Disentangling dynamic networks: Separated and joint expressions of functional connectivity patterns in time

• Published in: Human brain mapping Vol. 35; no. 12; pp. 5984 - 5995
• Main Authors: Leonardi, Nora, Shirer, William R., Greicius, Michael D., Van De Ville, Dimitri
• Format: Journal Article
• Language: English
• Published: New York, NY Blackwell Publishing Ltd 01.12.2014; Wiley-Liss; John Wiley & Sons, Inc; John Wiley and Sons Inc
• Subjects: Adolescent; Adult; Artificial Intelligence; Biological and medical sciences; Brain - physiology; Brain Mapping - methods; Cluster Analysis; Computer Simulation; dynamic functional connectivity; Electrodiagnosis. Electric activity recording; Feasibility Studies; Female; functional magnetic resonance imaging; Humans; Investigative techniques, diagnostic techniques (general aspects); Magnetic Resonance Imaging; Male; matrix factorization; Medical sciences; Miscellaneous. Technology; Models, Neurological; Nervous system; Neural Pathways - physiology; Neuropsychological Tests; Radiodiagnosis. Nmr imagery. Nmr spectrometry; Rest; resting state; Young Adult; Osteoarticular system; dynamic functional connectivity; matrix factorization; Nervous system diseases; resting state; Radiodiagnosis; Joint; Nuclear magnetic resonance imaging; functional magnetic resonance imaging; Functional imaging
• ISSN: 1065-9471; 1097-0193; 1097-0193
• DOI: 10.1002/hbm.22599

Resting‐state functional connectivity (FC) is highly variable across the duration of a scan. Groups of coevolving connections, or reproducible patterns of dynamic FC (dFC), have been revealed in fluctuating FC by applying unsupervised learning techniques. Based on results from k‐means clustering and sliding‐window correlations, it has recently been hypothesized that dFC may cycle through several discrete FC states. Alternatively, it has been proposed to represent dFC as a linear combination of multiple FC patterns using principal component analysis. As it is unclear whether sparse or nonsparse combinations of FC patterns are most appropriate, and as this affects their interpretation and use as markers of cognitive processing, the goal of our study was to evaluate the impact of sparsity by performing an empirical evaluation of simulated, task‐based, and resting‐state dFC. To this aim, we applied matrix factorizations subject to variable constraints in the temporal domain and studied both the reproducibility of ensuing representations of dFC and the expression of FC patterns over time. During subject‐driven tasks, dFC was well described by alternating FC states in accordance with the nature of the data. The estimated FC patterns showed a rich structure with combinations of known functional networks enabling accurate identification of three different tasks. During rest, dFC was better described by multiple FC patterns that overlap. The executive control networks, which are critical for working memory, appeared grouped alternately with externally or internally oriented networks. These results suggest that combinations of FC patterns can provide a meaningful way to disentangle resting‐state dFC. Hum Brain Mapp 35:5984–5995, 2014. © 2014 The Authors. Human Brain Mapping published by Wiley Periodicals, Inc.