Model selection for spectral parameterization.

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.

Trends in Dietary Vitamin A Intake Among US Adults by Race and Ethnicity, 2003-2018.

This study uses data from the 2003-2004 to 2017-2018 National Health and Nutrition Examination Surveys (NHANES) to assess whether a difference exists in dietary vitamin A intake as a marker of consumption of vitamin A­rich foods among Black, Hispanic, and White adults in the US.