Aperture Neuro

UK Biobank (UKB) brain imaging data is a one-of-a-kind resource for studying the links between the brain and demographic-, lifestyle- and genetic data. When establishing such links, it is crucial to account for confounding effects caused by the acquisition of fMRI images, as well as demographic confounding factors. UKB brain imaging confounds are constructed through variable selection by the proportion of variance explained in the Imaging Derived Phenotypes (IDPs), from tens of thousands of possible confounds. The current implementation of this pipeline is very computationally intensive and has a large memory footprint, largely due to the varying patterns of missing data in IDPs. This makes it impractical for many users of UK Biobank brain imaging data. We propose a fast and memory efficient multivariate pipeline for constructing imaging confounds using mean imputation combined with a bias-corrected estimator of R2, the proportion of confound variance explained in an IDP. Building on this, we also improve the pipeline in order to better select confounds that explain unique variance in IDPs, and non-imaging variables of interest, so called nIDPs. The new implementation leads to a more compact set of confounds that explains roughly the same amount of variance, and runs in around 1 hour on a single CPU.

Post-mortem magnetic resonance imaging (MRI) offers high resolution and histological correlation, so protocols have been developed by brain banks using hemispheres fixed by immersion in Neutral-buffered formalin (NBF), but they provide limited tissue samples. Conversely, anatomy laboratories could supply complete brains perfused either with a salt-saturated (SSS) or an alcohol-formaldehyde (AFS) solution. These fixation methods alter the brains molecular properties, potentially affecting MRI quality and structural characteristics. T1- and T2-weighted (T1w, T2w) contrasts change with NBF fixation, but the effects of SSS or AFS remain unknown. We compared T1w and T2w intensities of different regions of interest (ROIs), including subcortical white matter (WM), cortical and deep gray matter (GM), in brains fixed with NBF, SSS and AFS. We scanned 20 ex situ hemispheres (NBF-immersed=7; SSS-perfused=7; AFS-perfused=6) in a 3T MRI scanner using T1w (0.7mm3) and T2w (0.64mm3) sequences overnight. Mean intensities of 29 ROIs in T1w and T2w MRIs and GM-WM ratios were calculated and compared in brains fixed with the three solutions. We found that T1w images were more affected by the fixation process, inverting the contrast of in vivo T1w and reducing the GM-WM contrast in AFS-fixed brains. T2w images resembled in vivo scans and maintained a sharp contrast in brains fixed with the three solutions, although the GM-WM intensity ratios were lowered in SSS-fixed brains. In conclusion, brains fixed with SSS and AFS from anatomy laboratories could be used for MRI studies, especially with the T2w sequence that seems more appropriate for structural analyses in different ROIs.

BackgroundNeurodegenerative diseases are associated with brain atrophy. The volume of in vivo human brains is determined with various magnetic resonance imaging (MRI) measurement tools of which the validity has not been assessed against a gold standard. Here, we propose to validate the MRI brain volumes by scanning ex vivo-in situ specimens (i.e., anatomical heads), which allows the extraction of the brain after the scan to compare its volume with the gold standard water displacement method (WDM). MethodsWe acquired 3T MRI T2-weighted, T1-weighted, and MP2RAGE images of seven anatomical heads fixed with an alcohol-formaldehyde solution routinely used in anatomy laboratories and segmented the gray and white matter of the brain using two methods: 1) a manual intensity-based threshold segmentation using Display (MINC-ToolKit, McConnell BIC), and 2) an automatic Deep-Learning-based segmentation tool (SynthSeg). The brains were then extracted, and their volumes were measured with the WDM after the removal of their meninges and a midsagittal cut (to allow water penetration into the ventricles). Volumes from all methods were compared to the ground truth (WDM volumes) using a repeated-measures ANOVA. ResultsMean brain volumes, in cubic centimeters, were 1111.14{+/-}121.78 for WDM, 1020.29{+/-}70.01 for manual T2-weighted, 1056.29{+/-}90.54 for automatic T2-weighted, 1094.69{+/-}100.51 for automatic T1-weighted, 1066.56{+/-}96.52 for automatic MP2RAGE INV1, and 1156.18{+/-}121.87 for MP2RAGE INV2. All volumetry methods were significantly different (F=17.874; p<0.001) from the WDM volumes, except the automatic T1-weighted volumes. ConclusionWe demonstrate that SynthSeg accurately determines the brain volume in ex vivo-in situ T1-weighted MRI scans. Our results also suggest that given the contrast similarity between our ex vivo and in vivo sequences, the brain volumes of clinical studies are most probably sufficiently accurate, with some degree of underestimation depending on the sequence used.

Multiband (MB) or Simultaneous multi-slice (SMS) acquisition schemes allow the acquisition of MRI signals from more than one spatial coordinate at a time. Commercial availability has brought this technique within the reach of many neuroscientists and psychologists. Most early evaluation of the performance of MB acquisition employed resting state fMRI or the most basic tasks. In this study, we tested whether the advantages of using MB acquisition schemes generalize to group analyses using a cognitive task more representative of typical cognitive neuroscience applications. Twenty-three subjects were scanned on a Philips 3T scanner using five sequences up to eight-fold acceleration with MB-factors 1 to 4, SENSE factors up to 2 and corresponding TRs of 2.45s down to 0.63s, while they viewed (i) movies showing complex actions with hand object interactions and (ii) control movies without hand object interaction. Using random effects group-level, voxel-wise analysis we found that all sequences were able to detect the basic action observation network known to be recruited by our task. The highest t-values were found for sequences with MB4 acceleration. For the MB1 sequence, a 50% bigger voxel volume was needed to reach comparable t-statistics. The group-level t-values for resting state networks (RSNs) were also highest for MB4 sequences. Here the MB1 sequence with larger voxel size did not perform comparable to the MB4 sequence. Altogether, we can thus recommend the use of MB4 (and SENSE 1.5 or 2) on a Philips scanner when aiming to perform group-level analyses using cognitive block design fMRI tasks and voxel sizes in the range of cortical thickness (e.g. 2.7mm isotropic). While results will not be dramatically changed by the use of multiband, our results suggest that MB will bring a moderate but significant benefit.

The potential negative impact of head movement during fMRI has long been appreciated. Although a variety of prospective and retrospective approaches have been developed to help mitigate these effects, reducing head movement in the first place remains the most appealing strategy for optimizing data quality. Real-time interventions, in which participants are provided feedback regarding their scan-to-scan motion, have recently shown promise in reducing motion during resting state fMRI. However, whether feedback might similarly reduce motion during task-based fMRI is an open question. In particular, it is unclear whether participants can effectively monitor motion feedback while attending to task-related demands. Here we assessed whether a combination of real-time and between-run feedback could reduce head motion during task-based fMRI. During an auditory word repetition task, 78 adult participants (aged 19-81) were pseudorandomly assigned to receive feedback or not. Feedback was provided FIRMM software that used real-time calculation of realignment parameters to estimate participant motion. We quantified movement using framewise displacement (FD). We found that motion feedback resulted in a statistically significant reduction in participant head motion, with a small-to-moderate effect size (reducing average FD from 0.347 to 0.282). Reductions were most apparent in high-motion events. We conclude that under some circumstances real-time feedback may reduce head motion during task-based fMRI, although its effectiveness may depend on the specific participant population and task demands of a given study.

There has been a concerted effort by the neuroimaging community to establish standards for computational methods for data analysis that promote reproducibility and portability. In particular, the Brain Imaging Data Structure (BIDS) specifies a standard for storing imaging data, and the related BIDS App methodology provides a standard for implementing containerized processing environments that include all necessary dependencies to process BIDS datasets using image processing workflows. We present the BrainSuite BIDS App, which encapsulates the core MRI processing functionality of BrainSuite within the BIDS App framework. Specifically, the BrainSuite BIDS App implements a participant-level workflow comprising three pipelines and a corresponding set of group-level analysis workflows for processing the participant-level outputs. The Anatomical Pipeline extracts cortical surface models from a T1-weighted (T1w) MRI. It then performs surface-constrained volumetric registration to align the T1w MRI to a labeled anatomical atlas, which is used to delineate anatomical regions of interest in the MRI brain volume and on the cortical surface models. The Diffusion Pipeline processes diffusion-weighted imaging (DWI) data, with steps that include coregistering the DWI data to the T1w scan, correcting for susceptibility-induced geometric image distortion, and fitting diffusion models to the DWI data. The Functional Pipeline performs fMRI processing using a combination of FSL, AFNI, and BrainSuite tools. It coregisters the fMRI data to the T1w image, then transforms the data to the anatomical atlas space and to the Human Connectome Projects grayordinate space. The outputs of each pipeline can then be processed during group-level analysis. The outputs of the Anatomical Pipeline and the Diffusion Pipeline are analyzed using the BrainSuite Statistics Toolbox in R (bstr), which provides functionality for hypothesis testing and statistical modeling. The outputs of the Functional Pipeline can be analyzed using atlas-based or atlas-free statistical methods during group-level processing. These analyses include the application of BrainSync, which synchronizes the time-series data temporally and enables comparison of resting-state or task-based fMRI data across scans. We also present the BrainSuite Dashboard quality control system, which provides a browser-based interface for reviewing the outputs of individual modules of the participant-level pipelines across a study in real-time as they are generated. BrainSuite Dashboard facilitates rapid review of intermediate results, enabling users to identify processing errors and make adjustments to processing parameters if necessary. The comprehensive functionality included in the BrainSuite BIDS App provides a mechanism for rapidly deploying the BrainSuite workflows into new environments to perform large-scale studies. We demonstrate the capabilities of the BrainSuite BIDS App using structural, diffusion, and functional MRI data from the Amsterdam Open MRI Collections Population Imaging of Psychology dataset.

Functional magnetic resonance imaging (fMRI) in awake infants has the potential to reveal how the early developing brain gives rise to cognition and behavior. However, awake infant fMRI poses significant methodological challenges that have hampered wider adoption. The present work takes stock after the collection of a substantial amount of awake infant fMRI data across multiple studies from two labs at different institutions. These data were leveraged to glean insights on participant recruitment, experimental design, and data acquisition that could be useful to consider for future studies. Across 766 fMRI sessions with awake infants aged 1-36 months, the authors explored the factors that influenced how much usable data were obtained per session. The age of an infant predicted whether they would successfully enter the scanner (younger more likely) and, if they did enter, the number of minutes of functional data collected (linear, younger more) and retained after preprocessing with lab-specific protocols or harmonized motion exclusion thresholds (quadratic, 12-24 months more than younger and older). The amount of functional data retained was also influenced by assigned sex (female more), experimental paradigm (movies better than blocks and events), and stimulus content (social better than abstract). There were many differences in the research approach between labs making head-to-head comparisons difficult, but Yale was more likely to get infants into the scanner, MIT collected more data from infants who entered, and the amount of data retained after preprocessing did not differ statistically between labs (9 minutes). In addition, the authors assessed the value of attempting to collect multiple experiments per session, an approach that yielded more than one usable experiment averaging across all sessions. Although any given scan is unpredictable, these findings support the feasibility of awake infant fMRI and suggest practices to optimize future research.

A particularly challenging form of missing data is structured missingness, where sets of subjects and variables consistently have missing data. For tabular data from sub-studies or modalities, structured missingness can come from non-participation in followup studies, which creates large blocks of missing data. Canonical Correlation Analysis (CCA) is a multivariate modelling tool commonly used to link two different set of variables, and in neuroimaging has typically been used to find associations between imaging and non-imaging variables. Motivated by CCA, we propose a new method for covariance estimation from incomplete data that handles data with a mix of structured and unstructured missingness, assuming Missing at Random (MAR). Our proposed method is compared to existing methodology by way of evaluation on simulated data and on real data from subjects in the UK Biobank brain imaging cohort.

Functional magnetic resonance imaging (fMRI) in rodents holds great potential for advancing our understanding of brain networks. Unlike the human fMRI community, there remains no standardized resource in rodents for image processing, analysis and quality control, posing significant reproducibility limitations. Our software platform, Rodent Automated Bold Improvement of EPI Sequences (RABIES), is a novel pipeline designed to address these limitations for preprocessing, quality control, and confound correction, along with best practices for reproducibility and transparency. We demonstrate the robustness of the preprocessing workflow by validating performance across multiple acquisition sites and both mouse and rat data. Building upon a thorough investigation into data quality metrics across acquisition sites, we introduce guidelines for the quality control of network analysis and offer recommendations for addressing issues. Taken together, the RABIES software will allow the emerging community to adopt reproducible practices and foster progress in translational neuroscience.

ObjectiveResting-state functional MRI is increasingly used in the clinical setting and is now included in some diagnostic guidelines for severe brain injury patients. However, to ensure high-quality data, one should mitigate fMRI-related noise typical of this population. Therefore, we aimed to evaluate the ability of different preprocessing strategies to mitigate noise-related signal (i.e., in-scanner movement and physiological noise) in functional connectivity of traumatic brain injury patients. MethodsWe applied nine commonly used denoising strategies, combined into 17 pipelines, to 88 traumatic brain injury patients from the Epilepsy Bioinformatics Study for Anti-epileptogenic Therapy clinical trial (EpiBioS4Rx). Pipelines were evaluated by three quality control metrics across three exclusion regimes based on the participants head movement profile. ResultsWhile no pipeline eliminated noise effects on functional connectivity, some pipelines exhibited relatively high effectiveness depending on the exclusion regime. Once high-motion participants were excluded, the choice of denoising pipeline becomes secondary - although this strategy leads to substantial data loss. Pipelines combining spike regression with physiological regressors were the best performers, whereas pipelines that used automated data driven methods performed comparatively worse. ConclusionIn this study, we report the first large-scale evaluation of denoising pipelines aimed at reducing noise-related functional connectivity in a clinical population known to be highly susceptible to in-scanner motion and significant anatomical abnormalities. If resting-state functional magnetic resonance is to be a successful clinical technique, it is crucial that procedures mitigating the effect of noise be systematically evaluated in the most challenging populations, such as traumatic brain injury datasets.

Movement during MRI scanning complicates distinguishing between the different tissues in the brain (e.g., grey and white matter). Standard practice excludes scans based on researcher-determined visual quality thresholds. Unfortunately, children, elderly, and clinical populations are shown to move more, resulting in higher exclusion rates. This disproportionate exclusion creates systematic bias in the literature and makes research findings less generalizable. Furthermore, the artifacts caused by motion are demonstrated to continue to confound data, even after visual quality control has occurred. We aimed to minimize the confounding factor of systematic group differences in movement. To achieve this, we used a post-scanning statistical technique called propensity score matching (PSM) that matches control and patient populations on scan quality metrics, leading to more comparable groups, greater inclusion, and more generalizable results. We found that PSM can attenuate significant differences in scan quality between groups while allowing for greater sample diversity than standard exclusion protocols. Crucially, using PSM can also alter the results of neuroimaging-based analyses. Using three datasets (total n = 1536), we compared voxel based morphometry analyses based on different quality control protocols. In particular, we observed discrepant results between PSM and strict threshold exclusion, with PSM magnifying some regional group differences and diminishing others. Overall, PSM is a customizable way to mitigate the impact of confounds in neuroimaging research and a powerful method to help distinguish true effects from artifacts.

BackgroundPerforming fMRI scans of children can be a difficult task, as participants tend to move while being scanned. Head motion represents a significant confound in functional magnetic resonance imaging (fMRI) connectivity analyses, and methods to limit the impact of movement on data quality are needed. One approach has been to use shorter MRI protocols, though this potentially reduces the reliability of the results. ObjectiveHere we describe steps we have taken to limit head motion in an ongoing fMRI study of children undergoing a 60 minute MRI scan protocol. Specifically, we have used a mock scan protocol that trains participants to lie still while being scanned. We provide a detailed protocol and describe other in-scanner measures we have implemented, including an incentive system and the use of a weighted blanket. Materials and methodsParticipants who received a formal mock scan (n = 12) were compared to participants who had an informal mock scan (n = 7). A replication group of participants (n = 16), including five with autism spectrum disorder, who received a formal mock scan were also compared to the informal mock scan group. The primary measure of interest was the mean frame-to-frame displacement across eight functional runs during the fMRI protocol. ResultsParticipants in the formal mock scan and replication group tended to exhibit more low-motion functional scans than the informal mock scan group (P < 0.05). Across different functional scan conditions (i.e. while watching movie clips, performing an attention task, and during resting-state scans), effect sizes tended to be large (Hedges g > 0.8). ConclusionResults indicate that with appropriate measures, it is possible to achieve low-motion fMRI data in younger participants undergoing a long scan protocol.

The UK Biobank Imaging Study, with its dedicated cardiovascular magnetic resonance (CMR) sub-study, has re-defined the scale and scope of cardiovascular research, generating high-quality imaging data in 100,000 participants with linkage to rich genetic, demographic, lifestyle, and clinical data. The resource has enabled transformative discoveries across genomics, epidemiology, and biomedical engineering, and has served as a global blueprint for population imaging studies. Its success has been accelerated by an equitable data access model that fosters international collaboration. Looking ahead, efforts should focus on harmonisation across cohorts, adherence to rigorous methodological standards, and multidisciplinary collaboration to drive meaningful clinical translation - whether through direct improvements in patient care or experimental validation of imaging-derived insights. The UK Biobank CMR experience illustrates the power of large-scale imaging cohorts and sets a benchmark for future initiatives aimed at improving cardiovascular health through integrated, collaborative science. This paper provides an overview of the UK Biobank and its CMR sub-study, systematically reviews key publications, discusses methodological considerations, and highlights important future directions.

A critical step before data-sharing of human neuroimaging is removing facial features to protect individuals privacy. However, not only does this process redact identifiable information about individuals, but it also removes non-identifiable information. This introduces undesired variability into downstream analysis and interpretation. This registered report investigated the degree to which the so-called defacing altered the quality assessment of T1-weighted images of the human brain from the openly available "IXI dataset". The effect of defacing on manual quality assessment was investigated on a single-site subset of the dataset (N=185). By comparing two linear mixed-effects models, we determined that four trained human raters perception of quality was significantly influenced by defacing by modeling their ratings on the same set of images in two conditions: "nondefaced" (i.e., preserving facial features) and "defaced". In addition, we investigated these biases on automated quality assessments by applying repeated-measures, multivariate ANOVA (rm-MANOVA) on the image quality metrics extracted with MRIQC on the full IXI dataset (N=581; three acquisition sites). This study found that defacing altered the quality assessments by humans and showed that MRIQCs quality metrics were mostly insensitive to defacing.

The adoption of a standardized preprocessing workflow is vital for fostering community, sharing, and reproducibility. fMRIPrep has been a critical advancement towards this end, however, it is limited in its capacity to be applied to data across the lifespan, starting from infancy. Here, we introduce fMRIPrep Lifespan, an extension of fMRIPrep that extends the standardized processing from childhood to senescence to include neonatal, infant, and toddler structural and functional MRI data preprocessing. This effort involves a NiPreps integration of 1) a workflow akin to fMRIPrep optimized for MRI data in the first years of life (previously NiBabies) and 2) upstream enhancements to the entire NiPreps suite, including multi-echo data processing, modularization of workflow components, and convergence of processing with other popular workflows (ABCD-BIDS, Human Connectome Project Pipelines). Using data from the Baby Connectome Project (participants 1-43 months of age), we demonstrate that fMRIPrep Lifespan produces high-quality outputs across a wide age range. Moving forward, the scalable, modular infrastructure of fMRIPrep Lifespan will ensure adaptability to data from birth to old age while maintaining robust and reproducible frameworks for functional MRI research across the lifespan.

In spite of the great progress that has been made towards automating brain extraction in human magnetic resonance imaging (MRI), challenges remain in the automation of this task for mouse models of brain disorders. Researchers often resort to editing brain segmentation results manually when automated methods fail to produce accurate delineations. However, manual corrections can be labor-intensive and introduce interrater variability. This motivated our development of a new deep-learning-based method for brain segmentation of mouse MRI, which we call Mouse Brain Extractor. We adapted the existing SwinUNETR architecture (Hatamizadeh et al., 2021) with the goal of making it more robust to scale variance. Our approach is to supply the network model with supplementary spatial information in the form of absolute positional encoding. We use a new scheme for positional encoding, which we call Global Positional Encoding (GPE). GPE is based on a shared coordinate frame that is relative to the entire input image. This differs from the positional encoding used in SwinUNETR, which solely employs relative pairwise image patch positions. GPE also differs from the conventional absolute positional encoding approach, which encodes position relative to a subimage rather than the entire image. We trained and tested our method on a heterogeneous dataset of N=223 mouse MRI, for which we generated a corresponding set of manually-edited brain masks. These data were acquired previously in other studies using several different scanners and imaging protocols and included in vivo and ex vivo images of mice with heterogeneous brain structure due to different genotypes, strains, diseases, ages, and sexes. We evaluated our methods results against those of seven existing rodent brain extraction methods and two state-of-the art deep-learning approaches, nnU-Net (Isensee et al., 2018) and SwinUNETR. Overall, our proposed method achieved average Dice scores on the order of 0.98 and average HD95 measures on the order of 100 {micro}m when compared to the manually-labeled brain masks. In statistical analyses, our method significantly outperformed the conventional approaches and performed as well as or significantly better than the nnU-Net and SwinUNETR methods. These results suggest that Global Positional Encoding provides additional contextual information that enables our Mouse Brain Extractor to perform competitively on datasets containing multiple resolutions.

Predicting neuromarkers for cognitive abilities using fMRI has been a major focus of research in the past few years. However, it has recently been reported that many thousands of participants are required to obtain reproducible results (Marek et al (2022)). This appears to be a major impediment to obtaining neuromarkers from fMRI because large sample sizes are typically not available in neuroimaging studies. Here we show that the out-of-sample prediction accuracy can be dramatically improved by supplementing fMRI with readily available non-imaging information so that reliable predictive modeling becomes feasible even for small sample sizes. Specifically, we introduce a novel machine learning method that predicts intelligence from resting-state fMRI data, leveraging educational level as supplementary information. We refer to our approach as "semi-blind machine learning (SML)" because it operates under the assumption that supplementary information, such as educational level, is available for subjects in both the training and test sets. This setup closely mirrors real-world scenarios, especially in clinical contexts, where patient background information typically exists and can be utilized to boost prediction accuracy. However, guarding against bias is crucial. Subjects should not be categorized as more intelligent simply based on their higher education levels. Therefore, our approach contains a component explicitly designed for bias control. We have applied our method to three different data collections and observed marked improvements in prediction accuracies across a wide range of sample sizes. We anticipate that semi-blind machine learning provides a promising approach to fMRI-based predictive modelling with the potential for a wide range of future applications.

Meta-analyses are invaluable tools for navigating the rapidly expanding scientific literature. Given their high value, ensuring the quality of meta-analyses is paramount. We conducted a multifaceted overview, examining each step in a manual neuroimaging meta-analysis on a large scale. We used four novel datasets comprising over 14,000 papers, including fMRI meta-analyses, fMRI studies, studies included in meta-analyses, and studies associated with image data on NeuroVault. Regarding successes, two-thirds of meta-analyses stated that they followed PRISMA guidelines, and 65% included a flowchart describing their inclusion process. We point out several areas for improvement. Pre-registration was fairly rare (20%), and only half listed their exact search strategy. There could be a location bias in which papers are included, and many did not include enough studies to be robust against publication bias (68% of meta analyses have less than 30 studies included). We also offer ideas for future directions. As image based meta-analysis is the gold standard, we have indicated which topics have the most image data available. The potential redundancy of topics can be visualized in our paper, and we recommend future meta-analyses be in conversation with past ones by citing and discussing previous similar work. By addressing these findings, the neuroimaging community can collectively improve the field of neuroimaging meta-analyses.

ObjectivesMagnetic resonance imaging (MRI) is essential in both research and clinical settings, with quantitative MRI (qMRI) enhancing reproducibility and sensitivity. However, qMRI processing can be complex, especially for users with limited coding experience. We introduce Resomapper, an open-source, cross-platform tool that integrates established processing libraries into a unified, user-friendly workflow, simplifying qMRI analysis while promoting accessibility, reproducibility, and data sharing. Materials and MethodsResomapper is a Python-based pipeline designed for intuitive multiparametric MRI processing. It supports T1, T2, and T2* relaxometry, magnetization transfer imaging (MTI) and diffusion tensor imaging (DTI) model fitting. The software includes advanced preprocessing options such as denoising, Gibbs artifact removal, and bias field correction. Users can process data interactively through a sequential pipeline or via an automated JSON-configured workflow, both executable through simple command-line instructions. Resomapper ensures compatibility by converting raw MRI data from different formats into the standardized NIfTI format within a BIDS-like structure, enhancing reproducibility, scalability, and data management efficiency. To demonstrate its application, we present a brain MRI study carried out on healthy, adult C57BL/6J mice, both sexes. MRI acquisitions were conducted on a Bruker Biospec 7T system using a multiparametric MRI protocol that included anatomical T2W images, T2 and T2* maps, MTI and DTI. The data were processed with Resomapper and co-registered using ANTsPy. Finally, a region of interest (ROI)-based analysis was performed to examine differences between sexes and brain areas, focusing on the cortex (Cx), hippocampus, (HPC), thalamus (Thal), and hypothalamus (HTH). ResultsDifferences in all MRI parameters were found across brain regions, as expected. Additionally, a small significant sex difference in T2* was observed, with higher values in the thalamus and hypothalamus of female mice compared to males. This may reflect sex-specific responses to anesthesia. Moreover, this study also serves as a reference for standardized multiparametric qMRI studies in mice using Resomapper. ConclusionsBy integrating multiple processing tools into a single, accessible framework, Resomapper streamlines qMRI workflows and enables reproducible, high-quality image analysis. Thanks to its ability to handle diverse preprocessing techniques, multiple imaging modalities, and standardized data formats, the software proves to be a valuable resource for researchers with varying levels of programming expertise.

Current neuroimaging acquisition and processing approaches tend to be optimised for quality rather than speed. However, rapid acquisition and processing of neuroimaging data can lead to novel neuroimaging paradigms, such as adaptive acquisition, where rapidly processed data is used to inform subsequent image acquisition steps. Here we first evaluate the impact of several processing steps on the processing time and quality of registration of manually labelled T1-weighted MRI scans. Subsequently, we apply the selected rapid processing pipeline both to rapidly acquired multicontrast EPImix scans of 95 participants (which include T1-FLAIR, T2, T2*, T2-FLAIR, DWI & ADC contrasts, acquired in [~]1 minute), as well as to slower, more standard single-contrast T1-weighted scans of a subset of 66 participants. We quantify the correspondence between EPImix and single-contrast T1-weighted scans, using correlations between voxels and regions of interest across participants, measures of within- and between-participant identifiability as well as regional structural covariance networks. Furthermore, we explore the use of EPImix for the rapid construction of morphometric similarity networks. Finally, we quantify the reliability of EPImix-derived data using test-retest scans of 10 participants. Our results demonstrate that quantitative information can be derived from a neuroimaging scan acquired and processed within minutes, which could further be used to implement adaptive multimodal imaging and tailor neuroimaging examinations to individual patients. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=77 SRC="FIGDIR/small/430956v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@3ae0e9org.highwire.dtl.DTLVardef@1840d39org.highwire.dtl.DTLVardef@80339borg.highwire.dtl.DTLVardef@bc16cf_HPS_FORMAT_FIGEXP M_FIG Graphical abstract. C_FIG