Molecular mechanisms in plasticity in Alzheimer's: integration of the gut microbiota and EEG-derived entropy

Neurodegenerative diseases, particularly Alzheimer’s disease, affect over 55 million individuals worldwide. Despite extensive research, early detection remains limited by the absence of integrated multimodal frameworks combining biological, neurophysiological, and computational biomarkers. This study proposes a multimodal analytical framework integrating gut microbiome diversity indices, serum brain-derived neurotrophic factor (BDNF) levels, synaptic connectivity markers, and resting-state EEG-derived entropy measures to model presymptomatic Alzheimer’s disease. A convergent secondary data analysis approach will be applied using publicly available datasets, including the Alzheimer’s Disease Neuroimaging Initiative (ADNI), the preclinical Alzheimer’s gut microbiome dataset (dbGaP: Ferreiro et al.), and UK Biobank records. Analytical methods include statistical modeling and machine learning-based classification to evaluate associations between multimodal biomarkers and Alzheimer’s disease status. The central hypothesis is that a composite multimodal signature will demonstrate improved predictive performance compared to single-modality biomarkers such as CSF amyloid-β and APOE genotype. This framework aims to provide a basis for future validation studies investigating gut-brain axis interactions and neuroplasticity-related mechanisms in Alzheimer’s disease.