Modular Brain Network Organization Predicts Response to Cognitive Training in Older Adults.
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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2016-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC5179237?pdf=render |
| _version_ | 1818951534284636160 |
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| author | Courtney L Gallen Pauline L Baniqued Sandra B Chapman Sina Aslan Molly Keebler Nyaz Didehbani Mark D'Esposito |
| author_facet | Courtney L Gallen Pauline L Baniqued Sandra B Chapman Sina Aslan Molly Keebler Nyaz Didehbani Mark D'Esposito |
| author_sort | Courtney L Gallen |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-20T09:36:01Z |
| format | Article |
| id | doaj.art-4b4772bd8f264348b82f093f11fc41f3 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-20T09:36:01Z |
| publishDate | 2016-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-4b4772bd8f264348b82f093f11fc41f32022-12-21T19:45:00ZengPublic Library of Science (PLoS)PLoS ONE1932-62032016-01-011112e016901510.1371/journal.pone.0169015Modular Brain Network Organization Predicts Response to Cognitive Training in Older Adults.Courtney L GallenPauline L BaniquedSandra B ChapmanSina AslanMolly KeeblerNyaz DidehbaniMark D'EspositoCognitive 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.http://europepmc.org/articles/PMC5179237?pdf=render |
| spellingShingle | Courtney L Gallen Pauline L Baniqued Sandra B Chapman Sina Aslan Molly Keebler Nyaz Didehbani Mark D'Esposito Modular Brain Network Organization Predicts Response to Cognitive Training in Older Adults. PLoS ONE |
| title | Modular Brain Network Organization Predicts Response to Cognitive Training in Older Adults. |
| title_full | Modular Brain Network Organization Predicts Response to Cognitive Training in Older Adults. |
| title_fullStr | Modular Brain Network Organization Predicts Response to Cognitive Training in Older Adults. |
| title_full_unstemmed | Modular Brain Network Organization Predicts Response to Cognitive Training in Older Adults. |
| title_short | Modular Brain Network Organization Predicts Response to Cognitive Training in Older Adults. |
| title_sort | modular brain network organization predicts response to cognitive training in older adults |
| url | http://europepmc.org/articles/PMC5179237?pdf=render |
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