# Bridging Huntington’s disease research with big data science: Harmonized neuroimaging datasets from multiple studies

## TL;DR

This paper introduces a platform that harmonizes and distributes neuroimaging data from eight Huntington’s disease studies, making it easier for researchers to access and use.

## Contribution

The novel contribution is a unified platform that curates and harmonizes neuroimaging data from multiple HD studies into standardized formats.

## Key findings

- The platform combines data from eight studies into six harmonized BIDS datasets with 2,216 participants and 7,073 sessions.
- Raw data is converted to BIDS format, and processed data is generated using pipelines like Freesurfer and fmriprep.
- The platform provides both raw and processed data to support diverse research needs in HD.

## Abstract

Multiple neuroimaging datasets from Huntington’s disease (HD) studies are publicly available, but these datasets are in various formats, omit imaging metadata, and sometimes contain corrupt scans. We have created a platform to curate, harmonize, and distribute neuroimaging datasets from eight different studies: TRACK-HD, TRACKOn-HD, PREDICT-HD, IMAGE-HD, HD-YAS, SHIELD-HD, PEARL-HD, and LONGPDE10. The platform is organized into three conceptual levels to serve the research community with both raw and processed data. Raw data are converted into Brain Imaging Data Structure (BIDS) format, while processed data are obtained from pipelines such as Freesurfer and fmriprep. Studies that had followed the same participants were combined. After combining studies, the final six BIDS datasets include a total of 2,216 participants and 7,073 sessions. We outline tools, principles, and recommendations for future data management in HD research.