Corrigendum: Changes in primary somatosensory cortex following allogeneic hand transplantation or autogenic hand replantation.

[This corrects the article DOI: 10.3389/fnimg.2022.919694.].

Failure to Compensate: Patients With Nerve Injury Use Their Injured Dominant Hand, Even When Their Nondominant Is More Dexterous.

OBJECTIVE: To identify how individuals respond to unilateral upper extremity peripheral nerve injury via compensation (increased use of the nondominant hand). We hypothesized that injury to the dominant hand would have a greater effect on hand use (left vs right choices). We also hypothesized that compensation would not depend on current (postinjury) nondominant hand performance because many patients undergo rehabilitation that is not designed to alter hand use. DESIGN: Observational survey, single-arm. SETTINGS: Academic research institution and referral center. PARTICIPANTS: A total of 48 adults (N=48) with unilateral upper extremity peripheral nerve injury. Another 14 declined participation. Referred sample, including all eligible patients from 16 months at 1 nerve injury clinic and 1 hand therapy clinic. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Hand use (% of actions with each hand) via Block Building Task. Dexterity via Jebsen-Taylor Hand Function. RESULTS: Participants preferred their dominant hand regardless of whether it was injured: hand usage (dominant/nondominant) did not differ from typical adults, regardless of injured side (P>.07), even though most participants (77%) were more dexterous with their uninjured nondominant hand (mean asymmetry index, -0.16±0.25). The Block Building Task was sensitive to hand dominance (P=2 × 10-4) and moderately correlated with Motor Activity Log amount scores (r2=0.33, P<.0001). Compensation was associated only with dominant hand dexterity (P=3.9 × 10-3), not on nondominant hand dexterity, rehabilitation, or other patient and/or injury factors (P>.1). CONCLUSIONS: Patients with peripheral nerve injury with dominant hand injury do not compensate with their unaffected nondominant hand, even if it is more dexterous. For the subset of patients unlikely to recover function with the injured hand, they could benefit from rehabilitation that encourages compensation with the nondominant hand.

Measuring Activities of Daily Living in Stroke Patients with Motion Machine Learning Algorithms: A Pilot Study.

Measuring activities of daily living (ADLs) using wearable technologies may offer higher precision and granularity than the current clinical assessments for patients after stroke. This study aimed to develop and determine the accuracy of detecting different ADLs using machine-learning (ML) algorithms and wearable sensors. Eleven post-stroke patients participated in this pilot study at an ADL Simulation Lab across two study visits. We collected blocks of repeated activity ("atomic" activity) performance data to train our ML algorithms during one visit. We evaluated our ML algorithms using independent semi-naturalistic activity data collected at a separate session. We tested Decision Tree, Random Forest, Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost) for model development. XGBoost was the best classification model. We achieved 82% accuracy based on ten ADL tasks. With a model including seven tasks, accuracy improved to 90%. ADL tasks included chopping food, vacuuming, sweeping, spreading jam or butter, folding laundry, eating, brushing teeth, taking off/putting on a shirt, wiping a cupboard, and buttoning a shirt. Results provide preliminary evidence that ADL functioning can be predicted with adequate accuracy using wearable sensors and ML. The use of external validation (independent training and testing data sets) and semi-naturalistic testing data is a major strength of the study and a step closer to the long-term goal of ADL monitoring in real-world settings. Further investigation is needed to improve the ADL prediction accuracy, increase the number of tasks monitored, and test the model outside of a laboratory setting.

Interhemispheric transfer of post-amputation cortical plasticity within the human somatosensory cortex.

Animal models reveal that deafferenting forelimb injuries precipitate reorganization in both contralateral and ipsilateral somatosensory cortices. The functional significance and duration of these effects are unknown, and it is unclear whether they also occur in injured humans. We delivered cutaneous stimulation during functional magnetic resonance imaging (fMRI) to map the sensory cortical representation of the intact hand and lower face in a group of chronic, unilateral, upper extremity amputees (N = 19) and healthy matched controls (N = 29). Amputees exhibited greater activity than controls within the deafferented former sensory hand territory (S1f) during stimulation of the intact hand, but not of the lower face. Despite this cortical reorganization, amputees did not differ from controls in tactile acuity on their intact hands. S1f responses during hand stimulation were unrelated to tactile acuity, pain, prosthesis usage, or time since amputation. These effects appeared specific to the deafferented somatosensory modality, as fMRI visual mapping paradigm failed to detect any differences between groups. We conclude that S1f becomes responsive to cutaneous stimulation of the intact hand of amputees, and that this modality-specific reorganizational change persists for many years, if not indefinitely. The functional relevance of these changes, if any, remains unknown.