Nilearn
Advanced Machine Learning for Neuroimaging Data and Functional Connectivity Analysis.

A robust, BIDS-compliant preprocessing pipeline for functional MRI data.
fMRIPrep is a high-performance functional magnetic resonance imaging (fMRI) data preprocessing pipeline designed to provide an easily accessible, state-of-the-art interface that is robust to variations in scan acquisition protocols. Built on top of Nipype, fMRIPrep integrates tools from FSL, AFNI, ANTs, and FreeSurfer into a unified workflow. Its 2026 market position is defined by its role as the industry standard for reproducible neuroscience, adhering strictly to the Brain Imaging Data Structure (BIDS). The architecture follows a 'glass box' philosophy, providing detailed visual reports that allow researchers to inspect every step of the spatial normalization, motion correction, and susceptibility distortion correction. By containerizing the environment via Docker and Singularity, fMRIPrep eliminates the 'it works on my machine' problem, ensuring that results are bit-wise reproducible across different high-performance computing (HPC) environments. It is increasingly utilized in large-scale clinical trials and population-level studies like the UK Biobank to ensure data quality and standardization before statistical modeling.
Uses the ANTs 'SyN' algorithm to estimate susceptibility-induced distortions when dedicated fieldmaps are missing.
Advanced Machine Learning for Neuroimaging Data and Functional Connectivity Analysis.
The foundational Python abstraction layer for multi-modal neuroimaging data access and header manipulation.
The industry-standard suite for advanced FMRI, MRI, and diffusion brain imaging analysis.
The gold standard in automated brain MRI morphometry and surface-based analysis.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Seamless integration with FreeSurfer for cortical surface estimation and mapping functional data to surface templates.
The pipeline dynamically adjusts its steps based on available metadata in the BIDS JSON sidecars.
Generates comprehensive, interactive HTML reports with 'before and after' svg animations for registration steps.
Incorporates T2* estimation and weighted averaging for multi-echo acquisition sequences.
Implementation of Anatomical and Temporal Component Based Noise Correction for physiological noise removal.
Supports multiple template spaces including MNI, TemplateFlow-based custom templates, and native space.
Eliminating site-specific bias caused by different preprocessing software versions or manual pipelines.
Registry Updated:2/7/2026
Mapping fine-grained functional activation to the cortical surface for laminar fMRI studies.
Removing motion artifacts and physiological noise that mimic neural connectivity.