Machine learning methods applied to dMRI tractography for the study of brain connectivity
Wednesday, August 19, 2020
13:15 UTC
Pamela Guevara
Engineer
Department of Electrical Engineering, Faculty of Engineering, Universidad de Concepción, Chile
Recording
Open recording
Hosted by IIBCE on Brain Science70 minutes
Abstract
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.
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