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.

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.

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.

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.

Background: The corticoreticulospinal tract (CReST) is a major descending motor pathway in humans, but little is known about its relative innervation of proximal versus distal upper extremity (UE) muscles. In addition, CReST is believed to reorganize after corticospinal injury, but changes in its projections to different paretic muscles remain unknown. Here, we used transcranial magnetic stimulation (TMS) to probe the functional connectivity of the contralesional CReST to an arm muscle (biceps (BIC)) and an intrinsic hand muscle (first dorsal interosseous (FDI)) in healthy and stroke subjects.
Method(s): In this cross-sectional observational study, we examined 15 healthy (F: 7; mean age: 54 (44-81) years; mean UE Fugl-Meyer Assessment (FMA) score: 65 (63-66)) and 16 chronic stroke subjects (F: 10; mean age 62 (44-85) years; mean UE FMA score: 49 (23-64); mean time since stroke: 5 (0.5-14.4) years). We applied TMS to the contralesional hemisphere (assigned in healthy subjects) to elicit ipsilateral motor evoked potentials (iMEPs). We measured contralesional CReST functional connectivity (iMEP presence/absence) and projection strength (iMEP size; mV*ms) to the paretic BIC and FDI. We also measured paretic muscle maximum voluntary contraction and segmental FMA subscores. We examined differences in CReST projections between muscles and subject groups using Fisher's exact tests and general linear mixed models, and examined neurophysiologicalbehavioral relationships with Pearson's and Spearman's correlations.
Result(s): The contralesional CReST made functional connections to both muscles of most subjects (iMEP presence/absence: healthy BIC 14/1, healthy FDI 15/0; stroke BIC 11/5, stroke FDI 15/1). CReST functional connectivity did not differ between muscles in either healthy or stroke subjects (all p>0.172), and did not differ between subject groups for either muscle (all p=1.0). However, CReST projection strength for the muscles diverged between subject groups, manifesting as larger iMEPs in FDIs than BICs in healthy subjects (1.9 mV*ms, p=0.042) and larger iMEPs in BICs than FDIs in stroke subjects (1.0 mV*ms, p=0.042). Muscle iMEP sizes did not significantly differ between healthy and stroke subjects. Muscle strength related to iMEP size in only the paretic BIC of stroke subjects (r(6)=0.853, p=0.007). There was no relationship between FMA subscores and iMEP size for either muscle in either subject group.
Conclusion(s): Our findings indicate that the contralesional CReST has readily identifiable connections to the paretic BIC and FDI. In healthy subjects, the identification of a stronger CReST projection strength to the FDI challenges the notion of a proximal innervation bias by the reticulospinal tract. The shift in projection strength to the BIC after stroke reinforces the concept that the CReST reorganizes after CST injury, with circumscribed behavioral relevance. To confirm a recovery role of the CReST, a longitudinal observation of recovering behavior relating to changing CReST neurophysiology is required.

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.