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

Purpose: Peak 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.
Methods: PrVEP 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.
Results: Seven 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.
Conclusions: PCA 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 Relevance: We 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.