Using noise to distinguish between system and observer effects in multimodal neuroimaging

TL;DR

This paper shows how noise can help determine if differences in brain activity measurements come from the brain itself or from the measuring tools.

Contribution

The novel approach uses noise in generative models to distinguish system-level from observer-level effects in multimodal neuroimaging.

Findings

Noise in generative models can disentangle system and observer effects in cross-scale neuroimaging data.

In most subjects, time series show a mix of system- and observer-level contributions.

One subject's cross-scale difference was attributed solely to observer-level effects.

Abstract

It has become increasingly common to record brain activity simultaneously at more than one spatiotemporal scale. Here, we address a central question raised by such cross-scale datasets: do they reflect the same underlying dynamics observed in different ways, or different dynamics observed in the same way? In other words, to what extent can variation between modalities be attributed to system-level versus observer-level effects? System-level effects reflect genuine differences in neural dynamics at the resolution sampled by each device. Observer-level effects, by contrast, reflect artefactual differences introduced by the nonlinear transformations each device imposes on the signal. We demonstrate that noise, when incorporated into generative models, can help disentangle these two sources of variation.

We apply this noise-based approach to simultaneously recorded high-frequency broadband signals from macroelectrodes and microwires in the human hippocampus.

Most subjects show a complex mixture of system- and observer-level contributions to their time series. However, in one subject, the cross-scale difference is statistically attributable to an observer-level effect—i.e., consistent with the same dynamics at both microwire and macroelectrode scales.

This study shows that noise can be used in empirical datasets to determine whether cross-scale variation arises from differences in neural dynamics or differences in observer functions.

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