# Using multimodal imaging and the RDoC framework to predict risk factors for suicide attempt

> **NIH NIH R03** · YALE UNIVERSITY · 2020 · $83,750

## Abstract

Project Summary
Suicide is one of the greatest challenges facing Society today. Although research to date has
provided important insight into the risk factors for suicide, it has struggled to make a translational
impact on preventing suicide. New research strategies are therefore vital. The RDoC framework
offers a promising approach to researching suicide, by moving the focus away from mental
illness as the primary predictor of suicide, towards an emphasis on transdiagnostic dimensions,
novel predictors, and integration of multiple units of analysis. Identifying the neurobiology
underlying the transdiagnostic risk factors for suicide is an important step in understanding the
mechanisms that lead to suicide. The majority of neuroimaging research to date has been purely
correlational in nature, and often fails to generalize outside of the study sample. Predictive
modelling, on the other hand, uses cross-validation to increase generalizability, and allows for
the prediction of behavior, which is likely to have greater real-world utility. In this proposal, we
will use the RDoC framework, multimodal imaging and predictive modelling to identify the
molecular and network-level underpinnings of risk factors for suicide in a large, transdiagnostic
dataset. Specifically, we aim to use Connectome-based Predictive Modelling (CPM) to generate
and validate predictive models of suicide risk factors (depression severity, impulsivity, executive
dysfunction and sleep disturbances), transdiagnostically, based on existing functional
connectivity data (aim 1). Further, convergent research implicates glutamate dysfunction in
suicidality, and emerging evidence suggests a specific role for the metabotropic glutamate
receptor 5 (mGluR5) in suicide and suicide-related behaviors. Subjects in the existing dataset
also participated in PET imaging with and [18F]FPEB, a radioligand specific for the metabotropic
glutamate receptor 5 (mGluR5). We will investigate the association between mGluR5 and risk
factors for suicide transdiagnostically (aim 2). Finally, we propose to integrate fMRI and PET
data by using PET-weighted CPM to predict risk factors for suicide transdiagnostically (aim 3).
Here, we will assess whether combining molecular and circuit-level units of analysis increases
predictive performance and identifies novel predictive networks. In summary, we propose to use
cutting-edge multimodal imaging, computational modelling and the RDoC framework in an
innovative approach to gain a more comprehensive understanding of the neural mechanisms
underlying risk factors for suicide across psychiatric disorders. Findings of the proposed study
have the potential to inform risk models and identify novel molecular and network-level targets
for preventative and treatment strategies that are critically needed to reduce the suicide rate.

## Key facts

- **NIH application ID:** 9985179
- **Project number:** 5R03MH118609-02
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Sophie Holmes
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $83,750
- **Award type:** 5
- **Project period:** 2019-08-01 → 2022-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9985179

## Citation

> US National Institutes of Health, RePORTER application 9985179, Using multimodal imaging and the RDoC framework to predict risk factors for suicide attempt (5R03MH118609-02). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/9985179. Licensed CC0.

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