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Description

Multivariate classification methods have become an increasingly popular tool for identifying multiple regions of brain activity on an fMRI scan that together can differentiate disease physiology from control. We created a predictive model using fMRI data grouped by voxel count into the AAL atlas and were able to differentiate Chronic Fatigue Syndrome from control at a 80% accuracy rate. These results imply combining machine learning with fMRI data can be a powerful diagnostic tool.

Describe the new knowledge and additional skills the participant will gain after attending your presentation.: Attendees will learn how to apply a multivariate pattern classification algorithm to fMRI data. A successfully developed algorithm lends a predictive and diagnostic capability to an already widely available data source (fMRI scans).

Authors:

Destie Provenzano (Presenter)
Georgetown University

Stuart Washington, Georgetown University
James Baraniuk, Georgetown University

Presentation Materials:

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