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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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Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
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通过成像基因组数据来进行多模式深度学习,用于对精神分裂症进行分类.

Ayush Kanyal1, Badhan Mazumder1, Vince D Calhoun2

  • 1Department of Computer Science, Georgia State University, Atlanta, GA, United States.

Frontiers in psychiatry
|July 15, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的深度学习框架,用于使用脑成像和遗传数据检测精神分裂症. 多模式方法实现了79.01%的准确性,优于单模式方法.

关键词:
深度学习是一种深度学习.可解释的人工智能 (XAI)功能网络连接 (FNC) 功能网络连接图像学 遗传学 基因学多式联运是多式联运.精神分裂症是一种精神分裂症.单核酸多态 (单核酸多态,简称SNP)结构磁共振成像 (sMRI) 是一种结构性磁共振成像.

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科学领域:

  • 神经科学是一个神经科学.
  • 人工智能的人工智能
  • 遗传学 是一个遗传学.

背景情况:

  • 精神分裂症 (SZ) 是一种复杂的精神疾病,影响认知,情绪和行为.
  • 它的确切原因是未知的,但它涉及结构和功能性大脑变化和遗传因素.
  • 通过多种数据类型调查SZ对于更好的检测至关重要.

研究的目的:

  • 开发一个深度学习框架,以提高精神分裂症的检测.
  • 整合结构性MRI (sMRI),功能性MRI (fMRI) 和遗传数据 (SNP).
  • 通过结合多模式特征来提高分类准确性.

主要方法:

  • 使用DenseNet进行sMRI形态特征.
  • 应用1D CNN和LRP用于fMRI功能连接和SNP相关性.
  • 集成的功能使用极端梯度提升 (XGBoost) 进行分类.
  • 使用可解释的人工智能 (XAI) 来解释特征.

主要成果:

  • 多模式方法在将SZ从健康对照 (HC) 中分类时达到79.01%的准确性.
  • 这一性能超过了使用单个数据模式的方法的准确性.
  • XAI确定了重要的功能网络和SNP,为分类做出了贡献.

结论:

  • 一个深度学习框架有效地集成sMRI,fMRI和遗传数据,以改进SZ分类.
  • 可解释的人工智能为精神分裂症的关键生物标志物提供了见解.
  • 这种多模式战略为推进SZ检测和理解提供了一个有希望的途径.