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Neuroimaging Multi-Omics Fingerprints for Subtyping and Uncertainty-Aware Prediction of Alzheimer's Disease

Award Number: R15AG103945
ORGANIZATION: NATIONAL INSTITUTE ON AGING
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 09/01/2026
PERIOD OF PERFORMANCE END DATE: 08/31/2029

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Neuroimaging Multi-Omics Fingerprints for Subtyping and Uncertainty-Aware Prediction of Alzheimer's Disease - Project Summary/Abstract Alzheimer's disease (AD) is a leading cause of dementia worldwide, yet its progression is highly heterogeneous across individuals. A major limitation in biomedical imaging is the inability to connect neuroimaging phenotypes to their underlying molecular drivers, which limits the biological interpretability and clinical predictive power of neuroimages. While neuroimaging genetics has linked genetic variants to brain structure, it overlooks critical regulatory layers (transcriptomic and proteomic processes) that mediate the path from gene to brain. Moreover, most predictive models for AD progression output a single trajectory without quantifying prediction conﬁdence, making it difﬁcult to translate into clinical decision-making. The overall objective of this project is to develop computational imaging frameworks that integrate neuroimag- ing with multi-omics data (genomics, transcriptomics, and proteomics) to identify AD subtypes and model neu- rodegenerative progression with calibrated uncertainty. We hypothesize that neuroimaging multi-omics ﬁnger- prints capture the molecular-imaging relationships that explain phenotypic heterogeneity and improve the relia- bility of individualized progression predictions. Aim 1 constructs a knowledge-guided graph to characterize multi- modal imaging–omics interactions and identify interpretable AD subtypes. Aim 2 designs uncertainty-aware deep learning models to forecast neurodegenerative and cognitive trajectories. The project will use ANMerge data for model development and discovery, and evaluate generalizability on complementary data (AMP-AD, ADNI). Successful completion of this project will yield novel deep learning algorithms that unify multi-omics and neu- roimaging data to reveal interpretable subtypes and generate uncertainty-aware disease predictions. The out- comes will advance precision neuroimaging and medicine by providing generalizable computational frameworks applicable across Alzheimer's and other neurodegenerative diseases. Undergraduate students will participate in data integration, method implementation and evaluation, and result interpretation under close mentorship, gaining hands-on research experience at the intersection of neuroscience and artiﬁcial intelligence.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2026 ( Subtotal = $526,607 )
2026	2026	WAKE FOREST UNIVERSITY	1834 WAKE FOREST RD	WINSTON SALEM	NC	27109	FORSYTH	USA	Aging Research	000	1	8/13/2026	NEW	$526,607
														Subtotal = $526,607

Grand Total All Awards = $526,607

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Neuroimaging Multi-Omics Fingerprints for Subtyping and Uncertainty-Aware Prediction of Alzheimer's Disease

Award Number: R15AG103945

ORGANIZATION: NATIONAL INSTITUTE ON AGING

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 09/01/2026

PERIOD OF PERFORMANCE END DATE: 08/31/2029

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