ArticleScientific reports2019
Multifractal and entropy analysis of resting-state electroencephalography reveals spatial organization in local dynamic functional connectivity.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Article
- Diminished variability of alpha and beta band-limited power as a neural signature in schizophrenia.Translational psychiatry · 2026Article
- Multifractal Cascade Modeling Reveals Fundamental Limits of Current Neuroimaging Strategies.Sensors (Basel, Switzerland) · 2025Article
- Reduced temporal variability of cortical excitation/inhibition ratio in schizophrenia.Schizophrenia (Heidelberg, Germany) · 2025Article
- EEG entropy insights in the context of physiological aging and Alzheimer's and Parkinson's diseases: a comprehensive review.GeroScience · 2024Review
- Resting-state fractal brain connectivity is associated with impaired cognitive performance in healthy aging.GeroScience · 2024Article
- Fingerprints of decreased cognitive performance on fractal connectivity dynamics in healthy aging.GeroScience · 2024Article
- Combining detrended cross-correlation analysis with Riemannian geometry-based classification for improved brain-computer interface performance.Frontiers in neuroscience · 2024Article
- Multifractal Analysis in Neuroimaging.Advances in neurobiology · 2024Review
- Gait variability predicts cognitive impairment in older adults with subclinical cerebral small vessel disease.Frontiers in aging neuroscience · 2022Article
- Multiple-Resampling Cross-Spectral Analysis: An Unbiased Tool for Estimating Fractal Connectivity With an Application to Neurophysiological Signals.Frontiers in physiology · 2022Article
- Real-Time Algorithm for Detrended Cross-Correlation Analysis of Long-Range Coupled Processes.Frontiers in physiology · 2022Article
- Separating scale-free and oscillatory components of neural activity in schizophrenia.Brain and behavior · 2021Article
- Virtual Reality for Neurorehabilitation and Cognitive Enhancement.Brain sciences · 2021Review
- Decreased connection density and modularity of functional brain networks during n-back working memory paradigm.Brain and behavior · 2021Article
- Multifractal Dynamics in Executive Control When Adapting to Concurrent Motor Tasks.Frontiers in physiology · 2021Article
- Multifractal Functional Connectivity Analysis of Electroencephalogram Reveals Reorganization of Brain Networks in a Visual Pattern Recognition Paradigm.Frontiers in human neuroscience · 2021Article
- Space: A Missing Piece of the Dynamic Puzzle.Trends in cognitive sciences · 2020Review
- Multifractal and Entropy-Based Analysis of Delta Band Neural Activity Reveals Altered Functional Connectivity Dynamics in Schizophrenia.Frontiers in systems neuroscience · 2020Article
- Scale-Free Coupled Dynamics in Brain Networks Captured by Bivariate Focus-Based Multifractal Analysis.Frontiers in physiology · 2020Article
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4 authors.
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Abstract
Functional connectivity of the brain fluctuates even in resting-state condition. It has been reported recently that fluctuations of global functional network topology and those of individual connections between brain regions expressed multifractal scaling. To expand on these findings, in this study we investigated if multifractality was indeed an inherent property of dynamic functional connectivity (DFC) on the regional level as well. Furthermore, we explored if local DFC showed region-specific differences in its multifractal and entropy-related features. DFC analyses were performed on 62-channel, resting-state electroencephalography recordings of twelve young, healthy subjects. Surrogate data testing verified the true multifractal nature of regional DFC that could be attributed to the presumed nonlinear nature of the underlying processes. Moreover, we found a characteristic spatial distribution of local connectivity dynamics, in that frontal and occipital regions showed stronger long-range correlation and higher degree of multifractality, whereas the highest values of entropy were found over the central and temporal regions. The revealed topology reflected well the underlying resting-state network organization of the brain. The presented results and the proposed analysis framework could improve our understanding on how resting-state brain activity is spatio-temporally organized and may provide potential biomarkers for future clinical research.
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