TY - JOUR
T1 - Modular brain network organization predicts response to cognitive training in older adults
AU - Gallen, Courtney L.
AU - Baniqued, Pauline L.
AU - Chapman, Sandra B.
AU - Aslan, Sina
AU - Keebler, Molly
AU - Didehbani, Nyaz
AU - D'Esposito, Mark
N1 - Funding Information:
This work was supported by the National Institutes of Health (grant number NS79698 to MD and RC1-AG035954 to SBC), the Department of Defense Air Force Office of Scientific Research (National Defense Science and Engineering Graduate Fellowship 32 CFR 168a to CLG), and by grants from the Lyda Hill Foundation and the T. Boone Pickens Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Publisher Copyright:
© 2016 Gallen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2016/12
Y1 - 2016/12
N2 - 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.
AB - 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.
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U2 - 10.1371/journal.pone.0169015
DO - 10.1371/journal.pone.0169015
M3 - Article
C2 - 28006029
AN - SCOPUS:85007332885
SN - 1932-6203
VL - 11
JO - PloS one
JF - PloS one
IS - 12
M1 - e0169015
ER -