# Integrative computational models of latent behavioral and neural constructs in children: a longitudinal developmental big-data approach

> **NIH NIH R01** · STANFORD UNIVERSITY · 2020 · $789,412

## Abstract

Project Abstract
Impairments in cognitive systems that regulate the ability to adaptively engage with and respond to changing
stimuli and goals are a hallmark of psychopathology. Identifying the underlying cognitive and neural factors that
drive dysfunctional behavioral dynamics is a primary goal for psychiatric research. However conventional
methods are unable to reveal latent constructs that govern these dynamic processes. Novel computational
approaches are required to reveal latent behavioral dynamics and traits associated with psychopathology, and
their neural circuit basis, within the Research Domain Criteria (RDoC) framework. Most, if not all, psychiatric
disorders have a neurodevelopmental origin and are associated with atypical maturation of cognitive brain
networks. Cognition is a dynamic process, which relies on flexible inhibitory control, goal-directed beliefs
that impact moment-to-moment expectation, and the capacity to learn and adapt from prior decisions.
Developing dynamic latent behavioral models of cognition is significant in the context of psychopathology,
because deficits in inhibitory control, performance monitoring and belief updating are implicated in multiple
psychiatric disorders including ADHD, autism, and schizophrenia. Our overarching goal is to develop and
validate Hierarchical Latent Variable Dynamics (HLVD), a novel integrative computational approach for
discovering robust latent behavioral constructs and their neural circuit bases. The proposed studies will
leverage the longitudinal Adolescent Behavioral and Cognitive Development (ABCD) study, which has
generated unprecedented amounts of “Big Data” (N>5,000) for charting cognitive and brain development in
children and adolescents over time. Crucially, HLVD will be used to identify and validate novel latent constructs
of behavioral dynamics that are expected to be significant dimensional predictors of externalizing symptoms
and developmental psychopathology. The proposed studies will significantly enhance our understanding of
RDoC constructs and provide new insights into latent behavioral dynamics and traits associated with
psychopathology in the developing brain. Our studies are highly relevant to the mission of the NIMH initiative
RFA-MH-19-242, which seeks to accelerate research on neurodevelopment and trajectories of risk for mental
illness. Our innovative approach will ultimately aid in the development of biomarkers for early detection and
treatment of psychiatric disorders.

## Key facts

- **NIH application ID:** 9984536
- **Project number:** 5R01MH121069-02
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Vinod Menon
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $789,412
- **Award type:** 5
- **Project period:** 2019-07-26 → 2024-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9984536

## Citation

> US National Institutes of Health, RePORTER application 9984536, Integrative computational models of latent behavioral and neural constructs in children: a longitudinal developmental big-data approach (5R01MH121069-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9984536. Licensed CC0.

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