NDLI: Dynamic functional connectivity analysis of functional MRI based on copula time-varying correlation

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Dynamic functional connectivity analysis of functional MRI based on copula time-varying correlation

Content Provider	Semantic Scholar
Author	Lee, Namgil Kim, Jong-Min
Copyright Year	2019
Abstract	BACKGROUND Recent studies showed that functional connectivity (FC) in the human brain is not static but can dynamically change across time within time scales of seconds to minutes. NEW METHOD This study introduces a new statistical method called the copula time-varying correlation for dynamic functional connectivity (dFC) analysis from functional magnetic resonance imaging (fMRI) data. RESULTS Compared to other state-of-the-art statistical measures of dynamic correlation such as the dynamic conditional correlation (DCC), the proposed method can be effectively applied to data having asymmetric or non-normal distributions. COMPARISON WITH EXISTING METHODS Numerical simulations were conducted under various kinds of time-varying correlations and distributions, and it was demonstrated that the proposed method was superior to the DCC-based method for asymmetric and non-normal distributions. CONCLUSIONS FMRI data of 138 human participants watching a Pixar animated movie were analyzed by the proposed method based on five a priori selected brain regions in the cortex. Based on statistical group analysis results, it was discovered that (1) the correlation between the left temporoparietal junction (LTPJ) and the primary visual cortex (V1) and the correlation between the dorsal posterior cingulate cortex (dPCC) and V1 were significantly higher for older age groups (5yo-Adult) more often than for younger age groups (3yo-4yo), and (2) the right temporoparietal junction (RTPJ), LTPJ, and dPCC were significantly correlated in all age groups at most of the scanning time periods.
Starting Page	32
Ending Page	47
Page Count	16
File Format	PDF HTM / HTML
DOI	10.1016/j.jneumeth.2019.05.004
PubMed reference number	31100293
Journal	Medline
Volume Number	323
Alternate Webpage(s)	https://api.elsevier.com/content/article/pii/S016502701930144X
Alternate Webpage(s)	https://www.sciencedirect.com/science/article/pii/S016502701930144X?dgcid=api_sd_search-api-endpoint
Alternate Webpage(s)	https://doi.org/10.1016/j.jneumeth.2019.05.004
Journal	Journal of Neuroscience Methods
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

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