Testing group differences in state transition structure of dynamic functional connectivity models

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2018

DOI

View graph of relations

Understanding the origins of intrinsic time-varying functional connectivity remains a challenge in the neuroimaging community. However, some associations between dynamic functional connectivity (dFC) and behavioral traits have been observed along with gender differences. We propose a permutation testing framework to investigate dynamic differences between groups of subjects. In particular, we investigate differences in fractional occupancy, state persistency and the full transition probability matrix. We demonstrate our framework on resting state functional magnetic resonance imaging data from 820 healthy young adults from the Human Connectome Project considering two prominent dFC models, namely sliding-window k-means and the Gaussian hidden Markov model. The variables showing consistent significant dynamic differences were limited to gender and the degree of motion in the scanner. We observe for the data considered that a large sample size (here 500 subjects) is needed to to draw reliable conclusions about the significance of those variables. Our results point to dynamic features providing limited information with regard to behavioral traits despite a relatively large sample size.
Original languageEnglish
Title of host publicationProceedings of 2018 International Workshop on Pattern Recognition in Neuroimaging
PublisherIEEE
Publication date2018
Pages1-4
ISBN (print)978-1-5386-4291-7
DOIs
StatePublished - 2018
Event8th International Workshop on Pattern Recognition in Neuroimaging - Singapore, Singapore

Conference

Conference8th International Workshop on Pattern Recognition in Neuroimaging
LocationCentre for Life Sciences at the National University of Singapore
CountrySingapore
CitySingapore
Period12/06/201814/06/2018
CitationsWeb of Science® Times Cited: No match on DOI
Download as:
Download as PDF
Select render style:
APAAuthorCBE/CSEHarvardMLAStandardVancouverShortLong
PDF
Download as HTML
Select render style:
APAAuthorCBE/CSEHarvardMLAStandardVancouverShortLong
HTML
Download as Word
Select render style:
APAAuthorCBE/CSEHarvardMLAStandardVancouverShortLong
Word

ID: 151866753