Sailing in rough waters: Examining volatility of fMRI noise

Abstract Background The assumption that functional magnetic resonance imaging (fMRI) noise has constant volatility recently been challenged by studies examining heteroscedasticity arising from head motion and physiological noise. present study builds on this work using latest methods the field of financial mathematics to model fMRI volatility. Methods Multi-echo phantom human scans were used realised was estimated. Hurst parameter H ∈ (0, 1), which governs roughness/irregularity time series, Calibration H performed pathwise, well-established neural network calibration tools. Results In all experiments calibrated values within rough case, H  Discussion findings suggest is not only non-constant, but also substantially more irregular than a standard Brownian motion. Thus, further research needed examine impact such pronounced oscillations in have data analyses.