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Related Concept Videos

Attention-Deficit/Hyperactivity Disorder01:30

Attention-Deficit/Hyperactivity Disorder

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.
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Drug response models describe how pharmacological agents interact with biological systems to produce measurable effects. Baseline responses are inherent physiological activities without a drug significantly influencing the observed pharmacological outcomes. Depending on the drug response model employed, these baseline responses may combine with the drug's effect in either an additive or proportional manner.Additive Drug Response ModelIn the additive model, the drug effect is independent of the...

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Related Experiment Video

Updated: Jul 15, 2026

Event Related Potentials (ERPs) and other EEG Based Methods for Extracting Biomarkers of Brain Dysfunction: Examples from Pediatric Attention Deficit/Hyperactivity Disorder (ADHD)
10:02

Event Related Potentials (ERPs) and other EEG Based Methods for Extracting Biomarkers of Brain Dysfunction: Examples from Pediatric Attention Deficit/Hyperactivity Disorder (ADHD)

Published on: March 12, 2020

A Random Forest-Based Risk Prediction Model for Non-Response to Methylphenidate in Children with Attention Deficit

Yuan Lei1, Fang Li1, Linyan Xiao2

  • 1Department of Pediatrics, The Fourth Hospital of Changsha (Integrated Traditional Chinese and Western Medicine Hospital of Changsha, Changsha Hospital of Hunan Normal University), Changsha, Hunan, 410219, People's Republic of China.

Neuropsychiatric Disease and Treatment
|July 14, 2026
PubMed
Summary

A random forest model accurately predicts non-response to methylphenidate (MPH) in children with attention deficit hyperactivity disorder (ADHD). This tool aids in personalized treatment decisions for ADHD, identifying patients unlikely to benefit from MPH.

Keywords:
attention deficit hyperactivity disordermethylphenidateprediction modelrandom foresttreatment response

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Last Updated: Jul 15, 2026

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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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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The Adventures of Fundi Intervention Based on the Cognitive and Emotional Processing in Attention Deficit Hyperactive Disorder Patients

Published on: June 12, 2020

Area of Science:

  • Neuroscience
  • Pediatrics
  • Pharmacology

Background:

  • Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder in children.
  • Methylphenidate (MPH) is a first-line pharmacological treatment for ADHD.
  • Predicting individual treatment response to MPH is crucial for optimizing clinical management.

Purpose of the Study:

  • To develop and validate a prediction model for MPH non-response in pediatric ADHD.
  • To identify clinical and biological predictors of MPH treatment outcomes.
  • To provide decision support for individualized ADHD therapy.

Main Methods:

  • A prospective cohort of 150 children with ADHD receiving MPH was enrolled.
  • Patients were classified as responders or non-responders based on SNAP-IV scores after 3 months.
  • A random forest algorithm was employed to build the prediction model, evaluating feature importance.

Main Results:

  • Lower serum 25-hydroxyvitamin D [25(OH)D], cortisol, S100β protein, BDNF, and urinary catecholamines were observed in non-responders.
  • Combined ADHD subtype, higher SNAP-IV scores, lower 25(OH)D, and lower dopamine levels were independent risk factors for non-response.
  • The random forest model achieved high predictive performance (AUC=0.883) with 86.67% accuracy.

Conclusions:

  • A random forest-based prediction model effectively identifies children with ADHD unlikely to respond to MPH.
  • The model integrates clinical and laboratory data for accurate prediction of MPH treatment outcomes.
  • This approach supports personalized medicine strategies in pediatric ADHD management.