# The Neuroimaging Brain Chart Software Suite

> **NIH NIH U24** · UNIVERSITY OF PENNSYLVANIA · 2024 · $879,586

## Abstract

This study proposes to refine, integrate and disseminate the NeuroImaging Brain Chart (NIBCh) software
toolbox and machine learning (ML) model library, an ecosystem of software components enabling
constructive integration, statistical harmonization, and ML-centric data analyses across studies. NIBCh
enables large-scale analyses of multi-modal brain MRI data by mapping such data into a compact coordinate
system of informative neuroimaging signatures implemented by our library of ML models. The axes of this
coordinate system represent two types of information: 1) a variety of structural (sMRI and dMRI) and
functional connectomic (rsfMRI) imaging derived phenotypes (IDPs), such as multi-scale brain parcelations
and brain networks; 2) complex ML-based imaging signatures (ML-IDPs), which capture multi-variate
imaging patterns that reflect the heterogeneity of brain aging, neurodegeneration, as well as of
neuropsychiatic disorders and have been previously derived from carefully processed and curated data of
over 65,000 individuals. Using our software toolboxes (Tbx), researchers will be able to map new data into
NIBCh, and hence to use ML-IDP models trained in NIBCh, as well as perform statistical tests against NIBCh
normative ranges and compare their results with those of other studies using the same Tbx. The software
suite will include a set of containerized pre-processing and analysis pipelines, as well as statistical
harmonization and ML inference toolboxes, which will be accessible via a standalone python front-end
visualization, as cloud-based containers, and via a web-interface supported by our high-performance
computing cluster. Several dissemination plans are discussed, including a github user community, tutorials
at major technical and clinical meetings, and support of both standalone pipelines locally or on the cloud,
and web-based access of harmonization and ML inference modules.
 The over-arching primary goal of our program is to provide the software tools that will allow users to
contribute to an actively growing community-based dimensional neuroimaging system that will utilize
machine learning models to provide rich, yet precise, compact, concise, and informative representations of
brain structure, function and connectivity.

## Key facts

- **NIH application ID:** 10821322
- **Project number:** 5U24NS130411-02
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Christos Davatzikos
- **Activity code:** U24 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $879,586
- **Award type:** 5
- **Project period:** 2023-04-15 → 2028-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10821322

## Citation

> US National Institutes of Health, RePORTER application 10821322, The Neuroimaging Brain Chart Software Suite (5U24NS130411-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10821322. Licensed CC0.

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