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Machine Learning in Neuroimaging


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This training will consist of two main sections: (1) application of ML to brain images from a clinical archive to detect brain disorders and (2) extraction of brain features from a large publicly available dataset to better understand mental health. After a brief introduction to the fundamentals of brain imaging, the first part of the class will focus on using structural brain MRI to diagnose and predict autism. Next, a deep learning technique will be applied to estimate brain volume from head CT (computed tomography) images that have poor image contrast. This technique's potential for early detection and tracking Alzheimer's disease will be presented. In the second part of the class, resting-state functional MRI (rsfMRI) data will be used to identify brain markers that may help to better understand the gender disparity in mental health. The class will conclude with evidence that suggests that rsfMRI has individually unique patterns that may serve as brain markers of certain behavioral characteristics.

Please register if you would like to join; we will send instructions about the virtual session to all registrants in advance.


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Status Archived
Date Thursday, April 16th, 2020
Time 4:30pm - 6:30pm
Location Virtual Classroom
Leader Andrew Michael
Enrolled 90