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相关概念视频

Autism Spectrum Disorder01:19

Autism Spectrum Disorder

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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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相关实验视频

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Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
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基于动态图嵌入的自闭症谱系障碍功能连接中的动态生物标志物.

Yanting Liu1, Hao Wang1, Yanrui Ding2

  • 1School of Science, Jiangnan University, Wuxi, 214122, China.

Interdisciplinary sciences, computational life sciences
|December 7, 2023
PubMed
概括

研究人员开发了使用动态大脑网络 (DBN) 诊断自闭症谱系障碍 (ASD) 的新模型. 这些模型识别了改变的大脑连接模式,为早期ASD识别提供了潜在的生物标志物.

关键词:
在ASD中,使用的是ASD.生物标记物识别方法动态的大脑网络 动态的大脑网络图形嵌入式嵌入式俄罗斯-fMRI

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科学领域:

  • 神经科学是一个神经科学.
  • 发育障碍 发育障碍 发展障碍
  • 生物标志物发现发现

背景情况:

  • 自闭症谱系障碍 (ASD) 诊断由于其复杂的神经和发育性质而具有挑战性.
  • 动态大脑网络 (DBN) 提供了丰富的时空信息,对于理解ASD中大脑功能至关重要.
  • 识别大脑区域之间的动态通信模式是发现ASD诊断生物标志物的关键.

研究的目的:

  • 为ASD提出新的诊断模型,利用DBN的时空特征.
  • 研究动态图嵌入用于表示大脑区域之间的交互信息.
  • 为了确定潜在的基于DBN的生物标志物用于ASD识别.

主要方法:

  • 开发了两个诊断模型:dgEmbed-KNN和聚合-SVM.
  • 利用DBN和动态图嵌入的时空信息.
  • 分析聚合脑网络连接作为分类的特征.

主要成果:

  • 与传统和深度学习方法相比,dgEmbed-KNN模型的分类准确度略高.
  • 聚合-SVM模型在使用聚合的大脑网络连接来诊断ASD方面表现出强大的能力.
  • 在ASD中确定了特定大脑区域的过度和不足连接 (中枢后环,岛,小脑,尾状核,极) 和功能子网络 (DMN,视觉,听觉,突出网络) 内的/之间的异常动态相互作用.

结论:

  • DBN为ASD诊断和治疗提供了宝贵的见解.
  • 拟议的dgEmbed-KNN和Aggregation-SVM模型有效地利用DBN特征进行ASD识别.
  • 发现的动态连接性改变为ASD提供了潜在的DBN生物标志物.