Disruptions of network connectivity predict impairment in multiple behavioral domains after stroke

Deficits following stroke are classically attributed to focal damage, but recent evidence suggests a key role of distributed brain network disruption. We measured resting functional connectivity (FC), lesion topography, and behavior in multiple domains (attention, visual memory, verbal memory, language, motor, and visual) in a cohort of 132 stroke patients, and used machine-learning models to predict neurological impairment in individual subjects. We found that visual memory and verbal memory were better predicted by FC, whereas visual and motor impairments were better predicted by lesion topography. Attention and language deficits were well predicted by both. Next, we identified a general pattern of…

• AH
  American Heart Association

  Award: 14PRE19610010
• NI
  National Institute of General Medical Sciences

  Award: 5T32GM007200-39
• NI
  National Institute of Neurological Disorders and Stroke

  Award: P30 NS048056
• NI
  National Institute of Child Health and Human Development

  Award: 5R01HD061117