FiberNET: Deep learning to evaluate brain tract integrity worldwide and in AD

University Of Southern California, Los Angeles CA

Investigators

Linked publications & trials

Abstract

ABSTRACT Alzheimer’s disease (AD) threatens to devastate society worldwide. For every 5 years of age over age 65, the prevalence of AD doubles, costing an estimated $277 billion in the U.S. in 2018, a $20 billion increase from the previous year. Here we propose a coordinated global study of brain aging and AD, that uses novel approaches to assess the white matter microstructure of the brain’s neural pathways - a crucial brain metric that breaks down on the pathway from molecular AD pathology to clinical decline. With a novel deep learning tool, called FiberNET, we extract and analyze the brain’s white matter fiber bundles obtained from diffusion MRI (dMRI) scans across the world, and answer 3 key questions: how do the brain’s tracts age worldwide? How does tract aging depend on sex, Alzheimer’s genetic risk, and brain amyloid load? Can tract metrics predict clinical decline better, when combined with standard, accepted biomarkers of AD? The proposal unites experts in AD, neuroimaging, machine learning, and large-scale genomics, to relate new aging metrics (tract microstructure) to protective and adverse factors. Novel mathematics include innovations in picking up crossing fibers and tissue properties from multi- shell diffusion MRI, and convolutional neural nets to learn patterns of aging in neural pathways worldwide. We aim to (1) use FiberNET, our deep learning method, to extract tracts from brain dMRI scans worldwide, and create normative charts for normal tract aging in 20,000 people across the lifespan; (2) ask how the tract aging trajectory depends on sex, the AD protective genotype APOE2, risk genotype APOE4, and brain amyloid load measured with amyloid-sensitive PET. The proposed study will create standardized charts of white matter tract integrity across the lifespan to serve as a guidepost for normative white matter aging. We build on our ENIGMA- Lifespan work - which analyzed brain MRI data from 10,144 people from 91 cohorts - to create lifespan charts for the brain’s major tracts from dMRI, yielding fundamental normative information for comparisons of AD groups worldwide. This lifespan approach will aid the discovery of personal factors that accelerate aging relative to population norms (e.g., sex, APOE genotype, and amyloid load). To ensure the impact of the developments, we created a team of beta-testers to help test and refine the methods, that is tightly integrated into our ENIGMA consortium, which is dedicated to cross-cohort data harmonization. This global approach to aging and AD will offer a new source of power to “break the logjam” in discovering factors that affect the brain as we age.