Keywords: fMRI Analysis, fMRI, Denoising, DNN
Motivation: We expected that using a fMRI denoising neural network (DNN) that requires the denoised signal to correlate with a task design matrix in combination with a GLM analysis, can lead to biased results.
Goal(s): To find a DNN cost function that is independent of the task design.
Approach: We suggested a cost function that is based on the preserved frequency content and the correlation with motion regressors, with non-brain signals and with the non-denoised signal.
Results: We found that the DNN with the proposed cost function performed best in reducing the noise while preserving the BOLD signal.
Impact: Our intension with this abstract is to make researchers to think more carefully about the conditions included in their customized cost function in DNN-like denoising models and to check critically the denoised output.
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