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Deep Generative Analyses for fMRI data

Award Number: F30MH129086
ORGANIZATION: NATIONAL INSTITUTE OF MENTAL HEALTH
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: FELLOWSHIP/SCHOLARSHIP/STUDENT LOANS
PERIOD OF PERFORMANCE START DATE: 11/01/2023
PERIOD OF PERFORMANCE END DATE: 10/31/2025

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Deep Generative Analyses for fMRI data - ABSTRACT About 51.5 million people (1 in 5 US adults) lives with a mental illness (MI) and it is estimated that serious MI costs Americans about $193 billion in lost earnings, yearly. Given the high prevalence and social cost of MI, there has been a growing push for translating advances in neuroscience research into improvements in MI prevention and psychiatry care delivery. In this context, it has become increasingly evident that psychiatric diseases emerge as result of abnormalities in brain spatiotemporal dynamics and network connectivity. Furthermore, neuropsychiatric diseases typically have a high degree of individual variability in presentation, symptom severity, and treatment response. In this proposal, we aim to design new fMRI analysis methods capable of tackling the abovementioned challenges – i.e., capable of directly modeling brain spatiotemporal dynamics, while also capturing individual variability. More specifically, the main goal of this proposal is to extend a previously developed deep-generative fMRI analysis model (VAE-GAM) that produces interpretable spatial effect maps for each covariate (as in standard methods) while capturing nonlinear effects and correlations across voxels. To accomplish this goal, I propose to: 1) Model temporal dynamics directly by fitting a Recurrent Neural Network (RNN) to the VAE-GAM latent space; and 2) Capture individual differences by using a deep Mixed Effects Modeling framework to model individual subject maps as being the sum of a group-level baseline map and a subject-unique map, generated using a learned, subject-unique embedding vector. The expected outcome of this proposal is a flexible fMRI analysis toolset that will allow researchers and clinicians to identify new brain activity patterns linking high-level behavior in health and disease states. We believe such a model could be a step towards fulfilling the goal of delivering biologically-sound, computationally driven, and personalized health care for millions of patients afflicted by mental illness.


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: 2025 ( Subtotal = $42,689 )
2025	2025	DUKE UNIVERSITY	2200 W MAIN ST	DURHAM	NC	27705	DURHAM	USA	Mental Health Research Grants	000	2	11/30/2024	NON-COMPETING CONTINUATION	$40,845
2025	2025	DUKE UNIVERSITY	2200 W MAIN ST	DURHAM	NC	27705	DURHAM	USA	Mental Health Research Grants	001	2	12/31/2024	NON-COMPETING CONTINUATION	$1,280
2025	2025	DUKE UNIVERSITY	2200 W MAIN ST	DURHAM	NC	27705	DURHAM	USA	Mental Health Research Grants	002	2	9/8/2025	NON-COMPETING CONTINUATION	$564
														Subtotal = $42,689

Issue Date FY: 2023 ( Subtotal = $40,503 )
2023	2023	DUKE UNIVERSITY	2200 W MAIN ST	DURHAM	NC	27705	DURHAM	USA	Mental Health Research Grants	001	1	9/12/2023	NEW	$0
2023	2023	DUKE UNIVERSITY	2200 W MAIN ST	DURHAM	NC	27705	DURHAM	USA	Mental Health Research Grants	000	1	9/12/2023	NEW	$40,503
														Subtotal = $40,503

Grand Total All Awards = $83,192

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Deep Generative Analyses for fMRI data

Award Number: F30MH129086

ORGANIZATION: NATIONAL INSTITUTE OF MENTAL HEALTH

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: FELLOWSHIP/SCHOLARSHIP/STUDENT LOANS

PERIOD OF PERFORMANCE START DATE: 11/01/2023

PERIOD OF PERFORMANCE END DATE: 10/31/2025

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