BackgroundFunctional 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.MethodFunctional 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.ResultsReal 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.ConclusionTaken 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.
Unraveling the Mysteries of Cognitive-Impaired Alzheimer’s through Brain Connectivity and Complexity

Dr. David Lowemann, M.Sc, Ph.D., is a co-founder of the Institute for the Future of Human Potential, where he leads the charge in pioneering
Imagine the brain as a bustling city, with its numerous pathways and connections. In a groundbreaking study, scientists delved into the intricate dynamics of the brain by investigating the interplay between functional connectivity and complexity. Using a mathematical model, they analyzed how different regions of the brain communicate and discovered fascinating insights about mild cognitive-impaired Alzheimer’s disease (MCI-AD). Real electroencephalogram (EEG) signals from patients with MCI-AD showed reduced functional connectivity and complexity in specific brain regions. This finding was further validated through simulation studies with lesion-induced control groups. The simulations revealed that decreased connectivity led to a decrease in EEG complexity. The study highlights a direct relationship between functional connectivity and complexity in MCI-AD, providing new avenues for understanding this complex neurological condition.