Posterior Parietal Cortex

We defined six bilaterally defined regions of interest to cover the main anatomical areas that showed encoding for sequences in general (Figure 6A). Using probabilistic cytoarchitectonic maps (Fischl et al., 2008 (link)), only surface nodes that belonged to Brodman area (BA) 4 with maximal probability were included into the M1 ROI. To exclude mouth and leg representations, we further excluded all nodes that had a distance of more then 2.5 cm from the hand knob (Yousry et al., 1997 (link)). S1 was similarly defined as the hand-related aspect of BA 1,2, and 3. BA 6 was divided into a medial aspect (SMA/pre-SMA) and the lateral aspect superior to the crest of the middle frontal gyrus (PMd). The posterior parietal cortex was divided into an anterior region, including anterior, medial, and ventral IPS, and a posterior region, including the medial and lateral OPJ (Culham and Valyear, 2006 (link)).
For the analysis of the mean activity, we averaged all voxels within each ROI. For classification and decomposition analysis, we selected within each anatomical ROI and participant the 800 most activated surface nodes, causing each ROI to have a size of approximately 260 voxels. Because the MVPA measures are independent of the mean activity, this selection does not bias the results under the null-hypothesis. Classification analysis within each selected part of the ROI was performed using randomly drawn groups of 160 voxels, repeating this process 5000 times, and averaging the accuracy over all draws. This random-subspace approach increases the reliability of accuracy for ROI-based analyses (Diedrichsen et al., 2013 (link)).

The Kruskal-Wallis test was conducted to investigate differences in age between patients with ST, patients without ST, and HCs groups. Chi-square tests of homogeneity were used to investigate differences between the three groups in the distribution of gender, side of the arm used in the task, and to check the proportion of patients with dystonic tremor between patients with and without ST. An unpaired t-test was run to investigate differences in TWSTRS scores between patients with and without ST.
Data normality was tested for EMG and EEG variables by skewness and kurtosis values. The assumption of normality was satisfied for baseline EMG and for EMG power modulation values after the removal of one outlier (2.5 standard deviations (SDs); results were not affected by removing the outlier), but not for baseline EEG and ERSP values.
Experiment 1: For baseline EMG power, one-way analysis of variance (ANOVA) was used to investigate the effect of the factor group (patients with ST, patients without ST, and HCs) on EMG power pre-touch. To evaluate the effect of ST, mixed-design ANOVAs were used to test the effect of the factors time (post-touch, release) and group (patients with ST, patients without ST, and HC) on EMG power modulation.
For baseline EEG power, false discovery rate (FDR)-corrected Kruskal-Wallis tests with Dunn's post hoc comparisons were conducted to investigate differences in EEG power pre-touch between groups (patients with ST, patients without ST, HCs) across the different frequency bands and area on EEG power pre-touch. To evaluate the effect of ST, we performed a MANOVA with ERSP at different frequency bands as dependent variables (theta, alpha, beta, gamma), and group (patients with ST, patients without ST, and HCs), time (post-touch, release) and cortical area (sensorimotor vs posterior parietal cortex) as independent variables. Since MANOVA is robust to deviations from normality (Weinfurt 1995 ), we performed a parametric test regardless of the non-normal distribution of ERSP values. FDR correction was applied to MANOVA post hoc multiple univariate interaction effects.
Spearman's correlation assessed the relationship between ERSP associated with ST and EMG power modulation considering an FDR correction for multiple correlations. Outliers in correlation analysis were defined as data points with standardized residuals greater than ± 3 standard deviations.
Experiments 2, 3, and 4: Mixed-design ANOVAs used for experiment 1 analysis were repeated to compare EMG power modulation and ERSP between patients with ST and patients without ST in the no touch, passive, and examiner touch conditions.

Functional data was preprocessed using fMRIPrep^{30 (link)}; RRID:SCR_016216). Specifically, a reference volume and its skull-stripped version were generated using a custom methodology of fMRIPrep. Head-motion parameters with respect to the BOLD reference (transformation matrices, and six corresponding rotation and translation parameters) were estimated before any spatiotemporal filtering using mcflirt (FSL 5.0.9)^{36 (link)}. BOLD runs were slice-time corrected using 3dTshift from AFNI 201 602 07^{37 (link)} (RRID:SCR_005927). The BOLD reference was then co-registered to the T1w reference using bbregister (FreeSurfer) which implements boundary-based registration³⁸ . Co-registration was configured with six degrees of freedom. The BOLD time-series were resampled into standard MNI152NLin2009cAsym space. Several confounding time-series were calculated based on the preprocessed BOLD: Frame-wise displacement (FWD) was calculated from the six motion parameters and root-mean-square difference (RMSD) of the BOLD percentage signal in the consecutive volumes. Contaminated volumes were then detected and classified as outliers by the criteria FWD > 0.5 mm or RMSD > 0.3% and replaced with new volumes generated by linear interpolation of adjacent volumes. The three global signals are extracted within the cerebrospinal fluid (CSF), the white matter masks. A bandpass filter with cut-off frequencies of 0.01 and 0.09 Hz was used. Finally, the covariates corresponding to head motion (6 realignment parameters), outliers, and the BOLD time series from the subject-specific white matter and CSF masks were used in the connectivity analysis as predictors of no interest, and were removed from the BOLD functional time series using linear regression.
ROI-to-ROI connectivity analysis was performed in CONN toolbox using 11 CONN resting state network nodes composing 3 networks (Default Mode Network (DMN): medial pre-frontal cortex (MPFC), precuneus cortex (PCC), bilateral lateral parietal (LP); Salience Network (SN): anterior cingulate cortex (ACC), bilateral anterior insula (AI), rostral pre-frontal cortex (RPFC), and supramarginal gyrus (SMG); Fronto-parietal Network (FP): bilateral lateral pre-frontal cortex (LPFC) and posterior parietal cortex (PPC)^{39 (link)}. The mean BOLD time series was computed across all voxels within each ROI. Bivariate regression analyses were used to determine the linear association of the BOLD time series between each pair of regions for each subject. Both positive and negative correlations were examined. The resultant correlation coefficients were transformed into z-scores using Fisher’s transformation to satisfy normality assumptions. The within network-level FC was calculated as the average of the FCs within the networks of SN, DMN and FPN.