Functional Connectivity and Complexity in the Phenomenological Model of Mild Cognitive-Impaired Alzheimer's Disease - Das, Surya; Puthankattil, Subha D. (Frontiers in Computational Neuroscience, 2022)
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 Kuramoto mean field model. Method: Functional connectivity metrices are estimated using weighted phase lag index and complexity measure through popularly used complexity estimators such as Lempel Ziv complexity (LZC), fractal dimension (FD) and fluctuation-based dispersion entropy (FDispEn). Complexity measures are estimated on real and simulated EEG signals of mild cognitive impaired Alzheimer’s disease (MCI-AD) patients and controls. Complexity measures are further applied on simulated signals generated from lesion induced connectivity measures 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 MCI-AD patients exhibited reduced functional connectivity and complexity in anterior and central regions. Simulation study has also displayed significantly reduced regional complexity in the patient group with respect to control. Similar reduction in complexity was further evident in simulation studies with lesion induced control groups compared to non-lesion induced control groups. Conclusions: 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.