Working memory load-dependent changes in cortical network connectivity estimated by machine learning.

ABSTRACT: Working memory engages multiple distributed brain networks to support goal-directed behavior and higher order cognition. Dysfunction in working memory has been associated with cognitive impairment in neuropsychiatric disorders. It is important to characterize the interactions among cortical networks that are sensitive to working memory load since such interactions can also hint at the impaired dynamics in patients with poor working memory performance. Functional connectivity is a powerful tool used to investigate coordinated activity among local and distant brain regions. Here, we identified connectivity footprints that differentiate task states representing distinct working memory load levels. We employed linear support vector machines to decode working memory load from task-based functio

SUBMITTER: Eryilmaz H 

PROVIDER: S-EPMC8202087 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature