Department of Psychology, The University of Texas at Austin, Austin, United States.
Interacting Minds Centre, Aarhus University, Aarhus, Denmark.
Elife. 2022 Aug 30;11:e79277. doi: 10.7554/eLife.79277.
Functional magnetic resonance imaging (fMRI) has revolutionized cognitive neuroscience, but methodological barriers limit the generalizability of findings from the lab to the real world. Here, we present Neuroscout, an end-to-end platform for analysis of naturalistic fMRI data designed to facilitate the adoption of robust and generalizable research practices. Neuroscout leverages state-of-the-art machine learning models to automatically annotate stimuli from dozens of fMRI studies using naturalistic stimuli-such as movies and narratives-allowing researchers to easily test neuroscientific hypotheses across multiple ecologically-valid datasets. In addition, Neuroscout builds on a robust ecosystem of open tools and standards to provide an easy-to-use analysis builder and a fully automated execution engine that reduce the burden of reproducible research. Through a series of meta-analytic case studies, we validate the automatic feature extraction approach and demonstrate its potential to support more robust fMRI research. Owing to its ease of use and a high degree of automation, Neuroscout makes it possible to overcome modeling challenges commonly arising in naturalistic analysis and to easily scale analyses within and across datasets, democratizing generalizable fMRI research.
功能磁共振成像（fMRI）彻底改变了认知神经科学，但方法学上的障碍限制了实验室发现转化为现实世界的普遍性。在这里，我们介绍了 Neuroscout，这是一个用于分析自然主义 fMRI 数据的端到端平台，旨在促进稳健和可推广的研究实践的采用。Neuroscout 利用最先进的机器学习模型，使用自然主义刺激（如电影和叙述）自动注释来自数十项 fMRI 研究的刺激，使研究人员能够轻松地在多个具有生态有效性的数据集上测试神经科学假设。此外，Neuroscout 建立在一个强大的开放工具和标准生态系统之上，提供了一个易于使用的分析构建器和一个完全自动化的执行引擎，减轻了可重复性研究的负担。通过一系列元分析案例研究，我们验证了自动特征提取方法，并展示了其支持更稳健 fMRI 研究的潜力。由于其易用性和高度自动化，Neuroscout 使得克服自然主义分析中常见的建模挑战并在数据集内和跨数据集轻松扩展分析成为可能，使可推广的 fMRI 研究民主化。
