ArticleCognitive neurodynamics2019
Atypical temporal-scale-specific fractal changes in Alzheimer's disease EEG and their relevance to cognitive decline.
Article in Cognitive neurodynamics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- RIFT: A Fractal-Holographic Theory of Consciousness and Autopoietic Control.Brain sciences · 2026Article
- Schizophrenia detection via lobe-wise and overall EEG features using VMD and bayesian-optimized machine learning models.Frontiers in neuroscience · 2026Article
- Multimodal biomarker based on temporal complexity of eye movements and pupil diameter in attention-deficit/hyperactivity disorder.PLOS mental health · 2025Article
- Multi-Threshold Recurrence Rate Plot: A Novel Methodology for EEG Analysis in Alzheimer's Disease and Frontotemporal Dementia.Brain sciences · 2024Article
- A Comparative Study on Feature Extraction Techniques for the Discrimination of Frontotemporal Dementia and Alzheimer's Disease with Electroencephalography in Resting-State Adults.Brain sciences · 2024Article
- A Novel Metric for Alzheimer's Disease Detection Based on Brain Complexity Analysis via Multiscale Fuzzy Entropy.Bioengineering (Basel, Switzerland) · 2024Article
- Article
- Atypical instantaneous spatio-temporal patterns of neural dynamics in Alzheimer's disease.Scientific reports · 2024Article
- Dynamic topological data analysis: a novel fractal dimension-based testing framework with application to brain signals.Frontiers in neuroinformatics · 2024Article
- Fractal Dimension Analysis in Neurological Disorders: An Overview.Advances in neurobiology · 2024Review
- Clinical Sensitivity of Fractal Neurodynamics.Advances in neurobiology · 2024Article
- Stability analysis of fractional order memristor synapse-coupled hopfield neural network with ring structure.Cognitive neurodynamics · 2023Article
- Nonlinear and machine learning analyses on high-density EEG data of math experts and novices.Scientific reports · 2023Article
- The effects of repetitive transcranial magnetic stimulation and aerobic exercise on cognition, balance and functional brain networks in patients with Alzheimer's disease.Cognitive neurodynamics · 2023Article
- A new strategy for the early detection of alzheimer disease stages using multifractal geometry analysis based on K-Nearest Neighbor algorithm.Scientific reports · 2022Article
- Brain entropy, fractal dimensions and predictability: A review of complexity measures for EEG in healthy and neuropsychiatric populations.The European journal of neuroscience · 2022Review
- Four Methods to Distinguish between Fractal Dimensions in Time Series through Recurrence Quantification Analysis.Entropy (Basel, Switzerland) · 2022Article
- Alteration of Neural Network Activity With Aging Focusing on Temporal Complexity and Functional Connectivity Within Electroencephalography.Frontiers in aging neuroscience · 2022Article
- Functional Connectivity and Complexity in the Phenomenological Model of Mild Cognitive-Impaired Alzheimer's Disease.Frontiers in computational neuroscience · 2022Article
- EEG power spectral density in locked-in and completely locked-in state patients: a longitudinal study.Cognitive neurodynamics · 2021Article
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6 authors.
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Abstract
Recent advances in nonlinear analytic methods for electroencephalography have clarified the reduced complexity of spatiotemporal dynamics in brain activity observed in Alzheimer's disease (AD). However, there are far fewer studies exploring temporal scale dependent fractal properties in AD, despite the importance of studying the dynamics of brain activity within physiologically relevant frequency ranges. Higuchi's fractal dimension is a widely used index for evaluating fractality in brain activity, but temporal-scale-specific characteristics are lost due to its requirement of averaging over the entire range of temporal scales. In this study, we adapted Higuchi's fractal algorithm into a method for investigating temporal-scale-specific fractal properties. We then compared the values of the temporal-scale-specific fractal dimension between healthy control (HC) and AD patient groups. Our data indicate that relative to the HC group, the AD group demonstrated reduced fractality at both slow and fast temporal scales. Moreover, we confirmed that the fractality at fast temporal scales correlates with cognitive decline. These properties might serve as a basis for a useful approach to characterizing temporal neural dynamics in AD or other neurodegenerative disorders.
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