A mixed-modeling framework for whole-brain dynamic network analysis.

Autor:	Bahrami M; Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC, USA.; Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC, USA., Laurienti PJ; Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC, USA.; Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC, USA., Shappell HM; Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC, USA.; Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC, USA., Dagenbach D; Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC, USA.; Department of Psychology, Wake Forest University, Winston-Salem, NC, USA., Simpson SL; Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC, USA.; Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC, USA.
Jazyk:	angličtina
Zdroj:	Network neuroscience (Cambridge, Mass.) [Netw Neurosci] 2022 Jun 01; Vol. 6 (2), pp. 591-613. Date of Electronic Publication: 2022 Jun 01 (Print Publication: 2022).
DOI:	10.1162/netn_a_00238
Abstrakt:	The emerging area of dynamic brain network analysis has gained considerable attention in recent years. However, development of multivariate statistical frameworks that allow for examining the associations between phenotypic traits and dynamic patterns of system-level properties of the brain, and drawing statistical inference about such associations, has largely lagged behind. To address this need we developed a mixed-modeling framework that allows for assessing the relationship between any desired phenotype and dynamic patterns of whole-brain connectivity and topology. This novel framework also allows for simulating dynamic brain networks with respect to desired covariates. Unlike current tools, which largely use data-driven methods, our model-based method enables aligning neuroscientific hypotheses with the analytic approach. We demonstrate the utility of this model in identifying the relationship between fluid intelligence and dynamic brain networks by using resting-state fMRI (rfMRI) data from 200 participants in the Human Connectome Project (HCP) study. We also demonstrate the utility of this model to simulate dynamic brain networks at both group and individual levels. To our knowledge, this approach provides the first model-based statistical method for examining dynamic patterns of system-level properties of the brain and their relationships to phenotypic traits as well as simulating dynamic brain networks. (© 2022 Massachusetts Institute of Technology.)
Databáze:	MEDLINE
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