# Evaluating overlap and distinctiveness in neurocomputational loss and reward elements of the RDoC matrix

> **NIH NIH R01** · VIRGINIA POLYTECHNIC INST AND ST UNIV · 2021 · $788,923

## Abstract

PROJECT SUMMARY/ABSTRACT
Evidence indicates that disruptions in loss and reward valuation exist across traditional psychiatric diagnostic
categories, and these elements are featured in the NIMH Research Domain Criteria matrix. However,
validating these features of the RDoC matrix and determining the translational utility of loss and reward
valuation requires at least three critical advances: i) understanding the elements’ relational structure (i.e., to
what extent are loss and reward valuation linked or distinct), ii) establishing the functional relevance of
valuation measures (i.e., which features of loss and reward valuation are related to which symptoms), and iii)
determining the stability or lack thereof of the elements and relationships between the elements (i.e.,
determining which valuation features are state-like vs trait-like). To work toward validating valuation elements
and their relevance to psychopathology, we respond to RFA-MH-19-242 (Computational Approaches for
Validating Dimensional Constructs of Relevance to Psychopathology). Specifically, we take a data-driven,
computational psychiatry approach merging clinical and experimental data to delineate relationships among
computationally derived components of loss and reward valuation and with symptoms in a large sample of
participants with clinically significant mood, anxiety, or anhedonia (Aim 1). In Aims 2 and 3, we incorporate a
mechanistic trial to assess whether components of and relationships between loss and reward valuation are
sensitive to change a) over time, b) following 12 sessions of instructed valuation (Aim 2), or c) following
cognitive behavioral therapy (Aim 3). If successful, we believe there is immense opportunity to bridge
behaviorally-oriented clinicians and computational (neuro)scientists and advance the field by mapping
symptoms to neuromechanistic disease processes and spurring the development of new neurobehaviorally-
guided treatment approaches. As required by the RFA, this application assesses multiple constructs (loss and
reward valuation constructs and learning subconstructs) in the Negative and Positive Valence RDoC domains,
using multiple tasks and levels of data.

## Key facts

- **NIH application ID:** 10312509
- **Project number:** 1R01MH127773-01
- **Recipient organization:** VIRGINIA POLYTECHNIC INST AND ST UNIV
- **Principal Investigator:** PEARL H CHIU
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $788,923
- **Award type:** 1
- **Project period:** 2021-07-21 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10312509

## Citation

> US National Institutes of Health, RePORTER application 10312509, Evaluating overlap and distinctiveness in neurocomputational loss and reward elements of the RDoC matrix (1R01MH127773-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10312509. Licensed CC0.

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