ArticleFrontiers in neuroscience2020
Relationship Between Basic Properties of BOLD Fluctuations and Calculated Metrics of Complexity in the Human Connectome Project.
Article in Frontiers in neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A comprehensive analysis of brain network complexity in task-based fMRI using entropy: systematic review.Brain imaging and behavior · 2026Pooled it
- Fidelity of Spatiotemporal Patterns of Brain Activity Across Sampling Rate, Scan Duration, and Frequency Content.bioRxiv : the preprint server for biology · 2026Article
- Reduced brain entropy in migraine with partial restoration during attacks: A resting-state fMRI study.medRxiv : the preprint server for health sciences · 2025Article
- Resting-State Functional MRI Analyses for Brain Activity Characterization: A Narrative Review of Features and Methods.The European journal of neuroscience · 2025Review
- Time-varying synergy/redundancy dominance in the human cerebral cortex.Journal of physics. Complexity · 2025Article
- Whole-brain modular dynamics at rest predict sensorimotor learning performance.Network neuroscience (Cambridge, Mass.) · 2025Article
- Reliability of variability and complexity measures for task and task-free BOLD fMRI.Human brain mapping · 2024Article
- Spatial and Spectral Components of the BOLD Global Signal in Rat Resting-State Functional MRI.Magnetic resonance in medicine · 2023Article
- Comparison of Resting-State Functional MRI Methods for Characterizing Brain Dynamics.Frontiers in neural circuits · 2022Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Resting state functional MRI (rs-fMRI) creates a rich four-dimensional data set that can be analyzed in a variety of ways. As more researchers come to view the brain as a complex dynamical system, tools are increasingly being drawn from other fields to characterize the complexity of the brain's activity. However, given that the signal measured with rs-fMRI arises from the hemodynamic response to neural activity, the extent to which complexity metrics reflect neural complexity as compared to signal properties related to image quality remains unknown. To provide some insight into this question, correlation dimension, approximate entropy and Lyapunov exponent were calculated for different rs-fMRI scans from the same subject to examine their reliability. The metrics of complexity were then compared to several properties of the rs-fMRI signal from each brain area to determine if basic signal features could explain differences in the complexity metrics. Differences in complexity across brain areas were highly reliable and were closely linked to differences in the frequency profiles of the rs-fMRI signal. The spatial distributions of the complexity and frequency metrics suggest that they are both influenced by location-dependent signal properties that can obscure changes related to neural activity.
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