Quantifying impairment and disease severity using AI models trained on healthy subjects.

Automatic assessment of impairment and disease severity is a key challenge in data-driven medicine. We propose a framework to address this challenge, which leverages AI models trained exclusively on healthy individuals. The COnfidence-Based chaRacterization of Anomalies (COBRA) score exploits the decrease in confidence of these models when presented with impaired or diseased patients to quantify their deviation from the healthy population. We applied the COBRA score to address a key limitation of current clinical evaluation of upper-body impairment in stroke patients. The gold-standard Fugl-Meyer Assessment (FMA) requires in-person administration by a trained assessor for 30-45 minutes, which restricts monitoring frequency and precludes physicians from adapting rehabilitation protocols to the progress of each patient. The COBRA score, computed automatically in under one minute, is shown to be strongly correlated with the FMA on an independent test cohort for two different data modalities: wearable sensors (ρ = 0.814, 95% CI [0.700,0.888]) and video (ρ = 0.736, 95% C.I [0.584, 0.838]). To demonstrate the generalizability of the approach to other conditions, the COBRA score was also applied to quantify severity of knee osteoarthritis from magnetic-resonance imaging scans, again achieving significant correlation with an independent clinical assessment (ρ = 0.644, 95% C.I [0.585,0.696]).

Improvisation and live accompaniment increase motor response and reward during a music playing task.

Music provides a reward that can enhance learning and motivation in humans. While music is often combined with exercise to improve performance and upregulate mood, the relationship between music-induced reward and motor output is poorly understood. Here, we study music reward and motor output at the same time by capitalizing on music playing. Specifically, we investigate the effects of music improvisation and live accompaniment on motor, autonomic, and affective responses. Thirty adults performed a drumming task while (i) improvising or maintaining the beat and (ii) with live or recorded accompaniment. Motor response was characterized by acceleration of hand movements (accelerometry), wrist flexor and extensor muscle activation (electromyography), and the drum strike count (i.e., the number of drum strikes played). Autonomic arousal was measured by tonic response of electrodermal activity (EDA) and heart rate (HR). Affective responses were measured by a 12-item Likert scale. The combination of improvisation and live accompaniment, as compared to all other conditions, significantly increased acceleration of hand movements and muscle activation, as well as participant reports of reward during music playing. Improvisation, regardless of type of accompaniment, increased the drum strike count and autonomic arousal (including tonic EDA responses and several measures of HR), as well as participant reports of challenge. Importantly, increased motor response was associated with increased reward ratings during music improvisation, but not while participants were maintaining the beat. The increased motor responses achieved with improvisation and live accompaniment have important implications for enhancing dose of movement during exercise and physical rehabilitation.

Evidence and sources of placebo effects in transcranial direct current stimulation during a single session of visuospatial working memory practice.

Transcranial direct current stimulation (tDCS) can be used to non-invasively augment cognitive training. However, the benefits of tDCS may be due in part to placebo effects, which have not been well-characterized. The purpose of this study was to determine whether tDCS can have a measurable placebo effect on cognitive training and to identify potential sources of this effect. Eighty-three right-handed adults were randomly assigned to one of three groups: control (no exposure to tDCS), sham tDCS, or active tDCS. The sham and active tDCS groups were double-blinded. Each group performed 20 min of an adapted Corsi Block Tapping Task (CBTT), a visuospatial working memory task. Anodal or sham tDCS was applied during CBTT training in a right parietal-left supraorbital montage. After training, active and sham tDCS groups were surveyed on expectations about tDCS efficacy. Linear mixed effects models showed that the tDCS groups (active and sham combined) improved more on the CBTT with training than the control group, suggesting a placebo effect of tDCS. Participants' tDCS expectations were significantly related to the placebo effect, as was the belief of receiving active stimulation. This placebo effect shows that the benefits of tDCS on cognitive training can occur even in absence of active stimulation. Future tDCS studies should consider how treatment expectations may be a source of the placebo effect in tDCS research, and identify ways to potentially leverage them to maximize treatment benefit.

Corticospinal and corticoreticulospinal projections benefit motor behaviors in chronic stroke.

After corticospinal tract (CST) stroke, several motor deficits in the upper extremity (UE) emerge, including diminished muscle strength, motor control, and muscle individuation. Both the ipsilesional CST and contralesional corticoreticulospinal tract (CReST) innervate the paretic UE and may have different innervation patterns for the proximal and distal UE segments. These patterns may underpin distinct pathway relationships to separable motor behaviors. In this cross-sectional study of 15 chronic stroke patients and 28 healthy subjects, we examined two key questions: (1) whether segmental motor behaviors differentially relate to ipsilesional CST and contralesional CReST projection strengths, and (2) whether motor behaviors segmentally differ in the paretic UE. We measured strength, motor control, and muscle individuation in a proximal (biceps, BIC) and distal muscle (first dorsal interosseous, FDI) of the paretic UE. We measured the projection strengths of the ipsilesional CST and contralesional CReST to these muscles using transcranial magnetic stimulation (TMS). Stroke subjects had abnormal motor control and muscle individuation despite strength comparable to healthy subjects. In stroke subjects, stronger ipsilesional CST projections were linked to superior motor control in both UE segments, whereas stronger contralesional CReST projections were linked to superior muscle strength and individuation in both UE segments. Notably, both pathways also shared associations with behaviors in the proximal segment. Motor control deficits were segmentally comparable, but muscle individuation was worse for distal motor performance. These results suggest that each pathway has specialized contributions to chronic motor behaviors but also work together, with varying levels of success in supporting chronic deficits. Key points summary: Individuals with chronic stroke typically have deficits in strength, motor control, and muscle individuation in their paretic upper extremity (UE). It remains unclear how these altered behaviors relate to descending motor pathways and whether they differ by proximal and distal UE segment.In this study, we used transcranial magnetic stimulation (TMS) to examine projection strengths of the ipsilesional corticospinal tract (CST) and contralesional corticoreticulospinal tract (CReST) with respect to quantitated motor behaviors in chronic stroke.We found that stronger ipsilesional CST projections were associated with better motor control in both UE segments, whereas stronger contralesional CReST projections were associated with better strength and individuation in both UE segments. In addition, projections of both pathways shared associations with motor behaviors in the proximal UE segment.We also found that deficits in strength and motor control were comparable across UE segments, but muscle individuation was worse with controlled movement in the distal UE segment.These results suggest that the CST and CReST have specialized contributions to chronic motor behaviors and also work together, although with different degrees of efficacy.

Quantifying Impairment and Disease Severity Using AI Models Trained on Healthy Subjects.

Automatic assessment of impairment and disease severity is a key challenge in data-driven medicine. We propose a novel framework to address this challenge, which leverages AI models trained exclusively on healthy individuals. The COnfidence-Based chaRacterization of Anomalies (COBRA) score exploits the decrease in confidence of these models when presented with impaired or diseased patients to quantify their deviation from the healthy population. We applied the COBRA score to address a key limitation of current clinical evaluation of upper-body impairment in stroke patients. The gold-standard Fugl-Meyer Assessment (FMA) requires in-person administration by a trained assessor for 30-45 minutes, which restricts monitoring frequency and precludes physicians from adapting rehabilitation protocols to the progress of each patient. The COBRA score, computed automatically in under one minute, is shown to be strongly correlated with the FMA on an independent test cohort for two different data modalities: wearable sensors ($\rho = 0.845$, 95% CI [0.743,0.908]) and video ($\rho = 0.746$, 95% C.I [0.594, 0.847]). To demonstrate the generalizability of the approach to other conditions, the COBRA score was also applied to quantify severity of knee osteoarthritis from magnetic-resonance imaging scans, again achieving significant correlation with an independent clinical assessment ($\rho = 0.644$, 95% C.I [0.585,0.696]).

Data-Driven Quantitation of Movement Abnormality after Stroke.

Stroke commonly affects the ability of the upper extremities (UEs) to move normally. In clinical settings, identifying and measuring movement abnormality is challenging due to the imprecision and impracticality of available assessments. These challenges interfere with therapeutic tracking, communication, and treatment. We thus sought to develop an approach that blends precision and pragmatism, combining high-dimensional motion capture with out-of-distribution (OOD) detection. We used an array of wearable inertial measurement units to capture upper body motion in healthy and chronic stroke subjects performing a semi-structured, unconstrained 3D tabletop task. After data were labeled by human coders, we trained two deep learning models exclusively on healthy subject data to classify elemental movements (functional primitives). We tested these healthy subject-trained models on previously unseen healthy and stroke motion data. We found that model confidence, indexed by prediction probabilities, was generally high for healthy test data but significantly dropped when encountering OOD stroke data. Prediction probabilities worsened with more severe motor impairment categories and were directly correlated with individual impairment scores. Data inputs from the paretic UE, rather than trunk, most strongly influenced model confidence. We demonstrate for the first time that using OOD detection with high-dimensional motion data can reveal clinically meaningful movement abnormality in subjects with chronic stroke.

The distribution of transcallosal inhibition to upper extremity muscles is altered in chronic stroke.

OBJECTIVE: To determine if the distribution of transcallosal inhibition (TI) acting on proximal and distal upper extremity muscles is altered in chronic stroke. METHODS: We examined thirteen healthy controls and sixteen mildly to moderately impaired chronic stroke patients. We used transcranial magnetic stimulation (TMS) to probe TI from the contralesional onto ipsilesional hemisphere (assigned in controls). We recorded the ipsilateral silent period in the paretic biceps (BIC) and first dorsal interosseous (FDI). We measured TI strength, distribution gradient (TI difference between muscles), and motor impairment (Fugl-Meyer Assessment). RESULTS: Both groups had stronger TI acting on their FDIs than BICs (p < 0.001). However, stroke patients also had stronger TI acting on their BICs than controls (p = 0.034), resulting in a flatter distribution of inhibition (p = 0.028). In patients, stronger FDI inhibition correlated with less hand impairment (p = 0.031); BIC inhibition was not correlated to impairment. CONCLUSION: TI is more evenly distributed to the paretic FDI and BIC in chronic stroke. The relative increase in proximal inhibition does not relate to better function, as it does distally. SIGNIFICANCE: The results expand our knowledge about segment-specific neurophysiology and its relevance to impairment after stroke.

Dual-Tasking in Daily Activities Among Adults With and Without Stroke.

IMPORTANCE: In laboratory settings, dual-tasking is a performance strategy affected by dominance and stroke. However, the volitional use of dual-tasking has not been examined during naturalistic performance of activities of daily living (ADLs). OBJECTIVE: To examine dual-tasking in the context of ADLs and identify whether dominance and stroke influence its use. DESIGN: Cross-sectional, observational. SETTING: Academic medical center. PARTICIPANTS: Forty-three participants with chronic stroke and upper extremity (UE) motor impairment and 19 control participants without stroke. OUTCOMES AND MEASURES: We identified dual-tasking as the performance of dual-object primitives (DOPs), a functional strategy to manage two objects simultaneously. We videotaped participants performing feeding and toothbrushing tasks and identified the initiation and frequency of DOPs. We assessed whether these outcomes were influenced by UE dominance or paresis and whether among participants with stroke these outcomes were influenced by motor impairment (using the Fugl-Meyer Assessment) or cognitive impairment (using the Montreal Cognitive Assessment). RESULTS: DOP initiation was reduced on the nondominant side of control UEs and in the paretic UE of participants with stroke. After DOPs were initiated, however, their frequency was not significantly related to dominance or paresis. Among participants with stroke, DOP initiation but not DOP frequency was influenced by motor impairment, and neither were influenced by cognitive impairment. CONCLUSIONS AND RELEVANCE: The initiation of dual-tasking is curtailed in the nondominant and paretic UEs, extending previous laboratory-based findings to a more naturalistic setting. These results may reflect a demand on neural resources that is exceeded when these limbs are used. What This Article Adds: DOPs, a functional strategy to simultaneously engage two objects during ADLs, could serve as a behavioral marker of dual-tasking in real-world activities, supporting their investigation more broadly. Practicing DOPs in rehabilitation could also train the integration of dual-tasking strategies in activity execution.

PrimSeq: A deep learning-based pipeline to quantitate rehabilitation training.

Stroke rehabilitation seeks to accelerate motor recovery by training functional activities, but may have minimal impact because of insufficient training doses. In animals, training hundreds of functional motions in the first weeks after stroke can substantially boost upper extremity recovery. The optimal quantity of functional motions to boost recovery in humans is currently unknown, however, because no practical tools exist to measure them during rehabilitation training. Here, we present PrimSeq, a pipeline to classify and count functional motions trained in stroke rehabilitation. Our approach integrates wearable sensors to capture upper-body motion, a deep learning model to predict motion sequences, and an algorithm to tally motions. The trained model accurately decomposes rehabilitation activities into elemental functional motions, outperforming competitive machine learning methods. PrimSeq furthermore quantifies these motions at a fraction of the time and labor costs of human experts. We demonstrate the capabilities of PrimSeq in previously unseen stroke patients with a range of upper extremity motor impairment. We expect that our methodological advances will support the rigorous measurement required for quantitative dosing trials in stroke rehabilitation.

Chronic Stroke Sensorimotor Impairment Is Related to Smaller Hippocampal Volumes: An ENIGMA Analysis.

Background Persistent sensorimotor impairments after stroke can negatively impact quality of life. The hippocampus is vulnerable to poststroke secondary degeneration and is involved in sensorimotor behavior but has not been widely studied within the context of poststroke upper-limb sensorimotor impairment. We investigated associations between non-lesioned hippocampal volume and upper limb sensorimotor impairment in people with chronic stroke, hypothesizing that smaller ipsilesional hippocampal volumes would be associated with greater sensorimotor impairment. Methods and Results Cross-sectional T1-weighted magnetic resonance images of the brain were pooled from 357 participants with chronic stroke from 18 research cohorts of the ENIGMA (Enhancing NeuoImaging Genetics through Meta-Analysis) Stroke Recovery Working Group. Sensorimotor impairment was estimated from the FMA-UE (Fugl-Meyer Assessment of Upper Extremity). Robust mixed-effects linear models were used to test associations between poststroke sensorimotor impairment and hippocampal volumes (ipsilesional and contralesional separately; Bonferroni-corrected, P<0.025), controlling for age, sex, lesion volume, and lesioned hemisphere. In exploratory analyses, we tested for a sensorimotor impairment and sex interaction and relationships between lesion volume, sensorimotor damage, and hippocampal volume. Greater sensorimotor impairment was significantly associated with ipsilesional (P=0.005; ß=0.16) but not contralesional (P=0.96; ß=0.003) hippocampal volume, independent of lesion volume and other covariates (P=0.001; ß=0.26). Women showed progressively worsening sensorimotor impairment with smaller ipsilesional (P=0.008; ß=-0.26) and contralesional (P=0.006; ß=-0.27) hippocampal volumes compared with men. Hippocampal volume was associated with lesion size (P<0.001; ß=-0.21) and extent of sensorimotor damage (P=0.003; ß=-0.15). Conclusions The present study identifies novel associations between chronic poststroke sensorimotor impairment and ipsilesional hippocampal volume that are not caused by lesion size and may be stronger in women.

The use of wearable sensors to assess and treat the upper extremity after stroke: a scoping review.

PURPOSE: To address the gap in the literature and clarify the expanding role of wearable sensor data in stroke rehabilitation, we summarized the methods for upper extremity (UE) sensor-based assessment and sensor-based treatment. MATERIALS AND METHODS: The guideline outlined by the preferred reporting items for systematic reviews and meta-analysis extension for scoping reviews was used to complete this scoping review. Information pertaining to participant demographics, sensory information, data collection, data processing, data analysis, and study results were extracted from the studies for analysis and synthesis. RESULTS: We included 43 articles in the final review. We organized the results into assessment and treatment categories. The included articles used wearable sensors to identify UE functional motion, categorize motor impairment/activity limitation, and quantify real-world use. Wearable sensors were also used to augment UE training by triggering sensory cues or providing instructional feedback about the affected UE. CONCLUSIONS: Sensors have the potential to greatly expand assessment and treatment beyond traditional clinic-based approaches. This capability could support the quantification of rehabilitation dose, the nuanced assessment of impairment and activity limitation, the characterization of daily UE use patterns in real-world settings, and augment UE training adherence for home-based rehabilitation.IMPLICATIONS FOR REHABILITATIONSensor data have been used to assess UE functional motion, motor impairment/activity limitation, and real-world use.Sensor-assisted treatment approaches are emerging, and may be a promising tool to augment UE adherence in home-based rehabilitation.Wearable sensors may extend our ability to objectively assess UE motion beyond supervised clinical settings, and into home and community settings.

StrokeRehab: A Benchmark Dataset for Sub-second Action Identification.

Automatic action identification from video and kinematic data is an important machine learning problem with applications ranging from robotics to smart health. Most existing works focus on identifying coarse actions such as running, climbing, or cutting vegetables, which have relatively long durations and a complex series of motions. This is an important limitation for applications that require identification of more elemental motions at high temporal resolution. For example, in the rehabilitation of arm impairment after stroke, quantifying the training dose (number of repetitions) requires differentiating motions with sub-second durations. Our goal is to bridge this gap. To this end, we introduce a large-scale, multimodal dataset, StrokeRehab, as a new action-recognition benchmark that includes elemental short-duration actions labeled at a high temporal resolution. StrokeRehab consists of high-quality inertial measurement unit sensor and video data of 51 stroke-impaired patients and 20 healthy subjects performing activities of daily living like feeding, brushing teeth, etc. Because it contains data from both healthy and impaired individuals, StrokeRehab can be used to study the influence of distribution shift in action-recognition tasks. When evaluated on StrokeRehab, current state-of-the-art models for action segmentation produce noisy predictions, which reduces their accuracy in identifying the corresponding sequence of actions. To address this, we propose a novel approach for high-resolution action identification, inspired by speech-recognition techniques, which is based on a sequence-to-sequence model that directly predicts the sequence of actions. This approach outperforms current state-of-the-art methods on StrokeRehab, as well as on the standard benchmark datasets 50Salads, Breakfast, and Jigsaws.

NE-Motion: Visual Analysis of Stroke Patients Using Motion Sensor Networks.

A large number of stroke survivors suffer from a significant decrease in upper extremity (UE) function, requiring rehabilitation therapy to boost recovery of UE motion. Assessing the efficacy of treatment strategies is a challenging problem in this context, and is typically accomplished by observing the performance of patients during their execution of daily activities. A more detailed assessment of UE impairment can be undertaken with a clinical bedside test, the UE Fugl-Meyer Assessment, but it fails to examine compensatory movements of functioning body segments that are used to bypass impairment. In this work, we use a graph learning method to build a visualization tool tailored to support the analysis of stroke patients. Called NE-Motion, or Network Environment for Motion Capture Data Analysis, the proposed analytic tool handles a set of time series captured by motion sensors worn by patients so as to enable visual analytic resources to identify abnormalities in movement patterns. Developed in close collaboration with domain experts, NE-Motion is capable of uncovering important phenomena, such as compensation while revealing differences between stroke patients and healthy individuals. The effectiveness of NE-Motion is shown in two case studies designed to analyze particular patients and to compare groups of subjects.

Corticoreticulospinal tract neurophysiology in an arm and hand muscle in healthy and stroke subjects.

KEY POINTS: The corticoreticulospinal tract (CReST) is a descending motor pathway that reorganizes after corticospinal tract (CST) injury in animals. In humans, the pattern of CReST innervation to upper limb muscles has not been carefully examined in healthy individuals or individuals with CST injury. In the present study, we assessed CReST projections to an arm and hand muscle on the same side of the body in healthy and chronic stoke subjects using transcranial magnetic stimulation. We show that CReST connection strength to the muscles differs between healthy and stroke subjects, with stronger connections to the hand than arm in healthy subjects, and stronger connections to the arm than hand in stroke subjects. These results help us better understand CReST innervation patterns in the upper limb, and may point to its role in normal motor function and motor recovery in humans. ABSTRACT: The corticoreticulospinal tract (CReST) is a major descending motor pathway in many animals, but little is known about its innervation patterns in proximal and distal upper extremity muscles in humans. The contralesional CReST furthermore reorganizes after corticospinal tract (CST) injury in animals, but it is less clear whether CReST innervation changes after stroke in humans. We thus examined CReST functional connectivity, connection strength, and modulation in an arm and hand muscle of healthy (n = 15) and chronic stroke (n = 16) subjects. We delivered transcranial magnetic stimulation to the contralesional hemisphere (assigned in healthy subjects) to elicit ipsilateral motor evoked potentials (iMEPs) from the paretic biceps (BIC) and first dorsal interosseous (FDI) muscle. We operationalized CReST functional connectivity as iMEP presence/absence, CReST projection strength as iMEP size and CReST modulation as change in iMEP size by head rotation. We found comparable CReST functional connectivity to the BICs and FDIs in both subject groups. However, the pattern of CReST connection strength to the muscles diverged between groups, with stronger connections to FDIs than BICs in healthy subjects and stronger connections to BICs than FDIs in stroke subjects. Head rotation modulated only FDI iMEPs of healthy subjects. Our findings indicate that the healthy CReST does not have a proximal innervation bias, and its strong FDI connections may have functional relevance to finger individuation. The reversed CReST innervation pattern in stroke subjects confirms its reorganization after CST injury, and its strong BIC connections may indicate upregulation for particular upper extremity muscles or their functional actions.

Towards data-driven stroke rehabilitation via wearable sensors and deep learning.

Recovery after stroke is often incomplete, but rehabilitation training may potentiate recovery by engaging endogenous neuroplasticity. In preclinical models of stroke, high doses of rehabilitation training are required to restore functional movement to the affected limbs of animals. In humans, however, the necessary dose of training to potentiate recovery is not known. This ignorance stems from the lack of objective, pragmatic approaches for measuring training doses in rehabilitation activities. Here, to develop a measurement approach, we took the critical first step of automatically identifying functional primitives, the basic building block of activities. Forty-eight individuals with chronic stroke performed a variety of rehabilitation activities while wearing inertial measurement units (IMUs) to capture upper body motion. Primitives were identified by human labelers, who labeled and segmented the associated IMU data. We performed automatic classification of these primitives using machine learning. We designed a convolutional neural network model that outperformed existing methods. The model includes an initial module to compute separate embeddings of different physical quantities in the sensor data. In addition, it replaces batch normalization (which performs normalization based on statistics computed from the training data) with instance normalization (which uses statistics computed from the test data). This increases robustness to possible distributional shifts when applying the method to new patients. With this approach, we attained an average classification accuracy of 70%. Thus, using a combination of IMU-based motion capture and deep learning, we were able to identify primitives automatically. This approach builds towards objectively-measured rehabilitation training, enabling the identification and counting of functional primitives that accrues to a training dose.

The Pragmatic Classification of Upper Extremity Motion in Neurological Patients: A Primer.

Recent advances in wearable sensor technology and machine learning (ML) have allowed for the seamless and objective study of human motion in clinical applications, including Parkinson's disease, and stroke. Using ML to identify salient patterns in sensor data has the potential for widespread application in neurological disorders, so understanding how to develop this approach for one's area of inquiry is vital. We previously proposed an approach that combined wearable inertial measurement units (IMUs) and ML to classify motions made by stroke patients. However, our approach had computational and practical limitations. We address these limitations here in the form of a primer, presenting how to optimize a sensor-ML approach for clinical implementation. First, we demonstrate how to identify the ML algorithm that maximizes classification performance and pragmatic implementation. Second, we demonstrate how to identify the motion capture approach that maximizes classification performance but reduces cost. We used previously collected motion data from chronic stroke patients wearing off-the-shelf IMUs during a rehabilitation-like activity. To identify the optimal ML algorithm, we compared the classification performance, computational complexity, and tuning requirements of four off-the-shelf algorithms. To identify the optimal motion capture approach, we compared the classification performance of various sensor configurations (number and location on the body) and sensor type (IMUs vs. accelerometers). Of the algorithms tested, linear discriminant analysis had the highest classification performance, low computational complexity, and modest tuning requirements. Of the sensor configurations tested, seven sensors on the paretic arm and trunk led to the highest classification performance, and IMUs outperformed accelerometers. Overall, we present a refined sensor-ML approach that maximizes both classification performance and pragmatic implementation. In addition, with this primer, we showcase important considerations for appraising off-the-shelf algorithms and sensors for quantitative motion assessment.

A Taxonomy of Functional Upper Extremity Motion.

Background: Functional upper extremity (UE) motion enables humans to execute activities of daily living (ADLs). There currently exists no universal language to systematically characterize this type of motion or its fundamental building blocks, called functional primitives. Without a standardized classification approach, pooling mechanistic knowledge and unpacking rehabilitation content will remain challenging. Methods: We created a taxonomy to characterize functional UE motions occurring during ADLs, classifying them by motion presence, temporal cyclicity, upper body effector, and contact type. We identified five functional primitives by their phenotype and purpose: reach, reposition, transport, stabilize, and idle. The taxonomy was assessed for its validity and interrater reliability in right-paretic chronic stroke patients performing a selection of ADL tasks. We applied the taxonomy to identify the primitive content and motion characteristics of these tasks, and to evaluate the influence of impairment level on these outcomes. Results: The taxonomy could account for all motions in the sampled activities. Interrater reliability was high for primitive identification (Cohen's kappa = 0.95-0.99). Using the taxonomy, the ADL tasks were found to be composed primarily of transport and stabilize primitives mainly executed with discrete, proximal motions. Compared to mildly impaired patients, moderately impaired patients used more repeated reaches and axial-proximal UE motion to execute the tasks. Conclusions: The proposed taxonomy yields objective, quantitative data on human functional UE motion. This new method could facilitate the decomposition and quantification of UE rehabilitation, the characterization of functional abnormality after stroke, and the mechanistic examination of shared behavior in motor studies.

Differential Poststroke Motor Recovery in an Arm Versus Hand Muscle in the Absence of Motor Evoked Potentials.

Background. After stroke, recovery of movement in proximal and distal upper extremity (UE) muscles appears to follow different time courses, suggesting differences in their neural substrates. Objective. We sought to determine if presence or absence of motor evoked potentials (MEPs) differentially influences recovery of volitional contraction and strength in an arm muscle versus an intrinsic hand muscle. We also related MEP status to recovery of proximal and distal interjoint coordination and movement fractionation, as measured by the Fugl-Meyer Assessment (FMA). Methods. In 45 subjects in the year following ischemic stroke, we tracked the relationship between corticospinal tract (CST) integrity and behavioral recovery in the biceps (BIC) and first dorsal interosseous (FDI) muscle. We used transcranial magnetic stimulation to probe CST integrity, indicated by MEPs, in BIC and FDI. We used electromyography, dynamometry, and UE FMA subscores to assess muscle-specific contraction, strength, and inter-joint coordination, respectively. Results. Presence of MEPs resulted in higher likelihood of muscle contraction, greater strength, and higher FMA scores. Without MEPs, BICs could more often volitionally contract, were less weak, and had steeper strength recovery curves than FDIs; in contrast, FMA recovery curves plateaued below normal levels for both the arm and hand. Conclusions. There are shared and separate substrates for paretic UE recovery. CST integrity is necessary for interjoint coordination in both segments and for overall recovery. In its absence, alternative pathways may assist recovery of volitional contraction and strength, particularly in BIC. These findings suggest that more targeted approaches might be needed to optimize UE recovery.

Rethinking interhemispheric imbalance as a target for stroke neurorehabilitation.

OBJECTIVE: Patients with chronic stroke have been shown to have failure to release interhemispheric inhibition (IHI) from the intact to the damaged hemisphere before movement execution (premovement IHI). This inhibitory imbalance was found to correlate with poor motor performance in the chronic stage after stroke and has since become a target for therapeutic interventions. The logic of this approach, however, implies that abnormal premovement IHI is causal to poor behavioral outcome and should therefore be present early after stroke when motor impairment is at its worst. To test this idea, in a longitudinal study, we investigated interhemispheric interactions by tracking patients' premovement IHI for one year following stroke. METHODS: We assessed premovement IHI and motor behavior five times over a 1-year period after ischemic stroke in 22 patients and 11 healthy participants. RESULTS: We found that premovement IHI was normal during the acute/subacute period and only became abnormal at the chronic stage; specifically, release of IHI in movement preparation worsened as motor behavior improved. In addition, premovement IHI did not correlate with behavioral measures cross-sectionally, whereas the longitudinal emergence of abnormal premovement IHI from the acute to the chronic stage was inversely correlated with recovery of finger individuation. INTERPRETATION: These results suggest that interhemispheric imbalance is not a cause of poor motor recovery, but instead might be the consequence of underlying recovery processes. These findings call into question the rehabilitation strategy of attempting to rebalance interhemispheric interactions in order to improve motor recovery after stroke. Ann Neurol 2019;85:502-513.