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生物标志物生物标志物

Philip Nkwam¹, Ethan Draper², Jasmine Cakmak³

¹University of Lagos, Lagos, Nigeria.

Alzheimer's & dementia : the journal of the Alzheimer's Association

|December 24, 2025

概括

本研究引入了基于云的静态功能性MRI (rs-fMRI) 分析管道,以克服低收入和中等收入国家 (LMICs) 研究人员的计算障碍. 可访问的管道使得阿尔茨海默病 (AD) 研究的可复制的神经成像研究成为可能.

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.
全球健康全球健康

背景情况:

休息状态功能性MRI (rs-fMRI) 对于理解阿尔茨海默氏症 (AD) 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障碍,原因是资源有限和标准化管道.
为应对这些挑战,开发了一种新的,资源高效的基于云的rs-fMRI分析管道.

研究的目的:

为LMIC研究人员创建可复制和可访问的rs-fMRI分析管道.
为了使在资源有限的环境中进行可比的神经成像研究.
为了促进研究阿尔茨海默氏病 (AD) 在全球不同人口中的进展.

主要方法:

在Google Colab上使用Neurodesk开发了一个基于云的开放访问的rs-fMRI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.
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:

对17名受试者的初步分析表明,管道的效率是可靠的,每名受试者每时间点在约20分钟内生成可靠的功能连接地图.
完整的分析管道文档将在Protocol.io上公开提供,以确保透明度和可重复性.
该管道是为资源高效的rs-fMRI分析而设计的.

结论:

一个基于云的,低资源的rs-fMRI分析管道已经成功引入,解决了LMICs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在不同LMIC研究小组的通用性.

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