# Core 3 - Multi-Modal

> **NIH NIH P30** · UNIVERSITY OF MINNESOTA · 2020 · $58,229

## Abstract

Although MR techniques offer enormous potential for non-invasive in-vivo whole brain
measurements, the interpretation of the measurements can be greatly enhanced when combined
with classical neuroscience techniques. For example, although MR is particularly well suited for
population level studies of neuronal chemistry or activity, its temporal resolution is often limited by
hemodynamic factors. By combining high temporal resolution electrophysiology, such as provided by
extracellular or EEG electrodes, with MR measurements, a more complete picture of brain
metabolism and function is possible. One traditional challenge for such multimodal investigations is
the high barrier to entry that neuroscience investigators face in trying to state-of-the-art MR
techniques. The purpose of this core is to reduce this barrier to entry by encouraging and supporting
the integration of the cutting-edge MR methods available at the CMRR with traditional neuroscience
methodologies. This methodologies include behavioral measurements, electrophysiology,
histochemistry, optical imaging, and tract tracing. Because of the success of the core in supporting a
variety of such projects, as is documented in our Usage Tables, we propose to continue our efforts
through human and equipment resources to promote the incorporation of MR techniques, and to
assist in the design and implementation of multimodal

## Key facts

- **NIH application ID:** 10005500
- **Project number:** 5P30NS076408-09
- **Recipient organization:** UNIVERSITY OF MINNESOTA
- **Principal Investigator:** GEOFFREY M GHOSE
- **Activity code:** P30 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $58,229
- **Award type:** 5
- **Project period:** — → 2022-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10005500

## Citation

> US National Institutes of Health, RePORTER application 10005500, Core 3 - Multi-Modal (5P30NS076408-09). Retrieved via AI Analytics 2026-06-24 from https://api.ai-analytics.org/grant/nih/10005500. Licensed CC0.

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