# Mapping connectomes for disordered emotional states

> **NIH NIH U01** · STANFORD UNIVERSITY · 2020 · $745,640

## Abstract

PROJECT SUMMARY/ABSTRACT
Our objective is to use HCP protocols to acquire and make public a large dataset of imaging, behavioral, and
symptom data from patients with disordered emotional states. We will also develop and make public new
methods for examining how connectome disorganization gives rise to these disordered states at the level of
the individual patient. Psychopathology arising from enhanced negative emotion or from the loss of positive
emotional experience affects over 400 million people globally. Such states of disordered emotion cut across
multiple diagnostic categories and are compounded by accompanying disruptions in cognitive function. Not
surprisingly, therefore, these forms of psychopathology are a leading cause of disability. To address these
issues our investigative strategy is informed by the Research Domain Criteria (RDoC) initiative spearheaded
by NIMH. We focus on three RDoC domains and constructs: 1) acute threat within the Negative Valence
System (NVS) domain, a construct relevant to automatic reactions to fear and physical symptoms of anxiety; 2)
reward valuation and responsiveness within the Positive Valence System (PVS) domain, a construct involving
incentive salience, hedonic responses and symptoms of anhedonia; and 3) working memory within the
Cognitive System (CS) domain, a construct that implicates top-down regulation of cognitive rumination and
worry. Our approach is grounded in strict adherence to HPC protocols and a strong commitment to data
sharing. We unite complementary expertise, including (1) state-of-the-art MRI technology and data
management systems; (2) a field-leading Center for Reproducible Neuroscience; (3) a track record in leading
large-scale neuroradiology consortia; (4) leaders in RDoC-informed approaches to large-scale imaging in
depression and anxiety; and (5) pioneering statistical approaches for high-dimensional data. Our aims are to
(1) use the HCP protocols to acquire multi-modal data for 300 people aged 22-25 years of age who are
experiencing varying degrees of acute threat, loss of reward valuation/responsiveness, and difficulties in
working memory, (2) elucidate the nature of the relations among connectomes, symptoms, and behavior based
on networks related to the RDoC constructs of interest, and (3) to develop data-driven, machine-learning
methods to discover how connectomes for these constructs combine together to form naturally organized
clusters of people. Our data will advance a neurobiological model that maps network dysfunctions to specific
behaviors and symptoms. This model will provide a foundation for ultimately guiding more classifications and
treatment choices according to types of neural dysfunction rather than relying on diagnostic categories that are
agnostic to neurobiology.

## Key facts

- **NIH application ID:** 9925811
- **Project number:** 5U01MH109985-04
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Leanne Williams
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $745,640
- **Award type:** 5
- **Project period:** 2017-07-19 → 2022-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9925811

## Citation

> US National Institutes of Health, RePORTER application 9925811, Mapping connectomes for disordered emotional states (5U01MH109985-04). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/9925811. Licensed CC0.

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