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.

labapi is a Python library that enables computational workflows to connect to LabArchives' electronic lab notebook (ELN). Without an Application Programming Interface (API) connection, researchers must manually add workflow outputs through the LabArchives web interface, navigating to the appropriate page and uploading each output so that it appears with the experimental notes that provide context. labapi translates the flat LabArchives API into a Python object model following the existing hierarchy of the web interface, allowing workflows to navigate and modify notebook content through familiar paths. labapi enables researchers to build interconnected workflows that both read in and write data to LabArchives' ELN automatically. Researchers can inspect those outputs in the notebook, and later analysis code can read them back for another stage of analysis.

One of the challenges in the field of neuroimaging is that we often lack knowledge about the underlying truth and whether our methods can detect developmental changes. To address this gap, five research groups around the globe created simulated datasets embedded with their assumptions of the interplay between brain development, cognition, and behavior. Each group independently created the datasets, unaware of the approaches and assumptions made by the other groups. Each group simulated three datasets with the same variables, each with 10,000 participants over 7 longitudinal waves, ranging from 7 to 20 years-of-age. The independently created datasets include demographic data, brain derived variables along with behavior and cognition variables. These datasets and code that were used to generate the datasets can be downloaded and used by the research community to apply different longitudinal models to determine the underlying patterns and assumptions where the ground truth is known.

We developed a computational pipeline (now provided as a resource) for measuring morphological similarity between cortical surface sulci to construct a sulcal phenotype network (SPN) from each magnetic resonance imaging (MRI) scan in an adult cohort (n = 34,725; 45-82 years). Networks estimated from pairwise similarities of 40 sulci on 5 morphological metrics comprised two clusters of sulci, represented also by the bimodal distribution of sulci on a linear-to-complex dimension. Linear sulci were more heritable and typically located in unimodal cortex, and complex sulci were less heritable and typically located in heteromodal cortex. Aligning these results with an independent fetal brain MRI cohort (n = 228; 21-36 gestational weeks), we found that linear sulci formed earlier, and the earliest and latest-forming sulci had the least between-adult variation. Using high-resolution maps of cortical gene expression, we found that linear sulcation is mechanistically underpinned by trans-sulcal gene expression gradients enriched for developmental processes.

Background: Neuroimaging has contributed considerably to our understanding of brain development and its relationship to cognition and behavior. However, despite advancements in neuroimaging, replicability in research remains a key issue and there are no gold standard models that quantify neuroanatomical correlates of cognition, behavior and their interplay. Methods: Research groups across the globe have each independently created simulated datasets containing the interplay between brain development, behavior, and cognition. Each group has worked independently and unaware of the approaches and assumptions made by the other groups. Each group was provided the same number of variables and were instructed to create three datasets with each embedding how they envision the interplay between brain development, behavior, and cognition emerges throughout development. Results: We are releasing these simulated datasets to challenge/invite the research community to determine the underlying patterns and assumptions within the simulated datasets. Each dataset contains 10,000 participants over 7 longitudinal waves, ranging from age 7 to 20. The data can be found at https://socoden.github.io/Simulation/. The code and descriptions of the models that were used to create these datasets will be released mid 2025. The research community is invited to explore underlying models and data patterns in simulated datasets and submit their findings. Discussion: Findings from those in the community analyzing data will be evaluated by the groups that generated the simulated datasets. Evaluations will be qualitative and will be used to identify common themes across patterns in simulated datasets and models applied by the community, to learn which proposed neurodevelopment patterns were picked up, which ones were missed and what were common assumptions made. Lessons learned will be disseminated to the research community and may also be beneficial to test with real data as large developmental datasets, such as the ABCD Study and the China Child Brain Development (CCBD) Study, come to fruition.

One of the central objectives of contemporary neuroimaging research is to create predictive models that can disentangle the connection between patterns of functional connectivity across the entire brain and various behavioral traits. Previous studies have shown that models trained to predict behavioral features from the individual's functional connectivity have modest to poor performance. In this study, we trained models that predict observable individual traits (phenotypes) and their corresponding singular value decomposition (SVD) representations - herein referred to as latent phenotypes from resting state functional connectivity. For this task, we predicted phenotypes in two large neuroimaging datasets: the Human Connectome Project (HCP) and the Philadelphia Neurodevelopmental Cohort (PNC). We illustrate the importance of regressing out confounds, which could significantly influence phenotype prediction. Our findings reveal that both phenotypes and their corresponding latent phenotypes yield similar predictive performance. Interestingly, only the first five latent phenotypes were reliably identified, and using just these reliable phenotypes for predicting phenotypes yielded a similar performance to using all latent phenotypes. This suggests that the predictable information is present in the first latent phenotypes, allowing the remainder to be filtered out without any harm in performance. This study sheds light on the intricate relationship between functional connectivity and the predictability and reliability of phenotypic information, with potential implications for enhancing predictive modeling in the realm of neuroimaging research.

The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities. This paper is meant as a history of how the standard has developed and grown over time. We outline the principles behind the project, the mechanisms by which it has been extended, and some of the challenges being addressed as it evolves. We also discuss the lessons learned through the project, with the aim of enabling researchers in other domains to learn from the success of BIDS.

Does our mood change as time passes? This question is central to behavioural and affective science, yet it remains largely unexamined. To investigate, we intermixed subjective momentary mood ratings into repetitive psychology paradigms. Here we demonstrate that task and rest periods lowered participants' mood, an effect we call 'Mood Drift Over Time'. This finding was replicated in 19 cohorts totalling 28,482 adult and adolescent participants. The drift was relatively large (-13.8% after 7.3 min of rest, Cohen's d = 0.574) and was consistent across cohorts. Behaviour was also impacted: participants were less likely to gamble in a task that followed a rest period. Importantly, the drift slope was inversely related to reward sensitivity. We show that accounting for time using a linear term significantly improves the fit of a computational model of mood. Our work provides conceptual and methodological reasons for researchers to account for time's effects when studying mood and behaviour.

In mental health research, it has proven difficult to find measures of brain function that provide reliable indicators of mental health and well-being, including susceptibility to mental health disorders. Recently, a family of data-driven analyses have provided such reliable measures when applied to large, population-level datasets. In the current pre-registered replication study, we show that the canonical correlation analysis (CCA) methods previously developed using resting-state magnetic resonance imaging functional connectivity and subject measures (SMs) of cognition and behaviour from healthy adults are also effective in measuring well-being (a ‘positive–negative axis') in an independent developmental dataset. Our replication was successful in two out of three of our pre-registered criteria, such that a primary CCA mode's weights displayed a significant positive relationship and explained a significant amount of variance in both functional connectivity and SMs. The only criterion that was not successful was that compared to other modes the magnitude of variance explained by the primary CCA mode was smaller than predicted, a result that could indicate a developmental trajectory of a primary mode. This replication establishes a signature neurotypical relationship between connectivity and phenotype, opening new avenues of research in neuroscience with clear clinical applications.

The NIMH Healthy Research Volunteer Dataset is a collection of phenotypic data characterizing healthy research volunteers using clinical assessments such as assays of blood and urine, mental health assessments, diagnostic and dimensional measures of mental health, cognitive and neuropsychological functioning, structural and functional magnetic resonance imaging (MRI), along with diffusion tensor imaging (DTI), and a comprehensive magnetoencephalography battery (MEG). In addition, blood samples of healthy volunteers are banked for future analyses. All data collected in this protocol are broadly shared in the OpenNeuro repository, in the Brain Imaging Data Structure (BIDS) format. In addition, task paradigms and basic pre-processing scripts are shared on GitHub. There are currently few open access MEG datasets, and multimodal neuroimaging datasets are even more rare. Due to its depth of characterization of a healthy population in terms of brain health, this dataset may contribute to a wide array of secondary investigations of non-clinical and clinical research questions.

Does our mood change as time passes? This question is central to behavioural and affective science, yet it remains largely unexamined. To investigate, we intermixed subjective momentary mood ratings into repetitive psychology paradigms. We demonstrate that task and rest periods lowered participants' mood, an effect we call "Mood Drift Over Time." This finding was replicated in 19 cohorts totaling 28,482 adult and adolescent participants. The drift was relatively large (-13.8% after 7.3 minutes of rest, Cohen's d=0.574) and was consistent across cohorts. Behaviour was also impacted: participants were less likely to gamble in a task that followed a rest period. Importantly, the drift slope was inversely related to reward sensitivity. We show that accounting for time using a linear term significantly improves the fit of a computational model of mood. Our work provides conceptual and methodological reasons for researchers to account for time's effects when studying mood and behaviour.

The neuroimaging community is steering towards increasingly large sample sizes, which are highly heterogeneous because they can only be acquired by multi-site consortia. The visual assessment of every imaging scan is a necessary quality control step, yet arduous and time-consuming. A sizeable body of evidence shows that images of low quality are a source of variability that may be comparable to the effect size under study. We present the MRIQC Web-API, an open crowdsourced database that collects image quality metrics extracted from MR images and corresponding manual assessments by experts. The database is rapidly growing, and currently contains over 100,000 records of image quality metrics of functional and anatomical MRIs of the human brain, and over 200 expert ratings. The resource is designed for researchers to share image quality metrics and annotations that can readily be reused in training human experts and machine learning algorithms. The ultimate goal of the database is to allow the development of fully automated quality control tools that outperform expert ratings in identifying subpar images.

In this paper, we describe a Bayesian deep neural network (DNN) for predicting FreeSurfer segmentations of structural MRI volumes, in minutes rather than hours. The network was trained and evaluated on a large dataset (n = 11,480), obtained by combining data from more than a hundred different sites, and also evaluated on another completely held-out dataset (n = 418). The network was trained using a novel spike-and-slab dropout-based variational inference approach. We show that, on these datasets, the proposed Bayesian DNN outperforms previously proposed methods, in terms of the similarity between the segmentation predictions and the FreeSurfer labels, and the usefulness of the estimate uncertainty of these predictions. In particular, we demonstrated that the prediction uncertainty of this network at each voxel is a good indicator of whether the network has made an error and that the uncertainty across the whole brain can predict the manual quality control ratings of a scan. The proposed Bayesian DNN method should be applicable to any new network architecture for addressing the segmentation problem.

In order to obtain the sample sizes needed for robustly reproducible effects, it is often necessary to acquire data at multiple sites using different MRI scanners. This poses a challenge for investigators to account for the variance due to scanner, as balanced sampling is often not an option. Similarly, longitudinal studies must deal with known and unknown changes to scanner hardware and software over time. In this manuscript, we have explored scanner-related differences in the dataset recently released by the Adolescent Brain Cognitive Development (ABCD) project, a multi-site, longitudinal study of children age 9-10. We demonstrate that scanner manufacturer, model, as well as the individual scanner itself, are detectable in the resting and task-based fMRI results of the ABCD dataset. We further demonstrate that these differences can be harmonized using an empirical Bayes approach known as ComBat. We argue that accounting for scanner variance, including even minor differences in scanner hardware or software, is crucial for any analysis.

This is the first study to investigate functional brain activity in patients affected by autoimmune encephalitis with faciobrancial dystonic seizures (FBDS). Multimodal 3T MRI scans, including structural neuroimaging (T1-weighted, diffusion weighted) and functional neuroimaging (scene-encoding task known to activate hippocampal regions), were performed. This case series analysis included eight patients treated for autoimmune encephalitis with FBDS, scanned during the convalescent phase of their condition (median 1.1 years post-onset), and eight healthy volunteers. Compared to controls, 50% of patients showed abnormal hippocampal activity during scene-encoding relative to familiar scene-viewing. Higher peak FBDS frequency was significantly related to lower hippocampal activity during scene-encoding (p = 0.02), though not to markers of hippocampal microstructure (mean diffusivity, p = 0.3) or atrophy (normalized volume, p = 0.4). During scene-encoding, stronger within-medial temporal lobe (MTL) functional connectivity correlated with poorer Addenbrooke's Cognitive Examination-Revised memory score (p = 0.03). These findings suggest that in autoimmune encephalitis, frequent seizures may have a long-term impact on hippocampal activity, beyond that of structural damage. These observations also suggest a potential approach to determine on-going MTL performance in this condition to guide long-term management and future clinical trials.

Recent “representational” accounts suggest a key role for the hippocampus in complex scene perception. Due to limitations in scanner field strength, however, the functional neuroanatomy of hippocampal-dependent scene perception is unknown. Here, we applied 7 T high-resolution functional magnetic resonance imaging (fMRI) alongside a perceptual oddity task, modified from nonhuman primate studies. This task requires subjects to discriminate highly similar scenes, faces, or objects from multiple viewpoints, and has revealed selective impairments during scene discrimination following hippocampal lesions. Region-of-interest analyses identified a preferential response in the subiculum subfield of the hippocampus during scene, but not face or object, discriminations. Notably, this effect was in the anteromedial subiculum and was not modulated by whether scenes were subsequently remembered or forgotten. These results highlight the value of ultra-high-field fMRI in generating more refined, anatomically informed, functional accounts of hippocampal contributions to cognition, and a unique role for the human subiculum in discrimination of complex scenes from different viewpoints.SIGNIFICANCE STATEMENTThere is increasing evidence that the human hippocampus supports functions beyond just episodic memory, with human lesion studies suggesting a contribution to the perceptual processing of navigationally relevant, complex scenes. While the hippocampus itself contains several small, functionally distinct subfields, examining the role of these in scene processing has been previously limited by scanner field strength. By applying ultra-high-resolution 7 T fMRI, we delineated the functional contribution of individual hippocampal subfields during a perceptual discrimination task for scenes, faces, and objects. This demonstrated that the discrimination of scenes, relative to faces and objects, recruits the anterior subicular region of the hippocampus, regardless of whether scenes were subsequently remembered or forgotten.