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      Neural complexity and the spectral slope characterise auditory processing in wakefulness and sleep

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          Abstract

          Auditory processing and the complexity of neural activity can both indicate residual consciousness levels and differentiate states of arousal. However, how measures of neural signal complexity manifest in neural activity following environmental stimulation and, more generally, how the electrophysiological characteristics of auditory responses change in states of reduced consciousness remain under‐explored. Here, we tested the hypothesis that measures of neural complexity and the spectral slope would discriminate stages of sleep and wakefulness not only in baseline electroencephalography (EEG) activity but also in EEG signals following auditory stimulation. High‐density EEG was recorded in 21 participants to determine the spatial relationship between these measures and between EEG recorded pre‐ and post‐auditory stimulation. Results showed that the complexity and the spectral slope in the 2–20 Hz range discriminated between sleep stages and had a high correlation in sleep. In wakefulness, complexity was strongly correlated to the 20–40 Hz spectral slope. Auditory stimulation resulted in reduced complexity in sleep compared to the pre‐stimulation EEG activity and modulated the spectral slope in wakefulness. These findings confirm our hypothesis that electrophysiological markers of arousal are sensitive to sleep/wake states in EEG activity during baseline and following auditory stimulation. Our results have direct applications to studies using auditory stimulation to probe neural functions in states of reduced consciousness.

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          MEG and EEG data analysis with MNE-Python

          Magnetoencephalography and electroencephalography (M/EEG) measure the weak electromagnetic signals generated by neuronal activity in the brain. Using these signals to characterize and locate neural activation in the brain is a challenge that requires expertise in physics, signal processing, statistics, and numerical methods. As part of the MNE software suite, MNE-Python is an open-source software package that addresses this challenge by providing state-of-the-art algorithms implemented in Python that cover multiple methods of data preprocessing, source localization, statistical analysis, and estimation of functional connectivity between distributed brain regions. All algorithms and utility functions are implemented in a consistent manner with well-documented interfaces, enabling users to create M/EEG data analysis pipelines by writing Python scripts. Moreover, MNE-Python is tightly integrated with the core Python libraries for scientific comptutation (NumPy, SciPy) and visualization (matplotlib and Mayavi), as well as the greater neuroimaging ecosystem in Python via the Nibabel package. The code is provided under the new BSD license allowing code reuse, even in commercial products. Although MNE-Python has only been under heavy development for a couple of years, it has rapidly evolved with expanded analysis capabilities and pedagogical tutorials because multiple labs have collaborated during code development to help share best practices. MNE-Python also gives easy access to preprocessed datasets, helping users to get started quickly and facilitating reproducibility of methods by other researchers. Full documentation, including dozens of examples, is available at http://martinos.org/mne.
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            On the Complexity of Finite Sequences

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              Parameterizing neural power spectra into periodic and aperiodic components

              Electrophysiological signals exhibit both periodic and aperiodic properties. Periodic oscillations have been linked to numerous physiological, cognitive, behavioral and disease states. Emerging evidence demonstrates that the aperiodic component has putative physiological interpretations and that it dynamically changes with age, task demands and cognitive states. Electrophysiological neural activity is typically analyzed using canonically defined frequency bands, without consideration of the aperiodic (1/f-like) component. We show that standard analytic approaches can conflate periodic parameters (center frequency, power, bandwidth) with aperiodic ones (offset, exponent), compromising physiological interpretations. To overcome these limitations, we introduce an algorithm to parameterize neural power spectra as a combination of an aperiodic component and putative periodic oscillatory peaks. This algorithm requires no a priori specification of frequency bands. We validate this algorithm on simulated data, and demonstrate how it can be used in applications ranging from analyzing age-related changes in working memory to large-scale data exploration and analysis.
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                Author and article information

                Journal
                European Journal of Neuroscience
                Eur J of Neuroscience
                Wiley
                0953-816X
                1460-9568
                March 2024
                December 15 2023
                March 2024
                : 59
                : 5
                : 822-841
                Affiliations
                [1 ] Institute of Computer Science University of Bern Bern Switzerland
                [2 ] Zentrum für Experimentelle Neurologie, Department of Neurology Inselspital University Hospital Bern Bern Switzerland
                [3 ] Sleep–Wake–Epilepsy Center, NeuroTec, Department of Neurology, Inselspital, Bern University Hospital University of Bern Bern Switzerland
                Article
                10.1111/ejn.16203
                38100263
                a7601a0a-f3d6-4f7d-94b2-22330e5675b9
                © 2024

                http://onlinelibrary.wiley.com/termsAndConditions#vor

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