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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Background While recent works suggested that overweight/obesity may impair executive function (EF), the overweight/obesity-EF relationship has not been well studied in adolescents. Furthermore, no research has investigated adolescent EF impairments across the weight spectrum (e.g., underweight or thinness, normal, overweight/obesity), especially those with underweight condition, with the moderating effect of negative emotions in the weight-EF association being limitedly investigated. We aimed to determine whether overall and abdominal weight spectrum associated with EF impairments and to identity whether negative emotions moderate the weight-EF link in adolescents. Methods We applied a subsample of the SCHEDULE-A project. Adolescents (11–18 years) were recruited using a multi-stage cluster random sampling approach. We measured the overall and abdominal weight spectrum by body mass index z-score and waist-to-height ratio, respectively. We used the Behavior Rating Inventory of Executive Function (BRIEF) to evaluate adolescent EF in nature setting, and utilized the Depression Anxiety and Stress Scales (DASS-21) to assess three types of negative emotional status (i.e., depression, anxiety, and stress). Results Of the 1935 adolescents, 963 (49.8%) were male. We observed that abdominal, not overall, overweight was associated with the Global Executive Composite (GEC) impairment (OR = 1.59, 95% CI 1.07–2.35), particularly for inhibit, emotion control, shift, working memory, and monitor domains. Furthermore, depression moderated the abdominal overweight-GEC association (P = 0.032 for interaction term), especially for emotional control, working memory, and initiate dimensions. Moreover, we also found abdominal thinness was associated with the Metacognition Index problem (OR = 1.33, 95% CI 1.04–1.72), particularly for plan and monitor areas. Conclusions Both abdominal overweight and thinness were associated with adolescent EF, and depression would be a modifiable target to improve EF in adolescents with abdominal overweight. Future longitudinal studies are needed to investigate the causal relationship between abdominal weight spectrum and EF, as well as the underlying mechanisms among adolescents suffering from depression.

Background Sleep disturbances in women occur frequently throughout pregnancy. Previous studies have demonstrated that the increasing incidence of physiological and psychological illness is concurrent with increasing sleep deprivation and poor sleep quality in adults and children. Objectives The Shanghai Sleep Birth Cohort Study (SSBCS) was established to examine the effect of sleep disturbances during the third trimester on emotional regulation of mothers; to assess the effect of maternal sleep during pregnancy on the growth and development of children; and to explore the influence of children's sleep characteristics on physical and social‐emotional development. Population The study was conducted in the Renji Hospital in Pudong New District, Shanghai from May 2012 to July 2013. Women and their newborns who met the inclusion criteria and agreed to participate in this study were recruited to the SSBCS. Methods The follow‐up visits for children were conducted at the age of 42 days, 3, 6, 9, 12, 18, and 24 months, and 3, 4, and 6 years. Data on demographic factors, physical examination, sleep assessment, developmental and psychiatric assessment, diet records, and biological samples were collected throughout the study. Preliminary results A total of 277 pregnant women were recruited to the study; the response rate was 64.3%. 37.9% of the pregnant women had poor sleep quality and 12.0% suffered from depression. Infant sleep patterns changed during the first year of life, but most sleep characteristics showed little variation from 6 to 12 months. Conclusions The SSBCS is an on‐going prospective cohort study with follow‐up to 6 years. The detailed data on demographic factors, sleep assessment, physical examinations, neurodevelopmental and psychiatric assessment, diet records, and biological samples make this research platform an important resource for examining the potential effects of sleep characteristics on both maternal and child health.

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