EEG and brain network analysis in the early diagnosis of Alzheimer's disease

Abstract. The early diagnosis of Alzheimers Disease (AD) is critical for effective intervention and improved patient outcomes. This paper explores the role of electroencephalography (EEG) and brain network analysis in the early detection of AD, with a particular focus on neurofeedback training as a therapeutic intervention. We review the characteristic changes in EEG signals, such as decreased alpha power and increased theta power, and their implications for identifying early cognitive decline. Functional connectivity analysis using coherence and phase synchronization methods reveals disruptions in key brain networks, providing valuable biomarkers for early AD. We also discuss quantitative EEG (qEEG) techniques, including power spectral density and fractal dimension, which enhance diagnostic accuracy. Additionally, mathematical models like the Kuramoto model offer insights into network dynamics and predict disease progression. Integrating EEG with other diagnostic tools, such as MRI and PET, creates a comprehensive multimodal approach. This integrative framework enhances early diagnosis and informs personalized treatment strategies, representing a significant advancement in the field of AD research and clinical practice.