Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.06.19.161570v1?rss=1 Authors: Leppanen, J., Stone, H., Lythgoe, D. J., Williams, S., Horvath, B. Abstract: Background: Functional resonance magnetic imaging (fMRI) noise is usually assumed to have constant volatility. However this assumption has been recently challenges with a few studies examining heteroscedasticity arising from head motion and physiological noise. However, to our knowledge no studies have studied heteroscedasticity in scanner noise. Thus the aim of this study was to estimate the smoothness of fMRI scanner noise using latest methods from the field of financial mathematics. Methods: A multi-echo fMRI scan was performed on a phantom using two 3 tesla MRI units. The echo times were used as intra-time point data to estimate realised volatility. Smoothness of the realised volatility processes was examined by estimating the Hurst, H , parameter in the rough Bergomi model using neural network calibration. Results: All H < 0.5 and on average fMRI scanner noise was very rough with H {approx} 0.03. Substantial variability was also observed, which was caused by edge effects, whereby H was larger near the edges of the phantoms. Discussion: The findings challenge the assumption that fMRI scanner noise has constant volatility and add to the steady accumulation of studies suggesting implementing methods to model heteroscedasticity may improve fMRI data analysis. Additionally, the present findings add to previous work showing that the mean and normality of fMRI noise processes show edge effects, such that signal near the edges of the images is less likely to meet the assumptions of current modelling methods. Copy rights belong to original authors. Visit the link for more info