# Visceral neural circuits linking childhood threat and deprivation with stress physiology and affective symptoms in a transdiagnostic sample using high-field personalized brain mapping

> **NIH NIH R01** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2022 · $465,231

## Abstract

Project Summary/Abstract
Childhood adversity was recently posited to be psychiatry's greatest public health challenge, as it is a major
predictor of mood, anxiety and trauma-related (i.e., affective) disorders. Childhood adversity is also associated
with dysregulated physiological stress reactivity, which individuals with affective disorders also display. While
stress is thought to be a mechanism by which childhood adversity influences physical and mental health, few
studies have considered stress-related brain regions beyond corticoamygdalar and hippocampal structures
and little is known regarding how childhood adversity impacts specific, proximally stress-responsive neural
circuits. Central visceral circuits (CVCs) are implicated in affective psychopathology and are critical in the
control of stress responses. CVCs comprise visceromotor and viscerosensory pathways that reciprocally
connect hypothalamic and limbic forebrain regions to brainstem nuclei. Preliminary data show significant links
between: 1) childhood adversity and CVCs and 2) CVCs and affective symptoms. These results also suggest
opposing influences of childhood “threat” vs. “deprivation” on CVCs. “Threat” experiences include abuse and
other traumatic events, while “deprivation” comprises diminished environmental stimuli, such as low childhood
socioeconomic status or neighborhood deprivation. Interestingly, evidence suggests that threat blunts, while
deprivation heightens physiological stress reactivity (e.g., cortisol reactivity). Thus, we propose that threat and
deprivation may have different effects on the CVCs most proximal to the control of stress responses, including
understudied regions such as the brainstem nucleus of the solitary tract (NST), paraventricular nucleus of the
hypothalamus (PVN) and bed nucleus of the stria terminalis (BST). In the proposed, limitations of lower field
strength MRI are overcome with the improved signal-to-noise and unprecedented resolution of high-field MRI
at 7 Tesla. Multimodal neuroimaging will acquire specialized structurals, and resting-state, mental stress and
emotion-evoked, and white matter connectivity. Stress physiology measures will also be collected. Consistent
with the RDoC initiative, we will recruit a continuous and transdiagnostic community sample of 220 young
adults (ages 18-35) with a full range of childhood threat, deprivation and affective symptoms to examine: 1) the
effects of childhood threat and deprivation on CVC connectivity, 2) the effects of childhood threat and
deprivation on stress physiology and affective symptoms, and 3) the extent to which CVC connectivity
mediates the relationship between threat and deprivation, and physiological and affective outcomes.
Significance. Our proposal is congruent with NIMH Strategy 1.3, Map the connectomes for mental illnesses,
focusing on CVCs as the “connectome” of interest. Elucidating how CVCs may link childhood threat and
deprivation to stress physiology and affective symptoms ...

## Key facts

- **NIH application ID:** 10436264
- **Project number:** 5R01MH120065-04
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** Layla Banihashemi
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $465,231
- **Award type:** 5
- **Project period:** 2019-07-19 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10436264

## Citation

> US National Institutes of Health, RePORTER application 10436264, Visceral neural circuits linking childhood threat and deprivation with stress physiology and affective symptoms in a transdiagnostic sample using high-field personalized brain mapping (5R01MH120065-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10436264. Licensed CC0.

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