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阿尔茨海默氏症成像联盟

Amir Dehsarvi1, Lukas Frontzkowski1, Anna Dewenter1

  • 1Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Bavaria, Germany.

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概括
此摘要是机器生成的。

ADprep是一个新的,自动化的神经成像管道,协调阿尔茨海默病 (AD) 数据处理. 它为研究人员简化了多模式MRI和PET分析,改善了数据标准化和跨研究的共享.

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科学领域:

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

背景情况:

  • 对阿尔茨海默病 (AD) 研究来说,处理大规模的多模式MRI和PET数据至关重要.
  • 目前的神经成像数据处理是复杂的,需要专门的专业知识,导致跨站点的变化.
  • 标准化,用户友好的工作流程对于协调神经成像数据和减少预处理偏差至关重要.

研究的目的:

  • 开发ADprep,一个容器化,自动化的神经成像工具箱,用于处理多模式MRI和PET数据.
  • 建立一个用户友好的管道,不需要编程专业知识,促进数据协调和共享.
  • 在AD研究中生成各种大脑图谱的标准化输出,以便在AD研究中进行一致的神经成像分析.

主要方法:

  • ADprep使用nipype库,并根据大脑成像数据结构 (BIDS) 处理格式化的数据.
  • 它包括结构性MRI (体积测量,皮质厚度),功能性MRI (连接性) 和多追踪器PET (SUVR,部分体积校正) 的预处理.
  • 该管道在FreeSurfer空间生成标准化的输出,并通过基于云的 (GRIP) 和本地集群实现可用.

主要成果:

  • 在大型数据集 (ADNI,ADNI-DOD,A4) 上成功测试了ADprep,处理失败率低于4%.
  • 管道证明了高可重现性,与已建立的ADNI PET核心结果密切匹配,用于粉胺-PET (r=0.99) 和-PET (r=0.98).
  • 处理时间高效:MRI扫描大约1小时,PET扫描大约30分钟.

结论:

  • ADprep为多式联络神经成像数据提供了一个用户友好的,协调的管道.
  • 它使非专家用户能够处理各种神经成像数据集,产生适合直接统计分析的输出.
  • 该工具箱显著推进了阿尔茨海默病研究社区内的标准化神经成像分析和数据共享.