Detection of Autism Spectrum Disorder(ASD)is a crucial area of research,representing a foundational aspect of psychological studies.The advancement of technology and the widespread adoption of machine learning methodo...Detection of Autism Spectrum Disorder(ASD)is a crucial area of research,representing a foundational aspect of psychological studies.The advancement of technology and the widespread adoption of machine learning methodologies have brought significant attention to this field in recent years.Interdisciplinary efforts have further propelled research into detection methods.Consequently,this study aims to contribute to both the fields of psychology and computer science.Specifically,the goal is to apply machine learning techniques to limited data for the detection of Autism Spectrum Disorder.This study is structured into two distinct phases:data preprocessing and classification.In the data preprocessing phase,four datasets—Toddler,Children,Adolescent,and Adult—were converted into numerical form,adjusted as necessary,and subsequently clustered.Clustering was performed using six different methods:Kmeans,agglomerative,DBSCAN(Density-Based Spatial Clustering of Applications with Noise),mean shift,spectral,and Birch.In the second phase,the clustered ASD data were classified.The model’s accuracy was assessed using 5-fold cross-validation to ensure robust evaluation.In total,ten distinct machine learning algorithms were employed.The findings indicate that all clustering methods demonstrated success with various classifiers.Notably,the K-means algorithm emerged as particularly effective,achieving consistent and significant results across all datasets.This study is expected to serve as a guide for improving ASD detection performance,even with minimal data availability.展开更多
Genomic profiling in postmortem brain from autistic individuals has consistently revealed convergent molecular changes.What drives these changes and how they relate to genetic susceptibility in this complex condition ...Genomic profiling in postmortem brain from autistic individuals has consistently revealed convergent molecular changes.What drives these changes and how they relate to genetic susceptibility in this complex condition are not well understood.We performed deep single-nucleus RNA sequencing(snRNA-seq)to examine cell composition and transcriptomics,identifying dysregulation of cell type-specific gene regulatory networks(GRNs)in autism spectrum disorder(ASD).展开更多
ASD儿童具有高度异质性,各种症状的明确定义及机理尚未得到完全解析,以至于主次矛盾不突出、研究方向不明晰。文章以19世纪60年代罗马尼亚孤儿院案例开始,分析刺激不足对突触修剪的影响,并进一步把刺激分类为外源性和内源性两种,最终得...ASD儿童具有高度异质性,各种症状的明确定义及机理尚未得到完全解析,以至于主次矛盾不突出、研究方向不明晰。文章以19世纪60年代罗马尼亚孤儿院案例开始,分析刺激不足对突触修剪的影响,并进一步把刺激分类为外源性和内源性两种,最终得到皮层下中枢“熵增”的三大原因:发育不良、功能紊乱和器质性病变,解析了ASD核心症状之一重复刻板行为异质性的原因。Children with ASD are highly heterogeneous, and the clear definition and mechanism of various symptoms have not been fully resolved, so that the primary and secondary contradictions are not prominent, and the research direction is also not clear. This paper starts with the case of the Romania orphanage in the 1960s, analyzes the effect of insufficient stimulation on synaptic pruning, further classifies the stimulation into exogenous and endogenous two, and finally obtains the three major causes of “entropy increase” in the subcortical center: dysplasia, dysfunction and organic lesions, and analyzes the reasons for the heterogeneity of the repetitive stereotyped behavior.展开更多
文摘Detection of Autism Spectrum Disorder(ASD)is a crucial area of research,representing a foundational aspect of psychological studies.The advancement of technology and the widespread adoption of machine learning methodologies have brought significant attention to this field in recent years.Interdisciplinary efforts have further propelled research into detection methods.Consequently,this study aims to contribute to both the fields of psychology and computer science.Specifically,the goal is to apply machine learning techniques to limited data for the detection of Autism Spectrum Disorder.This study is structured into two distinct phases:data preprocessing and classification.In the data preprocessing phase,four datasets—Toddler,Children,Adolescent,and Adult—were converted into numerical form,adjusted as necessary,and subsequently clustered.Clustering was performed using six different methods:Kmeans,agglomerative,DBSCAN(Density-Based Spatial Clustering of Applications with Noise),mean shift,spectral,and Birch.In the second phase,the clustered ASD data were classified.The model’s accuracy was assessed using 5-fold cross-validation to ensure robust evaluation.In total,ten distinct machine learning algorithms were employed.The findings indicate that all clustering methods demonstrated success with various classifiers.Notably,the K-means algorithm emerged as particularly effective,achieving consistent and significant results across all datasets.This study is expected to serve as a guide for improving ASD detection performance,even with minimal data availability.
文摘Genomic profiling in postmortem brain from autistic individuals has consistently revealed convergent molecular changes.What drives these changes and how they relate to genetic susceptibility in this complex condition are not well understood.We performed deep single-nucleus RNA sequencing(snRNA-seq)to examine cell composition and transcriptomics,identifying dysregulation of cell type-specific gene regulatory networks(GRNs)in autism spectrum disorder(ASD).
文摘ASD儿童具有高度异质性,各种症状的明确定义及机理尚未得到完全解析,以至于主次矛盾不突出、研究方向不明晰。文章以19世纪60年代罗马尼亚孤儿院案例开始,分析刺激不足对突触修剪的影响,并进一步把刺激分类为外源性和内源性两种,最终得到皮层下中枢“熵增”的三大原因:发育不良、功能紊乱和器质性病变,解析了ASD核心症状之一重复刻板行为异质性的原因。Children with ASD are highly heterogeneous, and the clear definition and mechanism of various symptoms have not been fully resolved, so that the primary and secondary contradictions are not prominent, and the research direction is also not clear. This paper starts with the case of the Romania orphanage in the 1960s, analyzes the effect of insufficient stimulation on synaptic pruning, further classifies the stimulation into exogenous and endogenous two, and finally obtains the three major causes of “entropy increase” in the subcortical center: dysplasia, dysfunction and organic lesions, and analyzes the reasons for the heterogeneity of the repetitive stereotyped behavior.
