Biology subjects

Stylianou, O.

Publications and source records attributed to Stylianou, O..

3 recordsLinked to original sources

bioRxiv2026

30 Hz High-Definition Transcranial Alternating Current Stimulation at the Left Frontal Cortex Reduces the Spectral Slope of the EEG in the Contralateral Hemisphere

Background: High-definition transcranial alternating current stimulation (HD-tACS) is favored by the neurostimulation community for its precision and ability to influence neuronal dynamics. Yet, the exact mechanism by which the underlying brain structures are being affected remains unclear. We believe that the investigation of the aperiodic nature of the electroencephalograph (EEG) could shed light on the modulatory effects of HD-tACS. Methods: We analyzed the EEG of 9 participants during a compensatory tracking task (CTT) in two sessions, each with different HD-tACS protocols. Every session consisted of an initial period of no stimulation, followed by 30 Hz HD-tACS in the left motor (M30) or frontal (F30) cortex. We then isolated the aperiodic component of the EEG and calculated its spectral slope {beta}. Results and Discussion: {beta} decreased during F30 mainly in the right frontal cortex, indicating a shift towards higher frequencies and an increase of the excitatory/inhibitory balance. Additionally, we found that despite the long monotonus task the accuracy of the participants did not decrease, which might be attributed to the ability of both M30 and F30 to sustain attention for prolonged time. Finally, the change of CTT accuracy during the stimulation correlated with the {beta} of specific channels before the stimulation. This indicates the potential of {beta} to be used as a screening biomarker in future studies. In conclusion, we showed the ability of HD-tACS to alter EEG aperiodic dynamics and paved the way for future exploration of such dynamics in the field.

neuroscience↗

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↗