ArticleCerebral cortex (New York, N.Y. : 1991)2021
Interindividual Signatures of fMRI Temporal Fluctuations.
Article in Cerebral cortex (New York, N.Y. : 1991), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- VarCoNet: A Variability-Aware Self-Supervised Framework for Functional Connectome Extraction From Resting-State fMRI.Human brain mapping · 2026Article
- Spontaneous brain regional dynamics contribute to generalizable brain-behaviour associations.Nature human behaviour · 2026Article
- Differential engagement of thalamic nuclei orchestrates consciousness states across anesthesia, sleep, and disorders of consciousness.Communications biology · 2025Article
- Unraveling neural complexity: Exploring brain entropy to yield mechanistic insight in neuromodulation therapies for tobacco use disorder.Imaging neuroscience (Cambridge, Mass.) · 2024Article
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Authors and funding
6 authors.
Funding
Abstract
The complexity and variability of human brain activity, such as quantified from Functional Magnetic Resonance Imaging (fMRI) time series, have been widely studied as potential markers of healthy and pathological states. However, the extent to which fMRI temporal features exhibit stable markers of inter-individual differences in brain function across healthy young adults is currently an open question. In this study, we draw upon two widely used time-series measures-a nonlinear complexity measure (sample entropy; SampEn) and a spectral measure of low-frequency content (fALFF)-to capture dynamic properties of resting-state fMRI in a large sample of young adults from the Human Connectome Project. We observe that these two measures are closely related, and that both generate reproducible patterns across brain regions over four different fMRI runs, with intra-class correlations of up to 0.8. Moreover, we find that both metrics can uniquely differentiate subjects with high identification rates (ca. 89%). Canonical correlation analysis revealed a significant relationship between multivariate brain temporal features and behavioral measures. Overall, these findings suggest that regional profiles of fMRI temporal characteristics may provide stable markers of individual differences, and motivate future studies to further probe relationships between fMRI time series metrics and behavior.
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