Faculty Bibliography

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; β=0.16) but not contralesional (P=0.96; β=0.003) hippocampal volume, independent of lesion volume and other covariates (P=0.001; β=0.26). Women showed progressively worsening sensorimotor impairment with smaller ipsilesional (P=0.008; β=-0.26) and contralesional (P=0.006; β=-0.27) hippocampal volumes compared with men. Hippocampal volume was associated with lesion size (P<0.001; β=-0.21) and extent of sensorimotor damage (P=0.003; β=-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.

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.

KEY POINTS/CONCLUSIONS: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/UNASSIGNED: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.

PURPOSE/UNASSIGNED: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/UNASSIGNED: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/UNASSIGNED: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/UNASSIGNED: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.

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.

Introduction: Activities of daily living (ADLs) are performed through a sequence of fundamental units of motion, called primitives. We previously observed that during ADLs, one upper extremity (UE) may engage two objects simultaneously, such as turning on a faucet while holding a toothbrush. These dual-object primitives (DOPs) may demand increased neural resources, as they likely entail the simultaneous execution of two motor plans. Skilled movement by the nondominant healthy UE or the paretic UE has also been found to require increased neural activity. We posited that performance of DOPs would exceed the neural resources available to the nondominant or paretic side, reducing their performance on these sides. We also predicted that the frequency of DOP performance by the paretic UE would relate to its degree of motor impairment.
Method(s): We studied 19 right-hand dominant healthy subjects (10M:9F; 62.0 +/- 13.6 years) and 43 premorbidly right-hand dominant stroke subjects (23M:20F; 24L:19R paretic; 57.5 +/- 14.5 years; 5.7 +/- 6.5 years post stroke). We evaluated subjects on the UE Fugl-Meyer Assessment (FMA) and videotaped their performance of a feeding and toothbrushing task. We analyzed the videos to extract the incidence and count of DOP performance by each UE. To control for dominance and paresis, we normalized DOP counts to the total number of primitives performed by the UE. We used two-tailed Fisher's Exact tests to compare the incidence of DOPs performed by each UE, and Spearman's correlation to examine the relationship between FMA score and DOP frequency.
Result(s): In healthy subjects, the incidence of DOPs was lower on the nondominant than dominant side (12/19 vs. 19/19; p<0.01). In stroke subjects, the incidence of DOPs was lower on the paretic than nonparetic side (19/43 vs. 43/43; p<0.01). The laterality of paresis did not affect whether that UE would perform DOPs (11/19 dominant paretic vs. 8/24 nondominant paretic; p=0.132). In stroke subjects, lower FMA scores were related to a lower frequency of DOP performance on their paretic UE (rho=0.368, p=0.015).
Discussion(s): Our results suggest that UE laterality and impairment may impact DOP performance in healthy and stroke subjects, respectively. DOPs were less commonly performed by the nondominant UE and the paretic UE, and worse impairment was associated with lower DOP performance. We speculate that engaging two objects simultaneously requires additional neural resources that are unavailable to the nondominant or injured motor network. It is conceivable that the return of DOP performance by the paretic UE may track with the availability of a recovered neural substrate.

Introduction: With the corticospinal tract (CST), the corticoreticulospinal tract (CReST) is a major descending motor pathway with widespread bilateral innervation. In animals, CST damage causes a loss of motor control and prompts reorganization in the CReST, possibly with stronger connectivity to arm flexors (e.g. biceps (BIC)) than finger abductors (e.g. first dorsal interosseous (FDI)). CReST reorganization may also contribute to widespread muscle co-activations (i.e. abnormal synergy expression) in the paretic upper extremity (UE). Here, we posited that CReST reorganization after stroke targets the BIC more than the FDI in humans. We predicted that CReST activity, manifesting as abnormal synergy expression, would be more strongly evoked by skilled arm flexion than finger abduction in stroke patients.
Method(s): We studied the paretic UE of 14 chronic stroke patients (F: 8; mean age: 64 (44-85) years; mean post-stroke time: 5 (0.5-14.4) years) and the matched UE of 14 healthy controls (F: 6; mean age: 55 (36-81) years). Subjects used their arm or index finger to move an onscreen cursor through an arc-shaped channel while the remainder of the UE was restrained.We recorded effector kinematics with an infrared camera and electromyographic (EMG) signals from triceps (TRI), deltoid (DLT), BIC, extensor digitorum, flexor carpi radialis (FCR), flexor digitorum superficialis (FDS), and FDI. To quantify movement error, we calculated the average radial distance between the cursor path and the outer channel edge. To quantify abnormal muscle synergies, we applied a non-negative matrix factorization algorithm to the EMG data to identify muscle synergies and calculated the similarity of the synergy vectors between patients and controls; higher similarity scores indicate more normal synergy patterns. We calculated muscle co-activations using correlations between EMG signals of each muscle-pair. We examined group differences with independent t-tests and control-synergy relationships with correlations.
Result(s): Movement errors were higher in patients than controls for the arm (p<0.01) and trended higher for the finger (p=0.074). In the arm, movement errors were inversely related to synergy similarity scores (p<0.01). Higher errors also related to greater FDI-FCR, BIC-TRI, BIC-DLT, and TRI-DLT coactivation (all p<0.05). In the finger, movement errors were unrelated to synergy similarity scores. Lower movement errors related to greater FDSTRI co-activation (p<0.05).
Discussion(s): In the arm, we found that as motor control worsened, the expression of abnormal synergies increased, indicating that CReST activation may increase with loss of CST function. Muscle co-activation was widespread in the UE, in keeping with CReST's multilevel spinal branching. We did not find a relationship between motor control and synergy expression with finger movement, although the long-range co-contraction between the FDS and TRI may speak to a CST-driven stabilizing strategy. Our findings strengthen the notion that CReST reorganization after stroke may preferentially target the arm flexor and its synergies.

Introduction: Quantifying upper extremity (UE) motor impairment after stroke is impractical, limiting our ability to tailor rehabilitation training in real time. The current gold-standard measure of impairment, the Fugl-Meyer Assessment (FMA), is time-consuming and requires a trained assessor. The FMA furthermore does not assess functional motions in real-world contexts, which is exactly where we aim our rehabilitation interventions. Here, we took initial steps to develop an approach to automatically quantify UE motor impairment during functional task performance.
Method(s): We studied 51 chronic stroke patients (28F:23M; 57.7 (21.3-84.3) years old; 28L:23R paretic; FMA 43.1 (8-65)).We recorded upper body motion with 9 inertial measurement units (IMUs) while patients performed the FMA and a functional task (moving an object on a horizontal 8-target array). We trained a long short-term memory (LSTM) deep learning model to estimate FMA scores from the recorded motion (training set n=40; test set n=11; 4 LSTM layers with between-layer batch normalization; IMU data windows of 4s with slide of 1s). LSTM-generated impairment scores were computed from FMA motions or from functional motions. To ascertain the accuracy of the approach, we calculated the root mean square error (RMSE) and the Spearman correlation coefficient (rho) between the LSTM scores and the FMA scores from a trained expert. We also examined whether the performance of particular classes of functional primitives (i.e. reach, transport, or reposition) would be sufficient to accurately estimate impairment.
Result(s): Using motions from the FMA performance, our approach estimated FMA scores within 1.1 points of a trained assessor. Using motions from the functional task performance, our approach estimated FMA scores within 1.6 points. Correlation values between the FMA scores and LSTM scores were rho = 0.98 for FMA motions and rho = 0.96 for functional motions. Among the three functional primitives, reaches were the most informative for estimating the impairment scores (RMSE: 1.9 points), followed by transports (RMSE: 2.1 points), and repositions (RMSE: 2.8 points).
Discussion(s): We present a new approach that uses sensor-based motion capture and deep learning to automatically estimate UE motor impairment. This approach has high accuracy and shows high concurrent validity with the FMA, even when it assesses unrelated functional motions. Thus, it may be possible to directly measure impairment from performance of real-world functional tasks, which the FMA does not offer. Estimating impairment during stroke rehabilitation would enable clinicians to tailor treatment strategy in real time.