# Resting state connectivity signatures of obsessive compulsive symptoms

> **NIH NIH F30** · COLUMBIA UNIVERSITY HEALTH SCIENCES · 2022 · $50,252

## Abstract

PROJECT SUMMARY
Obsessive-compulsive disorder (OCD) is a disabling illness that exhibits bimodal timing of onset, with up to half
of cases beginning in childhood. Subclinical obsessive-compulsive symptoms (OCS) often precede the
development of clinically significant OCD, though symptoms in some children remit naturally over time.
However, the neural bases of OCS and their changes over development are poorly understood. Capitalizing on
large, publicly available datasets and sophisticated computational methods, I propose to examine functional
MRI signatures of OCS and their longitudinal trajectories in children and adolescents. Specifically, I will apply
machine learning to publicly available data from the Adolescent Brain Cognitive Development (ABCD) study, a
prospective community sample tracked longitudinally at 21 diverse sites across the United States, to reveal
whole-brain functional MRI patterns that correspond to OCS severity. Baseline data is already available from
approximately 12,000 children aged 9-10 whose families have committed to ongoing follow-up. I will then use
data from the independent Healthy Brain Network (HBN) study, which includes data from approximately 2,500
children from the New York City area, to statistically and clinically validate these patterns (Aim 1). I will then
combine baseline neuroimaging and clinical data with longitudinal follow-up clinical data from the ABCD study
to examine neural signatures that predict subsequent OCS trajectories (Aim 2). Finally, I will leverage data
collected from children with clinical-severity OCS (i.e., OCD) before and after gold-standard cognitive
behavioral therapy at the New York State Psychiatric Institute (NYSPI) to identify pre-treatment predictors of
response and remission (Exploratory Aim). Collectively, these aims will identify brain connectivity features that
correspond to reliable patterns in which OCS co-vary, which could hint at common mechanisms underlying
multiple symptoms and implicate specific circuits that can be targeted in future studies aimed at developing
and testing novel treatments and prevention strategies. Furthermore, this research proposal integrates a
detailed training plan that will bring me closer to my goal of becoming a physician-scientist focused on clinical
and computational psychiatry. Supported by the resources of both Columbia University Irving Medical Center
and the NYSPI, I will deepen my technical skills in MRI image processing, neuroimaging analysis, and machine
learning, while also improving my scientific writing, oral presentation, and clinical skills.

## Key facts

- **NIH application ID:** 10478918
- **Project number:** 5F30MH126504-02
- **Recipient organization:** COLUMBIA UNIVERSITY HEALTH SCIENCES
- **Principal Investigator:** Tracey Chen Shi
- **Activity code:** F30 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $50,252
- **Award type:** 5
- **Project period:** 2021-09-01 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10478918

## Citation

> US National Institutes of Health, RePORTER application 10478918, Resting state connectivity signatures of obsessive compulsive symptoms (5F30MH126504-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10478918. Licensed CC0.

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