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Abstract #0877

Modeling Motor Task Activation from Task-free fMRI with Machine Learning: Predictions and Accuracy in Individual Subjects

Elizabeth Zakszewski1, Alexander Cohen1, Chen Niu2, Xiao Ling2, Oiwi Parker Jones3, Saad Jbabdi3, Ming Zhang2, Maode Wang2, and Yang Wang1

1Medical College of Wisconsin, Milwaukee, WI, United States, 2First Affiliated Hospital of Xi'An Jiaotong University, Shaanxi Xi'an, China, 3Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, Oxford, United Kingdom

This study is aimed to apply a newly developed machine learning approach to predict individual motor performance from resting state functional MRI. Our data demonstrate that resting state fMRI even using conventional EPI protocols can predict individual motor performance. Our results suggest that the novel machine learning model could more accurately predict motor function at the individual level, compared to the independent component analysis method.

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