Large scale diffusion MRI tractography remains a significant challenge. Users must orchestrate a complex sequence of instructions that require many software packages with complex dependencies and high computational cost. We developed MaPPeRTrac, a diffusion MRI tractography pipeline that simplifies and vastly accelerates this process on a wide range of high performance computing environments. It fully automates the entire tractography workflow, from management of raw MRI machine data to edge-density visualization of the connectome. Data and dependencies, handled by the Brain Imaging Data Structure (BIDS) and Containerization using Docker and Singularity, are de-coupled from code to enable rapid prototyping and modification. Data artifacts are designed to be findable, accessible, interoperable, and reusable in accordance with FAIR principles. The pipeline takes full advantage of HPC resources using the Parsl parallel programming framework, resulting in creation of connectome datasets of unprecedented size. MaPPeRTrac is publicly available and tested on commercial and scientific hardware, so that it may accelerate brain connectome research for a broader user community.
bioRxiv Subject Collection: Neuroscience