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Abstract #3913

A comparison of model-based deconvolution methods for estimating the carbon-dioxide response function in resting-state fMRI

Seyedmohammad Shams1, Prokopis C Prokopiou2, Georgios Mitsis3, and J. Jean Chen4
1Rotman Research Institute, Baycrest, Toronto, ON, Canada, 2Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States, 3McGill University, Montreal, ON, Canada, 4Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada


The resting-state fMRI response to carbon-dioxide (CO2) fluctuations is useful for denoising and for physiological mapping. In this work, we compare model-based deconvolution methods for estimating this response function (CRF) in a wide range of signal-to-noise conditions and with various assumed ground-truth CRF shapes. We also propose an improved method using canonical correlation analysis to identify the optimal CRF adaptively. The best choice of method depends on the desired CRF parameter, such as timing (dynamics) or area (static cerebrovascular reactivity). The inverse-Logit and adaptive CCA methods provided the highest accuracy and robustness.

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