Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Photoreceptors and Visual Pathways01:22

Photoreceptors and Visual Pathways

5.6K
At the molecular level, visual signals trigger transformations in photopigment molecules, resulting in changes in the photoreceptor cell's membrane potential. The photon's energy level is denoted by its wavelength, with each specific wavelength of visible light associated with a distinct color. The spectral range of visible light, classified as electromagnetic radiation, spans from 380 to 720 nm. Electromagnetic radiation wavelengths exceeding 720 nm fall under the infrared category,...
5.6K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Networks of respiratory-muscular coupling in exercise and fatigue in young adults.

Physiological reports·2026
Same author

The vertical symphony: how pitch perception shapes spatial and affective mapping across different countries.

Cognitive processing·2026
Same author

Multimodal Detection of Pain and Anticipation Anxiety from Ultra-Short Duration Wearable Sensors Measurements.

Sensors (Basel, Switzerland)·2026
Same author

Technical note: a functional data analysis approach to analyze the light-adapted electroretinogram in children and adolescents.

Documenta ophthalmologica. Advances in ophthalmology·2026
Same author

Electroretinographic evaluation of single vision contact lenses with non-refractive opaque features.

Clinical & experimental optometry·2026
Same author

ERG-Graph: Graph Signal Processing of the Electroretinogram for Classification of Neurodevelopmental Disorders.

Bioengineering (Basel, Switzerland)·2026

Related Experiment Video

Updated: May 31, 2025

Electroretinogram Recording for Infants and Children under Anesthesia to Achieve Optimal Dark Adaptation and International Standards
08:38

Electroretinogram Recording for Infants and Children under Anesthesia to Achieve Optimal Dark Adaptation and International Standards

Published on: September 3, 2020

6.0K

Spectral Analysis of Light-Adapted Electroretinograms in Neurodevelopmental Disorders: Classification with Machine

Paul A Constable1, Javier O Pinzon-Arenas2, Luis Roberto Mercado Diaz2

  • 1Caring Futures Institute, College of Nursing and Health Sciences, Flinders University, Adelaide 5000, SA, Australia.

Bioengineering (Basel, Switzerland)
|January 24, 2025
PubMed
Summary

Electroretinograms reveal distinct patterns in autism spectrum disorder (ASD) and attention deficit/hyperactivity disorder (ADHD). Machine learning models show promise for classifying these conditions, particularly in two-group distinctions.

Keywords:
attention deficit hyperactivity disorderautismbiomarkerfeature selectionmedicationretinasex

More Related Videos

Using the Electroretinogram to Assess Function in the Rodent Retina and the Protective Effects of Remote Limb Ischemic Preconditioning
06:34

Using the Electroretinogram to Assess Function in the Rodent Retina and the Protective Effects of Remote Limb Ischemic Preconditioning

Published on: June 9, 2015

16.1K
Simultaneous Recording of Electroretinography and Visual Evoked Potentials in Anesthetized Rats
10:30

Simultaneous Recording of Electroretinography and Visual Evoked Potentials in Anesthetized Rats

Published on: July 1, 2016

12.3K

Related Experiment Videos

Last Updated: May 31, 2025

Electroretinogram Recording for Infants and Children under Anesthesia to Achieve Optimal Dark Adaptation and International Standards
08:38

Electroretinogram Recording for Infants and Children under Anesthesia to Achieve Optimal Dark Adaptation and International Standards

Published on: September 3, 2020

6.0K
Using the Electroretinogram to Assess Function in the Rodent Retina and the Protective Effects of Remote Limb Ischemic Preconditioning
06:34

Using the Electroretinogram to Assess Function in the Rodent Retina and the Protective Effects of Remote Limb Ischemic Preconditioning

Published on: June 9, 2015

16.1K
Simultaneous Recording of Electroretinography and Visual Evoked Potentials in Anesthetized Rats
10:30

Simultaneous Recording of Electroretinography and Visual Evoked Potentials in Anesthetized Rats

Published on: July 1, 2016

12.3K

Area of Science:

  • Neuroscience
  • Ophthalmology
  • Computational Psychiatry

Background:

  • Electroretinograms (ERGs) demonstrate observable differences between neurotypical individuals and those with autism spectrum disorder (ASD) or attention deficit/hyperactivity disorder (ADHD).
  • Previous research indicates potential for ERGs in differentiating these neurological conditions.

Purpose of the Study:

  • To investigate the efficacy of machine learning (ML) and feature selection techniques in classifying individuals with ASD and ADHD using ERG data.
  • To determine the accuracy of ML models in distinguishing between ASD, ADHD, ASD + ADHD, and control groups.

Main Methods:

  • Collected ERG data from four groups: ASD (n=77), ADHD (n=43), ASD + ADHD (n=21), and controls (n=137).
  • Applied standard time-domain and signal analysis features.
  • Evaluated various machine learning models for classification tasks.

Main Results:

  • Achieved a balanced accuracy (BA) of 0.87 for classifying ASD in male participants.
  • Achieved a BA of 0.84 for classifying ADHD in female participants.
  • Classification accuracy decreased significantly when including more groups: 0.70 for a three-group model (ASD, ADHD, control) and 0.53 for a four-group model (ASD, ADHD, ASD + ADHD, control).

Conclusions:

  • ERG data, when analyzed with machine learning, shows potential for a broad two-group classification of ASD or ADHD.
  • Model performance is influenced by participant sex and is limited when attempting to classify multiple distinct diagnostic groups simultaneously.