Aperture Neuro

Intracranial volume (ICV) is often estimated as part of automated neuroimaging processing pipelines and commonly used to account for and characterize cranial size. FreeSurfer's estimated intracranial volume (eTIV) produces implausible longitudinal estimates in developmental samples when using versions prior to FreeSurfer 8. Across two different adolescent datasets, we document eTIV changes of up to 39% between 1.5 - 2 year sessions for FreeSurfer 7, with errors more pronounced on Siemens scanners and in female participants. These errors are markedly reduced in FreeSurfer 8 and AssemblyNet. However, the widespread use of FreeSurfer 7 raises concern about developmental neuroimaging studies that utilize eTIV without quality control.

The choroid plexus (ChP) is a highly vascularized structure located in the brain ventricles that plays important roles in multiple central nervous system functions. ChP enlargement has been implicated in many neuropsychiatric conditions such as multiple sclerosis, depression, schizophrenia, and Alzheimer's disease. However, limitation in the accuracy of the current segmentation tools highlight the need for more reliable methods to quantify ChP volume in healthy and clinical human populations. The shape and size of the ChP vary drastically from person to person, and currently there are no large ChP datasets which allow comprehensive training of segmentation models. Here we introduce DataPlex, a collection of 700 structural MRI brain images where the lateral ventricle ChP has been manually labeled and checked by expert human raters. DataPlex is balanced and representative across a wide range of scanning parameters, demographics, and clinical profiles, and subject-level information will be available for each labeled image. DataPlex will be open source to support the creation of reliable segmentation tools for the human lateral ventricle ChP.

Quantitative magnetic resonance imaging (qMRI) supports both basic neuroscience and clinical research, providing the foundation for longitudinal studies of brain development, aging, and neurological disease. However, its validity rests on a little-studied assumption: that physical relocation of imaging hardware leaves underlying neuroimaging biomarkers intact. When research facilities expand or modernize, an entire scanner undergoes radical disruption--field ramp-down, physical transport, shielding reconstruction, and re-shimming. Whether qMRI metrics remain stable following such invasive procedures without introducing systematic biases is largely unquantified. Here, we systematically evaluated the impact of relocating a 3T clinical scanner over a distance of 30 km on a broad, multimodal suite of neuroimaging markers in healthy volunteers. To disentangle variance components, we benchmarked within- and cross-site stability using percent variation (PV; measurement precision), intraclass correlation coefficients (ICC; absolute reliability), and Spearman rank correlations (R2; spatial and order agreement). We show that scanner relocation introduces no greater variance than baseline within-site assessments. High-resolution brain structural morphometry from different sequences (multi-echo MPRAGE, compressed sensing MPR2RAGE) and quantitative relaxometry (compressed sensing MP2RAGE T1 mapping) demonstrated excellent stability (PV<1%, ICC=0.89-0.99, R2>0.93). Diffusion tensor (mean diffusivity: MD, fractional anisotropy: FA) and mean signal kurtosis (MSK) metrics from multi-shell diffusion MRI maintained low variability across white and deep gray matter (global cross-site ICC>0.90; global PV: MD<1.7%, MSK<1.2%, FA<3.8%; global R2: MSK>0.93; MD>0.90; FA>0.77), confirming relocation robustness for diffusion-derived microstructural markers. Resting-state functional networks displayed high cross-site reproducibility (ICC>0.90), while task-based fMRI activation peaks retained acceptable spatial and magnitude agreement (ICC=0.30-0.60) despite intrinsic physiological variability. In the absence of software or hardware upgrades, physical scanner relocation leaves qMRI biomarkers remarkably intact--providing the empirical foundation required to preserve data integrity across major facility transitions and multi-site longitudinal cohorts. Key points that summarize the paperO_LIHardware transitions do not compromise qMRI data integrity: Physical relocation of a 3T scanner (including field ramp-down, 30 km transport, and re-shimming) introduces no greater variance in quantitative MRI metrics than baseline within-site test-retest assessments. C_LIO_LIMultimodal biomarker robustness: Brain morphometry, T1 relaxometry, diffusion tensor and kurtosis, resting-state functional connectivity display comparable within- and cross-site stability reflected by different metrics (percent variation, intraclass correlation coefficient and Spearman correlation) C_LIO_LIEmpirical backing for longitudinal research: In the absence of hardware or software upgrades, multimodal qMRI biomarkers remain remarkably stable, reassuring researchers that long-term cohort studies and multi-site harmonization can survive major facility relocations. C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=175 SRC="FIGDIR/small/26363342v1_ufig1.gif" ALT="Figure 1"> View larger version (57K): org.highwire.dtl.DTLVardef@773d03org.highwire.dtl.DTLVardef@46a09borg.highwire.dtl.DTLVardef@da5bc0org.highwire.dtl.DTLVardef@1bdbf9d_HPS_FORMAT_FIGEXP M_FIG This study evaluates the impact of physically relocating a 3T MRI scanner on neuroimaging biomarkers. By comparing before and after relocation sessions on healthy volunteers, we conclude that the procedure does not introduce significant variability. Parameters for morphometry, T1 relaxometry, diffusion, and resting-state functional connectivity show high stability. Task-based fMRI showed greater within-site variability, but no increase in variability following relocation, consistent with the inherently higher variability of task-evoked responses.. The results confirm the integrity of qMRI data after infrastructure relocation, ensuring reliability for longitudinal studies. C_FIG

Neuroimaging research focusing on subcortical brain regions showed that they play a role in a wide range of cognitive functions and associated their structural alterations to various psychiatric disorders. Methods to analyse such structures were developed to observe changes, not simply in terms of volumes, but in terms of surfaces, which are able to detect subtle and local changes in shape and geometrical configuration. Such advanced analyses have however been largely restricted to command line environments with relatively limited access to Python users or researchers with low technical expertise. We present SubCortexMesh as a user-friendly toolbox which covers automated surface estimation from popular subcortical volume segmentations (FreeSurfer and the Functional Magnetic Resonance Imaging of the Brain Software Library (FSL)), computes shape-related vertex-wise metrics (thickness, surface area and curvature) for whole cohorts and includes statistical analyses. This is all done inside Python, automatically computing all subjects of a given preprocessed directory, with the explicit intent to minimise steps and manual coding required from the users. SubCortexMesh includes statistical tools which allow conventional random field theory-based cluster analyses on native subject metrics, standardised within common surface templates, thus containing a whole workflow necessary to run an up-to-date shape-wise subcortical analyses in one package.

There is increasing demand for quantitative measurements in neuroimaging research, including absolute quantification of regional myelin density. Fast whole-brain macromolecular proton fraction (MPF) imaging enables acquisition and reconstruction of quantitative maps of MPF, a biomarker of myelin, and has been used in many research applications. To date, no standard protocols have been published for parcellating MPF maps. FreeSurfer is a widely used software package for automated brain parcellation. It is designed for use with high-resolution T1-weighted anatomical images, typically acquired with an MPRAGE sequence. MPF maps inherently provide high contrast between white and gray matter and can potentially be used as input images for parcellation, enabling simultaneous quantitative tissue characterization and volumetric assessment. However, it is unclear how the use of MPF maps as input images with FreeSurfer software affect reliability and repeatability of regional MPF and volume estimates. We evaluated three FreeSurfer parcellation workflows for this purpose. The workflows differed in the amount of neuroimaging data required and computational intensity. MPF and MPRAGE data were acquired in two separate sessions for 11 adults. Reliability was evaluated using mean relative differences in estimates, Bland-Altman analysis, and intra-class correlation coefficients (ICCs). Repeatability was assessed using within-subject coefficients of variation (CVws). All three workflows produced similarly high within-subject repeatability across scans. However, reliability in regional MPF estimates was lower in gray matter than in white matter for workflows that used MPF maps as input. Workflows that use MPF maps as FreeSurfer input may be adequate for estimating mean MPF in GM and WM of cortical parcels and in subcortical regions. However, the MPRAGE-based workflow is recommended when reliable individual-level estimates or volume estimates are required.

Background: Ultra-low field (ULF) magnetic resonance imaging (MRI) offers accessible point-of-care neuroimaging, yet its capability to support multivariate network analyses remains understudied. Methods: We systematically evaluated the feasibility and reliability of Morphometric Similarity Networks (MSN) and Morphometric Inverse Divergence (MIND) networks derived from paired high-field (HF, 3T/1.5T) and ULF (64mT) scans in a primary cohort(N=23) and an independent replication cohort (N=19). We tested the impact of super-resolution processing, cortical parcellation granularity (Desikan-Killiany vs. Destrieux), and mesh resolution (fsnative vs. fsaverage5) on edge-level cross-scanner agreement (Lin's concordance correlation coefficient, CCC) and subject-level identifiability. Results: MIND networks demonstrated higher edge-level HF-ULF agreement (mean CCC = 0.95) and ULF reproducibility (mean CCC = 0.98) compared to MSNs (mean CCC = 0.86-0.87 and 0.91-0.94, respectively). In contrast, MSNs yielded higher subject-level differential identifiability across field strengths due to a broader distinction between within- and between-subject similarity. Reconstructing MIND networks on native high-resolution meshes (fsnative) maximised cross-scanner agreement, whereas coarser meshes and finer cortical parcellations enhanced individual network identifiability. Conclusions: Structural similarity networks can be reliably constructed from ULF MRI data. Optimal analytic choices depend on study objectives: MIND on native meshes maximises cross-scanner stability, whereas MSNs excel at preserving individual uniqueness, paving the way for globally scalable connectomic research.

Myelin supports rapid and efficient neural communication and is implicated in brain development, learning, aging, and neurological disease. Although numerous magnetic resonance imaging (MRI) approaches have been proposed to characterize myelin in vivo, they have rarely been evaluated together within the same participants and across repeated acquisitions. To address these gaps, we introduce the Myelin Imaging Reliability and ReprOducibility Resource (MIRROR), an openly available multimodal test--retest MRI dataset of diffusion MRI (dMRI), inhomogeneous magnetization transfer imaging (ihMT), quantitative susceptibility mapping (QSM), MP2RAGE, T1w/T2w ratio mapping, and transverse relaxometry in 22 healthy adults. We evaluated gray--white matter differentiation, intermetric correspondence, test--retest reliability, and participant specificity across 27 myelin-sensitive metrics. Gray--white matter differentiation was strong for dMRI derivatives, MP2RAGE-derived R1, T1w/T2w, and ihMT, while being substantially weaker for QSM measures. Intermetric spatial correlations were generally strongest within acquisition or modeling families. Test--retest reliability was generally high, particularly for dMRI-derived metrics, but was more variable among QSM-, ihMT-, and R2*-derived measures. Regional spatial profiles were also highly participant-specific, with discriminability approaching its upper bound for most metrics. All imaging data, derived maps, and processing code are openly released. MIRROR provides a benchmark for characterizing the measurement properties and practical tradeoffs of scalable myelin-sensitive MRI methods, supporting principled acquisition selection and accelerating methodological development and validation.

Population-graph models aggregate a query subject with an unlabeled cohort, so their reported performance can depend on the evaluation regime rather than on predictive information alone. Evaluation context has two separable axes: the visibility of the cohort during training, and the context available at inference. We study the training-time axis under a protocol fixed before analysis, on an ABIDE-I cohort of 871 subjects (20 acquisition sites). Two population-graph implementations are compared: a learned/adaptive graph (EV-GCN) and a static weighted graph (Parisot-GCN). Within each implementation the cohort, connectivity features, outer splits and training and selection rules are held fixed, and only the information available to the graph varies. The primary estimand is the implementation-by-training-context interaction: the difference between the P0-to-P1 AUC changes of the two implementations ({Delta}{Delta}AUC). Across three preselected outer splits (666, 1024, 2024) the direction is consistent and positive, with observed interaction range 0.06285-0.13090; uncertainty is assessed separately within each split by paired subject and site-cluster bootstrap resampling and is reported per split. Splits are replication units: they are not pooled and no cross-split average is reported, so the result is not a claim of invariance to arbitrary splits. These findings tie reported population-graph performance to the evaluation information regime and motivate explicit reporting of cohort visibility. The contribution is methodological: a two-axis taxonomy of information regimes, preselected cross-split evidence that the sensitivity of reported performance to cohort context depends on the implementation, and a reusable reporting checklist.

Background: Whole-brain segmentation of neonatal low-field MRI is needed for regional volumetric analysis, but established segmentation systems are designed around different field strengths, contrasts, and age ranges. Methods: We first applied complete end-to-end pipelines, including FreeSurfer, FSL-based processing, Infant FreeSurfer, FastSurfer, BrainSuite, SPM12/CAT12, iBEAT v2, the dHCP structural pipeline, and M-CRIB-S, directly to 0.35T neonatal T2-weighted images. Because those systems did not produce a complete 47-region output, we next tested released end-to-end AI segmentation models, including SynthSeg, FastSurfer, FastSurferVINN, and QuickNAT. These were highly sensitive to input format, contrast, voxel geometry, and the high-field training domain. We then decomposed the task into skull stripping, registration, super-resolution, atlas propagation, and tissue refinement and tested the available components individually. Results: No complete end-to-end pipeline or released AI model delivered a usable 47-region segmentation in direct application. Stage-wise testing showed that preprocessing, reconstruction, registration, atlas propagation, and expectation-maximization each solved only part of the problem. Their final assembly produced complete 47-region outputs across the neonatal cohort. Conclusions: Direct application of established pipelines was insufficient for 0.35T neonatal T2-weighted MRI. A stage-wise combination of compatible components provided the workable solution.

Neuroimaging is becoming an increasingly prominent tool to understand intimate partner violence (IPV) and partner-inflicted brain injury (BI). Yet, by nature of IPV and partner-inflicted BI, those most affected may be the least likely to be eligible, participate, and produce usable data in neuroimaging studies. A critical question therefore remains of who is represented in neuroimaging research on IPV and related BI. Here, we examined representativeness at three major stages of neuroimaging sample construction using screening records from 352 women and a subsequent cohort of 207 enrolled women with lifetime exposure to physical IPV. At the eligibility stage, three in five women eligible for behavioral research were deemed ineligible for magnetic resonance imaging (MRI) at initial screening. At the participation stage, the 61 women who underwent resting-state functional MRI (fMRI) had more education, were more likely to be employed, and reported lower psychiatric and neurobehavioral symptom burden than enrolled women who did not undergo neuroimaging. Finally, at the quality-control stage following fMRI, motion-based exclusion disproportionately removed women who had sustained the most partner-inflicted BIs. Our findings empirically reveal that eligibility, participation, and quality control each bias the demographic, clinical, abuse, and BI burden represented in an fMRI sample, and therefore have key implications for generalizability. Measuring and reporting representation bias can help identify whose experiences are underrepresented in the evidence, and accordingly reduce the risk that IPV neuroimaging research understates the very harms it seeks to characterize.

Background Large-scale retrospective studies often require researchers to adjudicate outcomes or cohort eligibility from unstructured clinical records. Manual review can provide reliable labels but is difficult to scale. We developed and evaluated a consensus workflow using multiple large language models (LLMs) to automatically assign high-confidence outcome labels while deferring ambiguous cases for manual review. Methods We included 6,718 brain MRI reports from 5,856 patients. The expert-adjudicated reference set included 680 reports from 675 patients: 372 normal (age-appropriate without significant intracranial pathology), 90 white matter hyperintensity-only (WMH-only; isolated nonspecific WMHs potentially ambiguous for binary classification), and 218 abnormal (other intracranial pathology) reports. We evaluated proprietary-inclusive (Qwen3/Kimi-Linear/GPT-5) and open-source (Qwen3/Kimi-Linear/DeepSeek-R1) ensembles against individual LLMs. Ensembles assigned a normal or abnormal label only when all models agreed; all other reports were classified as uncertain and deferred for manual review. Individual LLMs assigned normal or abnormal predictions and classified WMH-only predictions as uncertain. In the full cohort, we compared the prevalence of active central nervous system (CNS)-related International Classification of Diseases, Tenth Revision (ICD-10) diagnoses across the normal, uncertain, and abnormal groups generated by each ensemble. Results In the expert-adjudicated reference set, both ensembles achieved 100% precision for automatically assigned normal and abnormal reports. GPT-5 was the strongest individual LLM but had lower precision than either ensemble, with normal and abnormal precision of 98.9% and 94.1%, respectively, and 17 expert-label mismatches versus none for the consensus ensembles. In the full cohort, 1.6% of reports classified as normal were associated with CNS-related ICD-10 diagnoses, compared with 29.7% of uncertain and 51.3% of abnormal reports for the proprietary-inclusive ensemble; corresponding rates were 1.6%, 31.1%, and 51.6% for the open-source ensemble. Conclusions Requiring unanimous agreement across multiple LLMs enabled high-precision automatic adjudication while directing ambiguous cases to manual review. This strategy may reduce the burden of manual adjudication and support scalable construction of high-purity research cohorts from unstructured clinical text.

Multi-echo functional magnetic resonance imaging (fMRI) acquires signals with distinct contrast profiles across echo times (TEs). Empirical evidence suggests that different TEs capture distinct signal contributions with varying sensitivity to BOLD and non-BOLD processes from long to short TEs (Stroman, 2002; Krishnamurthy, 2023; Dong, 2024). Previous literature has not yet examined how brain states extracted from temporal dynamic functional connectivity (dFNC) evolve across TEs. Here we present a methodological and empirical study of TE-dependent evolution of dynamic connectivity states, with a phenomenological two-component model for interpretation. Multiple echoes were acquired with an echo planar time-resolved imaging (EPTI) sequence. TE-specific components were extracted via atlas-based group ICA (Neuromark 1.0), and brain connectivity states were generated via windowed dFNC and spatial ICA clustering of windows. Within-subject and group-level states evolved across TEs in network organization and variance. States exhibited TE-dependent variance profiles, with distinct peak echo times; and group-level points of state transition were independently mapped to TE-dependent transitions in variance. We modified a bi-exponential model to simulate and model changes in the dFNC time-course across TEs. The bi-exponential model revealed high consistency with subject states (R2 fit=0.86, res. error=0.55, 86% model convergence rate), suggesting a two-component signal system contributing to dynamic TE-dependent connectivity changes in state. A significant correlation between relative model terms was estimated on a cell-wise basis across states (r(24) = -0.43, p=0.03, 95% CI [-0.70, -0.04]). This suggests that specific bi-exponential signal parameters (i.e., short FNC1,TE=0, long FNC2,TE=0, and long FNC2,decay) were associated with the observed TE-dependent variations in connectivity states across TEs. In sum, our analyses provide downstream implications for TE-aware state analysis dependent on connectivity magnitude (FNCTE=0) and rate of connectivity decay (FNCdecay), in a two-component signal model, with future implications for TE-dependent state biomarkers.

Neuroimaging studies that pool data across clinical sites often suffer from site effects --- variability in imaging measures that arises from technical heterogeneity across sites as opposed to true biological signal. While numerous harmonization methods have been proposed to remove site effects from imaging features, less attention has been placed on how to incorporate harmonization models into common machine-learning pipelines. We demonstrate that current approaches for integration either suffer from data leakage, fail to fully remove site effects from the target data, or attenuate true biological associations during the harmonization process. To address these issues, we propose Multiple Imputation for Removing Technical Heterogeneity (MIRTH), which uses imputed outcomes to harmonize the test data. We benchmark MIRTH's performance using both simulated and real-world volumetric data from the Alzheimer's Disease Neuroimaging Initiative and the Baltimore Longitudinal Study of Aging, demonstrating that MIRTH can achieve high predictive accuracy while avoiding inflated performance when the outcome is imbalanced across clinical sites.

Purpose: We aim to acquire and reconstruct high signal-to-noise ratio (SNR), whole-brain, mesoscale (isotropic submillimeter) diffusion MRI (dMRI) in vivo in a clinically feasible scan time. Methods: We present ROVER-dMRI, a unified rotating-view acquisition and super-resolution reconstruction framework. Thick slices (up to 10 mm)acquired across many rotated orientations improve the base SNR, and a shared multi-resolution hash-encoded implicit neural representation (INR) with per-direction MLP head and a physics-informed, Bloch-simulated slice-profile forward model reconstructs these views into a continuous isotropic volume (up to 13x super-resolution) in a self-supervised manner. ROVER-dMRI was evaluated in vivo on human brain (0.78 mm isotropic and 0.5 mm isotropic) and monkey brain (0.6 mm isotropic) across MRI platforms with varying gradient performance, and benchmarked against gSlider-SMS and least-squares super-resolution reconstruction (LS-SRR). Results: At 0.78 mm isotropic resolution, ROVER-dMRI diffusion- weighted images achieved higher SNR than the vendor product sequence, whose images were limited by the noise floor, while its directionally encoded color (DEC) maps showed lower noise than gSlider. When reducing the number of views from 20 to 13 (reducing the scan time by 65% ), ROVER-dMRI acquisition/reconstruction preserved all fine details in the fiber orientations. In the monkey brain, its DEC map matched a 60-direction gSlider acquisition at only 40% of the scan time. ROVER-dMRI preserved coherent fiber-orientation-distribution (FOD) structure, including crossing fibers, and recovered fine details in the cerebellum at 0.5 mm isotropic, that are not visible at 0.78 mm resolution. Compared to state-of- the-art 0.5 mm methods, ROVER-dMRI reduced scan time by 73% while acquiring more diffusion directions. Conclusion: ROVER-dMRI offers a practical route to high-SNR whole-brain mesoscale diffusion imaging in vivo, with the pulse sequence and reconstruction pipeline released as open source. KEYWORDS: diffusion MRI, super-resolution, implicit neural representation, hash encoding, physics informed learning

Brain foundation models have shown promising advances in learning generalizable representations from functional magnetic resonance imaging (fMRI) data. However, existing models are typically tied to specific brain atlases and cannot be applied to fMRI data represented using different atlases without full retraining, thereby severely limiting their broader utility across neuroimaging studies. To address this bottleneck, we introduce AREION, atlas-agnostic representation learning of brain connectomes. AREION overcomes atlas dependency through two integrated innovations. First, a Brain Region Contextualizing technique maps brain regions and connectivity entries from diverse atlases into a unified representation space based on their spatial, functional, and morphological characteristics. Second, a Hierarchical Brain Transformer Encoder processes region-specific connectivity profiles as variable-length token sequences and jointly captures local functional structure and global brain functional organization, conditioned on brain regional context, without assuming a fixed number of regions. Together, these two techniques enable the learning of generalizable representations across brain atlases with different parcellation resolutions and connectivity dimensions. AREION is pretrained on a large-scale multi-atlas cohort of fMRI data via self-supervised modeling and evaluated across diverse downstream tasks. Extensive experiments show that it outperforms strong supervised baselines and fMRI foundation models while using substantially fewer parameters, demonstrating strong transferability to atlases unseen during pretraining, generalization to external datasets, and excellent interpretability at the brain regional level.

Resting-state connectivity can predict task-evoked fMRI activation, but correspondence with an individual task map may partly reflect a shared population pattern. We evaluated the Variational Resting-state-to-Task Prediction TransformeR (VRPTR), a three-dimensional encoder-decoder combining a compressed Transformer bottleneck, variational latent sampling, and multiscale skip connections. Models were trained in 360 healthy adults from the WU-Minn Human Connectome Project and evaluated in 40 held-out participants from the same dataset for the story-versus-math language contrast. VRPTR achieved mean voxel-map Pearson r=0.642 and Dice AUC=0.519, exceeding compact volumetric BrainSurfCNN-like and SWIFUN-like comparators by {Delta}r=0.0376 and 0.0335, respectively. A template fixed from the 360 training maps yielded residual correlation 0.364; VRPTR improved correlation but not mean absolute or root mean squared error over this template. A matched factorial ablation associated Transformer processing with improved map correlation ({Delta}r=0.0168); the variational main effect did not survive multiple-comparison correction. Component swaps indicated that subject-specific activation patterns depended more on multiscale skip features than on the bottleneck representation. Latent sampling produced a modest association between posterior standard deviation and voxelwise absolute error (mean Spearman {rho}=0.091). Raw 95% intervals covered only 4.7% of observed values. Five-fold calibration within the held-out cohort increased coverage to 94.8%, and spatially varying uncertainty modestly improved probabilistic scores over a constant-width model. Uncertainty also increased under input degradation. These findings support the contribution of Transformer processing to language-map prediction and distinguish it from subject-specific spatial conditioning and relative uncertainty estimation. Independent validation of calibrated uncertainty and evaluation in clinical cohorts remain necessary.

The cerebral cortex is organized along a gradient of laminar complexity, indexed principally by the emergence and granularity of layer IV and by the size of layer III neuron somata, and cortical types are the ordered categories along it. The Structural Model builds on that gradient to predict laminar connectivity, plasticity and disease vulnerability (Barbas and Rempel-Clower, 1997; Garcia-Cabezas et al., 2019). Existing digital cortical-type maps are area-level: they assign one type per classical area and cannot represent a type boundary that runs through an area. We release cyto7, an open, vertex-level, seven-type cortical-type atlas delineated on the fsaverage surface following the Garcia-Cabezas protocol and revised by the protocol's authors. We distribute the atlas with a reusable quality-control and support framework. A topological audit reports the map against four spatial rules that follow from cortical type analysis and the dual origin of the neocortex theory: neighbouring vertices differ by at most one type, the least complex cortex forms one continuous limbic belt, eulaminate II forms a single matrix enclosing a few holes, and eulaminate III and koniocortex form isolated islands. These were imposed during construction rather than tested, and the audit reports what imposing them cost. A vertex-wise benchmark against the existing area-level map, one type per von Economo-Koskinas area, shows exact agreement at 52.5% of vertices, with disagreement almost always a single level and concentrated inside the large, heterogeneous areas that a single areal label cannot resolve. Where the two maps disagree, cyto7's label better matches the independent multimodal profile in 60.1% of pooled feature-by-vertex comparisons, against 46% under a rotation null and 54% under granularity-matched alternative partitions (p = 0.020), the harder of the two. A per-vertex anatomical support score, computed from anatomy alone, states how strongly each hand-drawn label is backed by the anatomical evidence, and is deliberately not a calibrated error probability. We then validate cyto7 against independent brain measures not used to define it, under a spatial-autocorrelation (spin) null with FDR control. Intracortical myelin (T1w/T2w) increases, cortical thickness decreases, and the principal functional-connectivity gradient reverses along the type axis (all q of about 0.006 or less). Source-reconstructed resting-state MEG dynamics also track the axis: the intrinsic timescale shortens toward koniocortex (rho of about -0.46) and survives the null, as do the spectral centroid and the slow/fast band-power ratio, whereas the aperiodic 1/f exponent does not. The laminar marker genes are where cyto7 differs most from a quantisation of a continuous map: it shows a larger absolute correlation with the layer-IV marker RORB (rho = +0.78) and the layer-V marker FEZF2 (-0.59) than seven-level septiles of myelin, of the functional gradient or of a histological gradient, on four of five markers, while tying or losing to those septiles on the macroscale proxies. These are larger observed associations against three tested quantisations, not a test that the differences between maps are reliable, and they do not establish the accuracy of any individual boundary. Defined from anatomy alone, cyto7 recovers the sensorimotor-to-association hierarchy and, on these markers, some of the laminar signal underlying it. It is released with its full pipeline and an uncalibrated anatomical support score as a reusable substrate for structure-function and modelling studies.

Maximizing the morphological and molecular fidelity of preserved mammalian brain tissue is essential for basic neuroscience and brain banking, where tissue quality determines the reliability of downstream analyses. Aldehyde fixation and cryogenic storage are the most powerful preservation techniques available. However, how to best combine them is incompletely characterized. Here, we tested twelve different approaches to preserve the murine brain, including standard aldehyde perfusion fixation, variants of aldehyde-stabilized cryopreservation (ASC) using ethylene glycol (EG), aldehyde fixation followed by unprotected freezing, and several cryopreservation protocols without preceding aldehyde fixation, such as interleaved equilibration with vitrification solution. Preservation quality was assessed using light microscopy, transmission electron microscopy, and patch-level feature analysis with the DINOv2 vision foundation model. We found that ASC with sodium dodecyl sulfate (SDS)-mediated blood-brain barrier permeabilization preserved nuclear morphology, myelin periodicity, and neuropil texture comparable to standard aldehyde perfusion fixation, providing the first independent replication of ASC. Omitting SDS caused severe parenchymal dehydration, due to a mismatch between water and cryoprotectant transport. Aldehyde fixation followed by unprotected slow freezing confined ice damage primarily to perivascular zones, while fast freezing led to intranuclear clefts and cavities. Among non-fixation protocols, interleaved equilibration with vitrification solution preserved myelinated axon profiles and chromatin patterns, but induced perivascular edema. High concentration glycerol perfusion caused significant osmotic dehydration. Straight freezing of unfixed tissue without cryoprotectant was followed by membrane disruption, but resulted in the visualization of more structure than expected, likely as a result of structural restitution upon thawing. Taken together, our results provide a framework for matching preservation strategies to the needs of different types of brain banking and neuroscience research.

PurposeWith growing interest in pediatric MRS to understand the neurochemistry of brain development, it is essential to establish pediatric-specific metabolite T1 and T2 relaxation times for reliable quantification of metabolites. Furthermore, characterization of age-related changes in relaxation times may provide insight into developmental differences in tissue microstructure and cellular composition. Therefore, the goals of this study were two-fold: (1) to estimate metabolite T1 and T2 relaxation times in a pediatric cohort; and (2) to assess linear relationships between age and relaxation times. MethodsTI- and TE-series data were acquired from the bilateral thalamus in 20 and 25 participants aged between 0 and 7 years, respectively. Data were processed and modelled in Osprey 2.5.0. Metabolite T1 and T2 relaxation times of total N-acetylaspartate at 2.0 ppm (tNAA2.0), total creatine at 3.0 ppm (tCr3.0), total creatine at 3.9 ppm (tCr3.9), total choline (tCho), myo-inositol (mI) and the combined glutamate and glutamine (Glx) were estimated by fitting the modelled signal amplitudes to TI-series inversion recovery signal and TE-series exponential decay equations, respectively. Linear relationships with age were tested for metabolite T1 and T2 relaxation times using Spearman correlations. ResultstNAA2.0 and Glx showed significant changes in T1 with age. tNAA2.0, tCr3.0, tCho and mI showed significant decrease in T2 with age. ConclusionSeveral metabolites showed significant changes in relaxation times with age in the thalamus region. Therefore, use of age-specific relaxation correction for pediatric studies using the linear equations provided is encouraged for reliable quantification of metabolites.

Lesion Network Mapping (LNM) is a method used to map symptom-related circuits in the human brain. Recent work has raised concerns about the method and the specificity of LNM-derived networks, which often show high spatial overlap across unrelated conditions. Null models have been proposed to address the raised limitations1. Useful statistical tools in network neuroscience, they are reference procedures; in LNM, the normative connectome matrix C remains the source of the derived networks also under null modelling. Their broader role as a general solution for LNM therefore warrants careful evaluation. Here, we examine the proposed null models step-by-step and test them empirically across multiple datasets. We show that the proposed null-model correction effectively only replaces C with a transformed matrix C' (e.g., C' ~ C - mean(C)), one that is directly derived from C and retains much of its low-dimensional structure. Consequently, null-model-corrected LNM maps remain shaped by the same standard set of connectome-derived patterns and continue to show repetition and non-specificity across unrelated conditions. We discuss the scope and limitations of null models for LNM, showing that they can statistically benchmark LNMs, but that their application does not resolve the core limitation of non-specificity of LNM networks.