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Autism Spectrum Disorder

Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a neurodevelopmental condition marked by persistent deficits in social communication and interaction alongside restrictive and repetitive behaviors or interests. ASD is sometimes accompanied by intellectual impairment.
These core symptoms manifest differently among individuals, ranging from mild to severe. The disorder's complexity extends beyond its clinical presentation, encompassing a diverse range of biological, cognitive, and sociocultural influences.

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Updated: May 27, 2025

Investigating Social Cognition in Infants and Adults Using Dense Array Electroencephalography dEEG

Investigating Social Cognition in Infants and Adults Using Dense Array Electroencephalography dEEG

Published on: June 27, 2011

自闭症谱系障碍的检测使用来自电脑图的突出连接特征.

Zahrul Jannat Peya¹, Mahfuza Akter Maria¹, Sk Imran Hossain¹

¹Computer Science and Engineering Department, Khulna University of Engineering & Technology, Khulna 9203, Bangladesh.

International journal of neural systems

|February 18, 2025

概括

此摘要是机器生成的。

这项研究引入了一种使用脑电图 (EEG) 信号来检测自闭症谱系障碍 (ASD) 的新方法. 从连接特征地图 (CFM) 中提取的相互信息 (MI) 功能显示了区分自闭症与对照对象的最佳结果.

关键词:

自闭症谱系障碍自闭症谱系障碍连接功能地图的连接功能地图.电脑脑电图 (EEG) 是一种电脑电图.

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Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

Published on: September 12, 2011

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

相关实验视频

Last Updated: May 27, 2025

Investigating Social Cognition in Infants and Adults Using Dense Array Electroencephalography dEEG

Investigating Social Cognition in Infants and Adults Using Dense Array Electroencephalography dEEG

Published on: June 27, 2011

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

Published on: September 12, 2011

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

科学领域:

神经科学是一个神经科学.
生物医学工程生物医学工程
计算精神病学是一种计算精神病学.

背景情况:

自闭症谱系障碍 (ASD) 由于其变异性和缺乏明确的生物标志物而带来了重大诊断挑战.
电脑电图 (EEG) 信号,反映大脑活动,为神经发育障碍的检测提供了一个有希望的途径.
目前对ASD的诊断方法缺乏客观指标,需要先进的分析方法.

研究的目的:

从EEG信号中开发一种有效的特征提取方法,以区分患有自闭症和神经类型控制的个人.
评估各种连接特征在识别EEG数据中的ASD模式中的有效性.
通过先进的机器学习技术,建立一个强大的分类框架来检测ASD.

主要方法:

从EEG信号中提取六个突出的连接特征:交叉相关性 (XCOR),相锁定值 (PLV),皮尔森相关系数 (PCC),相互信息 (MI),规范化相互信息 (NMI) 和转移 (TE).
连接特征地图 (CFM) 的构建,以从提取的特征中表示空间信息.
使用对CFM应用的卷积神经网络 (CNN) 进行ASD和对照对象的分类.

主要成果:

连接特征地图 (CFM) 在区分自闭症与对照对象方面表现出卓越的表现,因为它们具有固有的空间信息.
相互信息 (MI) 功能在对ASD和对照参与者进行分类时表现出最高的准确性.
通过增加样本大小和数据细分,特别是MI功能,观察到性能改善.

结论:

使用CFM和CNN的拟议方法为从EEG数据中客观检测ASD提供了有希望的方法.
相互信息 (MI) 成为提高ASD分类准确性的关键特征.
建议在不同的数据集和更大的样本大小中进行进一步的验证,以巩固这种基于EEG的诊断工具的临床适用性.