Accurate lesion segmentation is critical in stroke rehabilitation research for the quantification of lesion burden and accurate image processing. Current automated lesion segmentation methods for T1-weighted (T1w) MRIs, commonly used in stroke research, lack accuracy and reliability. Manual segmentation remains the gold standard, but it is time-consuming, subjective, and requires neuroanatomical expertise. We previously released an open-source dataset of stroke T1w MRIs and manually-segmented lesion masks (ATLAS v1.2, N = 304) to encourage the development of better algorithms. However, many methods developed with ATLAS v1.2 report low accuracy, are not publicly accessible or are improperly validated, limiting their utility to the field. Here we present ATLAS v2.0 (N = 1271), a larger dataset of T1w MRIs and manually segmented lesion masks that includes training (n = 655), test (hidden masks, n = 300), and generalizability (hidden MRIs and masks, n = 316) datasets. Algorithm development using this larger sample should lead to more robust solutions; the hidden datasets allow for unbiased performance evaluation via segmentation challenges. We anticipate that ATLAS v2.0 will lead to improved algorithms, facilitating large-scale stroke research.

Stroke is a leading cause of long-term disability in the United States. Recent studies have shown that high doses of repeated task-specific practice can be effective at improving upper-limb function at the chronic stage. Providing at-home telerehabilitation services with therapist supervision may allow higher dose interventions targeted to this population. Additionally, muscle biofeedback to train patients to avoid unwanted simultaneous activation of antagonist muscles (co-contractions) may be incorporated into telerehabilitation technologies to improve motor control. Here, we present the development and feasibility of a low-cost, portable, telerehabilitation biofeedback system called Tele-REINVENT. We describe our modular electromyography acquisition, processing, and feedback algorithms to train differentiated muscle control during at-home therapist-guided sessions. Additionally, we evaluated the performance of low-cost sensors for our training task with two healthy individuals. Finally, we present the results of a case study with a stroke survivor who used the system for 40 sessions over 10 weeks of training. In line with our previous research, our results suggest that using low-cost sensors provides similar results to those using research-grade sensors for low forces during an isometric task. Our preliminary case study data with one patient with stroke also suggest that our system is feasible, safe, and enjoyable to use during 10 weeks of biofeedback training, and that improvements in differentiated muscle activity during volitional movement attempt may be induced during a 10-week period. Our data provide support for using low-cost technology for individuated muscle training to reduce unintended coactivation during supervised and unsupervised home-based telerehabilitation for clinical populations, and suggest this approach is safe and feasible. Future work with larger study populations may expand on the development of meaningful and personalized chronic stroke rehabilitation.

Accurate lesion segmentation is critical in stroke rehabilitation research for the quantification of lesion burden and accurate image processing. Current automated lesion segmentation methods for T1-weighted (T1w) MRIs, commonly used in rehabilitation research, lack accuracy and reliability. Manual segmentation remains the gold standard, but it is time-consuming, subjective, and requires significant neuroanatomical expertise. We previously released a large, open-source dataset of stroke T1w MRIs and manually segmented lesion masks (ATLAS v1.2, N=304) to encourage the development of better algorithms. However, many methods developed with ATLAS v1.2 report low accuracy, are not publicly accessible or are improperly validated, limiting their utility to the field. Here we present ATLAS v2.0 (N=955), a larger dataset of T1w stroke MRIs and manually segmented lesion masks that includes both training (public) and test (hidden) data. Algorithm development using this larger sample should lead to more robust solutions, and the hidden test data allows for unbiased performance evaluation via segmentation challenges. We anticipate that ATLAS v2.0 will lead to improved algorithms, facilitating large-scale stroke rehabilitation research.

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.

Importance: Virtual reality in head-mounted displays (HMD-VR) may be a valuable tool in occupational therapy to address anxiety. Findings from the virtual reality exposure therapy (VRET) literature may facilitate translation of HMD-VR to occupational therapy psychosocial practice. Objective: To explore how HMD-VR has been used to treat anxiety through VRET and could be translated to occupational therapy. Data Sources: We searched seven electronic databases for articles published between 2000 and 2020: CINAHL, Cochrane Library, Embase, ERIC, Ovid MEDLINE, PsycINFO, and Web of Science. Search terms included HMD-VR constructs, products, and therapy concepts. Study Selection and Data Collection: We used Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to report studies implementing VRET to treat anxiety. At least two reviewers assessed each citation, and a third resolved disagreements. Articles were included if they were in English, reported experimental data, and used HMD-VR. Letters, commentaries, book chapters, technical descriptions, theoretical papers, conference proceedings (≤4 pages), and reviews were excluded. Findings: Twenty-eight studies used HMD-VR to treat posttraumatic stress disorder (n = 3), specific phobias (n = 19), and performance-based social anxiety (n = 6); protocols and levels of evidence varied (randomized controlled trials, n = 11; controlled trials without randomization, n = 6; case–control or cohort studies, n = 11). Qualitative examination indicates HMD-VR is an effective treatment tool. Conclusions and Relevance: HMD-VR can be a valuable tool for occupational therapy to simulate environments where clients with anxiety disorders participate. Eliciting presence through multisensory features and body representation may enhance outcomes. What This Article Adds: Drawing from the VRET literature, this scoping review suggests that HMD-VR can be used by occupational therapy practitioners to simulate ecologically valid environments, evaluate client responses to fearful stimuli, and remediate anxiety though immersion in virtual tasks when participation in natural contexts is unfeasible. Having ecologically valid environments is particularly important for people with anxiety disorders because they need support to cope when they encounter triggers in everyday life environments.