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

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.

You might also read

Related Articles

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

Sort by
Same author

BundleWarp: Enhancing white matter tractometry and morphometry with precise neuronal mapping using streamline-based nonlinear registration.

Medical image analysis·2026
Same author

Surface-based tractography uncovers 'what' and 'where' pathways in prefrontal cortex.

Cortex; a journal devoted to the study of the nervous system and behavior·2026
Same author

Brain dissection photogrammetry: a tool for studying human white matter connections integrating ex vivo and in vivo multimodal datasets.

Nature communications·2025
Same author

Integrating direct electrical stimulation with brain connectivity predicts lesion-induced language impairment and recovery.

Communications medicine·2025
Same author

Anatomical insights into the superior longitudinal system from integrative in- vivo and ex-vivo mapping.

Communications biology·2025
Same author

Supervised white matter bundle segmentation in glioma patients with transfer learning.

Medical image analysis·2025
Same journal

Treadmill exercise rescues motor deficits in parkinsonian mice by modulating striatal D2-MSN activity: evidence from calcium imaging and chemogenetics.

Frontiers in systems neuroscience·2026
Same journal

Transfer learning for EEG-based BCIs: a comparative evaluation and optimization of data alignment methods.

Frontiers in systems neuroscience·2026
Same journal

The volatile anesthetic isoflurane causes global suppression of neuronal activity, disrupting hub neuron function in <i>Caenorhabditis elegans</i>.

Frontiers in systems neuroscience·2026
Same journal

Associative emotional memory encoding: insights from network stability analysis of an fMRI-driven bilinear dynamics.

Frontiers in systems neuroscience·2026
Same journal

The neurobiological basis of the awe experience in affective disorders: an exploratory EEG study.

Frontiers in systems neuroscience·2026
Same journal

Exploring the spiking neural autoencoder: from hyperexcitability to noise-driven compensation.

Frontiers in systems neuroscience·2026
See all related articles

Related Experiment Video

Updated: May 17, 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

ADHD diagnosis from multiple data sources with batch effects.

Emanuele Olivetti1, Susanne Greiner, Paolo Avesani

  • 1NeuroInformatics Laboratory, Bruno Kessler Foundation Trento, Italy ; Center for Mind and Brain Sciences, University of Trento Trento, Italy.

Frontiers in Systems Neuroscience
|October 13, 2012
PubMed
Summary
This summary is machine-generated.

Researchers addressed batch effects in Attention Deficit Hyperactivity Disorder (ADHD) neuroimaging data. Eliminating these biases significantly altered classification accuracy, necessitating revised conclusions for ADHD diagnosis.

Keywords:
ADHDBayesian hypothesis testingbatch effectdissimilarity spacemultivariate pattern classification

More Related Videos

The Adventures of Fundi Intervention Based on the Cognitive and Emotional Processing in Attention Deficit Hyperactive Disorder Patients
05:48

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

Published on: June 12, 2020

Using Brain Activation (nir-HEG/Q-EEG) and Execution Measures (CPTs) in a ADHD Assessment Protocol
13:09

Using Brain Activation (nir-HEG/Q-EEG) and Execution Measures (CPTs) in a ADHD Assessment Protocol

Published on: April 1, 2018

Related Experiment Videos

Last Updated: May 17, 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

The Adventures of Fundi Intervention Based on the Cognitive and Emotional Processing in Attention Deficit Hyperactive Disorder Patients
05:48

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

Published on: June 12, 2020

Using Brain Activation (nir-HEG/Q-EEG) and Execution Measures (CPTs) in a ADHD Assessment Protocol
13:09

Using Brain Activation (nir-HEG/Q-EEG) and Execution Measures (CPTs) in a ADHD Assessment Protocol

Published on: April 1, 2018

Area of Science:

  • Neuroscience
  • Medical Imaging
  • Computational Psychiatry

Background:

  • Attention Deficit Hyperactivity Disorder (ADHD) lacks a clear pathophysiological model and objective diagnostic biomarkers.
  • The ADHD-200 Consortium dataset offers rich, heterogeneous neuroimaging and phenotypic data for studying ADHD neural correlates.
  • Automated diagnosis systems for ADHD are crucial due to the disorder's prevalence and social costs.

Purpose of the Study:

  • To investigate the impact of data heterogeneity and batch effects on ADHD classification accuracy.
  • To propose and validate methods for mitigating batch effects in neuroimaging datasets.
  • To explore effective data representation and evaluation strategies for heterogeneous ADHD data.

Main Methods:

  • Utilized the ADHD-200 Consortium dataset comprising structural MRI and resting-state fMRI data from ~1000 participants.
  • Developed and applied a novel method to eliminate batch effects arising from multi-center data collection.
  • Employed dissimilarity representation and a test of independence for robust classification and evaluation on imbalanced data.

Main Results:

  • Eliminating batch effects caused a substantial decrease in classification accuracy, challenging previous findings.
  • The proposed methods demonstrated effective handling of data heterogeneity and biases.
  • The dissimilarity representation proved useful for creating effective feature spaces.

Conclusions:

  • Standard analyses of the ADHD-200 dataset may be confounded by batch effects, requiring re-evaluation of prior conclusions.
  • Accurate ADHD diagnosis necessitates careful consideration and correction of data heterogeneity.
  • Advanced methods for data representation and evaluation are vital for reliable automated ADHD diagnosis.