Classification of cocaine‐dependent participants with dynamic functional connectivity from functional magnetic resonance imaging data
Year: 2019
Type: article
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 component analysis was carried out on all participant data to compute spatially independent components (ICs). Eight ICs were selected manually as relevant brain networks, which were used to classify healthy versus cocaine-dependent participants. FC and DFC measures of the chosen IC pairs were used as features for the classification algorithm. Support Vector Machines were used for both feature selection/reduction and participant classification. Based on DFC with only seven IC pairs, participants were successfully classified with 95% accuracy (and with 90% accuracy with three IC pairs), whereas static FC yielded only 81% accuracy. Visual, sensorimotor, default mode, and executive control networks, amygdala, and insula played the most significant role in the DFC-based classification. These findings support the use of DFC-based classification of fMRI data as a potential biomarker for the identification of cocaine dependence.
Source: Journal of Neuroscience Research
DOI: 10.1002/jnr.24421
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
Awards: UT Southwestern Center for Translational Medicine (UL1/KL2/TL1), LIMBIC SENSITIVITY IN COCAINE ADDICTION, Limbic Sensitivity in Cocaine Addiction, Impulsivity, Neural Deficits, and Cocaine Relapse, UT-Southwestern Clinical and Translational Alliance for Research (UT-STAR) (TL1), Independent Component Analysis Based Support Vector Machine Classification Method, G8934398269, G9000138301
OpenAlex ID: W2938235323