# Predicting trajectories of psychopathology using multimodal neuroimaging and multi-task learning

> **NIH NIH F31** · WASHINGTON UNIVERSITY · 2024 · $42,574

## Abstract

PROJECT SUMMARY / ABSTRACT:
Most forms of psychopathology have been increasingly recognized as brain disorders that emerge early in
development and persist throughout the lifespan. Given the considerable costs of mental illness, it is imperative
to develop ways of identifying adolescents who are the most vulnerable, which may lead to more precise and
personalized interventions. Here, we propose using a novel predictive framework that may better capture the
neurodevelopmental origins of psychopathology, thereby yielding more accurate predictions of psychopathology.
Specifically, we plan to develop multi-task neural networks that are trained on spatial maps from three
neuroimaging modalities and yield simultaneous predictions of an individual’s age (“brain age”) and
psychopathology (“brain pathology”). By integrating predictions of brain age and brain pathology through this
novel multi-task framework, we may derive models with improved predictive power, which would also be useful
for uncovering the specific biomarkers that underlie each dimension of psychopathology. To investigate these
research questions and replicate our findings, we will use multimodal neurodevelopmental data from two of the
largest neuroimaging datasets that also contain three longitudinal timepoints – namely the Human Connectome
in Development (HCD) and the Adolescent Brain Cognitive Developmental (ABCD) samples. In contrast to using
single-task models, we hypothesize that the multi-task predictions of brain age and brain pathology would be
better able at detect individual differences at any given point in time (Aim 1) and such predictions would best
map onto within-subject changes throughout adolescence (Aim 2). Further, we will use multiple feature
importance methods to identify which brain areas and neural properties added the largest predictive power to
our most accurate models (Aim 3). This F31 proposal may prove useful in identifying adolescents who are most
vulnerable to psychopathology (“personalization”) and accessing risk earlier in the course of development
(“precision”). We will also make our deep learning models publicly available so that anyone could use them to
yield out-of-sample predictions, which may have wide-spread applications for neuroimaging researchers and
pediatric clinicians. Through the pursuit of these research objectives, the applicant will receive essential training
in the following areas: 1) deep/machine learning methods, 2) multimodal neuroimaging, 3) advanced
psychopathology, 4) conducting rigorous and reproducible research, 5) professional development as the
applicant progresses toward a career as an independent, NIH-funded academic researcher. The assembled
training team has substantial expertise in each of these subject domains. With their support, the applicant will
develop the theoretical, analytical, and professional aptitude needed to foster his research and career ambitions.
Altogether, this F31 proposal will be catalyst to help the app...

## Key facts

- **NIH application ID:** 10937073
- **Project number:** 5F31MH135640-02
- **Recipient organization:** WASHINGTON UNIVERSITY
- **Principal Investigator:** Robert J Jirsaraie
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $42,574
- **Award type:** 5
- **Project period:** 2023-09-20 → 2025-09-19

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10937073

## Citation

> US National Institutes of Health, RePORTER application 10937073, Predicting trajectories of psychopathology using multimodal neuroimaging and multi-task learning (5F31MH135640-02). Retrieved via AI Analytics 2026-06-25 from https://api.ai-analytics.org/grant/nih/10937073. Licensed CC0.

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