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

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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 publication2018 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Publication date2018
Article number8423966
ISBN (Print)9781538668597
DOIs
Publication statusPublished - 2018
Event2018 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2018 - Singapore, Singapore
Duration: 12 Jun 201814 Jun 2018

Conference

Conference2018 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2018
LandSingapore
BySingapore
Periode12/06/201814/06/2018

Event

2018 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2018

12/06/201814/06/2018

Singapore, Singapore

Event: Conference

ID: 56438364