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阿尔茨海默氏症成像联盟

Philip Nkwam1, Ethan Draper2, Jasmine Cakmak3

  • 1University of Lagos, Lagos, Nigeria.

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概括
此摘要是机器生成的。

这项研究引入了基于云的休息状态功能性MRI (rs-fMRI) 管道,以使阿尔茨海默氏病研究在低资源环境中成为可能. 这种可访问的工具通过克服神经成像分析中的计算障碍,赋予研究人员权力.

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科学领域:

  • 神经成像是一种神经成像.
  • 计算神经科学是一种神经科学.
  • 阿尔茨海默氏症疾病研究研究

背景情况:

  • 休息状态功能性MRI (rs-fMRI) 对于研究阿尔茨海默氏病 (AD) 中的大脑连接性至关重要.
  • 低收入和中等收入国家 (LMICs) 在fMRI数据分析方面面临重大挑战,原因是有限的计算资源和缺乏量身定制的预处理管道.
  • 这项工作通过开发可重复的,资源高效的基于云的rs-fMRI分析管道来解决这些障碍.

研究的目的:

  • 创建一个开放的,基于云的rs-fMRI分析管道.
  • 授权LMICs的研究人员进行可比的神经成像研究.
  • 克服用于AD研究的fMRI数据分析中的计算和标准化限制.

主要方法:

  • 通过Google Colab在Neurodesk上使用开源工具开发了一个开放式访问分析管道.
  • 使用了阿尔茨海默病新型或实验性治疗方法的症状前评估 (PREVENT-AD) 数据集 (75名受试者,12-48个月的随访).
  • 在数据子集上实施的标准fMRI预处理步骤和估计的功能连接指标 (ALFF,fALFF,ReHo).

主要成果:

  • 在对17名受试者的初步分析中,每个时间点每名受试者在约20分钟内生成了可靠的ALFF,fALFF和ReHo地图.
  • 完整的分析管道文档将在Protocol.io上提供,以确保透明度和可重复性.
  • 该管道是为资源有限的环境设计的,使用标准规格的谷歌云引擎.

结论:

  • 引入了基于云的,低资源的rs-fMRI分析管道,以解决LMIC研究环境中的计算限制.
  • 该管道将应用于PREVENT-AD数据集和正在进行的非洲痴呆症研究.
  • 未来的可靠性分析将评估管道对LMIC集团资源高效的rs-fMRI分析的概括性.