On spurious and real fluctuations of dynamic functional connectivity during rest
Open accessAbstract
Functional brain networks reconfigure spontaneously during rest. Such network dynamics can be studied by dynamic functional connectivity (dynFC); i.e., sliding-window correlations between regional brain activity. Key parameters-such as window length and cut-off frequencies for filtering-are not yet systematically studied. In this letter we provide the fundamental theory from signal processing to address these parameter choices when estimating and interpreting dynFC. We guide the reader through several illustrative cases, both simple analytical models and experimental fMRI BOLD data. First, we show how spurious fluctuations in dynFC can arise due to the estimation method when the window length is shorter than the largest wavelength present in both signals, even for deterministic signals with a fixed relationship. Second, we study how real fluctuations of dynFC can be explained using a frequency-based view, which is particularly instructive for signals with multiple frequency components such as fMRI BOLD, demonstrating that fluctuations in sliding-window correlation emerge by interaction between frequency components similar to the phenomenon of beat frequencies. We conclude with practical guidelines for the choice and impact of the window length.
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Common statistical approaches to assessing dynamic FC may not test against null models that properly account for random noise.
“Other work has called into question some of the common statistical approaches to assessing dynamic FC (Hindriks et al., 2016; Leonardi and Van De Ville, 2015), which may not test against null models that properly account for random noise.”
From Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature (2017) · cites this paper as Leonardi and Van De Ville, 2015
A 30-s time window is optimal for quantifying dynamic reconfiguration of networks in the frequency range 0.08-0.15 Hz.
“In a recent paper from Van De Ville and colleague (49), the authors demonstrate the optimality of a 30-s time window for quantifying dynamic reconfiguration of networks in the frequency range 0.08–0.15 Hz.”
From Dynamic reconfiguration of frontal brain networks during executive cognition in humans (2015) · cites this paper as [49]
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- Authors
- Leonardi, N., & Van De Ville, D.
- Affiliations
- University of Geneva (CH)
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2015-01
- Volume
- 104
- Pages
- 430-436
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2142566135
- Cited by
- 828
- References
- 21
- Citations by year
- 2026: 63 · 2025: 86 · 2024: 73 · 2023: 80 · 2022: 73 · 2021: 83 · 2020: 95 · 2019: 80 · 2018: 57 · 2017: 77 · 2016: 34 · 2015: 25 · 2014: 1
- FWCI
- 25.42
- Type
- journal article
- Language
- en
- Update notices
- erratum (2015-01-01)
- Open access
- bronze
- doi.org
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Archive ouverte UNIGE (University of Geneva) (publishedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
- Archive ouverte UNIGE (University of Geneva) (submittedVersion, cc-by)
- Funders
- Swiss National Science Foundation (PP00P2-146318)
- Center for Biomedical Imaging (CIBM)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Keywords
- dynamic functional connectivity, resting-state brain networks, sliding window correlation, window size, beat frequency, frequency-based analysis, network dynamics, signal processing theory
- MeSH terms
- Brain, Brain Mapping, Humans, Magnetic Resonance Imaging, Nerve Net, Signal Processing, Computer-Assisted, Artifacts
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