ArticleBrain and behavior2019
Assessment of dynamic functional connectivity in resting-state fMRI using the sliding window technique.
Article in Brain and behavior, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.
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45 citing papers in PubMed.
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- Normative brain-state trajectories reveal deviation from healthy aging in AD.bioRxiv : the preprint server for biology · 2026Article
- State-specific disruptions of dynamic functional connectivity in young migraine without aura: a hidden Markov model approach.Frontiers in neuroscience · 2026Article
- Altered dynamic functional connectivity in adults with rural left-behind experience: a resting-state fMRI study.Frontiers in psychiatry · 2026Article
- Dynamic, state-dependent characteristics of cognitive fluctuations in Lewy body dementia: a magnetoencephalography study.Brain communications · 2026Article
- Functional connectivity and graph theory of impaired central visual pathways in acute ischemic stroke based on functional magnetic resonance imaging.Quantitative imaging in medicine and surgery · 2025Article
- st-DenseViT: A Weakly Supervised Spatiotemporal Vision Transformer for Dense Prediction of Dynamic Brain Networks.Human brain mapping · 2025Article
- Existence of Dynamic Functional Connectivity Variations of Brain Networks in Psychogenic Non-Epileptic Seizures Through Resting-state Functional Magnetic Resonance Imaging.Journal of biomedical physics & engineering · 2025Article
- Evolution of the Rich Club Properties in Mouse, Macaque, and Human Brain Networks: A Study of Functional Integration, Segregation, and Balance.Neuroscience bulletin · 2025Article
- Sex Differences in Functional Gradients and Dynamic Functional Connectivity in Preschool-Aged Children With ASD.CNS neuroscience & therapeutics · 2025Article
- Aberrant static and dynamic functional connectivity of insular cortex in patients with trigeminal neuralgia.Scientific reports · 2025Article
- Studying time-resolved functional connectivity via communication theory: on the complementary nature of phase synchronization and sliding window Pearson correlation.bioRxiv : the preprint server for biology · 2025Article
- A Statistical Characterization of Dynamic Brain Functional Connectivity.Human brain mapping · 2025Article
- Abnormal neural network connectivity in heart failure with reduced and mild-ranged ejection fraction patients: an independent component and dynamic functional network connectivity analysis.Frontiers in neurology · 2025Article
- The alterations in brain network functional gradients and dynamic functional connectivity in Alzheimer's disease: a resting-state fMRI study.Frontiers in aging neuroscience · 2025Article
- Sliding window functional connectivity inference with nonstationary autocorrelations and cross-correlations.bioRxiv : the preprint server for biology · 2024Article
- Neonatal brain dynamic functional connectivity in term and preterm infants and its association with early childhood neurodevelopment.Nature communications · 2024Article
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3 authors.
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Abstract
introductionRecent studies related to assessing functional connectivity (FC) in resting-state functional magnetic resonance imaging have revealed that the resulting connectivity patterns exhibit considerable fluctuations (dynamic FC [dFC]). A widely applied method for quantifying dFC is the sliding window technique. According to this method, the data are divided into segments with the same length (window size) and a correlation metric is employed to assess the connectivity within these segments, whereby the window size is often empirically chosen.
methodsIn this study, we rigorously investigate the assessment of dFC using the sliding window approach. Specifically, we perform a detailed comparison between different correlation metrics, including Pearson, Spearman and Kendall correlation, Pearson and Spearman partial correlation, Mutual Information (MI), Variation of Information (VI), Kullback-Leibler divergence, Multiplication of Temporal Derivatives and Inverse Covariance.
resultsUsing test-retest datasets, we show that MI and VI yielded the most consistent results by achieving high reliability with respect to dFC estimates for different window sizes. Subsequent hypothesis testing, based on multivariate phase randomization surrogate data generation, allowed the identification of dynamic connections between the posterior cingulate cortex and regions in the frontal lobe and inferior parietal lobes, which were overall in agreement with previous studies.
conclusionsIn the case of MI and VI, a window size of at least 120 s was found to be necessary for detecting dFC for some of the previously identified dynamically connected regions.
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