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Related Concept Videos

Attention-Deficit/Hyperactivity Disorder01:30

Attention-Deficit/Hyperactivity Disorder

289
Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by persistent inattention, hyperactivity, and impulsivity. It affects approximately 5-8% of children globally, with around 60-70% of cases persisting into adulthood. ADHD has significant implications for educational attainment, social interactions, and occupational success.
Diagnostic Criteria and Symptoms
To diagnose ADHD, symptoms must manifest before age 12 and be evident across multiple settings....
289

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Related Experiment Video

Updated: Sep 9, 2025

Event Related Potentials ERPs and other EEG Based Methods for Extracting Biomarkers of Brain Dysfunction: Examples from Pediatric Attention Deficit/Hyperactivity Disorder ADHD
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Visual processing oscillates differently through time for adults with ADHD.

Pénélope Pelland-Goulet1,2,3,4,5, Martin Arguin1,2, Hélène Brisebois5,6

  • 1Psychology Department, Université de Montréal, Montréal, Québec, Canada.

Plos One
|September 2, 2025
PubMed
Summary
This summary is machine-generated.

Attention-Deficit/Hyperactivity Disorder (ADHD) impacts visual processing temporal features. Machine learning accurately distinguished ADHD individuals and medication use based on these unique visual performance markers.

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Area of Science:

  • Neuroscience
  • Cognitive Psychology
  • Psychiatry

Background:

  • Attention-Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder impacting ~3-4% of Canadian adults and 2.6% globally.
  • ADHD symptoms include inattention, hyperactivity, and impulsivity, affecting cognitive processes, yet its impact on perceptual oscillations remains understudied.

Purpose of the Study:

  • To investigate the temporal features of visual processing in adults with ADHD compared to neurotypical individuals.
  • To determine if temporal sampling techniques can reveal distinct visual processing patterns associated with ADHD.

Main Methods:

  • A visual word recognition task was employed using a temporal sampling technique to generate classification images.
  • These images reflected visual processing effectiveness based on stimulus temporal properties.
  • A machine learning algorithm was utilized to classify participants based on extracted temporal features.

Main Results:

  • Machine learning achieved 91.8% accuracy in classifying individuals with ADHD versus neurotypical controls using limited temporal features.
  • A high accuracy of 91.3% was achieved in classifying ADHD individuals based on psychostimulant medication usage.
  • Significant differences in temporal features of visual processing were observed between the groups.

Conclusions:

  • The study identified distinct behavioral markers in visual performance linked to ADHD, detectable through random temporal sampling.
  • Regular psychostimulant medication use in ADHD individuals also presents a discernible marker in visual processing.
  • These findings suggest that temporal sampling is a promising method for uncovering ADHD-related visual performance differences.