Beta and Tremor-Related Oscillations in the Motor Thalamus of Essential Tremor Patients

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1 IETF - WD Hutchison Feb 214 requesting $25, Beta and Tremor-Related Oscillations in the Motor Thalamus of Essential Tremor Patients 1. Specific aims of proposal The proposed study will examine beta oscillations (13-3 Hz) in the motor thalamus of essential tremor (ET) patients and will test the hypothesis that the beta rhythm plays a role in the suppression of tremor. Microelectrode recordings of the ventral thalamus will be collected during intraoperative mapping procedures for deep brain stimulation (DBS) surgery. The project will consist of four studies: a. A retrospective study using archived microelectrode recordings of the motor thalamus of essential tremor and pain patients. Beta oscillatory power in LFP and spike trains will be measured and mapped to the ventral thalamus according to the reconstructed microelectrode track. Beta oscillatory power will be measured in the ET group and compared to beta power in the pain patient group. b. The relationship between the severity of ET and beta oscillatory power will be assessed by examining the correlation between clinical tremor scores and beta power in the motor thalamus. The data set from the above retrospective study will also be used for this study. c. A prospective study of ET and pain patients examining beta activity during rest and tremor (ET only). Microelectrode recordings of the motor thalamus will be collected from upcoming DBS cases and neuronal activity in the beta band will be examined during rest and during tremor episodes. Pain patients will serve as controls for this maneuver. d. A study of the effect of patterned 5 Hz and 2 Hz stimulation on tremor. Using dual microelectrodes we will microstimulate with one microelectrode and record the effect on a nearby thalamic cell with the sentinel electrode while monitoring the effect on tremor reduction or arrest. Comparisons will be made between three microstimulation protocols: i.) Continuous 2 Hz high frequency stimulation (HFS) ii.) Theta burst protocol (TBP) at 5 Hz iii.) Beta burst protocol (BBP) at 2 Hz

2 Clinical Neurophysiology 123 (212) Contents lists available at ScienceDirect Clinical Neurophysiology journal homepage: Oscillatory activity in the globus pallidus internus: Comparison between Parkinson s disease and dystonia Moran Weinberger a, William D. Hutchison a,b, Mahan Alavi a,b, Mojgan Hodaie b,c, Andres M. Lozano b,c, Elena Moro b,d, Jonathan O. Dostrovsky a,b, a Department of Physiology, University of Toronto, Toronto, ON, Canada b Toronto Western Research Institute, Toronto, ON, Canada c Division of Neurosurgery, Toronto Western Hospital, UHN, University of Toronto, Toronto, ON, Canada d Division of Neurology, Toronto Western Hospital, UHN, University of Toronto, Toronto, ON, Canada article info highlights Article history: Accepted 4 July 211 Available online 16 August 211 Keywords: Basal ganglia GPi Microelectrode recordings Local field potential Oscillations 3% of globus pallidus neurons in Parkinson s disease patients have 11 3 Hz beta oscillatory activity. Globus pallidus firing is coherent with the local field potential (LFP) oscillatory activity in the beta range. In dystonia patients LFP and neuronal oscillatory activity frequency is 8 1 Hz and only 1% of neurons fire coherently. abstract Objective: Deep brain stimulation in the globus pallidus internus (GPi) is used to alleviate the motor symptoms of both Parkinson s disease (PD) and dystonia. We tested the hypothesis that PD and dystonia are characterized by different temporal patterns of synchronized oscillations in the GPi, and that the dopaminergic loss in PD makes the basal ganglia more susceptible to oscillatory activity. Methods: Neuronal firing and local field potentials (LFPs) were simultaneously recorded from the GPi in four PD patients and seven dystonia patients using two independently driven microelectrodes. Results: In the PD patients, beta (11 3 Hz) oscillations were observed in the LFPs and the firing activity of 3% of the neurons was significantly coherent with the LFP. However, in the dystonia group, the peak frequency of LFP oscillations was lower (8 2 Hz) and there was a significantly smaller proportion of neurons (1%) firing in coherence with the LFP (P <.1). Conclusions: These findings suggest that synchronization of neuronal firing with LFP oscillations is a more prominent feature in PD than in dystonia. Significance: This study adds to the growing evidence that dopaminergic loss in PD may increase the sensitivity of the basal ganglia network to rhythmic oscillatory inputs. Ó 211 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved. 1. Introduction Parkinson s disease (PD) and dystonia are movement disorders associated with basal ganglia (BG) dysfunction. PD is a neurodegenerative disorder, which is characterized by a severe loss of dopaminergic neurons in the substantia nigra pars compacta and motor symptoms such as rigidity and akinesia. Classical models of basal ganglia function are based on discharge rates in the BG Corresponding author. Address: Department of Physiology, Medical Sciences Building 332, 1 King s College Circle, University of Toronto, Toronto, ON, Canada M5S 1A8. Tel.: ; fax: address: j.dostrovsky@utoronto.ca (J.O. Dostrovsky). structures and predict that in PD, the dramatic decrease in dopamine concentration in the striatum results in an increased activity in the output nuclei, the globus pallidus internus (GPi) and substantia nigra pars reticulata, and over-inhibition of the thalamocortical drive (Albin et al., 1989; DeLong, 199). Conversely, dystonia, which is characterized by sustained co-contractions of agonist and antagonist muscles that lead to abnormal posture and involuntary movements, is suggested to result from decreased basal ganglia output that, in turn, leads to decreased inhibition of thalamic activity and consequently to increased excitability of the motor cortex (Vitek, 22). These predictions of the so-called rate model have been generally confirmed in animal models of PD (Miller and DeLong, 1987; /$36. Ó 211 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved. doi:1.116/j.clinph

3 M. Weinberger et al. / Clinical Neurophysiology 123 (212) Filion and Tremblay, 1991; Boraud et al., 1998) and are also supported by most recording studies in humans undergoing functional neurosurgery as a treatment for PD or dystonia (Lenz et al., 1998; Merello et al., 24; Starr et al., 25; Tang et al., 27). However, the predicted rate changes have not been found in all studies (Bergman et al., 1994; Wichmann et al., 1999; Lenz et al., 1999; Raz et al., 2; Hutchison et al., 23; Pessiglione et al., 25), and the model fails to explain the paradoxical improvement in the motor symptoms of both PD and dystonia by lesion or highfrequency stimulation in the GPi (Marsden and Obeso, 1994; Lozano et al., 1997; Lang et al., 1997; Lozano, 21; Vidailhet et al., 25). Instead, there is increasing evidence for changes in the activity patterns of basal ganglia neurons, which are associated with various basal ganglia-related neurological disorders. In PD, the incidence of neuronal bursting activity in the globus pallidus and subthalamic nucleus (STN) is increased (Filion and Tremblay, 1991; Hutchison et al., 1994; Boraud et al., 1998), most often in the context of increased synchronous neuronal and local field potential (LFP) oscillatory activity in both the STN and GPi (Bergman et al., 1994, 1998; Nini et al., 1995; Raz et al., 2; Magnin et al., 2; Brown et al., 21; Levy et al., 22a,b; Foffani et al., 25). It has been shown that the oscillatory discharges and LFPs observed in PD patients are rapidly reversed by treatment with dopaminergic medication (Brown et al., 21; Priori et al., 22; Levy et al., 22a), and are positively correlated with the patients response to the medication (Weinberger et al., 26; Ray et al., 28), suggesting that these oscillations might be a direct effect of dopamine deficiency. Oscillatory activity in the GPi has also been observed in patients with generalized or focal dystonia (Silberstein et al., 23; Starr et al., 25; Chen et al., 26a,b). Thus, the paradoxical amelioration of motor disabilities in PD and dystonia by GPi lesion or deep brain stimulation (DBS) might be explained if the different symptoms are dependent on abnormal, but different, patterns of synchronized oscillations in the basal ganglia circuits, so that eliminating this activity by lesion or DBS results in symptomatic improvement. Indeed, Silberstein et al. (23) have shown that PD and dystonia are characterized by different oscillatory patterns. The LFPs recorded from the DBS electrodes implanted in the GPi revealed that in PD the dominant frequency of oscillation is between 11 and 3 Hz, known as the beta range, whereas in dystonia the power of LFP beta oscillations is weaker and there is increased oscillatory activity in lower frequencies (4 1 Hz). In contrast to beta oscillatory activity which is related to rigidity and akinesia (Kuhn et al., 26, 28; Chen et al., 27; Ray et al., 28), the 4 1 Hz oscillations have been implicated in dyskinesias (Silberstein et al., 23, 25; Foffani et al., 25; Alonso-Frech et al., 26; Chen et al., 26a). In PD patients, beta activity in the GPi is mainly evident in the LFPs recorded from the macroelectrodes used for therapeutic highfrequency stimulation of this region (Brown et al., 21; Priori et al., 22; Silberstein et al., 23, 25). Although synchronized beta oscillatory neuronal firing in the GPi has been previously reported (Levy et al., 22b; Tang et al., 27), the relationship between neuronal firing and the LFP in GPi in PD patients remains to be elucidated. Previous studies in STN of PD patients have demonstrated a close association between oscillatory neuronal firing and the LFP in the beta frequencies (Kuhn et al., 25; Weinberger et al., 26; Moran et al., 28). This phenomenon was hypothesized to result from a greater sensitivity of basal ganglia neurons to oscillatory inputs in the dopamine depleted state (Magill et al., 21; Sharott et al., 25; Baufreton et al., 25; Walters et al., 27). This hypothesis can be examined in the GPi by comparing the relationship between neuronal firing and LFPs in PD and dystonia. This comparison is possible since the GPi is widely targeted to treat dystonia (Vidailhet et al., 25) and in some cases, levodopainduced dyskinesias in PD patients (Krack et al., 1998). In the present study, we investigated the relationship between neuronal discharges and the oscillatory local field potentials in the GPi in both PD and dystonia patients. The degree of association between neuronal firing and the LFP, as well as the predominant frequency of oscillations, was compared between PD and dystonia. In addition, we investigated the changes in GPi oscillatory activity during levodopa-induced dyskinesias in PD. 2. Methods 2.1. Patients We studied four PD patients undergoing stereotactic neurosurgery for the implantation of DBS electrodes in the left GPi (2 patients) or unilateral pallidotomy (2 patients). These four patients were the only PD patients who have been implanted in the GPi within the last 9 years in our center, since the STN has been our target of choice for PD. There were two women and two men who, at the time of operation were 4, 62, 63 and 7 years old and had a PD duration of 8, 16, 11 and 27 years, respectively. In two of these four patients the GPi was targeted because they were considered suboptimal candidates for STN DBS due to some cognitive decline and disabling levodopa-induced dyskinesia as the most important symptom. The other two patients had previously undergone bilateral STN DBS with insufficient benefits. For comparison purposes, we also studied one male and six females with primary dystonia (generalized, segmental or cervical) with a mean age (±SD) of 57.9 ± 1 years and disease duration of 2.5 ± 15 years. Six of the dystonia patients had bilateral and one had unilateral GPi surgery for DBS; one patient had had a previous left pallidotomy. Apart from local anesthetics, no sedatives or anesthetics (such as propofol) were administered during or before surgery. Presurgical clinical assessments of all patients were performed by movement disorder specialists at the Toronto Western Hospital. PD patients were assessed according to the motor part of the Unified Parkinson s Disease Rating Scale (UPDRS) (Fahn et al., 1987) before and after an acute levodopa challenge, whereas cervical dystonia patients were evaluated according to the Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS) (Comella et al., 1997) and segmental or generalized dystonia patients were rated with the Burke Fahn Marsden Dystonia Rating Scale (Burke et al., 1985). Demographic and clinical details of the patients are given in Table 1. The studies were performed with approval of the University Health Network Ethical Review Board, University of Toronto. Patients gave written and informed consent before surgery Recordings The recording procedures to localize the posteroventral GPi during stereotactic neurosurgery have been previously described (Hutchison et al., 1994; Lozano et al., 1996, 1998). Briefly, parasagittal trajectories were oriented at 2 mm from the midline. Microelectrode trajectories usually started 15 mm above target so that the recordings often began within the GPe. This would then be followed by the internal medullary lamina, which lies between the GPe and GPi. This in an area of white matter and is therefore identified by a significant decrease in overall activity. Occasionally, peripallidal border cells, characterized by a highly regular firing pattern and slower rate, were encountered within the lamina and borders of GPi. In both the PD and dystonia cases, entry into the GPi was marked by an overall increase in background noise and high frequency discharges. The bottom of the GPi was determined

4 36 M. Weinberger et al. / Clinical Neurophysiology 123 (212) Table 1 Demographic and clinical characteristics of the patients. Patient # and disease Age (years) and sex Disease duration (years) Pre-op. motor scores: UPDRS on/off (PD) b TWSTRS severity scale or BFMDRS movement scale (dystonia) Daily medications for the movement disorder 1 PD 7 F /8 Levodopa/carbidopa, amantadine, quetiapine, entacapone, lorazepam 2 PD a 4 F /55.5 Levodopa/carbidopa, pramipexole, amantidine, zoplicone 3 PD a 62 M /56.5 Levodopa/carbidopa, pramipexole, clonazepam 4 PD 63 M /41 Levodopa/carbidopa, amantadine, clozapine 5 Generalized dystonia Side and number of microelectrode trajectories within GPi from which recordings analyzed No. of GPi neurons analyzed L on L off condition L off condition L off condition 8 3 L off condition M 4 46 None R (2) + L Segmental dystonia 57 F None R + L Cervical dystonia 48 F 5 23 None R 15 8 Cervical dystonia 73 F NA NA NA R + L Cervical dystonia 53 F Trihexyphenydyl, R+L 5 8 clonazepam 1 Cervical dystonia 5 F 8 24 Clonazepam, lorazepam R + L Cervical dystonia 7 F 4 28 Clonazepam R %of oscillatory/ coherent neurons NA not available. a Patient had previous bilateral STN DBS. b Pre-operative UPDRS motor scores were assessed after 12-h anti-parkinsonian medication withdrawal (the defined off) and 1 h after an acute levodopa challenge (on). based on white matter (no spiking activity, decreased background noise) and identification of the optic tract, which lies close to the ventral border of the GPi. The optic tract could be identified by microstimulation to elicit visual percepts and/or by using a flashing strobe light to elicit a visual evoked response in the microelectrode recording. Sometimes it could be identified also by axonal electrical activity with narrow spikes or a subtle increase in high frequency noise. Another important anatomical landmark was the internal capsule that could be identified by stimulation-evoked tetanic contractions of contralateral muscles. Monopolar extracellular recordings of neuronal firing and LFPs were obtained simultaneously using two independently driven tungsten microelectrodes (about 25 lm tip length, axes 6 lm apart,.2-mx impedance at 1 khz) as described previously (Levy et al., 27). Recordings were amplified 5 1, times and filtered at 1 5 Hz (analog Butterworth filters: high-pass, one pole; low-pass, two poles; at 5 Hz amplitude was decreased by roughly 5%) using two Guideline System GS3 (Axon Instruments, Union City, CA) amplifiers. During the recordings, signals were monitored on a loudspeaker and displayed on a computer screen. Recorded signals were digitized at 1 khz and directly stored onto a computer hard drive with a CED 141 data acquisition system running Spike2 (Cambridge Electronic Design, Cambridge, UK). All recordings were performed in the resting state while the patients were awake and with local anesthesia. Patients were not asked to perform any task and epochs with voluntary movements were excluded. Limb movements were measured using electromyography (EMG) and/or triaxial accelerometers (with summated x y z signals) that were recorded simultaneously with the neuronal activity. An example of simultaneous GPi recordings of LFPs and neuronal discharge is shown in Fig. 1A. The first PD patient (#1) was given 1 mg of levodopa (1 tablet of Sinemet Ò, levodopa/carbidopa 1/25) in the morning of surgery. Microelectrode recordings started approximately 2 h later so that during the first trajectory, which lasted for about 1.5 h, the patient was in the on condition (i.e. less rigid and bradykinetic when compared to off condition ) and displayed levodopainduced dyskinesia (mostly in her right side of the body) almost continuously during the recording session. During the second microelectrode trajectory, which was performed at least 3.5 h after the last dose of levodopa, the effects of the medication were wearing-off and the patient presented with resting tremor, increased rigidity and bradykinesia, and no-dyskinesia. This track was therefore considered to be in the off condition state. The second PD patient (#2) was given 1 mg of levodopa (1 tablet of Sinemet Ò ) approximately 4 h before surgery to relieve the severity of her off-dystonia. In this patient, only one microelectrode trajectory was performed (see Table 1), during which the patient was rigid and bradykinetic and was therefore considered to be in the off condition. It should be noted that the off condition state of these PD patients might differ from the practically-defined off state in which patients are withdrawn from their anti-parkinsonian medication for at least 12 h before surgery (also see Section 4.1). All the PD patients had one GPi trajectory each and were in the 12 h OFF medication state. The seven dystonia patients were given antidystonic medications (usually benzodiazepines) prior to surgery Data analysis Action potentials arising from single- or multi-units were discriminated using template-matching tools in Spike2 (Cambridge Electronic Design, Cambridge, UK). Only recordings P17 s (mean ± SD: 42 ± 25 s) that were obtained during periods without voluntary movements (based on EMG and/or accelerometer recordings and observations) or artifacts were analyzed. Border cells were identified and excluded from the analyses. Spike times and unfiltered LFP data were imported into MATLAB (version 7.1, The MathWorks, Natick, MA) and spectral analyses were performed. The frequency and power of oscillatory firing and LFPs were calculated using the discrete Fourier transform according to Halliday et al. (1995), and significant neuronal oscillations were detected using shuffling of spike trains according to Rivlin-Etzion et al. (26). In addition, coherence and cross-correlation analyses (Rosenberg et al., 1989; Halliday et al., 1995) were used to evaluate the relationship between simultaneously recorded data from separate electrodes and between LFP and spike data recorded from

5 M. Weinberger et al. / Clinical Neurophysiology 123 (212) A Electrode 1 LFP.25 V Spikes 1 V Electrode 2 LFP.5 V B db Power spectrum: LFP a Coherence: LFP1-LFP2 d ms Coherence: LFP1-cell1 e Cross-corr: LFP1-LFP Time (ms) g db Power spectrum: LFP2 1-1 b Coherence: LFP2-cell1 f x Cross-corr: LFP1-cell Time (ms) h 4 x Cross-corr: LFP2-cell Time (ms) i db 1 5 Power spectrum: cell c Fig. 1. Example of synchronized neuronal and local field potential (LFP) beta oscillatory activity recorded from the globus pallidus internus (GPi) of a Parkinson s disease patient (#1) during the off condition. (A) Raw data showing LFP and neuronal discharge recorded simultaneously from the two microelectrodes. Spikes and LFP activity were derived by high- and low-pass filtering the raw signals at 125 and 1 Hz, respectively. (B) LFP and neuronal discharge power spectra, obtained from the pair of recording sites, and their corresponding coherence and cross-correlation functions. (a and b) LFP power spectra obtained from the recorded LFPs. Dotted line indicates 95% confidence interval of the estimated spectrum. (c) Neuronal power spectrum. Shaded area indicates 95% confidence interval. (d f) Coherence functions for each combination. Dotted line indicates 95% confidence limit. (g i) Cross-correlograms for each combination (the LFPs were band-pass filtered between 11 and 35 Hz). Dotted line indicates the 95% confidence interval. the same microelectrode. For a more detailed description of spectral analyses refer to our previous studies (Weinberger et al., 26, 29). Locations of recording sites were determined retrospectively according to the physiological landmarks observed intraoperatively and a reconstruction of the electrode trajectory using the Schaltenbrand and Wahren stereotactic atlas (Schaltenbrand and Wahren, 1977). From these reconstructions, recording sites were approximated to be either in the dorsal or ventral part of the GPi. In order to compare the relative LFP beta power in the dorsal and ventral GPi in each PD patient, the mean power across the 1-Hz band centered on the frequency of the peak power was calculated at each recording site. The LFP power at each recording site was then expressed as the percentage of the maximum power observed in the trajectory. Finally, the percentage values were averaged in each half of the GPi to give mean percentage of LFP beta power

6 362 M. Weinberger et al. / Clinical Neurophysiology 123 (212) in the dorsal and ventral GPi. This method of measuring LFP power changes in basal ganglia nuclei was used previously (Kuhn et al., 25; Weinberger et al., 26). 3. Results We analyzed recordings from 94 GPi neurons along four tracks in the four PD patients in the off condition, and from 273 neurons along 13 tracks in the seven dystonia patients. The mean recording durations (±SD) were 45.9 ± 23.6 and 42.7 ± 27.2 s, respectively (no statistical difference). In addition, 17 neurons were recorded in track 1 of PD patient #1 when the patient was in the on condition and the findings are reported separately below. The number of neurons and microelectrode trajectories that were analyzed in each patient are given in Table Firing rates of GPi neurons The mean firing rate (±SD) of GPi neurons recorded from PD patients in the off condition was 81.9 ± 4.7 Hz. In the dystonia group, the mean firing rate was 61.2 ± 3.6 Hz and was significantly lower than that from PD patients (P 6.1, Mann Whitney rank-sum test on the medians: 58.3 and 74.6 Hz, respectively) Coherence between neuronal firing and oscillatory LFPs in PD patients In the PD patients, beta (11 3 Hz) oscillatory activity was observed in the LFPs recorded from the GPi, and there was significant coherence between pairs of LFPs in 44 out of 52 sites (85%) where LFPs were recorded simultaneously from the two microelectrodes. Fig. 2 shows reconstructions of the electrode tracks in patients #1 and #2 and the locations of the oscillatory and non-oscillatory neurons. Of the 94 neurons recorded in the PD patients, the firing of 32 (34%) neurons was significantly coherent with the simultaneously recorded LFP at the beta frequencies. The frequency of peak coherence ranged between 15 and 3 Hz with a mean of 24.5 ± 4.2 Hz. Ten of these neurons were coherent with the LFPs recorded from both microelectrodes, whereas 18 neurons were coherent only with the LFP recorded from the same electrode. The remaining four neurons were coherent only with the LFP recorded from the other electrode. Fig. 1 shows an example of spectral analysis of simultaneous recordings of neuronal firing and LFP activity, in which significant coherence in the beta range was observed. Interestingly, although 34% of the neurons displayed significant coherence with the LFP, significant oscillatory firing was only observed in 12 (14%) neurons, of which 1 were coherent with the PD patient 1 % Max beta power P =.4 Outside GPi GPi uncorrelated GPi correlated Dorsal GPi Ventral GPi PD patient % Max beta power P =<.1 Dorsal GPi Ventral GPi Fig. 2. (Left) Reconstruction of the microelectrode tracks through the globus pallidus in PD patients #1 and #2. The GPi is colored in yellow. The neurons recorded during the off condition were plotted along the tracks where dark dots represent neurons that were not oscillatory or correlated with the LFP and red dots represent neurons that were oscillatory/correlated with the LFP. Open circles represent neurons that were recorded outside the GPi. Note that for each trajectory data from the two microelectrodes were combined. Dotted line illustrates the trajectory performed during the on condition in patient #1. Dashed line illustrates the division of the GPi into dorsal and ventral halves. (Right) Box plots of the relative LFP beta power in the dorsal and ventral GPi. LFP beta power was expressed as a percentage of the maximum power observed in each trajectory. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

7 M. Weinberger et al. / Clinical Neurophysiology 123 (212) LFP (see Fig. 1B for an example). The mean frequency of neuronal oscillations was 24. ± 5.7 Hz, and this was similar to the mean frequency of coherence with the LFP (P =.95, Mann Whitney ranksum test). Coherence between simultaneously recorded neurons was observed in only 5 out of 49 pairs Distribution of coherent neurons in dorsal and ventral regions of the GPi in PD Table 2 shows the numbers of oscillatory/coherent neurons in dorsal and ventral regions of the GPi for each of the PD patients. In 3 of the 4 patients the percentage of coherent neurons was higher in the ventral GPi however only in patient #2 (Fig. 2, right panel) was this difference significant, and although the mean percentage of coherent neurons was higher ventrally (45% vs 28%) this was not significant. The beta LFP power was not significantly different between the dorsal and ventral GPi except in patient #2 (medians: 4.7 and 11.4, respectively; P 6.1, rank-sum test) Neurons are less correlated to oscillatory LFPs in dystonia patients In the dystonia group, oscillatory activity was observed in the LFPs mostly in the frequencies between 8 and 2 Hz. Of 216 sites where LFPs were recorded simultaneously from the two microelectrodes, 193 (89%) showed significant coherence across this frequency band. Examples of the coherence between the two simultaneously recorded LFPs in each of the seven dystonia patients are shown in Fig. 3. Of 273 neurons recorded in these patients, the firing activity of 29 (1.6%) was significantly coherent with the simultaneously recorded LFP. This proportion was significantly lower than that observed in PD (P 6.1, v 2 test). Of these neurons, six were coherent with the LFPs recorded from both microelectrodes (see example in Fig. 4), whereas the vast majority (n = 23) were coherent only with the LFP recorded from the same electrode. In this group of patients, the mean frequency of coherence was 18 ± 4.1 Hz, and was determined to be significantly lower than the frequencies observed in PD (P 6.1, t-test). The proportion of correlated neurons in each of the dystonia patients varied from % to 19.5% with an average of 9% per patient (Table 1). In general, these neurons were evenly distributed within the dorsal and ventral halves of the GPi. In the dorsal GPi, the activity of 14 out of the 139 neurons (1.1%) was coherent with the LFP, whereas in the ventral part, the activity of 15 of the 12 neurons (12.5%) was coherent with the LFP. Significant oscillatory activity of neuronal discharges in the 8 2 Hz band was not observed in any of the dystonia patients. Because of the relatively small number of neurons that fired coherently with the LFP in each dystonia patient, we did not compare their distribution to that of the LFP power within the GPi. Table 2 Distribution of oscillatory/coherent cells in dorsal and ventral GPi of PD patients. Patient # No. of oscillatory/ coherent cells in dorsal GPi No. of oscillatory/ coherent cells in ventral GPi Total no. of oscillatory/ coherent cells in dorsal GPi Total no. of oscillatory/ coherent cells in ventral GPi Two-tail P-values (Fisher s exact test of no. of oscillatory/coherent cells dorsal vs ventral GPi) Beta LFP power medians dorsal/ventral GPi 1 7 (37%) 6 (5%) / (5.5%) 9 (37.5%) /11.4 < (17%) 2 (1%) / (71%) 3 (5%) / Total 14 (28%) 2 (45%) / P-value (rank-sum test of beta LFP power dorsal vs ventral GPi).9 Patient 3.9 Patient 4.9 Patient Patient 6.9 Patient 7.9 Patient Fig. 3. Examples of the coherence between simultaneously recorded LFPs that were obtained from the GPi in each of the six dystonia patients. Dotted line indicates 95% confidence limit.

8 364 M. Weinberger et al. / Clinical Neurophysiology 123 (212) Fig. 4. Example of synchronized neuronal and LFP activity recorded from a dystonia patient (#5). (a and b) LFP power spectra obtained from the recorded LFPs. Dotted line indicates 95% confidence interval of the estimated spectrum. (c) Neuronal power spectrum. Shaded area indicates 95% confidence interval. (d f) Coherence functions for each combination. Dotted line indicates 95% confidence limit. (g i) Cross-correlograms for each combination. Dotted line indicates the 95% confidence interval Changes in GPi oscillatory activity during levodopa-induced dyskinesia in PD In one of the PD patients (patient #1), we had the opportunity to perform one microelectrode track through the GPi during levodopa-induced dyskinesia. In this track, the LFPs revealed oscillatory activity at frequencies around 9 and 7 Hz. In addition, all sites (n = 16) in which LFPs were recorded simultaneously from the two electrodes showed significant coherence at these frequencies, whereas beta coherence was significantly reduced (from a median value of.5.2; P 6.1, rank-sum test). Fig. 5 shows examples of power spectra of simultaneously recorded LFPs and their corresponding coherence and cross-correlation functions, as were observed in each of the two microelectrode recordings performed in this patient. Importantly, we did not find significant coherence between the recorded LFP and spiking activity during periods of limb dyskinesia. Two out of the 17 (12%) neurons that were recorded in this track, showed significant coherence with the LFPs recorded from both microelectrodes at a frequency around 9 Hz. One of these two neurons also exhibited significant oscillatory firing at this frequency (Fig. 6). Examination of the firing rates of neurons recorded during levodopa-induced dyskinesia (n = 17) revealed no significant difference from the neurons recorded during the off condition (n = 73) in the two PD patients (mean firing rate of 74.9 ± 29.8 Hz compared to 79.3 ± 36.7 Hz, respectively; P =.66, t-test). 4. Discussion 4.1. GPi firing rates The firing rates of GPi neurons were significantly higher in the PD group compared to the dystonia group. This is consistent with the predictions of the rate model of the basal ganglia for PD (Albin et al., 1989; DeLong, 199) and dystonia (Vitek, 22). However, the absence of control data limits our ability to determine whether the firing rates observed in PD/dystonia were respectively higher/ lower than normal activity in humans. In the normal monkey, the mean GPi firing rate ranges between 55 and 8 Hz (Miller and DeLong, 1987; Filion and Tremblay, 1991; Boraud et al., 1996, 1998; Starr et al., 25; Leblois et al., 26). Thus, the mean rate of 79 Hz observed in our PD patients might be close to normal. Indeed, previous studies in PD patients that were in the practicallydefined off state (i.e. after 12-h withdrawal from anti-parkinsonian medication) have reported higher GPi firing of about 9 95 Hz (Tang et al., 25, 27; Starr et al., 25), which is similar to the firing observed in the GPi of MPTP monkeys (Filion and Tremblay, 1991; Boraud et al., 1996). This suggests that the off condition of our PD patients (about 4 h after the last dose of levodopa) might not be comparable to the defined off state in which anti-parkinsonian medications are withdrawn for a longer period of time prior to the recordings. In fact, the mean firing rate recorded during the on condition (75 Hz) was not significantly

9 M. Weinberger et al. / Clinical Neurophysiology 123 (212) A db -1-2 Power spectrum: LFP1 Coherence: LFP1-LFP B db 1-1 Power spectrum: LFP Coherence: LFP1-LFP Cross-corr: LFP1-LFP2-5 5 Time (ms) db No dyskinesias Power spectrum: LFP2 2-2 Cross-corr: LFP1-LFP2-5 5 Time (ms) db During dyskinesias Power spectrum: LFP Fig. 5. Examples of synchronized LFP activity recorded from the GPi of a Parkinson s disease patient (#1) during the on and off conditions. (A) LFP power spectra, obtained from a pair of recording sites during the off condition (no-dyskinesia, during the second track), and their corresponding coherence and cross-correlation functions. (B) Power spectra obtained from LFPs that were recorded simultaneously during the on condition (levodopa-induced dyskinesia, during the first track). different than that recorded during the off condition in our two patients. Since the motor UPDRS scoring was not systematically performed in our two PD patients during the microelectrode recordings, it was not possible to directly compare their off condition to the defined off state that was assessed preoperatively (and is shown in Table 1). According to pharmacokinetic and pharmacodynamic studies, levodopa carbidopa plasma levels peak.5 2 h after its administration, when the patients best motor status is reached (Deleu et al., 22), and could take up to 6 h to wear off completely, even though motor scores may decline to baseline after approximately 3 h (Fabbrini et al., 1987). It is therefore likely that the off condition in our two patients was associated with residual levels of dopamine in the system, which might explain the relatively low firing rates observed in their GPi. Alternatively, the low firing rates might be due to the fact that these PD patients suffered from levodopa-induced dyskinesia. Some studies have reported decreased firing rate in GPi neurons in PD patients with offperiod dystonia and levodopa-induced dyskinesia (Hallett, 2; Hashimoto et al., 21). In the dystonia group, on the other hand, the mean GPi firing rate found (6 Hz) is similar to that reported previously (Vitek et al., 1999; Merello et al., 24; Starr et al., 25). It is important to note, however, that in some studies the firing rates in the GPi of dystonia patients had a higher rate of discharge with a mean of about 75 Hz (Hutchison et al., 23; Tang et al., 27), which might reflect differences in the pathology underlying the dystonia or patient to patient variations GPi LFP oscillatory activity and its relationship to neuronal discharges in PD We found prominent beta oscillatory activity in the local field potentials recorded from the GPi of both PD patients during the off condition. This study is the first to demonstrate beta oscillations in GPi LFPs recorded from the focal, high-impedance, microelectrodes, and is consistent with data obtained from the large contact DBS macroelectrodes (Brown et al., 21; Silberstein et al., 23, 25; Foffani et al., 25). In addition, we were able to reveal significant coherence between simultaneously recorded LFPs, suggesting that these beta oscillations are widely distributed and synchronized within the GPi. Our results demonstrate for the first time that in PD, the neuronal activity of about 34% of the neurons in the GPi is significantly coherent with the simultaneously recorded LFP These data further confirm that the beta LFP recorded from the basal ganglia reflects, at least in part, synchronized activity in a population of local neurons (Kuhn et al., 25; Weinberger et al., 26; Chen et al., 26b). However, although 34% of the neurons fired coherently with the beta LFP oscillations, only 12% displayed significant beta oscillatory firing. This can be attributed to the fact that coherence analysis is generally more sensitive and reveals activity that may not reach significance in the power spectrum. Thus, we cannot rule out the possibility that the coherent, non-oscillatory, neurons might exhibit weak beta oscillations that failed to reach significance. Since a tendency toward neuronal synchronization in the GPi has been demonstrated in monkeys following MPTP treatment (Nini et al., 1995; Raz et al., 2) and in PD patients (Levy et al., 22b), it is likely that the relatively small number of oscillatory neurons found in the present study is due to the medication state of the patients (discussed in Section 4.1) Modulation of GPi oscillatory activity during levodopa-induced dyskinesia Interestingly, we found that although the firing rates of GPi neurons were not significantly different between the off condition and the on condition, the oscillatory patterns were different. During periods of levodopa-induced dyskinesia, beta activity in the local field potential was largely reduced and two new peaks around 9 and 7 Hz emerged. This finding is largely consistent with previous studies. High gamma activity around 7 Hz in the GPi (and also in STN) has been shown to increase in PD patients following dopaminergic medication (Brown et al., 21) and during

10 366 M. Weinberger et al. / Clinical Neurophysiology 123 (212) db Power spectrum: LFP1 1 a Coherence: LFP1-LFP2 d Coherence: LFP1-cell1 e Cross-corr: LFP1-LFP2 1 g Time (ms) db Power spectrum: LFP2 2 4 b Coherence: LFP2-cell1 f Cross-corr: LFP1-cell1.2 h Cross-corr: LFP2-cell1 i 4 2 Power spectrum: cell1 c db Time (ms) Time (ms) Fig. 6. Example of synchronized neuronal and LFP oscillatory 1 Hz activity recorded from the GPi of a Parkinson s disease patient (#1) during levodopa-induced dyskinesia. (a and b) LFP power spectra. Dotted line indicates 95% confidence interval of the estimated spectrum. (c) Neuronal power spectrum. Shaded area indicates 95% confidence interval. (d f) Coherence functions for each combination. Dotted line indicates 95% confidence limit. (g i) Cross-correlograms between simultaneously recorded data (LFPs were band-pass filtered between 8 and 2 Hz). Dotted line indicates the 95% confidence interval. voluntary movement (Cassidy et al., 22) in concurrence with a reduction in beta activity. Our findings suggest that gamma activity is also increased during levodopa-induced dyskinesia. Furthermore, it has been showed, in two PD patients, that 8 3 Hz oscillatory pallidal LFP activity is inversely correlated to the degree of levodopa-induced dyskinesia (Silberstein et al., 25), and that enhanced LFP oscillations within, and coherence between GPi and STN at low frequencies (<1 Hz) are observed only contralateral to the side of dyskinesia (Foffani et al., 25; Alonso-Frech et al., 26). Overall, our findings support the hypothesis that Parkinsonian dyskinesia is related to an altered balance between oscillatory rhythms within the basal ganglia network rather than to a further decrease in firing rates. Such altered balance between rhythms of oscillations has been also shown in the STN during rest tremor in PD patients (Weinberger et al., 29) GPi oscillatory activity in dystonia and its comparison to PD Our spectral analysis of the local field potentials in the GPi of dystonia patients revealed oscillatory activity at frequencies between 8 and 2 Hz. These frequencies are higher than those previously reported in dystonia (Liu et al., 22, 26; Silberstein et al., 23; Chen et al., 26b; Foncke et al., 27). By using microelectrode recordings, Chen et al. (26b) showed that in dystonia patients, 3 12 Hz oscillatory LFPs are maximal in the GPi and can be synchronized with the activity of 28% of the neurons. This activity was also shown to be coherent with dystonic EMG activity in the contralateral muscles (Liu et al., 22, 26; Chen et al., 26a; Foncke et al., 27). Furthermore, it has been demonstrated by Silberstein et al. (23) that the LFPs recorded from the DBS electrodes implanted in the GPi in dystonia patients have higher power in the 4 1 Hz band and less power in the 11 3 Hz band compared to PD patients, suggesting that the two diseases are characterized by different patterns of pallidal activity. It should be noted, however, that although the percentage of LFP power in the 4 1 Hz band was higher in patients with dystonia, a discrete peak was found consistently only in the 11 3 Hz band and not in the 4 1 Hz band; it should be noted however that this observation might be due to the attenuation of frequencies below 1 Hz in our study (see below). In addition, studies that have investigated movement-related changes in LFPs recorded from the GPi of dystonia patients revealed that movements significantly decrease LFP oscillations in the range of 8 2 Hz (Liu et al., 26, 28; Brucke et al., 28), which is equivalent to the frequency range observed in our dystonia patients and suggests that this frequency range might be somewhat analogous to the beta frequency range in PD. Here, not only did we observe 8 2 Hz LFP oscillations but we also found that the firing of 1% of the neurons was correlated with the LFP at those frequencies. This percentage of correlated

11 M. Weinberger et al. / Clinical Neurophysiology 123 (212) neurons, however, was significantly lower than that observed in PD. A possible explanation is that synchronized activity in dystonia tends to not be oscillatory and thus could not be detected by our coherence analysis. Whether this is the case or not, our findings provide further evidence for the hypothesis that dopaminergic loss at the level of the STN and GP in PD may increase the sensitivity of the network to rhythmic, perhaps of cortical origin, inputs in the beta range (Magill et al., 21; Sharott et al., 25; Baufreton et al., 25; Walters et al., 27), leading to the pathological synchronized oscillations within and between these structures (Bevan et al., 26). The fact that the frequencies of oscillations were broadly similar in all seven dystonia patients might suggest that these oscillatory frequencies (8 2 Hz) are not related to a specific pathology of dystonia but are more likely associated with the manifestation of dystonic symptoms. Nevertheless, the small number of dystonia patients in this study precludes a meaningful comparison between the three types of dystonia (generalized, segmental and cervical) Methodological constraints The number of PD patients that participated in this study is limited because of the low prevalence of patients with PD for which GPi surgery is indicated. The four PD cases were offered GPi surgery due to asymmetric severe drug-induced dyskinesias in two of the patients, and STN DBS-resistant off-dystonia in the remaining two. Thus, the pathology in these patients might share some similarities to the pathology in dystonia, and this could have influenced our results. Moreover, the possibility that chronic bilateral STN DBS in one of the PD patients could have resulted in some long-term physiological changes in the basal ganglia circuits cannot be ruled out. Another limitation was that these data were not obtained after overnight withdrawal from anti-parkinsonian medication since the patients could not tolerate the defined off condition. The patients were considered to be in either on condition during levodopa-induced dyskinesias, or off condition in the absence of levodopa-induced dyskinesia and reappearance of motor signs such as rigidity, bradykinesia and tremor, but the motor state was not evaluated using a standardized method (i.e. UPDRS). Despite these limitations, these results are unique and provide interesting insights regarding the possible pathophysiology of PD and dystonia. The lowest setting of our amplifier high-pass filter was 1 Hz and thus lower frequencies were attenuated. This has resulted in lower magnitudes of the power of the frequencies below 1 Hz (e.g. in the power spectra illustrated in the figures) and may also have reduced possible coherence in this range. However, we feel that this is unlikely to have confounded our main conclusions which were focused on oscillatory activity in the 8 2 Hz range. The method would have been sensitive enough to reveal oscillatory power and coherence around 5 Hz (where attenuation was about 5%). This study does not and cannot address oscillatory activity below 5 Hz in these patients. 5. Conclusion This study shows, for the first time, that the oscillatory LFPs recorded from the GPi in PD patients are closely associated with both significant and subthreshold beta oscillatory activity in populations of GPi neurons. In dystonia, the firing rates of GPi neurons and the frequencies of LFP oscillations are significantly lower than in PD, and the oscillatory LFPs are not as closely related to the neuronal firing. 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13 Experimental Neurology 217 (29) Contents lists available at ScienceDirect Experimental Neurology journal homepage: Enhanced synchronization of thalamic theta band local field potentials in patients with essential tremor Abdoul Kane a, William D. Hutchison a,b,c, Mojgan Hodaie b,c, Andres M. Lozano b,c, Jonathan O. Dostrovsky a,b, a Department of Physiology, University of Toronto, Toronto, ON, Canada b Toronto Western Research Inst., Toronto, ON, Canada c Toronto Western Hospital, Division of Neurosurgery, University of Toronto, Toronto, ON, Canada article info abstract Article history: Received 16 August 28 Revised 5 January 29 Accepted 4 February 29 Available online 2 February 29 Keywords: Tremor Thalamus Local field potentials Theta activity Synchronization Local field potentials (LFPs) were recorded in 13 patients from pairs of microelectrodes driven through thalamus during functional localization prior to implantation of a thalamic deep brain stimulation electrode for treatment of tremor or pain. Six patients had a history of essential tremor (ET), 3 of multiple sclerosis, and the remaining 4 had symptoms of chronic pain. Specific to the ET group was the observation that oscillatory field potentials recorded from the two microelectrodes in the motor thalamus (ventralis intermedius Vim, ventralis oralis posterior Vop) were highly coherent at frequencies characteristic of pathological tremor (4 7 Hz). This stands in contrast to the significantly more desynchronized state observed in the somatosensory thalamus (ventralis caudalis Vc) for that frequency band. In addition, higher frequency coherent oscillations typically associated with physiological tremor (8 12 Hz) were observed in the ET patients in motor thalamus and Vc and in motor thalamus of pain patients. An examination of the interfrequency correlation of the LFPs in Vim and Vop showed that the low frequency theta waves correlated with high frequency oscillations in the beta and gamma ranges. These findings are consistent with and extend those of other studies suggesting that alterations in thalamic oscillatory activity are involved in the pathophysiology of ET and furthermore suggest that increased synchronization in the 4 7 Hz range is related to the occurrence of tremor in the ET patient group. Furthermore, they support the idea that therapies such as lesions and high frequency stimulation of the motor thalamus are effective in reducing tremor symptoms since they destroy the abnormal low frequency synchronization in motor thalamus. 29 Elsevier Inc. All rights reserved. Introduction In the past few years it has become increasingly acknowledged that large scale oscillatory activity plays an important role in basic brain function (Boraud et al. 25; Buzsaki, 24; Engel et al., 1992; Von der Malsburg, 1995). Recent studies have also shown that several neurological disorders such as epilepsy, depression, pain, and dystonia are associated with significant changes in network patterns of activity (Eidelberg et al., 1998; Pujol et al., 2; Spencer, 22; Stone et al., 28), and also suggest that therapies such as deep brain stimulation (DBS) work by disrupting those pathological network patterns (Dostrovsky and Bergman, 24). Essential tremor is a movement disorder characterized by postural and action tremor with a frequency of 4 12 Hz. Although deep brain stimulation of the motor thalamus significantly improves those Corresponding author. Department of Physiology, Med Sci Bldg 332, 1 King's College Circle, University of Toronto, Toronto, ON, Canada M5S 1A8. Fax: address: j.dostrovsky@utoronto.ca (J.O. Dostrovsky). symptoms, the pathophysiology of ET remains unclear. The direct involvement of thalamic nuclei has been hypothesized since tremor cells are commonly found in the ventral intermediate nucleus (cerebellar receiving area, Vim) and, to a lesser extent, in the ventral caudal (somatosensory, Vc) and ventral oral posterior nuclei (pallidal receiving area, Vop) during in tremor in Parkinson's disease and other types of tremor (Brodkey et al., 24; Guiot et al., 1962; Hua and Lenz, 25; Lenz et al., 1988b, 1994) for patients with tremor symptoms. This was reaffirmed in a recent paper by Marsden et al. (2) where coherence was found between thalamic local field potentials (LFPs), cortical EEG and EMG signals during tremor activity. Some hints to the causes of ET have also been obtained through functional imaging experiments and neuronal recordings which implicate an overactivity of the cerebello-thalamocortical system (Molnar et al., 25; Boecker and Brooks, 1998). In this study, we investigated the spectral characteristics of LFPs in Vop, Vim, and Vc nuclei for a group of ET patients by assessing the coherence between LFPs recorded from two closely spaced ( 1 mm) microelectrodes. By comparing those characteristics to those observed in multiple sclerosis patients being treated for their tremor and in /$ see front matter 29 Elsevier Inc. All rights reserved. doi:1.116/j.expneurol

14 172 A. Kane et al. / Experimental Neurology 217 (29) chronic pain patients, we were able to assess their possible involvement in the pathophysiology of ET. The results suggest that exaggerated spatio-temporal coherence in the theta frequency band characterizes network activity in Vim and Vop for ET patients. Methods Patients We studied 6 patients with essential tremor (ET), 3 patients with tremor secondary to multiple sclerosis (MS), and 4 patients with pain, who were undergoing intraoperative microelectrode recordings in thalamus in order to obtain physiological data for localizing the target for DBS electrode placement. The study was approved by the University Health Network Ethical Review Board, University of Toronto. Patients gave written and informed consent prior to surgery. Surgical procedure and targeting The localization and mapping procedures in the thalamus are described in detail in previous publications (e.g. see Tasker et al., 1999; Lenz et al., 1988a). Briefly, the coordinates of the initial electrode trajectory were inferred from imaging-based identification of the locations of the anterior and posterior commissures which were then used to obtain appropriately scaled maps of the thalamus based on the standard atlas map of Schaltenbrand and Wharen (1977). Subsequent electrode tracks, when necessary, were guided by the physiological findings obtained from the first track. Neuronal recordings were usually made along several para-sagittal trajectories traversing respectively Vop, Vim, and Vc (see Fig. 1). The localization of those subnuclei was further validated by the functional characteristics of cells encountered during the recording and responses to microstimulation. The border of Vim and Vc could be easily determined by the presence in Vc but not in Vim of neurons responding to low intensity tactile stimuli and stimulation evoked parasthesia at low stimulation intensities. Vop and Vim were differentiated on the basis of neuronal responses to active and passive movements, firing rates, tremor reduction to microstimulation and reconstructions of the electrode tracks with the physiological findings on the scaled atlas maps (see Molnar et al., 25 for further details). Recordings Neuronal spike and LFP activity were recorded along with wrist flexor/extensor electromyography and accelerometer signals. The neuronal data were acquired with two independently driven microelectrodes (25 μm tip length, axes 6 μm apart,.1 MΩ impedance at 1 Hz) during the electrophysiological mapping procedure (see Levy et al., 27 and Weinberger et al., 26 for details). All recordings were amplified 5 1 times, band-pass filtered at 1 5 Hz (analog Butterworth filters; attenuation of about 5% at 5 Hz) using two Guideline System GS3 (Axon Instruments, Foster City, CA) amplifiers. The signals were digitized at 1 khz with a CED 141 using Spike2 software (Cambridge Electronic Designs, Cambridge, UK). Twenty second periods of recordings obtained simultaneously from pairs of electrodes in thalamus during periods of rest, without visible resting tremor, were selected for analysis. At each site analyzed the depths of the two electrode tips were about the same so that the distance between recording sites was about 1 mm. These recording sites were identified by their position along the electrode track (distance above or below the initial predicted target in thalamus). The LFP and in some cases also the spike event times data were then imported into MATLAB (version 7, The MathWorks, Natick, MA) for further analysis. Data analysis Standard Fourier transform techniques (Halliday et al., 1995) were used for the spectral analysis. Briefly, after the signals were downsampled to 1 khz and low-pass filtered at 8 Hz, spectra of LFP power were estimated by dividing the waveform signal into a number of sections of equal duration of 1.24 s (124 data points, 512 point overlap), each section was windowed (Hanning window) and the magnitude of the 124 discrete Fourier transform of each section were squared and averaged to form the power spectrum, yielding a frequency resolution of.97 Hz. Coherence analyses (Halliday et al., 1995; Jarvis and Mitra, 21; Rosenberg et al., 1989) were used to assess the relationship between simultaneously recorded signals from each of the two electrodes and also between microelectrode LFPs and the accelerometer signal. The coherence function provides a frequency domain bounded measure of association, taking on values between and 1, with in the case of independence and 1 in the case of a perfect linear relationship. Specifically, the coherence between two recordings, x t and y t, is defined by C = jf xtyt j2 jf xtxt jjf ytyt j Fig. 1. Sagittal brain map showing the electrode track traversing the Vim and Vc regions of thalamus. where f xtyt, f xtxt and f ytyt respectively describe the cross spectrum and autospectra of x t and y t. Peaks were considered significant if they exceeded the 95% confidence level, which is set equal to 1 (.5) L 1, where L is the number of disjoint sections used to estimate the spectrum (Halliday et al., 1995). Correlation interactions between different frequency components of the LFPs were investigated, as in Llinas et al. (1999), by estimation of a correlation function K defined by K(ω 1,ω 2 )=bp(ω 1 )P(ω 2 )N, where P is the mean-adjusted power at a given frequency and the brackets represent an averaging over multiple epochs (obtained by partitioning of each 2 s recording sample into non overlapping 5 ms data epochs). The spectrum in each epoch was estimated by application of the multitaper method (Percival and Walden, 1993), which allows for reduced bias in small size time series. Average spectra (mean inter-frequency correlation spectrum, mean coherence spectrum) for field potentials grouped by location (Vop, Vim, Vc) were computed for each patient type.

15 A. Kane et al. / Experimental Neurology 217 (29) Results A total of 121 pairs of recordings were collected from the 6 ET patients, 6 pairs from the 3 MS patients, and 5 pairs from the 4 pain patients. The individual autospectra of the LFP recordings from each of the electrodes as well as the coherence between them were calculated. Fig. 2 shows a typical example of LFP recordings and their power spectra and the coherence between them for a pair of recordings in the Vim of an ET patient. The autospectra of thalamic field potentials and the coherence between those signals are shown in Fig. 2B. Although LFP activity and accelerometer data were coherent over frequencies typically associated with tremor activity, this is not immediately apparent in the autospectra. However, the coherence plot of the recordings from the two microelectrodes clearly reveals the presence of tremor-related oscillations in Vim. In order to quantitatively evaluate whether there were consistent differences in the magnitudes of oscillation at different frequencies in the different thalamic nuclei (pallidal, cerebellar, or somatosensory receiving nuclei) and between patient groups we calculated the mean coherence between pairs of recordings in each region and each patient group. In the ET group the mean coherence was computed for 3 pairs of recordings in Vop, 7 pairs in Vim, and 35 pairs in Vc and averaged. Fig. 3 shows the average coherence spectra for all pairs of recordings in Vop, Vim, and Vc in the ET patients. The dotted lines indicate the one standard error away from the mean. Fig. 3. Plot of the average coherence between the two recording electrodes as a function of frequency. Coherence was computed for each pair of recordings from Vop (3 pairs), Vim (6 pairs), and Vc (31 pairs), for all 6 ET patients, and averaged. The dotted lines indicate the one standard error away from the mean. The horizontal dotted line indicates the level above which the coherence was significantly different from baseline (pb.5). Fig. 2. Example showing raw LFP and accelerometer recordings and their power spectrums and the coherence between them in a patient with essential tremor. (A) Top two traces show the raw unfiltered local field potentials obtained from a pair of microelectrodes in Vim and the bottom trace is the accelerometer recording of hand movements. (B) The left column shows the autospectra of the three recordings shown in A (for a 2 second period). The second column shows the coherence spectra between accelerometer and LFP1 and LFP2 respectively, and between LFP1 and LFP2.

16 174 A. Kane et al. / Experimental Neurology 217 (29) absence of 5 1 Hz activity in Vc. Fig. 3 also shows a strong trend for coherent oscillatory activity in the beta range (2 35 Hz) in all three nuclei in ET patients although the coherence did not reach statistical significance. In order to assess the significance of this synchronization with respect to the pathophysiology of ET we compared the ET findings with those obtained by similar spectral coherence analysis from control subjects (patients who were diagnosed with pain, or MS). Fig. 4 shows the coherence plots for each patient group in each of the three regions (there were no recordings from Vop in the pain group). The most pronounced difference between ET and the other groups was the significantly elevated coherence in the 5 to 15 Hz band in Vim and 1 to 15 Hz band in Vc. In order to determine whether there was a relationship between the activity in different frequency bands in Vim and Vop in the 6 ET patients, we performed an inter-frequency correlation anlaysis of the LFPs. As can be seen in Fig. 5, activity in the low frequency theta waves was correlated with high frequency oscillatory activity in the beta and gamma range up to about 6 Hz, especially in Vop. Discussion Fig. 4. Comparison of the average coherence spectra for the ET, MS, and pain patients. (A) Mean coherence for pairs of recordings from the pallidal receiving area of motor thalamus Vop. The number of pairs of recordings included in the averages are ET-3, MS-16. (B) Mean coherence for pairs of recordings from the cerebellar receiving area of motor thalamus Vim (ET-6, MS-25, pain 22). (C) Mean coherence for pairs of recordings from the ventrocaudal nucleus Vc. (ET-31, MS-19, pain 28). Especially noticeable is the presence of low frequency theta and alpha (4 7 Hz, 8 12 Hz) band oscillatory activity in Vim and Vop. This stands in contrast to the marked 1 12 Hz activity and virtual This study has revealed on the basis of spectral coherence analysis that there are marked differences in the oscillatory activity between Vop, Vim and Vc and furthermore that there are differences in the patterns of these activities between different patient groups. Of particular interest was the finding that in ET patients there was a marked elevation in the power of oscillations in the 5 to 15 Hz range in Vop and Vim suggesting the involvement of these regions in the etiology of ET. This study utilized coherence analysis of the signals recorded from two microelectrodes whose tips were approximately 1 mm apart. We believe that this is the first report of the use of this type of analysis for investigating oscillatory LFP activity and from our experience it provides a more sensitive and reliable measure of low frequency LFP oscillatory activity than spectral analysis of the recordings from a single electrode does. For frequencies lower than 5 Hz, we have consistently observed that significant coherence between the two recording electrodes can reliably detect spatially extended oscillations. It also gives valuable information about the origins of the recorded field potentials (locally generated or otherwise), and at the same time provides a measure of their spatial homogeneity. Fig. 5. Plots of the inter-frequency relationships in Vim and Vop in ET patients. The thalamic LFP power correlation patterns in Vim (average of 12 recording samples) are shown in A and those in Vop (6 recording samples) in B.

17 A. Kane et al. / Experimental Neurology 217 (29) Theta rhythmicity Thalamic theta waves are commonly reported in normal adult individuals during certain stages of sleep. Several studies have also emphasized their prevalence in awake subjects affected by neurological disorders such as pain (Llinas and Ribary, 21; Llinas et al., 21; Marsdenet al.,2), or Parkinson's disease (Sarnthein and Jeanmonod, 27). This study suggests that the involvement of theta rhythmicities in the pathophysiology of ET has to do with the strength of their coherence in Vim and Vop. The relationship of theta waves to clinical tremor is rather intriguing. It is possible that those low frequency waves drive spatially and functionally segregated groups of thalamic cells into the slow firing patterns that are often correlated to tremor activity (Guiot et al., 1962; Hua and Lenz, 25). It is also possible that symptoms arise from an abnormal cross frequency modulation (Fig. 5), in which 4 7 Hz thalamic oscillations would influence the generation of pathological beta or gamma activity (Llinas et al., 1999). The mechanisms underlying the emergence of theta rhythms in motor thalamus are unclear. Even the origin of recorded field potentials is difficult to ascertain. Unlike some laminar structures, where LFPs are believed to arise essentially from contributions by EPSPs and dendritic action potentials (Mitzdorf, 1985), thalamic motor nuclei exhibit a complex non-laminated structure (Ilinsky and Kultas-Ilinsky, 22; Percheron et al., 1996). Thalamocortical (TC) cells there receive inputs from a variety of afferents including spinothalamic cells (excitatory or inhibitory), corticothalamic neurons (excitatory), local circuit neurons (inhibitory), GABAergic neurons from the thalamic reticular nucleus and the globus palllidus, and excitatory cells from cerebellar nuclei. This is further complicated by redundancies and interactions in the connectivity patterns. The high coherence in the theta range observed in the ET patients was unlikely to have resulted from oscillatory neuronal firing (tremor cells) and reafferent activity. The recordings included in this study were from periods without visible resting tremor. Although Vim neurons do sometimes have oscillatory activity during periods when there is no tremor, this is not usually common. Even during tremor only some of the cells fire in an oscillatory pattern and not all of these are coherent with the tremor. For example, in the study of Hua et al. (1998) in ET patients during tremor only 26 of 73 of the neurons had tremor-related activity, only 37% had sensory responses and of those only 33% were related to tremor. Similarly, in a different study, Marsden et al. (2) reported coherence between Vim LFPs and EMG during tremor in only one of three ET patients examined. Furthermore, oscillatory firing of neurons does not necessarily lead to oscillatory local field potentials and oscillatory neuronal firing is not necessarily synchronous with the LFP. For example, we have observed many cases of neurons in the subthalamic nucleus whose firing is oscillatory but not coherent with the LFP (e.g. see Weinberger et al. 26). These observations also indicate that the LFPs are not contaminated by the unit activity and moreover suggest that the LFPs recorded from the microelectrode are much more likely to reflect synchronized inputs to the neurons (e.g. dendritic membrane oscillations) rather than axonal spiking activity. It is important to note also that in the current study the low frequency activity in the LFP was observed even in the absence of simultaneously recorded unit activity. Furthermore, we only very rarely found occurrences of synchronization between units simultaneously recorded from the two electrodes and we rarely found samples for which LFP and unit activity recorded from the same electrode had significant linear correlation over the theta band. Slow thalamocortical rhythms have been extensively investigated, especially in regards to their relationship to sleep. It is currently believed that sleep spindles (4 14 Hz) essentially emerge from the interactions between thalamic reticular neurons and thalamocortical relay cells, are synchronized in large scale assemblies by corticothalamic feedback to the thalamic reticular nucleus, and are terminated by barrages of depolarizing inputs from cerebellum and cortex (Andersen and Andersson, 1968; Bazhenov et al., 2). Although the theta rhythms we report do not have the characteristic features of spindle waves they may share similar generating mechanisms. Their persistence may be symptomatic of a dysfunction in the way the various thalamic afferents interact. Indeed, many of the results obtained over the last decade suggest that ET is accompanied by an increase in the excitatory input from cerebellum to thalamus (Boecker and Brooks, 1998). Somatotopic and biophysical changes in the motor thalamus have been reported as well (Kiss et al., 23; Molnar et al., 25). At present, high frequency stimulation of Vim has essentially replaced thalamic lesion surgeries as a therapy for otherwise intractable tremor. The mechanisms by which both techniques achieve their spectacular effect on tremor remain unknown (McIntyre et al., 24; Miocinovic et al., 26; Vitek, 22) although recent evidence suggests that DBS works by disrupting pathological network synchronization and preventing the emergence of aberrant low frequency oscillations (Miocinovic et al., 26). The observations reported in this paper speak for a role of stimulation and lesion therapy involving the disruption of pathological spatio-temporal synchrony. Coherence over the 8 12 Hz band In patients with essential tremor, Marsden et al. (2) have previously shown that thalamic (Vim) field potentials and cortical EEGs were coherent over the 8 12 Hz frequency band. Schnitzler et al. (26), using MEG recordings, have also reported similar activity in the thalamus of healthy subjects. Both studies have related those oscillations to physiological tremor. The intra thalamic coherence (in Vim, Vop, Vc) over the 8 12 Hz band featured prominently in most of the samples analyzed in the present paper. It is also remarkable that such coherence was, on average, greater in ET patients compared to MS and pain patients. One possible interpretation is that the same alterations in ET patients that lead to synchrony over pathological tremor frequencies also affect the extent of synchronization over the 8 12 Hz band. The analysis of inter-frequency relationships has previously been used by Llinas and coworkers for analysis of cortical MEG signals in different patient groups and related to thalamic dysfunction. The interactions such as we observed are believed to be indicative of pathology given that low and high frequency oscillations correspond to different thalamocortical functional states (Llinas et al., 1999). Thus the findings of this study showing elevated theta and alpha power in motor thalamus and apparently abnormal inter-frequency relationships provide further evidence that the symptoms in ET patients are likely to be due to alterations in thalamic function which are manifest at least in part by abnormalities in oscillatory patterns. Acknowledgments We wish to thank all the patients who participated in this study and Yu Yan Poon for her help with acquiring clinical data. The work was supported by the Canadian Institutes of Health Research grant to JOD (MOP-4255). AML holds a Tier 1 Canada Research Chair in Neurosciences. References Andersen, P., Andersson, S.A., Physiological Basis of the Alpha Rhythm. Appleton- Century-Crofts, New-York. Bazhenov, M., Timofeev, I., Steriade, M., Sejnowski, T., 2. Spiking-bursting activity in the thalamic reticular nucleus initiates sequences of spindle oscillations in thalamic networks. J. Neurophysiol. 84, Boecker, H., Brooks, D.J., Functional imaging of tremor. Mov. Disord. 13, Boraud, T., Brown, P., Goldberg, J.A., Graybiel, A.M., Magill, P.J., 25. Oscillations in the basal ganglia, the good, the bad and the unexpected. In: Bolam, JP, Ingham, CA, Magill, PJ (Eds.), The Basal Ganglia VIII. Springer Science and Business Media, New York, pp

18 176 A. Kane et al. / Experimental Neurology 217 (29) Brodkey, J.A., Tasker, R.R., Hamani, C., McAndrews, M.P., Dostrovsky, J.O., Lozano, A.M., 24. Tremor cells in the human thalamus: differences among neurological disorders. J. Neurosurg. 11 (1), Buzsaki, G., 24. Large-scale recording of neuronal ensembles. Nat. Neurosci. 7, Dostrovsky, J., Bergman, H., 24. Oscillatory activity in the basal ganglia; relationship to normal physiology and pathophysiology. Brain 127, Eidelberg, D., Moeller, J.R., Antonini, A., Kazumata, K., Nakamura, T., Dhawan, V., Spetsieris, P., deleon, D., Bressman, S.B., Fahn, S., Functional brain networks in DYT1 dystonia. Ann. Neurol. 44, Engel, A.K., Konig, P., Kreiter, A.K., Schillen, T.B., Singer, W., Temporal coding in the visual-cortex new vistas on integration in the nervous-system. 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19 Candidate reference letter. RE: BASA, DIELOR Dear Admissions Committee, It is my pleasure to write a strong letter of recommendation for Mr. Dielor Basa to support his application to the Master of Science program in the Department of Physiology at the University of Toronto. I have known Dielor since February 213 and most recently I have interacted with Dielor on a regular basis in my own neurosurgical operating room. As a stereotactic and functional neurosurgeon, deep brain stimulation (DBS) for movement disorders is one of my sub-specialty interests. For these operations Dr. William Hutchinson s neurophysiology team performs microelectrotode recordings (MERs) in the human brain of our patients during surgery. These MERs help to optimize final positioning of a DBS electrode in the appropriate nucleus of the brain. Given that the patient is often awake during these procedures, this type of surgery also provides a unique opportunity to ask fundamental neurophysiological research questions in the operating theatre. Dr Hutchison and his team are leaders in the field in this regard as evidenced by the high impact journals in which his team publishes in. Dielor has been a key member of this neurophysiology team and has attended all of my movement disorders DBS operations since Fall of 213. In this capacity he has demonstrated impressive progression towards independence in a very short time which bodes well for his research training. Dielor is now fully capable of performing MERs in human subjects without his supervisor present and I have felt comfortable with him taking the lead on this even if Dr. Hutchinson is away. This is an impressive accomplishment in relatively short order. Important qualities that would make an excellent graduate student that Dielor has demonstrated clearly to me are as follows: i) he picks up new techniques very quickly ii) his troubleshooting skills when the recordings or the experiments are not going forward as planned are excellent iii) he demonstrates a keen eye for observation when analyzing electrophysiological data in the operating room. iv) perhaps most importantly he is not one to give up in the face of technical challenges that may arise. I also note that in the time he has been working with Dr. Hutchinson s team he has already developed a project on thalamic oscillations in Parkinsons disease and essential tremor and prepared a paper for submission in this regard. These attributes, in addition to his professionalism in all of his interactions, put Dielor in a fine position to not only succeed in graduate school but I am confident that he will excel. Therefore I strongly support the application of Dielor Basha for the Master s level program in Physiology at the University of Toronto. ---

20 Suneil K. Kalia MD PhD FRCSC Staff Neurosurgeon - Toronto Western Hospital Scientist - Toronto Western Research Institute Assistant Professor - University of Toronto West Wing 4-428, Toronto Western Hospital 399 Bathurst Street. Toronto, ON. M5T 2S8 phone: fax: suneil.kalia@uhn.ca

21 ietf #;itl;ii*,., CONFLICT OF INTEREST QUESTIONNAIRE Giant Proposal Submissions ir lf?f h-^-l ^J h:-^^l^-a '^, pursuant to the purposes and interests of the policy adopted by the IETF Board of Directors requiring disclosure of certain interests, a copy of which has been furnished to me, I hereby state that I or relatives have the following affiliations or interests and have taken part in the following transactions which, when considered in conjunction with my relationship to the lnternational EssentialTremor Foundation (IETF) might create or be a.conflict of interest. (Write "none" where applicable). 1.,Advisory Board or Panel Affiliation Nots 5 2. Consulting, Speakers Bureau or Contractual Services Nort e 3, Research/Grant Support [r] orrg 4. Financial or Material Support g not otheruvise listed. NoN I hereby acknowledge the information given is complete and accurate to the best of my knowledge and beiief. I understand that failure to Jccurately disclose a potential interest may cause revocation of my grant award if approved, I also understand that if any of the above circumstances change, that I am to complete a new questionnaire.,r : $ignature GrantConflic ,po Box 145 Lenexa, Kansas oos I usa 888,387,3667 (tollfree) (local)l essentialtremor.org

22 IETF - WD Hutchison Feb 214 requesting $25, Figure 1. High frequency microstimulation in the motor thalamus of an ET patient produces tremor arrest and abolishes the tremor rhythm of the motor thalamic neuron.

23 IETF - WD Hutchison Feb 214 requesting $25, Figure 2. Spike power spectrogram showing the modulation of beta-oscillatory spikes during non-tremulous movement and during a tremor episode. Beta activity is sustained while the patient is at rest but desynchronized with passive flexion-extension of the related limb. The beta desynchronization occurs at the onset of movement and not prior to it. The bottom trace shows the spontaneous emergence of tremor in a PD patient and the concomitant replacement of the beta rhythm with tremor frequency.

24 IETF - WD Hutchison Feb 214 requesting $25, Figure 3. Theta-burst (5Hz) microstimulation of the motor thalamus induces peripheral tremor in an ET patient.

25 IETF - WD Hutchison Feb 214 requesting $25, Figure 4. A demonstration of intrinsic 5Hz oscillations in the motor thalamus in an ET patient. Stimulation with a single burst results in a spontaneous rebound burst at 5.8 Hz. Stimulation with 5 Hz theta burst entrains the neuron to a 5Hz rhythm.

26 IETF - WD Hutchison Feb 214 requesting $25, Figure 5. Stimulation parameters for high frequency stimulation, theta burst and beta burst.