Christopher Johnson Markiewicz

International data privacy regulations impede the pooling of research data for collaborative analysis. We introduce Neurobagel, a federated network enabling cohort discovery across locally governed, access-controlled datasets. Through intuitive graphical tools and a decentralized query infrastructure, Neurobagel facilitates harmonization, control, and discovery of data according to local regulations. Today, Neurobagel is deployed by consortia and data platforms in Europe, North America, Asia, and Australia, supporting diverse and evolving regulatory frameworks.

Molecular neuroimaging with positron emission tomography (PET) and single-photon emission computed tomography (SPECT) enables quantification of specific molecular targets in the living brain. Despite its scientific impact, molecular neuroimaging research has historically faced challenges due to high costs, small sample sizes, laboratory-specific analysis pipelines, and limited large-scale data sharing. These factors have hindered reproducibility and the broader reuse of valuable PET datasets. The OpenNeuroPET initiative was established to address these barriers by developing standards, infrastructure, and open-source tools currently focused on PET. Through collaborations across Europe and North America, OpenNeuroPET has supported the PET extension of the Brain Imaging Data Structure (PET–BIDS), providing a standardized framework for PET datasets and metadata. Building on PET–BIDS, tools such as PET2BIDS, ezBIDS, and BIDSCoin facilitate data conversion and curation. In parallel, OpenNeuro now hosts PET–BIDS datasets for open sharing, while complementary platforms such as PublicnEUro provide controlled-access pathways designed to support compliance with the European Union General Data Protection Regulation (GDPR). Emerging open-source workflows further support automated, reproducible PET analysis, promoting harmonization across centers. Together, these developments mark an important step toward an open molecular neuroimaging community in which datasets, software, and workflows can be transparently shared, reused, and scaled for collaborative research.

Electromyography (EMG) is fundamental to clinical assessment, rehabilitation, neuromuscular research, and human-machine interfaces. Despite decades of use, no widely adopted standard exists for organizing and sharing EMG data, limiting reusability and large-scale data aggregation. We present EMG-BIDS, an extension to the Brain Imaging Data Structure (BIDS) that standardizes the organization of EMG recordings. EMG-BIDS addresses challenges unique to EMG, including diverse electrode types (surface or intramuscular, single channel to high-density arrays), heterogeneous electrode placements across anatomical locations, montages (e.g., monopolar or bipolar sensor designs), and the critical need for transparent documentation of sensor positioning. The specification introduces hierarchical coordinate systems that link local electrode grids to anatomical landmarks, enabling precise and reproducible placement documentation. EMG-BIDS is now part of BIDS as of version 1.11.0, supported by existing tools, including MNE-BIDS and EEGLAB. We demonstrate the specification through public datasets, including high-density surface EMG recordings. EMG-BIDS provides the foundation for FAIR (Findable, Accessible, Interoperable, Reusable) EMG data sharing, enabling meta-analyses, multi-site studies, and machine learning applications that require standardized, well-documented datasets.

International data privacy regulations impede the pooling of research data for collaborative analysis. We introduce Neurobagel, a federated network enabling cohort discovery across locally governed, access-controlled datasets. Through intuitive graphical tools and a decentralized query infrastructure, Neurobagel facilitates harmonization, control, and discovery of data according to local regulations. Today, Neurobagel is deployed by consortia and data platforms in Europe, North America, Asia, and Australia, supporting diverse and evolving regulatory frameworks.

The field of neuroimaging has witnessed an exponential increase in data production and availability, fueled by technical advances that have enabled large-scale data collection, as well as by changes in policies and funding initiatives that support open data-sharing programs. As the number of openly available datasets grows larger, the need for efficient, standardized, and flexible tools to index, inspect, and fetch neuroimaging data is becoming a pressing need. OpenNeuro is a popular data hosting service in neuroimaging research; it allows to share publicly neuroimaging data stored following the Brain Imaging Data Structure (BIDS) standard. Although tools such as bids2table (https://childmindresearch.github.io/bids2table/bids2table.html) or Neurobagel Query enable interrogating BIDS datasets (including those on OpenNeuro) and the associated metadata, researchers that require using a subset of one or multiple datasets stored in data repositories typically need to fetch the entire dataset, and to use dedicated tools to interrogate and select the relevant data, hindering interoperability and scalability. Furthermore, existing tools to interrogate BIDS datasets only enable interrogating the metadata, and miss other features that can only be obtained by inspecting the actual volumetric data. Conversely, command-line tools allowing to fetch datasets from remote servers (e.g., Cohort Creator) do not provide data inspection or metadata-based filtering capabilities. Thus, a tool to efficiently interrogate and filter neuroimaging data hosted on public repositories is missing.We present NiQuery , a principled, open-source Python library designed to address these challenges by providing a robust framework for querying neuroimaging datasets, inspecting metadata, performing subset selection, and aggregating results. NiQuery seeks to facilitate reproducible research by enabling transparent access to the content of neuroimaging datasets, including volume-specific metadata.

Open data infrastructures are rapidly reshaping the landscape of neuroscience by enabling greater reproducibility through transparency, reusability, and collaboration, thus providing a foundation for sustainable and scalable scientific progress. This paper presents a systematic audit of the Neuroelectromagnetic Data Archive and Repository (NEMAR), a platform supporting the sharing and reuse of EEG, MEG, and iEEG datasets standardized through the Brain Imaging Data Structure (BIDS) format. Drawing on a detailed analysis of over 300 publicly available datasets, we assess the composition, metadata quality, and population coverage of data in NEMAR. Our findings reveal significant momentum in data sharing practices, particularly in EEG, and highlight key challenges, including inconsistent metadata, underrepresentation of clinical populations, and variable adherence to data standards. These insights highlight the importance of targeted support for policy and cultural development to ensure open repositories like NEMAR are not only accessible but also equitable, reusable, and scientifically robust.

The Brain Imaging Data Structure (BIDS) is an increasingly adopted standard for organizing scientific data and metadata. It facilitates easier and more straightforward data sharing and reuse. BIDS currently encompasses several biomedical imaging and non-imaging techniques, and as more research groups begin to use it, additional experimental techniques are being incorporated into the standard, allowing diverse experimental methods to be stored within the same cohesive structure. Here, we present an extension for magnetic resonance spectroscopy (MRS) data, termed MRS-BIDS.

SynopsisMotivation: Create an open-source alternative to FSL’s eddy without commercial limitations thatgeneralizes over problems beyond eddy current distortions of diffusion MRI.Goals: Evaluate the tool’s performance vis-à-vis with corresponding tools.Approach: We implement a Gaussian Process regressor model and a Bayesian optimizationlayer on an existing registration framework, and estimate the alignment of neuroimaging dataenabled by the eddymotion framework.Results: Using publicly available neuroimaging data, we provide evidence about theeffectiveness of our volume-to-volume modeling framework for generalized artifact identificationand correction in neuroimaging.ImpactWe present eddymotion, an open-source framework for volume-to-volume artifact estimationinspired by FSL eddy. Our tool allows for easy alternative model implementation, it can be usedfor non-dMRI imaging modalities and does not have a restrictive license.

The adoption of a standardized preprocessing workflow is vital for fostering community, sharing, and reproducibility. fMRIPrep has been a critical advancement towards this end, however, it is limited in its capacity to be applied to data across the lifespan, starting from infancy. Here, we introduce fMRIPrep Lifespan, an extension of fMRIPrep that extends the standardized processing from childhood to senescence to include neonatal, infant, and toddler structural and functional MRI data preprocessing. This effort involves a NiPreps integration of 1) a workflow akin to fMRIPrep optimized for MRI data in the first years of life (previously NiBabies) and 2) upstream enhancements to the entire NiPreps suite, including multi-echo data processing, modularization of workflow components, and convergence of processing with other popular workflows (ABCD-BIDS, Human Connectome Project Pipelines). Using data from the Baby Connectome Project (participants 1-43 months of age), we demonstrate that fMRIPrep Lifespan produces high-quality outputs across a wide age range. Moving forward, the scalable, modular infrastructure of fMRIPrep Lifespan will ensure adaptability to data from birth to old age while maintaining robust and reproducible frameworks for functional MRI research across the lifespan.

Functional near-infrared spectroscopy (fNIRS) is an increasingly popular neuroimaging technique that measures cortical hemodynamic activity in a non-invasive and portable fashion. Although the fNIRS community has been successful in disseminating open-source processing tools and a standard file format (SNIRF), reproducible research and sharing of fNIRS data amongst researchers has been hindered by a lack of standards and clarity over how study data should be organized and stored. This problem is not new in neuroimaging, and it became evident years ago with the proliferation of publicly available neuroimaging datasets. To solve this critical issue, the neuroimaging community created the Brain Imaging Data Structure (BIDS) that specifies standards for how datasets should be organized to facilitate sharing and reproducibility of science. Currently, BIDS supports dozens of neuroimaging modalities including MRI, EEG, MEG, PET, and many others. In this paper, we present the extension of BIDS for NIRS data alongside tools that may assist researchers in organizing existing and new data with the goal of promoting public disseminations of fNIRS datasets.