AUTHOR=Bringas Vega Maria L. , Guo Yanbo , Tang Qin , Razzaq Fuleah A. , Calzada Reyes Ana , Ren Peng , Paz Linares Deirel , Galan Garcia Lidice , Rabinowitz Arielle G. , Galler Janina R. , Bosch-Bayard Jorge , Valdes Sosa Pedro A. 

TITLE=An Age-Adjusted EEG Source Classifier Accurately Detects School-Aged Barbadian Children That Had Protein Energy Malnutrition in the First Year of Life

JOURNAL=Frontiers in Neuroscience

VOLUME=Volume 13 - 2019

YEAR=2019

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2019.01222

DOI=10.3389/fnins.2019.01222

ISSN=1662-453X

ABSTRACT=We provide an electroencephalographic (EEG) based statistical classifier that correctly identifies children with histories of Protein Energy Malnutrition (PEM) in the first year of life and distinguished them from controls with 0.82% accuracy (area under the ROC curve).  Our previous result in the same participants achieved equivalent accuracy but was based on scalp quantitative EEG features which precluded anatomical interpretation. In contrast, we now employ BC-VARETA, a novel high-resolution EEG source imaging method with minimal leakage and maximal sparseness. This allowed us to identify, to our knowledge for the first time, a classifier in the source space. The EEGs was recorded in 1978 in 108 children who were 5 to 11 years and were participants in the 45+ year longitudinal Barbados Nutrition Study.  The PEM cohort experienced moderate-severe protein-energy malnutrition limited to the first year of life; healthy controls were classmates who were matched by age, gender and handedness. In the current study, we employed a machine learning approach based on the elastic net to create a stable sparse classifier. Interestingly, the classifier was driven predominantly by nutrition group differences in alpha activity in the lingual gyrus. This structure is part of the pathway associated with generating alpha rhythm generation which increases with normal maturation. The PEM group showed a significant decrease in alpha activity, suggesting a developmental delay in brain maturation. Childhood malnutrition is still a serious worldwide public health problem. Its consequences are particularly severe when present during early development- deficits during this critical period are permanent and predict impaired cognitive and behavioral functioning over the lifespan. Our EEG source classifier may provide a functionally interpretable diagnostic technology to study the effects on the brain of malnutrition, with wide applicability in low resources settings.