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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 ( ) is a common metric of coupling in FC s...
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Published in: | Frontiers in neuroscience 2024-11, Vol.18, p.1422085 |
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Main Authors: | , , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites |
Online Access: | Get full text |
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Summary: | 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 (
) is a common metric of coupling in FC studies. Yet
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 (MDC
). Firstly, we showed that MDC
had higher accuracy compared to
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 MDC
we could construct networks of healthy populations with significantly different properties compared to
networks. Based on our results, we believe that MDC
is a valid alternative to
that should be incorporated in future FC studies. |
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ISSN: | 1662-4548 1662-453X 1662-453X |
DOI: | 10.3389/fnins.2024.1422085 |