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bioRxiv · 10.1101/2020.12.11.420158

Developmental effects on pattern visual evoked potentials characterized by principal component analysis

Patterson Gentile, C.·Joshi, N. R.·Ciuffreda, K.·Arbogast, K.·Master, C.·Aguirre, G.
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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 11-19 years at two timepoints. 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 intra-subject reliability of the PC coefficients between the two timepoints. 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 timepoints (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 males compared to females. 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 youth that can be used to compare healthy and pathologic pediatric cohorts.

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Patterson Gentile, C., Joshi, N. R., Ciuffreda, K., Arbogast, K., Master, C., Aguirre, G.. 2020-12-11. Developmental effects on pattern visual evoked potentials characterized by principal component analysis. https://doi.org/10.1101/2020.12.11.420158

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