# Redefine Trans-Neuropsychiatric Disorder Brain Patterns through Big-Data and Machine Learning

> **NIH NIH RF1** · UNIVERSITY OF MARYLAND BALTIMORE · 2021 · $1,220,179

## Abstract

Abstract
This application will combine the strengths of two large scale NIH-funded initiatives to understand disorder-
related patterns in the human brain: Connectomes Related to Human Disease (CRHD) and Enhancing
Neuroimaging Genetics through Meta-Analysis (ENIGMA). We will develop and evaluate novel brain
vulnerability metrics - based on the idea of polygenic risk scores – that we expect to better predict diagnosis
and cognitive performance than standard neuroimaging measures. We define a metric of “vulnerability” by
quantifying the similarity between each individual's brain pattern and deficit patterns in neuropsychiatric
disorders. The Regional Vulnerability Index (RVI) uses Big Data meta-analyses to quantify the similarity
between an individual and meta-analytical deficit effect size patterns based on large and diverse international
samples. The Machine Learning-Vulnerability Index (MVI) is trained using Big Data mega-analytic samples to
quantify the similarity for individual brain patterns to those learned from patients and controls. We will compute
novel, cross-domain vulnerability metrics to phenotype each of the N=3,350 CHRD individuals across three
mainly psychiatric (schizophrenia-spectrum and psychosis disorder, major depression, and bipolar disorder),
three mainly neurological (epilepsy, mild cognitive impairment, and Alzheimer's disease) and three
neuroimaging domains (structural, diffusion, and resting state functional MRI). Our Specific Aims merge CRHD
and ENIGMA data to test four hypotheses: 1) Neuropsychiatric illnesses not only impact an isolated region or
circuit, but are associated with deficit patterns across multiple brain regions and circuits that can be unique to
each illness; 2) such deficit patterns are informative of cognitive deficits in patients; 3) similarity with the deficit
patterns will have higher specificity and sensitivity than any traditional neuroimaging metric or trait; and 4)
similarity at the voxel- and vertex-based level may lead to development of high-resolution vulnerability indices.
We will test these hypotheses by performing patient-control sensitivity and specificity analyses in the
corresponding CRHD illness groups; study the degree of separation vs. commonality across psychiatric and
neurological disorders; and evaluate pattern differences at specific stages of the illnesses, such as Alzheimer's
disease. We will use multivariate mediation analyses to link vulnerability to variance in cognitive domains
ascertained by CHRD. This short, intensive project will benefit the community-at-large by populating the
CHRD/HCP database with novel multimodal brain phenotypes extracted and homogenized using standard
ENIGMA workflows, enriched with Open Science approaches.

## Key facts

- **NIH application ID:** 10186960
- **Project number:** 1RF1MH123163-01A1
- **Recipient organization:** UNIVERSITY OF MARYLAND BALTIMORE
- **Principal Investigator:** PETER V. KOCHUNOV
- **Activity code:** RF1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $1,220,179
- **Award type:** 1
- **Project period:** 2021-04-01 → 2024-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10186960

## Citation

> US National Institutes of Health, RePORTER application 10186960, Redefine Trans-Neuropsychiatric Disorder Brain Patterns through Big-Data and Machine Learning (1RF1MH123163-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10186960. Licensed CC0.

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