Emerging Evidence for Putative Neural Networks and Antecedents of Pediatric Anxiety in the Fetal, Neonatal, and Infant Periods

Associations between early trajectories of amygdala development and later school-age anxiety in two longitudinal samples

Amygdala function is implicated in the pathogenesis of autism spectrum disorder (ASD) and anxiety. We investigated associations between early trajectories of amygdala growth and anxiety and ASD outcomes at school age in two longitudinal studies: high- and low-familial likelihood for ASD, Infant Brain Imaging Study (IBIS, n = 257) and typically developing (TD) community sample, Early Brain Development Study (EBDS, n = 158). Infants underwent MRI scanning at up to 3 timepoints from neonate to 24 months. Anxiety was assessed at 6-12 years. Linear multilevel modeling tested whether amygdala volume growth was associated with anxiety symptoms at school age. In the IBIS sample, children with higher anxiety showed accelerated amygdala growth from 6 to 24 months. ASD diagnosis and ASD familial likelihood were not significant predictors. In the EBDS sample, amygdala growth from birth to 24 months was associated with anxiety. More anxious children had smaller amygdala volume and slower rates of amygdala growth. We explore reasons for the contrasting results between high-familial likelihood for ASD and TD samples, grounding results in the broader literature of variable associations between early amygdala volume and later anxiety. Results have the potential to identify mechanisms linking early amygdala growth to later anxiety in certain groups.

Hair brain-derived neurotrophic factor (BDNF) as predictor of developing psychopathological symptoms in childhood

BACKGROUND

Dysregulation in the expression of neurotrophins is implicated in the pathophysiology of several mental disorders. Peripheral brain-derived neurotrophic factor (BDNF) can be measured in hair and might represent a marker of adequate neuroplasticity regulation. In early developmental periods, neuroplasticity regulation might be particularly important, but BDNF markers have not yet been analyzed in this regard. We used the hair-BDNF concentration (HBC) to investigate the prediction of emerging symptoms of anxiety/depressive and attention-deficit hyperactivity disorder (ADHD) in the developmentally crucial period from preschool to school age.

METHODS

117 children (58 girls, 59 boys) participated in a longitudinal study at the ages of 4-5 (T1) and 8 (T2) years. At T1, HBC was measured in a 3 cm hair segment. At T1 and T2, symptom domains were assessed using a multi-method (clinical interview, questionnaire) and multi-informant approach.

RESULTS

T1 HBC was significantly negatively associated with T1 anxiety/depressive symptoms (r = -0.27) and predicted T2 anxiety disorder symptoms (r = -0.34) after controlling for the T1 symptoms. T1 HBC also predicted T2 depressive disorder symptoms (r = -0.18) but was not associated with ADHD symptom development.

LIMITATIONS

BDNF hair analysis is a new method with a not yet large number of studies on methodological issues. Our study adds evidence to the validity of the method.

CONCLUSIONS

Prediction of anxiety/depressive symptom development by HBC was shown. As this study was the first to use HBC in this context, cross-validation is necessary and worthwhile. HBC might prove to constitute a useful, non-invasive early marker of risk for anxiety/depressive disorders in childhood.

A Genetic Neural Net Model for the Relationship between Pre-School and Attention in Early Childhood

This paper uses an algorithm of a genetic neural network to conduct an in-depth study and analysis of the attention relationship in pre-school education of young children. In face expression images, expression features are often concentrated and distributed in some local regions, while convolutional neural networks are homogeneous for feature extraction of facial expression images. To address the problem of the nonuniform distribution of expression features, a facial expression recognition method based on the spatial domain attention mechanism is proposed. A comparison of the differences between group and grade before the intervention using a two-way multivariate ANOVA revealed a nonsignificant interaction effect with a value of 0.90 for the group-grade interaction effect and an F-value of 0.36. The group's main effect had a value of 0.90 and an F-value of 0.36, with the experimental and control groups being equal groups on multiple dependent variables. The salient features in face expression images that are more important for facial expression recognition can be selected adaptively, and relatively high weights are assigned to these salient features. Experimental results on the image library show that the introduction of a spatial domain attention mechanism can improve the average recognition accuracy of face expressions by about 1.1% and 1.0%, respectively. A hybrid domain attention mechanism-based face expression recognition method is proposed to extract important expression features from both the spatial domain and the channel domain. Experimental results on the image library show that combining the spatial domain attention mechanism and the channel domain attention mechanism can improve the average recognition accuracy by about 2.1% and 1.5%, respectively. The use of rule games was effective in developing children's sustained and selective attention; the effect of rule games on the sustained and selective attention of older boys and girls did not show significant gender differences.