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.
Functional Connectivity and Complexity in the Phenomenological Model of Mild Cognitive-Impaired Alzheimer's Disease
Surya Das,Subha D. Puthankattil
Published 2022 in Frontiers in Computational Neuroscience
ABSTRACT
PUBLICATION RECORD
- Publication year
2022
- Venue
Frontiers in Computational Neuroscience
- Publication date
2022-06-06
- Fields of study
Medicine, Computer Science
- Identifiers
- External record
- Source metadata
Semantic Scholar, PubMed
CITATION MAP
EXTRACTION MAP
CLAIMS
CONCEPTS
- complexity measures
Signal-complexity metrics applied to the recorded and simulated EEG time series, including the named estimators in the abstract.
Aliases: complexity metrics
- control group
The healthy comparison group and its corresponding simulation condition used as the reference for the patient analyses.
Aliases: controls, control condition
- electroencephalogram (eeg) signals
The recorded brain electrical time series and model-generated signals that were analyzed for connectivity and complexity.
Aliases: EEG signals, EEG
- functional connectivity
The synchrony-based coupling between EEG regions estimated to characterize communication across brain areas in the study.
Aliases: FC
- kuramoto mean-field model
A neurocomputational oscillator model used to generate simulated EEG and to relate connectivity changes to signal complexity.
Aliases: Kuramoto model, mean-field Kuramoto model
- lesion-induced connectivity matrix
A modified connectivity matrix with induced lesions that was used to drive alternative simulation conditions.
Aliases: lesioned connectivity matrix, lesion-induced matrix
- mild cognitive-impaired alzheimer's disease (mci-ad)
A patient group with mild cognitive impairment due to Alzheimer disease used as the clinical comparison condition in the EEG and simulation analyses.
Aliases: MCI-AD, mild cognitive impairment due to Alzheimer's disease
- weighted phase lag index
A phase-lag-based connectivity estimator used to compute functional connectivity from EEG signals while reducing volume-conduction effects.
Aliases: wPLI
REFERENCES
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