基于大规模自闭症fMRI数据，用独立成分分析提取无图谱约束的脑网络。
ICA-based Resting-State Networks Obtained on Large Autism fMRI Dataset ABIDE
- 用ICA从ABIDE数据中提取无图谱依赖的静息态脑网络
- 首次公开可复现的全脑网络数据集，支持更灵活的神经机制研究
- 适合自闭症神经机制探索与新型分析方法开发的研究者
功能性磁共振成像（fMRI）在脑功能研究中日益重要，尤其用于识别自闭症谱系障碍（ASD）患者与健康对照之间的潜在神经特征。自闭症大脑成像数据交换计划（ABIDE）通过其大规模数据共享推动了这一研究。尽管ABIDE提供多种图谱预处理的数据，但独立成分分析（ICA）在降维中的应用仍不足。本文填补该空白，基于ABIDE预处理数据，采用ICA构建并公开了静息态脑网络（RSNs）：https://github.com/SjirSchielen/groupICAonABIDE。这些网络突破图谱限制，揭示了不受先验空间约束的神经激活簇，为自闭症研究提供了与主流图谱方法互补的新视角。该资源将有助于推动更深入的神经机制分析及新方法开发。
原文摘要 · Abstract (English)
Functional magnetic resonance imaging (fMRI) has become instrumental in researching brain function. One application of fMRI is investigating potential neural features that distinguish people with autism spectrum disorder (ASD) from healthy controls. The Autism Brain Imaging Data Exchange (ABIDE) facilitates this research through its extensive data-sharing initiative. While ABIDE offers data preprocessed with various atlases, independent component analysis (ICA) for dimensionality reduction remains underutilized. We address this gap by presenting ICA-based resting-state networks (RSNs) from preprocessed scans from ABIDE, now publicly available: https://github.com/SjirSchielen/groupICAonABIDE. These RSNs unveil neural activation clusters without atlas constraints, offering a perspective on ASD analyses that complements the predominantly atlas-based literature. This contribution provides a valuable resource for further research into ASD, potentially aiding in developing new analytical approaches.
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