Adam G. Thomas

The existence and mechanistic bases of sex effects on human subcortical anatomy remain unclear. To address these gaps, we combined structural neuroimaging of sex differences in subcortical volume and shape across three independent cohorts (combined N=2,886) with neuroimaging maps of sex chromosomal and endocrine effects from three clinical cohorts (combined N=313, including sex chromosome aneuploidies and isolated gonadotropin-releasing hormone deficiency). We found that human subcortical anatomy shows focal sex differences of moderate-strong effect size during young adulthood (male-biased surface area overlying lateral/ventral putamen and pallidum, nucleus accumbens, ventral posterior complex of thalamus, anterior hippocampus, and medial and central nuclei of amygdala; female-biased surface area overlying caudate head, posterior/dorsal pallidum, mediodorsal, lateral dorsal, lateral geniculate, and medial geniculate nuclei of thalamus, posterior hippocampus, and basolateral and lateral nuclei of amygdala). These regional sex differences show strong within-sample reproducibility at modest sample sizes (N≥200) and are replicable in a fully independent mid-adulthood cohort. Strikingly, we find that the topography of adult subcortical sex differences is already established by pre/early puberty (age 9 years), and the same holds for cortical sex differences. Comparison with neuroimaging data from rare disorder cohorts reveals that subcortical sex differences show a statistically significant spatial and directional congruence with the anatomical effects of X-chromosome dosage in the striatum and effects of testicular hormones in the amygdala. Together, these findings provide a reference map for the fine-grained spatial patterning of sex differences in adult subcortical shape, demonstrating its replicability, establishment before pubertal progression, and spatial concordance with potentially causal sex chromosomal and gonadal effects.

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.

Summary: 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.

Open data sharing is increasingly mandated by research funders, journals, and institutions, yet large-scale compliance measurement remains challenging. We analyzed 951,949 open access biomedical research articles published between January 2024 and June 2025 using a dual-source pipeline: PDF-based text extraction (MinerU) where PDFs were available and PMC XML otherwise, followed by algorithmic detection of data sharing statements (oddpub v7.2.3), enriched with funder, journal, and institutional metadata from OpenAlex. The corpus included 294,172 PDF-covered articles (30.9%) and 657,777 XML-only articles (69.1%). We found an over-all open data rate of 8.7%, rising to 11.7% among funder-linked articles (those with at least one funder identified in the metadata). Rates varied more than tenfold across the research ecosystem: leading major funders reached observed open data rates of 20–24%, while top journals reached observed rates of 70–86%, with corrected estimates as high as 92.9% (Nature Genetics) after adjusting for XML-only coverage limitations. PDF-based detection identified approximately 52% more data sharing statements than XML-based methods on the same articles. These observed rates differ markedly across funders and journals, and current overall sharing remains far below universal compliance. These patterns provide an empirical baseline against which future policy changes can be measured. An interactive dashboard at https://www.opensciencemetrics.org enables stakeholders to explore and benchmark these results.

The neurobiological accompaniments of well-established sex differences in human behavior and disease remain unclear — in part due to a lack of large, diverse functional neuroimaging studies. We address this gap using over 700 h of fMRI data across seven tasks from 978 individuals with extensive structural and behavioral measures. We find that sex differences in task-activation are widespread (85% of cortex) and reproducible, largely task-specific, of small to moderate effect size, and unaligned with brain volume differences. While machine learning can classify sex from brain activation, volume, or behavior, these data types provide orthogonal information. Brain-wide association studies reveal that links between brain activation and behavior are highly conserved between sexes. The few subtle sex differences in brain-behavior linkage that do exist are not preferentially localized to sex-biased behaviors. Our findings clarify the nature of sex differences in human brain function and their links with neuroanatomy and behavior, providing a useful foundation for future research.

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.

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.

Sex chromosomes are a fundamental aspect of sex-biased biology, but the extent to which homologous X–Y gene pairs (‘the gametologs’) contribute to sex-biased phenotypes remains hotly debated. Although these genes tend to exhibit large sex differences in expression throughout the body (XX females can express both X members, and XY males can express one X and one Y member), there is conflicting evidence regarding the degree of functional divergence between the X and Y members. Here we develop and apply co-expression fingerprint analysis to characterize functional divergence between the X and Y members of 17 gametolog gene pairs across >40 human tissues. Gametolog pairs exhibit functional divergence between the sexes that is driven by divergence between the X versus Y members (assayed in males), and this within-pair divergence is greatest among pairs with evolutionarily distant X and Y members. These patterns reflect that X versus Y gametologs show coordinated patterns of asymmetric coupling with large sets of autosomal genes, which are enriched for functional pathways and gene sets implicated in sex-biased biology and disease. Our findings suggest that the X versus Y gametologs have diverged in function and prioritize specific gametolog pairs for future targeted experimental studies.

Large biobanks have dramatically advanced our understanding of genetic influences on human brain anatomy. However, most studies have combined rather than compared male and female participants. Here we screen for sex differences in the common genetic architecture of over 1000 neuroanatomical phenotypes in the UK Biobank and establish a general concordance between male and female participants in heritability estimates, genetic correlations, and variant-level effects. Notable exceptions include higher mean heritability in the female group for regional volume and surface area phenotypes; between-sex genetic correlations that are significantly below 1 in the insula and parietal cortex; and a common variant with stronger effect in male participants mapping to RBFOX1 - a gene linked to multiple neuropsychiatric disorders more common in men. This work suggests that common variant influences on human brain anatomy are largely consistent between males and females, with a few exceptions that will guide future research in growing datasets.

By brain imaging repositories (e.g., OpenNeuro (Markiewicz et al., 2021), PublicnEUro (Public nEUro, 2023)), data management tools (e.g., COINS (Landis et al., 2016), XNAT (Marcus et al., 2007)) and computational platforms (e.g. BrainLife (Hayashi S, 2023)). BIDS allows data to be shared much more efficiently, enables the development of automated data analysis pipelines, and improves reproducibility.