Assessing the reproducibility of template-based clustering for tractography in healthy human adults

The dataset was processed and analyzed to assess effect of intersubject variability on the reliability of template-based clustering methods. Two datasets - (1) Human Connectome Project and (2) MyConnectome project was used for this analysis. Additionally, two template-clustering approaches - (1) spectral clustering and (2) QuickBundles was assessed. Reliability of template-based clustering approaches was assessed for both deep white matter tracts and the superficial white matter. The dataset contains the associated Jupyter notebooks, code, processed template and analysis data. All data was processed in containerized environments. The majority of analysis is performed with Python 3 and an R add-on for evaluating along-tract agreement using the "ICC" package. Additionally, MRtrix3 is used for working .mif and .tck files. Original data for the MyConnectome project can be accessed via OpenNeuro. Data was reorganized upon download such that each session was treated as a "subject" in BIDS organization. Original data can also be downloaded for the Human Connectome Project via the HCP database. Data was also reorganized upon download to be BIDS compliant. Data was provided in part by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University. Data for the MyConnectome project were obtained from the OpenNeuro database (ds000031).