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Fehring, J.

Publications and source records attributed to Fehring, J..

3 recordsLinked to original sources

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↗

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↗

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↗