title: Modular Brain Network Organization Predicts Response to Cognitive Training in Older Adults.
abstract: Cognitive training interventions are a promising approach to mitigate cognitive deficits common in aging and, ultimately, to improve functioning in older adults. Baseline neural factors, such as properties of brain networks, may predict training outcomes and can be used to improve the effectiveness of interventions. Here, we investigated the relationship between baseline brain network modularity, a measure of the segregation of brain sub-networks, and training-related gains in cognition in older adults. We found that older adults with more segregated brain sub-networks (i.e., more modular networks) at baseline exhibited greater training improvements in the ability to synthesize complex information. Further, the relationship between modularity and training-related gains was more pronounced in sub-networks mediating "associative" functions compared with those involved in sensory-motor processing. These results suggest that assessments of brain networks can be used as a biomarker to guide the implementation of cognitive interventions and improve outcomes across individuals. More broadly, these findings also suggest that properties of brain networks may capture individual differences in learning and neuroplasticity. Trail Registration: ClinicalTrials.gov, NCT#00977418.
authors.affiliation: Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley CA, United States of America.
authors.forename: Courtney L
authors.initials: CL
authors.lastname: Gallen
authors.affiliation: Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley CA, United States of America.
authors.forename: Pauline L
authors.initials: PL
authors.lastname: Baniqued
authors.affiliation: Center for BrainHealth®, The University of Texas at Dallas, Dallas TX, United States of America.
authors.forename: Sandra B
authors.initials: SB
authors.lastname: Chapman
authors.affiliation: Center for BrainHealth®, The University of Texas at Dallas, Dallas TX, United States of America.
authors.forename: Sina
authors.initials: S
authors.lastname: Aslan
authors.affiliation: Center for BrainHealth®, The University of Texas at Dallas, Dallas TX, United States of America.
authors.forename: Molly
authors.initials: M
authors.lastname: Keebler
authors.affiliation: Center for BrainHealth®, The University of Texas at Dallas, Dallas TX, United States of America.
authors.forename: Nyaz
authors.initials: N
authors.lastname: Didehbani
authors.affiliation: Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley CA, United States of America.|Department of Psychology, University of California, Berkeley, Berkeley CA, United States of America.
authors.forename: Mark
authors.initials: M
authors.lastname: D'Esposito
mesh_terms: D000368:Aged; D000375:Aging; D003071:Cognition / Q000502:physiology*; D000066492:Cognitive Aging; D005260:Female; D006801:Humans; D007206:Individuality; D007858:Learning / Q000502:physiology; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D009415:Nerve Net; D009473:Neuronal Plasticity / Q000502:physiology