ArticleFrontiers in computational neuroscience2022
Functional Connectivity and Complexity in the Phenomenological Model of Mild Cognitive-Impaired Alzheimer's Disease.
Article in Frontiers in computational neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
- Post-stroke rehabilitative mechanisms in individualized fatigue level-controlled treadmill training in rats.Neural regeneration research · 2026Article
- Excitation-inhibition homeostasis in Alzheimer's disease: a selective multiscale review of mechanisms, sex differences, and therapeutic opportunities.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026Review
- Entropy as a Tool for Quantifying Biomedical Signals: Gaps and Opportunities in Clinical Methodological Approaches-A Scoping Review.Biomedical engineering and computational biology · 2026Review
- Modeling of whole brain sleep electroencephalogram using deep oscillatory neural network.Frontiers in neuroinformatics · 2025Article
- Is EEG Entropy a Useful Measure for Alzheimer's Disease?Actas espanolas de psiquiatria · 2024Review
- Clinical Sensitivity of Fractal Neurodynamics.Advances in neurobiology · 2024Article
- Alzheimer's Disease: Insights from Large-Scale Brain Dynamics Models.Brain sciences · 2023Review
- Patients with Alzheimer's disease dementia show partially preserved parietal 'hubs' modeled from resting-state alpha electroencephalographic rhythms.Frontiers in aging neuroscience · 2023Article
- Phase Lag Index of Resting-State EEG for Identification of Mild Cognitive Impairment Patients with Type 2 Diabetes.Brain sciences · 2022Article
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2 authors.
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Abstract
Background: Functional connectivity and complexity analysis has been discretely studied to understand intricate brain dynamics. The current study investigates the interplay between functional connectivity and complexity using the Kuramoto mean-field model. Method: Functional connectivity matrices are estimated using the weighted phase lag index and complexity measures through popularly used complexity estimators such as Lempel-Ziv complexity (LZC), Higuchi's fractal dimension (HFD), and fluctuation-based dispersion entropy (FDispEn). Complexity measures are estimated on real and simulated electroencephalogram (EEG) signals of patients with mild cognitive-impaired Alzheimer's disease (MCI-AD) and controls. Complexity measures are further applied to simulated signals generated from lesion-induced connectivity matrix and studied its impact. It is a novel attempt to study the relation between functional connectivity and complexity using a neurocomputational model. Results: Real EEG signals from patients with MCI-AD exhibited reduced functional connectivity and complexity in anterior and central regions. A simulation study has also displayed significantly reduced regional complexity in the patient group with respect to control. A similar reduction in complexity was further evident in simulation studies with lesion-induced control groups compared with non-lesion-induced control groups. Conclusion: Taken together, simulation studies demonstrate a positive influence of reduced connectivity in the model imparting a reduced complexity in the EEG signal. The study revealed the presence of a direct relation between functional connectivity and complexity with reduced connectivity, yielding a decreased EEG complexity.
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