Noise-Driven Separation of System and Device Effects in Multimodal Neuroimaging

Simultaneous recordings across spatiotemporal scales using different neuroimagingmodalities are becoming increasingly common. However, this introduces a core ambiguity: do observed differences between signals reflect genuine differences in neural dynamics, or are they artefacts of the distinct ways that devices measure and transform those dynamics? In this study, we address how to disentangle these effects—specifically, how to isolate measurement artefacts introduced by the observer (i.e., the device) from the true dynamics of the system (i.e., the neural system). We show that stochastic fluctuations—typically viewed as a confound — can in fact be exploited when incorporated into generative models. Using synthetic data, we demonstrate that Bayesian model comparison more accurately distinguishes between system- and observer-level differences when models are augmented with noise, compared to noise-free cases. We apply this technique to empirical data and show that cross-scale variation between simultaneously recorded high-frequency broadband signals from macroelectrodes and microwires in the human hippocampus is better explained by differences in observer functions than by underlying system dynamics. These findings suggest that noise, when appropriately modelled, can enhance inference in multimodal neuroimaging by revealing the origin of signal variation across scales.