ArticleFrontiers in human neuroscience2023
Fractal dimension analysis of resting state functional networks in schizophrenia from EEG signals.
Article in Frontiers in human neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Quantitative assessment of neurodevelopmental maturation: a comprehensive systematic literature review of artificial intelligence-based brain age prediction in pediatric populations.Frontiers in neuroinformatics · 2024Pooled it
- Topographic Reorganization of EEG Complexity During Visual Mental Imagery: Insights from Lempel-Ziv Complexity in High-Density EEG.Brain topography · 2026Article
- Cognitive Processing and EEG Complexity.Entropy (Basel, Switzerland) · 2026Review
- Correlation of frontal lobe electroencephalogram asymmetry and cognitive prognosis in schizophrenia.World journal of psychiatry · 2026Article
- Diminished spatial dynamics and maladaptive spatial complexity link resting brain network disruption to cognition in schizophrenia.bioRxiv : the preprint server for biology · 2025Article
- Tracking dynamic EEG connectivity in schizophrenia and bipolar disorder.Scientific reports · 2025Article
- Dynamic Evolution of EEG Complexity in Schizophrenia Across Cognitive Tasks.Entropy (Basel, Switzerland) · 2025Article
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Abstract
Fractal dimension (FD) has been revealed as a very useful tool in analyzing the changes in brain dynamics present in many neurological disorders. The fractal dimension index (FDI) is a measure of the spatiotemporal complexity of brain activations extracted from EEG signals induced by transcranial magnetic stimulation. In this study, we assess whether the FDI methodology can be also useful for analyzing resting state EEG signals, by characterizing the brain dynamic changes in different functional networks affected by schizophrenia, a mental disorder associated with dysfunction in the information flow dynamics in the spontaneous brain networks. We analyzed 31 resting-state EEG records of 150 s belonging to 20 healthy subjects (HC group) and 11 schizophrenia patients (SCZ group). Brain activations at each time sample were established by a thresholding process applied on the 15,002 sources modeled from the EEG signal. FDI was then computed individually in each resting-state functional network, averaging all the FDI values obtained using a sliding window of 1 s in the epoch. Compared to the HC group, significant lower values of FDI were obtained in the SCZ group for the auditory network (
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