External Validation of a Dynamic Prediction Model for Upper Limb Function After Stroke.

Objective: To externally validate the dynamic prediction model for prediction of upper limb (UL) function 6 months after stroke. The dynamic prediction model has been developed and cross-validated on data from 4 Dutch studies. Design: Data from a prospective Danish cohort study were used to assess prediction accuracy. Setting: A Danish neurorehabilitation hospital. Participants: In this external validation study, follow-up data for 80 patients in the subacute phase after stroke (N=80), mean age 64 (SD11), 43% women, could be obtained. They were assessed at 2 weeks, 3 months, and 6 months after stroke with the Action Research Arm Test (ARAT), Fugl-Meyer Motor Assessment upper limb (FMA), and Shoulder Abduction (SA) Finger Extension (FE), (SAFE) test. Intervention: Not applicable. Main Outcome Measures: Prediction accuracy at 6 months was examined for 3 categories of ARAT (0-57 points): mild (48-57), moderate (23-47), and severe (0-22). Two individual predictions of ARAT scores at ±6 months post-stroke were computed based on, respectively, baseline (2 weeks) and 3 months ARAT, FE, SA values. The absolute individual differences between observed and predicted ARAT scores were summarized. Results: The prediction model performed best for patients with relatively good UL motor function, with an absolute error median (IQR) of 3 (2-9), and worst for patients with severe UL impairment, with a median (IQR) of 30 (3-39) at baseline. In general, prediction accuracy substantially improved when data obtained 3 months after stroke was included compared with baseline at 2 weeks after stroke. Conclusion: We found limited clinical usability due to the lack of prediction accuracy 2 weeks after stroke and for patients with severe UL impairments. The dynamic prediction model could probably be refined with data from biomarkers.

Machine Learning for Motor Imagery Wrist Dorsiflexion Prediction in Brain-Computer Interface Assisted Stroke Rehabilitation.

Stroke is a life-changing event that can affect the survivors' physical, cognitive and emotional state. Stroke care focuses on helping the survivors to regain their strength; recover as much functionality as possible and return to independent living through rehabilitation therapies. Automated training protocols have been reported to improve the efficiency of the rehabilitation process. These protocols also decrease the dependency of the process on a professional trainer. Brain-Computer Interface (BCI) based systems are examples of such systems where they make use of the motor imagery (MI) based electroencephalogram (EEG) signals to drive the rehabilitation protocols. In this paper, we have proposed the use of well-known machine learning (ML) algorithms, such as, the decision tree (DT), Naive Bayesian (NB), linear discriminant analysis (LDA), support vector machine (SVM), ensemble learning classifier (ELC), and artificial neural network (ANN) for MI wrist dorsiflexion prediction in a BCI assisted stroke rehabilitation study conducted on eleven stroke survivors with either the left or right paresis. The doubling sub-band selection filter bank common spatial pattern (DSBS-FBCSP) has been proposed as feature extractor and it is observed that the ANN based classifier produces the best results.

Plasticity and response to action observation: a longitudinal FMRI study of potential mirror neurons in patients with subacute stroke.

BACKGROUND: Action observation has been suggested as a possible gateway to retraining arm motor function post stroke. However, it is unclear if the neuronal response to action observation is affected by stroke and if it changes during the course of recovery. OBJECTIVE: To examine longitudinal changes in neuronal activity in a group of patients with subacute stroke when observing and executing a bimanual movement task. METHODS: Eighteen patients were examined twice using 3-T functional magnetic resonance imaging; 1 to 2 weeks and 3 months post stroke symptom onset. Eighteen control participants were examined once. Image time series were analyzed (SPM8) and correlated with clinical motor function scores. RESULTS: During action observation and execution, an overlap of neuronal activation was observed in the superior and inferior parietal lobe, precentral gyrus, insula, and inferior temporal gyrus in both control participants and patients (P < .05; false discovery rate corrected). The neuronal response in the observation task increased from 1 to 2 weeks to 3 months after stroke. Most activated clusters were observed in the inferior temporal gyrus, the thalamus and movement-related areas, such as the premotor, supplementary and motor cortex (BA4, BA6). Increased activation of cerebellum and premotor area correlated with improved arm motor function. Most patients had regained full movement ability. CONCLUSIONS: Plastic changes in neurons responding to action observation and action execution occurred in accordance with clinical recovery. The involvement of motor areas when observing actions early and later after stroke may constitute a possible access to the motor system.