# Predictive Markers and Mechanisms of Persistent Psychotic-like Experiences in Children: An Adolescent Brain and Cognitive Development Study Analysis

> **NIH NIH K01** · UNIVERSITY OF CALIFORNIA AT DAVIS · 2023 · $174,722

## Abstract

PROJECT TITLE
Predictive Markers and Mechanisms of Persistent Psychotic-like Experiences in Children: An Adolescent
Brain and Cognitive Development Study Analysis
PROJECT SUMMARY/ABSTRACT
My long-term career goal is to establish a highly influential and independent research program and become a
broad spectrum leader and innovator in computational psychiatry. I therefore propose the following training goals:
1) To gain formalized training in computational psychiatry methods and statistics, including deep
learning and structural equation modeling, 2) to obtain a deeper understanding of the field of
developmental psychopathology, and 3) to acquire training in developing and sustaining an independent
investigator position with his own laboratory. To meet the first goal, I will meet regularly with University of
California Davis (UCD) faculty co-mentors Ian Davidson and Emilio Ferrer, attend relevant UCD classes, and
complete a Research Plan with the following Specific Aims: 1) Using deep learning from Adolescent Brain and
Cognitive Development (ABCD) Study data (demographic/clinical information, neurocognitive testing,
neuroimaging data, and environmental metrics), predict child psychotic-like experience (PLE) distress scores
the following year (i.e., baseline data predicting year one PQ-BC distress, year one data predicting year two
distress, etc.), and 2) Using rules-based guidance from deep learning using explainable AI (XAI) algorithms, use
sequential, structural equation modeling (SEM) of latent profiles (i.e., longitudinal trajectories, e.g., emerging,
absent, remitting, persistent) to test hypotheses regarding the longitudinal mechanism(s) which may lead to
persistent PLEs in the ABCD cohort. In Aim 1, XAI methods will be used identify the rules used by the deep
learner to make decisions; in Aim 2, these rules will then be used to create latent constructs for modeling profiles
of longitudinal trajectories. If r2>0.80 is consistently achieved for each iterative analysis in Aim 1, it suggests that
ABCD instruments may predict PLE severity one year after measurement at a potentially clinically implementable
level and help generate future hypotheses for personalized interventions aimed at reducing risk for persistent
PLE distress. For Aim 2, insight will be gained with regard to how brain structure, brain function, neurocognitive
ability, and environmental influences interact to influence the time course of PLE expression. To meet the
second goal, I will receive mentoring including guided readings via monthly meetings with co-mentor Dr. Ellen
Leibenluft, a world-renowned psychiatrist specializing in developmental research and Section Chief at the NIMH.
I will also attend developmental classes, seminars, and workshops offered by UCD. To meet the third goal, I
will attend weekly meetings with mentor Dr. Cameron Carter, from whom I will gain overarching career guidance.
This will include learning how to create and oversee a laboratory, obtaining recom...

## Key facts

- **NIH application ID:** 10691886
- **Project number:** 5K01MH125096-03
- **Recipient organization:** UNIVERSITY OF CALIFORNIA AT DAVIS
- **Principal Investigator:** Jason Smucny
- **Activity code:** K01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $174,722
- **Award type:** 5
- **Project period:** 2021-09-01 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10691886

## Citation

> US National Institutes of Health, RePORTER application 10691886, Predictive Markers and Mechanisms of Persistent Psychotic-like Experiences in Children: An Adolescent Brain and Cognitive Development Study Analysis (5K01MH125096-03). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10691886. Licensed CC0.

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