Loss of brain inter-frequency hubs in Alzheimer's disease

TL;DR

This study uses a multi-layer network approach to analyze multi-frequency brain connectivity in Alzheimer's disease, revealing abnormal inter-frequency hub distribution and its correlation with cognitive impairment, with potential diagnostic applications.

Contribution

Introduces a multi-layer network method to analyze multi-frequency brain networks in AD, identifying disrupted inter-frequency hubs and their diagnostic potential.

Findings

AD shows decreased multi-participation coefficient indicating disrupted inter-frequency connectivity.

Regional connectivity alterations are localized in association areas and cingulate cortex.

MPC correlates with memory impairment and improves classification accuracy for AD.

Abstract

Alzheimer's disease (AD) causes alterations of brain network structure and function. The latter consists of connectivity changes between oscillatory processes at different frequency channels. We proposed a multi-layer network approach to analyze multiple-frequency brain networks inferred from magnetoencephalographic recordings during resting-states in AD subjects and age-matched controls. Main results showed that brain networks tend to facilitate information propagation across different frequencies, as measured by the multi-participation coefficient (MPC). However, regional connectivity in AD subjects was abnormally distributed across frequency bands as compared to controls, causing significant decreases of MPC. This effect was mainly localized in association areas and in the cingulate cortex, which acted, in the healthy group, as a true inter-frequency hub. MPC values significantly correlated with memory impairment of AD subjects, as measured by the total recall score. Most predictive regions belonged to components of the default-mode network that are typically affected by atrophy, metabolism disruption and amyloid-beta deposition. We evaluated the diagnostic power of the MPC and we showed that it led to increased classification accuracy (78.39%) and sensitivity (91.11%). These findings shed new light on the brain functional alterations underlying AD and provide analytical tools for identifying multi-frequency neural mechanisms of brain diseases.