학술논문
Spectral quantitative and semi-quantitative EEG provide complementary information on the life-long effects of early childhood malnutrition on cognitive decline
Document Type
article
Author
Fuleah A. Razzaq; Ana Calzada-Reyes; Qin Tang; Yanbo Guo; Arielle G. Rabinowitz; Jorge Bosch-Bayard; Lidice Galan-Garcia; Trinidad Virues-Alba; Carlos Suarez-Murias; Ileana Miranda; Usama Riaz; Vivian Bernardo Lagomasino; Cyralene Bryce; Simon G. Anderson; Janina R. Galler; Maria L. Bringas-Vega; Pedro A. Valdes-Sosa
Source
Frontiers in Neuroscience, Vol 17 (2023)
Subject
Language
English
ISSN
1662-453X
Abstract
ObjectiveThis study compares the complementary information from semi-quantitative EEG (sqEEG) and spectral quantitative EEG (spectral-qEEG) to detect the life-long effects of early childhood malnutrition on the brain.MethodsResting-state EEGs (N = 202) from the Barbados Nutrition Study (BNS) were used to examine the effects of protein-energy malnutrition (PEM) on childhood and middle adulthood outcomes. sqEEG analysis was performed on Grand Total EEG (GTE) protocol, and a single latent variable, the semi-quantitative Neurophysiological State (sqNPS) was extracted. A univariate linear mixed-effects (LME) model tested the dependence of sqNPS and nutritional group. sqEEG was compared with scores on the Montreal Cognitive Assessment (MoCA). Stable sparse classifiers (SSC) also measured the predictive power of sqEEG, spectral-qEEG, and a combination of both. Multivariate LME was applied to assess each EEG modality separately and combined under longitudinal settings.ResultsThe univariate LME showed highly significant differences between previously malnourished and control groups (p