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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.
Diagnostic Criteria and Symptoms
To diagnose ADHD, symptoms must manifest before age 12 and be evident across multiple settings....
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Types of Hypothesis Testing01:11

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There are three types of hypothesis tests: right-tailed, left-tailed, and two-tailed.
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The actual hypothesis testing begins by considering two hypotheses. They are termed  the null hypothesis and the alternative hypothesis. These hypotheses contain opposing viewpoints.
The null hypothesis, denoted by H0 is a statement of no difference between the variables—they are not related. This can often be considered the status quo. As  a result if you cannot accept the null, it requires some action.
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Related Experiment Video

Updated: Jul 17, 2025

Using Brain Activation nir-HEG/Q-EEG and Execution Measures CPTs in a ADHD Assessment Protocol
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Subtype classification of attention deficit hyperactivity disorder with hierarchical binary hypothesis testing

Yuan Gao1, Huaqing Ni1, Ying Chen2

  • 1College of Information Science and Engineering, Hohai University, Nanjing, People's Republic of China.

Journal of Neural Engineering
|August 30, 2023
PubMed
Summary

This study introduces a new framework for diagnosing attention deficit hyperactivity disorder (ADHD) subtypes using brain connectivity, achieving over 97% accuracy. This method identifies key brain connections for improved ADHD diagnosis and treatment.

Keywords:
ADHD subtypesbinary hypothesis testingbrain functional connectivitydeep learningmulticlass classification

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Area of Science:

  • Neuroscience
  • Medical Imaging
  • Machine Learning

Background:

  • Accurate diagnosis of attention deficit hyperactivity disorder (ADHD) subtypes is crucial for effective treatment in children.
  • Current machine learning methods using MRI data achieve less than 80% accuracy for ADHD subtype diagnosis.

Purpose of the Study:

  • To enhance the accuracy of ADHD subtype diagnosis.
  • To identify reliable biomarkers for ADHD subtypes.
  • To develop an effective framework for classifying ADHD subtypes.

Main Methods:

  • Proposed a hierarchical binary hypothesis testing (H-BHT) framework utilizing brain functional connectivity (FC) as input.
  • Employed a two-stage decision tree strategy for subtype classification.
  • Extracted typical FC in both stages to identify discriminative biomarkers.

Main Results:

  • Achieved an average accuracy of 97.1% and a kappa score of 0.947 on ADHD-200 resting-state fMRI datasets.
  • Identified discriminative functional connectivity patterns between ADHD subtypes.
  • Demonstrated the effectiveness of the H-BHT framework in ADHD subtype recognition.

Conclusions:

  • The H-BHT framework offers a highly accurate method for ADHD subtype classification.
  • The identified FC patterns serve as potential biomarkers for ADHD subtypes.
  • This approach provides a valuable reference for multiclass classification in mental health.