Irreversible functional network deformation induced by preprocessing scheme order in multivariate EEG recordings.

IF 2.9 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Frontiers in Neuroinformatics Pub Date : 2026-09-28 eCollection Date: 2026-01-01 DOI:10.3389/fninf.2026.1907920
J Daniel Arzate-Mena, Paola V Olguín-Rodríguez, Maria Corsi-Cabrera, Erik A López-Alvarado, Markus F Müller
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引用次数: 0

Abstract

Introduction: The analysis of Electroencephalographic recordings is non-trivial, the order in which filtering and re-referencing are applied during multivariate signal preprocessing has a critical and underappreciated consequence for functional network estimation.

Methods: Using EEG sleep recordings from healthy subjects, synthetic data from controlled numerical models (Nf-Tori and IAAFT surrogates), and surrogate-based statistical testing, we provide a quantitative comparison of two preprocessing schemes that differ solely in the order of filtering and re-referencing (filtering followed by re-referencing-FR, and re-referencing followed by filtering-RF).

Results: We show that the RF scheme induces spurious cross-correlations that increase systematically with the frequency band and are strongly modulated by the power spectral exponent β of the signal. Critically, for signals with steep power spectra (β ≥2), a regime that characterizes slow-wave sleep, epileptic post-ictal periods, anesthesia, neonatal, and patients in vegetative states, the RF scheme produces artificial correlations that are irreversible. They cannot be eliminated post-hoc even when surrogate-corrected cross-correlations (SAC-matrix) are applied. The mechanism underlying this effect is due to the high-amplitude slow-frequency components that dominate the median reference signal over extended time intervals, embedding redundant information across all channels. In contrast, the FR scheme introduces only small, systematic negative shifts in cross-correlations that are not statistically significant and can be effectively corrected using surrogate-based methods.

Conclusions: We therefore recommend the FR scheme combined with surrogate-corrected cross-correlations for all applications involving signals with β ≥2. Although this finding is demonstrated in EEG, it applies to any multivariate recording where spatial re-referencing and band-pass filtering are applied sequentially.

多变量脑电记录中预处理方案顺序引起的不可逆功能网络变形。
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来源期刊
Frontiers in Neuroinformatics
Frontiers in Neuroinformatics MATHEMATICAL & COMPUTATIONAL BIOLOGY-NEUROSCIENCES
CiteScore
4.80
自引率
5.70%
发文量
132
审稿时长
14 weeks
期刊介绍: Frontiers in Neuroinformatics publishes rigorously peer-reviewed research on the development and implementation of numerical/computational models and analytical tools used to share, integrate and analyze experimental data and advance theories of the nervous system functions. Specialty Chief Editors Jan G. Bjaalie at the University of Oslo and Sean L. Hill at the École Polytechnique Fédérale de Lausanne are supported by an outstanding Editorial Board of international experts. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics and the public worldwide. Neuroscience is being propelled into the information age as the volume of information explodes, demanding organization and synthesis. Novel synthesis approaches are opening up a new dimension for the exploration of the components of brain elements and systems and the vast number of variables that underlie their functions. Neural data is highly heterogeneous with complex inter-relations across multiple levels, driving the need for innovative organizing and synthesizing approaches from genes to cognition, and covering a range of species and disease states. Frontiers in Neuroinformatics therefore welcomes submissions on existing neuroscience databases, development of data and knowledge bases for all levels of neuroscience, applications and technologies that can facilitate data sharing (interoperability, formats, terminologies, and ontologies), and novel tools for data acquisition, analyses, visualization, and dissemination of nervous system data. Our journal welcomes submissions on new tools (software and hardware) that support brain modeling, and the merging of neuroscience databases with brain models used for simulation and visualization.
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