Trial and Subject information
All experimental protocols were approved by the University of Pittsburgh Institutional Review Board (Protocol STUDY19090210) under an abbreviated IDE. The study is published on ClinicalTrial.gov number NCT04512690.
Subjects between 21-70 years of age who had suffered from an ischemic or hemorrhagic stroke more than 6 months prior to the start of the study were eligible for participation. All subjects had hemiparesis affecting their upper limb and had a pre-study FM-UE score between 7 and 45. Prior to the study, participants were screened via a medical evaluation. Candidates with severe co-morbidities, previously implanted medical devices, claustrophobia, or who were pregnant, or breastfeeding were excluded from the study. Subjects were not receiving any anti-spasticity, anti-epileptic, or anti-coagulation medications for the duration of the study period.
After screening and pre-study baselines, subjects were implanted with clinical spinal cord leads. Starting from day 4 post-implant, subjects underwent experimental assessments 5 times per week, 4 hours per day, for a total of 14 sessions. During sessions, scientific measurements of joint torque, movement kinematics, muscle activity, and performance in robotic tasks and simulated activities of daily living were performed with and without stimulation. The implants were removed by post-implant day 29. Study follow up was performed at least 4 weeks after explant. Each assessment and their associated procedures are described in detail below. Assessments during the first 5 to 7 sessions were focused on designing an optimal stimulation strategy that was then maintained for the remaining sessions.
In this work, we report results from the first 2 subjects participating in our trial, both of whom were caucasian females. SCS01 (31 years) had a right thalamic hemorrhagic stroke secondary to a cavernous malformation 9 years prior to participation in the study. Her interim history involved several bleeding events with eventual ablation of the malformation with gamma knife radiosurgery. At the time of her participation in our trial, her post stroke residual was a left-sided spastic hemiparesis for which she was receiving botulinum injections in her biceps, brachioradialis, and pronator teres. Botulinum treatments were suspended starting 6 months prior to the study period and continuing through the end of the study. In the years between her initial infarct and participation, she also underwent a C5-6 anterior cervical discectomy and fusion to treat cervical stenosis as well as a flexor tendon lengthening surgery due to spasticity and suffered arm and wrist fractures in her affected arm. For SCS01, we included in this work, analysis of 138 isometric force test repetitions at multiple joints (54 stim off and 84 stim on) and 36 planar reaches (18 with SCS and 18 without SCS). We also report the results of simulated activities of daily living and other motor tasks that were performed at least 1 session per week (see Figure 4).
SCS02 (47 years) had a right ischemic middle cerebral artery stroke secondary to a right carotid dissection resulting in a large MCA territory infarct 3 years prior to participation in the study. Her post stroke residual at the time of participation was a left-sided spastic hemiparesis complicated by a left wrist flexion contracture despite treatment with splinting. For SCS02, we included in this work, analysis of 42 isometric force tests repetitions at multiple joints (21 stim off and 21 stim on) and 57 planar reaches (38 with SCS and 19 without SCS) that were obtained across multiple days during the study. We also report the results of simulated activities of daily living and other motor tasks that were performed at least 1 session per week (see Figure 4).
Both subjects successfully completed the protocol with no serious adverse events. SCS01 experienced phlebitis several days after the explant procedure at the end of the study that was resolved with oral antibiotics.
Lead Implant and Explant
General anesthesia was induced using propofol and maintained using sevoflurane and propofol at levels that allowed for reliable somatosensory evoked potential monitoring. Short-acting paralytic was used for intubation, but no additional paralytic was given to facilitate intraoperative monitoring of SCS evoked EMG. Both subjects were placed prone and affixed in a 3-pin Mayfield head holder. The back and neck were prepared and draped in typical sterile fashion and prophylactic antibiotics were administered. A small incision was made over the T1-T2 laminas using fluoroscopic guidance, and the tissue was dissected to expose the fascia. A Tuohy needle was inserted into the T1-T2 epidural interspace and used to guide the placement of a clinically approved 8 contact percutaneous spinal lead (PN 977A260, Medtronic). The first (rostral) lead was threaded rostrally and steered in situ using fluoroscopy towards the lateral aspect of the spinal cord such that the most distal contact was positioned at the base of the C3 vertebral body.
To confirm placement of the distal lead and ensure that we could recruit motor pools of the upper arm as proximal as the trapezius, we delivered current controlled monopolar stimulation using an intraoperative neuromonitoring system (Xltek Protektor, Natus Medical). Stimulation pulses were delivered at 1-2 Hz on representative electrodes of the array and we measured compound muscle action potentials (CMAPs) using intramuscular needle electrodes (ipsilateral trapezius, anterior deltoid, medial deltoid, posterior deltoid, biceps, triceps, pronator teres, wrist flexors, wrist extensors, abductor pollicis, and abductor digiti minimi; and contralateral bicep and wrist extensors). We also recorded contralateral activity to ensure that SCS did not induce cross-over effects to the other arm. Once satisfied with the lead placement, the Tuohy needle was removed, and the lead was sutured to the fascia to prevent lead migration.
The second (caudal) lead was placed through the same incision and T1-T2 interspace, this time, ensuring that the most proximal contact was positioned at the T1 vertebral body. As before, intraoperative electrophysiology was performed to ensure proper placement, verifying that SCS could recruit motor pools of the most distal muscles in the hand including abductor pollicis and abductor digiti minimi. Once in final position, the two leads (rostral and caudal) overlapped to provide complete coverage of spinal segments C4 to T1. The distal ends of both leads were tunneled subcutaneously and exited through a separate stab incision over the left flank. Both incisions were closed, and the externalized portion of the leads were covered.
To explant the arrays at the conclusion of the study period, the patients were prepared in a similar fashion to the implantation surgery. The upper thoracic incision was re-opened, and the lead wires were cut and removed proximally. The distal end of the leads were removed through the lateral exit wound and both incisions were closed.
To evaluate the specificity of SCS in recruiting individual motor pools, recruitment curves were performed on each of the 16 contacts. Stimulation was delivered at 1-2 Hz on one electrode at a time with gradually increasing current amplitude while simultaneously recording CMAPs from all muscles. The peak-to-peak amplitude of the SCS-induced CMAPs were measured, one for each stimulus amplitude, and normalized to the maximum amplitude recorded on that muscle across all measured trials.
Frequency Dependent Suppression
To validate that stimulation was activating dorsal sensory afferent fibers and not directly recruiting ventral motor efferent fibers, we evaluated the stimulation frequency dependent response of CMAP amplitudes across several representative electrodes. Current amplitude was fixed at a level above the motor threshold (the amplitude above which CMAPs were reliably induced). Pulse frequency was then increased from 1-2 Hz up to 20 Hz and the relative, normalized, peak-to-peak amplitude of CMAP responses were compared.
X-ray images were acquired at weekly timepoints in both axial and sagittal views to ensure the stability of lead position.
MRI was acquired using a 3-T Prisma System (Siemens) using a 64-channel head and neck coil. A T1-weighted structural image was captured using a magnetization-prepared rapid gradient echo (MPRAGE) sequence (TR = 2300 ms; TE = 2.9 ms; FoV = 256 × 256 mm2; 192 slices, slice thickness = 1.0 mm, in-plane resolution = 1.0 × 1.0 mm). Lesion segmentation was performed manually for each slice of the sequence using the MRIcron image viewer (NITRC) and the resulting region of interest (ROI) was smoothed on all planes using a gaussian smoothing kernel with a full-width at half-maximum of 2mm. MRIcro_GL (NITRC) was used to visualize and export the resulting segmented overlays.
High definition fiber tracking (HDFT)
The same 3-T MRI scanner was configured to use a diffusion spectrum imaging scheme to capture a total of 257 diffusion samples. The maximum b-value used was 4000 s/mm² and the in-plane resolution and slice thicknesses were 2 mm. The accuracy of b-table orientation was examined by comparing fiber orientations with those of a population-averaged template52.
The diffusion data were reconstructed in the MNI space using q-space diffeomorphic reconstruction53 to obtain the spin distribution function54. A diffusion sampling length ratio of 1.25 was used. The output resolution in diffeomorphic reconstruction was 2 mm isotropic. The restricted diffusion was quantified using restricted diffusion imaging55. The tensor metrics were calculated and a deterministic fiber tracking algorithm56 was used to reconstruct the cortico-spinal tract fibers. A tractography atlas52 was used to map left and right cortico-spinal tracts with a distance tolerance of 16 mm. For the fiber tracking, we used: an anisotropy threshold of 0.035, an angular threshold of 50 degrees, and a step size of 1 mm. Tracks with lengths shorter than 10 mm or longer than 200 mm were discarded. A total of 1,000,000 seeds were placed. Topology-informed pruning57 was applied to the tractography with 16 iterations to remove false connections. We then calculated the mean fractional anisotropy (FA) values for left and right cortico-spinal tract and the percentage of asymmetry was computed using Stinear’s formula:
Where FAL is the mean FA value of the CST in the lesioned hemisphere and FAH is the mean FA value of CST in the intact hemisphere.
Custom Stimulation Controller
During the trial, SCS was delivered using a clinical grade, single channel, current controlled stimulator (DS8R, Digitimer) and a high-current compliant 1-to-8 multiplexer (D188, Digitimer). Current could be delivered to any contact by connecting it to the multiplexer and selecting the associated output channel. A custom-built microcontroller-based (Arduino Due, Arduino) control unit set pulse timing, amplitude, and output channel for each stimulus. Pulse width, inter-pulse interval, and waveform shape were fixed by the stimulator which ensured proper charge balancing and safe operation. Each pulse was a cathodic-first, biphasic square waveform with 200 us (SCS01) to 400 us (SCS02) monophase pulse width and 10 us inter-pulse interval. Cathodic and anodic phases were equivalent in amplitude and duration.
The control unit triggered each stimulus with a digital trigger pulse and set pulse amplitude using a continuous analog signal between 0-3.3 V. The DS8R hardware was configured for safety such that it could not produce amplitudes higher than 10.23 mA. Despite the stimulator comprising a single current source, the control unit’s firmware enabled semi-synchronous stimulation across multiple channels by rapidly switching the output channel after each pulse (Extended Data Figure 2 e and 5 and). The time between pulses on separate channels was measured to be 2.2 ms, giving enough time for the multiplexer to fully switch output channels. During the study, we used this system to deliver stimulation on up to 4 separate spinal electrodes at up to 100 Hz. All programmable stimulation settings were configurable using a graphical user interface (GUI) developed in MATLAB which communicated with the control unit via a virtual serial port over a USB connection. Stimulation frequency, channel, duration, latency, and amplitude could all be configured manually via the GUI. Each channel could also be set to deliver a single pulse, a pulse train of fixed duration or pulse count, or continuous stimulation (Extended Data Figure 2).
A custom command protocol was implemented to facilitate communication between the GUI and control unit (Extended Data Figure 2 b and d). Communication was always initiated by the GUI with a command packet comprising the length in bytes of the packet, a 1-byte command, and 0-6 bytes of data. Possible commands included triggering or terminating stimulation, clearing the current configuration, reading or writing a parameter, configuring the microcontroller to accept new parameters (program mode), saving parameters, and an initialization handshake. When writing parameters, the length and command bytes were followed by the parameter to be set, the channel (if applicable), and the value to be written. When reading parameters, the data payload comprised only the parameter to be read. All commands were followed by a response packet from the microcontroller comprising the length of the packet, an echo of the command received, a data payload if applicable (for example when reading parameters), and a status byte indicating whether the command was executed correctly.
To assess muscle activity during movement, surface electromyography (sEMG) was recorded using a wireless EMG system (Trigno, Delsys Inc.). Up to 14 synchronized wireless sensors (Avanti Trigno, Delsys Inc.) were used to amplify, digitize, and wirelessly transmit EMG signals to a base station unit. Each sensor sampled the analog signal at 1925.925 Hz and applied a hardware bandpass filter of 20-800 Hz. Once the signals were received by the base station, they were converted back to an analog waveform and resampled at 2500 Hz by a data acquisition system (PCI-6255, National Instruments) for synchronization with other task events. The Trigno system has a known, fixed wireless latency of 59.6 ms.
At the beginning of each experimental session, the arm and hand were cleaned using isopropyl alcohol. Skin safe adhesive was used to secure the EMG sensors to the subject’s arm. Depending on the muscles of interest for a particular experiment, we recorded from up to 14 individual muscles of the arm and hand; including the trapezius, anterior deltoid, medial deltoid, posterior deltoid, biceps, triceps, pronator teres, wrist flexors, wrist extensors, extensor digitorum, and abductor pollicis, whose locations were identified by palpation while the subject was instructed to perform simple movements. Sensors were then carefully removed at the end of each session.
Single joint isometric torque
Maximum isometric strength was measured for the shoulder, elbow, and wrist joints (when possible) using a robotic torque dynamometer (HUMAC NORM, CSMi). To measure torque, the robot’s manipulandum was positioned and held at a fixed angle and the subject was asked to apply their maximum force while flexing or extending the joint under test for a sustained period of 5 seconds followed by a 10 to 15 second break. This procedure was repeated 5 times to complete a set. For each joint, the system was configured such that the joint was at a nominal and comfortable angle and so that it was aligned with the manipulandum’s center of rotation. The HUMAC NORM’s suggested configurations were used, when possible, but SCS02 was unable to support the weight of her arm and so was placed in a seated position to measure elbow and shoulder torques instead of the suggested supine position. In addition, a splint was used to secure SCS02’s hand to the manipulandum to assist her in holding the handle securely and a counterweight was used where appropriate to offset the mass of the manipulandum and allow for more sensitive measurements. The maximum torque value within each repetition was considered for analysis.
Grip force was measured using a hand dynamometer. Participants were asked to hold the dynamometer and apply their maximum grasping force for five seconds. Each measurement comprised the highest force produced on each of 3 attempts and data were combined across days to assemble enough data for statistical comparison.
Planar reach and pull kinematics
To evaluate upper limb motor control during directed reach and pull movements, we used a robotic augmented reality exoskeleton system (KINARM, BKIN Technologies). Participants were secured in a modified wheelchair and their arms were suspended in the exoskeleton to remove the effects of gravity. The platform displayed virtual targets onto a dichroic augmented reality display in front of the subject that allowed them to visualize their hand position relative to the virtual graphics. The robot’s motorized joints permitted the application of a mechanical load to the subject’s movements.
Center Out Task
For this task, the participants were asked to reach from a central starting position to one of 3 targets displayed using the AR display, then return to the starting position. On each trial the starting position was displayed, and the robot moved the subject’s arm into position, locking it in place. Next the target was presented, and the exoskeleton was unlocked. An audio cue was played after a randomized 100 to 700 ms delay indicating that the subject could begin their movement. The participant was given 10 (SCS01) or 15 (SCS02) seconds to complete each trial. A target was considered acquired when the subject’s index finger was within a 0.5 cm radius of the target center for 500 ms. An audio cue indicated the end of the reach phase. If the subject was unable to reach the target, the robot returned the arm to the starting position and the next target was presented. If the trial was successful, the subject’s finger was positioned in the center of the target in preparation for the pull phase and locked in place. After a 500 ms delay, the arm was unlocked followed by a final audio cue after another 100-700 ms delay indicating the start of the pull phase, and the subject was required to return their hand to the starting position. In some trials, a load of -30 was applied isotropically to the movement using the exoskeleton to increase the task difficulty. Each target was presented 6 times in random order (unless otherwise noted). For each subject, appropriate targets were selected based on their individual range of motion.
The following metrics were calculated for each trajectory to compare kinematic quality. Trajectory smoothness was calculated as the number of peaks in the velocity profile for both the reach and pull phases. We also measured the total time of the combined reach and pull phases. Total path length was calculated and normalized to the Euclidean distance between the starting position and the target; more efficient movements had a lower value. Finally, the variance of each trajectory was calculated as the mean deviation of the actual trajectory from the mean trajectory calculated across all 5 repetitions of the movement.
Open-Ended Reaching Task
The subject was presented with 3 equally spaced horizontal lines (approximately 15, 25, and 30 cm away from the participant) and was asked to reach from a starting position to the furthest line they could. In this way we assessed how far the subject could reach in an open-ended manner.
During each task, the participant started with their hand as close to their body as they could (maximum elbow flexion). After a verbal cue, they began their movement with the goal of passing the farthest line possible. Once the subject indicated that they had reached their maximum distance, another verbal cue indicated that they should return to their initial position. Task events were manually labeled during the trial by the experimenter. Each set comprised 5 repetitions.
As in the center-out task, a set of metrics was calculated for each trajectory; reach and pull phases were considered separately. Movement duration was calculated as the time it took from the beginning of each phase for the subject to cross the second horizontal line (25 cm) during reach and the first horizontal line during pull (15 cm). Maximum distance was measured as the axial distance between the point closest to the subject and the point furthest from the subject in each phase. Range of motion of the elbow during the task was considered as the angle difference between the most acute and most obtuse elbow angles achieved during each phase. As a metric of smoothness, the number of peaks in the elbow angle velocity profile was counted. Total path length measured the total length of the trajectory from the starting point to the second line (25 cm; reach phase) or from the end position to the first line (15 cm; pull phase) and was normalized by the phase duration. Finally, as a measure of variance, we calculated the distribution of each trajectory timepoint from the mean trajectory. A distribution skewed towards the left indicated that more samples were close to the mean trajectory, whereas a distribution with values towards the right indicated large deviations from the mean trajectory and therefore more variance.
Fast reaching task
The participant was presented with 6 targets, all axially equidistant from the subject, but at varying heights and lateral positions. The 3 “lower” targets were at table surface height and the 3 “upper” targets were raised to require shoulder flexion beyond 90 degrees. There was a left, center, and right target at each height. A 7th position was placed directly in front of the subject and was used as a “home” position. Starting with their arm outside the working area, the subject was asked to first touch the home position then touch each of the 6 targets, returning to the home position after each target. For this task, we asked the subject to perform the sequence as fast as possible. The total time it took to reach all 6 targets was recorded. For ease of comparison, the average time to acquire one target was calculated from this total.
Robotic 3D reaching task
As an alternative to the fast-reaching task, we used an exoskeleton robot (ARMEO POWER, Hocoma) to assist 3D movements when the subject was unable to lift their arm against the force of gravity (SCS02). This robotic system provides motorized support at each joint of the arm and measures kinematic variables in real time allowing for a subject’s real-world movements to be displayed in a virtual video game environment. For this task, objects were presented within a virtual room and the subject was asked to reach toward each object and move it to a different position within the room (ARMEO POWER cleanup game). The robot was configured to provide 50% weight support and assist movements at the “Low Support” setting. Game difficulty was set to “Easy”. Each game lasted 3 minutes and the goal was to move as many objects as possible within the time limit. We then calculated the average time per object to get a comparable measure to the fast-reaching task.
The Fugl-Meyer Upper-Extremity assessment is a standardized evaluation of upper limb motor control and sensory function58. It includes 7 categories of assessments including passive and active range of motion, joint pain, proprioception, and tactile sensation. In total, there are 126 possible points. However, all scores reported in this manuscript correspond to the “Motor Function” sub-score which has a maximum value of 66. A trained medical professional conducted and scored the exam at 4 different timepoints: pre-study, mid-study (approximately 2 weeks after implant), end-of-study (4 weeks), and post-study (1 month after explant).
Modified Ashworth Scale
To ensure that SCS was not exacerbating joint spasticity, we performed the Modified Ashworth Scale (MAS) each session day at the beginning of the session. This assessment involves passive manipulation of each joint, and ranking spasticity levels from 0-4 (0 being no spasticity). A trained medical professional performed and scored the assessment each day. Here we report both a full breakdown of all joint scores measured on each day for both subjects as well as a “summary score”. The summary score was taken to be the average score across all joints for each day.
Box and Blocks
When possible, we also evaluated the subject’s performance in the “Box and Blocks” task. This is a standardized assessment in which a participant must grasp one small block at a time from one side of a box, lift it over a divider, and drop the block in the other half of the box. The total number of blocks moved from one side to the other within 1 minute was the subject’s score.
Activities of Daily Living
Drawing a spiral
We asked the subject to draw a spiral shape using a marker on a plain piece of white printer paper taped down to a table. The goal of the task was to make the curves as smooth as possible and attempt not to overlap each of the concentric rings. The subject was allowed to comfortably position the pen in their hand using their unaffected hand before starting to draw.
We placed a full, sealed can of soup on a table in front of the participant. The subject was asked to grasp the object from the side, requiring them to supinate their forearm, lift the can, and place it at an adjacent target. This task evaluated the subject’s ability to reach, grasp, lift, and release a moderately heavy object. Here, the subject was not allowed to use their unaffected arm to assist in grasping the object.
In an alternative object manipulation task, we asked the subject to hold a wooden plank with vertical dowels (similar to a tower of Hanoi toy) on their lap using their unaffected hand. We then placed a metal cylinder over one of the dowels. The subject was required to grasp the cylinder, lift it off of the first dowel, align it and place it onto a second dowel, and release the cylinder. An experimenter helped to position the hand on the cylinder before the start of the trial. All other movements were performed by the subject entirely on their own.
As a measure of hand dexterity, we positioned a wooden panel with a shackle-style key-actuated lock on a table in front of the subject, who was asked simply to open the lock using their affected limb. To do this task, the participant was required to grasp and stabilize the lock with one hand (e.g. the unaffected hand), use a pinch grip to grasp the key with the other hand (e.g. the affected hand), and supinate the forearm to twist the key and unlock the lock. The subject then removed the lock from its latch on the wooden panel, replaced it by realigning the shank with the latch, and relocked the lock by aligning and pressing the shank back into the body.
The subject was presented with small bite sized portions of food on a plate and a plastic fork. They were tasked with first picking up the fork from a table, using it to secure a piece of food, and perform the complex movement of orienting the food toward their mouth in preparation to eat it. Here, the subject was required to initiate picking up the fork with their affected hand but was allowed to reposition it using their unaffected hand before attempting to pick up the food.
Isometric contraction (root mean square analysis)
During isometric contractions, EMG was acquired from appropriate muscles using wireless sensors as described above. Empirically, we observed that deltoid EMG signals contained stimulation artifact during trials where stimulation was active due to the proximity of deltoid muscles to the stimulating electrodes. We removed these artifacts by blanking the signal coinciding with stimulation pulses. All signals were bandpass filtered (25-300 Hz, 5th order Butterworth digital filter) and the root mean square (rms) value was calculated from the filtered data over the full duration of each trial for statistical analysis.
Planar reaching (muscle synergy analysis)
Coordinated movements such as reaching and pulling require the timed co-activation of appropriate muscles to produce accurate and controlled limb motion. We measured which muscles were simultaneously active during planar reaching movements by calculating muscle synergies using non-negative matrix factorization (NNMF), a dimensionality reduction technique59.
EMG pre-processing was different for SCS01 and SCS02 due to large amplitude stimulation artifacts present in SCS02’s EMG data that were not present for SCS01. For SCS01, stimulation artifact was removed by blanking and the resulting data were bandpass filtered (20-500 Hz, 5th order Butterworth digital filter). For SCS02, EMG were first bandpass filtered using a narrower pass band (10-200 Hz, 5th order Butterworth, digital filter) to remove high frequency components of the stimulation artifact. Notch filters (5th order Butterworth) at 50, 100, and 150 Hz were then used to remove low frequency harmonics of the stimulation artifact. The resulting signals from both subjects were rectified, low-pass filtered (5 Hz, 5th order Butterworth digital filter), and normalized to the maximum EMG value recorded from that muscle over the whole day. Processed EMG was extracted from the reach and pull phases of each movement. Muscle synergies were identified using NNMF.
NNMF decomposes the EMG signals into a synergy activation matrix using the temporal correlation between the activity of individual muscles59. The result is a set of one-dimensional timeseries signals for each muscle synergy identified. Each synergy in-turn comprises contributions from multiple muscles as described by a synergy vector. We implemented NNMF with two factors which were selected by observing the point-of-inflection in the residuals vs. number of synergies curve60. For each phase of the movement (reach and pull), the primary synergy for that movement was identified as the one that most positively correlated (increased) with the movement. All repetitions of the movement were used to perform the dimensionality reduction. Finally, the contributions of deltoid and elbow muscles were quantified and compared using the primary synergy’s synergy vector.
All statistical comparisons of means presented in this manuscript were performed using the bootstrap method, a non-parametric approach which makes no distributional assumptions on the observed data. Instead, bootstrapping uses resampling to construct empirical confidence intervals for quantities of interest. For each comparison (e.g. comparing stimulation on vs stimulation off for shoulder torque in SCS01, shown in Figure 1f), we construct bootstraped samples by drawing a sample with replacement from observed measurements, while preserving the number of measurements in each condition. We construct 10,000 bootstrap samples and, for each, calculate the difference in means of the resampled data. A 95% confidence interval for the difference in means is obtained by identifying the 2.5th and 97.5th quantiles for the resulting values. The null hypothesis of no difference in the mean was rejected if 0 was not included in the 95% confidence interval. If more than one comparison was being performed at once, we used a Bonferroni correction by dividing this alpha value by the number of pairwise comparisons being performed.
Comparison of distributions
Statistical comparison of distributions was done using a two-sample Kolmogorov-Smirnov (KS) non-parametric test using MATLAB. Again, an alpha value of 0.05 was used. Here, we used this test to compare the variability of kinematic trajectories during 2D planar reaching (the open-ended reaching task). The deviations of each trajectory from the mean trajectory were used to build a distribution of deviations. The resulting distributions for two conditions (stimulation off and stimulation on) could then be compared using the KS test.
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