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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.

Research-active healthcare institutions are associated with improved patient outcomes and staff satisfaction. However, research funding in the United Kingdom remains disproportionately concentrated in established academic centres, limiting opportunities for newer institutions - often located in regions with greater health need - to develop research capacity. This entrenches health inequalities and restricts the pipeline of clinical researchers in underserved areas. We used a case study methodology to explore how one new United Kingdom medical school, situated within a teaching-focused university and region of relative socioeconomic disadvantage, built research capacity and supervisory infrastructure from the ground up. Drawing on internal expertise and infrastructure, strategic partnerships and national funding schemes, we examined the structural enablers and barriers encountered in establishing a locally relevant research ecosystem. A phased approach to capacity building was employed, starting with internal resources and strategic collaborations. Supervisory infrastructure was developed through networked partnerships, enabling undergraduate and postgraduate research opportunities. The creation of thematic research groups evolved into recognized research centres. This foundation enabled successful bids for competitive external funding, including undergraduate and postgraduate research schemes, which in turn developed research capacity. We highlight how equitable access to research opportunities - particularly for students from widening participation backgrounds - was embedded within the curriculum and supported by funded placements. Our experience demonstrates that early, targeted investment in research infrastructure, even in settings with low baseline research activity, can generate sustainable capacity, increase participation and reduce regional disparities in research engagement. To promote equity in research funding and reduce health inequalities, national funding bodies should adopt more inclusive investment strategies that actively support emerging centres. Structural reform is needed to ensure that funding mechanisms do not solely reward existing capacity but also foster its development in underserved regions. Our findings offer a scalable model for building sustainable research ecosystems in new or underfunded centres, aligned with local health needs and population outcomes.

Brain tissue segmentation of infant magnetic resonance (MR) images is important for studying typical and atypical brain development. The infant brain undergoes rapid changes throughout the first years of postnatal life, making tissue segmentation difficult for most existing algorithms. We introduce a deep neural network BIBSNet (Baby and Infant Brain Segmentation Neural Network), an open-source model for robust and generalizable brain tissue segmentation leveraging data augmentation and a large sample size of manually annotated images. Model training included MR brain images from 90 participants with an age range of 0-8 months (median age 4.6 months). Using manually annotated real images along with synthetic segmentation images produced using SynthSeg, the model was trained using a 10-fold procedure. Model performance was assessed by comparing BIBSNet, and joint label fusion (JLF) inferred segmentations to ground truth segmentations, and an ad-hoc analysis with iBeat inferred segmentation, using Dice Similarity Coefficient (DSC). Additionally, MR data along with the FreeSurfer compatible segmentations were processed with the DCAN labs infant-ABCD-BIDS processing pipeline from ground truth, JLF, and BIBSNet to produce anatomical and resting state functional derivatives to further assess model performance on processed derivatives. BIBSNet outperforms JLF based on DSC comparisons especially with gray matter (BIBSNet = 0.849, JLF = 0.713) and white matter (BIBSNet = 0.862, JLF = 0.791). Additionally, with processed derived metrics, BIBSNet inferred segmentations outperforms JLF inferred segmentations across nearly all anatomical and functional metrics. Ad-hoc analyses of cortical segmentations - iBeat does not perform subcortical segmentations - showed that there is no significant difference between iBeat and BIBSNet segmentation for infants 0-5 months, but iBeat performed significantly better for infants 6-8 months. BIBSNet shows marked improvement over JLF across all age groups analyzed. The BIBSNet model is 600x faster compared to JLF at segmentation inference, produces FreeSurfer-compatible segmentation labels, and can be easily included in other processing pipelines. BIBSNet provides a viable alternative for segmenting the brain in the earliest stages of development.

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.

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.

This article presents a publicly accessible multimodal dataset comprising audiovisual recordings of unscripted German monologues and a corresponding time-resolved linguistic speech annotation corpus. The audiovisual dataset consists of more than 300 minutes of natural speech material recorded from six speakers across 60 takes. Recordings were captured using a Canon EOS 700D camera and a Neumann KM-184 microphone, with audio sampled at 48 kHz and video at 25 frames per second. Speakers produced spontaneous monologues on self-selected everyday topics under controlled laboratory conditions. Recording sessions included variations such as background babble noise presented via in-ear headphones and optional visual modifications, including the use of glasses or lipstick. Audio and video were acquired simultaneously, synchronized during post-processing, and segmented into individual stories. The accompanying speech annotation corpus provides detailed linguistic information aligned with the audiovisual material at millisecond resolution. An annotation pipeline combining established tools-OCTRA, G2P, MAUS, PHO2SYL, and the RFTagger-was used to derive orthographic transcripts, canonical phonological representations, phonetic segmentations, syllabification, and part-of-speech tags. Manual correction steps were applied to ensure transcription accuracy and to improve the quality of forced alignment. Each recording is accompanied by an events.tsv file containing time-stamped word, phoneme, and pause annotations; a JSON sidecar describing variable metadata; and a machine-actionable HED-formatted event file to support integration with neuroimaging standards such as BIDS. The structure of the resource follows a consistent file-naming scheme to ensure reliable linkage between audiovisual recordings and speech annotations. This combined audiovisual and linguistic resource supports a wide range of reuse applications, including acoustic-phonetic analysis, linguistic and neurolinguistic research, annotation benchmarking, and the development or evaluation of speech-processing tools. The resource's naturalistic content and high temporal precision enable detailed examination of spontaneous speech and facilitate replication-oriented research across disciplines.

Winnicott (1945a) suggested that some types of aggressive behaviour of the child returning from sustained separation from their parents may be regarded as an expression of hope, one in which they can yield their forms of defensive self-sufficiency to trust a parent again. The author parses Winnicott's later various approaches to understanding both instinctual and reactive aggression and how applying his later views can obfuscate the meaning of aggression in nuisance-making behaviour. The author offers a specific definition of nuisance in the analysis of adults and how it manifests itself in the analytic context. In the adult patients he describes, anger and hostility have become featured as expressions of grievance or greed regarding earlier ruptures in an experience of the parent's or analyst's registration of the patient's needs for attention. He considers two clinical contexts involving the conscious reluctance to agree with the analyst's interpretations as well as making demands on the analyst outside the setting as forms of nuisance. He explores how the analyst needs to hold two psychic realities - the patient's hope that is expressed in their nuisance-making as well as the analyst's limits in absorbing the patient's own self-destructive hatred of dependency. Holding these two realities helps to transform these self-destructive bids for attention into an opportunity for mourning. The analyst is required to work with his or her own subtle experiences of disturbance including hostility, helplessness and the pull to act out roles in the patient's earlier life.

Firearm-related injuries cause far-reaching harm, yet information about the value the public assigns to the benefits of prevention is limited. We surveyed California adults from the Ipsos KnowledgePanel (N = 2870) about their willingness to pay (WTP), in taxes or donations, to prevent firearm homicides, firearm suicides, and deaths from mass shootings. WTP was calculated using a double-bounded dichotomous choice contingent valuation model with a log-logistic error distribution. The mean WTP estimate for a program preventing 1 in 10 deaths ranged from $85.16 annually in donations to prevent firearm suicides to $145.63 in additional taxes to prevent deaths from mass shootings. In general, firearm owners were willing to pay less than non-owners; however, Black firearm owners reported the largest WTP, among subgroups and overall. Most respondents were willing to pay the sum-total of their bids to prevent all 3 types of firearm injury; of those, maximum WTP, on average, was $508.08 annually in donations or $534.82 in additional taxes. This implies a statewide total of up to $6.9 billion in perceived benefit. As resources for prevention, intervention, and supportive services are threatened or terminated, these findings underscore the substantial public demand for investments in firearm injury reduction efforts.

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.

Understanding the neural mechanisms of adolescent substance use is a critical public health issue, with direct implications for bolstering prevention and treatment strategies. Yet this effort is challenging because substance use is multi-faceted, substance use facets change over time, and commonly used brain network features are not optimized to capture both local and global aspects of intrinsic connectivity. In this study, we aimed to address these issues. We operationalized adolescent substance use along three dimensions-intent, access, and family-developmental history-and trained predictive models of each facet at mulitple timepoints using traditional and emergent (connectome embedding) metrics of resting-state connectivity. Trait impulsivity, a known risk factor, was also examined. Using Baseline and 2 Year Follow-Up data from the ABCD Bids Community Collection (ABCC), we found that prediction was more successful at follow-up than baseline. At baseline, predictive accuracy was modest and intent to use substances was the most accurately predicted facet. Prediction accuracies at follow-up were much higher, with access and family-developmental history being better predicted, signaling a developmental shift in the brain-behavior mapping of substance use vulnerabilities. Tradtional and emergent metrics of connectivity performed similarity. These findings suggest that the neurobiological correlates of substance use are dynamic across adolescence, possibly reflecting changing phenotypes. More broadly, these results underscore the importance of modeling distinct substance use facets and accounting for developmental timing to understand risk trajectories, while contributing to a growing literature that shows early-developing individual differences are predictive of later outcomes.