EMG recordings, preprocessing and subsequent analyses Rationale: It could be argued that trem or in the PD group may alter functional

connectivity within the motor system and thereby provide a trivial source of differences between PD patients and control subjects. To control for this factor, muscle activity in the most-affected arm (sampled with electromyography; EMG) was measured during MR-scanning in all 41 PD patients and in a subgroup of 23 out of 36 controls. We used this signal to reveal brain regions involved in tremor generation, and to remove - through multiple regression - tremor-related variance from the data. M ethods: We used a pair of carbon wired MRI compatible sintered silver/silver- chloride electrodes (Easycap, Herrsching-Breitbrunn, Germany), placed 3 cm apart along the muscle bellies of the forearm muscles (flexor or extensor, depending on the tremor characteristics). A neutral electrode was placed on the head of the ulna. Following amplification and A/D conversion (Brain Products GmBH, Gilching, Germany), an optical cable fed the EMG signal to a dedicated PC outside the MR room for further off-line analysis. A hardware filter with 250-Hz low-pass filter and a 10 s tim e constant was applied before amplification of the signals, after which the EMG was digitized at 5000 Hz. MR artifact correction followed the method described by (Allen et al., 2000;van Duinen et al., 2005). After MR artifact correction the data was down-sampled to 1000 Hz, band-pass filtered (allowing frequencies between 25 and 250 Hz, to remove possible movement artifacts) and rectified to enhance the information on EMG burst-frequency (tremor) of the signal, thereby recovering the low frequency EMG content (Myers et al., 2003). Subsequent analyses were performed in Matlab (MathWorks, Natick, MA), consisting of a time-frequency analysis on the preprocessed EMG data using the FieldTrip toolbox for EEG/MEG analysis (http:// www.ru.nl/neuroim aging/fieldtrip). For each segment, we calculated the EMG power between 0.5 - 20 Hz in steps of 0.1 s using 2 s Hanning tapered windows, which resulted in a 0.5 Hz resolution. By averaging over all tim e-points we obtained an average power spectrum across segments. The peak frequency between 2 and 8 Hz. (i.e. the frequency corresponding to the parkinsonian tremor) was determined for each individual patient after visual inspection of the average power spectrum. Then, EMG power at the individual trem or frequency was extracted. This lead to a regressor of 265 values (one for each scan) describing the scan-by-scan fluctuations in EMG power at the tremor frequency. We also calculated the EMG amplitude (by taking the square root of the EMG power) and we log-transformed the EMG power to remove outliers, leading to 3 tremor-related EMG regressors (power, amplitude and log of power). Last, we applied a z-transformation to each of these three regressors and convolved them with the hemodynamic response function (hrf) as implemented in SPM5 (http://www.fil.ion.ucl.ac.uk/spm). In the 41 PD patients, these three regressors

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were added to the first-level multiple regression model, effectively accounting for and removing tremor-related variance from the BOLD signal. This allowed us to investigate cortico-striatal connectivity, while controlling for tremor-related BOLD fluctuations. Given the lack of any activity in the rectified EMG signal between 2 and 8 Hz, we did not include these regressors to the first-level model in the controls.

To test whether this analysis was sufficiently sensitive to account for tremor-related effects, we assessed the variance uniquely explained by the tremor-related BOLD fluctuations (using only one regressor describing the changes in power of the rectified EMG during scanning) in the context of a multiple regression analysis that considered the same nuisance variables described above. Each patient-specific contrast image describing the tremor-related effects was entered into a one-sample t-test (random effects analysis). To test for consistent effects contralateral to the affected body side across the whole group, the 13 patients who were left-side affected (the other 28 patients were right-side affected), had their contrast images flipped along the axial plane. Given their known involvement in motor execution, we focused our analyses on the primary motor cortex (MNI coordinates [-36 -23 49]) contralateral to the most-affected side, and on the cerebellum (MNI coordinates [20 -53 -19]) ipsilateral to the most-affected side (Hanakawa et al., 2003). Previous work in tremor-dominant PD, using methods similar to the current study, has shown that (only) these tw o regions specifically decreased their activity in response to deep brain stimulation of the contralateral ventral intermediate (Vim) nucleus of the thalamus, which diminished trem or amplitude (Fukuda et al., 2004). In addition, within the contralateral motor cortex, the reduction in brain activity correlated directly with the reduction in tremor amplitude. Accordingly, we corrected for multiple comparisons [FDR-corrected at p<0.05; (Genovese et al., 2002)] in tw o spherical volumes of interest (radius of 10 mm) centered at those coordinates.

Results: During fMRI scanning we were able to reliably quantify the parkinsonian tremor: EMG traces showed a clear peak at 4.5 Hz. across patients, a peak that was absent in the controls (Suppl. Fig. 1). This finding is consistent with previous EMG studies performed outside the fMRI scanner (Findley et al., 1981 ;O'Suilleabhain and Matsumoto, 1998). Variations in right-lateralized hand trem or amplitude accounted for a significant amount of variance in tw o portions of the m otor system directly related to movement execution: the hand area of the left primary m otor cortex (BA 4; [-36 -24 56], t(41)=3.91, p=0.014 FDR-corrected) and the right cerebellar vermis ([12 -54 -22], t(41)=3.52, p=0.015 FDR-corrected). Importantly, these tw o regions showed an almost complete overlap with areas that were equally correlated with the posterior putamen for both PD patients and controls (Suppl. Fig. 1). Conversely, these tw o regions were spatially separated from areas that were differentially correlated with the posterior putamen for patients and controls. This finding underscores the validity of our

Al tere d c or tic o-stri ata l c o n n e c t iv it y in PD | 53

o 5 10 15 20

Frequency(Hz)

S u p p l. Fig 1 Trem or-related brain activity

A) Plot of rectified EMG power (y-axis) as a function of frequency (x-axis), averaged across 41 PD patients (in red) and 23 matched controls (in blue). PD patients showed a clear peak of EMG activity around 4.5 Hz. (tremor-frequency), which was absent in the controls. B-C) Maps of cerebral activity related to variations in EMG-power (at the peak tremor- frequency) in 41 PD patients. The image represents an SPM t-map (thresholded at p<0.01 uncorrected, overlaid on anatomical images of a representative subject from the MNI series) describing tremor-related activity in the 41 PD patients. D-E) Tremor-related cerebral activity (in orange, same as in B-C) is shown together with connectivity maps for the posterior putamen (in red, equal connectivity between PD and controls, same as in Fig. 2A; in cyan, decreased connectivity for PD patients compared to controls, same as in Fig. 3A). It can be clearly seen that there was no overlap between brain regions showing tremor-related cerebral activity (in orange) and brain regions showing differential connectivity with the posterior putamen in PD (in cyan). Conversely, brain regions with tremor-related brain activity (in orange) were a subset of the regions with equal posterior putamen connectivity across patients and controls (in red).

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procedure to isolate tremor-related brain activity, and it further indicates that between-groups differences are unlikely to be caused by the presence of tremor.

2. Anatomical analyses (voxel based morphometry)

Rationale: It could be argued that differences in functional connectivity across groups were caused by anatomical changes in the PD group. Therefore, we performed a voxel based m orphom etry (VBM) analysis on the 77 subjects considered in this study. This VBM analysis tested whether cortical gray matter volume differs between PD patients and matched healthy controls.

M ethods: VBM analyses were done in SPM8. We segmented the anatomical MRI scan of each subject into gray matter, white matter, cerebrospinal fluid, and extra-cerebral compartments (e.g. out-of-brain, skull, skin). We used the DARTEL toolbox (Ashburner, 2007) to create a group-specific anatomical template and register all individual gray matter images to this template. All images were subsequently normalized to MNI space, while correcting for volume changes induced by normalization. Last, we smoothed all gray matter images using a kernel of 8 mm FWHM, and we performed a two-sample t-test on these smoothed images, directly comparing PD patients with controls. We also included age, gender, as well as total gray and white matter as covariates, since these factors have been shown to have a great impact on gray matter volume (Good et al., 2001).

Results: There were no significant differences in gray matter volume across groups (Suppl. Fig. 2), even when lowering the statistical threshold to a lenient threshold of p<0.001 uncorrected, and even when restricting our search to a particular region of interest (i.e. those supra-threshold voxels identified in the contrasts "posterior putamen: controls >PD”, and "anterior putamen: PD>controls” - see main body of the report).

We conclude that gray matter changes do not explain the pattern of results that we found. The results of this VBM analysis are consistent with previous work that also failed to show gray matter abnormalities in PD patients (Feldmann et al., 2008;Price et al., 2004)), but see (Burton et al., 2004).