**FMRIPrep: A Robust Preprocessing Pipeline for Functional MRI** Preprocessing of functional MRI (fMRI) data involves multiple steps to clean and standardize the data before statistical analysis. The complexity of these workflows has increased with advancements in MR data acquisition and image processing techniques. To address this challenge, the authors introduce fMRIPrep, an analysis-agnostic tool designed for robust and reproducible preprocessing of task-based and resting-state fMRI data. fMRIPrep automatically adapts a best-in-breed workflow to the specific characteristics of any dataset, ensuring high-quality preprocessing with minimal manual intervention. By incorporating visual assessment checkpoints into an iterative integration framework for software testing, fMRIPrep demonstrates robust performance on a diverse set of fMRI data from 54 studies in the OpenfMRI repository. Key features of fMRIPrep include: 1. **Robustness**: fMRIPrep self-adapts to the idiosyncrasies of input datasets. 2. **Quality**: It produces high-quality preprocessing outcomes. 3. **Transparency**: It provides comprehensive visual reports and a citation boilerplate for detailed documentation. 4. **Ease-of-use**: It is user-friendly and minimizes manual intervention. The workflow integrates state-of-the-art tools from various neuroimaging software packages and is built on the Brain Imaging Data Structure (BIDS). fMRIPrep is evaluated through a comprehensive validation framework, including fault-discovery testing and quality assurance testing. Results show that fMRIPrep yields high-quality results and improves spatial precision by reducing uncontrolled smoothing compared to commonly used preprocessing tools like FSL's feat. fMRIPrep aims to enhance the reliability and reproducibility of fMRI research by providing a high-quality, robust, easy-to-use, and transparent preprocessing workflow.**FMRIPrep: A Robust Preprocessing Pipeline for Functional MRI** Preprocessing of functional MRI (fMRI) data involves multiple steps to clean and standardize the data before statistical analysis. The complexity of these workflows has increased with advancements in MR data acquisition and image processing techniques. To address this challenge, the authors introduce fMRIPrep, an analysis-agnostic tool designed for robust and reproducible preprocessing of task-based and resting-state fMRI data. fMRIPrep automatically adapts a best-in-breed workflow to the specific characteristics of any dataset, ensuring high-quality preprocessing with minimal manual intervention. By incorporating visual assessment checkpoints into an iterative integration framework for software testing, fMRIPrep demonstrates robust performance on a diverse set of fMRI data from 54 studies in the OpenfMRI repository. Key features of fMRIPrep include: 1. **Robustness**: fMRIPrep self-adapts to the idiosyncrasies of input datasets. 2. **Quality**: It produces high-quality preprocessing outcomes. 3. **Transparency**: It provides comprehensive visual reports and a citation boilerplate for detailed documentation. 4. **Ease-of-use**: It is user-friendly and minimizes manual intervention. The workflow integrates state-of-the-art tools from various neuroimaging software packages and is built on the Brain Imaging Data Structure (BIDS). fMRIPrep is evaluated through a comprehensive validation framework, including fault-discovery testing and quality assurance testing. Results show that fMRIPrep yields high-quality results and improves spatial precision by reducing uncontrolled smoothing compared to commonly used preprocessing tools like FSL's feat. fMRIPrep aims to enhance the reliability and reproducibility of fMRI research by providing a high-quality, robust, easy-to-use, and transparent preprocessing workflow.