Biao Cai1, Gemeng Zhang1, Aiying Zhang1, Wenxing Hu1, Julia M Stephen2, Tony W Wilson3, Vince D Calhoun4, Yu-Ping Wang1. 1. Biomedical Engineering Department, Tulane University, New Orleans, LA, USA. 2. The Mind Research Network, Albuquerque, NM, USA. 3. Department of Neurological Sciences, University of Nebraska Medical Center (UNMC), Omaha, NE, USA. 4. Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) (Georgia State University, Georgia Institute of Technology, Emory University), Atlanta, GA 30030, USA.
Abstract
BACKGROUND: Functional magnetic resonance imaging (fMRI) has been implemented widely to study brain connectivity. In particular, time-varying connectivity analysis has emerged as an important measure to uncover essential knowledge within the network. On the other hand, independent component analysis (ICA) has served as a powerful tool to preprocess fMRI data before performing network analysis. Together, they may lead to novel findings. METHODS: We propose a new framework (GICA-TVGL) that combines group ICA (GICA) with time-varying graphical LASSO (TVGL) to improve the power of analyzing functional connectivity (FNC) changes, which is then applied for neuro-developmental study. To investigate the performance of our proposed approach, we apply it to capture dynamic FNC using both the Philadelphia Neurodevelopmental Cohort (PNC) and the Pediatric Imaging, Neurocognition, and Genetics (PING) datasets. RESULTS: Our results indicate that females and males in young adult group possess substantial difference related to visual network. In addition, some other consistent conclusions have been reached by using these two datasets. Furthermore, the GICA-TVGL 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. COMPARISON WITH EXISTING METHOD: The performance of sliding window approach is largely affected by the window size selection. In addition, it also assumes temporal locality hypothesis. CONCLUSION: Our proposed framework provides a feasible method to investigate brain dynamics and has the potential to become a widely used tool in neuroimaging studies.
BACKGROUND: Functional magnetic resonance imaging (fMRI) has been implemented widely to study brain connectivity. In particular, time-varying connectivity analysis has emerged as an important measure to uncover essential knowledge within the network. On the other hand, independent component analysis (ICA) has served as a powerful tool to preprocess fMRI data before performing network analysis. Together, they may lead to novel findings. METHODS: We propose a new framework (GICA-TVGL) that combines group ICA (GICA) with time-varying graphical LASSO (TVGL) to improve the power of analyzing functional connectivity (FNC) changes, which is then applied for neuro-developmental study. To investigate the performance of our proposed approach, we apply it to capture dynamic FNC using both the Philadelphia Neurodevelopmental Cohort (PNC) and the Pediatric Imaging, Neurocognition, and Genetics (PING) datasets. RESULTS: Our results indicate that females and males in young adult group possess substantial difference related to visual network. In addition, some other consistent conclusions have been reached by using these two datasets. Furthermore, the GICA-TVGL 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. COMPARISON WITH EXISTING METHOD: The performance of sliding window approach is largely affected by the window size selection. In addition, it also assumes temporal locality hypothesis. CONCLUSION: Our proposed framework provides a feasible method to investigate brain dynamics and has the potential to become a widely used tool in neuroimaging studies.
Authors: Laura Murray; J Michael Maurer; Alyssa L Peechatka; Blaise B Frederick; Roselinde H Kaiser; Amy C Janes Journal: Cogn Neurosci Date: 2021-03-18 Impact factor: 2.550
Authors: Malvina N Skorska; Nancy J Lobaugh; Michael V Lombardo; Nina van Bruggen; Sofia Chavez; Lindsey T Thurston; Madison Aitken; Kenneth J Zucker; M Mallar Chakravarty; Meng-Chuan Lai; Doug P VanderLaan Journal: Front Endocrinol (Lausanne) Date: 2022-07-22 Impact factor: 6.055