Faculty Bibliography

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.

BACKGROUND AND OBJECTIVES:Functional outcomes after stroke are strongly related to focal injury measures. However, the role of global brain health is less clear. In this study, we examined the impact of brain age, a measure of neurobiological aging derived from whole-brain structural neuroimaging, on poststroke outcomes, with a focus on sensorimotor performance. We hypothesized that more lesion damage would result in older brain age, which would in turn be associated with poorer outcomes. Related, we expected that brain age would mediate the relationship between lesion damage and outcomes. Finally, we hypothesized that structural brain resilience, which we define in the context of stroke as younger brain age given matched lesion damage, would differentiate people with good vs poor outcomes. METHODS:We conducted a cross-sectional observational study using a multisite dataset of 3-dimensional brain structural MRIs and clinical measures from the ENIGMA Stroke Recovery. Brain age was calculated from 77 neuroanatomical features using a ridge regression model trained and validated on 4,314 healthy controls. We performed a 3-step mediation analysis with robust mixed-effects linear regression models to examine relationships between brain age, lesion damage, and stroke outcomes. We used propensity score matching and logistic regression to examine whether brain resilience predicts good vs poor outcomes in patients with matched lesion damage. RESULTS:= 0.004). DISCUSSION:We provide evidence that younger brain age is associated with superior poststroke outcomes and modifies the impact of focal damage. The inclusion of imaging-based assessments of brain age and brain resilience may improve the prediction of poststroke outcomes compared with focal injury measures alone, opening new possibilities for potential therapeutic targets.

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/CONCLUSIONS: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/CONCLUSIONS:The results expand our knowledge about segment-specific neurophysiology and its relevance to impairment after stroke.

IMPORTANCE/OBJECTIVE: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/METHODS:Cross-sectional, observational. SETTING/METHODS:Academic medical center. PARTICIPANTS/METHODS:Forty-three participants with chronic stroke and upper extremity (UE) motor impairment and 19 control participants without stroke. OUTCOMES AND MEASURES/METHODS: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/CONCLUSIONS: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.

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.