Abstract #3136

Testing for regional anatomical and physiological biases in fMRI signal fluctuations using surface-based deep learning

Olivia Viessmann¹, Divya Varadarajan¹, Adrian V Dalca^1,2, Bruce Fischl^1,2, Michael Bernier¹, Lawrence L Wald^1,2, and Jonathan R Polimeni^1,2

¹Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States, ²Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States

We provide a proof of concept that surface-based CNNs can predict anatomical and physiological data from fMRI signals. Specifically, we trained CNNs to predict local cortical thickness, cortical orientation to the B₀-field and MR angiography data to demonstrate that this information exists in the resting-state timeseries and can be extracted and possibly used for variance and bias reduction. Our results suggest that deep learning is able to identify non-linear relationships between the fMRI data and these anatomical and physiological biases.

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