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

Modeling in Therapy01:26

Modeling in Therapy

Modeling, a key technique in therapy, uses observational learning to help clients acquire and practice new skills by watching therapists demonstrate desired behaviors. This approach, rooted in Albert Bandura's concept of vicarious learning, plays a significant role in therapeutic interventions for various psychological conditions, including social anxiety, ADHD, and depression.
Participant Modeling
Participant modeling involves therapists demonstrating calm and effective behaviors in situations...
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

Early deviations from normative brain morphology and cortical microstructure in schizophrenia spectrum disorders.

Translational psychiatry·2026
Same author

Calibration of MRI-based reference intervals to new samples.

Imaging neuroscience (Cambridge, Mass.)·2026
Same author

Response to interpreting cross-sectional comparisons of menopausal status and brain outcomes.

Psychological medicine·2026
Same author

Short Report: An Examination of Children's Autism Traits and Their Association With Family Experience.

Autism research : official journal of the International Society for Autism Research·2026
Same author

Trajectory of irritability in children and youth in Ontario, Canada, throughout the COVID-19 pandemic.

JCPP advances·2026
Same author

A systematic review and meta-analysis examining the impact of placement instability on the mental health outcomes of care experienced children and young people.

JCPP advances·2026

Related Experiment Video

Updated: May 16, 2026

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging
12:21

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

Published on: September 12, 2011

Subgrouping autism and ADHD based on structural MRI population modelling centiles.

Clara Pecci-Terroba1, Meng-Chuan Lai2,3,4,5,6, Michael V Lombardo7

  • 1Department of Psychology, University of Cambridge, Cambridge, UK.

Molecular Autism
|June 4, 2025
PubMed
Summary

Population modeling identified distinct subgroups in autism and attention deficit hyperactivity disorder (ADHD), revealing varied neuroanatomical patterns. Method selection significantly impacts subgroup identification in these neurodevelopmental conditions.

Keywords:
ADHDAutismNeuroimagingPopulation modellingStructural MRISubgrouping

More Related Videos

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 Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance
09:01

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

Published on: May 7, 2014

Related Experiment Videos

Last Updated: May 16, 2026

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging
12:21

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

Published on: September 12, 2011

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 Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance
09:01

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

Published on: May 7, 2014

Area of Science:

  • Neuroscience
  • Developmental Psychology
  • Biostatistics

Background:

  • Autism and ADHD are heterogeneous neurodevelopmental conditions with complex, variable neurobiology.
  • Existing neuroimaging studies show inconsistent results regarding characteristic neuroanatomical profiles.
  • Identifying homogeneous subgroups within or across these conditions is crucial for clinical insight.

Purpose of the Study:

  • To apply population modeling for clustering neurodevelopmental heterogeneity in autism and ADHD.
  • To compare a novel semi-supervised machine learning algorithm (HYDRA) with traditional clustering approaches.
  • To investigate neuroanatomical differences and subgroup characteristics within autism and ADHD datasets.

Main Methods:

  • Utilized a multi-site dataset with global and regional centile scores of cortical thickness, surface area, and grey matter volume.
  • Employed HYDRA, a novel semi-supervised machine learning algorithm, for population modeling and clustering.
  • Compared HYDRA's performance against a traditional clustering method.

Main Results:

  • Identified distinct subgroups within autism and ADHD, and across diagnoses, exhibiting varied neuroanatomical alterations compared to controls.
  • Subgroups were characterized by unique combinations of increased or decreased morphometric patterns.
  • No significant clinical differences were found across the identified subgroups.

Conclusions:

  • Population modeling is a valuable tool for exploring heterogeneity and subgrouping in autism and ADHD.
  • Distinct subgroups with specific neuroanatomical alteration patterns were identified.
  • Results underscore the critical influence of algorithm choice and feature selection in subgroup analysis, emphasizing the need for detailed methodological reporting.