ADNI | Active Investigations

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Active Investigations

There are many active research projects accessing and applying shared ADNI data. Use the search above to find specific research focuses on the active ADNI investigations. This information is requested annually as a requirement for data access.

Principal Investigator
Principal Investigator's Name:	Zhen-Qi Liu
Institution:	McGill University
Department:	Neurology and Neurosurgery
Country:
Proposed Analysis:	RATIONALE \| The complexity of neurodegenerative diseases arise from the currently-unclear molecular mechanism and diverse manifestation of symptoms and progression tracks. Existing literature and recent perspectives strongly suggest a cross-disease landscape that may be characterized by mesoscale network organizations (Crossley et al., 2014, Heuvel and Sporns, 2019). HYPOTHESIS \| Our central hypothesis is that multiscale cortical disconnection profiles are organized as a globally-clustered (transdiagnostic) and locally-distributed (individualized) structure, with meaningful clinical and multi-omics manifestations. SPECIFIC AIMS \| By studying the cortical disconnection profiles using a novel machine learning framework, we will map the network latent space (Aim #1), identify transdiagnostic subtypes and individual signatures (Aim #2), and relate to multi-modal phenotypes (Aim #3) METHODS \| We propose to use a data-driven graph representation learning model (InfoGraph; Sun, F.-Y. et al., 2020) to study the putative network latent space of healthy and diseased brains, with the ability to discover transdiagnostic subtypes and individual traits. We will use HCP (Human Connectome Project, N>1000) and UKB (UK Biobank, N>10000) for healthy population; PD (PPMI, Parkinson’s Progression Markers Initiative), FTD (GENFI, Genetic FTD Initiative), and AD (ADNI, Alzheimer's Disease Neuroimaging Initiative) for diseased populations. EXPECTED OUTCOME \| The proposed project will map the diverse landscape of neurodegenerative diseases and provide an improved understanding of multiscale transdiagnostic features for accurate subtype discovery. By adapting to individual profiles and reflecting on existing knowledge, the proposal will provide detailed individual signatures for personalized therapeutic plans.
Additional Investigators