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Abstract #5467

Predicting individual language task activation from resting state fMRI using a novel data-driven approach

Elizabeth Zakszewski1, Alexander Cohen1, Oiwi Parker Jones2, Saad Jbabdi2, and Yang Wang1

1Medical College of Wisconsin, Milwaukee, WI, United States, 2Oxford 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 language network based on the resting state functional MRI (rs-fMRI). Despite the presence of significant variability of language network across subjects, the predicated language maps match excellently with the language task fMRI derived activation maps at the individual level. Our results suggest that rs-fMRI can be used as a promising clinical tool for mapping language network by using the novel processing approach.

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