Machine learning for brain imaging
published: Feb. 17, 2015, recorded: September 2014, views: 7800
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
In this talk, I would like to showcase a few examples of machine learning problems that arise when using brain imaging to understand brain function and its pathologies. I'll first introduce brain images and to derive statistical features. Then I'll discuss how prediction from these images is useful for diagnostic purposes, but also as a windows to understand the brain. I'll highlight specific challenges that arise when learning predictive models from brain maps, and details solutions put forward by our group, namely spatial penalties. Moving beyond well-posed statistical maps, I'll show how a combination of unsupervised modeling and supervised learning can predict phenotypic traits from spontaneous brain activity, recorded without controlling the subjects behavior. Finally, I'll detail how our work builds upon and nourishes a Python software stack that we leverage to interact with practitioners.
Download slides: mlpmsummerschool2014_varoquaux_brain_imaging.pdf (20.9 MB)
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !