# An Individualized, Multidimensional Dimensional Approach to Psychopathology

> **NIH NIH R01** · YALE UNIVERSITY · 2021 · $842,579

## Abstract

A primary challenge facing functional neuroimaging is the translation of research findings to the clinical setting.
In part, fMRI has struggled as a clinical tool due to the lack of functional phenotypes that characterize patients.
To address this, we have developed connectome-based predictive modeling (CPM) to identify and validate
predictive models of behavior/symptoms based on functional connectivity data. The promise of this approach is
that by developing predictive models based on the functional organization of an individual’s brain, we may be
able to extract a rich connectivity phenotypes to aid in the clinical characterization of patients. This approach
has the potential to improve our ability to categorize patients in otherwise heterogeneous groups and monitor
the effectiveness of treatment interventions. To do this, modeling methods are needed that are designed to
generalize across multiple behaviors, symptoms and diagnostic groups. In this proposal, we will push forward
several major developments in CPM focused on generating transdiagnostic models for three specific behaviors
(attention, working memory, and fluid intelligence) and factors from clinical tests, that will lead to functional
phenotypes. We will collect a battery of continuous performance tasks in a spectrum of (N=300) individuals.
We propose three specific aims: (1) To characterize node-boundary x dimensional construct effects; (2) To
preform unidimensional and multi-dimensional CPM to predict RDoC constructs; (3) To evaluate the extent to
which subjects with similar functional phenotypes cluster into symptom based or DSM-5 categorical clusters.
This aim will also allow us to investigate the functional networks that vary with symptom and to investigate
categorical subtleties in these symptom based phenotypes. The significance of transdiagnostic predictive
models of behavior from functional connectivity data lay in their ability to delineate clinically relevant
information from any individual (i.e. patient or control). The current lack of transdiagnostic predictive models
limits the clinical utility of fMRI, providing a framework for, and generating, these models could have important
implications in translating fMRI into a viable clinical tool. The innovation of this proposal is fourfold: 1) the
collection of a novel trans-diagnostic data set to be made publicly available; 2) the development of an
approach to generate personalized functional atlases to account for individual differences in anatomy; 3) the
development of methods to delineate meaningful functional phenotypes to assess symptoms, and 4) to provide
a means for comparing alignment of subjects on symptom dimensions versus DSM-5 categories using these
functional phenotypes. These developments will be validated using a combination of novel data to be collected
here as well as 3 publicly available data sets. The final deliverables will yield tools for measuring functional
phenotypes reflecting symptom scores suitable ...

## Key facts

- **NIH application ID:** 10191052
- **Project number:** 5R01MH121095-03
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** R Todd Constable
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $842,579
- **Award type:** 5
- **Project period:** 2019-08-01 → 2024-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10191052

## Citation

> US National Institutes of Health, RePORTER application 10191052, An Individualized, Multidimensional Dimensional Approach to Psychopathology (5R01MH121095-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10191052. Licensed CC0.

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