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Abstract #5466

Predicting Individual Task Performance From Resting State fMRI: Effects of Training Task Data Quality

Alexander D. Cohen1, Elizabeth Zakszewski1, and Yang Wang1

1Radiology, Medical College of Wisconsin, Milwaukee, WI, United States

Resting state functional MRI (rs-fMRI) has been used to predict individual task activation by training a model to map rs-fMRI networks to task performance. This study used a multiband, multi-echo acquisition to collect motor task fMRI as training data. The effects of echo combination and denoising of the training-task data on rs-fMRI predictions were examined. Multi-echo task data resulted in increased predictive accuracy of the model. These results suggest the quality of the training-task data affects the accuracy of the prediction model.

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