# Community-supported open-source software for computational neuroanatomy

> **NIH NIH R01** · TRUSTEES OF INDIANA UNIVERSITY · 2024 · $524,710

## Abstract

This project catalyzes research on brain networks by developing computational methods and
software tools for analysis of diffusion MRI (dMRI) data and validating them in vivo.
Understanding brain networks and their relation to neural computation is a major challenge in
contemporary neuroscience. The proper function of brain networks is also inextricably tied to
neurological, cognitive and psychiatric health. The networks that connect distinct regions in the
brain are composed of large bundles that contain the axons of millions of neurons. DMRI is the
only currently available method to measure the trajectory and physical properties of these
bundles in the human brain non-invasively and a large body of research using dMRI has
substantially contributed to our understanding of the way in which differences in brain
connections contribute to individual differences across a spectrum of behaviors and clinical
conditions. Progress in research and methods development has also translated into increasing
use of dMRI in clinical applications. The project is led by the founders of the Diffusion Imaging in
Python (DIPY) software project who have been working to invigorate the neuroimaging user and
developer community by developing, implementing, disseminating, and maintaining important
software tools. The proposed project pursues a next generation phase of development, in which
the overall objective is to generate a platform to better utilize dMRI data in accordance with
BRAIN 2.0 targets. To address current barriers to progress, we plan to address the following
aims in the current proposal: Aim 1 will develop new tractometry methods that enhance the
interpretability of dMRI data; Aim 2 will introduce new pre-processing algorithms including
methods for susceptibility correction; Aim 3 will focus on improving computational performance
using parallel computing within a single node (e.g., via use of graphical processing units) and
across nodes (i.e., distributed computing); Aim 4 will focus on the validation of DIPY methods
using publicly available human and non-human primate data. Overall, this work will enable
impactful brain research and will facilitate subsequent clinical adoption of advanced
computational methods using dMRI data.

## Key facts

- **NIH application ID:** 10890175
- **Project number:** 5R01EB027585-05
- **Recipient organization:** TRUSTEES OF INDIANA UNIVERSITY
- **Principal Investigator:** Eleftherios Garyfallidis
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $524,710
- **Award type:** 5
- **Project period:** 2018-08-01 → 2027-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10890175

## Citation

> US National Institutes of Health, RePORTER application 10890175, Community-supported open-source software for computational neuroanatomy (5R01EB027585-05). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10890175. Licensed CC0.

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