An excess of saturated fatty acids and simple sugars in the diet is a known environmental
risk factor of Alzheimer's disease (AD) but the holistic view of the interacting processes …

Abstract The Electrophysiology Professional Interest Area (EPIA) and Global Brain
Consortium endorsed recommendations on candidate electroencephalography (EEG) …

Brain functional networks have been traditionally studied considering only interactions
between pairs of regions, neglecting the richer information encoded in higher orders of …

In this paper, we explore the utility of resting-state EEG measures as potential biomarkers for
the detection and assessment of cognitive decline in mild cognitive impairment (MCI) and …

Purpose Alzheimer's disease (AD) is a progressive, incurable human brain illness that
impairs reasoning and retention as well as recall. Detecting AD in its preliminary stages …

Computational analysis of electroencephalographic (EEG) signals have shown promising
results in detecting brain disorders, such as Alzheimer's disease (AD). AD is a progressive …

Recent years have witnessed a surge of sophisticated computer-aided diagnosis techniques
involving Artificial Intelligence (AI) to accurately diagnose and classify Alzheimer's disease …

Increasing life expectancy has led to an aging population, which has consequently
increased the prevalence of dementia. Alzheimer's disease (AD), the most common form of …

Background Neuronal hyperexcitability and inhibitory interneuron dysfunction are frequently
observed in preclinical animal models of Alzheimer's disease (AD). This study investigates …

Objective. This study aims to design and implement the first deep learning (DL) model to
classify subjects in the prodromic states of Alzheimer's disease (AD) based on resting-state …