Stylianou, O. | Search bioRxiv

bioRxiv2024

Whole-Body Networks: A Holistic Approach for Studying Aging

Aging is a multiorgan disease, yet the traditional approach is to study each organ in isolation. Such organ-specific studies allowed us to gather invaluable information regarding the pathomechanisms that contribute to senescence. But we believe that a big-picture exploration of the whole-body network (WBN) during aging could be complementary. In this study, we analyzed the functional magnetic resonance imaging (fMRI), breathing rate and heart rate time series of a young and an elderly group during eyes-open resting-state. By exploring the time-lagged coupling between the different organs we constructed WBNs. First, we showed that our analytical pipeline could identify regional differences in the networks of both populations, allowing us to proceed with the remaining of the analysis. By comparing the WBNs of young and elderly, a complex relationship emerged where some connections were stronger and some weaker in the elderly. Finally, the interconnectivity and segregation of the WBNs negatively correlated with the short-term memory of the young participants. This study: i) validated our methods, ii) identified differences between the two groups and iii) showed correlation with behavioral metrics. We are at the edge of a paradigm shift on how aging-related research is conducted and we believe that our methodology should be implemented in more complex mental and/or physical tasks to better demonstrate the alterations of WBNs as we age.

neuroscience↗

bioRxiv2024

Multiscale Detrended Cross-Correlation Coefficient: Estimating Coupling in Nonstationary 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. Pearsons 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 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. Author SummaryThe brain consists of a vastly interconnected network of regions. To estimate the connection strength of such networks the coupling between different brain regions should be calculated. This can be achieved by using a series of statistical methods that capture the connection strength between signals originating across the brain, one of them being Pearsons correlation (rP). Despite its benefits, rP is not suitable for realistic estimation of brain network architecture. In this study, we introduced a novel estimator called multiscale detrended cross-correlation coefficient (MDC3). Firstly, we showed that MDC3 was more accurate than rP using simulated signals with known connection strength, as well as simulated brain activity emerging from realistic brain simulations. Next, we constructed brain networks based on real-life brain activity, recorded using two different methodologies. 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 studies of brain networks.

neuroscience↗