fMRI Acoustic Noise Enhances Parasympathetic Activity in Humans

Background: Functional magnetic resonance imaging (fMRI) is one of the most important neuroimaging techniques; nevertheless, the acoustic noise of the MR scanner is unavoidably linked to the process of data acquisition. We hypothesized that the auditory noise of the scanner has an effect on autonomic activity. Methods: We measured heart rate variability (HRV) while exposing 30 healthy subjects to fMRI noise. In doing so, we demonstrated an increase in parasympathetic nervous system (PNS) activity compared to silence and white noise and a decrease in sympathetic nervous system (SNS) activity compared to white noise. Conclusions: The influence of MR scanner noise on the autonomic nervous system should be taken into account when performing fMRI experiments.

Auditory Stimulation Modulates Resting-State Functional Connectivity in Unresponsive Wakefulness Syndrome Patients

Passive listening to music is associated with several psychological and physical benefits in both, healthy and diseased populations. In this fMRI study, we examined whether preferred music has effects on the functional connectivity within resting-state networks related to consciousness. Thirteen patients in unresponsive wakefulness syndrome (UWS) and 18 healthy controls (HC) were enrolled. Both groups were exposed to different auditory stimulation (scanner noise, preferred music, and aversive auditory stimulation). Functional connectivity was analyzed using a seed-based approach. In HC, no differences were found between the three conditions, indicating that their networks are already working at high level. UWS patients showed impaired functional connectivity within all resting-state networks. In addition, functional connectivity of the auditory network was modulated by preferred music and aversive auditory stimulation. Hence, both conditions have the potential to modulate brain activity of UWS patients.

Sailing in rough waters: examining volatility of fMRI noise

1.AbstractBackgroundFunctional resonance magnetic imaging (fMRI) noise is usually assumed to have constant volatility. However this assumption has been recently challenged in 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.MethodsA 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 is examined by estimating the Hurst parameter, a parameter H ∈ (0, 1) governing the roughness (Hölder continuity) of paths in the rough Bergomi model, introduced in [2]. The rough Bergomi model a member of the family of rough stochastic volatility models. A family of models which was recently popularised in mathematical finance by observations indicating that volatility in financial markets is best described by stochastic models where volatility can be modulated by the Hurst parameter H, which usually calibrates to values H ∈ (0, 0.5) (the rough case), hence inspiring the name of the model family. In this work, calibration of the Hurst parameter H is performed pathwise, using recently developed neural network calibration tools.ResultsIn all experiments the volatility calibrates to values well within the rough case H < 0.5 and on average fMRI scanner noise was very rough with H ≈ 0.03. Substantial variability was also observed, which was caused by edge effects, whereby H was larger near the edges of the phantoms.DiscussionThe findings challenge the assumption that fMRI scanner noise has constant volatility (in fact, the lower the value of H, the more pronounced the oscillations of the volatility, and hence the more “severe” is the violation of the constant volatility assumption) 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.