Biology subjects

Stier, C.

Publications and source records attributed to Stier, C..

5 recordsLinked to original sources

bioRxiv2024

Neurophysiological correlates of cortical hierarchy across the lifespan

The brain processes information along a hierarchical structure, forming a gradient of cortical hierarchy from sensorimotor areas to transmodal areas. Here, we aim to understand which aspects of neural dynamics characterize this gradient and whether the respective spatial distribution varies across the lifespan. Therefore, we extracted neurophysiological features from magnetoencephalography recordings in 350 participants between 18 and 88 years during rest. Among traditional features related to the power spectrum, delta power (1-4 Hz) showed the most robust association with cortical hierarchy, increasing along this axis. Beyond traditional features, we employed comprehensive time-series characterization and identified a novel hierarchy-sensitive feature capturing the variability of the signals mean over time. This feature increases along the cortical hierarchy, suggesting that higher-level brain areas exhibit more dynamic and context-dependent activity patterns. Furthermore, we highlight changes in the gradient of brain dynamics across the lifespan. Alpha power distribution, for instance, exhibits a posterior-anterior gradient in young adults that becomes less pronounced with increasing age. Further, the change of the autocorrelation and auto mutual information function along the cortical hierarchy is heavily modulated by age. These findings reveal simple but robust neurophysiological markers of cortical hierarchy and highlight the dynamic nature of the brains organization throughout life.

neuroscience↗

bioRxiv2024

Extensive MEG time-series phenotyping unveils neural markers predictive of age

Understanding the evolving dynamics of the brain throughout life is pivotal for anticipating and evaluating individual health. While previous research has described age effects on spectral properties of neural signals, it remains unclear which ones are most indicative of age-related processes. This study addresses this gap by analyzing resting-state data obtained from magnetoencephalography in 350 adults (18-88 years). We employed advanced time-series analysis at the brain region level and machine learning to predict age. While traditional spectral features achieved low to moderate accuracy, over a hundred novel time-series features proved superior. Notably, temporal autocorrelation emerged as the most robust predictor of age. Distinct patterns of autocorrelation within the visual and temporal cortex were most informative, offering a versatile measure of age-related signal changes for comprehensive health assessments based on brain activity.

neuroscience↗

bioRxiv2024

Beyond oscillations - A novel feature space for characterizing brain states

Our moment-to-moment conscious experience is paced by transitions between states, each one corresponding to a change in the electromagnetic brain activity. One consolidated analytical choice is to characterize these changes in the frequency domain, such that the transition from one state to the other corresponds to a difference in the strength of oscillatory power, often in pre-defined, theory-driven frequency bands of interest. Today, the huge leap in available computational power allows us to explore new ways to characterize electromagnetic brain activity and its changes. Here we leveraged an innovative set of features on an MEG dataset with 29 human participants, to test how these features described some of those state transitions known to elicit prominent changes in the frequency spectrum, such as eyes-closed vs eyes-open resting-state or the occurrence of visual stimulation. We then compared the informativeness of multiple sets of features by submitting them to a multivariate classifier (SVM). We found that the new features outperformed traditional ones in generalizing states classification across participants. Moreover, some of these new features yielded systematically better decoding accuracy than the power in canonical frequency bands that has been often considered a landmark in defining these state changes. Critically, we replicated these findings, after pre-registration, in an independent EEG dataset (N=210). In conclusion, the present work highlights the importance of a full characterization of the state changes in the electromagnetic brain activity, which takes into account also other dimensions of the signal on top of its description in theory-driven frequency bands of interest.

neuroscience↗

bioRxiv2023

Respiratory modulations of cortical excitabilityand interictal spike timing in focal epilepsy - a case report

Brain activity in focal epilepsy is marked by a pronounced excitation-inhibition (E:I) imbalance and interictal epileptiform discharges (IEDs) observed in periods between recurrent seizures. As a marker of E:I balance, aperiodic neural activity and its underlying 1/f characteristic reflect the dynamic interplay of excitatory and inhibitory currents. Recent studies have independently assessed 1/f changes both in epilepsy and in the context of body-brain interactions in neurotypical individuals where the respiratory rhythm has emerged as a potential modulator of excitability states in the brain. Here, we investigate respiration phase-locked modulations of E:I balance and their involvement in the timing of spike discharges in a case report of a 25 year-old focal epilepsy patient using magnetoencephalography (MEG). We show that i) respiration differentially modulates E:I balance in focal epilepsy compared to N = 40 neurotypical controls and ii) IED timing depends on both excitability and respiratory states. These findings overall suggest an intricate interplay of respiration phase-locked changes in excitation and the consequential susceptibility for IED generation and we hope they will spark interest in subsequent work on body-brain coupling and E:I balance in epilepsy.

neuroscience↗

bioRxiv2022

Adult lifespan trajectories of neuromagnetic signals and interrelations with cortical thickness

Oscillatory power and phase synchronization map neuronal dynamics and are commonly studied to differentiate the healthy and diseased brain. Yet, little is known about the course and spatial variability of these features from early adulthood into old age. Leveraging magnetoencephalography (MEG) resting-state data in a cross-sectional adult sample (n = 350), we probed lifespan differences (18-88 years) in connectivity and power and interaction effects with sex. Building upon recent attempts to link brain structure and function, we tested the spatial correspondence between age effects on cortical thickness and those on functional networks. We further probed a direct structure-function relationship at the level of the study sample. We found MEG frequency-specific patterns with age and divergence between sexes in low frequencies. Connectivity and power exhibited distinct linear trajectories or turning points at midlife that might reflect different physiological processes. In the delta and beta bands, these age effects corresponded to those on cortical thickness, pointing to co-variation between the modalities across the lifespan. Structure-function coupling was frequency-dependent and observed in unimodal or multimodal regions. Altogether, we provide a comprehensive overview of the topographic functional profile of adulthood that can form a basis for neurocognitive and clinical investigations. This study further sheds new light on how the brains structural architecture relates to fast oscillatory activity.

neuroscience↗