Multiscale detrended cross-correlation coefficient: estimating coupling in non-stationary neurophysiological signals
The brain consists of a vastly interconnected network of regions, the connectome. By estimating the statistical interdependence of neurophysiological time series, we can measure the functional connectivity (FC) of this connectome. Pearson’s correlation (rP) is a common metric of coupling in FC studi...
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Frontiers Media S.A.
2024-11-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2024.1422085/full |
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| author | Orestis Stylianou Orestis Stylianou Orestis Stylianou Gianluca Susi Gianluca Susi Martin Hoffmann Martin Hoffmann Isabel Suárez-Méndez Isabel Suárez-Méndez David López-Sanz David López-Sanz Michael Schirner Michael Schirner Michael Schirner Michael Schirner Michael Schirner Petra Ritter Petra Ritter Petra Ritter Petra Ritter Petra Ritter |
| author_facet | Orestis Stylianou Orestis Stylianou Orestis Stylianou Gianluca Susi Gianluca Susi Martin Hoffmann Martin Hoffmann Isabel Suárez-Méndez Isabel Suárez-Méndez David López-Sanz David López-Sanz Michael Schirner Michael Schirner Michael Schirner Michael Schirner Michael Schirner Petra Ritter Petra Ritter Petra Ritter Petra Ritter Petra Ritter |
| author_sort | Orestis Stylianou |
| collection | DOAJ |
| description | The brain consists of a vastly interconnected network of regions, the connectome. By estimating the statistical interdependence of neurophysiological time series, we can measure the functional connectivity (FC) of this connectome. Pearson’s correlation (rP) is a common metric of coupling in FC studies. Yet rP does not account properly for the non-stationarity of the signals recorded in neuroimaging. In this study, we introduced a novel estimator of coupled dynamics termed multiscale detrended cross-correlation coefficient (MDC3). Firstly, we showed that MDC3 had higher accuracy compared to rP and lagged covariance using simulated time series with known coupling, as well as simulated functional magnetic resonance imaging (fMRI) signals with known underlying structural connectivity. Next, we computed functional brain networks based on empirical magnetoencephalography (MEG) and fMRI. We found that by using MDC3 we could construct networks of healthy populations with significantly different properties compared to rP networks. Based on our results, we believe that MDC3 is a valid alternative to rP that should be incorporated in future FC studies. |
| format | Article |
| id | doaj-art-b3f471f99258460cbe4e295f074772c3 |
| institution | Kabale University |
| issn | 1662-453X |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj-art-b3f471f99258460cbe4e295f074772c32024-11-13T06:21:06ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2024-11-011810.3389/fnins.2024.14220851422085Multiscale detrended cross-correlation coefficient: estimating coupling in non-stationary neurophysiological signalsOrestis Stylianou0Orestis Stylianou1Orestis Stylianou2Gianluca Susi3Gianluca Susi4Martin Hoffmann5Martin Hoffmann6Isabel Suárez-Méndez7Isabel Suárez-Méndez8David López-Sanz9David López-Sanz10Michael Schirner11Michael Schirner12Michael Schirner13Michael Schirner14Michael Schirner15Petra Ritter16Petra Ritter17Petra Ritter18Petra Ritter19Petra Ritter20Berlin Institute of Health at Charité, Universitätsmedizin Berlin, Berlin, GermanyCharité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Department of Neurology with Experimental Neurology, Berlin, GermanyDepartment of Surgery, Immanuel Clinic Rüdersdorf, University Clinic of Brandenburg Medical School, Berlin, GermanyDepartment of Structure of Matter, Thermal Physics and Electronics, Complutense University of Madrid, Madrid, SpainCenter for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, SpainBerlin Institute of Health at Charité, Universitätsmedizin Berlin, Berlin, GermanyCharité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Department of Neurology with Experimental Neurology, Berlin, GermanyDepartment of Structure of Matter, Thermal Physics and Electronics, Complutense University of Madrid, Madrid, SpainCenter for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, SpainCenter for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, SpainDepartment of Experimental Psychology, Cognitive Processes and Speech Therapy, Complutense University of Madrid, Madrid, SpainBerlin Institute of Health at Charité, Universitätsmedizin Berlin, Berlin, GermanyCharité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Department of Neurology with Experimental Neurology, Berlin, GermanyBernstein Focus State Dependencies of Learning & Bernstein Center for Computational Neuroscience, Berlin, GermanyEinstein Center for Neuroscience Berlin, Berlin, GermanyEinstein Center Digital Future, Berlin, GermanyBerlin Institute of Health at Charité, Universitätsmedizin Berlin, Berlin, GermanyCharité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Department of Neurology with Experimental Neurology, Berlin, GermanyBernstein Focus State Dependencies of Learning & Bernstein Center for Computational Neuroscience, Berlin, GermanyEinstein Center for Neuroscience Berlin, Berlin, GermanyEinstein Center Digital Future, Berlin, GermanyThe brain consists of a vastly interconnected network of regions, the connectome. By estimating the statistical interdependence of neurophysiological time series, we can measure the functional connectivity (FC) of this connectome. Pearson’s correlation (rP) is a common metric of coupling in FC studies. Yet rP does not account properly for the non-stationarity of the signals recorded in neuroimaging. In this study, we introduced a novel estimator of coupled dynamics termed multiscale detrended cross-correlation coefficient (MDC3). Firstly, we showed that MDC3 had higher accuracy compared to rP and lagged covariance using simulated time series with known coupling, as well as simulated functional magnetic resonance imaging (fMRI) signals with known underlying structural connectivity. Next, we computed functional brain networks based on empirical magnetoencephalography (MEG) and fMRI. We found that by using MDC3 we could construct networks of healthy populations with significantly different properties compared to rP networks. Based on our results, we believe that MDC3 is a valid alternative to rP that should be incorporated in future FC studies.https://www.frontiersin.org/articles/10.3389/fnins.2024.1422085/fullfunctional connectivityfunctional connectomenon-stationary signalsbrain networksstatistical interdependence |
| spellingShingle | Orestis Stylianou Orestis Stylianou Orestis Stylianou Gianluca Susi Gianluca Susi Martin Hoffmann Martin Hoffmann Isabel Suárez-Méndez Isabel Suárez-Méndez David López-Sanz David López-Sanz Michael Schirner Michael Schirner Michael Schirner Michael Schirner Michael Schirner Petra Ritter Petra Ritter Petra Ritter Petra Ritter Petra Ritter Multiscale detrended cross-correlation coefficient: estimating coupling in non-stationary neurophysiological signals Frontiers in Neuroscience functional connectivity functional connectome non-stationary signals brain networks statistical interdependence |
| title | Multiscale detrended cross-correlation coefficient: estimating coupling in non-stationary neurophysiological signals |
| title_full | Multiscale detrended cross-correlation coefficient: estimating coupling in non-stationary neurophysiological signals |
| title_fullStr | Multiscale detrended cross-correlation coefficient: estimating coupling in non-stationary neurophysiological signals |
| title_full_unstemmed | Multiscale detrended cross-correlation coefficient: estimating coupling in non-stationary neurophysiological signals |
| title_short | Multiscale detrended cross-correlation coefficient: estimating coupling in non-stationary neurophysiological signals |
| title_sort | multiscale detrended cross correlation coefficient estimating coupling in non stationary neurophysiological signals |
| topic | functional connectivity functional connectome non-stationary signals brain networks statistical interdependence |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2024.1422085/full |
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