Developmental Effects on Pattern Visual Evoked Potentials Characterized by Principal Component Analysis.

Abstract

PurposePeak amplitude and peak latency in the pattern reversal visual evoked potential (prVEP) vary with maturation. We considered that principal component analysis (PCA) may be used to describe age-related variation over the entire prVEP time course and provide a means of modeling and removing variation due to developmental age.MethodsPrVEP was recorded from 155 healthy subjects ages 11 to 19 years at two time points. We created a model of the prVEP by identifying principal components (PCs) that explained >95% of the variance in a “training” dataset of 40 subjects. We examined the ability of the PCs to explain variance in an age- and sex-matched “validation” dataset (n = 40) and calculated the intrasubject reliability of the PC coefficients between the two time points. We explored the effect of subject age and sex upon the PC coefficients.ResultsSeven PCs accounted for 96.0% of the variability of the training dataset and 90.5% of the variability in the validation dataset with good within-subject reliability across time points (R > 0.7 for all PCs). The PCA model revealed narrowing and amplitude reduction of the P100 peak with maturation, and a broader and smaller P100 peak in male subjects compared to female subjects.ConclusionsPCA is a generalizable, reliable, and unbiased method of analyzing prVEP. The PCA model revealed changes across maturation and biological sex not fully described by standard peak analysis.Translational RelevanceWe describe a novel application of PCA to characterize developmental changes of prVEP in youths that can be used to compare healthy and pathologic pediatric cohorts.