Melanie Ganz

Brain magnetic resonance imaging (MRI) is widely used as a diagnostic and monitoring tool for most neurological conditions. Although clinical practice produces large volumes of data, they are typically underutilised for training machine learning models that could substantially improve the feasibility, accuracy, and implementation of diagnostic tools in clinical settings. Instead, such models are usually trained on small, highly curated datasets and evaluated under controlled conditions, which limits their generalisability to the variability of real-world clinical imaging. This limitation is also reflected in publicly available datasets. And, it is particularly evident in epilepsy-related abnormalities, for which such datasets are scarce.The PMG-BrainDrugs dataset is the first open polymicrogyria (PMG) dataset including patients across the full age range. PMG is a malformation of cortical development characterised by abnormal cortical layering. The associated abnormalities are heterogeneous and can sometimes be difficult to detect. The dataset is comprised of 208 subjects, including 119 patients with radiologically confirmed PMG and 89 controls with negative findings. Each scanning session may include several anatomical imaging modalities (T1-, T2-, and T2*-weighted, or FLAIR sequences). For 27 subjects, 2 or 3 time points are available. The release of this dataset includes minimally processed data (with only defacing applied) as well as masks of PMG abnormalities. The dataset is available at DOI: 10.70883/GBSQ9852.This dataset aims to bridge the gap between curated research datasets and the variability encountered in routine clinical practice. In doing so, it offers a valuable resource for developing and evaluating more robust and generalisable methods for the detection of PMG and related cortical malformations.

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.

Objective: To estimate the prevalence of epilepsy-associated malformations of cortical development (MCDs) in Eastern Denmark, and to validate whether epilepsy prevalence in the same population is consistent with national estimates. Methods: A retrospective cohort study of people registered with ICD-10 code DG40* and/or DZ033A from 1998 up to 1 July 2023 was conducted. The study population was defined as all living residents in Eastern Denmark with at least one recorded hospital-patient contact within the year preceding 1 July 2023. Magnetic resonance imaging (MRI) availability was required to assess presence of any MCD. MRI radiology reports were manually reviewed or evaluated using a language model to identify MCDs, including encephalocele, focal cortical dysplasia (FCD), hemimegalencephaly, heterotopia, hypothalamic hamartoma, lissencephaly, polymicrogyria and schizencephaly. Prevalence estimates were calculated for each MCD subtype and for epilepsy overall, and compared with the available literature. Results: On 1 July 2023, 28,739 people met inclusion criteria, and 14,434 had an available brain MRI, including radiological description of possible MCDs. The prevalence per 100,000 population was 1044.6 (95% CI 1032.6 to 1056.6) for epilepsy and 32.1 (95% CI 30.1 to 34.3) for any MCD associated with seizures. Reported MCD prevalence in the literature, when existent, was derived from pediatric age-ranged selected cohorts, except for FCD. No prevalence estimates for hemimegalencephaly and heterotopia were identified. Significance: We presented the first population-based estimates of seizure-associated MCD prevalence in a large all-age cohort. Direct comparison with prior literature was prevented due to differences in study design and population structure, but epilepsy prevalence was consistent with previously reported national estimates. Key points: First prevalence estimates of malformation of cortical development presenting with seizures on a large all-age cohort. Epilepsy prevalence estimates align with Denmark’s nationwide estimates previously reported. Language models used on nation-wide registries can contribute to elucidate the epidemiology of rare conditions.

Background: Major depressive disorder (MDD) is clinically heterogeneous, hindering identification of reproducible biomarkers. Using a semi-supervised machine learning approach, HYDRA, we previously identified two neuroanatomical dimensions from structural MRI in medication-free MDD from COORDINATE-MDD consortium. These dimensions (D1, D2) showed differential responses to selective serotonin reuptake inhibitor (SSRI) antidepressants and placebo. External replication in UK Biobank linked D2, characterized by widespread subtle neuroanatomical reductions, to an immuno-metabolic profile. Here, we examined whether these dimensions are detectable early in the course of illness. Methods: We applied the pre-trained model to structural MRI data from the multisite PRONIA cohort, comprising individuals with recent-onset depression (ROD; n = 377; mean age 25.8 years, SD 6.0; 51.3% female) and healthy controls (n = 267; mean age 25.5 years, SD 6.4; 61.0% female). Participants were assigned to clusters (C1, C2) corresponding to the previously identified dimensions (D1, D2). Clusters were compared on clinical symptom profiles, peripheral inflammatory markers, and in a subset (n = 107), proteomic ageing indices. Results: Two neuroanatomical clusters were identified in PRONIA. C1 (n = 265) showed higher negative symptom severity and elevated interleukin-2 levels. C2 (n = 140) was associated with higher residual proteomic age. Overall depressive symptom severity did not differ significantly between clusters. Conclusions: Neuroanatomical dimensions of MDD are reproducible and detectable at illness onset. Associations with negative symptom severity, inflammatory signalling, and proteomic ageing suggest these dimensions capture biologically meaningful heterogeneity early in depression. These findings support a biologically informed framework for stratified treatment approaches in MDD.

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.

Motion-induced artefacts are a common challenge in pediatric magnetic resonance imaging (MRI), often degrading image quality and leading to frequent use of sedation or general anesthesia (GA) to minimise movement and ensure image clarity. However, repeated exposure to GA raises both logistical challenges and potential neurodevelopmental concerns. This dataset includes cerebral MRI scans from 47 children aged 4 to 11 years, who were clinically referred for imaging under GA. The participants were trained and afterwards imaging was performed without GA. MRI scans were acquired with or without motion correction using a marker-less tracking system integrated into the standard clinical workflow. Each MRI sequence is accompanied by radiologist-based quality ratings, automated reference-free quality metrics, and motion traces. For standardisation, all image data are structured in compliance with the Brain Imaging Data Structure (BIDS) format. This dataset provides a valuable benchmark for testing image-based motion correction methods and developing motion-tolerant imaging strategies, addressing a critical gap in a publicly available clinical pediatric MRI data acquired under realistic motion conditions.

The prevalence of psychiatric diagnoses is increasing globally, with major depressive disorder (MDD) among the most prevalent and a leading contributor to the growing strain on mental healthcare systems. Addressing these challenges requires large-scale, high-quality health data; therefore, we selected Denmark as a case study where rich register data is available. In Denmark, the average wait time is two years for access to psychiatric care. Artificial intelligence (AI) for healthcare is considered a potential solution for triage and resource allocation. Although Denmark is often considered a homogeneous society, its sociodemographic and socioeconomic factors differ across the country, including immigration rates. There are also regional differences in the prevalence of psychiatric diagnoses. However, we have yet to understand how AI models account for such regional inequities. To examine this, we study MDD, the most costly mental health diagnosis for the Danish healthcare System, as a use case. Our analysis consists of two parts. First, we conducted an observational association analysis to characterize the regional, sociodemographic, and socioeconomic factors that influence MDD diagnosis in Denmark. We find disparities in the odds of obtaining an MDD diagnosis according to region of care and sociodemographic factors. Secondly, we develop two MDD prediction models, utilizing low and high levels of information about individual heritage, respectively. We assess the performance of the two predictive models across the five administrative regions in Denmark, and their generalizability when fine-tuned on data from only a single region. We observe that the AI models varied in model performance across the different administrative regions, immigration status, and heritage. We also find that including more nuanced variables that account for immigration status, heritage, and socioeconomic status (SES) lead to an improved model performance on all regions, for both immigrants and people of Danish origin. These findings illustrate the importance of assessing the fairness of AI models deployed in high-risk domains such as psychiatry and approaching sociodemographic identity with cultural sensitivity. Lastly, we call on researchers to acknowledge the inequities that persist across geographical regions and to account for these disparities in future work.

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.

Psilocybin and other serotonergic drugs acutely alter human brain function and large-scale connectivity as measured with BOLD fMRI, but whether these effects are frequency-specific remains unknown. We applied multitaper spectral and cross-spectral analyses to resting-state fMRI data from 28 healthy volunteers scanned multiple times acutely following oral psilocybin administration (0.2 – 0.3 mg/kg), together with plasma psilocin measurements, to estimate psilocin associations with temporal frequency-specific activity and connectivity. Psilocybin produced a selective reduction in low-frequency spectral power (0.01 – 0.06 Hz ) and an increase in spectral entropy, with the strongest effects in transmodal networks. We also observed a reduction in low-frequency connectivity energy explained by the unimodal/transmodal axis. These findings demonstrate that psilocin induces spatially distributed, frequency-dependent alterations, suggesting that broadband fMRI analyses may obscure low-frequency dynamics. Frequency-resolved approaches may offer greater sensitivity for characterizing psychedelic effects on brain activity.