Abstract
Alzheimer's disease (AD) is characterized by the pathological accumulation of amyloid plaques and hyperphosphorylated tau proteins, leading to disruptions in synaptic transmission and neural circuit alterations. Despite advancements in therapies to delay disease progression, there is a pressing need for simple, non-invasive, and accessible biomarkers to evaluate their effectiveness. Quantitative electroencephalography (qEEG), a computational method for quantifying brain electrical activity, is increasingly applied in AD research. We highlight the application of qEEG biomarkers, including power spectrum analysis (oscillatory activity within frequency bands), functional connectivity (coherent neural couplings) and effective connectivity (directional neural interactions), microstates (brief, stable states of the brain network), and non-linear analyses (e.g., entropy and EEG network analysis). These biomarkers can reflect real-time neural dynamics, making them ideal tools for diagnosis and monitoring the progression AD and mild cognitive impairment (MCI). It has been shown that decreased α power and increased θ power within the qEEG spectrum correlate with enhanced AD severity. Data from microstate analysis have demonstrated significant variations in temporal dynamics in patients with AD. Non-linear measures, such as entropy, have identified marked reductions in neural complexity in AD and MCI patients, indicating that they may serve as early diagnostic markers. Compared to traditional neuroimaging techniques, such as magnetic resonance imaging (MRI) or positron emission tomography (PET), qEEG is known to be cost-effective and facilitates real-time monitoring. Overall, qEEG biomarkers are promising for advancing AD research due to their non-invasive nature, affordability, and ability to capture real-time neural activity. Integrating qEEG with multimodal neuroimaging and clinical profiles may facilitate earlier identification and precision therapies. Future research should focus on standardizing protocols, validating biomarkers across diverse cohorts, and exploring their potential in large-scale clinical trials.