TY - JOUR
T1 - Disruptions of network connectivity predict impairment in multiple behavioral domains after stroke
AU - Siegel, Joshua Sarfaty
AU - Ramsey, Lenny E.
AU - Snyder, Abraham Z.
AU - Metcalf, Nicholas V.
AU - Chacko, Ravi V.
AU - Weinberger, Kilian
AU - Baldassarre, Antonello
AU - Hacker, Carl D.
AU - Shulman, Gordon L.
AU - Corbetta, Maurizio
PY - 2016/7/26
Y1 - 2016/7/26
N2 - 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.
AB - 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.
KW - Functional connectivity
KW - Interhemispheric
KW - Language
KW - Memory
KW - Stroke
UR - http://www.scopus.com/inward/record.url?scp=84979516775&partnerID=8YFLogxK
U2 - 10.1073/pnas.1521083113
DO - 10.1073/pnas.1521083113
M3 - Article
C2 - 27402738
AN - SCOPUS:84979516775
SN - 0027-8424
VL - 113
SP - E4367-E4376
JO - Proceedings of the National Academy of Sciences of the United States of America
JF - Proceedings of the National Academy of Sciences of the United States of America
IS - 30
ER -