Prediction and correction of physiological noise in fMRI using machine learning

Ash, T; Suckling, J; Walter, M; Ooi, C; Tempelmann, C; Carpenter, A; Williams, G

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Prediction and correction of physiological noise in fMRI using machine learning

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Zitation

Ash, T., Suckling, J., Walter, M., Ooi, C., Tempelmann, C., Carpenter, A., et al. (2011). Prediction and correction of physiological noise in fMRI using machine learning. Poster presented at 19th Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine (ISMRM 2011), Montréal, Canada.

Zitierlink: https://hdl.handle.net/21.11116/0000-0002-4C3C-7

Zusammenfassung

We present a support vector machine based technique for recreation of partially or fully absent physiological recording data, to allow detrending of physiological noise to occur even in the absence of complete recordings of the physiological cycles. The technique uses a multi-class SVM to predict phase of each physiological cycle from fMRI image data, after training on prior data. Using these predicted phase values as inputs to physiological detrending tool RETROICOR show similar impact on Fourier transforms of the data as using recorded values, showing that they are accurate enough for use as inputs to detrending tools.