# Bridging diffusion MRI and chemical tracing for validation and inference of fiber architectures

> **NIH NIH R01** · MASSACHUSETTS GENERAL HOSPITAL · 2022 · $659,897

## Abstract

Project summary: This project will collect a unique, multi-modal, multi-contrast dataset with tracer injections
and diffusion MRI in the same macaque brains. We will use this dataset to develop novel algorithms for
inferring local fiber architectures from diffusion MRI. The goal is to overcome the limitations of current methods
for diffusion orientation reconstruction, which are designed to resolve fiber crossings but not to distinguish
between crossings and other configurations, such as branching, turning, fanning, etc. More broadly, the
proposed dataset will allow us to investigate organizational principles of brain pathways and to provide a
testbed for the neuroimaging community to evaluate the accuracy of diffusion tractography and microstructural
modeling techniques. The project is a collaboration between groups with extensive expertise in diffusion MRI
methodological development (MGH Martinos Center) and anatomical tracer studies (University of Rochester).
We have previously collected high-resolution ex vivo diffusion MRI data on a set of macaque brains that had
also received tracer injections. We have recently used these data in an open tractography challenge, with the
participation of research teams from around the world. This was the first challenge of its kind to provide
diffusion MRI data suitable for all state-of-the-art diffusion reconstruction methods (e.g., multi-shell or Cartesian
grid sampling), in addition to providing the tracer injections in the same brains as the MRI scans. Our own
preliminary studies and the challenge itself offer several insights into the performance of state-of-the-art
tractography methods. For example, our results indicate that, while most tractography methods would require
their parameters to be tuned differently to achieve optimal accuracy for different cortical seed regions, there
are approaches that are robust across cortical areas. Furthermore, our results suggest that errors occur
frequently in areas where the fiber architecture is not well modeled by a crossing. Thus there is a need for
novel tractography approaches that go beyond the crossing-fiber paradigm. Here we propose to develop such
an approach. Our prior work included injection sites in the frontal, prefrontal, and cingulate cortices only. Here
we propose to investigate the extent to which our prior findings generalize across the brain, by performing
tracer injections that sample a wider range of cortical areas. Furthermore, we will extend our acquisition
protocol to acquire data appropriate not only for tractography, but also for microstructural and myelin mapping.
These data will allow us to answer a broader range of questions about tractography, microstructure, and their
intersection. Beyond the methodological development proposed in this project, the data will also be an
invaluable resource to the neuroimaging community, providing researchers with a framework for the objective
assessment of current diffusion MRI analysis methods and id...

## Key facts

- **NIH application ID:** 10318985
- **Project number:** 5R01NS119911-02
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** Anastasia Yendiki
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $659,897
- **Award type:** 5
- **Project period:** 2020-12-15 → 2024-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10318985

## Citation

> US National Institutes of Health, RePORTER application 10318985, Bridging diffusion MRI and chemical tracing for validation and inference of fiber architectures (5R01NS119911-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10318985. Licensed CC0.

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