Replication data for the paper 'Closed-loop peripheral stimulation reduces tremor and is associated with decreased thalamic oscillations and group I inhibition'

Abstract

Replication data for the paper 'Closed-loop peripheral stimulation reduces tremor and is associated with decreased thalamic oscillations and group I inhibition' The source data folder contains four main subfolders: 1. “Intracerebral local field potentials”. This contains .json files from Medtronic Percept PC neurostimulator. Specifically, there is anonymized data from patient 12 that was recorded during assessments carried out before (PRE) and after (POST) active and sham stimulation sessions.2. “Disynaptic group I inhibition”. This contains .mat files recorded from patient 2 during assessments carried out before (PRE) and after (POST) active and sham stimulation sessions.3. “Long-term kinematics”: This contains .mvnx files from patient 14 that were recorded in sessions 1, 5, 6 and 10 from the long-term phase of the study. Specifically, there are files from each kinematic assessment carried out before and after stimulation session, when the participant maintained upper limb postures that elicited tremor (keeping the arms extended, flexing the elbows, holding two paper cups to the mouth).4. “Clinical assessments”. This folder contains a .xlsx file with the results from the short and long-term clinical assessments. -------------------------------------------------------------------------------------------------Intracerebral local field potentials To extract .json files from Medtronic Percept PC neurostimulator, the reader can use the MATLAB Toolbox available in https://github.com/yohannthenaisie/percepttoolbox. Once extracted, it is possible to load raw LFP data from patient 12 that was recorded during assessments carried out before (PRE) and after (POST) active and sham session. It is also possible to plot spectrograms for each channel. -------------------------------------------------------------------------------------------------Disynaptic group I inhibition Each .mat file can be opened using either Matlab or Python. Column number 2 from variable ‘Data’ contains raw EMG data from FCR. As explained in the manuscript, conditioned FCR H-reflexes were obtained by delivering a conditioning stimulus to the radial nerve at an intensity just below MT, at one of three conditioning test intervals: 1 ms before the median nerve stimulus (-1 ms), simultaneously (0 ms), or 1 ms after (+1 ms). Please note that time +99 corresponds to unconditioned H-reflex and time +20 is used to assess Ia presynaptic inhibition and was not evaluated in this study. Ten repetitions of each condition were delivered in randomized order, with an interstimulus interval of 5 ± 0.2 s to minimize post-activation depression. Raw EMG signals were digitally band-pass filtered using a second-order Butterworth filter (20-500 Hz). Trials were then segmented over a 500-ms window following the median nerve test stimulus. The peak-to-peak amplitude of the FCR H-reflex was measured within the 15-30 ms interval and averaged across the ten repetitions of each condition. Each conditioned H-reflex was expressed as a ratio relative to the corresponding unconditioned H-reflex amplitude. For each participant, the conditioning test interval producing the greatest inhibition at PRE was selected for subsequent analysis. The percentage change from PRE to POST was calculated according to the following equation: Change (%) = (1 - RatioPOST / RatioPRE) X 100where RatioPRE and RatioPOST represent the ratio between the conditioned and unconditioned H-reflex amplitudes before and after the intervention, respectively. Positive values indicate an increase in inhibition, whereas negative values indicate a decrease. -------------------------------------------------------------------------------------------------Long-term kinematics The .mvnx format is a human-readable XML format that contains sensor data, segment kinematics, joint angles, and subject information. Xsens .mvnx files can be accessed using either Python or MATLAB. The corresponding Xsens MVNX reader files can be downloaded from the Xsens/Movella resources (https://www.xsens.com/support/software-documentation). ### Python The following files are required: - mvn.py- main_mvnx.py- load_mvnx.py- mvnx_file_accessor.py ### MATLAB The following files are required: - load_mvnx.m- main_mvnx.m These files allow .mvnx recordings to be loaded and the different motion and kinematic signals stored in them to be accessed. ## Accessing signals in Python After loading an .mvnx file, signals can be retrieved for a specific body segment and axis. For example, the right hand is identified as: SEGMENT_RIGHT_HAND = 10 The available axis indices are: AXIS_ALL = -1 AXIS_X = 0AXIS_Y = 1AXIS_Z = 2 AXIS_W = 3 For three-dimensional signals, such as hand free acceleration, the available components are the X, Y, and Z axes. For example, the Z component of the right-hand free acceleration can be accessed by selecting: segment = SEGMENT_RIGHT_HANDaxis = AXIS_Z