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

Joshua Sarfaty Siegel, Lenny E. Ramsey, Abraham Z. Snyder, Nicholas V. Metcalf, Ravi V. Chacko, Kilian Weinberger, Antonello Baldassarre, Carl D. Hacker, Gordon L. Shulman, Maurizio Corbetta

Research output: Contribution to journalArticlepeer-review

253 Scopus citations

Abstract

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 deficitswere well predicted by both. Next,we identified a general pattern of physiological network dysfunction consisting of decrease of interhemispheric integration and intrahemispheric segregation, which strongly related to behavioral impairment in multiple domains. Network-specific patterns of dysfunction predicted specific behavioral deficits, and loss of interhemispheric communication across a set of regions was associated with impairment across multiple behavioral domains. These results link key organizational features of brain networks to brain- behavior relationships in stroke.

Original languageEnglish
Pages (from-to)E4367-E4376
JournalProceedings of the National Academy of Sciences of the United States of America
Volume113
Issue number30
DOIs
StatePublished - Jul 26 2016

Keywords

  • Functional connectivity
  • Interhemispheric
  • Language
  • Memory
  • Stroke

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