# CRCNS: Computational neuroimaging of the human

> **NIH NIH R01** · BAYLOR COLLEGE OF MEDICINE · 2020 · $179,511

## Abstract

Human brainstem serves many plays critical roles in health and disease. Unfortunately, it has
been vastly under-studied because of its physical inaccessibility in animal models, and its low
contrast-to-noise ratio (CNR) for functional magnetic resonance imaging (fMRI) in human
studies. At conventional fMRI field strengths, CNR is an order-of-magnitude lower in brainstem
than in cerebral cortex. Recently, ultra-high-field (UHF) scanners are becoming more-and-more
available for fMRI. UHF is particularly attractive for brainstem because it offers a tremendous
boost in CNR over conventional field strengths. Here we propose a panoply of measurements,
methods, and modeling to open up brainstem fMRI to more general use at UHF. Our work will
be directed toward a particular brainstem nucleus called superior colliculus (SC), a small
structure (-9-mm across) that is associated with eye movements and the orientation of
attention. SC is also involved in a number of diseases. For example, deterioration of SC's
structural integrity, functional properties, and connections have been observed in dementia with
Lewy bodies, Alzheimer's disease, progressive supranuclear palsy, amyotrophic lateral
sclerosis, and cervical dystonia. Our novel methods will enable us to distinguish visual
responses, in the superficial layers of SC, from somtaosensation in the deep layers of SC. We
can therefore relate the representation of tactile sensation to maps of visual space. We can then
determine how this mapping is affected by changes in limb position to begin to understand
coordinate transformations in human SC. The availability of such high-resolution methods for
deep-brain structures will have transformative impact on brain research, both basic and clinical.
Basic research will benefit from the higher resolution and contrast-to-noise ratio of our UHF MR
acquisition strategies, as well as the precision and sensitivity of our surface-based analysis
methods. The safety of fMRI should also enable use of our methods as adjuncts to treatment
protocols using 7T scanners. Thus, this work is well aligned with Goal 1 of NIBIB's Strategic
Plan, the development of innovative technologies that integrate engineering with physical and
life sciences to solve complex problems and improve health.

## Key facts

- **NIH application ID:** 9968333
- **Project number:** 5R01EB027586-03
- **Recipient organization:** BAYLOR COLLEGE OF MEDICINE
- **Principal Investigator:** DAVID B RESS
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $179,511
- **Award type:** 5
- **Project period:** 2018-09-11 → 2022-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9968333

## Citation

> US National Institutes of Health, RePORTER application 9968333, CRCNS: Computational neuroimaging of the human (5R01EB027586-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9968333. Licensed CC0.

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