PhD Scientific Days 2022

Neurosciences II.

Multiple-Resampling Cross-Spectral Analysis: An Unbiased Tool for Estimating Fractal Connectivity with an Application to Neurophysiological Signals

Előadó neve

Dr. Czoch, Ákos

Előadó munkahelye

Semmelweis Egyetem, Molekuláris Orvostudományok Doktori Iskola

Előadó telefonszáma

06202825728

Előadó e-mail címe

czoch97@gmail.com

Az előadás címe

Multiple-Resampling Cross-Spectral Analysis: An Unbiased Tool for Estimating Fractal Connectivity with an Application to Neurophysiological Signals

Szerző(k) neve és munkahelye

Frigyes Sámuel Rácz(1,2), Ákos Czoch(1), Zalán Káposzta(1), Orestis Stylianou(1,3), Péter Mukli(1,4), András Eke(1,5)
1. Department of Physiology, Faculty of Medicine, Semmelweis University, Budapest, Hungary
2. Department of Neurology, Dell Medical School, The University of Texas at Austin, Austin, TX, United States
3. Institute of Translational Medicine, Semmelweis University, Budapest, Hungary
4. Oklahoma Center for Geroscience and Healthy Brain Aging, Department of Biochemistry & Molecular Biology, The University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States
5. Department of Radiology & Biomedical Imaging, School of Medicine, Yale University, New Haven, CT, United States

Bemutatás módja

Szóbeli

Szekció

Neurosciences II.

Language of the presentation

English

Preferred session

Neurosciences

Összefoglaló szövege

Introduction: Investigating scale-free (i.e., fractal) functional connectivity in the brain has recently attracted increasing attention. Although numerous methods have been developed to assess the fractal nature of functional coupling, these typically ignore that neurophysiological signals are assemblies of broadband, arrhythmic activities as well as oscillatory activities at characteristic frequencies such as the alpha waves. While contribution of such rhythmic components may bias estimates of fractal connectivity, they are also likely to represent neural activity and coupling emerging from distinct mechanisms. Aims: Irregular-resampling auto-spectral analysis (IRASA) was recently introduced as a tool to separate fractal and oscillatory components in the power spectrum of neurophysiological signals by statistically summarizing the power spectra obtained when resampling the original signal by several non-integer factors. Here we introduce multiple-resampling cross-spectral analysis (MRCSA) as an extension of IRASA from the univariate to the bivariate case, namely, to separate the fractal component of the cross-spectrum between two simultaneously recorded neural signals by applying the same principle. Methods: MRCSA does not only provide a theoretically unbiased estimate of the fractal cross-spectrum (and thus its spectral exponent) but also allows for computing the proportion of scale-free coupling between brain regions. As a demonstration, we apply MRCSA to human electroencephalographic recordings obtained in a word generation paradigm. Results: We show that the cross-spectral exponent as well as the proportion of fractal coupling increases almost uniformly over the cortex during the rest-task transition, likely reflecting neural desynchronization. Conclusion: Our results indicate that MRCSA can be a valuable tool for scale-free connectivity studies in characterizing various cognitive states, while it also can be generalized to other applications outside the field of neuroscience. Funding: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors

University and Doctoral School

Semmelweis University, Doctoral School of Molecular Medicine

Supervisor

Dr. Frigyes Sámuel Rácz

Publication of my abstract

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

Kind

Szabad

Status

elfogadva

Accepted presentation method

szóbeli

Előadás fájl jóváhagyás

nem rendelkezett róla

Előadó

6870

Start

15:30

End

15:45

Authors (legacy)

Frigyes Sámuel Rácz(1,2), Ákos Czoch(1), Zalán Káposzta(1), Orestis Stylianou(1,3), Péter Mukli(1,4), András Eke(1,5)
1. Department of Physiology, Faculty of Medicine, Semmelweis University, Budapest, Hungary
2. Department of Neurology, Dell Medical School, The University of Texas at Austin, Austin, TX, United States
3. Institute of Translational Medicine, Semmelweis University, Budapest, Hungary
4. Oklahoma Center for Geroscience and Healthy Brain Aging, Department of Biochemistry & Molecular Biology, The University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States
5. Department of Radiology & Biomedical Imaging, School of Medicine, Yale University, New Haven, CT, United States