oscillations are detected in STN, they will also be present in SNr and will be coherent with STN oscillations

46 CHAPTER 2 - GENERAL METHODS This chapter will provide a detailed description of the methods used in the experiments conducted for my thesis. Patients in this study were operated by Dr. Andres Lozano or Dr. Mojgan Hodaie. Patients were clinically assessed by Dr. Elena Moro, and the clinical data were collected by Yu-Yan Poon. Setting up of the recording devices and intraoperative microelectrode recordings of the patients were carried out by Dr. William Hutchison, Ian Prescott, Dr. Akihiro Yugeta, and me. Parts of the data analysis were done with MATLAB scripts written by Dr. Neil Mahant. All data analyses and statistics were performed by me Patients and consent We studied 16 advanced PD patients undergoing stereotactic neurosurgery for the implantation of DBS electrodes in STN. The group consisted of eight women and eight men who, at the time of operation, had a mean age of 56 yr (range 42 67) and a mean duration of PD of 13.4 ± 3.8 (mean ± SD) years. Clinical assessment of all the patients was performed prior to surgery by a movement disorders specialist at Toronto Western Hospital using the Unified Parkinson s Disease Rating Scale (UPDRS) (Fahn et al., 1987) before and after an acute levodopa (L-dopa) administration (Moro et al., 2002). Each patient was recorded at rest after the overnight withdrawal of their antiparkinsonian medication (off state). Demographic details of the patients are given in Table 2.T1. The studies were performed with approval of the University Health Network Ethical Research Review Board, University of Toronto. Patients gave written and informed consent before surgery. 36

47 37 Table 2.T1: Demographic and clinical characteristics of the PD patients Pre-op motor Patient Age (years) and Disease duration scores: Side and number of microelectrode trajectories within # sex (years) UPDRS on/off STN (PD) 1 63 M 10 16/48 R + L off 2 48 F 20 20/55 R + L off 3 60 M 18 15/35.5 L off condition 4 42 M /43 R + L off 5 67 F /24.5 R + L off 6 52 F /23 R + L off 7 49 M 9 4.5/21 R + L off 8 58 M 13 10/24 R + L off 9 53 M 13 4/23.5 R + L off F 19 20/33 R off condition F 11 12/39.5 R + L off M 10 6/26 R + L off F 13 9/31 R + L off F 10 29/43 L off condition 15 52M NA 59.5/22 R + L 16 44F NA 47.5/NA R + L

48 Intraoperative neuronal recordings Operative procedures Here, a brief description of the operative procedures is provided. However, details can be found in the previous publications by our group (e.g. Hutchison et al., 1994; Lozano et al., 1996; Hutchison et al., 1998). Prior to surgery, preoperative MRI images were obtained (Signa, 1.3 T, General Electric, Milwaukee, Wis) after a stereoactic frame (Leksell G, Elekta, Inc, Atlanta, Ga) was affixed to the patient s heads. MRI images were then used to locate the frame coordinates for the anterior and posterior commissures (AC, PC). These values were then entered into a computer program that allowed for shrinkage and expansion of the standard stereotactic human brain atlas map (Schaltenbrand and Wahren, 1977) in order to fit the patient s AC-PC length and subsequently plot the electrode trajectories. In this thesis, trajectory is defined as a single penetration made by the pair of closely spaced parallel microelectrodes and track is defined as the path of an individual microelectrode. Following this initial targeting of the nucleus, the patient was brought into the operating room, where under local anesthesia, and depending on the type of operation (unilateral or bilateral), one or two trephine holes (25 mm) were drilled in the skull. Through this opening, microelectrode recordings were performed to obtain the physiological mapping of the target area. Microelectrode recording trajectories usually spanned an area from 10 mm above the target area to 5 mm below it. Microelectrodes were guided into the brain using a manual hydraulic microdrive. All patients were awake during the mapping procedure. Using an isolated constant current stimulator (Axon system GS3000), microstimulation of various targets was delivered to evoke sensory or motor responses. These were mostly trains of square wave pulses (pulse width 150 µs or 200 µs) at 100 to 300 Hz and up

49 39 to a maximum of 100 µa. Based on microelectrode recording and stimulation a physiological map of the target area was obtained Physiological targeting a - Subthalamic Nucleus A detailed description of microelectrode recording use in the localization of the target for DBS electrode in STN is provided elsewhere (Hutchison et al., 1998). Briefly, orientation of parasagittal trajectories was adjusted at 12 mm lateral from the midline, 2-4 mm posterior to the mid-commissural point and 3 mm below the AC-PC line. Typically, a microelectrode trajectory begins approximately 10 mm above STN and passes first through such areas like thalamic reticular nucleus and/or anterior thalamus, zona incerta, and then into STN and finally SNr. The dorsal border of STN is characterized by an increase in the background activity and higher frequency of neuronal discharges. In addition, upon entering dorsal STN, a beta frequency flutter on the audio monitor is frequently detected. Recording of neuronal activity and LFPs defined the full dorsoventral extent of the STN (4-6 mm). Further, in order to localize the motor portion of STN, patients were asked to perform active and passive movements, which evoke responses in many of the motor neurons. The ventral border of STN is denoted by reduced background noise and less oscillatory LFP activity until the electrodes reach SNr. Approximate recording locations within STN were obtained by calculating the distance with respect to dorsal border of STN. For the ease of comparison across patients, values of recording depths were normalized with respect to the dorsal and ventral borders of STN with 0 and 1 denoting dorsal and ventral borders of nuclei respectively.

50 b - Substantia nigra pars reticulata Microelectrode recordings of SNr were performed using the same trajectory that was used for STN. Usually, SNr is detected after a 1 mm gap below the ventral border of STN and its neurons are characterized by higher-frequency, lower amplitude discharges that are more regular and less oscillatory compared to STN. Even in the absence of the gap, difference in the rate and pattern of firing of STN and SNr neurons is used in determining the border between STN and SNr. In addition, high frequency microstimulation using 0.5 second trains of 200 Hz and current pulses of 3-5µA was delivered at sites with large amplitude and well-isolated spikes. Following the termination of the stimulation train, SNr units were clearly inhibited for several hundred milliseconds. However, thresholds for inducing similar effects on STN neurons are much higher (>10 µa) and only a few (if any) STN neurons are inhibited following the high frequency microstimulation (Filali et al., 2004; Lafreniere-Roula et al., 2009), and others show a rebound burst effect. This method of confirming SNr neurons is quite powerful specifically in the border region between STN and SNr, and in addition to assessment of background neuronal activity, presence of gap in neuronal activity, and firing rate and pattern, has been extensively used by our group over the past several years to identify SNr (Lafreniere-Roula et al., 2009) Microelectrode setup For all the patients in this study, a dual microelectrode recording method was used (Levy et al., 2007), which allowed for simultaneous recordings from two microelectrodes. Microelectrodes used in this study were parylene-c coated tungsten microelectrodes with an exposed tip of µm length that were plated with gold and platinum to a final impedance of approximately MΩ at 1 khz. Each of these microelectrodes was then inserted into a 30-guage stainless steel

51 41 tube (Small Parts Inc., HTX-25 tubing) that was joined to the microelectrode using epoxy resin. Stainless tubes were insulated with polyimide tubing. The dual microelectrode guideline set up were constructed by soldering two adjacent 23-gauge, thin walled stainless steel tubes (HTX- 23TW; Small Parts Inc., Miami Lakes, FL) forming the inner guide tubes into which, two identical microelectrodes were independently inserted. These were then set into a mediolateral position with respect to each other and relative to the patient. This construct fits easily into the standard stereotactic frame outer guide tube, which, in our setup, was constructed from 17 gauge stainless steel tubing (HTX-17; Small Parts Inc.). The microelectrodes were parallel and were separated by a mediolateral distance of ~ 600 μm. Each microelectrode was driven independently into the brain with a manual hydraulic microdrive. This allows for recording from pairs of neurons and/or LFPs at variable distances axially. A photograph of the dual microdrive head stage assembly is shown in Figure 2.F1.

52 42 Figure 2.F1. A: head stage and detachable guide tubes assembly. Note the adequate separation of the two microdrive units that was provided by the gradual 30-degree curve in one of the guide tubes. B: microelectrodes extending from the distal end of dual guide tubes with tip dimensions of 25 μm (we300325a, Microprobe). Microelectrodes had mediolateral orientation with respect to patients and were centered approximately 300 μm off the center axis of the stereotactic frame guide tube. Taken with permission from Levy et al. (2007) Dual microelectrode technique for deep brain stereotactic surgery in humans. Neurosurgery 60 (4 Supp 2): Single unit and local field potential recordings During the mapping procedure, single unit activity and local field potentials were both recorded from the microelectrodes. Recordings were monopolar and both microelectrodes shared a common ground consisting of the stainless steel guide tube and frame attachments to the head. Microelectrodes were connected to the amplifiers head stage through their proximal ends, and shared a common ground with them. Recordings were amplified 5,000 to 10,000 times using two Guideline System GS3000 (Axon Instruments, Union City, CA) amplifiers and filtered at 10 to 5,000 Hz (analog Butterworth filters: high pass, one pole; low-pass, two pole; at 5 khz gain was

53 43 roughly 50%). During the mapping procedure, recording signals were displayed on a computer screen and monitored on a loudspeaker. Any movement or muscle activities of the limbs were also monitored with accelerometers and wrist flexor/extensor EMGs. The spike/lfp signals were digitized at 10 khz with a CED 1401 (Cambridge Electronic Design [CED], Cambridge, UK) and at the end the recording procedures stored onto a hard drive Experimental design For the purpose of our experiment, we systematically varied the distance between the two microelectrodes. Once the microelectrodes enter the dorsal STN and beta frequency flutter on the audio monitor was detected, one of the two was held stationary, while the other one was advanced toward the ventral border of STN and then into SNr over a distance of ~ 4-6 mm. Sequential sec segments of data were collected at sites of varying distance apart where well-isolated single units were recorded. These recordings were used to describe effect of distance on STN - STN coherence. In the second part of the experiment, the electrode in SNr was held stationary and the initial stationary electrode in dorsal STN was advanced down in a similar fashion to detect well isolated single units and to measure segments of data with different spatial separation between STN-SNr. Figure 2.F2 shows the schematic of the recording in STN and SNr with the location of the electrodes track on the customized Schaltenbrand and Wahren at 12mm from the midline.

54 44 Figure 2.F2. Reconstruction of microelectrode recording track through the subthalamic nucleus (STN) and substantia nigra pars reticulata (SNr) and examples of neuronal recordings. On the right, sagittal 12.0 mm lateral stereotactic map (Schaltenbrand and Wahren, 1977) along with the systematic variation of distance between microelectrodes within the STN and between the STN and SNr is shown. Examples of recording traces (1 - sec duration) that are used as anatomical landmarks due to their characteristic electrophysiological properties are shown on the left. Voa and Vop, ventro oralis anterior and posterior nuclei of the thalamus ; Vim, ventro intermediate nucleus of the thalamus.

55 Data analysis Spectral analysis of neuronal discharges and local field potentials For analysis, only sites with good recordings were selected. We only chose recordings of 12 seconds length that were free of artifacts and were not obtained during periods with voluntary movements (based on analyses of activity in EMG and/or accelerometer recordings). Single- or multi-unit neuronal activities were discriminated using template-matching tools in Spike2 (Cambridge Electronic Design, Cambridge, UK). Because the present thesis is solely concerned with local field potential (LFP) activity in the beta-band (11Hz-30Hz) region, both microelectrode channels where fed through a 2nd order butterworth bandpass filter between 11Hz and 30Hz. Spike times and LFP data were imported into MATLAB (version 6.5, The MathWorks, Natick, MA) for spectral and cross-correlation analyses a - Power spectrum Local field potentials Discrete Fourier transform and its derivations calculated according to Halliday et al., (1995) were the main tools used for analyzing LFP recordings. Signals were first down-sampled to a common sampling rate of 1 khz, then autospectra of the LFP were estimated by dividing the records into a number of disjoint sections of equal duration of 1024 points (1.024 seconds) affording a frequency resolution of ~1 Hz (0.98 Hz). Each section was windowed (Hanning window) and the magnitude of the 1024 discrete Fourier transforms of each section was squared. The power spectrum was then estimated by averaging across these discrete sections. The power value was then transformed into a logarithmic scale and expressed in decibel (db) units. Since

56 46 this estimation of the power spectrum has a distribution analogous to a chi-squared distribution (χ²), the 95% confidence intervals were given according to the χ² distribution (Jarvis and Mitra, 2001). However, degree of freedom values were given on the basis of the number of windowed sections. Neuronal discharges Spectral analysis of spiking activities was performed similarly to the LFPs on the basis of Fourier transform principals according to Halliday et al. (1995). First, spike times were converted to a logical function (zeros and ones) where each neuronal spike is stored along with a vector. In the case of point processes, spectral analysis was performed by correlating the sinusoids and co-sinusoids of the complex Fourier exponential with the times of occurrence of the events. This method highlights periodic components in the discharge since presence or absence of specific periodicities in the spike timing will lead to increase/decrease of correlation at the particular frequency of periodicity that is expected by chance alone (Halliday et al., 1995). Detection of significant oscillation in the spike train was performed through interspike intervals (ISIs) shuffling technique (Rivlin-Etzion et al., 2006). This technique uses the time differences between adjacent spikes (first-order ISIs) to generate a new spike train, the spectrum of which is determined solely by the first-order ISIs of the original spike trains. However, higher-order effects (the time difference between spikes that are separated by one spike or more) are abolished by the shuffling process. Oscillatory patterns that are generated by the higher-order ISIs are detected through comparison of the original spectrum with the new one. Accuracy of the estimate is enhanced by repeating the shuffling process 100 times and averaging the results. The corrected spectrum is then obtained by subtracting the original spectrum from the new spectrum, and its peaks are considered significant only when they exceed the upper 95% confidence limit.

57 47 Similar to LFPs, 95% confidence intervals were given according to the χ² distribution and are frequency independent and solely depend on the degrees of freedom b - Coherence and cross-correlation Coherence is a frequency domain function that provides the measure of association between two signals (Rosenberg et al., 1989; Halliday et al., 1995). In our study, we used it extensively to evaluate the relationship between simultaneously recorded LFPs and spikes from the two electrodes, and also between LFP and spike data recorded from one microelectrode. It takes values between 0 and 1, with 0 being the sign of independence and 1 indicating perfect coupling. Coherence is estimated by direct substitution of appropriate spectra as: fxy 2 /fxxfyy with 95% confidence level of 1- (0.05) 1/(L-1) ; where fxx and fyy are the autospectra of the component processes, fxy is the cross-spectral density, and L is the number of windowed sections. It is important to note that prior to the analyses, LFPs did not undergo further low-pass filtering, and as a result, we cannot rule out the possibility of small contamination with the neuronal firing recorded from the same microelectrode. This is specifically notable in above 40 Hz coherence of LFPs with spiking activity recorded both by one microelectrode. However, it is of less concern as the present investigation is solely with regard to local field potential (LFP) activity in the betaband (11Hz-30Hz) region. Cross-correlation is a time-domain measure of the relationship between two samples of data. It is defined by the inverse Fourier transform of the cross-spectrum fxy. Even though, it can be estimated within the time domain, estimation within the frequency domain facilitates the formation of confidence limits (Halliday et al., 1995). The upper and lower 95% confidence limits for the estimated value of cross-correlation are given by 0 ± 1.96 (var cross-corr ) 1/2 based on

58 48 the hypothesis that two independent processes give a cross-correlation value of zero. Crosscorrelation provides further information on how two signals are related, however, only coherence estimates determined whether such a relationship was significant or not Statistical comparison All the statistical tests in this work were performed with SigmaStat software (version 3.1, Systat Software, Richmond, CA). Comparisons of beta frequency peaks and powers in STN and SNr of each Parkinson`s disease patient were performed using unpaired student t-tests. Paired t-tests were performed to compare neuronal and beta LFPs before/during different movement tasks. Simple and multiple regression analyses were also performed to find how different categories were related. The R 2 values were then used to calculate the P value with the following formula: t = r/( (1 r 2 )/(n 2)) 1/2 ; where n is the sample size. All values are expressed as mean ± standard deviations (SD), and a P value of less than 0.05 was considered to be a significant difference in all statistical tests.

59 CHAPTER 3 - RESULTS We analyzed recordings from 188 STN and 44 SNr neurons along 23 tracks in sixteen PD patients in the off condition. The mean recording durations ( SD) were s and s respectively (no statistical difference). The number of neurons and microelectrode trajectories that were analyzed in each patient are given in Table 3.T Beta LFP power progressively declines over the full extent of STN We confirmed a progressive attenuation in beta LFP power over the dorsoventral extent of STN. Figure 3.F1B shows an example of how beta LFP declines over the full extent of STN in a single case and Fig 3.F2A shows the peak beta power in seven patients where we maintained one microelectrode stationary and guided the other one dorsoventrally into STN. Only the trajectories with more than 4 mm (5.1 ± 0.4) length of STN were included in the graph. As the figure shows, beta LFP is highest at dorsal STN and tends to decline sharply as the microelectrode reaches central STN (R 2 = 0.51, P < ; two-tailed unpaired t test). Figure 3.F1B illustrates an example of beta LFP decline in one PD patient with one of the microelectrodes being guided dorsoventrally into STN and another remaining stationary in SNr. Note stable but relatively low beta power in a single site in SNr (red) over time with the patient at rest. Beta power was high in dorsal STN and progressively declined as the microelectrode moved into ventral STN (black). Moreover, the peak of beta power shifted slightly to a lower peak frequency from dorsal to ventral STN and became close to the beta peak in SNr. 49

60 Beta oscillation are widely distributed and synchronized within and between STN and SNr Data were analyzed from pairs of recordings within STN/STN (n=111) STN and SNr (n= 42), and within SNr (n= 17) at rest. Table 3.T1 summarizes the number of recoding pairs where LFPs and/or neuronal oscillatory activity were coherent (coherence of greater than 0.2; P < 0.05). Beta LFP coherence was significant between pairs in STN (92%), while significant coherence was observed between 32% of neuronal pairs. Figure 3.F2B shows that beta LFP was significantly coherent throughout the full extent of STN, while coherence between neuronal pairs sharply declined as the distance between microelectrodes increased (Figure 3.F2C). Interestingly, there was significant coherence between beta LFPs in STN and SNr (86%), but significant neuronal coherence was observed in only 5% of STN-SNr neuronal pairs. Figure 3.F3 illustrates an example of significant coherence of spiking activity between STN and SNr. There was also significant coherence between 88% of the pairs of LFPs in dorsal SNr. Table 3.T1: Number and percent significance of recording pairs in STN and SNr of PD patients Number of Pairs % Significant Coherence LFP1/LFP2 % Significant Coherence Cell 1/Cell2 STN/STN N = (92%) 36 (32%) STN/SNr N = (86%) 2 (5%) SNr/SNr N = (88%) 0%

61 Spatial extent of LFP beta oscillatory activity in STN is a better predictor of motor symptoms of PD in the OFF state To probe the relationship between the spatially extended beta LFPs and the motor features of Parkinsonism, we looked at cross-spectral coherence between two microelectrodes as we systematically varied the distance between them. Only trajectories with more than 4 mm of STN were included. Figure 3.F4 (top left) shows how coherence changed over distance in seven PD patients. We plotted the regression lines of each track (top right), and used their slopes as a measure of spatial extension of beta LFP coherence. Interestingly, this measure of spatially extended beta LFP correlates with UPDRS motor scores in off, but not on state (Figure 3.F4, bottom) with the R 2 = 0.52, (off), and R 2 = 0.08 (on) with non-significant p values. However, analysis of beta LFP power across 15 PD patients showed a significant negative correlation with mupdrs scores (Figure 3.F5), R 2 = 0.45 (off), and R 2 = 0.35 (on), P < 0.05; two-tailed unpaired t test Comparison of neuronal and LFP beta oscillatory activity in STN and SNr In total, 188 STN and 44 SNr cells were analyzed in 16 PD patients. Table 3.T2 summarizes the comparisons between STN and SNr cell/lfp recordings within each of the patients. The mean firing rate of STN and SNr neurons (± SD) were 57 ± 18 Hz and 81 ± 19 Hz respectively. On average, 46% of STN neurons were oscillatory/coherent with simultaneously recorded LFPs, yet no oscillatory SNr neurons were detected. However, 25% of SNr neurons were coherent with simultaneously recorded LFPs. It is important to note that the statistical measure used to capture significant single-unit oscillatory activity is different from the test of significant coupling with simultaneously recorded LFPs. This may yield significant cell/lfp coherence of a neuron whose independent activity is not significantly oscillatory. Powers and frequency peaks of beta LFPs in

62 52 STN and SNr of each patient were also compared. Figure 3.F6 demonstrates how power of beta LFPs varies from STN to SNr within each PD patient. Beta LFP power in STN is significantly higher than that of SNr in seven of the sixteen PD patients (P < 0.05, two-tailed unpaired t tests). In terms of beta frequency peak, significant differences were observed in three patients (bottom) (P < 0.05, two-tailed unpaired t tests); with the peak of beta frequency being slightly lower in SNr as it is also reflected in Figure 3.F1B. In an attempt to compare beta LFP powers in STN and SNr across all the sixteen PD patients, we normalized all the values of beta LFP powers according to highest and lowest values at each recording site, calculated the mean in each patient, and excluded the ones with more than fifty percent standard deviations. As it was expected, power of beta LFPs of STN and SNr are significantly positively correlated (Figure 3.F8, R 2 = 0.81, P < ; two-tailed unpaired t test) Ratios of beta LFP powers in STN and SNr The question of whether beta band LFPs detected in SNr are result of physiological coupling or volume conduction from STN is critical to the interpretation of our findings. One way of addressing this question is to look at the ratios of beta LFP powers in STN and SNr of each PD patient. If the LFP oscillatory activity in SNr was solely due to volume conduction from SNr, we would expect the ratios to be constant from one patient to another. Figure 3.F6 demonstrates how the ratios of LFP powers varied from one patient to another.

63 Phase differences between oscillatory activity of pairs of neurons Another distinguishing feature between near field volume conduction and physiological coupling is the presence of a phase difference between activities recorded by microelectrodes at each recording site. In the case of volume conduction, no such difference should be observed. However, physiological coupling necessitates a certain extent of variance as it is also reflected in neural conduction or synaptic delay. Figure 3.F9 illustrates an example of a phase delay between STN and SNr in one of the PD patients. One microelectrode is in central STN (right) and another in dorsal SNr (left). There is a phase delay of 2.0 milliseconds, with STN leading SNr, suggesting that beta activity in SNr is not simply a result of near field volume conduction from STN.

64 54 Figure 3.F1. A: Location of the electrode tracks on the customized Schaltenbrand and Wahren 12mm from the midline atlas map. B: Power spectrum analysis comparison at different distances within STN. The control stationary microelectrode (red) was in the dorsal portion of SNr in a PD patient off medication. Note the generally stable beta power at the fixed SNr site over time with the patient at rest (t refers to the elapsed times, and arrows show the depths where STN and SNr spectra were calculated). However there was a progressive decline in beta power with increasing distance from dorsal to ventral STN (black). Moreover, it appears that the peak of highest beta power shifts from dorsal to ventral STN and becomes closer to the beta peak in SNr. The data are from patient [2567].

65 55 Figure 3.F2. A: Scatterplot showing the progressive attenuation of beta power over the dorsoventral extent of STN in seven PD patients (R 2 = 0.51, P < ; two-tailed unpaired t test). Only tracks with more than 4mm of STN were included. One microelectrode was maintained at dorsal STN and another was driven dorsoventrally. The power of the beta band LFP at each depth was normalized according to the highest power in each track. The depth was normalized by dividing the depth by the total exent of STN for that track. Scatterplots showing coherence between LFPs (B) and Cells (C and D) as a function of the distance between the two electrodes over the whole extent of STN with their corresponding regression lines (dotted lines). Coherence between beta LFPs drops 11% per millimeter distance in STN (B), while significant neuronal coherence is mostly confined to the 2 mm area of dorsal STN (C) controlling for the effect of distance (D) (Closed squares represent significant coherences).

66 56 Figure 3.F3. Example of coherence between a SNr cell and a STN cell in one of the PD patients with electrode 1 at depth 1.14 in SNr and electrode 2 at depth 0.34 in STN (0 denoting dorsal border and 1 ventral border of STN). Dotted line indicates 95% confidence limit.

67 Figure 3.F4. A: Left: Change in cross-spectral beta band LFP coherence between two microelectrodes as the distance was systematically varied between them in seven PD patients. Only trajectories with more than 4 mm of STN (5.1 ± 0.4) were included. Right: Corresponding regression lines of change in beta band LFP over the full extent of STN. B: Correlation of spatial extension of beta LFP (as measured by the slope of regression lines of change in beta LFP) with UPDRS motor scores in off (left) and on (right) states. Scatterplots show positive correlations between these measures of spatially extended beta LFP and motor impairments of 57

68 58 PD in both off (left) and on (right) states with R 2 = 0.52 (off), and R 2 = 0.08 (on), with nonsignificant p values of two-tailed unpaired t test. Figure 3.T5. Scatterplots showing negative correlations between beta LFP power and UPDRS motor scores in off (left) and on (right) states of fifteen PD patients with R 2 = 0.45 (off), and R 2 = 0.35 (on) P < 0.05; two-tailed unpaired t test. Data of patients with more than fifty percent standard deviations of beta LFP means were excluded.

69 59 Figure 3.F6. Comparison of β power (Normalized with respect to highest β power in each track) in STN (black) and SNr (grey) of sixteen PD patients, shows a clear decline in beta power from STN to SNr. Significant difference was observed in seven patients (P < 0.05, two-tailed unpaired t tests).

70 60 Figure 3.F7. Comparison of β peak frequency (Hz) in STN (black) and SNr (grey) of the sixteen PD patients. Beta peak frequency between STN and SNr is significantly different in three patients (P < 0.05, two-tailed unpaired t tests).