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Automating Reproducible Connectivity Processing Pipelines on High Performance Computing Machines
Paul B Camacho1,2,3, Evan D Anderson3,4, Aaron T Anderson3,5, Hillary Schwarb3, Tracey M Wszalek3, and Brad P Sutton1,2,3
1Neuroscience Program, University of Illinois at Urbana-Champaign, Urbana, IL, United States, 2Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States, 3Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, United States, 4Decision Neuroscience Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL, United States, 5Stephens Family Clinical Research Institute, Carle Foundation Hospital, Urbana, IL, United States
We developed a wrapper for performing resting-state functional connectivity analysis via Singularity images on high-performance computing systems. This pipeline provides network-based statistics, data quality, and documentation.
Flow of pipeline from raw DICOMs to end-user entry into a Research Electronic Data Capture (REDCap) study management database. XNAT: Extensible Neuroimaging Archive Toolkit, HPC: High Performance Computing system, Slurm: Slurm Workload Manager, HeuDiConv: Heuristic-centric DICOM Converter, MRIQC: Magnetic Resonance Imaging Quality Control, fMRIPrep: Function Magnetic Resonance Imaging Preprocessing, xcpEngine: the XCP Engine, BCT: Brain Connectivity Toolbox.
Comparison of global efficiency as calculated by the Brain Connectivity Toolbox MATLAB functions for two common structural atlases (Automated Anatomical Labeling 116, Desikan-Killiany) and the Power 264 functional atlas after three methods of denoising in xcpEngine (36 motion parameters, 36 motion parameters and Power scrub method, and ICA-AROMA).