# Mapping Heterogeneity of Neuroanatomical Imaging Signatures of Psychosis via Pattern Analysis

> **NIH NIH R01** · UNIVERSITY OF PENNSYLVANIA · 2020 · $658,144

## Abstract

Neuropsychiatric disorders are characterized by highly heterogeneous and frequently overlapping clinical
phenotypes. Understanding the neurobiological underpinnings of these clinical symptoms has been a central goal
in neuropsychiatric research and has been largely facilitated by MRI and associated analytical methods that have
found reproducible neuroanatomical abnormalities. However, the neuroanatomical heterogeneity in these
disorders is also high. Therefore, attempting to find a unique neuroanatomical signature of a complex
neuropsychiatric disorder using commonly used current techniques is hampered by such heterogeneity.
Personalized disease treatment calls for fine quantification of heterogeneity and for more precise placement of
each individual patient into a multi-dimensional spectrum of neuroanatomical alterations found in neuropsychiatric
disorders. In the proposed project we focus on the neuroanatomy of psychosis. To this end, we leverage a unique
set of pooled cohorts from 10 sites, including (1) adults with chronic schizophrenia-spectrum (non-affective)
psychotic disorders (n=749), (2) individuals with first-episode (FE) psychosis (n=665), and matched healthy
controls (N=1,483). This large cohort will allow us to test our first hypothesis, namely that neuroanatomical
phenotypes of these patients will display high heterogeneity, which will allow us to define neuroanatomical
dimensions of pathology. Our second hypothesis is that this heterogeneity will relate to clinical phenotypes in
chronic schizophrenia spectrum patients, as well as to longitudinal outcome in FE psychosis. We leverage newly
developed pattern analysis and semi-supervised machine learning techniques designed to quantify heterogeneity
of complex patterns of neuroanatomical abnormalities. Our goal is to arrive at a new “NeuroAnatomical
Coordinate system of PSychosis”(NAC-PS), with each dimension reflecting a different neuroanatomical pattern of
brain alterations in this spectrum, which will allow us to measure patient positions and trajectories in this
spectrum, as they evolve across time and treatment. We propose to: Aim1: Develop inter-site harmonization
methods for imaging data, and hence establish a methodological platform for constructive integration of structural
imaging data from multiple sites. Using these methods, we will generate a resource of 2,897 datasets with
advanced neuroanatomical measurements; Aim 2: investigate the heterogeneity of anatomical patterns related to
psychosis at the population level, using novel group analysis methods which model the neuroanatomical
phenotype of disease as a collection of directions of deviation from normal anatomy. This will define a spectrum
of neuroanatomical patterns of psychosis, rather than seeking a single dominant pattern; Aim 3: Develop MRI-
based classification, subtyping, and outcome prediction on an individual patient basis, under this heterogeneity;
Aim 4: Relate baseline neuroanatomical patterns to longitudin...

## Key facts

- **NIH application ID:** 9942277
- **Project number:** 5R01MH112070-04
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Christos Davatzikos
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $658,144
- **Award type:** 5
- **Project period:** 2017-09-19 → 2022-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9942277

## Citation

> US National Institutes of Health, RePORTER application 9942277, Mapping Heterogeneity of Neuroanatomical Imaging Signatures of Psychosis via Pattern Analysis (5R01MH112070-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9942277. Licensed CC0.

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