PhD Scientific Days 2026

Neurosciences

From Noise to Physiology: Assessing Sleep Neural Time Series Using Multi-Scale Entropy and Power-Law Modulated Multi-Scale Entropy

Előadó neve

Magyar, Tárek Zoltán, MSc

Neptune code

AUCNFL

Előadó munkahelye

Institute of Behavioural Sciences, Semmelweis University

Előadó telefonszáma

+36204470808

Előadó e-mail címe

magyar.tarek@phd.semmelweis.hu

Az előadás címe

From Noise to Physiology: Assessing Sleep Neural Time Series Using Multi-Scale Entropy and Power-Law Modulated Multi-Scale Entropy

Szerző(k) neve és munkahelye

Tárek Zoltán Magyar^1,2, Dr. Martin Dresler³, Dr. Orsolya Szalárdy¹, Dr. Yevgenia Rosenblum³, Prof. Dr. Róbert Bódizs¹

1: Institute of Behavioural Sciences, Semmelweis University, Hungary
2: Selye János Doctoral College for Advanced Studies, Semmelweis University, Hungary
3: Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Netherlands

Bemutatás módja

Szóbeli

Szekció

Neurosciences

Language of the presentation

English

Preferred session

Neurosciences

Összefoglaló szövege

Besides canonical sleep electroencephalography (EEG) features, advances in signal processing have enabled the extraction of aperiodic (non-oscillatory) and complexity-based metrics that capture complementary properties of neural activity. Among these, multi-scale entropy (MSE) quantifies signal irregularity across temporal scales, whereas power-law modulated multi-scale entropy (pMSE) extends this approach by incorporating the aperiodic, power-law characteristic of EEG signals. As a result, pMSE emphasizes the balance between randomness and order that characterizes healthy biological systems. In this study, we compare MSE vs pMSE in differentiating between sleep signals and the consistency of the results over datasets.
MSE and pMSE were computed from EEG recordings recorded by the human Healthy Brain Study (n = 863), and a mouse sleep deprivation experiment (n = 8), as well as simulated signals with controlled spectral exponents ranging from 1.5 to 3.5. We evaluated the ability of MSE and pMSE to differentiate signals with varying spectral exponents over a range of time scales up to 500 ms. Likewise, we compared their ability to capture sleep homeostatic adjustments relative to baseline with ANOVA.
In simulations, both MSE and pMSE curves were sensitive to changes in spectral exponents, indicating that entropy estimates depend on the power-law structure of the signal. However, only pMSE showed a clear correspondence between physiological and simulated signals with matching spectral exponents across time scales up to 400 ms. In addition, pMSE, within the 300-350 ms time scale, captured homeostatic adjustments induced by sleep deprivation (F(1, 7) = 7.034; p = .024; ηp = .501), whereas MSE did not.
By incorporating power-law spectral dynamics into multiscale entropy estimation, pMSE more accurately maps physiological sleep EEG signals and better captures homeostatic changes observed in a rodent sleep deprivation paradigm compared to conventional MSE.
Funding: Research supported by the Dutch Research Council (NWO), Alzheimer Nederland Early Career Grant, the Hungarian Ministry of Innovation and Technology (TKP2021-EGA-25 and TKP2021-NKTA-47), and the National Research, Development and Innovation Office of Hungary (K146792,2023-1.2.1-ERA_NET-2023-00004), as well as the EU Joint Programme Neurodegenerative Disease (JPND2022-120)

University

Semmelweis University

Supervisor

Prof. Dr. Róbert Bódizs; Dr. Orsolya Szalárdy

Publication of my abstract

I give consent to the publication of my abstract on the website of the congress.

phd.section.field

in doctoral studies after complex exam (PhD)

Kind

Szabad

Status

elfogadva

Accepted presentation method

szóbeli

Előadás fájl jóváhagyás

jóváhagyta

Előadó

7442

Start

16:00

End

16:10