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Using noise to distinguish between system and observer effects in multimodal neuroimaging.
Erik D Fagerholm1, Hirokazu Tanaka2, Gregory Scott3
1First Department of Neurology, St. Anne's University Hospital and Faculty of Medicine, Masaryk University, Brno, Czechia.
Frontiers in Computational Neuroscience
|November 3, 2025
Summary
Noise-based generative models help distinguish true neural dynamics from device artifacts in cross-scale brain recordings. This approach clarifies whether observed differences stem from system-level neural variations or observer-level measurement effects.
Area of Science:
- Neuroscience
- Signal Processing
- Computational Biology
Background:
- Simultaneous recording of brain activity at multiple spatiotemporal scales is increasingly common.
- Interpreting these cross-scale datasets requires distinguishing genuine neural dynamics from measurement artifacts.
Purpose of the Study:
- To develop and apply a noise-based method for disentangling system-level (true neural dynamics) and observer-level (device-induced artifacts) effects in cross-scale brain recordings.
- To investigate the contributions of system- and observer-level effects in simultaneously recorded human hippocampal data.
Main Methods:
- Utilized generative models incorporating noise to analyze simultaneously recorded high-frequency broadband signals.
- Applied the noise-based approach to data from macroelectrodes and microwires in the human hippocampus.
Main Results:
- Most subjects exhibited a mixture of system- and observer-level contributions to their recorded neural signals.
- In one subject, the observed cross-scale differences were statistically attributable solely to observer-level effects, suggesting consistent underlying dynamics.
Conclusions:
- Noise can serve as a powerful tool in empirical datasets to differentiate between cross-scale variations arising from neural dynamics versus measurement functions.
- This methodology provides a framework for accurately interpreting multi-scale neural recordings.
