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Related Concept Videos

Attention-Deficit/Hyperactivity Disorder01:30

Attention-Deficit/Hyperactivity Disorder

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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.
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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.
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Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
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Related Experiment Video

Updated: Jun 3, 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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Functional imaging derived ADHD biotypes based on deep clustering: a study on personalized medication therapy

Aichen Feng1,2, Dongmei Zhi3, Yuan Feng4

  • 1Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.

Eclinicalmedicine
|January 7, 2025
PubMed
Summary

Researchers identified two distinct subtypes of Attention Deficit Hyperactivity Disorder (ADHD) using neuroimaging and clinical data. One subtype showed better recovery with methylphenidate compared to atomoxetine, suggesting personalized ADHD treatment approaches.

Keywords:
Adolescent brain and cognitive development (ABCD) studyAttention deficit hyperactivity disorder (ADHD)Biological subtype detectionDeep clusteringGraph convolutional network (GCN)

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Area of Science:

  • Neuroscience
  • Computer Science
  • Psychiatry

Background:

  • Attention Deficit Hyperactivity Disorder (ADHD) is a common neurodevelopmental disorder with no clear link between subtypes and medication effectiveness.
  • Objective neuroimaging markers are needed for categorizing ADHD biotypes to enable personalized treatment strategies.

Purpose of the Study:

  • To develop and validate a deep learning model for identifying ADHD biotypes using functional network connectivity and phenotypic data.
  • To explore potential differences in treatment response between identified ADHD biotypes.

Main Methods:

  • A graph convolution network for biological subtype detection (GCN-BSD) was proposed, integrating functional network connectivity (FNC) and non-imaging phenotypic data.
  • The GCN-BSD model was applied to a large ADHD cohort from the Adolescent Brain and Cognitive Development (ABCD) study for discovery and validated on an independent dataset with longitudinal medication data.

Main Results:

  • Two ADHD biotypes were identified in 1069 ADHD patients from the ABCD study and validated in 130 ADHD adolescents.
  • Biotype 1 exhibited significantly better recovery in psychosomatic symptoms (p < 0.05) when treated with methylphenidate versus atomoxetine, after adjusting for baseline scores.
  • Differences in cognitive performance and hyperactivity/impulsivity symptoms were also observed between the biotypes.

Conclusions:

  • Imaging-driven, biotype-guided approaches show promise for personalized ADHD treatment.
  • Deep learning algorithms can help delineate ADHD biotypes, potentially improving medication effectiveness and guiding treatment selection.