# Functional Anatomic Studies of Self-Affect:  A Multimodal Approach

> **NIH NIH R01** · DARTMOUTH COLLEGE · 2020 · $587,777

## Abstract

Project Summary
How the self is experienced is central to healthy emotional functioning as well as many disturbances in
psychological functioning. This competing renewal uses structural, functional, and resting-state neuroimaging,
coupled with passive smartphone sensing technology and ecological momentary assessments, to examine the
affective components of self. Understanding the factors that contribute to changes in the affective aspects of
self that result from environmental stressors has the potential to provide important insights into the
development of mental disorders and help identify individuals who might be in greatest need of early
intervention or treatment. Research findings during the prior two award periods (R01 MH059282) revealed
several key brain regions involved in processing information related to self. Moreover, we discovered that
structural and functional connectivity between these regions and other brain regions known to be involved in
emotional processes are associated with measures of self-affect. The overarching goal of this research is to
examine how brain connectivity and activity is related to change in subjective distress and associated
functional impairment. An exciting aspect of the proposed work is that we will take advantage of the university
setting to follow a large cohort of participants over their four years of college to assess how changes in self-
affect are predicted by relevant brain networks as well as how those networks change over time. Tasks
assessing self-affect will be performed during scanning. Given that approximately 30% of participants are
likely to develop a significant subjective distress, one goal is to examine whether there are biomarkers that
predict these outcomes. Additional scanning studies will induce interpersonal distress to examine the
temporary inductions of affect on task performance. This project will use recently developed applications of
network analysis to assess resting state connectivity in brain circuitry and its relation to self-affect and health-
relevant outcomes. The guiding hypothesis of this research is that individual differences in the integrity of these
networks can predict individual differences in vulnerability to stress and their relation to self-affect. The specific
aims of the study are: (1). Characterize neural networks that give rise to self-affect using diffusion tensor
imaging, resting state functional connectivity, and task-related functional imaging. In addition, multivariate
pattern analysis and representation similarity analysis will be used to classify participants as having high or low
self-affect (e.g., self-esteem, depression, anxiety); (2). Examine how changes in self-affect that occur over time
are reflected by changes within relevant brain networks and are predicted by baseline network connectivity;
and (3). Examine how induced interpersonal distress impacts self-affect and related functional connectivity
across networks. Understanding the factors t...

## Key facts

- **NIH application ID:** 9975226
- **Project number:** 5R01MH059282-15
- **Recipient organization:** DARTMOUTH COLLEGE
- **Principal Investigator:** JAMES V HAXBY
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $587,777
- **Award type:** 5
- **Project period:** 2000-11-01 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9975226

## Citation

> US National Institutes of Health, RePORTER application 9975226, Functional Anatomic Studies of Self-Affect:  A Multimodal Approach (5R01MH059282-15). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9975226. Licensed CC0.

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