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Classification of familial and non-familial ADHD using auto-encoding network and binary hypothesis testing.

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    This study used deep learning and MRI scans to differentiate between familial and non-familial attention-deficit/hyperactivity disorder (ADHD). The AI model successfully identified distinct neurobiological markers for ADHD subtypes, offering new diagnostic potential.

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    Area of Science:

    • Neuroimaging
    • Artificial Intelligence
    • Genetics

    Background:

    • Family history is a significant risk factor for Attention-Deficit/Hyperactivity Disorder (ADHD).
    • Distinguishing between familial ADHD (ADHD-F) and non-familial ADHD (ADHD-NF) using neuroimaging has not been previously explored.
    • Multimodal Magnetic Resonance Imaging (MRI) offers detailed brain structure and function insights.

    Purpose of the Study:

    • To investigate the efficacy of deep learning algorithms combined with multimodal MRI in differentiating ADHD subtypes.
    • To identify specific neuroimaging biomarkers that distinguish ADHD-F from ADHD-NF and controls.
    • To develop a robust, generalizable framework for identifying neurodevelopmental disorder markers.

    Main Methods:

    • Utilized T1-weighted and diffusion-weighted MRI data from 438 children (129 ADHD-F, 159 ADHD-NF, 150 controls).
    • Employed a deep learning pipeline involving feature selection (t-test, mutual information, Lasso) and an auto-encoder.
    • Implemented a binary-hypothesis strategy with leave-one-out testing nested within five-fold cross-validation.

    Main Results:

    • The model achieved moderate accuracy in classifying ADHD subtypes against controls and between ADHD-F and ADHD-NF (AUCs ranging from 0.67 to 0.70).
    • Key differentiating metrics included mean diffusivity (MD) in the fornix, fractional anisotropy (FA) in the inferior fronto-occipital fasciculus, and cortical thickness in various brain regions.
    • Distinct neuroimaging patterns were identified for ADHD-F vs. controls, ADHD-NF vs. controls, and ADHD-F vs. ADHD-NF.

    Conclusions:

    • A semi-supervised deep learning framework can reliably differentiate familial and non-familial ADHD using multimodal MRI data.
    • Advanced deep learning techniques can identify robust and generalizable neurobiological markers for neurodevelopmental disorders.
    • This approach holds promise for improving diagnostic precision in ADHD.