A GICA-TVGL framework to study sex differences in resting state fMRI dynamic connectivity

Biao Cai; Julia M. Stephen; Tony W. Wilson; Vince D. Calhoun; Yu-Ping Wang

doi:10.1117/12.2549282

10 March 2020 A GICA-TVGL framework to study sex differences in resting state fMRI dynamic connectivity

Biao Cai, Julia M. Stephen, Tony W. Wilson, Vince D. Calhoun, Yu-Ping Wang

Proceedings Volume 11313, Medical Imaging 2020: Image Processing; 113132F (2020) https://doi.org/10.1117/12.2549282
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

Functional magnetic resonance imaging (fMRI) has been implemented widely to study brain connectivity. In the context of fMRI, independent component analysis (ICA) is a powerful tool, which extracts patterns from the data without requiring prior knowledge. Recently, time-varying connectivity analysis has emerged as an important measure to uncover essential knowledge within the network. In this study, we propose a new framework that combines group ICA (GICA) with time varying graphical LASSO (TVGL) to improve the power of analyzing functional network connectivity (FNC) changes. To investigate the performance of our proposed approach, we apply it to capture dynamic FNC using the Pediatric Imaging, Neurocognition, and Genetics (PING) datasets. Our results indicate that females and males of young adults do not show large FNC differences though some slight variations have been found. For instance, females exhibited stronger interdomain FNC and greater correlation in occipital-frontal components for some specific states in comparison to males. In addition, the TVGL-GICA model indicated that females had a higher probability to stay in a stable state. Males had a higher tendency to remain in a globally disconnected mode. Our proposed framework provides a feasible method to investigate brain dynamics accurately and has the potential to become a useful tool in neuroimaging studies.

Citation Download Citation

Biao Cai, Julia M. Stephen, Tony W. Wilson, Vince D. Calhoun, and Yu-Ping Wang "A GICA-TVGL framework to study sex differences in resting state fMRI dynamic connectivity", Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 113132F (10 March 2020); https://doi.org/10.1117/12.2549282

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