Abstract #0905
The minimal processing pipeline for arterial spin labeling data from the Human Connectome Project Lifespan studies of Aging and Development
Flora Kennedy McConnell1,2,3, Jack Toner1,2, Thomas Kirk4,5, Yuriko Suzuki5, Martin Craig1,2, Timothy S. Coalson6, Michael P. Harms7, Matthew F. Glasser6,8, and Michael A. Chappell1,2,3,5
1Radiological Sciences, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom, 2Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, United Kingdom, 3Nottingham Biomedical Research Centre, Queen's Medical Centre, University of Nottingham, Nottingham, United Kingdom, 4Institute of Biomedical Engineering, University of Oxford, Oxford, United Kingdom, 5Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom, 6Department of Neuroscience, Washington University School of Medicine, Washington University in St Louis, St Louis, MO, United States, 7Department of Psychiatry, Washington University School of Medicine, Washington University in St Louis, St Louis, MO, United States, 8Department of Radiology, Washington University School of Medicine, Washington University in St Louis, St Louis, MO, United States
Synopsis
The arterial spin labelling (ASL) data from the Human Connectome Project (HCP) Lifespan studies can provide a source of unusually high-resolution hemodynamic measures from >2500 individuals with ages 5-21 and 37-100+. This work presents a summary of the minimal ASL processing pipeline used to provide pre-processed calibrated perfusion and arterial arrival time measurements for the cortical surface and subcortical volumes, from these individuals. The pipeline accounts for slice-wise image intensity variations resulting from the simultaneous multi-slice acquisition used to achieve high spatial resolution. These measures and this pipeline will be made available to the global neuroscience and neuroimaging communities.
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