Building the overview we wish existed when starting out.
| Tool | Description | Added |
What it's written in — most tools are usable from the command line regardless. |
|
|
|
|---|---|---|---|---|---|---|
| Data Conversion | ||||||
| dcm2niix | Converts DICOM files to NIfTI format with BIDS-compatible JSON sidecar output. | C++ | Multi-modal | |||
| HeuDiConv | Flexible DICOM-to-BIDS converter using user-defined heuristic files to handle complex naming schemes. | Python | Multi-modal | |||
| BIDScoin | DICOM-to-BIDS conversion with a GUI-configurable mapping and a plugin system. | Python | Multi-modal | |||
| ReproIn | Scanner naming convention and setup for fully automatic BIDS-ready data organization at acquisition time. | Python | Multi-modal | |||
| BIDS Validator | Checks whether a dataset complies with the BIDS specification before sharing or pipeline submission. | JavaScript | Multi-modal | |||
| Dcm2Bids | Reorganises NIfTI files from dcm2niix into BIDS structure using a JSON configuration file. | Python | Multi-modal | |||
| Quality Control | ||||||
| MRIQC | Extracts image quality metrics from structural and functional MRI and produces visual reports. | Python | Multi-modal | |||
| mrQA | Checks MRI datasets for protocol compliance and flags deviations in acquisition parameters. | Python | Multi-modal | |||
| wonkyconn | Evaluates residual motion artefacts in fMRI functional connectivity and generates visual QC reports. | Python | fMRI | |||
| Preprocessing | ||||||
| fMRIPrep | Robust preprocessing pipeline for task-based and resting-state fMRI with automated decision-making and visual QC reports. | Python | fMRI | |||
| dMRIPrep | Preprocessing pipeline for diffusion MRI following the fMRIPrep design framework. | Python | Diffusion | |||
| sMRIPrep | Structural MRI preprocessing pipeline; used standalone or as the anatomical component of fMRIPrep. | Python | Structural | |||
| Nibabies | fMRIPrep adapted for infant brain MRI, with templates and workflows suited to developing brains. | Python | fMRI | |||
| HALFpipe | Containerized fMRI pipeline wrapping fMRIPrep with smoothing, filtering, confound regression, and interactive QA. | Python | fMRI | |||
| FreeSurfer | Cortical surface reconstruction, parcellation, and cortical thickness analysis from structural MRI. | C++ | Structural | |||
| CAT12 | SPM toolbox for voxel-based and surface-based morphometry of structural MRI data. | MATLAB | Structural | |||
| FSL | Comprehensive library for analysis of fMRI, structural, and diffusion MRI data. | C++ | Multi-modal | |||
| AFNI | Suite of programs for fMRI preprocessing, regression modelling, and statistical analysis. | C | fMRI | |||
| Tedana | Multi-echo fMRI denoising — separates BOLD signal from noise using T2* decay across echo times. | Python | fMRI | |||
| FastSurfer | Deep learning-based cortical surface reconstruction and parcellation; produces FreeSurfer-compatible output in minutes. | Python | Structural | |||
| SynthStrip | Skull-stripping tool using deep learning; works across contrasts and resolutions without retraining. | Python | Multi-modal | |||
| SynthSeg | Brain segmentation via deep learning trained on synthetic data; handles any contrast and resolution without retraining. | Python | Multi-modal | |||
| C-PAC | Configurable Pipeline for the Analysis of Connectomes — flexible fMRI preprocessing and connectivity analysis with GUI configuration. | Python | fMRI | |||
| ASLPrep | Arterial spin labeling preprocessing pipeline from the NiPreps family, producing CBF maps and QC reports. | Python | ASL | |||
| Mindboggle | Automated brain morphometry and cortical labeling; computes shape measures from FreeSurfer and ANTs surface outputs. | Python | Structural | |||
| Nighres | High-resolution brain MRI processing tools optimized for 7T data, including laminar and columnar analysis. | Python | Structural | |||
| fMRIDenoise | Automated pipeline for benchmarking fMRI denoising strategies across multiple confound models and QC metrics. | Python | fMRI | |||
| LayNii | Layer-fMRI analysis tools for cortical layerification, columnarization, layer-smoothing, and VASO analysis. | C++ | fMRI | |||
| TrUE-Net | Triplanar U-Net ensemble for WMH segmentation on FLAIR, with pretrained models and fine-tuning support. | Python | Structural | |||
| LST-AI | Deep learning ensemble (3× 3D U-Net) for MS and WMH lesion segmentation with automatic McDonald criteria annotation. | Python | Structural | |||
| SHIVA-WMH | 3D U-Net for WMH segmentation optimized for detecting small punctate lesions in younger subjects. | Python | Structural | |||
| segcsvd | CNN-based WMH segmentation on FLAIR and perivascular space segmentation on T1, using SynthSeg-derived anatomical context. | Python | Structural | |||
| HyperMapp3r | Bayesian CNN for WMH segmentation with uncertainty estimation. | Python | Structural | |||
| DeepWMH | Annotation-free WMH segmentation using deep learning trained without manually labeled data. | Python | Structural | |||
| wmh_seg | Transformer-based U-Net for WMH segmentation validated across 1.5T, 3T, and 7T FLAIR. | Python | Structural | |||
| W2MHS | Random forest-based WMH segmentation and quantification toolbox for aging and Alzheimer's research. | MATLAB | Structural | |||
| LST | SPM toolbox for lesion segmentation with lesion growth (LGA) and lesion prediction (LPA) algorithms. | MATLAB | Structural | |||
| UBO Detector | Cluster-based fully automated WMH extraction pipeline with regional quantification in lobes and arterial territories. | MATLAB | Structural | |||
| SIAM | Contrast-, resolution-, and pathology-robust head tissue segmentation trained from synthetic data; handles T1, T2, and FLAIR volumes. | Python | Multi-modal | |||
| intensity-normalization | Collection of MRI intensity normalization methods including Z-score, Nyul histogram matching, WhiteStripe, and deep learning approaches. | Python | Multi-modal | |||
| micapipe | Multimodal MRI processing pipeline for cortical and subcortical analysis of structural, diffusion, and functional data. | Python | Multi-modal | |||
| Spinal Cord Toolbox | Comprehensive open-source toolbox for processing and analysis of spinal cord MRI, covering registration, segmentation, and template-based analysis. | Python | Structural | |||
| fmripost-aroma | BIDS App for running ICA-AROMA denoising on fMRIPrep derivatives, removing motion-related noise components from functional MRI. | Python | fMRI | |||
| hMRI toolbox | SPM toolbox for creating quantitative MRI maps (T1, MT, PD, R2*) from multi-parameter mapping acquisitions for in vivo histology. | MATLAB | Structural | |||
| QUIT | Set of tools for processing quantitative MR images, with utilities and fitting methods for T1, T2, and magnetization transfer. | C++ | Structural | |||
| GOUHFI | Contrast- and resolution-agnostic segmentation tool for subcortical and cortical parcellation, optimised for ultra-high field MRI (>3T). | Python | Structural | |||
| SynthSR | Deep learning super-resolution that turns images of any orientation, resolution, and contrast into 1 mm isotropic MP-RAGE. | Python | Structural | |||
| Registration & Normalization | ||||||
| ANTs | Toolkit for deformable image registration, segmentation, and normalization of brain images. | C++ | Multi-modal | |||
| FSL FLIRT/FNIRT | FSL tools for linear (FLIRT) and nonlinear (FNIRT) brain image registration. | C++ | ||||
| Templateflow | Version-controlled archive of brain MRI templates and atlases with a Python API for programmatic access. | Python | Multi-modal | |||
| ANTsPy | Python interface to ANTs for image registration, segmentation, and template building without writing C++ code. | Python | Multi-modal | |||
| Harmonization | ||||||
| neuroCombat | Reference Python implementation of ComBat for removing scanner and site batch effects from neuroimaging feature matrices. | Python | Multi-modal | |||
| neuroCombat R | R implementation of ComBat for harmonizing multi-site neuroimaging data by removing scanner and site batch effects. | R | Multi-modal | |||
| neuroHarmonize | Extends neuroCombat with GAM-based nonlinear covariate modeling and direct NIFTI image harmonization support. | Python | Multi-modal | |||
| ComBatFamily | Unified R package wrapping ComBat, ComBat-GAM, CovBat, and longCombat under a single interface for multi-site harmonization. | R | Multi-modal | |||
| longCombat | Adapts ComBat for longitudinal multi-scanner imaging data by modeling subject-level random effects alongside site batch effects. | R | Multi-modal | |||
| CovBat | Extends ComBat to remove site effects from the covariance structure of neuroimaging features, not just their mean and variance. | R | Multi-modal | |||
| Harmonizer | Wraps ComBat as a scikit-learn transformer to integrate site-effect correction into cross-validated machine learning pipelines without data leakage. | Python | Multi-modal | |||
| dMRIharmonization | Harmonizes diffusion MRI data across scanners and sites using RISH (Rotational Invariant Spherical Harmonic) features. | Python | Diffusion | |||
| RISH-GLM | RISH-based diffusion MRI harmonization that does not require matched training subjects across sites. | Python | Diffusion | |||
| dmri-harmonization | Cross-scanner diffusion MRI harmonization using adaptive dictionary learning to match signal distributions across sites. | Python | Diffusion | |||
| HACA3 | Harmonizes structural MRI across sites using deep learning with disentangled representations of anatomy, contrast, and acquisition artifacts. | Python | Multi-modal | |||
| Statistical Analysis | ||||||
| SPM | Statistical parametric mapping framework for fMRI and voxel-based morphometry analysis. | MATLAB | ||||
| Nilearn | Machine learning and statistical tools for neuroimaging data, including GLMs and multivariate decoding. | Python | fMRI | |||
| Fitlins | Tool for estimating linear models defined by the BIDS Stats-Models specification, applied to BIDS-formatted datasets. | Python | fMRI | |||
| NiMARE | Python library for coordinate- and image-based neuroimaging meta-analysis, implementing ALE, MKDA, and other methods. | Python | fMRI | |||
| FSL Randomise | Permutation-based nonparametric inference for neuroimaging group-level statistics. | C++ | ||||
| ENIGMA VBM | Fully automated DARTEL VBM pipeline with QC and sensitivity analyses, standardized for multi-site mega-analysis. | MATLAB | ||||
| ENIGMA Toolbox | Python/MATLAB ecosystem for accessing 80+ ENIGMA working group datasets and contextualizing findings with connectomic and transcriptomic data. | Python/MATLAB | ||||
| IBMMA | Image-based meta- and mega-analysis framework for mass-univariate analysis across voxel, vertex, and connectome features from multi-site data. | Python | Structural | |||
| Neuromaps | Maps brain annotations onto standard surfaces and compares them against transcriptomic and receptor reference atlases. | Python | Multi-modal | |||
| BrainSMASH | Generates spatially autocorrelation-preserving surrogate brain maps for null hypothesis testing of brain-behavior correlations. | Python | Multi-modal | |||
| PALM | Permutation analysis of linear models for neuroimaging; supports complex designs, exchangeability blocks, sign-flipping, and TFCE. | MATLAB | Multi-modal | |||
| Neurosynth Compose | Web platform for reproducible neuroimaging meta-analysis with PRISMA-guided study curation, integrated with NeuroStore. | Web | fMRI | |||
| GingerALE | Coordinate-based meta-analysis using Activation Likelihood Estimation to identify consistent activation foci across studies. | Java | fMRI | |||
| SDM-PSI | Seed-based d Mapping with Permutation of Subject Images — hybrid CBMA tool that combines coordinate and image-based data. | MATLAB | fMRI | |||
| SnPM | Nonparametric permutation testing toolbox for SPM; controls for multiple comparisons without distributional assumptions. | MATLAB | fMRI | |||
| DiagnoseHarmonisation (DHARM) | In-development library for applying and assessing MRI harmonisation algorithms at the summary-measure level; also a centralised reference for validated harmonisation methods from the literature. | Python | Multi-modal | |||
| TAPAS | Suite of computational psychiatry tools (HGF, rDCM, PhysIO, and more) now maintained as individual packages under the ComputationalPsychiatry GitHub organization. | MATLAB/Python | fMRI | |||
| BrainIAK | Python toolkit for advanced fMRI analysis including shared response modelling, Bayesian RSA, and searchlight decoding. | Python | fMRI | |||
| FEMA | Fast and efficient mixed-effects algorithm for mass-univariate whole-brain analysis; designed for large-sample studies such as ABCD with voxelwise, vertexwise, and connectivity matrix support. | MATLAB | Multi-modal | |||
| ENIGMA Disease Working Group Stats | Batch GLM and effect-size scripts for ROI and vertexwise meta-analysis across ENIGMA disease working groups. | R | Multi-modal | |||
| PCNtoolkit | Python toolbox for probabilistic normative modelling of neuroimaging and clinical data, estimating individualized deviation scores from a reference population. | Python | Multi-modal | |||
| multiverse | R package for declaring and running multiverse analyses — systematically exploring all reasonable analytical choices and summarising their effect on results. | R | Multi-modal | |||
| VertexWiseR | R package for extracting, analyzing, and visualizing cortical and subcortical vertex-wise data from FreeSurfer, CAT12, and fMRIPrep outputs. | R | Structural | |||
| SubCortexMesh | Converts subcortical segmentation volumes to surface meshes and computes vertex-wise metrics for surface-based analysis. | Python | Structural | |||
| CanlabCore | Object-oriented MATLAB toolbox for fMRI data analysis, including GLM, mediation, and machine learning on brain images; the core of the Canlab toolboxes. | MATLAB | fMRI | |||
| nltools | Python toolbox for analyzing fMRI data, covering multivariate prediction, functional connectivity, and mediation analysis. | Python | fMRI | |||
| neuropredict | Automated estimation and comparison of predictive accuracy across neuroimaging features, with rigorous cross-validation. | Python | Multi-modal | |||
| NeuroRA | Python toolbox for representational similarity analysis of multimodal neural data, including fMRI, EEG, and behavioural measures. | Python | fMRI | |||
| GIFT | MATLAB toolbox for independent component analysis of neuroimaging data across fMRI, EEG, and PET, with multiple ICA algorithms. | MATLAB | fMRI | |||
| JuBrain Anatomy Toolbox | SPM toolbox that integrates probabilistic cytoarchitectonic brain maps with functional data, assigning anatomy to fMRI results. | MATLAB | fMRI | |||
| Connectivity | ||||||
| XCP-D | Robust fMRI post-processing pipeline for denoising, parcellation, and connectivity analysis; supports fMRIPrep, NiBabies, and HCP outputs. | Python | fMRI | |||
| CONN | MATLAB toolbox for functional connectivity with seed-based, ROI-to-ROI, and ICA analysis methods. | MATLAB | fMRI | |||
| BrainSpace | Toolbox for gradient decomposition and manifold learning of functional and structural connectivity matrices. | Python/MATLAB | fMRI | |||
| nibetaseries | Beta series estimation for task fMRI connectivity using least-squares separate or least-squares all approaches. | Python | fMRI | |||
| ENIGMA Tractometry Toolbox | Standardized white matter tract-based morphometry protocol for multi-site diffusion MRI mega-analysis across ENIGMA working groups. | Python | Diffusion | |||
| Brain Connectivity Toolbox | MATLAB and Python toolbox for complex network analysis of structural and functional brain connectivity data. | MATLAB | ||||
| Lead-DBS | MATLAB toolbox for DBS electrode reconstruction and connectome-based analysis using postoperative MRI and CT imaging. | MATLAB | ||||
| Functionnectome | Python package that combines fMRI functional signal across distant voxels using anatomical priors of structural brain circuits. | Python | fMRI | |||
| DeepDisco | Deep learning tool that generates white matter disconnectivity maps from binary lesion masks, bypassing tractography. | Python | Structural | |||
| netneurotools | Network Neuroscience Lab toolbox for network construction, null models, and statistical analysis of brain connectivity data. | Python | Multi-modal | |||
| BCBToolKit | Software package with several tools to indirectly assess brain disconnection from focal lesions. | Java | Structural | |||
| Diffusion Analysis | ||||||
| MRtrix3 | Suite for diffusion MRI processing, tractography, and connectome construction using constrained spherical deconvolution. | C++ | Diffusion | |||
| DIPY | Python library for diffusion MRI analysis including reconstruction, tractography, registration, and simulation. | Python | Diffusion | |||
| DSI Studio | Tractography tool for diffusion MRI with deterministic fiber tracking and connectometry analysis. | C++ | Diffusion | |||
| TractSeg | Deep learning-based white matter tract segmentation directly from diffusion MRI, without full tractography. | Python | Diffusion | |||
| TBSS | Voxelwise cross-subject analysis of diffusion data projected onto a mean FA skeleton. | C++ | Diffusion | |||
| Tracula | Automated probabilistic tractography of major white matter pathways using FreeSurfer's anatomical priors. | Python | Diffusion | |||
| XTRACT | Automated tractography of white matter bundles using standardized protocols across species. | C++ | Diffusion | |||
| Scilpy | Python diffusion MRI processing toolbox from the Sherbrooke Connectivity Imaging Lab, covering tractography, filtering, and tractometry. | Python | Diffusion | |||
| QSIPrep | BIDS-compatible preprocessing pipeline for diffusion MRI with distortion correction, motion correction, and denoising. | Python | Diffusion | |||
| TORTOISE | Suite of programs for preprocessing, tensor model fitting, and tractography of diffusion MRI data. | C++ | Diffusion | |||
| pyAFQ | Automated fiber quantification for diffusion MRI — delineates white matter bundles and computes tract profiles of tissue properties along them. | Python | Diffusion | |||
| NBLtools | Preprocessing pipeline for diffusion MRI with automated quality control and correction. | Python | Diffusion | |||
| StarTrack | Analyses diffusion MRI using DTI, spherical deconvolution, and whole-brain tractography, with interactive display of fibre orientation distributions. | C++ | Diffusion | |||
| dmipy | Python toolbox for reproducible diffusion MRI microstructure estimation using modular multi-compartment models. | Python | Diffusion | |||
| MITK Diffusion | Diffusion MRI reconstruction, tractography, and visualization; part of the Medical Imaging Interaction Toolkit from DKFZ. | C++ | Diffusion | |||
| Visualization | ||||||
| FSLeyes | Image viewer from the FSL team for overlaying brain images, statistical maps, and atlases. | Python | Multi-modal | |||
| wb_view | Connectome Workbench viewer for surface and volume neuroimaging data, designed for HCP-style CIFTI files. | C++ | Multi-modal | |||
| Nilearn plotting | Python functions for plotting brain maps, glass brains, and statistical overlays on MRI templates. | Python | fMRI | |||
| NiReports | Visual reporting library that generates the QC HTML pages used by fMRIPrep and MRIQC. | Python | Multi-modal | |||
| ITK-SNAP | Interactive tool for segmentation of 3D medical images with manual editing and automatic active contour methods. | C++ | Multi-modal | |||
| ggseg | R package for plotting brain atlas segmentations as ggplot2 geoms, supporting cortical and subcortical parcellations. | R | Structural | |||
| MRIcroGL | GPU-accelerated volume rendering and visualization of NIfTI brain images with MIP and raycasting modes. | Pascal | Multi-modal | |||
| TrackVis | Visualizes and analyzes fiber tract data from diffusion MRI tractography with interactive 3D display. | C++ | Diffusion | |||
| TractEdit | Interactive tool for virtual dissection and manual refinement of diffusion MRI tractography. | Python | Diffusion | |||
| PySurfer | Python library for visualization and statistical analysis of cortical surface representations from neuroimaging data. | Python | Structural | |||
| pycortex | Interactive 3D surface viewer for fMRI data; renders cortical activations on inflated and flat maps in a browser. | Python | fMRI | |||
| brainchop | In-browser 3D brain MRI segmentation using deep learning; runs fully client-side without uploading data. | JavaScript | Structural | |||
| 3D Slicer | Open-source platform for medical image informatics, processing, and 3D visualization; widely used for MRI segmentation and registration. | C++/Python | Multi-modal | |||
| NiChord | Python package for creating chord diagrams to visualize brain networks and functional connectivity. | Python | fMRI | |||
| Workflow Managers | ||||||
| Nipoppy | Manages the full neuroimaging workflow from raw data through BIDS conversion, pipeline execution, and derivative extraction. | Python | Multi-modal | |||
| Nipype | Pipeline framework wrapping FSL, SPM, FreeSurfer, and others into reproducible Python workflows. | Python | Multi-modal | |||
| Brainlife.io | Cloud platform for running containerized neuroimaging pipelines with provenance tracking. | Web | ||||
| Neurodesk | Containerized neuroimaging desktop environment with 100+ analysis tools, accessible via browser. | Docker | Multi-modal | |||
| DataLad | Distributed data management system for version-controlled datasets and reproducible analyses. | Python | Multi-modal | |||
| Clinica | Software platform for clinical neuroimaging studies, with standardized pipelines for structural and diffusion MRI data. | Python | Multi-modal | |||
| QuNex | Integrative platform for HCP-style processing of structural, functional, and diffusion MRI across large cohorts. | Python | Multi-modal | |||
| Pydra | Next-generation dataflow engine from the NiPype team for building reproducible, scalable neuroimaging workflows. | Python | Multi-modal | |||
| Neurodocker | Generates custom Dockerfiles and Singularity recipes for reproducible neuroimaging environments with any combination of tools and versions. | Python | Multi-modal | |||
| Snakemake | Workflow management system for building reproducible, scalable data analysis pipelines in Python. | Python | Multi-modal | |||
| fmriflows | Suite of dependent fMRI analysis pipelines covering anatomical and functional preprocessing, univariate GLM, and multivariate pattern analysis. | MATLAB | fMRI | |||
| NARPS Open Pipelines | Codebase reproducing the 70 analysis pipelines from the Botvinik-Nezer et al. (2020) Nature study on analytical variability in fMRI. | Python | fMRI | |||
| Libraries | ||||||
| nipy | Foundational Python package for fMRI analysis including model fitting, statistical testing, and signal processing. | Python | fMRI | |||
| nitime | Python library for timeseries analysis of neuroscience data, with tools for spectral analysis, coherence, and granger causality. | Python | fMRI | |||
| PyBIDS | Python library for querying, loading, and writing BIDS-formatted datasets. | Python | Multi-modal | |||
| Nibabel | Python library for reading and writing common neuroimaging file formats including NIfTI, GIFTI, and CIFTI. | Python | Multi-modal | |||
| neuropythy | Python library for cortical surface mesh analysis, registration, and retinotopic mapping; complements nibabel with tools for working with FreeSurfer surfaces. | Python | Structural | |||
| epgpy | Python library for simulating MRI signals using the Extended Phase Graph (EPG) formalism, with extensions for diffusion, magnetization transfer, and sequence optimization. | Python | Multi-modal | |||
| KomaMRI.jl | GPU-accelerated, Pulseq-compatible MRI simulation framework for designing and testing pulse sequences. | Julia | Multi-modal | |||
| MONAI | PyTorch-based open-source framework for deep learning in medical imaging, with pre-built transforms, networks, and training workflows for MRI segmentation and classification. | Python | Multi-modal | |||
| NIDL | Deep learning library for neuroimaging (anatomical volumes, surfaces, and fMRI), following the PyTorch training design and scikit-learn model API. | Python | Multi-modal | |||
| qMRLab | MATLAB/Python toolbox for simulation, analysis, and visualization of quantitative MRI data including T1, T2, magnetization transfer, and diffusion models. | MATLAB/Python | Multi-modal | |||
| ClinicaDL | Python library for reproducible deep learning in neuroimaging, providing pipelines for data preparation, training, and evaluation on MRI and PET. | Python | Multi-modal | |||
| Datasets | ||||||
| OpenNeuro | Free and open platform for sharing and analyzing BIDS-formatted neuroimaging datasets. | |||||
| NeuroVault | Repository for sharing unthresholded statistical brain maps from published studies. | |||||
| Human Connectome Project | High-resolution structural, functional, and diffusion MRI from 1,200 healthy adults. | |||||
| ADNI | Longitudinal MRI data from participants across the Alzheimer's disease spectrum. | |||||
| OASIS | Cross-sectional and longitudinal structural brain MRI datasets for aging and dementia research. | |||||
| IXI | Around 600 structural MRI scans from healthy subjects collected at three London hospitals. | |||||
| ABCD | Longitudinal brain imaging study tracking 10,000+ adolescents across the US. | |||||
| UK Biobank | Population-level brain imaging from 100,000+ UK participants; access requires application. | |||||
| HBCD | Longitudinal study of early brain and cognitive development tracking children from birth to age ten with structural and functional MRI. | |||||
| NeuroStore | Centralized database of 30,000+ neuroimaging studies with pre-extracted activation coordinates, powering Neurosynth Compose. | |||||
| BrainMap | Curated database of neuroimaging coordinates and metadata from peer-reviewed studies; powers GingerALE meta-analyses. | |||||
| ATLAS | Open stroke dataset of T1-weighted MRI scans with manually segmented lesion masks; version 3 includes 1,453 subjects. | Structural | ||||
| HBN | Open pediatric dataset from the Child Mind Institute with structural and functional MRI alongside behavioral measures. | Multi-modal | ||||
| NeuroBagel | Ecosystem for distributed neuroimaging dataset harmonization and search. | |||||
| Consortia | ||||||
| ENIGMA | Global consortium for large-scale neuroimaging genetics meta-analyses spanning 80+ working groups and countries. | |||||
| ReproNim | NIH center for reproducible neuroimaging computation — training, tools, and standards. | |||||
| INCF | International Neuroinformatics Coordinating Facility — standards, training, and infrastructure for neuroscience. | |||||
| CONP | Canadian Open Neuroscience Platform — data sharing and open science infrastructure for neuroimaging research. | |||||
| OHBM Open Science SIG | OHBM special interest group advancing open sharing of ideas, data, and tools in neuroimaging. | |||||
| Standards & Protocols | ||||||
| ENIGMA Protocols | Standardized imaging protocols for cortical, subcortical, DTI, and VBM analysis used across ENIGMA working groups. | |||||
| protocols.io | Platform for sharing and discovering versioned, citable step-by-step research protocols. | |||||
| BIDS specification | Community standard for organizing and describing neuroimaging datasets in a consistent file and metadata structure. | Web | Multi-modal | |||
| BIDS Validator | Checks datasets for compliance with the BIDS standard, flagging missing files and metadata errors. | JavaScript | Multi-modal | |||
| ENIGMA FreeSurfer Protocol | Standardized scripts for cortical thickness, surface area, and subcortical volume extraction and QC from FreeSurfer outputs across ENIGMA working groups. | MATLAB | Structural | |||
| ENIGMA DTI-TBSS Protocol | Standardized pipeline for registering FA images to the ENIGMA-DTI template and performing tract-based spatial statistics with ROI extraction. | MATLAB | Diffusion | |||
| ENIGMA DTI Preprocessing Guidelines | Standardized preprocessing scripts for diffusion MRI eddy current correction and EPI distortion correction across ENIGMA-DTI sites. | Shell | Diffusion | |||
| ENIGMA-CNV Protocol | Protocols and scripts for the ENIGMA copy number variant working group, covering neuroimaging QC and analysis steps for CNV carriers. | Shell | Multi-modal | |||
| Preregistration & Publishing | ||||||
| OSF | Open platform for preregistration, data and code sharing, and DOI-based archiving of study materials. | |||||
| AsPredicted | Lightweight preregistration platform — answer a short fixed questionnaire to lock hypotheses before data collection. | |||||
| bioRxiv | Preprint server for biology and neuroscience; shares findings publicly before peer review. | |||||
| NeuroLibre | Reproducible neuroscience preprints with live, executable code and data embedded in the publication. | |||||
| Zenodo | Archives code, data, and posters with citable DOIs; integrates directly with GitHub releases. | |||||
| PROSPERO | International registry for preregistering systematic reviews and meta-analyses before they begin. | |||||
| PreReg | Build a study plan from a concise preregistration to a full Registered Reports manuscript; run by the Leibniz Institute for Psychology (ZPID). | |||||
| Community | ||||||
| NeuroStars | Q&A forum for neuroinformatics, neuroimaging methods, and tool support. | |||||
| NITRC | Registry of neuroimaging analysis tools, datasets, and computational resources with a searchable database. | |||||
| MR-Hub | Community directory of open-source MRI reconstruction and simulation software, maintained by the ISMRM. | |||||
| Andy's Brain Book | Step-by-step online tutorials covering fMRI, structural MRI, diffusion, and FreeSurfer analysis using FSL, SPM, and AFNI. | |||||
| Andy's Brain Blog | Blog and linked video tutorials by Andy Jahn covering practical MRI analysis workflows. | |||||
| Neural Strategies | Russ Poldrack's newsletter: thoughts on minds, brains, and AI, with a heavy dose of coding. | |||||
| Better Code, Better Science | Open online book by Russ Poldrack on writing readable, robust, and reproducible scientific code, including AI-assisted coding practices. | |||||
| K-Space Explorer | Interactive tool for visualising k-space and understanding how MRI images are formed, with real-time inverse Fourier transforms. | |||||
| fMRI-Resources | Curated page of fMRI information and resources — useful websites, analysis software, and brain-anatomy references. | fMRI | ||||
| Events | ||||||
| Neurohackademy | Summer institute in neuroimaging and data science at the University of Washington. | |||||
| OHBM Brainhack | Annual hackathon for collaborative neuroimaging projects, co-located with the OHBM annual meeting. | |||||
| Brainhack | Worldwide network of collaborative hackathons for open neuroscience tool development, with tutorials, proceedings, and a code of conduct. | |||||
| MRI Together | Annual ESMRMB workshop on open and reproducible MRI science, run virtually with talks, discussions, and hands-on sessions. | |||||
| Software Carpentry | Hands-on workshops teaching researchers foundational computing skills — the Unix shell, version control with Git, and programming in Python or R. | |||||
| General-Purpose Tools | ||||||
| Git | Distributed version control system for tracking changes in code and collaborating with others. | C | ||||
| Docker | Packages software and its dependencies into containers that run identically across machines; the basis for BIDS Apps. | Go | ||||
| conda | Cross-platform package and environment manager for Python and other languages; mamba is a faster drop-in reimplementation. | Python | ||||
| uv | Fast Python package and project manager written in Rust, replacing pip and virtualenv workflows. | Rust | ||||
| Pixi | Fast package and environment manager built on the conda ecosystem, for reproducible per-project environments. | Rust | ||||
| Jupyter | Interactive notebooks for writing and running code, text, and figures together; the standard surface for exploratory analysis. | TypeScript | ||||
| DataLad | Version control for datasets — tracks and shares data alongside code using Git and git-annex. | Python | ||||
| Pydantic | Data validation library for Python using type annotations; checks that data has the expected structure. | Python | ||||
| SDV (Synthetic Data Vault) | General-purpose Python library for generating synthetic tabular, relational, and time-series data, used for privacy-preserving data sharing and augmentation. | Python | ||||