Tractography datasets, calculated from dMRI, represent the main WM structural connections in the brain. Thanks to advances in image acquisition and processing, the complexity and size of these datasets have constantly increased, also containing a large amount of artifacts. We present some examples of algorithms, most of them based on classical machine learning approaches, to analyze these data and identify common connectivity patterns among subjects.

MRI data quality is crucial to the result. This workshop talks some aspects we need to pay attention during the data acquisition, including FoV/slice brain coverage, synchronization between image acquisition and stimulus presentation, instruction to participant, real time quality monitoring, the usage of physiological data. Prior to the meeting, we are collecting questions for Xiangrui on anything related to mri protocol/parameters: https://www.tricider.com/admin/2YW93TsWZJ3/2DBkJUoE5Ot