<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Patterson Gentile C</submitter><funding>NEI NIH HHS</funding><pagination>1</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC8024780</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>10(4)</volume><pubmed_abstract>&lt;h4>Purpose&lt;/h4>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.&lt;h4>Methods&lt;/h4>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. W</pubmed_abstract><journal>Translational vision science &amp; technology</journal><pubmed_title>Developmental Effects on Pattern Visual Evoked Potentials Characterized by Principal Component Analysis.</pubmed_title><pmcid>PMC8024780</pmcid><funding_grant_id>P30 EY001583</funding_grant_id><pubmed_authors>Arbogast KB</pubmed_authors><pubmed_authors>Master C</pubmed_authors><pubmed_authors>Joshi NR</pubmed_authors><pubmed_authors>Ciuffreda KJ</pubmed_authors><pubmed_authors>Aguirre GK</pubmed_authors><pubmed_authors>Patterson Gentile C</pubmed_authors></additional><is_claimable>false</is_claimable><name>Developmental Effects on Pattern Visual Evoked Potentials Characterized by Principal Component Analysis.</name><description>&lt;h4>Purpose&lt;/h4>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.&lt;h4>Methods&lt;/h4>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. W</description><dates><release>2021-01-01T00:00:00Z</release><publication>2021 Apr</publication><modification>2025-04-26T08:20:41.778Z</modification><creation>2025-04-06T12:38:03.674Z</creation></dates><accession>S-EPMC8024780</accession><cross_references><pubmed>34003980</pubmed><doi>10.1167/tvst.10.4.1</doi></cross_references></HashMap>