Functional connectivity change as shared signal dynamics
Open accessAbstract
Background An increasing number of neuroscientific studies gain insights by focusing on differences in functional connectivity-between groups, individuals, temporal windows, or task conditions. We found using simulations that additional insights into such differences can be gained by forgoing variance normalization, a procedure used by most functional connectivity measures. Simulations indicated that these functional connectivity measures are sensitive to increases in independent fluctuations (unshared signal) in time series, consistently reducing functional connectivity estimates (e.g., correlations) even though such changes are unrelated to corresponding fluctuations (shared signal) between those time series. This is inconsistent with the common notion of functional connectivity as the amount of inter-region interaction.New method Simulations revealed that a version of correlation without variance normalization - covariance - was able to isolate differences in shared signal, increasing interpretability of observed functional connectivity change. Simulations also revealed cases problematic for non-normalized methods, leading to a "covariance conjunction" method combining the benefits of both normalized and non-normalized approaches.Results We found that covariance and covariance conjunction methods can detect functional connectivity changes across a variety of tasks and rest in both clinical and non-clinical functional MRI datasets.Comparison with existing method(s) We verified using a variety of tasks and rest in both clinical and non-clinical functional MRI datasets that it matters in practice whether correlation, covariance, or covariance conjunction methods are used.Conclusions These results demonstrate the practical and theoretical utility of isolating changes in shared signal, improving the ability to interpret observed functional connectivity change.
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In BOLD data, covariance is a more reliable indicator of coupling strength than connectivity.
“others have shown that covariance is a more reliable marker of coupling strength in BOLD data (Cole et al., 2016).”
From The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance (2016) · cites this paper as Cole et al., 2016
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- Authors
- Cole, M. W., Yang, G. J., Murray, J. D., Repovš, G., & Anticevic, A.
- Affiliations
- Rutgers, The State University of New Jersey (US)
- Yale University (US)
- University of Ljubljana (SI)
- Corresponding author
- Cole, Michael W.
- Venue
- Journal of Neuroscience Methods
- Publisher
- Elsevier BV
- Published
- 2016-02
- Volume
- 259
- Pages
- 22-39
- ISSN
- 0165-0270 (ISSN-L)
- PMC
- PMC4715953
- OpenAlex
- W2178864546
- Cited by
- 60
- References
- 64
- Citations by year
- 2025: 4 · 2024: 6 · 2023: 5 · 2022: 8 · 2021: 7 · 2020: 4 · 2019: 5 · 2018: 14 · 2017: 5 · 2016: 1 · 2015: 1
- FWCI
- 4.21
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of Neuroscience Methods (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- 16 NIH Institutes and Centers
- NIH Blueprint for Neuroscience Research
- McDonnell Center for Systems Neuroscience at Washington University
- National Institute of Health Center of Biomedical Research Excellence (COBRE) (1P20RR021938-01A2)
- 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
- functional connectivity, variance normalization, covariance, correlation, functional MRI
- MeSH terms
- Adult, Brain, Computer Simulation, Humans, Magnetic Resonance Imaging, Models, Neurological, Schizophrenia, Connectome
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