Functional Connectivity and Complexity in the Phenomenological Model of Mild Cognitive-Impaired Alzheimer's Disease

Abstract

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.

Patients with Alzheimer’s disease dementia show partially preserved parietal ‘hubs’ modeled from resting-state alpha electroencephalographic rhythms

“…We used a low number of scalp electrodes to record rsEEG activity (i.e., 19 electrodes placed according to 10–20 system), two standard bivariate (LLC) and multivariate (iCoh) techniques estimating the interrelatedness of the rsEEG activity at electrode pairs, and well-known graph indexes in line with the general methodology of several previous successful studies; those studies investigated the graph-based rsEEG topology in ADD patients based on ‘synchronization likelihood’, ‘phase lag index’, ‘synchronization likelihood’, ‘generalized composite multiscale entropy vector’, and ‘mutual information’ techniques applied to rsEEG data recorded from ≤19 scalp electrodes ( Stam et al, 2007a ; De Haan et al, 2009 ; Engels et al, 2015 ; Yu et al, 2016 ; Song et al, 2019 ; Das and Puthankattil, 2022 ; Franciotti et al, 2022 ).…”

“…Moreover, beyond the ‘small worldness’ property, ADD patients were characterized by abnormalities in the following graph network indexes: (1) a shift of the ‘betweenness centrality’ center of mass from posterior to anterior alpha rhythms in relation to disease severity, as revealed by the ‘phase lag index’ ( Engels et al, 2015 ); (2) a parietal and occipital loss of the network organization from theta and alpha rhythms, as revealed by the ‘phase lag index’ ( Yu et al, 2016 ); (3) hub rearrangement and functioning at different rsEEG frequency bands, as revealed by several interrelatedness measures ( Stam et al, 2007a ; De Haan et al, 2009 ; Frantzidis et al, 2014 ; Engels et al, 2015 ; Song et al, 2019 ; Das and Puthankattil, 2022 ); (4) lower ‘global efficiency’, increased ‘local efficiency’, and lower resilience of cortical networks from the rsEEG alpha and beta rhythms, as revealed by the Granger ‘directed transfer function’ ( Afshari and Jalili, 2017 ); and (5) reduced graph ‘local and global efficiency’ values from lower inward and outward directions of the interrelatedness derived from the whole-band rsEEG activity by another Granger measure based on a conditional multivariate vector autoregression model. Notably, the maximum abnormalities of the ‘hub degree’ were observed at parietal electrodes ( Franciotti et al, 2019 ), whereas no changes in the global network organization from the whole-band rsEEG activity were found by ‘mutual information’ measures of that interrelatedness ( Franciotti et al, 2022 ).…”

Phase Lag Index of Resting-State EEG for Identification of Mild Cognitive Impairment Patients with Type 2 Diabetes

“…According to the above findings, the functional connectivity decline between brain channels or regions is linked to cognitive impairment. This association may be the result of a reduction in the processing of local information brought on by synaptic degeneration and the death of cortical neurons, which leads to a gradual loss of connectivity between some cortical areas [ 30 ].…”

Alzheimer’s Disease: Insights from Large-Scale Brain Dynamics Models

“…In the existing study, the relation between functional connectivity and complexity was explored using the Kuramoto mean-field model in the MCI-AD condition, and certain variations were found in the anterior and central regions in the results, specifically including the frontal and temporal lobes [ 36 ]. And in Zimmermann et al’s study, the researchers attempted to identify biophysical model neural parameters that were associated with cognition along the spectrum from healthy controls to mild cognitive impairment (MCI) to AD, modeled the limbic subnetwork as well as the entire brain, and found that neurodegeneration and functional changes occurred first in the limbic and temporal regions of the brain and, later, in the motor and sensory areas [ 40 ].…”

“…Using neurodynamic model simulations, researchers have demonstrated that the disconnection of macroscopic anatomical structures in the brains of AD patients leads to abnormal changes in metastability, and this revealed an important relationship between metastability, cognition, and the efficiency of anatomical structures [ 30 , 31 ]. In addition, other researchers have used models that can analyze the characteristics of the evolutionary process to identify changes in alpha rhythms [ 32 , 33 ] and excitatory neurons [ 6 , 34 ] in the brains of AD patients and areas of abnormality [ 31 , 35 , 36 ] between AD patients and controls.…”