Attention-deficit/hyperactivity disorder (ADHD) is one of the most common brain diseases among children. The current criteria of ADHD diagnosis mainly depend on behavior analysis, which is subjective and inconsistent, especially for children. The development of neuroimaging technologies, such as magnetic resonance imaging (MRI), drives the discovery of brain abnormalities in structure and function by analyzing multimodal neuroimages for computer-aided diagnosis of brain diseases. This paper proposes a multimodal machine learning framework that combines the Boruta based feature selection and Multiple Kernel Learning (MKL) to integrate the multimodal features of structural and functional MRIs and Diffusion Tensor Images (DTI) for the diagnosis of early adolescent ADHD. The rich and complementary information of the macrostructural features, microstructural properties, and functional connectivities are integrated at the kernel level, followed by a support vector machine classifier for discriminating ADHD from healthy children. Our experiments were conducted on the comorbidity-free ADHD subjects and covariable-matched healthy children aged 9–10 chosen from the Adolescent Brain and Cognitive Development (ABCD) study. This paper is the first work to combine structural and functional MRIs with DTI for early adolescents of the ABCD study. The results indicate that the kernel-level fusion of multimodal features achieves 0.698 of AUC (area under the receiver operating characteristic curves) and 64.3% of classification accuracy for ADHD diagnosis, showing a significant improvement over the early feature fusion and unimodal features. The abnormal functional connectivity predictors, involving default mode network, attention network, auditory network, and sensorimotor mouth network, thalamus, and cerebellum, as well as the anatomical regions in basal ganglia, are found to encode the most discriminative information, which collaborates with macrostructure and diffusion alterations to boost the performances of disorder diagnosis.

Background Wolf-Hirschhorn syndrome (WHS) is a contiguous gene syndrome caused by partial 4p deletion highly variable in size in individual patients. The core WHS phenotype is defined by the association of growth delay, typical facial characteristics, intellectual disability and seizures. The WHS critical region (WHSCR) has been narrowed down and NSD2 falls within this 200 kb region. Only four patients with NSD2 variants have been documented with phenotypic features in detail. Case presentation Herein, we report the case of a 12-year-old boy with developmental delay. He had dysmorphic facial features including wide-spaced eyes, prominent nasal bridge continuing to forehead, abnormal teething and micrognathia. He also had mild clinodactyly of both hands. Using whole-exome sequencing, we identified a pathogenic mutation in NSD2 [c.4029_4030insAA, p.Glu1344Lysfs*49] isolated from peripheral blood DNA. Sanger confirmation of this variant revealed it as a de novo truncating variant in the family. Conclusion Here, we reported a boy with de novo truncating variant in NSD2 with atypical clinical features comparing with 4p16.3 deletion related WHS. Our finding further supported the pathogenesis of truncating variants in NSD2 and delineated the possible symptom spectrum caused by these variants.

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