BSD: A Bayesian Framework for Parametric Models of Neural Spectra

• Published in: The European journal of neuroscience Vol. 61; no. 10; pp. e70149 - n/a
• Main Authors: Medrano, Johan, Alexander, Nicholas A., Seymour, Robert A., Zeidman, Peter
• Format: Journal Article
• Language: English
• Published: France Wiley Subscription Services, Inc 01.05.2025; John Wiley and Sons Inc
• Subjects: Adult; Bayes Theorem; Bayesian analysis; Bayesian inference; Brain - physiology; EEG; Electroencephalography - methods; Female; group‐level analysis; Humans; Male; Mathematical models; MEG; Models, Neurological; neural oscillations; Oscillations; Parameter estimation; Signal Processing, Computer-Assisted; spectral analysis; Statistical analysis; Technical Note; Young Adult; group‐level analysis; spectral analysis; Bayesian inference; neural oscillations; EEG; MEG
• ISSN: 0953-816X; 1460-9568; 1460-9568
• DOI: 10.1111/ejn.70149

ABSTRACT The analysis of neural power spectra plays a crucial role in understanding brain function and dysfunction. While recent efforts have led to the development of methods for decomposing spectral data, challenges remain in performing statistical analysis and group‐level comparisons. Here, we introduce Bayesian spectral decomposition (BSD), a Bayesian framework for analysing neural spectral power. BSD allows for the specification, inversion, comparison and analysis of parametric models of neural spectra, addressing limitations of existing methods. We first establish the face validity of BSD on simulated data and show how it outperforms an established method [fit oscillations and one‐over‐f (FOOOF)] for peak detection on artificial spectral data. We then demonstrate the efficacy of BSD on a group‐level study of electroencephalography (EEG) spectra in 204 healthy subjects from the LEMON dataset. Our results not only highlight the effectiveness of BSD in model selection and parameter estimation but also illustrate how BSD enables straightforward group‐level regression of the effect of continuous covariates such as age. By using Bayesian inference techniques, BSD provides a robust framework for studying neural spectral data and their relationship to brain function and dysfunction. We introduce Bayesian spectral decomposition (BSD): By performing Bayesian inversion of parametric models of neural spectral power, BSD enables straightforward group‐level analysis of spectral content and robust detection of spectral peaks. We demonstrate how BSD reveals the ageing effect on the spectral content of resting state occipital EEG signals.