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Socioeconomic status effects on children's vocabulary brain development

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Abstract

Recent advancements in neuroimaging have made new ways of breaking down the dynamic interplay of genes and environmental influences, which affect the structure of accessible brain development. The impact on language abilities and verbal short-term memory of pre-school children from socioeconomic status (SES). Children from lower SES show less linguistic abilities and communication skills compared to higher SES children. The challenges of children's vocabulary brain development difference between lower and higher SES continues sometimes grows with age. Therefore this paper, machine learning assisted vocabulary development framework (MLVDF), has proposed a multimodal assessment of young children's brains' intellectual ability and capabilities. The proposed methodology is focused on the estimation and comparison with more developed strategies of nonverbal abilities using similar video interfaces. Machine learning technologies are used to determine a minimum number of variables capable of predicting particular children's cognitive skills. Thus, the findings highlight the complex effect of verbal and cognitive abilities on children's language and life experience. The results are obtained by the analysis of SES in Children's brain development as Cognitive skills ratio is 86.6%, Verbal abilities ratio is 87.12%, Children's brain capability ratio is 87.6%, Increasing the memory ratio is 83.5%, Learning attitude ratio is 93.8%, Reduce the effect of socioeconomic status ratio is 84.25%, Reading efficiency ratio is 87.6%.

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Objectives: One of the major challenges facing psychiatry is how to incorporate biological measures in the classification of mental health disorders. Many of these disorders affect brain development and its connectivity. In this study, we propose a novel method for assessing brain networks based on the combination of a graph theory measure (eigenvector centrality) and a one-class support vector machine (OC-SVM). Methods: We applied this approach to resting-state fMRI data from 622 children and adolescents. Eigenvector centrality (EVC) of nodes from positive- and negative-task networks were extracted from each subject and used as input to an OC-SVM to label individual brain networks as typical or atypical. We hypothesised that classification of these subjects regarding the pattern of brain connectivity would predict the level of psychopathology. Results: Subjects with atypical brain network organisation had higher levels of psychopathology (p < 0.001). There was a greater EVC in the typical group at the bilateral posterior cingulate and bilateral posterior temporal cortices; and significant decreases in EVC at left temporal pole. Conclusions: The combination of graph theory methods and an OC-SVM is a promising method to characterise neurodevelopment, and may be useful to understand the deviations leading to mental disorders.
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Childhood socioeconomic status (SES) is associated with cognitive achievement throughout life. How does SES relate to brain development, and what are the mechanisms by which SES might exert its influence? We review studies in which behavioral, electrophysiological and neuroimaging methods have been used to characterize SES disparities in neurocognitive function. These studies indicate that SES is an important predictor of neurocognitive performance, particularly of language and executive function, and that SES differences are found in neural processing even when performance levels are equal. Implications for basic cognitive neuroscience and for understanding and ameliorating the problems related to childhood poverty are discussed.
Impacts of school closures on physical and mental health of children and young people: a systematic review
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