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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
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The Use of fMRI Regional Analysis to Automatically Detect ADHD Through a 3D CNN-Based Approach.

Perihan Gülşah Gülhan1, Güzin Özmen2

  • 1Department of Electrical and Electronics Engineering, Institute of Science, Selcuk University, Konya, Turkey.

Journal of Imaging Informatics in Medicine
|July 19, 2024
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Summary

This study shows that a 3D convolutional neural network (CNN) effectively classifies attention deficit hyperactivity disorder (ADHD) using brain activity data. This deep learning approach offers a promising tool for ADHD diagnosis support.

Keywords:
3D CNNAttention deficit hyperactive disorderFunctional MRIRegional Analysis

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Attention deficit hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder impacting attention, hyperactivity, and impulsivity, typically emerging in childhood.
  • Accurate diagnosis of ADHD is crucial for timely intervention and management.
  • Current diagnostic methods can be subjective; objective biomarkers from neuroimaging are actively sought.

Purpose of the Study:

  • To develop and evaluate a novel deep learning model for ADHD classification using functional magnetic resonance imaging (fMRI) data.
  • To investigate the efficacy of a 3D convolutional neural network (CNN) architecture for analyzing spontaneous brain activity in individuals with ADHD.
  • To compare the performance of the proposed 3D CNN model against a fully connected neural network (FCNN) for ADHD diagnosis.

Main Methods:

  • Utilized the ADHD-200 database, including datasets from NeuroImage (NI), New York University (NYU), and Peking University (PU).
  • Employed fractional amplitude of low-frequency fluctuations (fALFF) and regional homogeneity (ReHo) data processed with a 3D CNN.
  • Implemented fivefold cross-validation to manage dataset imbalance and assessed model generalizability through cross-dataset training and testing.

Main Results:

  • The 3D CNN achieved classification accuracies of 76.19% (NI), 69.92% (NYU), and 70.77% (PU) for fALFF data.
  • The 3D CNN demonstrated superior performance compared to the FCNN across all tested datasets.
  • For cross-dataset generalizability, the 3D CNN attained 69.48% accuracy on fALFF data when tested on the PU dataset after training on NI and NYU.

Conclusions:

  • The 3D CNN approach is an effective method for classifying ADHD using fALFF data derived from fMRI.
  • The study validates the potential of deep learning, specifically 3D CNNs, as a decision support system for ADHD diagnosis.
  • The findings highlight the utility of analyzing spontaneous brain activity patterns for objective ADHD assessment.