An average sliding window correlation method for dynamic functional connectivity
Open accessAbstract
Sliding window correlation (SWC) is utilized in many studies to analyze the temporal characteristics of brain connectivity. However, spurious artifacts have been reported in simulated data using this technique. Several suggestions have been made through the development of the SWC technique. Recently, it has been proposed to utilize a SWC window length of 100 s given that the lowest nominal fMRI frequency is 0.01 Hz. The main pitfall is the loss of temporal resolution due to a large window length. In this work, we propose an average sliding window correlation (ASWC) approach that presents several advantages over the SWC. One advantage is the requirement for a smaller window length. This is important because shorter lengths allow for a more accurate estimation of transient dynamicity of functional connectivity. Another advantage is the behavior of ASWC as a tunable high pass filter. We demonstrate the advantages of ASWC over SWC using simulated signals with configurable functional connectivity dynamics. We present analytical models explaining the behavior of ASWC and SWC for several dynamic connectivity cases. We also include a real data example to demonstrate the application of the new method. In summary, ASWC shows lower artifacts and resolves faster transient connectivity fluctuations, resulting in a lower mean square error than in SWC.
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The sliding average temporal window method effectively aligns high-frequency remote sensing data with hourly precipitation observations on a consistent temporal scale, achieving temporal consistency for multi-source data matching.
“This method effectively aligns the high-frequency remote sensing data with the hourly precipitation observations on a consistent temporal scale, thus achieving temporal consistency for multi-source data matching.”
From A Machine Learning-Based Quality Control Algorithm for Heavy Rainfall Using Multi-Source Data (2025) · cites this paper as [50]
The Mathematical Characterization of ASWC section describes averaging consecutive sliding-window correlations to improve estimates of functional connectivity, not aligning datasets on a common temporal scale. Searches for remote sensing, hourly precipitation, and temporal alignment found no passage in our copy addressing the claimed application. (LLM, conf=0.78)
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- Authors
- Vergara, V. M., Abrol, A., & Calhoun, V. D.
- Affiliations
- Mind Research Network (US)
- Corresponding author
- Vergara, Victor M.
- Abrol, Anees
- Calhoun, Vince D.
- Venue
- Human Brain Mapping
- Publisher
- Wiley
- Published
- 2019-01-19
- Volume
- 40
- Issue
- 7
- Pages
- 2089-2103
- ISSN
- 1065-9471 (ISSN-L)
- 1097-0193
- PMC
- PMC6865616
- OpenAlex
- W2908590807
- Cited by
- 53
- References
- 42
- Citations by year
- 2026: 6 · 2025: 10 · 2024: 4 · 2023: 3 · 2022: 9 · 2021: 4 · 2020: 12 · 2019: 5
- FWCI
- 4.36
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Human Brain Mapping (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (2R01EB005846, R01REB020407, P20GM103472, P30GM1227, P30GM122734)
- National Science Foundation (1539067/1631819)
- 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
- sliding window correlation, dynamic functional connectivity, functional MRI, temporal resolution, high-pass filtering
- MeSH terms
- Brain, Brain Mapping, Humans, Magnetic Resonance Imaging, Models, Neurological, Nerve Net, Artifacts
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