Collegiate athlete brain data for white matter mapping and network neuroscience.

Area of Science:

• Neuroimaging
• Brain Connectomics
• Sports Medicine Research

Background:

• Limited open-access datasets exist for studying the neurological effects of collegiate athletics.
• Collecting high-quality neuroimaging data, particularly diffusion-weighted magnetic resonance imaging (dMRI), presents significant challenges.
• Understanding the impact of repetitive head impacts in athletes requires robust reference data.

Purpose of the Study:

• To introduce and share a comprehensive dataset of processed dMRI data and associated derivatives.
• To provide a reproducible preprocessing pipeline for dMRI analysis.
• To establish a reference dataset for investigating the effects of long-term athletic participation on brain structure and function.

Main Methods:

• Acquisition of minimally processed dMRI data from collegiate athletes and non-athletes.
• Application of advanced dMRI signal modeling techniques.
• Generation of full-brain tractograms and segmentation of major white matter tracts.
• Construction of structural connectivity matrices.
• Utilizing reproducible web services for data processing (brainlife.io).

Main Results:

• A curated dataset including processed dMRI data, diffusion signal models, tractograms, white matter tract segmentations, and structural connectivity matrices.
• A fully reproducible preprocessing pipeline accompanying the dataset.
• Data collected from two collegiate athlete groups and one non-athlete control group.

Conclusions:

• The shared dataset and pipeline address a critical need for open-access resources in neuroimaging research.
• This dataset serves as a valuable reference for studying the neurological consequences of collegiate sports participation.
• Future research can leverage this resource to investigate the impact of repetitive head impacts and other athletic exposures on brain health.