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.