# Diffeomorphometry applied to functional connectivity in schizophrenia using ultrahigh resolution MRI

> **NIH NIH R21** · JOHNS HOPKINS UNIVERSITY · 2022 · $220,187

## Abstract

Project Summary
Schizophrenia is a disorder with a life-time world-wide prevalence of ~0.5% and devastating consequences
for affected individuals, their families, and society. Improved understanding of the disruptions in cortical
circuitry thought to underlie schizophrenia could go a long way toward addressing challenges in
understanding the pathophysiology of schizophrenia, as well as the challenges of improving schizophrenia
nosology, diagnosis, and treatment. Our guiding hypothesis is that schizophrenia is a consequence of
cortical-cortical and cortical-subcortical functional dysconnectivity, and particularly the circuitry between the
thalamus and the cortex. Understanding this dysconnectivity could have profound implications for the field.
In this proposal, we will develop a new and potentially power method for the analysis of functional connectivity
between thalamic subregions and deep and superficial layers of the cortex, using a sophisticated
mathematical approach (diffeomorphometry) to precisely determine the thickness of the cortex at specified
regions of the brain. In Aim 1, we will optimize this method for both resting and activated thalamocortical
connectivity, using ultra-high field strength (7 Tesla) fMRI. In Aim 2, we will test the protocols developed in
Aim 1 in 20 individuals with schizophrenia compared to 20 healthy controls. We will determine if subdividing
the cortex using the diffeomorphometric approach will more clearly delineate the dysconnectivity in patients,
with the potential benefit that investigations can use smaller sample sizes, and that more subtle clinical factors
associated with aberrant connectivity can be discerned. In addition, this improved resolution at the cortex
may facilitate more nuanced understanding of the specific thalamic origins of aberrant signals and the
differences in dysconnectivity across different cortical layers. Overall, the goal is to establish methods that
can be used to further develop functional connectivity as a biomarker useful in nosology and prognosis, and
in the prediction and monitoring of treatment response.

## Key facts

- **NIH application ID:** 10348847
- **Project number:** 1R21MH128715-01
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** RUSSELL L MARGOLIS
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $220,187
- **Award type:** 1
- **Project period:** 2022-01-17 → 2023-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10348847

## Citation

> US National Institutes of Health, RePORTER application 10348847, Diffeomorphometry applied to functional connectivity in schizophrenia using ultrahigh resolution MRI (1R21MH128715-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10348847. Licensed CC0.

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