# Core 2 - Analysis

> **NIH NIH P30** · UNIVERSITY OF MINNESOTA · 2020 · $148,851

## Abstract

The analysis and visualization of high field magnetic resonance imaging (MRI) and
spectroscopy (MRS) data require cutting-edge computational resources and expertise, because
of their increasing size and complexity. Although these advances will undoubtedly improve our
understanding of brain development, aging and disorders, it can also be a significant barrier for
investigators. Therefore, the overall goal of this core is to provide unique and easily accessible
processing resources for human and animal neuroimaging research, which will empower
researchers to efficiently produce state-of-the-art results, and generate high-quality images from
CMRR's high and ultrahigh field MR scanners. Aim 1: To provide MRI data reconstruction and
pre-processing expertise and tools, including parallel imaging, artifacts and distortion correction.
Aim 2: To provide expertise and computational resources for MRI data analysis, including
registration, segmentation, diffusion models fitting (tensor, crossing fibers models,
microstructure), tractography, and fMRI statistical analysis. Aim 3: To provide quantitative
imaging analysis capabilities, including generating quantitative brain maps of relaxation times.
Aim 4: To provide processing and metabolite quantification capabilities for MRS. Aim 5: To
provide data storage, organization and visualization capabilities.

## Key facts

- **NIH application ID:** 10005499
- **Project number:** 5P30NS076408-09
- **Recipient organization:** UNIVERSITY OF MINNESOTA
- **Principal Investigator:** Christophe Lenglet
- **Activity code:** P30 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $148,851
- **Award type:** 5
- **Project period:** — → 2022-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10005499

## Citation

> US National Institutes of Health, RePORTER application 10005499, Core 2 - Analysis (5P30NS076408-09). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10005499. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*