# An Artificial Intelligence Foundation Model for Functional Neuroimaging: Personalized Prediction, Treatment Stratification, and Biotype Discovery in Major Depressive Disorder

> **NIH MH K23** · STANFORD UNIVERSITY · 2026 · $192,192

## Abstract

PROJECT SUMMARY
Major depressive disorder (MDD) is a leading cause of disability, with substantial individual and societal costs.
The heterogeneity of MDD and the lack of predictive tools for individualized treatment present significant
challenges to effective care. This proposal aims to leverage recent advances in foundation models, a type of
artificial intelligence (AI) that has demonstrated remarkable success in natural language processing, to develop
a neuroimaging-based tool that can aid in prognostication, treatment stratification, and biotype discovery in
MDD. Foundation models are pretrained on massive datasets, enabling them to learn generalizable features
that can then be adapted to smaller, more specific datasets. This approach is ideally suited for psychiatric
neuroimaging, where clinical datasets are scarce; however, non-clinical datasets like the Human Connectome
Project and UK Biobank are extensive. I have developed a functional prototype by adapting a transformer
architecture to analyze functional magnetic resonance imaging (fMRI) time-series data and training it on the UK
Biobank. Preliminary data generated using this prototype indicate strong potential for this approach. Applying
this innovative technique to psychiatry holds great promise for advancing the understanding and treatment of
MDD. To achieve this, I propose three specific aims. Aim 1: Use pooled fMRI data from individuals with MDD
to fine-tune the pretrained model to decode depression severity and uncover MDD biotypes; Aim 2: Use pooled
fMRI scans from longitudinal treatment data to fine-tune the pretrained model to predict antidepressant
response and identify neural circuits of treatment response; Aim 3: Prospectively evaluate the performance of
MRI-based treatment prediction models in a pilot clinical trial. If successful, this work will yield a novel
neurocomputational framework for personalized treatment stratification and significantly advance our
understanding of MDD neurobiology and heterog

## Key facts

- **NIH application ID:** 11302439
- **Project number:** 1K23MH139900-01A1
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Teddy  Akiki
- **Activity code:** K23 (R01, R21, SBIR, etc.)
- **Funding institute:** MH
- **Fiscal year:** 2026
- **Award amount:** $192,192
- **Award type:** 1
- **Project period:** 2026-04-15T00:00:00 → 2031-03-31T00:00:00

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11302439

## Citation

> US National Institutes of Health, RePORTER application 11302439, An Artificial Intelligence Foundation Model for Functional Neuroimaging: Personalized Prediction, Treatment Stratification, and Biotype Discovery in Major Depressive Disorder (1K23MH139900-01A1). Retrieved via AI Analytics 2026-07-03 from https://api.ai-analytics.org/grant/nih/11302439. Licensed CC0.

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