Steffen Bollmann

Interactive visualization is essential for collaborative analysis of complex neuroimaging data. Modern workflows span multiple computing environments including notebooks, integrated development environments, and standalone applications. All these workflows rely on visualization for quality control, exploration, and interpretation. However, visualization tools are typically siloed, forcing users to switch between environments and disrupting interactive analysis. Here, we address this challenge by integrating a shared visualization module directly into these diverse environments, providing a consistent visualization experience across formats, representations, and usage contexts. We developed a generalizable ecosystem of open-source tools built around the NiiVue rendering engine, supporting four primary use cases. First, we provide integrated file viewer capability for VS Code and JupyterLab. Second, we showcase three different architectures for standalone desktop applications. Third, we integrate NiiVue for Python notebooks, Julia, and R. Finally, we integrate NiiVue into traditional web applications. This unified ecosystem enables immediate, in-place inspection of neuroimaging data across the majority of modern computing platforms, eliminating the need to export data to external viewers. The tools support interactive inspection, dashboard creation, and the development of custom analysis applications within users’ existing workflows. The components are validated by automated continuous-integration testing, including unit tests and browser visualization tests against stored reference targets. By embedding visualization directly into commonly used computing environments, this ecosystem lowers the barrier to interactive data exploration and streamlines neuroimaging workflows. Visualization becomes an integral component of automated, reproducible, and shareable analysis pipelines, supporting more efficient collaboration and interpretation of complex neuroimaging data. By sharing a common rendering core, file format support, and interaction model, users can transfer visualization skills seamlessly across environments.

Neuroimaging data management and processing are tedious and error-prone, prompting reproducibility concerns. Globally, studies with heterogeneous infrastructure and governance policies lead to eclectic data processing and sharing, necessitating standardization of data workflows to ensure reusability and comparability of multi-centric datasets. The Nipoppy neuroinformatics framework facilitates such standardization by combining specification, protocol, and software to manage study-level data workflows. With its adoption, researchers can share standardized, derived datasets enabling efficient, reproducible, and inclusive research.

Neuroimaging education presents both conceptual and technical challenges: one must master complex foundational principles while navigating technical requirements including software installation, dependency management, and cross-platform compatibility. These tasks are valuable learning opportunities, but they can be time-consuming, which may shift focus away from learning about core neuroimaging concepts. While neuroimaging education benefits from many established resources, interactive notebooks can further integrate instruction with execution, providing a hands-on complement to existing educational materials. Here, we showcase NeurodeskEDU, an emerging learning resource developed using Neurodesk, an open-source, containerised neuroimaging platform, which enables neuroimaging workflows to be executed in a fully reproducible fashion through interactive Jupyter Notebooks. The notebooks integrate code, visualisations, narrative documentation, and direct access to open datasets into analysis workflows. Containerised software delivery reduces installation overhead while ensuring consistent execution across computing platforms. Community contributions undergo automated testing via GitHub Actions to ensure notebook quality and functionality, while persistent DOIs establish notebooks as formally citable scholarly outputs. This new community learning resource provides multiple deployment modes: zero-install cloud execution, downloadable notebooks for local use, static HTML documentation, and interactive slideshows for teaching scenarios. Through three use cases, we demonstrate how such interactive notebooks consolidate fragmented educational workflows, enable seamless multi-tool integration, and support scalable deployment from workshops to self-directed learning. By establishing educational materials as scholarly outputs with systematic quality assurance and version control, this work addresses ongoing challenges in computational publishing: maintaining reproducibility as software evolves, facilitating community contributions, and providing formal academic recognition for educational scholarship.

Topographic organization, whereby neighboring cortical locations encode neighboring features in sensory or cognitive space, is a fundamental principle of brain function. Existing approaches for obtaining individual-specific topographic maps either require resource-intensive functional neuroimaging or, when relying on population atlases, lack precision for individual-level inference. Here, we introduce deepRetinotopy toolbox, a deep learning-based application for predicting the functional topographic organization of human visual cortex from cortical anatomy alone. DeepRetinotopy toolbox produces accurate retinotopic maps across diverse experimental conditions, imaging sites, and scanner types. We demonstrate how predicted maps can be utilized to automatically generate individual-specific visual area boundaries, overcoming common biases in manual annotations. Finally, we applied our method to 11,060 anatomical scans, which allowed us to quantify age-related changes in the functional organization of visual cortex predictable from anatomy alone, underscoring the method’s broad utility for scalable, anatomy-based functional brain mapping.

Neuroimaging education presents both conceptual and technical challenges: one must master complex foundational principles while navigating technical requirements including software installation, dependency management, and cross-platform compatibility. These tasks are valuable learning opportunities, but they can be time-consuming, which may shift focus away from learning about core neuroimaging concepts. While neuroimaging education benefits from many established resources, interactive notebooks can further integrate instruction with execution, providing a hands-on complement to existing educational materials. Here, we showcase NeurodeskEDU, an emerging learning resource developed using Neurodesk, an open-source, containerised neuroimaging platform, which enables neuroimaging workflows to be executed in a fully reproducible fashion through interactive Jupyter notebooks. The notebooks integrate code, visualisations, narrative documentation, and direct access to open datasets into analysis workflows. Containerised software delivery reduces installation overhead while ensuring consistent execution across computing platforms. Community contributions undergo automated testing via GitHub Actions to ensure notebook quality and functionality, while persistent DOIs establish notebooks as formally citable scholarly outputs. This new community learning resource provides multiple deployment modes: zero-install cloud execution, downloadable notebooks for local use, static HTML documentation, and interactive slideshows for teaching scenarios. Through three use cases, we demonstrate how such interactive notebooks consolidate fragmented educational workflows, enable seamless multi-tool integration, and support scalable deployment from workshops to self-directed learning. By establishing educational materials as scholarly outputs with systematic quality assurance and version control, this work addresses ongoing challenges in computational publishing: maintaining reproducibility as software evolves, facilitating community contributions, and providing formal academic recognition for educational scholarship.

Topographic organization, whereby neighboring cortical locations encode neighboring features in sensory or cognitive space, is a fundamental principle of brain function. Existing approaches for obtaining individual-specific topographic maps either require resource-intensive functional neuroimaging or, when relying on population atlases, lack precision for individual-level inference. Here, we introduce deepRetinotopy toolbox , a deep learning-based application for predicting the functional topographic organization of human visual cortex from cortical anatomy alone. DeepRetinotopy toolbox produces accurate retinotopic maps across diverse experimental conditions, imaging sites, and scanner types. We demonstrate how predicted maps can be utilized to automatically generate individual-specific visual area boundaries, overcoming common biases in manual annotations. Finally, we applied our method to 11,060 anatomical scans, which allowed us to quantify age-related changes in the functional organization of visual cortex predictable from anatomy alone, underscoring the method’s broad utility for scalable, anatomy-based functional brain mapping.

Neuroimaging research has increasingly embraced open science practices, including the use and sharing of publicly available datasets. However, researchers still face significant challenges in accessing and preparing such datasets, from navigating through different storage systems and tools to complying with data privacy regulations. Moreover, the varied dependencies used by these tools often hinder reproducibility and accessibility, which in turn can negatively impact the reliability of analyses. In this paper, we show how Neurodesk streamlines both the use of open data and the preparation of datasets for sharing publicly. By integrating data access and standardized processing, Neurodesk maximizes the utility of open data initiatives while promoting accessibility and equity in the scientific community. We demonstrate Neurodesk’s capabilities through real-world use cases and outline ongoing developments toward secure, scanner-integrated data processing, further supporting privacy-preserving collaboration in neuroscience.

Computational tools are essential to neuroimaging research, yet their development, use, and citation are inconsistently addressed in current scientific publishing frameworks. Despite increasing adoption of the FAIR and FAIR4RS principles, challenges remain in ensuring the reproducibility, accessibility, and attribution of research software. This article presents how Neurodesk, an open-source, community-driven platform improves the transparency, portability, and sustainability of neuroimaging workflows. Neurodesk uses containerisation to encapsulate complete software environments that are portable across operating systems and computing infrastructures. Each container and workflow can be versioned and assigned a persistent DOI to support formal citation and long-term access. Through concrete use cases, we illustrate how Neurodesk facilitates code review, supports methodological extension, and fosters sustainable tool development. We argue that platforms like Neurodesk provide a practical infrastructure for enacting cultural and technical shifts in publishing, making research software a primary research output. By integrating attribution, standardisation, and reproducibility into everyday research workflows, Neurodesk exemplifies how future scientific publishing can more effectively recognise and preserve computational contributions.

Brain stimulation shows promise as an intervention to enhance executive function, particularly when paired with cognitive training. To optimize such approaches, we must understand the potential role of individual differences in intervention outcomes. We investigated the combined effects of multi-session multitasking training and prefrontal transcranial direct current stimulation (tDCS) on generalization of performance benefits, focusing on how cortical morphology predicts performance improvements. One hundred seventy-eight individuals underwent 7 Tesla MRI before completing multisession training with online stimulation. A cognitive task battery assessed improvements in trained and untrained tasks pre- and post-training. Stimulating the left or right prefrontal cortex at 1 mA during multitasking training enhanced transfer to a visual search task. Critically, cortical morphology predicted stimulation efficacy for inducing transfer. Cortical thickness in regions beneath the stimulating anode was related to reaction time changes in the most difficult visual search condition but only for the left and right 1 mA multitasking training groups. Performance was not related to cortical thickness for the groups receiving sham stimulation, 2 mA stimulation, or 1 mA stimulation with a control training task. These results highlight the importance of individual anatomical differences in modulating tDCS efficacy and identifying specific neuroanatomical features that predict generalized performance gains from combining tDCS with cognitive training.