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.

This abstract and the presentation materials are available to members only; a login is required.

Join Here