Aperture Neuro

BackgroundManganese-enhanced magnetic resonance imaging (MEMRI) is a powerful approach for mapping brain-wide neural activity and axonal projections in vivo. Yet standardized computational frameworks for voxel-wise and atlas-based characterization of brain states across large experimental cohorts remain limited. New methodHere, we present methodological advances for preprocessing and statistical analysis of MEMRI datasets to support scalable, reproducible cohort-level analyses. Quality assurance metrics were developed to evaluate images, cohort-level anatomical alignment, and intensity normalization. Using simulated data, we optimized smoothing, effect-size, and cluster-size thresholds to balance sensitivity and specificity in voxel-wise statistical mapping. We developed InVivoSegment software to apply to our new InVivo Atlas for segmentation of MEMRI data and interpretation of brain-wide activity. ResultsQuality assurance analyses established benchmarks for Mn(II)-induced signal- and contrast-to-noise evaluation, precise cohort-level alignment at 100 m isotropic resolution, and robust intensity normalization. Balanced accuracy and Youdens J statistics were calculated from simulated true positive and noise-only intensities, which defined optimal parameters for smoothing kernel, cluster-size and effect-size thresholds during voxel-wise mapping. Segmentation of simulated data demonstrated reliable transformation of voxel-wise results into regional summaries and identified secondary thresholds that minimize noise-driven artifacts. Comparison with existing methodsApproach to optimize correction parameters for statistical mapping using simulated images improves voxel- and segment-wise sensitivity compared to FDR/FWE-based correction procedures. ConclusionsThese methodological advances enable scalable, reproducible, brain-wide quantification of longitudinal changes in MEMRI studies, strengthen mechanistic investigation of brain-state dynamics relevant to human health, and provide broadly applicable tools for neuroimaging analyses beyond MEMRI applications. HighlightsO_LIQuantitative assurance of image quality complements visual assessment for cohort-level batch processing. C_LIO_LIOptimization of parameters using simulated noise-only images with and without investigator-embedded signal for voxel-wise mapping. C_LIO_LIA new software, "InVivoSegment" together with a labeled atlas, automates reliable user-friendly segmentation of voxel-wise data. C_LIO_LIMethodological advances in MEMRI data processing and computational analyses support scalable voxel- and segment-wise quantification of brain-wide neural activity. C_LI

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.

1.Quantitative neuropathology has advanced through whole-slide imaging and digital histology platforms. Yet, these measurements rarely align with neuroimaging coordinate frameworks that may be useful for spatial modeling and other applications. QNPtoVox, short for quantitative neuropathology to voxels, is a reproducible, modular pipeline that transforms quantitative metrics generated by digital pathology software (HALO) into voxel-based maps registered to a standard common coordinate (MNI) template. The workflow integrates digital histopathology, gross tissue photography, ex-vivo MRI, and nonlinear registration to generate spatially standardized 3D pathology representations. This Methods article provides a complete procedural description, including required materials, step-wise instructions, operator-dependent checkpoints, expected outputs, reproducibility evaluation, and troubleshooting. QNPtoVox enables voxel-level integration of neuropathology with neuroimaging tools, unlocking existing histopathology datasets for computational modeling and cross-cohort harmonization.

Functional magnetic resonance imaging (fMRI) quality assurance has traditionally relied on static, geometrically regular phantoms that cannot generate the dynamic signal changes fMRI analysis pipelines are designed to detect. Here we present the Signal Generating (SiGn) anthropomorphic brain phantom, a 3D-printed cortical model derived from an individual participants structural MRI, filled with tissue-mimicking agar gels and coupled to a hemin-based infusion system that produces controlled, time-varying T *-weighted signal changes. We validated the phantom across two scanning sessions on a 3 T Siemens MAGNETOM Vida scanner, demonstrating that hemin infusion produced spatially localised activation detectable by standard general linear model analyses. Because the phantoms geometry is derived from real participant anatomy, its functional data can be coregistered and spatially normalised to standard brain templates through the same pipeline applied to human data, enabling end-to-end assessment of how each preprocessing step affects a known ground-truth signal. To support adoption and reproducibility, we openly release the full resource at https://doi.org/10.60809/drum.31411158, including 3D-printable STL model files, tissue-mimicking gel recipes, the BIDS-formatted dataset, preprocessing and analysis scripts, and a containerised reproducibility workflow; the corresponding archival container image is also deposited on Zenodo at https://doi.org/10.5281/zenodo.19495290. This framework is intended to lower the barrier for other groups to fabricate, scan, and analyse an equivalent device on their own hardware, adapt it to specific research questions, and iteratively improve the design, thereby supporting more rigorous and transparent fMRI quality assurance practices across the neuroimaging community.

Over the past two decades, open-source research software such as SPM, AFNI and FSL formed the substrate for advancements in the brain functional magnetic resonance imaging (fMRI) field. The spinal cord fMRI field has matured substantially over the past decade, yet there is limited research software tailored for processing cord fMRI data that has distinct noise sources, unique challenges, niche processing requirements and special needs. Spinal cord fMRI data analysis is a different beast, involving specialized pre- and post-processing steps due to the cords unique anatomy and higher distortions/physiological noise, thus requiring extensive and careful quality assessment. Building upon 10+ years of research and development, we present Neptune - a user-interface-based MATLAB toolbox. With 30,000+ lines of in-house code, it is designed to be easy to use and does not require programming knowledge. Neptune builds on our previously published 15-step pre-processing pipeline (Barry et al., 2016) and presents a 19-step pipeline with new processing steps, and enhancements to existing steps. Neptune has a 4-step post-processing pipeline aimed at fMRI connectivity modeling. It generates extensive and novel quality control visuals to enable a thorough assessment of data quality, and displays them in an elegant webpage format. We demonstrate the utility of Neptune on our 7T data. Certain features of the popular Spinal Cord Toolbox (SCT) are integrated into Neptune, and users can import/export between Neptune and other software such as FSL and SPM. The availability of this open-source, easy-to-use software will benefit the spinal cord fMRI community, and also tip the cost-benefit balance for brain fMRI researchers to invest in learning new software to conduct important neuroscientific and clinical research using spinal cord fMRI.

Consistent, high-quality data is key to the success of fMRI studies given the many confounding factors and undesired signals that contaminate these data. Several quality assurance (QA) metrics exist for fMRI (e.g., temporal signal-to-noise ratio (TSNR), percent ghosting, motion estimates), but none of them leverage relationships between echoes that are part of multi-echo (ME) fMRI acquisitions. Here, we fill this gap by proposing a new QA metric for for ME-fMRI that quantifies the likelihood a given ME scan is dominated by BOLD (Blood Oxygenation Level-Dependent) fluctuations. We refer to this metric as pBOLD; the probability of the signal change being primarily BOLD contrast-dominated. Having an estimate of overall BOLD weighting - both before and after preprocessing - is meaningful because BOLD is the intrinsic contrast mechanism used in fMRI to infer neural activity. We introduce pBOLD to the neuroimaging community by first describing the theoretical principles supporting the metric. Next, we validate pBOLD efficacy using a small dataset (N=7 scans) of constant- and cardiac-gated scans that have distinct levels of contributing BOLD fluctuations. Third, we apply pBOLD to a larger publicly available ME dataset (N=439 scans), to evaluate six different pre-processing pipelines, and show how pBOLD provides complementary information to TSNR. Our results show that ME-based denoising increases both pBOLD and TSNR relative to basic denoising; however, including the global signal (GS) as a regressor only improves TSNR, but worsens pBOLD. Further analyses looking at the BOLD-like characteristics of the GS and its relationship to cardiac and respiratory traces suggest that the observed decrease in pBOLD is likely due to a decrease in BOLD fluctuations of neural origin contributing to the GS, and not due to contributions from other physiological BOLD fluctuations (i.e., respiratory and cardiac function). Finally, we also demonstrate how pBOLD can be applied as a data quality metric, by showing how higher pBOLD results in better ability to predict phenotypes based on whole-brain functional connectivity matrices.

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.

Multi-site diffusion MRI studies face scanner-induced variability that can obscure biological signal. Harmonization methods such as ComBat have been developed to address this, but have been evaluated primarily on diffusion scalar metrics. Whether scanner reproducibility differs across fundamentally distinct tract-derived representations has not been systematically compared. Here, we compared the reproducibility of three metric families (diffusion, shape, and connectivity) across 36 association tracts using the MASiVar dataset (5 subjects, 4 scanners, 27 sessions). We assessed intraclass correlation coefficients (ICC) and multivariate subject discrimination at baseline, under dimensionality reduction, and after ComBat harmonization. At baseline, shape metrics showed the highest reproducibility (median ICC 0.69), followed by connectivity (0.49) and diffusion (0.34). Shape and connectivity achieved comparable subject discrimination (both 1.75), significantly exceeding diffusion (1.23). ComBat harmonization improved all families but harmonized diffusion (0.58) remained below unharmonized shape (0.69), indicating that metric family selection remains consequential even after harmonization. Under low-dimensional representation, connectivity showed the largest gains (ICC 0.86, subject discrimination 3.0), exceeding other families at any dimensionality. Analysis of principal component loadings identified a small number of cortical regions per tract (median 6) that capture 95% of the reproducible connectivity signal, providing a per-tract reference for selecting the most informative regions in future multi-site studies. These findings indicate that the choice of which tract-derived metrics to analyze in multi-site studies deserves at least as much consideration as how to harmonize them.

Preprocessing and analysis of neuroimaging data are technically demanding, often requiring a combination of multiple software tools, modality-specific pipelines, and extensive parameter tuning to match dataset characteristics. These complexities make it difficult to document workflows in sufficient detail to ensure complete transparency and reproducibility. To address these challenges, we introduce QuNex recipes, a framework for defining and executing complete neuroimaging workflows - encompassing data onboarding, preprocessing, and analysis - in a transparent, machine- and human-readable format. Recipes are implemented as an integrated feature of the Quantitative Neuroimaging Environment & Toolbox (QuNex), a containerized, open-source platform for end-to-end multimodal and multi-species neuroimaging processing. The recipes framework enables seamless integration of QuNex commands with custom scripts and external tools, capturing every processing step and parameter setting. A fully reproducible study can thus be shared and replicated by providing only (a) the QuNex version used, (b) the recipe file, and (c) the data. This approach standardizes workflow specification, enhances transparency, and enables one-command replication of complex neuroimaging analyses. By providing a standardized way to describe and share workflows, recipes facilitate open exchange of best practices and reproducible methods within the neuroimaging community.

Real-time functional magnetic resonance imaging (fMRI) offers a powerful means of studying brain function adaptively, enabling experimental parameters to be updated dynamically in response to ongoing neural activity. However, current approaches remain limited by complex infrastructure requirements, bespoke implementations, and a lack of flexible frameworks for closed-loop neuroimaging, with many primarily focusing on neurofeedback experimental designs. Here we present AutoNeuro, an open-source framework for real-time fMRI acquisition, preprocessing, feature extraction, and adaptive experimental control. AutoNeuro connects directly to the MRI scanner, receiving reconstructed slices as soon as they become available, and streams them into a modular analysis pipeline designed for low-latency processing. Neural features are estimated at the temporal resolution of acquisition and are passed to a Bayesian optimisation agent that selects task conditions to maximise a user-defined objective function. Experimental conditions are represented within a bounded "experiment space", allowing heterogeneous conditions to be explored within a common coordinate system. We demonstrate AutoNeuro in a real-time fMRI experiment in which the system adaptively sampled task conditions to obtain a continuous map of brain response to the range of conditions contained within the experimental space. The system operated within the temporal constraints of real-time preprocessing and analysis, maintaining stable model estimates across iterations, converging on experimental conditions most relevant to the measured brain metric. These results establish AutoNeuro as a flexible platform for closed-loop neuroimaging, supporting hypothesis-driven optimisation as well as exploratory mapping of brain metrics across large experimental spaces.

BackgroundDeveloping generalizable neuroimaging models is often hindered by limited labeled data which has led to an increased interest in unsupervised inverse learning. Existing approaches often neglect geometric principles and struggle with diverse pathologies. We propose a symmetry-informed inverse learning foundation model to address these shortcomings for robust and efficient anomaly detection in brain MRI. MethodsOur framework employs a reconstruction-to-embedding pipeline, trained exclusively on healthy brain MRI slices. A 2D U-Net uses a novel, symmetry-aware masking strategy to reconstruct a disorder-free slice. Difference maps are embedded into a 1024-dimensional latent space via a Beta-VAE. Anomaly scoring is performed using Mahalanobis distance. We evaluated generalization by fine-tuning on external lesion datasets, BraTS Africa (SSA), and the ADNI-derived Alzheimers disease cohort (Alz). ResultsOn the source metastasis (Mets) dataset, the framework achieved high performance (AB1+MSE: 99.28% accuracy, 99.79% sensitivity). Generalization to the external lesion dataset (SSA) was robust, with the Symmetry ROC configuration achieving 91.93% accuracy. Transfer to the Alzheimers dataset (Alz) was more challenging, achieving a peak accuracy of 70.54% with a high false-positive rate, suggesting difficulty in separating subtle, diffuse changes. ConclusionThe symmetry-informed inverse learning framework establishes a robust foundation model for neuroimaging, showing strong performance for focal lesions and successful generalization under domain shift. Limitations in diffuse neurodegeneration underscore the necessity for richer representations and multimodal integration to improve future foundation models. Summary StatementA symmetry-informed inverse learning framework trained on normal brain MRI achieved high accuracy for detecting focal lesions and demonstrated strong generalization across external datasets under domain shift. Key Points[bullet] A symmetry-informed disorder-free reconstruction framework trained only on normal brain MRI achieved 99.28% accuracy and 99.79% sensitivity for metastasis detection on the BrainMetShare dataset, demonstrating non-inferior performance compared with all but one strategy while offering improved computational efficiency. [bullet]The model generalized effectively to an external tumor dataset (BraTS SSA), achieving up to 91.93% accuracy using receiver operating characteristic-optimized thresholding with minimal fine-tuning. [bullet]Embedding-based anomaly detection using Mahalanobis distance enabled consistent separation between normal and abnormal slices, supporting robust and interpretable anomaly detection across datasets.

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.

Functional brain mapping is an important tool to understand the organization of the human brain, both at the group level, but also to an increasing degree at the level of the individual. There are currently two main approaches to do so. Resting-state fMRI relies on inter-regional correlations of random fluctuations of the signal. In contrast, task-based localizers typically use a single-contrast between a task of interest and a matched control task to identify the location of a functional region in an individual brain. In this paper, we propose and evaluate a third approach: the use of multi-task batteries for both localization of a single functional region and parcellation of multiple functional regions. We show that multi-task localizers produce more consistent estimation of a single functional region across subjects than the single-contrast approach using the same amount of fMRI data. Furthermore, we demonstrate that the multi-task approach is sensitive to true inter-individual differences in region size, and does not suffer the same influence of signal-to-noise ratio that biases the single-contrast localizer. We then address the question of how to select tasks for the battery, and present a data-driven strategy that optimizes the characterization of a brain structure of interest. We show that such batteries outperform randomly selected batteries both for building individual parcellations as well as individual connectivity models. Finally, we demonstrate that an interspersed design - where all tasks are presented in each imaging run - yields more reliable results than splitting the tasks across different runs. We present an open source toolbox for the implementation of multi-task batteries, along with a library containing group-averaged activity patterns that can be used to optimize battery selection for different brain structures of interest.

The aim of this study was to evaluate the contributions of age, sex, and MRI vendor to variance in Diffusion Tensor Imaging (DTI) metrics, with a focus on understanding the impact of these factors in large-scale healthy brain datasets. A dataset of 2,700 DTI scans from healthy controls across multiple sites and MRI vendors was analyzed. The DTI scalar metrics fractional anisotropy (FA) and mean diffusivity (MD) were processed and the influence of age, sex, vendor, and brain atlas selection were determined. A statistical analysis was conducted and revealed significant (p<0.05) age-related differences in DTI metrics, with older participants showing reduced FA and increased MD, in line with known microstructural changes. Sex differences were observed, with females exhibiting slightly higher FA and lower MD in certain brain regions. Vendor variability was also noted, with all three MRI vendors showing significant differences in FA with Siemens machines typically exhibiting higher FA values and GE machines lower FA values (i.e. FASiemens > FAPhilips > FAGE). Atlas selection also highlighted some specific ROI behaviour (e.g. tapetum of the corpus callosum) as one of the most significant regions of interest (ROIs) in the JHU-Tracts atlas that demonstrated a large amount of deterioration with age, particularly in females. These findings emphasize the need to account for biological factors such as age and sex, as well as technical factors like ROI selection and MRI vendor, when interpreting DTI data. The results demonstrate the potential of large-scale, multi-vendor datasets to uncover meaningful biological trends, while also addressing the challenges of scanner-specific variability. Although previous work has shown sex and age differences, this is the first large scale DTI analysis that has included age, sex, and MRI vendor as sources of variance in one model.

Low statistical power remains a persistent concern in functional magnetic resonance imaging (fMRI) research, largely due to small sample sizes. Although prior work has documented gradual increases in sample size over time, it remains unclear whether structural factors in the publication process are associated with study design characteristics such as sample size. This review addresses this gap by analyzing a large sample of fMRI studies to assess how institutional prestige, journal impact factor, and journal review practices are associated with sample size. We analyzed articles published in 2021-2024 reporting new fMRI data collection in adult humans and including a measure of memory. We found studies with specialized populations, such as patient populations, had smaller sample sizes, as did studies with task-based designs compared to resting-state designs. We also found larger sample sizes were associated with journals with a double-blind review process. Institutional prestige was positively associated with sample size such that more highly ranked institutions tended to have larger samples, but there was no interaction between review type (single-vs. double-blind) and prestige, indicating this difference is not likely due to reviewer bias. Journal impact factor was not associated with sample size, however institutional prestige score predicted journal impact factor. These results suggest structural factors at the institutional level likely have a stronger influence on published study sample size than reviewer practices or biases.

The Adolescent Brain Cognitive Development (ABCD) Study is the largest U.S.-based neuroimaging initiative of adolescent brain maturation. Diffusion MRI (dMRI) provides unique insights into white matter organization, yet applying advanced processing pipelines and managing technical variability across scanning environments remains challenging at scale. To address these issues, we present ABCD-BIDS Community Collection (ABCC) release 3.1.0, including a curated resource of more than 24,000 fully processed ABCD dMRI datasets. ABCC provides fully processed images, nuanced image quality metrics, advanced microstructural measures, and person-specific bundle tractography. Evaluating these rich data revealed that measures of diffusion restriction and non-Gaussianity--in particular the intracellular volume fraction from NODDI and return-to-origin probability from MAP-MRI--were highly sensitive to neurodevelopment and robust to variation in image quality. Additionally, harmonization of microstructural features markedly improved the cross-vendor generalizability of developmental effects. Together, ABCC accelerates reproducible, rigorous research on adolescent white matter development.

Foundation models (FMs) for brain magnetic resonance imaging (MRI) are increasingly adopted as pretrained backbones for clinical tasks such as brain age prediction, disease classification, and anomaly detection. However, if FM embeddings (internal representations) shift systematically across MRI scanners, downstream analyses built on them may reflect acquisition hardware rather than biology. No study has yet quantified this cross-scanner reproducibility. Here, we assess the cross-scanner reliability of brain MRI FM embeddings and investigate which design factors (pretraining strategy, network architecture, embedding dimensionality, and pretraining dataset scale) best explain the observed differences. Using the ON-Harmony travelling-heads dataset (20 participants, eight scanners, three vendors), we evaluate the embeddings of five architecturally diverse FMs and a FreeSurfer morphometric baseline via within- and between-scanner intraclass correlation coefficient (ICC), variance decomposition, and scanner fingerprinting. Reliability spanned the full spectrum: biology-guided models achieved good-to-excellent cross-scanner ICC (AnatCL: 0.970 [95\% confidence interval (CI): 0.94, 0.98]; y-Aware: 0.809 [0.63, 0.88]), matching or surpassing FreeSurfer (0.926 [0.83, 0.96]), whereas purely self-supervised models fell below the poor threshold (BrainIAC: 0.453, BrainSegFounder: 0.307, 3D-Neuro-SimCLR: 0.247), with 23--58\% of embedding variance attributable to scanner identity. The strongest correlate of cross-scanner reliability among the models evaluated was pretraining strategy: incorporating biological metadata (cortical morphometrics, age) into the contrastive objective produced scanner-robust embeddings, whereas architecture, dimensionality, and dataset scale did not predict reliability.

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

Opfically pumped magnetometer-based magnetoencephalography (OPM-MEG) has recently emerged as a powerful neuroimaging approach in cognifive neuroscience, extending beyond the limitafions of convenfional cryogenic systems with greater experimental flexibility and wearable recording. Despite these advantages, standardised data analysis frameworks specifically tailored to OPM technology are sfill lacking, leading to variability in processing choices and reduced reproducibility across laboratories and hardware plafforms. We introduce OPM-FLUX, a comprehensive and fully documented end-to-end analysis pipeline developed for OPM-MEG data. The pipeline defines a clear sequence of preprocessing, noise suppression, arfifact handling, spectral analysis, evoked response analysis along with recommended parameter seftings. It also includes source reconstrucfion to idenfify where in the brain the signals originate. In addifion, OPM-FLUX supports mulfivariate paftern analysis (MVPA), enabling fime-resolved decoding of cognifive processes from sensor level data. OPM-FLUX is implemented in MNE-Python and distributed as interacfive Jupyter Notebooks that combine executable code with detailed methodological explanafions and graphical outputs. The pipeline further provides standardized reporfing templates and a data acquisifion Standard Operafing Procedure to facilitate preregistrafion, consistent documentafion, and standard pracfices across research sites. The workflow is demonstrated using openly available datasets acquired from both Cerca/QuSpin and FieldLine OPM systems during a visuospafial aftenfion paradigm that modulates alpha, beta, and gamma oscillafions and elicits event-related responses. By supporfing mulfiple OPM plafforms and promofing consistent methodological choices, OPM-FLUX enhances transparency, comparability, and replicafion in OPM-MEG research. The pipeline also serves as an educafional resource for students and researchers entering the field and is designed to evolve alongside ongoing technological and methodological advances in OPM-based brain imaging.

Many functional magnetic resonance imaging (fMRI) studies conclude that two conditions engage "overlapping, yet partly distinct" patterns of activation. Yet, there is currently no commonly accepted method for determining the extent of this overlap. While correlations between activation patterns can serve as a measure of their correspondence, empirical correlations are strongly biased towards zero due to measurement noise, preventing their use in testing hypotheses about the actual degree of pattern correspondence. In this paper, we derive the maximum-likelihood estimate for the correlation of the true (noise-less) activation patterns and examine its behavior in the low signal-to-noise regime that is typical for fMRI studies. We show that although the maximum-likelihood estimate corrects for much of the influence of measurement noise, it is ultimately biased. We examine different ways of drawing inferences about the size of the underlying true correlations. We find that a subject-wise bootstrap on the maximum-likelihood group estimate performs best over the tested conditions. We extend the proposed method to test more general hypotheses about the representational geometry of activation patterns for more conditions, and highlight best practices, as well as common pitfalls and problems, in testing such hypotheses.