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Abstract #0590

A Machine Learning Case for a Higher Order Control Plexus in the Frontal Pole Cortex

Nishant Zachariah 1 , Zhihao Li 2,3 , Jason Langley 2 , Shiyang Chen 2 , Mark Davenport 1 , Justin Romberg 1 , and Xiaoping Hu 2

1 Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States, 2 Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, United States, 3 Institute of Affective and Social Neuroscience, Shenzhen University, Shenzhen, Guangdong, China

In this study, we demonstrate a previously undiscovered function of Frontal Pole Cortex(FPC) in the regulation higher order cognitive tasks. We leverage machine learning techniques to data mine state of the art fMRI time series to uncover the role of the FPC. Remarkably, we are able to show that by using the time series of only 4 voxels (of > 900,000), with only a linear classifier, we are able to predict with >90% accuracy which of 7 tasks + resting state activity that a subject was performing. The most common location of these voxels across subjects is in the FPC.

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