The collaboration
lightniing is a collaboration of equal peers. Each team brings distinct expertise to a common goal: open, portable, high-performance neuroimaging infrastructure that researchers can use, inspect, and extend.
The brainchop team developed the lean MeshNet convolutional neural network for volumetric segmentation. Its efficient design enables these methods to run across a wide range of environments, including locally in web browsers using WebGPU. Applications include parcellation and segmentation, brain extraction, and lesion detection.
The DIPY (Diffusion Imaging in Python) team develops comprehensive open-source tools for analyzing computational neuroimaging data, with a strong focus on diffusion MRI. Its library provides advanced algorithms for fiber tractography, microstructure imaging, registration, reconstruction, and denoising, making state-of-the-art processing pipelines accessible and reproducible across diverse computing environments.
The fideus labs team has led the development of ITK-Wasm, bringing the proven ITK scientific computing library to the web. Its browser-based platforms and tools enable high-performance medical image processing and interactive spatial-data analysis directly in web applications. The team maintains core infrastructure and spatial-data tooling across the open-source scientific software ecosystem, including community datasets in scalable, web-optimized formats.
The Neurodesk team develops open-source infrastructure for accessible and reproducible neuroimaging. Its eponymous Neurodesk platform enables neuroimaging analyses to run across personal computers, high-performance computing, and cloud environments while addressing software dependencies, version conflicts, and platform incompatibilities.
The NiiVue team has developed and maintained free, open-source neuroimaging software for more than 25 years. Its visualization platforms include MRIcro, MRIcron, MRIcroGL, Surfice, and the community-driven NiiVue. The team also maintains core image-processing tools, including dcm2niix and niimath.
The NeuroImaging PREProcessing toolS (NiPreps) team develops a community-driven ecosystem of standardized, open-source pipelines for reproducible neuroimaging preprocessing. Its framework includes widely adopted workflows such as fMRIPrep and MRIQC, alongside specialized pipelines for structural, diffusion, infant, and PET imaging. By decoupling preprocessing from downstream statistical analysis, NiPreps ensures transparent, robust, and reusable data processing across diverse acquisition parameters, clinical populations, and species.
The Penn Lifespan Informatics & Neuroimaging Center (PennLINC) builds reproducible software, open data pipelines, and scalable informatics tools to map human brain development. Its research integrates multi-modal neuroimaging, network science, and machine learning to define normal patterns of brain maturation and understand the developmental origins of neuropsychiatric illness. The center maintains major community resources, including the Reproducible Brain Charts (RBC), the dMRI data of the ABCD-BIDS Community Collection (ABCC), and core analytical workflows like QSIPrep, QSIRecon, and XCP-D.