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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 for organizing, sharing, and analyzing molecular neuroimaging data. 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 enable GDPR-compliant controlled access. Emerging open-source workflows and BIDS applications further support automated, reproducible PET preprocessing and quantitative analysis, promoting harmonized processing across centers. Together, these developments mark an important step toward an open molecular neuroimaging ecosystem in which datasets, software, and workflows can be transparently shared, reused, and scaled for collaborative research.

Mycosis fungoides (MF), the most common cutaneous T-cell lymphoma, is challenging to diagnose in its early stages because clinical and histopathologic features often overlap with benign inflammatory dermatoses (BIDs), leading to misclassification and delayed treatment. To evaluate whether a self-supervised AI system can support diagnostic decision-making among MF and common inflammatory mimics. This retrospective two-center study included patients with confirmed diagnoses of MF or BIDs. A self-supervised multimodal system integrating whole-slide histopathology and routine clinical variables was developed to perform multiclass differential classification across MF and common BIDs. Cases were partitioned into training, internal validation, and independent external validation cohorts at the patient level. Clinical utility was assessed in a reader study in which dermatopathologists reviewed cases with and without system assistance. Across 786 WSIs from 532 patients, the multimodal model achieved macro-AUCs greater than 0.85 in both validation sets, with macro-balanced accuracy of 0.837 (95% CI, 0.778-0.897; n=106) internally and 0.762 (95% CI, 0.724-0.802; n=260) externally, representing improved performance over the unimodal histopathology model. In the reader study, AI assistance was associated with improved macro-balanced accuracy among both junior (0.778 to 0.818) and senior (0.805 to 0.859) dermatopathologists, accompanied by consistent improvements in macro-averaged sensitivity and specificity across all diagnostic categories. Interpretability analyses generated heatmaps that were generally consistent with recognized histopathologic features of both MF and BIDs, aligning with dermatopathologist interpretation. This multimodal, self-supervised system may support dermatopathologists by providing interpretable, probability-based guidance for the classification of MF and its common inflammatory mimics.

The ethical and legal imperative to share research data without causing harm requires careful attention to privacy risks. While mounting evidence demonstrates that data sharing benefits science, legitimate concerns persist regarding the potential leakage of personal information that could lead to reidentification and subsequent harm. We reviewed metadata accompanying neuroimaging datasets from heterogeneous studies openly available on OpenNeuro, involving participants across the lifespan-from children to older adults-with and without clinical diagnoses, and including associated clinical score data. Using metaprivBIDS (https://github.com/CPernet/metaprivBIDS), a software application for BIDS-compliant tsv/json files that computes and reports different privacy metrics (k-anonymity, k-global, l-diversity, SUDA, PIF), we found that privacy is generally well maintained, with serious vulnerabilities being rare. Nonetheless, issues were identified in nearly all datasets and warrant mitigation. Notably, clinical score data (e.g., neuropsychological results) posed minimal reidentification risk, whereas demographic variables-age, sex assigned at birth, sexual orientations, race, income, and geolocation-represented the principal privacy vulnerabilities. We outline practical measures to address these risks, enabling safer data sharing practices.

Overdose prevention centers (OPCs) are interventions to reduce overdose mortality and support health care engagement. In the US, concerns have been raised that OPCs may be associated with reduced economic activity in their surrounding neighborhoods. To evaluate changes in the local economic activity in New York City (NYC), measured by neighborhood-level foot traffic and consumer spending, following the opening of the first 2 publicly recognized OPCs in the US. This cohort study used anonymized mobility and spending data from June 1, 2021, to June 13, 2022, for the areas surrounding the East Harlem and Washington Heights OPCs in NYC. These neighborhoods were defined using 5-minute and 10-minute walking buffers and Business Improvement Districts (BIDs). Synthetic control donors included walking buffers and BIDs around syringe service programs without OPCs and opioid treatment programs that were operational as of OPCs' opening. Analyses were conducted from February to July 2025. Opening of the 2 NYC OPCs on November 30, 2021. Primary outcomes were foot traffic and in-person consumer spending within 10-minute walking buffers. Secondary analyses considered 5-minute walking buffers and BIDs. Augmented synthetic control models were adjusted for neighborhood-level demographic and socioeconomic features, with fit assessed using root mean squared error before OPC opening. Permutation tests and conformal inference were used to assess significance. A total of 27 biweekly observations (13 in pre-OPC and 14 in post-OPC periods) were analyzed. The 10-minute walking buffer analyses captured 1259 consumer spending sites and 7816 foot traffic sites across 2 treated buffers and 56 donor buffers. In East Harlem, the average treatment effect on the treated (ATT) estimate (SE) was -$21.96 ($40.53) for consumer spending (P = .16) and 1.28 (5.40) visits for foot traffic (P = .19). In Washington Heights, ATT (SE) estimates were $14.94 ($37.38) for consumer spending (P = .13) and 0.44 (3.54) visits for foot traffic (P = .97). Secondary analyses produced consistent results. No statistically significant results were observed at any post-OPC time point. This cohort study found that OPC opening was not associated with significant changes in local economic activity. Given the absence of observed economic harms, policy debates should instead focus on the public health implications of OPCs.

High-density intracranial recordings during naturalistic language processing are critical for advancing models of speech perception. However, open, well-annotated high-density ECoG resources for tonal languages such as Mandarin remain scarce. We present a publicly available high-density ECoG dataset from four participants undergoing awake craniotomy who listened to continuous, sentence-level Mandarin drawn from the Annotated Speech Corpus of Chinese Discourse (ASCCD). Signals were recorded with 128-256-channel subdural grids and synchronized with the audio; ECoG signals were down-sampled to 400 Hz, filtered in the high-gamma range (70-150 Hz), and used to derive high-gamma amplitude. The release follows BIDS-iEEG and is distributed as NWB files, with derivatives including high-gamma amplitude; word- and syllable-level alignments; Pinyin; lexical tone and stress tiers; prosodic break indices; mel-spectrograms; F0 and formants; and electrode localization on individual anatomy with projections to MNI space. This resource supports fine-grained investigations of lexical tone, syllabic structure, and higher-level linguistic representations during naturalistic listening.

To produce a list of prioritised research questions in paediatric critical care transport (PCCT) medicine. Two round modified Delphi method using the Paediatric Critical Care Society (PCCS) members. UK PCCS acute transport group and national PCCT teams. A total of 145 research questions were submitted in round 1, with 93 included in round 2 of the prioritisation process. Following round 2, a modified Hanlon method was applied along with a detailed literature review and the top 10 research priorities for PCCT were identified. This is the first study to prioritise topics for research in PCCT medicine. We identified top research priorities that the PCCT teams feel are important to take forward in future research. This will allow further bids for national funding to be directed towards areas of national importance.

Anesthesia has revolutionized surgical practice and offers a controlled model to study the neurobiology of consciousness. Functional magnetic resonance imaging (fMRI) studies have shown that anesthesia primarily disrupts connectivity across association cortices, suggesting that impaired integration between higher-order brain regions underlies unconsciousness. However, traditional fMRI paradigms are limited in detecting covert consciousness. Here, we present an fMRI dataset acquired from 26 healthy volunteers performing mental imagery tasks (tennis, navigation, and hand squeeze) and a motor response task under graded propofol sedation. The dataset captures brain activity across varying sedation levels, including instances of volitional mental imagery despite behavioral unresponsiveness. Prior analyses using this dataset have investigated the anterior insula's role in conscious access and asymmetric neural dynamics during loss and recovery of consciousness. This openly available dataset, formatted according to BIDS standards and has been released via OpenNeuro, provides a resource for exploring the neural mechanisms of anesthesia and consciousness with the unique feature of mental imagery, traditionally used only during assessment of disorders of consciousness.

The Brain Imaging and Neurophysiology Dataset (BIND) represents one of the largest multi-institutional, multimodal, clinical neuroimaging repositories, comprising 1.8 million brain scans from 38,942 patients, linked to full-text reports and neurophysiological recordings. This comprehensive dataset addresses critical limitations in neuroimaging research by providing a rich and diverse set of large-scale multimodal data. BIND integrates de-identified data from Massachusetts General Hospital, Brigham and Women's Hospital, and Stanford University, including 1,723,699 MRI scans (1.5, 3 and 7 Tesla), 54,137 CT scans, 5,093 PET scans, and 526 SPECT scans, converted to standardized NIfTI format following BIDS organization. The dataset spans the full age spectrum and encompasses diverse neurological conditions alongside healthy subjects. We deployed Large Language Models to extract structured clinical metadata from 84,960 reports to extract standardized clinical information. All imaging data are linked to previously published EEG and polysomnography recordings, facilitating future multimodal analyses. BIND is freely accessible for academic research through the Brain Data Science Platform (https://bdsp.io/). This resource facilitates large-scale neuroimaging studies, machine learning applications, and multimodal brain research to accelerate discoveries in clinical neuroscience.

Quantitative imaging is increasingly used for both clinical and research applications in musculoskeletal (MSK) disorders. Its widespread use, coupled with an assortment of modalities and vendors, has led to a diverse range of analysis methods and challenges in reproducing results both within and across centers. Clearly, consensus is needed to establish consistent data organization principles and thus permit the use of standardized image processing pipelines. Here, we-members of the Open and Reproducible Musculoskeletal Imaging Research (ORMIR) community-introduce the ORMIR-MIDS data format, which is derived from the existing Brain and Medical Imaging Data Structures (BIDS and MIDS) and extends them to MSK applications. ORMIR-MIDS comprises both a standard specification and a set of software tools for data conversion and organization. The latter permits the conversion of image data to a standardized format, in an organized folder structure, and produces up to 3 metadata files: important data-processing information, patient data, and complete image header tags to allow conversion back to the original format. The tool currently supports a range of imaging modalities and sub-modalities relevant to the MSK system, with more to come in the future. A suite of test data is also provided to demonstrate the functionality of the software, and the file system structure and associated image files and metadata after conversion of these test data are demonstrated here. With ORMIR-MIDS, we provide an open specification for multimodal MSK imaging, alongside tools for creating compliant datasets. Adherence to our standard will improve harmonization of imaging data across vendors and institutions and permit the development of reproducible processing pipelines and data repositories for MSK research.

Dermatologic imaging has been rapidly expanding, with over 70% of related PubMed articles published since 2016 and over a million images across international research challenges and large-scale datasets with skin images. To improve data quality and usability, standardizing dermatologic imaging data for non-protected health information (non-PHI) research systems is essential. While the International Skin Imaging Collaboration (ISIC) has advanced standards in skin imaging, the field lacks a generalizable infrastructure to organize and describe imaging data for non-PHI research systems. This results in inconsistently labeled, heterogeneous datasets that hinder data integration, scalability, and interoperability. To address this gap, we propose the Dermatology Imaging Data Structure (DermIDS), inspired by the Brain Imaging Data Structure (BIDS) for neuroimaging. This structured framework aims to improve usability across datasets, reveal metadata gaps, and enable scalable artificial intelligence (AI)/machine learning (ML)-ready workflows. To illustrate this system, we curated and processed 1,000,692 images with DermIDS. We demonstrate that DermIDS (1) supports multimodal photographic data acquired from clinical photography, general photography, dermoscopy, reflectance confocal microscopy, and surface 3D imaging; (2) facilitates image-specific technical and clinical metadata organization; and (3) streamlines quality control and harmonization. Across all images, 1,256 unique metadata features were identified. However, 70% of clinical metadata features and 98% of technical metadata features were present in less than 100k images, highlighting key gaps and demonstrating the utility of DermIDS in revealing inconsistencies and opportunities for standardization. Our work supports large-scale analysis and harmonization, laying the foundation for AI/ML-ready workflows to advance dermatologic imaging research.

Publicly available, large-scale medical imaging datasets are crucial for developing and validating artificial intelligence (AI) models and conducting retrospective clinical research. However, multimodal datasets that integrate functional and anatomical imaging with high-quality radiology reports across diverse malignancies remain scarce. Here, we present PETWB-REP, a curated dataset comprising whole-body 18F-Fluorodeoxyglucose (FDG) Positron Emission Tomography/Computed Tomography (PET/CT) scans and corresponding radiology reports from 490 patients. The cohort encompasses a broad spectrum of malignancies, including but not limited to lung, liver, breast, prostate, and ovarian cancers. Distinct from existing resources, PETWB-REP is organized following the Brain Imaging Data Structure (BIDS) standard, providing both raw data (with 3D de-facing for privacy) and processed derivatives (SUV-converted and registered). Each case includes bilingual (Chinese and English) de-identified textual reports and structured clinical metadata. This dataset is uniquely positioned to support multi-center validation and cross-disciplinary research in medical imaging, radiomics, automated report generation, and multimodal representation learning.

This dataset was acquired and curated to explore the spectrum of Motor Neuron Disease (MND) and Fronto-Temporal Dementia (FTD) with Ultra-High Field Magnetic Resonance Imaging (7 Tesla) and compare these to non-neurodegenerative disease controls (known colloquially as "The 7 T hEalthy Ageing study [7TEA]"). Twenty people living with neurodegenerative disease and 14 non-neurodegenerative controls underwent a comprehensive multimodal MRI protocol including structural, diffusion, quantitative MRI, resting state, and task fMRI, alongside cognitive testing and genetic screening. This dataset combines detailed imaging phenotypes with extensive clinical characterisations. It facilitates investigations into the spectrum of MND and FTD, has provided a basis for developing novel quantitative biomarkers, and supports the exploration of interactions between imaging features and clinical progression. The availability of this dataset supports various research avenues, from detailed hippocampal subfield analyses, network connectivity assessments, and multimodal genetic, cognitive, and imaging studies. The dataset is published on OpenNeuro (dataset ds007036) and is curated in the Brain Imaging Data Structure (BIDS) standard.

Motion-induced artefacts in MRI are a common occurrence but can obscure pathologies or be falsely identified as pathological. Reacquiring motion-corrupted scans is expensive, and thus retrospective and prospective motion correction methods have been introduced. Although motion correction shows promise, there is a lack of exhaustive testing on its efficacy with respect to full clinical cerebral MRI protocols. Here we present a dataset (n = 22) to facilitate future research, which includes data with and without intentional motion, and with and without prospective motion correction, across six MRI sequences included in a full clinical cerebral MRI protocol. Motion was captured by an external tracking device, and the dataset includes the motion data as derived motion transforms. For standardization, all image data are fully BIDS-compliant. Raw k-space data are available as well. As the dataset pairs motion-free data with motion-corrupted data, it can be used to develop or test different motion-correction or k-space reconstruction methods.