Abstract #4004

confounder: A BIDS/fMRIPrep app for efficiently assessing task-based GLM model fit with and without experimental confounds

Suzanne T Witt¹, Kathryne Van Hedger¹, Olivia Walton Stanley², Ali Khan², and Jörn Diedrichsen²

¹BrainsCAN, University of Western Ontario, London, ON, Canada, ²University of Western Ontario, London, ON, Canada

Denoising task-based fMRI data is important for increasing SNR and has implications for data interpretation. There is not currently a method or tool available for directly comparing the relative impact of including experimental confounds in a first-level GLM. Here we present a new BIDS/fMRIPrep app, confounder, that allows users to efficiently assess model fit for first-level, task-based GLM models with and without experimental confounds. The app provides users with model-fit information such as regional R², as well as additional information regarding data quality, such as the correlation between experimental confounds and predicted BOLD signal.

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