# C-PAC: A configurable, compute-optimized, cloud-enabled neuroimaging analysis software for reproducible translational and comparative

> **NIH NIH R24** · CHILD MIND INSTITUTE, INC. · 2020 · $545,239

## Abstract

ABSTRACT
The BRAIN Initiative is designed to leverage sophisticated neuromodulation, electrophysiological recording,
and macroscale neuroimaging techniques in human and non-human animal models in order to develop a
multilevel understanding of human brain function. However, the necessary tools for organizing, processing and
analyzing neuroimaging data generated through these efforts are not widely available as coherent and easy-to-
use software packages. Gaps are particularly apparent for nonhuman data (i.e., monkey, rodent), as most of
the existing processing and analytic software packages are specifically designed for human imaging. Methods
have been proposed for addressing the challenges inherent to the processing of nonhuman data (e.g., brain
extraction, tissue segmentation, spatial normalization, brain parcellation, temporal denoising); to date, these
have not been readily integrated into an easy-to-use, robust, and reproducible analysis package. Similarly,
many of the sophisticated machine learning and modeling methods developed for neuroimaging analyses are
inaccessible to most researchers because they have not been integrated into easy-to-use pipeline software. As
a result, translational and comparative neuroimaging researchers patch together neuroinformatics pipelines
that use various combinations of disparate software packages and in-house code.
We propose to extend the Configurable Pipeline for the Analysis of Connectomes (C-PAC) open-source
software to provide robust and reproducible pipelines for functional and structural MRI data. We will integrate
the various disparate image processing and analysis methods used to handle the challenges of nonhuman
imaging data, into a single, open source, configurable, easy-to-use end-to-end analysis pipeline package that
is accessible locally or via the cloud. The end product will not only improve the quality, transparency and
reproducibility of nonhuman translational and comparative imaging, but also enable new avenues of scientific
inquiry through our inclusion of methods that are yet to be applied to nonhuman imaging data (e.g., gradient-
based cortical parcellation methods, hyperalignment). Specific aims of the proposed work include to: 1)
Integrate neuroimaging processing and analysis methods optimized for BRAIN Initiative data, 2) Implement
strategies for carrying out comparative studies of human and non-human populations, and 3) Extend C-PAC to
include cutting-edge analytical strategies for identifying mechanisms of brain function. All development will
occur “in the open” using GitHub and other collaborative tools to maximally involve participation in the C-PAC
project. Annual hackathons will be held to collaborate with investigators from BRAIN Initiative awards and other
neuroinformatics development projects to integrate their tools with C-PAC. Hands-on training will be held to
train investigators on optimal use of the newly developed tools.

## Key facts

- **NIH application ID:** 9954159
- **Project number:** 5R24MH114806-03
- **Recipient organization:** CHILD MIND INSTITUTE, INC.
- **Principal Investigator:** Richard Cameron Craddock
- **Activity code:** R24 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $545,239
- **Award type:** 5
- **Project period:** 2018-09-01 → 2023-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9954159

## Citation

> US National Institutes of Health, RePORTER application 9954159, C-PAC: A configurable, compute-optimized, cloud-enabled neuroimaging analysis software for reproducible translational and comparative (5R24MH114806-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9954159. Licensed CC0.

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