Classification of cocaine-dependent participants with dynamic functional connectivity from functional magnetic resonance imaging data.

ABSTRACT: Static functional connectivity (FC) analyses based on functional magnetic resonance imaging (fMRI) data have been extensively explored for studying various psychiatric conditions in the brain, including cocaine addiction. A recently emerging, more powerful technique, dynamic functional connectivity (DFC), studies how the FC dynamics change during the course of the fMRI experiments. The aim in this paper was to develop a computational approach, using a machine learning framework, to determine if DFC features were more successful than FC features in the classification of cocaine-dependent patients and healthy controls. fMRI data were obtained from of 25 healthy and 58 cocaine-dependent participants while performing a motor response inhibition task, stop signal task. Group independent compone

SUBMITTER: Sakoglu U 

PROVIDER: S-EPMC6530930 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature