Predictive models based on dynamic functional connectivity measures could help to predict acute motor impairment and long-term recovery in stroke survivors. These models could be used to personalize rehabilitation and improve stroke recovery.
Why this matters
Stroke is the leading cause of long-term disability in adults. However, predicting level of chronic post-stroke disability can be challenging.
Initial motor impairment in the acute post-stroke period is a good overall predictor of chronic impairment but may not be sufficiently accurate for use at the single-patient level.
Novel imaging approaches may be more accurate; in particular, dynamic functional network connectivity analyses based on magnetic resonance imaging (MRI) may enhance ‘temporal resolution’ when assessing cerebral function and substantially improve predictive capabilities.