BrainScape:一个开源框架,用于整合和预处理解剖MRI数据集
Muhammad Nabi Yasinzai1, Remika Mito2, Mangor Pedersen1
1Department of Psychology & Neuroscience, Auckland University of Technology, Auckland, New Zealand.
Imaging neuroscience (Cambridge, Mass.)
|October 27, 2025
概括
BrainScape是一个新的框架,集成了160个不同的MRI数据集,自动化预处理和人口统计附件,用于可复制的神经科学研究. 这种工具可以提高大规模研究的数据可访问性和分析性.
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.
- 数据科学数据科学数据科学
背景情况:
- 磁共振成像 (MRI) 数据对于理解大脑结构和功能至关重要.
- 大数据集对于神经科学中的机器学习至关重要,但整合多样化的数据仍然具有挑战性.
- 现有的数据集往往缺乏人口变异性,限制了概括性和临床相关性.
研究的目的:
- 介绍BrainScape,这是一个开源框架,用于自动化多种MRI数据集的集成和预处理.
- 解决数据组织,格式化和元数据不一致性在异质神经成像资源中的挑战.
- 通过提高数据的可访问性和分析,促进大规模,可复制和包容的神经科学研究.
主要方法:
- 开发了一个开源的,基于插件的Python框架 (BrainScape),用于自动化MRI数据处理.
- 策划了160个公开可用的MRI数据集的集合,包括超过27,000个受试者和46,000个扫描.
- 实现了数据下载,组织,验证,预处理和人口统计附件的模块,确保可追溯性和可重复性.
主要成果:
- 成功将160个异构的MRI数据集集成为一个统一的集合 (BrainScape数据集).
- 布莱恩斯凯普框架自动化了预处理和人口统计数据的附加,保留了原始数据结构和元数据.
- 综合数据集包括多模式MRI数据 (T1w,T2w,T1Gd,FLAIR) 和主要的人口统计数据 (年龄,性别,手性).
结论:
- BrainScape提供了一个强大的解决方案,用于汇总多样化的MRI数据集,克服常见的数据集成挑战.
- 该框架通过实现神经科学研究的自动化,透明和可配置的工作流来促进开放科学.
- BrainScape加速了数据驱动的发现,并促进了脑成像研究的包容性和可重现性.
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