BrainForge: an online data analysis platform for integrative neuroimaging acquisition, analysis, and sharing.

Area of Science:

• Neuroimaging
• Computational Neuroscience
• Medical Informatics

Background:

• Neuroimaging research faces challenges in data management, computational resource allocation, and result reproducibility.
• Existing platforms often lack integrated solutions for archiving, processing, and sharing complex neuroimaging datasets.
• The need for accessible, high-performance computing solutions is critical for advancing neuroimaging studies.

Purpose of the Study:

• To introduce BrainForge, a cloud-enabled, web-based platform designed to streamline neuroimaging data analysis.
• To provide researchers with a unified solution for data archiving, processing, and sharing.
• To address common obstacles in neuroimaging research, including software compatibility, reproducibility, and computational demands.

Main Methods:

• BrainForge offers a cloud-based, web-accessible interface for neuroimaging data archiving and processing.
• It utilizes high-performance computing clusters for analyses, supporting multiple MRI modalities (structural, functional, diffusion, arterial spin labeling).
• Analyses are containerized within Singularity, integrating popular tools like Nipype, SPM, GIFT, and FreeSurfer, and accepting the BIDS format.

Main Results:

• BrainForge enables effortless data archiving and processing on high-performance computing infrastructure.
• The platform facilitates quick sharing of analysis results with collaborators.
• It supports preprocessing and group-level analyses for various MRI data types.

Conclusions:

• BrainForge provides a comprehensive solution for neuroimaging data analysis, enhancing reproducibility and accessibility.
• The platform effectively tackles challenges related to software, computational resources, and data sharing in research.
• Its integrated approach empowers researchers to accelerate discoveries in neuroimaging.