Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI?
Open accessAbstract
During the last several years, the focus of research on resting-state functional magnetic resonance imaging (fMRI) has shifted from the analysis of functional connectivity averaged over the duration of scanning sessions to the analysis of changes of functional connectivity within sessions. Although several studies have reported the presence of dynamic functional connectivity (dFC), statistical assessment of the results is not always carried out in a sound way and, in some studies, is even omitted. In this study, we explain why appropriate statistical tests are needed to detect dFC, we describe how they can be carried out and how to assess the performance of dFC measures, and we illustrate the methodology using spontaneous blood-oxygen level-dependent (BOLD) fMRI recordings of macaque monkeys under general anesthesia and in human subjects under resting-state conditions. We mainly focus on sliding-window correlations since these are most widely used in assessing dFC, but also consider a recently proposed non-linear measure. The simulations and methodology, however, are general and can be applied to any measure. The results are twofold. First, through simulations, we show that in typical resting-state sessions of 10 min, it is almost impossible to detect dFC using sliding-window correlations. This prediction is validated by both the macaque and the human data: in none of the individual recording sessions was evidence for dFC found. Second, detection power can be considerably increased by session- or subject-averaging of the measures. In doing so, we found that most of the functional connections are in fact dynamic. With this study, we hope to raise awareness of the statistical pitfalls in the assessment of dFC and how they can be avoided by using appropriate statistical methods.
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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 Hindriks et al., 2016
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- Authors
- Hindriks, R., Adhikari, M. H., Murayama, Y., Ganzetti, M., Mantini, D., Logothetis, N. K., & Deco, G.
- Affiliations
- Universitat Pompeu Fabra (ES)
- Max Planck Institute for Biological Cybernetics (DE)
- ETH Zurich (CH)
- Institució Catalana de Recerca i Estudis Avançats (ES)
- Corresponding author
- Hindriks, R.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2016-02
- Volume
- 127
- Pages
- 242-256
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC4758830
- OpenAlex
- W2180423080
- Cited by
- 632
- References
- 47
- Citations by year
- 2026: 33 · 2025: 60 · 2024: 62 · 2023: 55 · 2022: 62 · 2021: 56 · 2020: 59 · 2019: 75 · 2018: 67 · 2017: 77 · 2016: 24 · 2015: 1
- FWCI
- 33.49
- Type
- journal article
- Language
- en
- Update notices
- erratum (2016-05-15)
- Open access
- hybrid
- doi.org
- Licence
- Locations
- NeuroImage (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- RECERCAT (Consorci de Serveis Universitaris de Catalunya) (publishedVersion, other-oa)
- Lirias (submittedVersion, cc-by-nc-nd)
- Max Planck Digital Library (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Repositori digital de la UPF (Universitat Pompeu Fabra) (submittedVersion, cc-by-nc-nd)
- VU Research Portal (submittedVersion)
- Repository for Publications and Research Data (ETH Zurich) (submittedVersion, cc-by-nc-nd)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- ETH Zürich Research Collection
- Funders
- Wellcome Trust (101253/Z/13/Z., 101253)
- Royal Society (101253/Z/13/Z)
- FP7 Ideas: European Research Council (295129)
- FP7 Information and Communication Technologies
- 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
- dynamic functional connectivity, resting-state fMRI, sliding window correlation, statistical power, BOLD signal, macaque, research participants, statistical pitfalls
- MeSH terms
- Animals, Brain, Brain Mapping, Humans, Image Processing, Computer-Assisted, Macaca, Magnetic Resonance Imaging, Male, Neural Pathways, Rest
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