Biology subjects

Lopez-Sanz, D.

Publications and source records attributed to Lopez-Sanz, D..

3 recordsLinked to original sources

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↗

bioRxiv2024

Effects of Alzheimer's disease plasma marker levels on multilayer centrality in healthy individuals

Finding early and non-invasive biomarkers that help identify individuals in the earliest stages of the Alzheimers disease continuum is paramount. Electrophysiology and plasma biomarkers are great candidates in this pursuit. Furthermore, the combination of functional connectivity metrics with graph-theory analyses allows for a deeper understanding of network alterations. Despite this, this is the first MEG study to assess multilayer centrality considering inter-band connectivity in an unimpaired population at high risk of Alzheimers disease. Our objective is twofold. First, to address the relationship between a compound centrality score designed to overcome previous inconsistencies stemming from the use of various individual metrics, and plasma pathology markers of Alzheimers disease in unimpaired individuals with elevated levels of the latter. Lastly, to evaluate whether hubs centrality is more affected by the pathology. 33 individuals with available MEG recordings and elevated plasma pathology markers were included. A compound centrality score for each brain source of every subject was calculated combining widely used centrality metrics, considering intra- and inter-band connections. Spearman correlations were carried out to address the association between each nodes centrality score and biomarkers levels. Next, to test whether greater associations were found in hubs, a correlation between the obtained rho and the grand-average of the centrality score was carried out. Increasing concentrations of p-tau231 were associated with greater centrality within the network of posterior areas, which increased their connectedness in the theta range with the remaining areas, regardless of the latters frequency range. The opposite relationship was found for left areas, that decreased their connectedness in the gamma frequency range. Hubs centrality was significantly more affected by p-tau231 levels. Our results expand previous literature demonstrating early network reorganizations associated with elevated plasma p-tau231 in cognitively unimpaired individuals. Multilayer centrality increases in the theta band in posterior regions are congruent with previous results and theoretical models, that predict a longitudinal evolution towards a loss of centrality. On the other hand, the changes in multilayer centrality found in the gamma band could be associated with inhibitory neuron dysfunction, classical in AD pathology. Lastly, hubs were more likely to increase their centrality in association to p-tau231, thus corroborating hubs vulnerability.

neuroscience↗

bioRxiv2023

When Maturation is Not Linear: Brain Oscillatory Activity in the Process of Aging as Measured by Electrophysiology

Changes in brain oscillatory activity are commonly used as biomarkers both in cognitive neuroscience and in neuropsychiatric conditions. However, little is known about how its profile changes across maturation. Here we use regression models to characterize magnetoencephalography power changes within classical frequency bands in a sample of 792 healthy participants, covering the range 13 to 80 years old. Our results reveal complex, non-linear trajectories of power changes that challenge the linear model traditionally reported. Moreover, these trajectories also exhibit variations across cortical regions. Remarkably, we observed that increases in slow wave activity are associated with a better cognitive performance across the lifespan, as well as with larger gray matter volume for elderlies, while fast wave activity decreases with adulthood. These results suggest that elevated power in low-frequency resting-state activity during aging may reflect a proxy for deterioration, rather than serving as a compensatory mechanism, as usually interpreted. In addition, it enhances our comprehension of both neurodevelopment and the aging process by highlighting the complexity and regional specificity of changes in brain rhythms. Furthermore, our findings have potential implications for understanding cognitive performance and structural integrity.

neuroscience↗