Characterizing dynamic functional connectivity in the resting brain using variable parameter regression and Kalman filtering approaches
Abstract
The cognitive activity of the human brain benefits from the functional connectivity of multiple brain regions that form specific, functional brain networks. Recent studies have indicated that the relationship between brain regions can be investigated by examining the temporal interaction (known as functional connectivity) of spontaneous blood oxygen level-dependent (BOLD) signals derived from resting-state functional MRI. Most of these studies plausibly assumed that inter-regional interactions were temporally stationary. However, little is known about the dynamic characteristics of resting-state functional connectivity (RSFC). In this study, we thoroughly examined this question within and between multiple functional brain networks. Twenty-two healthy subjects were scanned in a resting state. Several of the RSFC networks observed, including the default-mode, motor, attention, memory, auditory, visual, language and subcortical networks, were first identified using a conventional voxel-wise correlation analysis with predefined region of interests (ROIs). Then, a variable parameter regression model combined with the Kalman filtering method was employed to detect the dynamic interactions between each ROI and all other brain voxels within each of the RSFC maps extracted above. Experimental results revealed that the functional interactions within each RSFC map showed time-varying properties, and that approximately 10-20% of the voxels within each RSFC map showed significant functional connectivity to each ROI during the scanning session. This dynamic pattern was also observed for the interactions between different functional networks. In addition, the spatial pattern of dynamic connectivity maps obtained from neighboring time points had a high similarity. Overall, this study provides insights into the dynamic properties of resting-state functional networks.
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The functional connectome exhibits temporal instability that allows flexible switching between multiple configurations within a scanning session.
“Mounting evidence suggests that the functional connectome exhibits temporal instability that allows it to flexibly switch between multiple different configurations within a scanning session (Allen et al., 2014; Chang and Glover, 2010; Kang et al., 2011; Shen et al., 2015).”
From Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature (2017) · cites this paper as Kang et al., 2011
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- Authors
- Kang, J., Wang, L., Yan, C., Wang, J., Liang, X., & He, Y.
- Affiliations
- Beijing Normal University (CN)
- University of British Columbia (CA)
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2011-06
- Volume
- 56
- Issue
- 3
- Pages
- 1222-1234
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W1964724088
- Cited by
- 111
- References
- 72
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 4 · 2023: 2 · 2022: 6 · 2021: 6 · 2020: 8 · 2018: 7 · 2017: 10 · 2016: 10 · 2015: 14 · 2014: 18 · 2013: 10 · 2012: 8
- FWCI
- 6.34
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Science Foundation of China (81030028, 30870667)
- Beijing Natural Science Foundation (7102090)
- Scientific Research Foundation for the Returned Overseas Chinese Scholars
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Keywords
- resting-state functional connectivity, dynamic functional connectivity, time-varying connectedness, Kalman filter, functional brain networks, default mode network, motor network, attentional networks, memory networks, auditory network, visual network, language network, subcortical network
- MeSH terms
- Adolescent, Algorithms, Brain, Brain Mapping, Computer Simulation, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Nerve Net, Neural Pathways, Regression Analysis, Rest, Young Adult
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