Spectral quantitative and semi-quantitative EEG provide complementary information on the life-long effects of early childhood malnutrition on cognitive decline

Objective This study compares the complementary information from semi-quantitative EEG (sqEEG) and spectral quantitative (spectral-qEEG) to detect life-long effects of early childhood malnutrition on brain. Methods Resting-state EEGs ( N = 202) Barbados Nutrition Study (BNS) were used examine protein-energy (PEM) middle adulthood outcomes. sqEEG analysis was performed Grand Total (GTE) protocol, a single latent variable, Neurophysiological State (sqNPS) extracted. A univariate linear mixed-effects (LME) model tested dependence sqNPS nutritional group. compared with scores Montreal Cognitive Assessment (MoCA). Stable sparse classifiers (SSC) also measured predictive power sqEEG, spectral-qEEG, combination both. Multivariate LME applied assess each modality separately combined under longitudinal settings. Results The showed highly significant differences between previously malnourished control groups p < 0.001); age 0.01) significant, no interaction group detected. Childhood 0.02) 0.003) predicted MoCA in adulthood. SSC demonstrated that spectral-qEEG had highest (mean AUC 0.92 ± 0.005). Finally, multivariate spectral-qEEG+sqEEG models log-likelihood (−479.7). Conclusion research has extended our prior work long-term impact Our findings significantly linked accelerated cognitive aging at 45–51 years age. While produced comparable results, indicated combining yielded better performance than either method alone, suggesting multimodal approach could be advantageous for future investigations. Significance Based findings, utilizing GTE valuable diagnostic tool detecting lasting impacts malnutrition. Notably, not been explored or reported as biomarker assessing Furthermore, observations suggest offers unique features captured by lead its improvement.