Deep learning models for neuroimaging have largely been developed for individual tasks, limiting knowledge transfer across applications. Here we introduce GenFAR, a modular deep learning framework that learns general, clinically informed features from brain MRIs. We trained this modular architecture on 49,246 individuals across 11 cohorts, using 17 diverse classification and regression tasks spanning cognition, clinical, diagnosis, demographics, and biomarkers. This yields aggregated, focused feature sets that capture rich, clinically- and biologically-relevant brain representations. We developed a sequential learning approach where tasks progressively build on previously learned representations. Through an analysis of 5,000 task sequences, we identified an optimal sequence length of six tasks and introduced a Donor Score metric to quantify each task's contribution to downstream performance. This analysis revealed five consistently strong donor tasks (Age, AD/MCI, MMSE, Hypertension, Hyperlipidemia) that formed the base of our sequential model. We demonstrated the utility of our learned representation, in various tasks beyond those included in the training set, to serve as the foundation for specialized secondary predictors. We further showed that using the learned feature representation can substantially increase the sample efficiency of secondary deep learning training tasks and models, as well as improve their accuracy.
V. Bashyam, G. Erus, Junhao Wen et al.· 0 citations
INTRODUCTION: Determining whether plasma biomarkers are preferentially associated with Alzheimer's disease (AD)-related rather than age-related brain atrophy patterns may clarify their prognostic and diagnostic clinical use. METHODS: Using data from the Baltimore Longitudinal Study of Aging (N=818), we examined cross-sectional plasma A{beta}42/A{beta}40, GFAP, NfL, p-tau181, and p-tau217 measurements obtained while participants were cognitively unimpaired (CU). During follow-up, 104 participants developed mild cognitive impairment (MCI)/dementia (74 due to AD, 24 due to non-AD, 6 unknown etiology). 2,293 longitudinal brain MRIs were used to quantify multidimensional atrophy pattern scores reflecting brain age (SPARE-BA), AD-like patterns (SPARE-AD), and five dominant dimensions of atrophy (R-indices). We investigated the associations of plasma biomarkers and atrophy pattern scores at index visit with conversion to MCI/dementia due to AD. We then examined the associations of plasma biomarkers with longitudinal change in pattern scores using linear mixed effects models. RESULTS: p-tau181, A{beta}42/A{beta}40 (Lumipulse), and p-tau217 were associated with incident MCI/dementia due to AD but not non-AD etiologies, while GFAP was associated with incident MCI/dementia due to both AD and non-AD. All four biomarkers were associated with longitudinal SPARE-AD changes. A{beta}42/A{beta}40 (Quanterix and Lumipulse), p-tau181, and p-tau217 were associated with longitudinal parieto-temporal atrophy. p-tau217 was the only biomarker associated with longitudinal medial temporal lobe atrophy. We did not find associations between plasma biomarkers and SPARE-BA or R-indices capturing subcortical, diffuse cortical, or perisylvian atrophy. DISCUSSION: Among CU individuals, plasma p-tau217 was associated with subsequent MCI/dementia due to AD and showed the most extensive associations with longitudinal AD-related brain atrophy.
M. Bilgel, I. Shah, J. Cooper et al.· medRxiv· 0 citations
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