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基于复杂值 fMRI 数据的空间源相位图的动态功能网络连接:对精神分裂症的应用.

Wei-Xing Li1, Qiu-Hua Lin1, Bin-Hua Zhao1

  • 1School of Information and Communication Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China.

Journal of neuroscience methods
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概括

新的动态空间功能网络连接 (dsFNC) 使用复杂值的fMRI数据捕获大脑阶段信息,改善精神分裂症检测和识别潜在的生物标志物.

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科学领域:

  • 神经成像是一种神经成像.
  • 功能性MRI分析
  • 精神疾病 精神疾病

背景情况:

  • 动态空间功能网络连接 (dsFNC) 通常使用只有大小的fMRI数据.
  • 完整的fMRI数据包含有价值的复杂值相位信息,经常被忽视.
  • 精神障碍可能会影响dSFNC检测到的功能性大脑变化.

研究的目的:

  • 引入一种新的dsFNC方法,利用复杂值fMRI数据的空间源相 (SSP) 地图.
  • 评估这种新方法,SSP-dsFNC,捕捉动态功能连接的能力.
  • 使用SSP-dsFNC来区分精神分裂症 (SZs) 和健康对照 (HCs) 的人.

主要方法:

  • 从复杂值的fMRI数据中导出SSP地图,以创建SSP-dsFNC.
  • 使用相互信息进行量化连接.
  • 采用统计分析和马尔科夫链来评估跨时间窗口的动态变化.
  • 根据连接差异和马尔科夫链过渡进行分类的SZ和HC.

主要成果:

  • 与只有大小的方法相比,SSP-dsFNC显示出更大的动态范围.
  • 在使用SSP-dsFNC的SZ和HC之间观察到显著的差异.
  • 该方法有效地利用了复杂值fMRI数据的完整大脑信息.

结论:

  • SSP-dsFNC为精神分裂症患者的功能变化提供了更高的敏感性.
  • 与现有方法相比,这种方法确定了额外的有意义的大脑连接.
  • 在SSP-dsFNC中,SZ与HC的分类准确度提高了14.6%.
  • 这些发现表明SSP-dsFNC作为精神病障碍的成像生物标志物的潜力.