Functional magnetic resonance imaging (fMRI) is a standard tool to investigate the neural correlates of cognition. fMRI noninvasively measures brain activity, allowing identification of patterns evoked by tasks performed during scanning. Despite the long history of this technique, the idiosyncrasies of each dataset have led to the use of ad-hoc preprocessing protocols customized for nearly every different study. This approach is time consuming, error prone and unsuitable for combining datasets from many sources. Here we showcase fMRIPrep (http://fmriprep.org), a robust tool to prepare human fMRI data for statistical analysis. This software instrument addresses the reproducibility concerns of the established protocols for fMRI preprocessing. By leveraging the Brain Imaging Data Structure to standardize both the input datasets (MRI data as stored by the scanner) and the outputs (data ready for modeling and analysis), fMRIPrep is capable of preprocessing a diversity of datasets without manual intervention. In support of the growing popularity of fMRIPrep, this protocol describes how to integrate the tool in a task-based fMRI investigation workflow.

功能磁共振成像（fMRI）是研究认知的神经相关性的标准工具。fMRI无创地测量大脑活动，从而可以识别扫描过程中执行的任务引起的模式。尽管这项技术已有很长的历史，但是每个数据集的特质都导致了针对几乎所有不同研究定制的临时预处理协议的使用。这种方法耗时，容易出错并且不适用于组合来自许多来源的数据集。在这里，我们展示了fMRIPrep（http://fmriprep.org），这是一种强大的工具，可以为统计分析准备人类fMRI数据。该软件仪器解决了已建立的fMRI预处理协议的重现性问题。通过利用脑成像数据结构来标准化输入数据集（由扫描仪存储的MRI数据）和输出数据（可用于建模和分析的数据），fMRIPrep能够预处理多种数据集而无需人工干预。为了支持fMRIPrep的日益普及，该协议描述了如何将工具集成到基于任务的fMRI调查工作流程中。