# Deep machine learning to delineate trajectories of vulnerability and transition to mental illness in youth

> **NIH NIH R00** · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · 2022 · $249,000

## Abstract

Most mental illness originates in youth: 50% of mental illness is diagnosed by the age of 14, and 75% by 24.
Manifest illness emerges after a period of ~8-10 years. Remarkably, functional MRI can detect disrupted patterns
of brain function associated with these vulnerable trajectories. However, the emergence of manifest mental
illness reflects a complex and dynamic interplay of many biological, developmental, social and environmental
factors that may be risky or protective, shared across illness categories, co-vary, and currently lack specificity.
Moreover, the ability of fMRI and serial assessment to detect clinically-relevant shifts in dynamic risk trajectories
of mental illness is poorly defined. Charting mental illness trajectories to optimize intervention is a critical need.
A major obstacle is the identification of specific neural-behavioral risk pathways that shape vulnerable
developmental trajectories into various categories of manifest mental illness. The long-term goal of my research
is to develop computational approaches to advance our understanding of risk trajectories in the mental health of
youth. Consequently, the overall objective of this study is to leverage advanced machine learning methods to
illuminate dynamic trajectories of mental illness progression, to characterize sustained mental health and to
quantify the incremental predictive value of serial and functional MRI assessment. My central hypothesis is that
specific risk pathways that have specific brain functional correlates characterize discrete peri-adolescent
trajectories in major mental illness categories. Further, that functional MRI metrics and serial assessment
significantly improve case prediction. This study will leverage a new, unprecedented opportunity to dissect risk
trajectories by applying deep machine learning to ‘big data’ from 20,000 diverse youth, including longitudinal
data. I will characterize the behavior and specificity of dynamic risk trajectories and their functional neural
correlates in 5 major peri-adolescent mental illness categories and determine the incremental predictive value
of adding functional MRI to bio-psycho-social data and continuing assessment after age 10, the developmental
inflection point for mental illness. Further innovation will accrue from the use of a cloud computing infrastructure
to perform the research. My rationale is that elucidating specific risk trajectories and how to best monitor them
in peri-adolescence will stimulate more refined prevention and early intervention strategies in youth mental
health, known to improve outcomes and reduce resource use. Concomitantly, I will obtain capstone training in
advanced machine learning methods, programming and cloud computing infrastructure, building on my KL2
training in machine learning, data science and the acquisition and analysis of fMRI and bio-psycho-social data.
An interdisciplinary mentorship team of international experts in data science, developmental psychopathology,
b...

## Key facts

- **NIH application ID:** 10470317
- **Project number:** 5R00MH118359-04
- **Recipient organization:** UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH
- **Principal Investigator:** Nina de Lacy
- **Activity code:** R00 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $249,000
- **Award type:** 5
- **Project period:** 2019-07-19 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10470317

## Citation

> US National Institutes of Health, RePORTER application 10470317, Deep machine learning to delineate trajectories of vulnerability and transition to mental illness in youth (5R00MH118359-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10470317. Licensed CC0.

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