Establish Diagnostic and Prognostic Models for Preclinical AD Patients Based on Multimodal MRI, Behavioral, Genetic, and Plasma Biomarkers

Overview

To establish the diagnostic and prognostic models that could help the preclinical identification of subjects at higher risk of clinical progression to mild cognitive impairment and dementia based on combined features of baseline demographic, cognitive, behavioral, multimodal MRI, genetic, and plasma data.

Alzheimer's disease (AD) is a global concern. Due to the lack of effective therapeutic methods targeting late-stage AD patients, it is critical to investigate brain alterations in the preclinical stage to pave the way for early diagnosis and intervention. Structural and functional magnetic resonance imaging (MRI) has been proven to be an effective and non-invasive approach to explore the neural mechanisms underlying neurological disorders. Genetic factors such as apolipoprotein E and plasma biomarkers play important roles in AD development and progression. However, the interaction effects of risk genes and different pathologic pathways implicated in the pathogenesis of AD remain unclear. Furthermore, the diagnostic and prognostic models that could predict future cognitive decline or clinical progression based on objective features derived from baseline demographic, cognitive, behavioral, multimodal MRI, genetic, and plasma data need to be further explored. We aim to investigate the neural basis underlying early cognitive deficits using structural and functional MRI data combined with novel analytical methods such as dynamic functional connectivity, surface-based morphometry, graph theory, multilayer network, functional-structural coupling, hidden Markov model, and connectome gradient mapping. Secondly, to explore the interaction effects of risk genes, which may help a better illustration of different biological pathways implicated in the pathogenesis of Alzheimer's disease. Thirdly, to investigate the divergent and dynamic abnormalities of multimodal imaging markers across different stages of Alzheimer's disease and their associations with plasma biomarkers, which may enhance our understanding of the neuropathological mechanisms. Fourthly, to provide scientific evidence on the potential targets for early intervention of neurodegenerative diseases. Lastly, to establish the diagnostic and prognostic models that could help the preclinical identification of subjects at higher risk of clinical progression to mild cognitive impairment and dementia based on combined features of baseline multimodal biomarkers. These studies may help a better understanding of the neural and biological basis underlying AD and pave the way for early diagnosis and intervention.