High-Throughput Image-Based Cohort Phenotyping

Advanced medical research, like understanding of the brain or personalized medicine, is facing the challenge to understand the correlation and effect model between environmental or genetic influence and the observed resulting phenotypes (e.g. morphological structures, function, variability) in healthy or pathologic tissue. The use case High-Throughput Image-Based Cohort Phenotyping will involve neuroimaging as piloting image domain to establish time-efficient parallel processing on High-Performance Computing (HPC) clusters as well as highly robust but flexible processing pipelines, efficient data mining techniques, uncertainty management, sophisticated machine learning and inference approaches. Such analyses are not only of high value for systems neuroscience and medical science, but also could be generalized for other disciplines searching for causalities between image-based observations and underlying mechanisms.

The German Cancer Research Center (DKFZ) and the Institute of Neuroscience and Medicine - Structural and functional organisation of the brain (INM-1) work on this use case in a joint effort.

Data for the image were provided 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.