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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00159816%3A_____%2F25%3A00082238" target="_blank" >RIV/00159816:_____/25:00082238 - isvavai.cz</a>

Result on the web

<a href="https://www.frontiersin.org/journals/computational-neuroscience/articles/10.3389/fncom.2025.1693279/full" target="_blank" >https://www.frontiersin.org/journals/computational-neuroscience/articles/10.3389/fncom.2025.1693279/full</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3389/fncom.2025.1693279" target="_blank" >10.3389/fncom.2025.1693279</a>

Result language

angličtina

Original language name

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

Original language description

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

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

30103 - Neurosciences (including psychophysiology)

Project

<a href="/en/project/NW25-04-00226" target="_blank" >NW25-04-00226: Enhancing epilepsy treatment with temporal interference of electric fields and advanced computational strategies</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Frontiers in Computational Neuroscience

ISSN

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e-ISSN

1662-5188

Volume of the periodical

19

Issue of the periodical within the volume

neuvedeno

Country of publishing house

CH - SWITZERLAND

Number of pages

9

Pages from-to

nestránkováno

UT code for WoS article

001605117300001

EID of the result in the Scopus database

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