Aperture Neuro

IntroductionPostmortem human brain magnetic resonance imaging (MRI) offers a unique opportunity to study finer neuroanatomical details and enables direct correlations with gold standard histological and immunohistochemical assessments. However, to prevent tissue decay, postmortem brains are preserved in fixative solutions which can alter tissue properties and exert substantial impacts on the MRI signals. The present study investigates the impact of formalin fixation, the most commonly used solution for postmortem human brain preservation, on different quantitative MRI contrasts. Methods142 intact human brain hemispheres immersed in 10% formalin for a range of fixation durations (between 0 days and 20 years) were imaged in a 3T MRI scanner. A subset of 10 brains were further scanned repeatedly at days 0, 3, 10, 20, 30, 60, 90, and 120 to allow for better characterization of the initial transient effects of fixation. Voxel-wise T1 and T2* relaxation, T1/T2 ratio, and myelin water fraction (MWF) maps were generated for each specimen and timepoint, and linear and nonlinear models were used to examine the spatiotemporal changes associated with progressive fixation. ResultsAll investigated metrics were significantly impacted by formalin fixation, albeit at different rates and with differing regional patterns. T1 and T2* relaxation time decreased as a result of progressive fixation, whereas T1/T2 ratio and MWF measures increased. T1 relaxation and T1/T2 ratio showed nonlinear patterns with initially accelerated changes that decelerate in the first few months, whereas T2* relaxation and MWF changes followed a more linear trend. ConclusionFormaldehyde fixation exerts systematic changes on quantitative MRI signals that can be modeled and adjusted for to allow for harmonized comparisons of MRI metrics across brains fixed for differing durations. The distinct temporal trajectories observed across metrics highlight the need to account for fixation duration in study design and downstream analyses, particularly when integrating datasets acquired under heterogeneous conditions. Our findings provide a quantitative framework for correcting fixation-induced biases, thereby improving the interpretability and reproducibility of postmortem MRI studies.

There is growing interest in identifying brain function underlying adolescent cognition, personality, and psychopathology. One promising approach is Precision Functional Mapping (PFM) of MRI functional connectivity, a data-intensive method for characterizing individualized brain networks. Foundational studies suggest that PFM can detect stable, task-responsive, and clinically relevant networks. Studies demonstrate that both functional connectivity reliability and network stability improve with increasing data quantity, although benchmark estimates vary across populations, preprocessing pipelines, and MRI acquisition approaches. Accordingly, it is important to understand how PFM performs in adolescent populations and with multi-echo fMRI acquisition. In a case study of eight youth (ages 10-17), we applied PFM to 80-minutes of combined resting-state and task-based fMRI. The resulting networks were highly modular, consistent with adult templates, and without evidence of structural registration artifacts. Functional connectivity reliability compared favorably to prior single-echo studies, with multivariate similarity and ICC estimates showing early stabilization around 10-15 minutes despite continued improvement with additional data. Trait-like stability increased gradually with acquisition time and a Bayesian algorithm (MS-HBM) demonstrated higher stability than Infomap. Across algorithms, stability was greatest in sensory networks (somatomotor, auditory, visual). Furthermore, when evaluating task-based responses to threat and attention paradigms, only the auditory network consistently benefited from individualized mapping over group template networks. These findings suggest that, with constrained scanning time, PFM is especially effective for characterizing sensory and perceptual networks in adolescents. Bridging the methodological divide between deeply sampled individual cases and large-scale developmental studies will require further innovation and validation.

High-resolution postmortem (ex vivo) magnetic resonance imaging enables detailed examination of brain anatomy at spatial scales not achievable in vivo and provides a unique opportunity to link morphometric measurements with the underlying pathology. Despite these advantages, robust computational tools for automated anatomical segmentation and cortical surface reconstruction remain limited, particularly in postmortem infant brains. Incomplete myelination, thinner cortical ribbons, small-scale neuroanatomy, as well as an evolving tissue contrast combined with fixation-induced signal alterations and variability in postmortem preparation make standard neuroimaging pipelines unusable for postmortem infant MRI. In this work, we introduce a one-of-its-kind multi-modal high-resolution postmortem infant MRI dataset and a unified computational framework that combines deep learning-based volumetric segmentation with surface-based cortical reconstruction and anatomical parcellation in native subject space resolution. To address the pronounced domain shift inherent to postmortem MRI, we develop a postmortem-specific synthetic data generation engine (PostSynth) that explicitly models fixation-driven postmortem imaging characteristics. In particular, we incorporate postmortem-specific altered gray-white matter contrast, laminar cortical intensity heterogeneity, specimen-specific bias fields, and background signal characteristics associated with immersion media: phenomena not typically observed in in vivo data or captured by generic contrast-agnostic synthesis methods. We benchmark our framework against a set of widely used contrast-agnostic and foundational brain segmentation models, demonstrating improved anatomical consistency and segmentation performance in high-resolution postmortem infant data. The code is publicly available as part of the purple-mri package.

BackgroundNeuronavigation based on the standard MNI template (MNI-protocol) offers a cost-effective alternative to the gold-standard individualized T1-weighted MRI approach (T1-protocol). However, it remains unclear whether the reduced anatomical precision of the MNI-protocol compromises its functional efficacy, creating a critical need to verify protocol interchangeability. ObjectiveWe aimed to determine whether the MNI-protocol yields targeting efficacy comparable to the T1-protocol by specifically testing their functional and biophysical equivalence. MethodsWe employed a novel tri-level within-subject framework. The behavioral level assessed functional efficacy via the size congruity effect (SCE) during TMS to the right intraparietal sulcus (IPS). Anatomical accuracy (coil-to-cortex distances) and electromagnetic efficacy (E-field simulations) were evaluated across three distinct regions (right IPS, left dorsolateral prefrontal cortex, and left primary motor cortex) to assess regional generalizability. ResultsThe MNI-protocol demonstrated functional similarity to the T1-protocol, yielding behavioral outcomes that were statistically indistinguishable. This functional equivalence was corroborated by electromagnetic analyses, which revealed nearly identical induced E-field magnitudes and spatial distributions across all three target regions. Although the T1-protocol achieved significantly shorter coil-to-cortex distances, this anatomical advantage did not confer any measurable functional benefit. ConclusionThe MNI-protocol produced behavioral and electromagnetic outcomes equivalent to the T1-protocol. These findings validate the MNI-protocol as a scientifically sound and scalable alternative to individualized MRI-guided targeting, supporting its broader application in diverse research and clinical settings. HighlightsO_LIFunctional equivalence of MNI-vs. T1-guided TMS was systematically tested. C_LIO_LIA novel tri-level framework compared behavioral, anatomical, and E-field metrics. C_LIO_LIMNI- and T1-guided targeting yielded comparable behavioral and E-field outcomes. C_LIO_LIAnatomical proximity does not ensure better behavior or stronger E-field strength. C_LIO_LIMNI-guided targeting offers a robust, practical alternative to individual MRI. C_LI

BackgroundMagnetic resonance imaging (MRI) is critical for acute stroke triage, but time-consuming, and often requires contrast injection for perfusion imaging. This study aimed to synthesize T-map perfusion maps from routinely available, non-contrast DWI and FLAIR using deep generative models. We hypothesized that relevant perfusion information could be inferred from these modalities to streamline imaging and reduce reliance on dynamic susceptibility contrast perfusion. MethodsAcute MRI data from 355 patients with anterior circulation stroke, including dynamic susceptibility contrast perfusion, were retrospectively collected from two European centers (Heidelberg: 2010-2018; Bordeaux: 2021-2022). Six versions of a denoising diffusion probabilistic model (DDPM) and a GAN architecture were trained to generate synthetic T-max perfusion maps from DWI, FLAIR, and infarct core mask as inputs. Performance was assessed by comparing synthetic and ground truth T-max maps using image similarity metrics. Regions with T-max >6s were compared using Dice coefficients, and mismatch volume distributions were analyzed. An ablation study quantified the contribution of each input. ResultsThe best performance was achieved by a DDPM with a 2.5D architecture using DWI, FLAIR, infarct core mask, and a perfusion-weighted loss function. It produced synthetic perfusion T-max maps with high similarity to ground truth under 110 seconds. The model showed strong spatial overlap for T-max >6s regions in internal validation (average Dice = 0.82, SD = 0.08), and external validation average (Dice 0.59, SD = 0.13), respectively. Synthetic maps closely matched ground-truth mismatch distributions, capturing key perfusion patterns. The infarct core mask played a critical role in model performance, alongside DWI and FLAIR inputs. ConclusionsWe propose a non-invasive, scalable framework to generate synthetic T-max perfusion maps from non-contrast MRI. This approach could expand access to perfusion data in acute stroke, shorten imaging protocols, and accelerate treatment decisions by eliminating the need for contrast-enhanced acquisition. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/684079v2_ufig1.gif" ALT="Figure 1"> View larger version (94K): org.highwire.dtl.DTLVardef@164235forg.highwire.dtl.DTLVardef@14e5489org.highwire.dtl.DTLVardef@190214eorg.highwire.dtl.DTLVardef@17a9e3a_HPS_FORMAT_FIGEXP M_FIG C_FIG

Much of a typical individuals mental life is characterized by spontaneous thoughts that occur independently of external stimuli. In prior studies, ongoing mental experiences and their neural correlates have been captured using thought probes presented at random intervals during functional Magnetic Resonance Imaging (fMRI). However, this approach results in temporally imprecise estimates of brain activity relative to the arising of mental experience. In this preregistered, proof-of-concept study, we aimed to improve temporal precision using a novel method termed real-time fMRI-triggered experience-sampling (rt-fMRI-ES). We analyzed blood-oxygenation-level-dependent signals in real time during a wakeful resting state (n=60) to trigger thought probes from spontaneous activations within two regions: the dorsal anterior insular cortex (daIC; a key region within salience network) and posteromedial cortex (PMC; a key region within default mode network). We tested two preregistered hypotheses: (H1) Ratings of arousal time-locked to daIC-activation trials are higher than ratings time-locked to non-daIC-activation trials; (H2) Ratings of external-attention time-locked to PMC-activation trials are lower than ratings time-locked to non-PMC-activation trials. After applying preregistered exclusion criteria, 42 participants (1243 trials) and 49 participants (1429 trials) were included in H1 and H2 analyses, respectively. We did not find evidence in support of H1, but we did find evidence in support of H2, as external-attention ratings were significantly lower for trials triggered by PMC activation compared to other trial types. Taken together, we successfully developed and validated the rt-fMRI-ES method, offering a novel technique to efficiently capture spontaneous thoughts based on ongoing neural activity. Preregistered Stage 1 Recommendationhttps://osf.io/sd4hu (Date of in-principle acceptance: 07/24/2024; under temporary private embargo)

Diffusion-weighted magnetic resonance imaging (dMRI)-based tractometry enables the quantification of white matter tissue properties in living humans while preserving anatomical specificity. Although tractometry is highly reproducible when the same scanner and acquisition protocol are used, its generalizability across scanners and protocols remains unclear. To address this gap, we performed a traveling-head experiment involving five subjects to evaluate tractometry across progressively different acquisition conditions, including multiple scanners, different scanner models, and two distinct protocols. Tractometry was performed for 20 major white matter tracts using diffusion tensor imaging metrics, neurite orientation dispersion and density imaging (NODDI) metrics, and a semi-quantitative ratio metric (T1w/b0). Generalizability across dataset pairs was quantified using the intraclass correlation coefficient (ICC). Tractometry showed consistently high ICCs when the scanner and protocol were identical; however, ICCs declined as differences in scanner model and acquisition protocol increased. Fractional anisotropy and orientation dispersion index retained relatively high ICCs across these comparisons, whereas other metrics showed marked declines when scanners or protocols differed. ComBat harmonization partially mitigated these declines, but ICCs did not reach the levels observed for datasets acquired using identical scanners and protocols. Finally, the minimum detectable change (MDC) for tractometry in datasets pooled across scanners and protocols varied by tract; for example, the optic radiation showed a lower MDC than the cingulum hippocampus. These findings highlight both the strengths and limitations of tractometry in multisite studies and highlight the importance of quantifying scanner- and protocol-dependent effects for specific metrics and tracts when interpreting measurements from heterogeneous datasets.

PurposeTo present a first-of-its-kind artificial intelligence (AI-)integrated MR pulse sequence that detects out-of-voxel (OOV) artifacts in real-time (within-TR) and responds prospectively by updating the crusher gradient scheme. MethodsPer Excitation Real-time Execution & Guided Responses with Integrated Neural-network Evaluation (PEREGRINE), developed for deployment of deep learning models and sequence updates, operated time-domain (TD) and frequency-domain (FD) convolutional autoencoders that detect OOV artifacts. Scans without (AI-off) and with (AI-on) updates were collected from the prefrontal cortex of healthy volunteers using edited MRS. The degree of OOV contamination (OOV score) was quantified per transient based upon the prevalence of OOV signals in the TD and FD data. OOV scores above a user-defined threshold triggered an update of the gradient scheme, iterating through 48 permutations (6 axis transpositions x 8 polarity flips). ResultsWithin each 2-second TR, PEREGRINE successfully provided single-transient OOV scores and updated gradients accordingly. No difference was observed between the OOV scores from the full ("Full" condition) AI-on and AI-off sessions due to the AI-on scan cycling over better and worse gradient permutations relative to the AI-off scan. However, the AI-on scan had significantly lower OOV scores than the AI-off scan when selecting the transients where PEREGRINE persisted ("Dwell" condition) on a given gradient permutation. Ultimately, Fit Quality Number (FQN), from linear combination modeling, improved significantly for the AI-on compared to the AI-off scan. ConclusionPEREGRINE enabled an AI-integrated sequence allowing for real-time evaluation and reduction of OOV artifacts, identifying gradient modifications that produced less OOV contamination.

Neuroimaging data management and processing are tedious and error-prone, prompting reproducibility concerns. Globally, studies with heterogeneous infrastructure and governance policies lead to eclectic data processing and sharing, necessitating standardization of data workflows to ensure reusability and comparability of multi-centric datasets. The Nipoppy neuroinformatics framework facilitates such standardization by combining specification, protocol, and software to manage study-level data workflows. With its adoption, researchers can share standardized, derived datasets enabling efficient, reproducible, and inclusive research.

Foundation models pre-trained on large neuroimaging datasets offer a promising approach to overcome the limited sample sizes typical of mental health imaging studies, yet their generalization across diverse clinical populations remains unclear. We present the first systematic benchmark of four publicly available structural MRI foundation models -- AnatCL, BrainIAC, 3D-Neuro-SimCLR, and SwinBrain -- on tasks relevant to mental health research. Using T1-weighted MRI from Parkin-sons Progression Markers Initiative (PPMI), Healthy Brain Network (HBN), and Nathan Kline Institute (NKI), we evaluate these models on sex classification, brain age prediction, and Parkinsons disease (PD) classification, benchmarking against models trained from FreeSurfer-derived cortical thickness and cortical surface area features, as well as an un-trained CNN baseline. Although some individual foundation models out-performed FreeSurfer on particular tasks and datasets, 3D-Neuro-SimCLR demonstrated the most consistent performance overall, with the notable exception of HBN sex classification, and all models failed to classify early-stage Parkinsons disease above chance. Notably, untrained CNNs achieved performance comparable to or exceeding FreeSurfer in multiple instances, establishing them as computationally efficient reference models. The cross-model feature correlation analysis reveals that foundation model representations correlate differently with traditional cortical measurements. These findings position structural MRI foundation models, particularly 3D-Neuro-SimCLR and AnatCL, as promising avenues to boost the performance of neuroimaging predictive models in mental health.

Spontaneous thought is pervasive in everyday human cognition, yet datasets capturing its neural dynamics under minimally interrupted conditions remain limited. The current dataset was acquired from a think-aloud functional MRI experiment in which 118 participants continuously verbalized their spontaneous thoughts during 10-minute scanning sessions. The raw MRI data and verbal transcripts with sentence-level timestamps were previously released and analyzed in our prior study examining neural activity associated with thought transitions. Building on that release, we additionally provide preprocessed MRI data, speech transcriptions with word-level timestamps aligned to image acquisition, large language model-generated ratings of transcribed thoughts across emotional and sensory dimensions, and self-report survey measures assessing personality, mental health, and cognitive abilities. Validation analyses demonstrated activation in expected cortical regions associated with speech production and sensory content identified from transcript annotations, agreement between language model and human ratings, and adequate internal consistency of survey measures, supporting the datasets overall quality. This dataset enables reuse for investigations of spontaneous thought, speech generation, and individual differences using naturalistic functional MRI data.

PurposeHigh-density RF receive arrays are required to realize the inherently available SNR and parallel imaging advantages at ultrahigh field strengths, which are essential for high-resolution functional and anatomical brain MRI. This study aims to systematically assess the impacts of often-overlooked parasitic losses associated with various RF coil components, as these losses can degrade the realized SNR and cause significant deviation from the ultimate intrinsic SNR (uiSNR; the theoretical upper bound of available SNR). In addition, we seek to detail engineering solutions to each of these loss mechanisms in pursuit of achieving a higher fraction of the uiSNR limit. MethodsA 16-channel loop-folded dipole transceiver array was developed for 10.5T human head applications and paired with a fully-updated 64-channel receive-only loop array. The optimization of the receive array considered several factors, including (but not limited to) coil dimensions to accommodate a larger population, the size and number of loops to enhance SNR and parallel imaging performance, and circuit design strategies to minimize parasitic losses. The SNR and parallel imaging performance of the receive array were quantitatively assessed by comparison with the uiSNR, as well as existing high-channel-count receive arrays at 7T and 10.5T. Finally, the complete 16-channel transmit, 80-channel receive coil array was safety validated for human use and employed for high-resolution functional and anatomical MRI at 10.5T. ResultsInitial results show that the 80-channel array, featuring larger loops in an overlapped layout with optimized circuitry, significantly improves the SNR and approaches the uiSNR limit in a large fraction of the head, while maintaining or enhancing the parallel imaging performance compared to previously used non-overlap layout. ConclusionThis study suggests that, although the traditionally used high-channel-count loop receive array technology can approach the uiSNR limit in the >10T regime, meticulous design optimization--including systematic assessment and minimization of parasitic losses--has become increasingly critical for achieving this goal in this new field-strength territory.

A common approach for investigating high-level visual cortex with functional magnetic resonance imaging (fMRI) is to define regions of interest (ROIs) in individual participants using functional activation clusters and anatomical landmarks. Although highly productive, this approach requires manual decisions about which clusters correspond to specific canonical regions, limiting reproducibility and posing challenges in populations with lower signal-to-noise ratios, such as children. The Group-Constrained Subject-Specific (GSS) approach reduces this subjectivity by using group-level parcels to constrain subject-specific functional ROI definition. However, the original GSS parcel set provides limited coverage of the occipital place area (OPA) and does not include more recently characterized scene-selective regions. Here, we introduce an updated and expanded set of GSS parcels for scene-selective cortex. Using a larger adult sample and dynamic scene stimuli, we generated updated parcels for OPA, parahippocampal place area (PPA), and retrosplenial complex (RSC), and for the first time, delineated a parcel for a newly discovered scene-selective region in the superior parietal lobule (superior place area; SPA). We evaluated these parcels in independent adult and pediatric datasets by testing whether they improve cross-subject coverage while preserving functional selectivity. The updated OPA parcel increased cross-subject coverage relative to the original parcel by Julian and colleagues. Moreover, ROIs defined using the updated parcels showed equal or greater scene selectivity across OPA, PPA, and RSC, indicating improved functional ROI definition without sacrificing specificity. Across scene-selective regions, the updated parcels reliably identified scene-selective cortex and reproduced canonical response profiles and in pediatric data. These parcels provide more complete and reliable coverage of the scene-processing network, supporting objective and reproducible ROI definition across adult and pediatric fMRI datasets. HighlightsO_LIExpanded group-constrained parcels improve coverage of scene-selective cortex C_LIO_LIDynamic stimuli yield improved cross-subject overlap for OPA C_LIO_LINew parcel introduced for the scene-selective region in the superior parietal lobule, now called superior place area (SPA) C_LIO_LIUpdated parcels reproduce canonical response profiles in adult data C_LIO_LIParcels reliably identify scene-selective voxels in pediatric datasets C_LI

Human brain tissue preserved in biorepositories is foundational for the structural, cellular, and biomolecular research necessary for a mechanistic understanding of neurological diseases. Realizing the research potential of these valuable resources requires well-characterized research-relevant tissue that can be efficiently identified by investigators and incorporated into the conceptual and computational frameworks of interdisciplinary research. Several large-scale efforts to improve research reliability and reproducibility have sought to characterize and annotate the processes by which these samples are collected, yet limited progress has been made on standardizing spatial information for these samples. Biorepositories systematically collect brain tissue according to a brain sampling protocol (BSP) that differs between institutions, yet explicit spatial information regarding the samples may not be documented in standard operating procedures (SOPs). The amount of anatomical location details available to investigators are inconsistent across biorepositories and typically lack sufficient anatomical precision to ensure correspondence with samples from other biorepositories or research relevant brain regions specified by neuroimaging, functional, or disease-susceptibility criteria. Here, we introduce a pipeline for developing a Spatial Atlas for Mapping Protocol Locations of Ex vivo Samples (SAMPLES), which uses a neuroimaging framework to create a 3D representation of a BSP through a metrically precise digital instantiation of the procedures for brain extraction, segmentation, slicing, and sampling on a modern digital brain template. SAMPLES incorporates modern neuroinformatics conventions to create explicit 3D labels of BSP-defined samples that can be interactively visualized with freely available neuroimaging software. We illustrate the pipeline by developing an atlas for the protocol from the University of Washington BioRepository and Integrated Neuropathology laboratory (UW BRaIN SAMPLES). By providing an explicit, computable reference, SAMPLES atlases can support the efficient identification, referencing, and utilization of postmortem samples for interdisciplinary research. These capabilities enable biorepository workflows, data harmonization across biorepositories, and integration with antemortem neuroimaging.

One of the prominent methods in neuroimaging data processing is SSM-PCA, which is based on principal component analysis and allows for the identification of diagnostically significant patterns in the form of statistical maps. We developed software, PIE Toolbox, employs SSM-PCA and classification based on the obtained diagnostic patterns revealed from functional and structural tomographic brain imaging. The program supports the entire analysis pipeline including preprocessing of brain images, diagnostic patterns extraction, building classification models, and prediction based on them. The resulting diagnostic patterns are weighted principal components obtained through SSM-PCA, or their linear combinations. PIE Toolbox allows selection of relevant structural and functional brain patterns, computation of their expression values in regions of interest, classification using support vector machines, and evaluation of model performance via cross-validation. This approach enables the use of patterns as features of intergroup differences for individual diagnosis. The software has been validated on both simulated and ADNI datasets.

The metabolic demands of the human brain are met by a complex vascular architecture, yet our characterization of this network remains incomplete. While we have mapped the macroscopic vessels on the brains surface and the microscopic capillaries within small tissue samples, the mesoscopic scale consisting of the penetrating vessels that plunge through cortex remains an anatomical terra incognita. Mapping the interface between the macroscopic and microscopic scales is essential to understanding the critical vascular supply that sustains brain health. Here, we leveraged the BigBrain dataset and developed custom detection and tracing algorithms to reveal a whole-cortex record of the mesoscopic vascular network. We find that vascular density is not uniform across the cortex, but is a heterogeneous landscape that shows clear relationships to traditional areal boundaries. While based on a single human specimen, our results constitute a reference for human mesoscopic angioarchitecture and demonstrates the power of repurposing high-resolution histological atlases. Ultimately, this work lays the groundwork for validating recently developed in vivo MRI techniques for imaging the human cerebrovascular system at mesoscale.

BACKGROUNDIn tetralogy of Fallot (ToF), changes to right ventricular (RV) function (as seen by strain or TAPSE) relate to altered myocardial structure. Direct three-dimensional anatomical evidence supporting these changes remains limited. OBJECTIVESTo non-destructively characterize myocardial architecture in pediatric ToF hearts using Hierarchical Phase-Contrast Tomography (HiP-CT) and structure tensor analysis. METHODSTwenty ToF and control pediatric hearts were imaged at the European Synchrotron, ESRF. Myocyte orientation was assessed through structure tensor analysis and distributed high-performance computing. A region-specific framework was developed for analysis of the RV. The predominant direction of myocardial aggregates (their helical angle) was compared across ventricular regions. RESULTSSignificant differences in orientation were found in all ToF segments vs controls (left ventricle, RV inlet, RV outflow tract, septum; p < 0.001). Myocytes in the ToF RV inlet were more circumferential overall, with regional heterogeneity. Contrary to traditional models, no discrete middle layer was found in the ToF RV, instead, a shift towards more circumferentially orientated myocytes and disrupted septal and outflow components was observed. RV contribution to the septum was greater in ToF (47.3% vs 34.0% ; p = 0.0026) with extension of ventricular insertion points disrupting septal architecture. There were more longitudinally oriented myocytes in the ToF RVOT, consistent with hypertrophied septo-parietal trabeculations. LV structure in ToF demonstrated a greater proportion of circumferentially oriented myocytes vs controls. CONCLUSIONSWe reveal profound alterations in ToF myocardial organization which broadly align with clinical observations and provide the first open-access HiP-CT congenital heart disease data as a basis for future computational modelling.

Objectives To evaluate whether class-conditional conformal prediction (CP) can provide reliable uncertainty quantification (UQ) under severe class imbalance and distribution shift, using multiple sclerosis (MS) diagnosis from magnetic resonance imaging (MRI) as a clinical exemplar. Methods We evaluated marginal and class-conditional CP using 720 T2-weighted MRI scans (142 MS, 578 controls). A convolutional neural network trained on 3 T data was evaluated under distribution shift (1.5 T acquisitions and synthetic image degradations). Through 100 Monte Carlo experiments, we assessed coverage guarantees, class-specific performance, and relationships between calibration set size, coverage variance, and uncertainty. Results Marginal CP severely under-covered the minority MS class (16.9% mean coverage at 1.5 T vs. 95.2% for controls) despite valid population-level guarantees. Class-conditional CP dramatically improved MS coverage to 77.5% at 1.5 T and 85.8% at 3 T, significantly reducing severe undercoverage (<80%) frequency while maintaining >89% control coverage. Minority class coverage variance increased due to limited calibration samples, matching theoretical Beta-binomial predictions. CP maintained validity under distribution shift; prediction set sizes scaled monotonically with shift severity, yielding clinically interpretable UQ. Conclusions Class-conditional CP successfully mitigates systematic undercoverage of minority disease classes while maintaining validity under distribution shift. The approach offers a practical, model-agnostic solution for uncertainty quantification applicable across clinical AI systems, though increased coverage variance for less represented conditions reflects fundamental statistical constraints. By characterizing these variance trade-offs, this framework enables more reliable deployment of diagnostic AI in heterogeneous clinical environments across diverse medical domains where minority disease class detection is critical.

IntroductionCoronary artery disease (CAD) increases the risk of cerebrovascular events, yet early brain injury in this population remains poorly characterized. White matter hyperintensities (WMHs), a biomarker of cerebrovascular lesions, are prevalent in CAD and are linked to risk of stroke. Beyond total burden, spatial distribution of WMHs carries pathological significance and is critical for understanding CAD-related injury. While clinical outcomes including coronary revascularization procedure and myocardial infarction influence CAD prognosis, their impact on WMH burden remains unclear. MethodsThis study investigated regional WMH burden in CAD and its relationship with clinical characteristics. 82 adults over 50 years participated, including 44 individuals with CAD and 38 controls. WMHs were segmented from fluid attenuated inversion recovery and T1-weighted MRI and categorized as total, periventricular, deep, and superficial regions. History of myocardial infarction and coronary revascularization (coronary artery bypass grafting (CABG) and percutaneous coronary intervention (PCI)), was obtained from medical files. ResultsIndividuals with CAD exhibited higher total, periventricular, and deep WMH volumes than controls. Participants who underwent CABG had higher superficial WMH volumes than those with PCI, suggesting greater disease severity influences WMH burden. ConclusionCAD is characterized by a distinct pattern of cerebrovascular vulnerability, with revascularization procedures influencing WMH burden

We present the TopBrain 2025 Challenge, the first benchmark for fine-grained multiclass segmentation of the whole brain vasculature in both computed tomography angiography (CTA) and magnetic resonance angiography (MRA). Building on the TopCoW challenge, TopBrain scales vessel annotation from the Circle of Willis to the entire brain, introducing a dataset of 90 annotated volumes across 48 landmark vessel classes spanning arterial and venous systems, of which 50 training volumes are publicly released. Vessel definitions were consolidated from established neuroanatomical references into a unified annotation scheme, and vessel caliber measurements along the centerline are reported for the first time across the whole brain vascular anatomy. To address the unique challenges of multiclass brain vessel segmentation, we propose an evaluation framework that accounts for detection in segmentation performance, assesses anatomical plausibility, and introduces novel contamination metrics that characterize inter-class prediction errors. Fifteen teams from over 220 registered participants submitted algorithms to the benchmark. The top-performing teams built on nnUNet with principled system design choices, achieving around 80% Dice scores, near-zero invalid neighbor counts, over 60% F1 scores for side-road vessels, and below 18% foreground contamination ratio. Larger vessels are easier to segment, while smaller and more complex vessels remain the true bottleneck. The annotated datasets and podium-finish algorithms are made publicly available on Zenodo.