Catalyst Awardee

Project Description

An Integrated Graph Convolutional Network Multimodal Platform (GCN-MP) for Early Detection and Prediction of Late Onset Alzheimer’s Disease

Victor OK Li, ScD | The University of Hong Kong; Jacqueline CK Lam, PhD (Co-Lead); David C Rubinsztein, BSc (Med), PhD; James B Rowe, B.M. B.Ch., PhD; Lawrence Cheung, PhD; Yang Han, PhD; Jocelyn Downey, PhD
Competition Sponsor: Research Grants Council of the Hong Kong Special Administrative Region, China
Award Year: 2023


Alzheimer’s disease (AD) is a leading cause of death worldwide. Early prediction of Late Onset Alzheimer’s disease (LOAD) is critical to timely intervention before irreversible brain damage.  Accurate early LOAD prediction is challenging due to the need for a wide range of multimodal assessments, which can be time-consuming, costly, and invasive, preventing those who might develop symptomatic LOAD from timely diagnosis and therapeutic interventions.  An AI-driven-multimodal LOAD prediction based on readily available data as inputs to an AI model trained on heterogenous data of high-dimensional modalities/features across different diseases/populations, can be desirable for early LOAD prediction/intervention. Our transformative multimodal  platform aims, FIRST, to develop an integrated Graph Convolutional Network Multimodal Platform (GCN-MP) technology, to fuse small datasets of different modalities/features/diseases/populations via a multimodal GCN model for accurate prediction of LOAD onset age; SECOND, to uncover early biomarkers and principal pathways driving LOAD onset. Five novelties have been proposed, including, heterogeneous multimodal inputs, multimodal data fusion, potential causal pathways identification, and integrated multimodal risk scores (IMRS) based on high-saliency complementary modalities. Our work will greatly facilitate clinicians and at-risk AD populations to more conveniently and accurately predict LOAD onset via the IMRS and onset age prediction, based on their available data types, and advance our understanding of LOAD etiology. Our team’s two consecutive grants awarded by NAM, and the numerous works done on AI-driven prediction and estimation and in neurological studies from our members in HKU-Cambridge-AI-for-Neuro-disease-Research-Platform have prepared us to achieve these meaningful goals of AI for Social Good.

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