Optically pumped magnetometers (OPMs) are new, room-temperature alternatives to superconducting quantum interference devices (SQUIDs) for measuring the brain’s magnetic fields. The most used OPM in MagnetoEncephaloGraphy (MEG) are based on alkali atoms operating in the spin-exchange relaxation-free (SERF) regime. These sensors do not require cooling but have to be heated. Another kind of OPM, based on the parametric resonance of ⁴He atoms are operated at room temperature, suppressing the heat dissipation issue. They also have an advantageous bandwidth and dynamic range more suitable for MEG recordings. We quantitatively assessed the improvement (relative to a SQUID magnetometers array) in recording the magnetic field with a wearable ⁴He OPM-MEG system through data simulations. The OPM array and magnetoencephalography forward models were based on anatomical MRI data from an adult, a nine-year-old child, and 10 infants aged between one month and two years. Our simulations showed that a ⁴He OPMs array offers markedly better spatial specificity than a SQUID magnetometers array in various key performance areas (e.g., signal power, information content, and spatial resolution). Our results are also discussed regarding previous simulation results obtained for alkali OPM.

The effects of anesthetic agents like propofol on brain activity provide critical insights into how consciousness is altered during anesthesia. This article presents a computational approach to modeling the impact of propofol on the brain’s electrical activity using the COALIA model, a large-scale neural mass model of human EEG designed for consciousness research. Propofol enhances GABA_A-mediated synaptic inhibition, leading to characteristic EEG signatures such as alpha oscillations (8-12 Hz) and slow-wave oscillations (0.1-1.5 Hz). We model these effects by modulating the parameters of postsynaptic potentials and synaptic strength, simulating the action of propofol at both the molecular and network levels. COALIA’s detailed representation of thalamo-cortical and cortico-cortical networks provides realistic simulations of brain dynamics under anesthesia, closely replicating experimental EEG data. Our findings highlight the importance of network-level interactions in producing the distinctive EEG changes seen during propofol-induced unconsciousness. This research paves the way for future applications in personalized anesthesia monitoring and consciousness assessment through EEG modeling.

Simulating the electrical activity of the human uterus has become a crucial tool for understanding the underlying biophysical phenomena, such as uterine contractions during pregnancy. Finite element models (FEM) offer valuable insights into these dynamics by providing a scalable framework to explore the propagation of electrical signals at the cellular and tissue levels. This study presents a finite element-based bidomain model to simulate the excitation propagation across the human uterus. Our model integrates cellular-level electrophysiological properties with tissue-level electrical propagation using the FEniCS Python library. A three-dimensional, realistic representation of uterine tissue is employed to simulate excitation patterns, contributing to a deeper understanding of uterine electrophysiology. The model can also be adapted to investigate pathological conditions such as preterm labor and test potential interventions. The developed simulation framework provides a scalable solution for the numerical challenges posed by solving complex, non-linear ordinary differential equations (ODEs) associated with uterine electrical activity. These simulations could offer a foundation for future research on uterine function and its related disorders.

Vigilance plays a vital role in numerous high-risk professions, where real-time monitoring of vigilance levels is highly beneficial. Brain-Computer Interfaces (BCIs) utilizing EEG signals, along with machine learning and deep learning algorithms, present a promising solution for the classification and monitoring of vigilance levels. This study aimed to create a dataset for training such models by evaluating four experimental paradigms: the Hitchcock Air Traffic Controller Task (ATC), the simultaneous line task, the successive line task, and the Oddball task. Subjective reports and behavioral performance were analyzed to determine the effectiveness of these tasks in inducing vigilance decrements over time. The findings reveal that both the ATC and simultaneous line tasks effectively induced significant declines in subjective vigilance ratings and behavioral performance. In contrast, the Oddball task was less successful in generating a noticeable vigilance decrement. This research demonstrates the potential of the ATC and simultaneous line tasks to induce vigilance variations, providing valuable datasets for training vigilance detection algorithms. Additionally, it highlights the importance of considering non-linear fluctuations in vigilance and the need for more advanced techniques to accurately classify different vigilance states. Such improvements in vigilance monitoring could substantially enhance safety and well-being in critical work environments.