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

Carlyn Patterson Gentile1, Nabin R Joshi2, Kenneth J Ciuffreda2

  • 1Children's Hospital of Philadelphia, Philadelphia, PA, USA.

Translational Vision Science & Technology
|May 18, 2021
PubMed
Summary
This summary is machine-generated.

Principal component analysis (PCA) effectively models developmental changes in pattern reversal visual evoked potentials (prVEP) in adolescents. This reliable method reveals age and sex differences in prVEP not apparent with standard peak analysis.

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Area of Science:

  • Neuroscience
  • Ophthalmology
  • Biostatistics

Background:

  • Visual evoked potentials, specifically pattern reversal visual evoked potential (prVEP), are influenced by maturation.
  • Standard analysis of prVEP focuses on peak amplitude and latency, potentially overlooking complex developmental variations.

Purpose of the Study:

  • To apply principal component analysis (PCA) to model age-related variations in the entire prVEP time course.
  • To develop a method for analyzing and potentially removing developmental variations in prVEP data.
  • To investigate the utility of PCA for characterizing developmental changes in pediatric cohorts.

Main Methods:

  • PCA was used to model prVEP data from 155 healthy adolescents (ages 11-19) across two time points.
  • A model was created using principal components (PCs) explaining >95% of variance in a training dataset (n=40).
  • The model's generalizability, reliability, and the influence of age and sex on PC coefficients were assessed using a validation dataset (n=40).

Main Results:

  • Seven PCs explained 96.0% of training data variance and 90.5% of validation data variance, demonstrating good reliability (R > 0.7).
  • PCA revealed maturational changes including a narrowing and amplitude reduction of the P100 peak.
  • The analysis identified differences between sexes, with male subjects exhibiting broader and smaller P100 peaks compared to females.

Conclusions:

  • PCA offers a generalizable, reliable, and unbiased approach for analyzing prVEP data.
  • The PCA model captures developmental and sex-related prVEP changes more comprehensively than traditional peak analysis.
  • This novel PCA application can aid in comparing healthy and pathological pediatric prVEP cohorts.