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Attention-Deficit/Hyperactivity Disorder01:30

Attention-Deficit/Hyperactivity Disorder

131
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....
131

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ADHD diagnosis using structural brain MRI and personal characteristic data with machine learning framework.

Dhruv Chandra Lohani1, Bharti Rana1

  • 1Department of Computer Science, University of Delhi, Delhi, India.

Psychiatry Research. Neuroimaging
|August 3, 2023
PubMed
Summary

Automated diagnosis of attention-deficit/hyperactivity disorder (ADHD) using structural MRI and personal characteristics achieved 75% accuracy. This approach aids in objective ADHD classification by analyzing brain structure and individual data.

Keywords:
Atlas-based feature extractionFeature selectionK-nearest neighboursLogistic regressionRandom forestSupport vector machine

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Area of Science:

  • Neuroimaging
  • Computational Psychiatry
  • Machine Learning

Background:

  • Automatic diagnosis of attention-deficit/hyperactivity disorder (ADHD) remains a significant challenge, necessitating objective and non-invasive methods.
  • Structural magnetic resonance imaging (sMRI) offers insights into brain morphology, potentially aiding in ADHD classification.
  • Integrating personal characteristics (PC) with neuroimaging data may enhance diagnostic accuracy.

Purpose of the Study:

  • To develop and evaluate an automated diagnostic system for ADHD classification using structural MRI and PC data.
  • To identify salient neuroimaging and personal characteristic features for distinguishing ADHD from typically developing children (TDC).
  • To compare the performance of various machine learning classifiers for ADHD diagnosis.

Main Methods:

  • An age-balanced dataset of 316 ADHD and 316 TDC individuals was utilized.
  • Volumetric gray matter (GM) features (AAL3 atlas) and cortical thickness (CT) features (Destrieux atlas) were extracted from sMRI scans.
  • Feature selection was performed using minimum redundancy maximum relevance (mRMR) and ensemble feature selection (EFS).
  • Five classifiers (k-NN, logistic regression, linear SVM, RBSVM, Random Forest) were trained and evaluated using a 10-fold cross-validation scheme.

Main Results:

  • The highest classification accuracy of 75% was achieved using CT and PC features with radial-based SVM (RBSVM) and linear SVM classifiers, combined with EFS.
  • Analysis revealed increased GM volume in 15 brain regions and decreased cortical thickness in 27 brain regions in ADHD individuals compared to TDC.
  • Different feature combinations were explored across seven experimental setups.

Conclusions:

  • Automated ADHD classification is feasible using structural MRI and personal characteristic data.
  • Cortical thickness and personal characteristics, analyzed with SVM variants and EFS, show promise for ADHD diagnosis.
  • Observed structural brain differences (GM volume increase, CT decrease) provide potential biomarkers for ADHD.