ArticleFrontiers in neuroscience2025
Seven unique frequency profiles for scoring vigilance states in preclinical electrophysiological data.
Article in Frontiers in neuroscience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Roles of NRXN1 in neuropsychiatric disorders: from genetic lesion to molecular mechanism.Frontiers in neuroscience · 2026Review
- Alpha-neurexins in health and disease.Frontiers in molecular neuroscience · 2025Review
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2 authors.
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Abstract
Manual scoring of longitudinal electroencephalographical (EEG) data is a slow and time-consuming process. Current advances in the application of machine-learning and artificial intelligence to EEG data are moving fast; however, there is still a need for expert raters to validate scoring of EEG data. We hypothesized that power-frequency profiles are determining the state and 'set the framework' for communication between neurons. Based on these assumptions, a scoring method with a set frequency profile for each vigilance state, both in sleep and awake, was developed and validated. We defined seven states of the functional brain with unique profiles in terms of frequency-power spectra, coherence, phase-amplitude coupling, α exponent, functional excitation-inhibition balance (fE/I), and aperiodic exponent. The new method requires a manual check of wake-sleep transitions and is therefore considered semi-automatic. This semi-automatic approach showed similar α exponent and fE/I when compared to traces scored manually. The new method was faster than manual scoring, and the advanced outcomes of each state were stable across datasets and epoch length. When applying the new method to the
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