Wilson Luc E, Castanheira Jason da Silva, Kinder Benjamin Lévesque, Baillet Sylvain
Montreal Neurological Institute, McGill University, Montreal QC, Canada.
bioRxiv. 2024 Aug 6:2024.08.01.606216. doi: 10.1101/2024.08.01.606216.
Neurophysiological brain activity comprises rhythmic (periodic) and arrhythmic (aperiodic) signal elements, which are increasingly studied in relation to behavioral traits and clinical symptoms. Current methods for spectral parameterization of neural recordings rely on user-dependent parameter selection, which challenges the replicability and robustness of findings. Here, we introduce a principled approach to model selection, relying on Bayesian information criterion, for static and time-resolved spectral parameterization of neurophysiological data. We present extensive tests of the approach with ground-truth and empirical magnetoencephalography recordings. Data-driven model selection enhances both the specificity and sensitivity of spectral and spectrogram decompositions, even in non-stationary contexts. Overall, the proposed spectral decomposition with data-driven model selection minimizes the reliance on user expertise and subjective choices, enabling more robust, reproducible, and interpretable research findings.
神经生理学大脑活动包括节律性（周期性）和无节律性（非周期性）信号成分，目前人们越来越多地研究这些成分与行为特征和临床症状的关系。当前用于神经记录频谱参数化的方法依赖于用户依赖的参数选择，这对研究结果的可重复性和稳健性提出了挑战。在这里，我们引入一种基于贝叶斯信息准则的模型选择原则方法，用于神经生理学数据的静态和时间分辨频谱参数化。我们用真实数据和经验性脑磁图记录对该方法进行了广泛测试。数据驱动的模型选择提高了频谱和频谱图分解的特异性和敏感性，即使在非平稳情况下也是如此。总体而言，所提出的数据驱动模型选择的频谱分解最大限度地减少了对用户专业知识和主观选择的依赖，从而能够获得更稳健、可重复和可解释的研究结果。
