Work

BSD: A Bayesian Framework for Parametric Models of Neural Spectra

Year: 2025

Type: article

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.

Source: European Journal of Neuroscience

Authors: Johan Medrano, Nicholas A. Alexander, Robert A. Seymour, Peter Zeidman

Institutions: UCL Queen Square Institute of Neurology, University College London

Language: English

Topic: Neural dynamics and brain function

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

Open Access status: hybrid

Estimated APC paid: $3,560

Funders: Wellcome Trust, Medical Research Council

Awards: Wellcome Centre for Human Neuroimaging, Wellcome Centre for Human Neuroimaging, G5882669802, G5939908983, Characterising the neural mechanisms of human memory at high resolution