A Novel, Wearable Inertial Measurement Unit for Stroke Survivors: Validity, Acceptability, and Usability.

OBJECTIVE: To establish the concurrent validity, acceptability, and sensor optimization of a consumer-grade, wearable, multi-sensor system to capture quantity and quality metrics of mobility and upper limb movements in stroke survivors. DESIGN: Single-session, cross-sectional. SETTING: Clinical research laboratory. PARTICIPANTS: Thirty chronic stroke survivors (age 57 (10) years; 33% female) with mild to severe motor impairments participated. INTERVENTIONS: Not Applicable. MAIN OUTCOME MEASURES: Participants donned 5 sensors and performed standardized assessments of mobility and upper limb (UL) movement. True/false, positive/negative time in active movement for the UL were calculated and compared to criterion-standards using an accuracy rate. Bland-Altman plots and linear regression models were used to establish concurrent validity of UL movement counts, step counts, and stance time symmetry of MiGo against established criterion-standard measures. Acceptability and sensor optimization were assessed through an end-user survey and decision matrix. RESULTS: Mobility metrics showed excellent association with criterion-standards for step counts (video: r=0.988, P<.001, IMU: r=0.921, P<.001) and stance-time symmetry (r=0.722, P<.001). In the UL, movement counts showed excellent to good agreement (paretic: r=0.849, P<.001, nonparetic: r=0.672, P<.001). Accuracy of active movement time was 85.2% (paretic) and 88.0% (nonparetic) UL. Most participants (63.3%) had difficulty donning/doffing the sensors. Acceptability was high (4.2/5). CONCLUSIONS: The sensors demonstrated excellent concurrent validity for mobility metrics and UL movements of stroke survivors. Acceptability of the system was high, but alternative wristbands should be considered.

Wearable Technology to Capture Arm Use of People With Stroke in Home and Community Settings: Feasibility and Early Insights on Motor Performance.

OBJECTIVE: The objectives of this study were to establish the short-term feasibility and usability of wrist-worn wearable sensors for capturing the arm and hand activity of people with stroke and to explore the association between factors related to the use of the paretic arm and hand. METHODS: Thirty people with chronic stroke were monitored with wrist-worn wearable sensors for 12 hours per day for a 7-day period. Participants also completed standardized assessments to capture stroke severity, arm motor impairments, self-perceived arm use, and self-efficacy. The usability of the wearable sensors was assessed using the adapted System Usability Scale and an exit interview. Associations between motor performance and capacity (arm and hand impairments and activity limitations) were assessed using Spearman correlations. RESULTS: Minimal technical issues or lack of adherence to the wearing schedule occurred, with 87.6% of days procuring valid data from both sensors. The average sensor wear time was 12.6 (standard deviation [SD] = 0.2) hours per day. Three participants experienced discomfort with 1 of the wristbands, and 3 other participants had unrelated adverse events. There were positive self-reported usability scores (mean = 85.4/100) and high user satisfaction. Significant correlations were observed for measures of motor capacity and self-efficacy with paretic arm use in the home and the community (Spearman correlation coefficients = 0.44-0.71). CONCLUSIONS: This work demonstrates the feasibility and usability of a consumer-grade wearable sensor for capturing paretic arm activity outside the laboratory. It provides early insight into the everyday arm use of people with stroke and related factors, such as motor capacity and self-efficacy. IMPACT: The integration of wearable technologies into clinical practice offers new possibilities to complement in-person clinical assessments and to better understand how each person is moving outside of therapy and throughout the recovery and reintegration phase. Insight gained from monitoring the arm and hand use of people with stroke in the home and community is the first step toward informing future research with an emphasis on causal mechanisms with clinical relevance.

Multi-Site Identification and Generalization of Clusters of Walking Behaviors in Individuals With Chronic Stroke and Neurotypical Controls.

BACKGROUND: Walking patterns in stroke survivors are highly heterogeneous, which poses a challenge in systematizing treatment prescriptions for walking rehabilitation interventions. OBJECTIVES: We used bilateral spatiotemporal and force data during walking to create a multi-site research sample to: (1) identify clusters of walking behaviors in people post-stroke and neurotypical controls and (2) determine the generalizability of these walking clusters across different research sites. We hypothesized that participants post-stroke will have different walking impairments resulting in different clusters of walking behaviors, which are also different from control participants. METHODS: We gathered data from 81 post-stroke participants across 4 research sites and collected data from 31 control participants. Using sparse K-means clustering, we identified walking clusters based on 17 spatiotemporal and force variables. We analyzed the biomechanical features within each cluster to characterize cluster-specific walking behaviors. We also assessed the generalizability of the clusters using a leave-one-out approach. RESULTS: We identified 4 stroke clusters: a fast and asymmetric cluster, a moderate speed and asymmetric cluster, a slow cluster with frontal plane force asymmetries, and a slow and symmetric cluster. We also identified a moderate speed and symmetric gait cluster composed of controls and participants post-stroke. The moderate speed and asymmetric stroke cluster did not generalize across sites. CONCLUSIONS: Although post-stroke walking patterns are heterogenous, these patterns can be systematically classified into distinct clusters based on spatiotemporal and force data. Future interventions could target the key features that characterize each cluster to increase the efficacy of interventions to improve mobility in people post-stroke.

A metric for characterizing the arm nonuse workspace in poststroke individuals using a robot arm.

An overreliance on the less-affected limb for functional tasks at the expense of the paretic limb and in spite of recovered capacity is an often-observed phenomenon in survivors of hemispheric stroke. The difference between capacity for use and actual spontaneous use is referred to as arm nonuse. Obtaining an ecologically valid evaluation of arm nonuse is challenging because it requires the observation of spontaneous arm choice for different tasks, which can easily be influenced by instructions, presumed expectations, and awareness that one is being tested. To better quantify arm nonuse, we developed the bimanual arm reaching test with a robot (BARTR) for quantitatively assessing arm nonuse in chronic stroke survivors. The BARTR is an instrument that uses a robot arm as a means of remote and unbiased data collection of nuanced spatial data for clinical evaluations of arm nonuse. This approach shows promise for determining the efficacy of interventions designed to reduce paretic arm nonuse and enhance functional recovery after stroke. We show that the BARTR satisfies the criteria of an appropriate metric for neurorehabilitative contexts: It is valid, reliable, and simple to use.

Understanding stroke survivors' preferences regarding wearable sensor feedback on functional movement: a mixed-methods study.

BACKGROUND: In stroke rehabilitation, wearable technology can be used as an intervention modality by providing timely, meaningful feedback on motor performance. Stroke survivors' preferences may offer a unique perspective on what metrics are intuitive, actionable, and meaningful to change behavior. However, few studies have identified feedback preferences from stroke survivors. This project aims to determine the ease of understanding and movement encouragement of feedback based on wearable sensor data (both arm/hand use and mobility) for stroke survivors and to identify preferences for feedback metrics (mode, content, frequency, and timing). METHODS: A sample of 30 chronic stroke survivors wore a multi-sensor system in the natural environment over a 1-week monitoring period. The sensor system captured time in active movement of each arm, arm use ratio, step counts and stance time symmetry. Using the data from the monitoring period, participants were presented with a movement report with visual displays of feedback about arm/hand use, step counts and gait symmetry. A survey and qualitative interview were used to assess ease of understanding, actionability and components of feedback that users found most meaningful to drive lasting behavior change. RESULTS: Arm/hand use and mobility sensor-derived feedback metrics were easy to understand and actionable. The preferred metric to encourage arm/hand use was the hourly arm use bar plot, and similarly the preferred metric to encourage mobility was the hourly steps bar plot, which were each ranked as top choice by 40% of participants. Participants perceived that quantitative (i.e., step counts) and qualitative (i.e., stance time symmetry) mobility metrics provided complementary information. Three main themes emerged from the qualitative analysis: (1) Motivation for behavior change, (2) Real-time feedback based on individual goals, and (3) Value of experienced clinicians for prescription and accountability. Participants stressed the importance of having feedback tailored to their own personalized goals and receiving guidance from clinicians on strategies to progress and increase functional movement behavior in the unsupervised home and community setting. CONCLUSION: The resulting technology has the potential to integrate engineering and personalized rehabilitation to maximize participation in meaningful life activities outside clinical settings in a less structured environment.

Data-driven biomarkers outperform theory-based biomarkers in predicting stroke motor outcomes.

Chronic motor impairments are a leading cause of disability after stroke. Previous studies have predicted motor outcomes based on the degree of damage to predefined structures in the motor system, such as the corticospinal tract. However, such theory-based approaches may not take full advantage of the information contained in clinical imaging data. The present study uses data-driven approaches to predict chronic motor outcomes after stroke and compares the accuracy of these predictions to previously-identified theory-based biomarkers. Using a cross-validation framework, regression models were trained using lesion masks and motor outcomes data from 789 stroke patients (293 female/496 male) from the ENIGMA Stroke Recovery Working Group (age 64.9±18.0 years; time since stroke 12.2±0.2 months; normalised motor score 0.7±0.5 (range [0,1]). The out-of-sample prediction accuracy of two theory-based biomarkers was assessed: lesion load of the corticospinal tract, and lesion load of multiple descending motor tracts. These theory-based prediction accuracies were compared to the prediction accuracy from three data-driven biomarkers: lesion load of lesion-behaviour maps, lesion load of structural networks associated with lesion-behaviour maps, and measures of regional structural disconnection. In general, data-driven biomarkers had better prediction accuracy - as measured by higher explained variance in chronic motor outcomes - than theory-based biomarkers. Data-driven models of regional structural disconnection performed the best of all models tested (R2 = 0.210, p < 0.001), performing significantly better than predictions using the theory-based biomarkers of lesion load of the corticospinal tract (R2 = 0.132, p< 0.001) and of multiple descending motor tracts (R2 = 0.180, p < 0.001). They also performed slightly, but significantly, better than other data-driven biomarkers including lesion load of lesion-behaviour maps (R2 =0.200, p < 0.001) and lesion load of structural networks associated with lesion-behaviour maps (R2 =0.167, p < 0.001). Ensemble models - combining basic demographic variables like age, sex, and time since stroke - improved prediction accuracy for theory-based and data-driven biomarkers. Finally, combining both theory-based and data-driven biomarkers with demographic variables improved predictions, and the best ensemble model achieved R2 = 0.241, p < 0.001. Overall, these results demonstrate that models that predict chronic motor outcomes using data-driven features, particularly when lesion data is represented in terms of structural disconnection, perform better than models that predict chronic motor outcomes using theory-based features from the motor system. However, combining both theory-based and data-driven models provides the best predictions.

Moderate-intensity cardiovascular exercise performed before motor practice attenuates offline implicit motor learning in stroke survivors but not age-matched neurotypical adults.

The acute impact of cardiovascular exercise on implicit motor learning of stroke survivors is still unknown. We investigated the effects of cardiovascular exercise on implicit motor learning of mild-moderately impaired chronic stroke survivors and neurotypical adults. We addressed whether exercise priming effects are time-dependent (e.g., exercise before or after practice) in the encoding (acquisition) and recall (retention) phases. Forty-five stroke survivors and 45 age-matched neurotypical adults were randomized into three sub-groups: BEFORE (exercise, then motor practice), AFTER (motor practice, then exercise), and No-EX (motor practice alone). All sub-groups practiced a serial reaction time task (five repeated and two pseudorandom sequences per day) on three consecutive days, followed 7 days later by a retention test (one repeated sequence). Exercise was performed on a stationary bike, (one 20-min bout per day) at 50% to 70% heart rate reserve. Implicit motor learning was measured as a difference score (repeated-pseudorandom sequence response time) during practice (acquisition) and recall (delayed retention). Separate analyses were performed on the stroke and neurotypical groups using linear mixed-effects models (participant ID was a random effect). There was no exercise-induced benefit on implicit motor learning for any sub-group. However, exercise performed before practice impaired encoding in neurotypical adults and attenuated retention performance of stroke survivors. There is no benefit to implicit motor learning of moderately intense cardiovascular exercise for stroke survivors or age-matched neurotypical adults, regardless of timing. Practice under a high arousal state and exercise-induced fatigue may have attenuated offline learning in stroke survivors.

Long-term forecasting of a motor outcome following rehabilitation in chronic stroke via a hierarchical bayesian dynamic model.

BACKGROUND: Given the heterogeneity of stroke, it is important to determine the best course of motor therapy for each patient, i.e., to personalize rehabilitation based on predictions of long-term outcomes. Here, we propose a hierarchical Bayesian dynamic (i.e., state-space) model (HBDM) to forecast long-term changes in a motor outcome due to rehabilitation in the chronic phase post-stroke. METHODS: The model incorporates the effects of clinician-supervised training, self-training, and forgetting. In addition, to improve forecasting early in rehabilitation, when data are sparse or unavailable, we use the Bayesian hierarchical modeling technique to incorporate prior information from similar patients. We use HBDM to re-analyze the Motor Activity Log (MAL) data of participants with chronic stroke included in two clinical trials: (1) the DOSE trial, in which participants were assigned to a 0, 15, 30, or 60-h dose condition (data of 40 participants analyzed), and (2) the EXCITE trial, in which participants were assigned a 60-h dose, in either an immediate or a delayed condition (95 participants analyzed). RESULTS: For both datasets, HBDM accounts well for individual dynamics in the MAL during and outside of training: mean RMSE = 0.28 for all 40 DOSE participants (participant-level RMSE 0.26 ± 0.19-95% CI) and mean RMSE = 0.325 for all 95 EXCITE participants (participant-level RMSE 0.32 ± 0.31), which are small compared to the 0-5 range of the MAL. Bayesian leave-one-out cross-validation shows that the model has better predictive accuracy than static regression models and simpler dynamic models that do not account for the effect of supervised training, self-training, or forgetting. We then showcase model's ability to forecast the MAL of "new" participants up to 8 months ahead. The mean RMSE at 6 months post-training was 1.36 using only the baseline MAL and then decreased to 0.91, 0.79, and 0.69 (respectively) with the MAL following the 1st, 2nd, and 3rd bouts of training. In addition, hierarchical modeling improves prediction for a patient early in training. Finally, we verify that this model, despite its simplicity, can reproduce previous findings of the DOSE trial on the efficiency, efficacy, and retention of motor therapy. CONCLUSIONS: In future work, such forecasting models can be used to simulate different stages of recovery, dosages, and training schedules to optimize rehabilitation for each person. Trial registration This study contains a re-analysis of data from the DOSE clinical trial ID NCT01749358 and the EXCITE clinical trial ID NCT00057018.

Multi-site identification and generalization of clusters of walking behaviors in individuals with chronic stroke and neurotypical controls.

Background: Walking patterns in stroke survivors are highly heterogeneous, which poses a challenge in systematizing treatment prescriptions for walking rehabilitation interventions. Objective: We used bilateral spatiotemporal and force data during walking to create a multi-site research sample to: 1) identify clusters of walking behaviors in people post-stroke and neurotypical controls, and 2) determine the generalizability of these walking clusters across different research sites. We hypothesized that participants post-stroke will have different walking impairments resulting in different clusters of walking behaviors, which are also different from control participants. Methods: We gathered data from 81 post-stroke participants across four research sites and collected data from 31 control participants. Using sparse K-means clustering, we identified walking clusters based on 17 spatiotemporal and force variables. We analyzed the biomechanical features within each cluster to characterize cluster-specific walking behaviors. We also assessed the generalizability of the clusters using a leave-one-out approach. Results: We identified four stroke clusters: a fast and asymmetric cluster, a moderate speed and asymmetric cluster, a slow cluster with frontal plane force asymmetries, and a slow and symmetric cluster. We also identified a moderate speed and symmetric gait cluster composed of controls and participants post-stroke. The moderate speed and asymmetric stroke cluster did not generalize across sites. Conclusions: Although post-stroke walking patterns are heterogenous, these patterns can be systematically classified into distinct clusters based on spatiotemporal and force data. Future interventions could target the key features that characterize each cluster to increase the efficacy of interventions to improve mobility in people post-stroke.

Understanding preferences of stroke survivors for feedback provision about functional movement behavior from wearable sensors: a mixed-methods study.

Background: In stroke rehabilitation, wearable technology can be used as an intervention modality by providing timely, meaningful feedback on motor performance. Stroke survivors' preferences may offer a unique perspective on what metrics are intuitive, actionable, and meaningful to change behavior. However, few studies have identified feedback preferences from stroke survivors. This project aims to determine stroke survivors' satisfaction with feedback from wearable sensors (both mobility and arm/hand use) and to identify preferences for feedback type and delivery schedule. Methods: A sample of 30 chronic stroke survivors wore a multi-sensor system in the natural environment over a 1-week monitoring period. The sensor system captured time in active movement of each arm, arm use ratio, step counts and stance time symmetry. Using the data from the monitoring period, participants were presented with a movement report with visual displays of quantitative and qualitative feedback. A survey and qualitative interview were used to assess ease of understanding, actionability and components of feedback that users found most meaningful to drive lasting behavior change. Results: Arm/hand use and mobility sensor-derived feedback metrics were easy to understand and actionable. The preferred metric to encourage arm/hand use was the hourly arm use bar plot, and similarly the preferred metric to encourage mobility was the hourly steps bar plot, which were each ranked as top choice by 40% of participants. Participants perceived that quantitative (i.e., step counts) and qualitative (i.e., stance time symmetry) mobility metrics provided complementary information. Three main themes emerged from the qualitative analysis: 1) Motivation for behavior change, 2) Real-time feedback based on individual goals, and 3) Value of experienced clinicians for prescription and accountability. Participants stressed the importance of having feedback tailored to their own personalized goals and receiving guidance from clinicians on strategies to progress and increase functional movement behavior in the unsupervised home and community setting. Conclusion: The resulting technology has the potential to integrate engineering and personalized rehabilitation to maximize participation in meaningful life activities outside clinical settings in a less structured environment-one where stroke survivors live their lives.

Wearable technology to capture arm use of stroke survivors in home and community settings: feasibility and early insights on motor performance.

Objective: To establish short-term feasibility and usability of wrist-worn wearable sensors to capture arm/hand activity of stroke survivors and to explore the association between factors related to use of the paretic arm/hand. Methods: 30 chronic stroke survivors were monitored with wrist-worn wearable sensors during 12h/day for a 7-day period. Participants also completed standardized assessments to capture stroke severity, arm motor impairments, self-perceived arm use and self-efficacy. Usability of the wearable sensors was assessed using the adapted System Usability Scale and an exit interview. Associations between motor performance and capacity (arm/hand impairments and activity limitations) were assessed using Spearman's correlations. Results: Minimal technical issues or lack of adherence to the wearing schedule occurred, with 87.6% of days procuring valid data from both sensors. Average sensor wear time was 12.6 (standard deviation: 0.2) h/day. Three participants experienced discomfort with one of the wristbands and three other participants had unrelated adverse events. There were positive self-reported usability scores (mean: 85.4/100) and high user satisfaction. Significant correlations were observed for measures of motor capacity and self-efficacy with paretic arm use in the home and the community (Spearman's correlation ρs: 0.44-0.71). Conclusions: This work demonstrates the feasibility and usability of a consumer-grade wearable sensor to capture paretic arm activity outside the laboratory. It provides early insight into stroke survivors' everyday arm use and related factors such as motor capacity and self-efficacy. Impact: The integration of wearable technologies into clinical practice offers new possibilities to complement in-person clinical assessments and to better understand how each person is moving outside of therapy and throughout the recovery and reintegration phase. Insights gained from monitoring stroke survivors arm/hand use in the home and community is the first step towards informing future research with an emphasis on causal mechanisms with clinical relevance.

Adaptive control is reversed between hands after left hemisphere stroke and lost following right hemisphere stroke.

Human motor adaptability is of utmost utility after neurologic injury such as unilateral stroke. For successful adaptive control of movements, the nervous system must learn to correctly identify the source of a movement error and predictively compensate for this error. The current understanding is that in bimanual tasks, this process is flexible such that errors are assigned to, and compensated for, by the limb that is more likely to produce those errors. Here, we tested the flexibility of the error assignment process in right-handed chronic stroke survivors using a bimanual reaching task in which the hands jointly controlled a single cursor. We predicted that the nondominant left hand in neurotypical adults and the paretic hand in chronic stroke survivors will be more responsible for cursor errors and will compensate more within a trial and learn more from trial to trial. We found that in neurotypical adults, the nondominant left hand does compensate more than the right hand within a trial but learns less trial-to-trial. After a left hemisphere stroke, the paretic right hand compensates more than the nonparetic left hand within-trial but learns less trial-to-trial. After a right hemisphere stroke, the paretic left hand neither corrects more within-trial nor learns more trial-to-trial. Thus, adaptive control of visually guided bimanual reaching movements is reversed between hands after the left hemisphere stroke and lost following the right hemisphere stroke. These results indicate that responsibility assignment is not fully flexible but depends on a central mechanism that is lateralized to the right hemisphere.

Corpus Callosal Microstructure Predicts Bimanual Motor Performance in Chronic Stroke Survivors: a Preliminary Cross-Sectional Study.

BACKGROUND: Microstructural changes in the corpus callosum (CC) are associated with more severe motor impairment in the paretic hand, poor recovery, and general disability. The purpose of this study was to determine if CC microstructure predicts bimanual motor performance in chronic stroke survivors. METHODS: We examined the relationship between the fractional anisotropy (FA) across the CC, in both the sensorimotor and non-sensorimotor regions, and movement times for two self-initiated and self-paced bimanual tasks in 41 chronic stroke survivors. Using publicly available control datasets (n = 52), matched closely for imaging acquisition parameters, we also explored the effect of stroke and age on callosal microstructure. RESULTS: In mild-to-moderate chronic stroke survivors with relatively localized lesions to the motor areas, lower callosal FA values, suggestive of a more disorganized microstructure, were associated with slower bimanual performance. Associations were strongest for the primary motor fibers (b = -2.19 ± 1.03, p = .035), followed closely by premotor/supplementary motor (b = -2.07 ± 1.07, p = .041) and prefrontal (b = -1.92 ± 0.97, p = .05) fibers of the callosum. Secondary analysis revealed that compared to neurologically age-similar adults, chronic stroke survivors exhibited significantly lower mean FA in all regions of the CC, except the splenium. CONCLUSION: Remote widespread changes in the callosal genu and body are associated with slower performance on cooperative bimanual tasks that require precise and interdependent coordination of the hands. Measures of callosal microstructure may prove to be a useful predictor of real-world bimanual performance in chronic stroke survivors.

Essential Role of Social Context and Self-Efficacy in Daily Paretic Arm/Hand Use After Stroke: An Ecological Momentary Assessment Study With Accelerometry.

OBJECTIVE: To determine the momentary effect of social-cognitive factors, in addition to motor capability, on post-stroke paretic arm/hand use in the natural environment. DESIGN: A 5-day observational study in which participants were sent 6 Ecological Momentary Assessment (EMA) prompts/day. SETTING: Participants' daily environment. PARTICIPANTS: Community-dwelling, chronic stroke survivors with right-dominant, mild-moderate upper extremity paresis (N=30). INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Time duration of bimanual and unimanual paretic arm/hand use indexed by accelerometry; social-cognitive factors (social context, self-efficacy, mood) captured by EMA; motor capability of the paretic limb measured by Fugl-Meyer Upper Extremity Motor Assessment (FM). RESULTS: After accounting for participants' motor capability, we found that momentary social context (alone or not) and self-efficacy significantly predicted post-stroke paretic arm/hand use behavior in the natural environment. When participants were not alone, paretic arm/hand movement increased both with and without the less-paretic limb (bimanual and unimanual movements, P=.018 and P<.001, respectively). Importantly, participants were more likely to use their paretic arm/hand (unimanually) if they had greater self-efficacy for limb use (P=.042). EMA repeated-measures provide a real-time approach that captures the natural dynamic ebb and flow of social-cognitive factors and their effect on daily arm/hand use. We also observed that people with greater motor impairments (FM<50.6) increase unimanual paretic arm/hand movements when they are not alone, regardless of motor capability. CONCLUSIONS: In addition to motor capability, stroke survivors' momentary social context and self-efficacy play a role in paretic arm/hand use behavior. Our findings suggest the development of personalized rehabilitative interventions which target these factors to promote daily paretic arm/hand use. This study highlights the benefits of EMA to provide real-time information to unravel the complexities of the biopsychosocial (ie, motor capability and social-cognitive factors) interface in post-stroke upper extremity recovery.

Paired associative stimulation applied to the cortex can increase resting-state functional connectivity: A proof of principle study.

INTRODUCTION: There is emerging evidence that high Beta coherence (hBc) between prefrontal and motor corticies, measured with resting-state electroencephalography (rs-EEG), can be an accurate predictor of motor skill learning and stroke recovery. However, it remains unknown whether and how intracortical connectivity may be influenced using neuromodulation. Therefore, a cortico-cortico PAS (ccPAS) paradigm may be used to increase resting-state intracortical connectivity (rs-IC) within a targeted neural circuit. PURPOSE: Our purpose is to demonstrate proof of principle that ccPAS can be used to increase rs-IC between a prefrontal and motor cortical region. METHODS: Eleven non-disabled adults were recruited (mean age 26.4, sd 5.6, 5 female). Each participant underwent a double baseline measurement, followed by a real and control ccPAS condition, counter-balanced for order. Control and ccPAS conditions were performed over electrodes of the right prefrontal and motor cortex. Both ccPAS conditions were identical apart from the inter-stimulus interval (i.e ISI 5 ms: real ccPAS and 500 ms: control ccPAS). Whole brain rs-EEG of high Beta coherence (hBc) was acquired before and after each ccPAS condition and then analyzed for changes in rs-IC along the targeted circuit. RESULTS: Compared to ccPAS500 and baseline, ccPAS5 induced a significant increase in rs-IC, measured as coherence between electrodes over right prefrontal and motor cortex, (p <.05). CONCLUSION: These findings demonstrate proof of principle that ccPAS with an STDP derived ISI, can effectively increase hBc along a targeted circuit.

Retrospective Analysis of Task-Specific Effects on Brain Activity After Stroke: A Pilot Study.

Background: Evidence supports cortical reorganization in sensorimotor areas induced by constraint-induced movement therapy (CIMT). However, only a few studies examined the neural plastic changes as a function of task specificity. This retrospective analysis aims to evaluate the functional brain activation changes during a precision and a power grasp task in chronic stroke survivors who received 2-weeks of CIMT compared to a no-treatment control group. Methods: Fourteen chronic stroke survivors, randomized to CIMT (n = 8) or non-CIMT (n = 6), underwent functional MRI (fMRI) before and after a 2-week period. Two behavioral measures, the 6-item Wolf Motor Function Test (WMFT-6) and the Motor Activity Log (MAL), and fMRI brain scans were collected before and after a 2-week period. During scan runs, participants performed two different grasp tasks (precision, power). Pre to post changes in laterality index (LI) were compared by group and task for two predetermined motor regions of interest: dorsal premotor cortex (PMd) and primary motor cortex (MI). Results: In contrast to the control group, the CIMT group showed significant improvements in the WMFT-6. For the MAL, both groups showed a trend toward greater improvements from baseline. Two weeks of CIMT resulted in a relative increase in activity in a key region of the motor network, PMd of the lesioned hemisphere, under precision grasp task conditions compared to the non-treatment control group. No changes in LI were observed in MI for either task or group. Conclusion: These findings provide preliminary evidence for task-specific effects of CIMT in the promotion of recovery-supportive cortical reorganization in chronic stroke survivors.

Different Patterns of Neural Activity Characterize Motor Skill Performance During Acquisition and Retention.

Motor performance and learning have distinct behavioral and neural signatures and can be uniquely modulated by various informational and motivational factors. Contemporary frameworks describe four different motor learning mechanisms mapped onto specific neural regions which are key for motor skill acquisition: error-based learning (cerebellum), reinforcement learning (basal ganglia), cognitive strategies (prefrontal cortex), and use-dependent learning (motor cortex). However, little is known about the neural circuits engaged during skill acquisition that are modulated specifically by practice-based performance improvement and those that predict recall performance. Based on previous work, we hypothesize that brain activity during practice in primary motor cortex and basal ganglia (1) is associated with trial-by-trial practice performance and (2) is predictive of immediate recall performance. Leveraging the contemporary framework, we use a well-known task paradigm that primarily relies upon cognitive strategy, reinforcement, and use-based learning mechanisms to test our hypotheses. Forty neurotypical young adults were asked to practice a pinch force tracking task. Participants received performance feedback after each trial during practice. We used whole brain analysis of functional magnetic resonance imaging (fMRI) and behavioral performance measures (i.e., time-on-target and self-efficacy) during the practice phase to determine which brain activation patterns are (1) associated with trial-by-trial tracking performance and (2) predictive of immediate no-feedback retention performance. We observed brain activations in the frontal orbital cortex, putamen, amygdala, and insula correlated with tracking performance improvement during practice. In contrast, a different set of performance-related activated regions were observed that were associated with immediate retention performance that included the primary motor cortex, superior frontal gyrus, somatosensory cortex, angular gyrus, and parietal gyrus. Our findings demonstrate that improved practice performance and recall of a sensorimotor skill are correlated with distinct neural activity patterns during acquisition, drawing on different motor learning mechanisms during encoding. While motor performance improvements depend on both cortical and subcortical regions, motor skill recall depends primarily on prefrontal and motor cortices. We discuss possible interpretations for why our hypothesis regarding basal ganglia activity and retention performance was not supported. Understanding the different neural mechanisms engaged in motor performance and learning may inform novel interventions to enhance motor skill learning.

Feasibility of an Online Langerian Mindfulness Program for Stroke Survivors and Caregivers.

Mindfulness is promising for individuals with neurological disorders and their caregivers to improve psychological well-being. The potential application of a Langerian mindfulness intervention, focused on attention to variability, however, is still unknown. The objective of the study was to determine the feasibility (usability, satisfaction, and potential effectiveness on psychological well-being) of an online mindfulness intervention for stroke survivors and caregivers. Using mixed methods, 11 stroke survivors and three caregivers participated in a 3-week, online, Langerian mindfulness intervention. A semi-structured interview assessed the intervention's usability and gathered feedback. Self-reported measures about psychological well-being were documented remotely 3 times (preintervention, postintervention, and 1-month follow-up). Qualitatively, participants were highly satisfied with the intervention and reported subjective benefits, but the usability of the online platform was poor. None of the self-reported measures changed over time. This study provided evidence of feasibility of an online Langerian mindfulness intervention in a new population: stroke survivors and caregivers.