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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