Standardized structural connectivity mapping of the AOMIC-ID1000 dataset: A multi-scale tractography resource

This dataset contains derivative structural connectomes computed from the AOMIC dataset (https://openneuro.org/datasets/ds003097/versions/1.2.1). We employed a standardized pipeline consisting of QSIprep and QSIrecon (incorporating MRtrix3 methodologies) to generate the diffusion-based matrices. The derivatives are provided across a range of parcellation schemes. For a mapping of the atlas nomenclature and dimensions, please refer to the provided atlas dictionary. { "schaefer_156": "atlas_4S156Parcels_sift_invnodevol_radius2_count_connectivity", "schaefer_256": "atlas_4S256Parcels_sift_invnodevol_radius2_count_connectivity", "schaefer_456": "atlas_4S456Parcels_sift_invnodevol_radius2_count_connectivity", "aicha_384": "atlas_AICHA384Ext_sift_invnodevol_radius2_count_connectivity", "brainnetome_246Ext": "atlas_Brainnetome246Ext_sift_invnodevol_radius2_count_connectivity", "gordon_333Ext": "atlas_Gordon333Ext_sift_invnodevol_radius2_count_connectivity", "AAL_116": "atlas_AAL116_sift_invnodevol_radius2_count_connectivity"} Acknowledgements The project development was supported by the IFORES Career Kickstart grant (D/107-30310), Faculty of Medicine, University Duisburg-Essen. We thank the support from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – Project-ID 422744262 – TRR 289; and Project-ID 316803389 – SFB 1280 “Extinction Learning”. We also thank the Institute for Artifitial Intelligence in Medicine (IKIM), for providing high-performance computing infrastructure, at the University of Duisburg-Essen and University Medicine Essen, Germany. The authors would like to express their gratitude to the creators of the Amsterdam Open MRI Cohort (AOMIC) for providing the open-access Diffusion-Weighted Imaging datasets (ID1000) used in this study.