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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14110%2F25%3A00143820" target="_blank" >RIV/00216224:14110/25:00143820 - isvavai.cz</a>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

30210 - Clinical neurology

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

FRONTIERS IN COMPUTATIONAL NEUROSCIENCE

ISSN

1662-5188

e-ISSN

1662-5188

Svazek periodika

19

Číslo periodika v rámci svazku

October 2025

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

9

Strana od-do

1-9

Kód UT WoS článku

001605117300001

EID výsledku v databázi Scopus

2-s2.0-105020593542