ArticleBrain research bulletin2026
Classification of familial and non-familial ADHD using auto-encoding network and binary hypothesis testing.
Article in Brain research bulletin, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Family heritage is one of the most powerful risk factors for attention-deficit/hyperactivity disorder (ADHD). Children with familial ADHD (ADHD-F) and non-familial ADHD (ADHD-NF) have both shared and distinct behavioral characteristics and clinical outcomes, with their neuropathological underpinnings under-investigated. In this study, we utilized an autoencoder-based deep learning architecture within the binary hypothesis framework to identify the most important structural- and diffusion-MRI-based neural signatures that robustly discriminate 129 children with ADHD-F, 159 AHDH-NF, and 150 matched controls. Nested leave-one-out and five-fold methods were used for double cross-validations of the results. Classification accuracy, sensitivity, specificity, and area under the curve (AUC) were applied to evaluate the model performance. The model achieved accuracies of 65.4 ± 3.2, 67.0 ± 2.2, and 62.0 ± 4.9, with corresponding AUCs of 65.6 ± 4.8, 67.6 ± 5.2, and 65.8 ± 5.2, for ADHD-F vs. controls, ADHD-NF vs. controls, and ADHD-F vs. ADHD-NF, respectively. The most informative features for successful ADHD-F vs. control discrimination were mean diffusivity (MD) of right fornix, MD of left parahippocampal cingulum, and cortical thickness of right inferior parietal cortex. The key contributors for successful ADHD-NF vs. control discrimination were fractional anisotropy (FA) of left inferior fronto-occipital fasciculus, MD of right fornix, and cortical thickness of right medial orbitofrontal cortex. The highlighted features for successful ADHD-F vs. ADHD-NF discrimination were volume of left cingulate cingulum tract, volume of right parietal segment of the superior longitudinal fasciculus, and cortical thickness of right fusiform cortex. Our binary hypothesis semi-supervised deep learning framework robustly discriminated familial vs. non-familial ADHD and provided validated neural features that have potential to serve as distinct treatment targets of ADHD-F vs. ADHD-NF.
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