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相关概念视频

Biological Causes of Schizophrenia01:29

Biological Causes of Schizophrenia

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Schizophrenia, a severe psychiatric disorder, arises from a complex interplay of biological factors, including genetic predisposition, structural brain abnormalities, neurotransmitter dysregulation, and developmental irregularities. These factors collectively contribute to the onset and progression of the disorder, which typically manifests in late adolescence or early adulthood.
Genetic Factors in Schizophrenia
The genetic basis of schizophrenia is strongly supported by family and twin...
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相关实验视频

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Modeling the Functional Network for Spatial Navigation in the Human Brain
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基于Connectome的精神分裂症预测使用结构连接 - 深图神经网络 (sc-DGNN)

P Udayakumar1, R Subhashini1

  • 1School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, India.

Journal of X-ray science and technology
|May 31, 2024
PubMed
概括

一种新的深度图神经网络模型通过分析大脑连接来准确预测精神分裂症. 这种先进的方法表现出比传统的机器学习技术更高的性能,用于诊断大脑疾病.

科学领域:

  • 神经科学是一个神经科学.
  • 大脑的连接性 大脑的连接性
  • 机器学习在医学中的应用

背景情况:

  • 了解人类大脑的连接组织对于洞察认知过程和障碍至关重要.
  • 大脑结构和功能连接分析有助于理解神经疾病.

研究的目的:

  • 为了提高大脑疾病问题,特别是精神分裂症的预测准确度.
  • 研究与精神分裂症相关的不连接的子网络和图形结构.

主要方法:

  • 利用了88名受试者的扩散磁共振成像 (dMRI) 数据.
  • 开发并应用了一个结构连接深度图形神经网络 (sc-DGNN) 模型.
  • 与三种经典机器学习 (ML) 和五种深度学习 (DL) 模型进行sc-DGNN性能比较.

主要成果:

  • 该sc-DGNN模型显示了与精神分裂症相关的脱节的优异预测性能.
  • 与ML和DL方法相比,实现了更高的准确性,灵敏度,特异性,精度,F1得分和ROC曲线下的面积 (AUC).
  • sc-DGNN实现了93%的准确率,显著超过了72%的准确率的线性差异分析 (LDA).

结论:

关键词:
在Connectome中使用Connectome.脑部疾病 脑部疾病连接矩阵连接矩阵.图表测量尺度 图表测量尺度神经网络的神经网络的神经网络神经成像是一种神经成像.蛋白质是一种蛋白质.

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  • 拟议的sc-DGNN模型有效地区分了精神分裂症患者和健康个体.
  • 深度图形神经网络为改善精神疾病的诊断准确性提供了一个有希望的途径.
  • 结构连接分析与先进的DL模型相结合,对大脑疾病研究具有重大潜力.