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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.

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.

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.

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.

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.

Here, we present a large-scale, multi-center dataset of combined magnetoencephalographic (MEG) and electroencephalographic (EEG) recordings, along with eye-tracking data and high-resolution structural MRI (T1); complementing with iEEG and fMRI datasets that are shared in accompanying data papers. The data was obtained through an adversarial collaboration between advocates of two neuroscientific theories of consciousness: the Global Neuronal Workspace Theory and the Integrated Information Theory. The dataset includes recordings from 100 individuals (mean age 22.79 ± 3.59 years, 54 female, all right-handed) across two research centers (UK and China), using a standardized data collection protocol. During the experiment, participants were asked to perform a non-speeded Go/No-Go target detection task, during which they were exposed to visual stimuli from four distinct categories (faces, objects, letters, false fonts) presented at different orientations (front, left, right view), and for varying durations (0.5, 1.0, 1.5 s), under different task conditions. The quality of the data was assessed and organized according to the Brain Imaging Data Structure (BIDS). It is accompanied by extensive metadata to enhance reusability.

Automated seizure detection and localization from intracranial EEG requires validated benchmark datasets with expert annotations, yet existing open datasets lack multi-expert consensus annotations and exclude stimulation-induced seizures. We present stereotactic EEG recordings from 83 seizures (46 spontaneous, 37 stimulation-induced) across 32 patients (19 from the University of Pennsylvania, 13 from the Children's Hospital of Philadelphia) with drug-resistant epilepsy. Three board-certified epileptologists independently annotated each seizure for onset time, onset channels, and channels seizing at 10 seconds post-onset using a standardized protocol. Consensus annotations were determined through majority voting. Inter-rater agreement was κ = 0.64 for onset channels and κ = 0.62 for spread channels. Individual rater agreement with consensus was κ = 0.81 for onset and κ = 0.80 for spread. Agreement metrics did not differ between spontaneous and stimulation-induced seizures. All data follow Brain Imaging Data Structure (BIDS) standards and include electrode localizations, patient demographics, and clinical outcomes. This dataset enables the validation of seizure onset and spread detection and localization against human expert performance and supports comparative analysis of seizure networks across spontaneous and stimulation-induced seizures.

Macaque MRI bridges non-invasive systems neuroscience with cellular and circuit-level mechanisms, but preprocessing remains fragmented across tools that are difficult to integrate, adapt to non-human primate acquisitions, and deploy reproducibly. We present Brainana, an automated, BIDS-compatible preprocessing framework for macaque neuroimaging. Brainana integrates structural and functional preprocessing, cortical surface reconstruction, quality control, transform tracking, and atlas projection within a containerized package, with cloud access for users without local compute. It incorporates macaque-trained deep learning models for brain extraction and tissue segmentation, conformation to standardize variable acquisitions, and surface reconstruction optimizations for macaque neuroanatomy. Across 23 imaging sites, Brainana processed data spanning heterogeneous scanners, protocols, species, and resolutions, yielding accurate anatomical correspondence across 130 monkeys, reliable native-space cortical surfaces, localized task-evoked activations, and reproducible brain-wide resting-state correlation structure. Brainana enables reproducible, scalable, and accessible macaque MRI preprocessing that supports cross-study comparison and multimodal integration across spatial scales, from neurons to networks. .

This work presents a multimodal dataset containing synchronized electroencephalography (EEG), electromyography (EMG), and kinematic recordings acquired during wrist motor tasks performed with a three degree of freedom robotic exoskeleton (BiomechWrist) coupled to a virtual interface. Designed as a normative baseline and benchmark resource for studying electrophysiological biomarkers and motor performance in healthy individuals, the dataset includes recordings from 45 healthy participants, each completing 320 trials of standardized wrist movements. The exoskeleton operated in transparent mode (actuators de-energized) to capture voluntary movements through high resolution encoders. Data are formatted according to the Brain Imaging Data Structure (BIDS) standard and follow FAIR principles, comprising raw biosignals, encoder trajectories, event markers, and derived performance metrics. To assess data quality, we provide subject level validation analyses, including power spectral density (PSD) and event related desynchronization/synchronization (ERDS) for EEG, as well as an EMG-Kinematic coupling analysis through Electromechanical Delay (EMD), and kinematic trajectory evaluation with performance metrics (accuracy, execution time, trajectory efficiency). This dataset supports research on wrist rehabilitation technologies and biomarker driven neuromodulation therapies, while also enabling studies in biosignal processing, artifact removal, machine learning for motor intention decoding, and the development of brain computer interfaces (BCI) and assistive devices targeting wrist mobility.

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.

Non-invasive EEG-based brain-computer interfaces (BCI) for handwriting imagery can support the restoration of fine writing abilities in individuals with motor impairments. However, the development of high-performance decoding algorithms is constrained by scarce training datasets. To address this, we present the first open EEG dataset dedicated to handwriting imagery. The dataset comprises 32-channel EEG recordings (sampled at 1000 Hz) from 21 healthy participants across two sessions separated by at least 24 hours. A dual-paradigm design captures multidimensional neural features: a Chinese character stroke imagery task (five basic strokes, 200 trials per session) and a Pinyin single-vowel imagery task (six vowels, 240 trials per session). After rigorous quality screening, 18,480 standardized trials are provided, ensuring completeness, reliability, and adherence to the Brain Imaging Data Structure (BIDS) standard. This dataset enables the development and evaluation of algorithms for non-invasive BCI and supports research on restoring writing-based communication in individuals with motor impairments.

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.

Functional near-infrared spectroscopy (fNIRS) and diffuse optical tomography (DOT) are rapidly evolving toward wearable, multimodal, and data-driven, AI-supported neuroimaging in the everyday world. However, current analytical tools are fragmented across platforms, limiting reproducibility, interoperability, and integration with modern machine learning (ML) workflows. Cedalion is a Python-based open-source framework designed to unify advanced model-based and data-driven analysis of multimodal fNIRS and DOT data within a reproducible, extensible, and community-driven environment. Cedalion integrates forward modelling, photogrammetric optode co-registration, signal processing, GLM Analysis, DOT image reconstruction, and ML-based data-driven methods within a single standardized architecture based on the Python ecosystem. It adheres to SNIRF and BIDS standards, supports cloud-executable Jupyter notebooks, and provides containerized workflows for scalable, fully reproducible analysis pipelines that can be provided alongside original research publications. Cedalion connects established optical-neuroimaging pipelines with ML frameworks such as scikit-learn and PyTorch, enabling seamless multimodal fusion with EEG, MEG, and physiological data. It implements validated algorithms for signal-quality assessment, motion correction, GLM modelling, and DOT reconstruction, complemented by modules for simulation, data augmentation, and multimodal physiology analysis. Automated documentation links each method to its source publication, and continuous-integration testing ensures robustness. This tutorial paper provides seven fully executable notebooks that demonstrate core features. Cedalion offers an open, transparent, and community extensible foundation that supports reproducible, scalable, cloud- and ML-ready fNIRS/DOT workflows for laboratory-based and real-world neuroimaging.