Human Brain Mapping

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.

Brain age models can systematically mispredict age for specific demographic groups, risking biased estimates of neurological health. We present an approach to improve accuracy and reduce racial disparities using synthetic T1 images generated via style-transfer with SuperSynth, an open-source FreeSurfer tool that produces intensity harmonized isotropic images regardless of the input's contrast or resolution. We refer to the original scans as the real domain and their SuperSynth-derived counterparts as the synthetic domain. Because each synthetic image derives from the participant's own scan, comparisons between domains hold anatomy and demographic composition constant. We audited fairness by training a neural network across thirteen racial compositions on a diverse cohort (683 White, 605 Black, 431 Asian) in both domains. Our findings highlight three key insights. First, the synthetic domain enhanced both fairness and accuracy, and even models trained on a single demographic showed reduced racial disparity purely from switching domain. The same pattern appeared in two independently developed pretrained models. Second, models trained on synthetic data demonstrated superior out-of-distribution robustness in external clinical testing. Third, augmenting imbalanced datasets with synthetic minority images closed 61% of the fairness gap without requiring new data collection, and unlike explicitly supplying race labels, does not require race as a model input at deployment. Representational analyses indicated that appearance standardization changes which features drive age prediction rather than removing demographic information from the images. By utilizing synthetic data, our approach offers a robust pathway to more equitable and generalizable brain age models.

Evidence from in-silico, ex vivo and invasive animal studies suggested the function of neural tissue to be inextricably linked to its structural and microstructural organization. However, this link has never been assessed non-invasively in humans. Here, we established a simple quantitative framework linking cerebral oxygen metabolism to gray matter microstructural features in healthy humans using advanced non-invasive MRI methods. The cerebral metabolic rate of oxygen consumption (CMRO2) was quantified with calibrated fMRI, whereas microstructural properties were estimated from diffusion MRI data fitted to the Soma and Neurite Density Imaging (SANDI) model, yielding soma radius (R soma) and soma volume fraction (fsoma) metrics, from which we derived maps of soma surface area density (SAD) and soma numerical density (ND) under the assumption of a spherical soma. CMRO2 was positively associated with SAD (r = 0.29, p < 0.01), fsoma (r = 0.28, p < 0.05) and ND (r = 0.25, p < 0.05), and negatively associated with Rsoma (r = -0.23, p < 0.05) across cortical brain regions. These relationships are consistent with predictions from experimental and theoretical studies, providing the first quantitative evidence that cerebral metabolic demand is coupled to microstructure in humans. The positive association of energy consumption with SAD is consistent with the notion that both the number of synapses (energy-demanding structures), and the energy required to maintain a membrane potential, scale with soma surface area. Our findings establish a non-invasive MRI framework for investigating the microstructural determinants of brain energy metabolism in health and potentially in disease.

Researchers increasingly have access to neuroimaging data with repeated measures within subjects. Linear mixed-effects modeling offers a valuable way to analyze such data, but its application remains limited in the neuroimaging literature due to high computational cost and a lack of mainstream analysis packages. Here we benchmark a recently published algorithm, the Fast and Efficient Mixed-Effects Algorithm (FEMA), against a conventional implementation (MATLAB's fitlme, restricted maximum likelihood) across 94,830 functional connectivity edges. In contrast to the Adolescent Brain Cognitive Development (ABCD) Study Dataset used in the development and testing of FEMA, the dataset used in the current analysis has a smaller sample size (n = 378) and comprises individuals with chronic pelvic pain scanned at up to four visits over three years. Despite these differences, the two implementations produced highly concordant results: fixed-effect estimates and test statistics correlated at r [&ge;] 0.999 (Lin's concordance correlation coefficient [&ge;] 0.999), and the two approaches reached the same statistical conclusion for 99.98% of edges. FEMA completed each analysis roughly 20 times faster. Agreement was weaker for the estimated variance components, where FEMA attributed systematically less variance to the participant random effect, yielding a slightly lower intraclass correlation in approximately 82% of edges. We further show that most residual disagreement reflected FEMA's default variance-grid resolution rather than the estimator itself, and could be eliminated at negligible computational cost. These findings support the use of FEMA for connectome-wide analysis of repeated-measures neuroimaging data.

The Alzheimer's Disease Neuroimaging Initiative (ADNI) is widely used to train machine learning models for Alzheimer's disease, yet whether it provides consistent ground truth for predictive modeling has not been systematically tested. In this paper, we showed that ADNI contains three interacting sources of bias with direct implications for machine learning: (a) diagnostic label inconsistency, (b) technical measurement drift, and (c) longitudinal survivor bias. A substantial proportion of cases, particularly within intermediate stages, fall outside ADNI's own diagnostic thresholds. MRI field strength and evolving processing pipelines introduce significant technical variability in hippocampal volume, while cohort survivor bias arising from differential retention of participants across phases further distorts longitudinal estimates of disease progression. These findings indicated that ADNI does not provide the stable, internally consistent labels often required in machine learning applications. We proposed a practical framework for diagnostic validation, feature harmonization, and cohort accounting, offering guidance for building more robust and biologically meaningful predictive models from large-scale neuroimaging cohorts.

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.

Brain maturation and aging are characterized by widespread changes in structural and functional brain organization. The extent to which these modalities capture shared versus distinct signatures of lifespan development and aging remains unclear. In this study, we applied connectome-based predictive modeling (CPM) to connectomes derived from diffusion MRI and resting-state functional MRI in Human Connectome Project Development, Young Adult, Aging, and combined Lifespan cohorts to predict chronological age. Both structural and functional connectomes significantly predicted age across cohorts, demonstrating that age-related information is distributed throughout whole-brain connectivity patterns. Prediction performance varied across lifespan stages, with generally stronger performance in the Development, Aging, and Lifespan cohorts than in Young Adulthood. Structural and functional models produced significantly correlated predicted ages in several cohorts, indicating shared age-related information across modalities. Structural-functional convergence analyses revealed limited and variable correspondence between modality-specific predictive features, suggesting that structural and functional connectomes capture both shared and complementary aspects of brain maturation and aging. Network-level analyses demonstrated that age-predictive information was distributed across canonical brain networks, including cerebellar, frontoparietal, salience, somatomotor, and subcortical networks, with patterns varying across cohorts and modalities. Multimodal CPM improved prediction relative to unimodal models in several cohorts, supporting the complementary contribution of structural and functional connectivity to age prediction. Cross-cohort, cross-sex, and cross-modality analyses further characterized the generalizability and shared information of predictive models across lifespan stages, sexes, and imaging modalities. This work demonstrates the utility of CPM for characterizing distributed structural and functional connectivity patterns associated with chronological age across the lifespan and provides a foundation for future studies of individual variability in brain maturation and aging.

Age of first alcohol use is associated with alterations in brain development and an increased risk of problematic substance use later in life. However, it remains unclear if neurobiological alterations associated with first-use age are present in young adulthood and how these alterations may be associated with different patterns of adult substance use. In this study, we employed SuperBigFLICA, a semi-supervised multimodal imaging data fusion approach, to identify joint structural morphometric and functional MRI (fMRI) brain profiles present in adulthood that are most predictive of age of alcohol first-use in the Human Connectome Project-Young Adult cohort (n=622). Our model achieved moderate performance (r=0.22 in external validation), identifying multimodal structure-function profiles predictive of first-use largely involving frontal regions. We then utilized Canonical Correlation Analysis to determine whether these multimodal brain profiles were differentially linked to patterns of adult substance use behaviors. We identified three significant brain modes (Mode 1: r = 0.2750, p = 0.014; Mode 2: r = 0.2249, p = 0.005; Mode 3: r = 0.1568, p = 0.029) associated with distinct profiles of alcohol and cannabis use. Together, we identified multimodal brain patterns in young adults that predict age of first use of alcohol and demonstrated that resulting multimodal structure-function brain profiles capture distinct multivariate patterns of adult substance use, providing a more integrated view of substance use neurobiology.

Brain metabolic connectivity is increasingly studied using positron emission tomography (PET), but multiple analytical approaches exist with unknown comparability. Here we compared metabolic connectivity approaches in 80 participants who underwent simultaneous dynamic PET with 18F-fluorodeoxyglucose and functional magnetic resonance imaging (fMRI) scans and cognitive testing. Six connectivity approaches were compared, including the traditional across-subject method (often labelled metabolic covariance) and five approaches that estimate individual-level connectivity: across-subject metabolic covariance with individual scores derived using leave one out (LOO); functional PET (fPET) within-subject correlations of regional timeseries; and three approaches that use distance-based measures to estimate individual-level connectivity based on within-region tracer distribution and kinetic compartment C1 and C2 time activity curves. fMRI connectivity was also calculated. Each method showed distinct topological, behavioural, structural and genetic signatures. fPET connectivity demonstrated the strongest cognitive associations and largest age-related declines, with unique predictive power for cognition independent of age. Tracer distribution showed strong age group discrimination although with mixed direction of age and cognitive effects. Metabolic covariance (LOO) and kinetic C2 showed no significant age group differences and their cognition associations reflected age-related variance. Gene expression associations implicated a shared core glycolytic-insulin machinery in all approaches, but the direction of the association with regional connectivity diverged across approaches and unique gene signatures further distinguished most methods. Collectively, these results indicate that the optimal connectivity approach depends on the research question. fPET connectivity with dynamic scans offers utility for cognition and ageing studies, tracer distribution for single-scan designs with careful interpretation of age and cognition relationships, kinetic approaches for novel biological insights into glucose kinetics and fMRI for multi-modal integration. Metabolic covariance (LOO) should be used with caution for brain-behaviour investigations. Researchers should weigh trade-offs between physiological specificity, analytical complexity, acquisition requirements and interpretability when selecting a metabolic connectivity approach.

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. Automated segmentation of glioma subregions on multiparametric MRI can be improved by changing either the network architecture or the information presented to the network. These two design levers are usually studied together, which makes their individual contributions difficult to see. We present an integrated analysis of two completed, independent studies, each of which isolates one lever. Methods. In the architecture-level study (Slice), SIBA-UNet - a 2D U-Net combining depthwise separable convolution, inverted-bottleneck blocks and attention-guided skip fusion - was trained on 128 x 128 multimodal slices from BraTS 2020 with a patient-level split, and a sigmoid decision threshold was selected on validation data. In the representation-level study (Subtract), CMSR-3D augmented the four standard MRI sequences with four voxel-wise cross-modal subtraction maps (T1-T1CE, T2-FLAIR, FLAIR-T1CE, T2-T1CE) and evaluated 4-, 6- and 8-channel inputs with an identical 3D U-Net on 1,251 BraTS-GLI 2023 cases (1,001/125/125 split). Results. SIBA-UNet reached an overall validation Dice of 0.8601 at a validation-selected threshold of 0.51, with reported subregion Dice of 0.8795 (necrotic core), 0.8501 (edema) and 0.9125 (enhancing tumor); the source ablation was only partially reported. With the architecture held fixed, the 8-channel CMSR-3D configuration gave the highest mean Dice among the evaluated inputs (0.8699 vs. 0.8534 for the 4-channel baseline) and the lowest mean HD95 (5.9859 vs. 8.2308), whereas either two-map subset alone changed mean Dice by at most 0.0037. Conclusions. Architecture design and input-representation design are separable levers for brain tumor segmentation. The controlled representation ablation indicates that the complete set of subtraction maps was more informative than either subset under a fixed 3D U-Net. Both studies were evaluated internally on their respective datasets, and their numerical results are not directly compared because the datasets, dimensionality and evaluation protocols differ.

Focal lesions are common after complicated mild-to-severe traumatic brain injury (cmsTBI) in children, yet it is unclear whether the location of focal damage relates to cognitive outcome beyond overall lesion burden. Combining 13 international cohorts through the ENIGMA Pediatric cmsTBI working group, we conducted voxelwise lesion-symptom mapping (VLSM) to test whether focal lesion location relates to cognitive outcome, and how this compares with overall lesion volume. From 357 patients with imaging that passed quality assurance, 67 had a focal lesion visible on T1-weighted MRI along with at least one performance-based cognitive outcome (D-KEFS Trail Making conditions and WISC/WAIS Coding). We tested whether lesion location related to cognitive outcome using VLSM, adjusting for age, sex, and time since injury, and separately tested total lesion volume as a predictor of each outcome. Lesions in the right middle frontal gyrus were associated with poorer performance on number sequencing (D-KEFS Trail Making condition 2), a timed visuomotor sequencing task (false discovery rate corrected). In separate regression analyses, greater lesion volume was associated with poorer number-letter switching and motor speed. The switching association remained significant after adjusting for injury severity, whereas the motor-speed association did not. In the largest lesion sample assembled in pediatric cmsTBI to date, focal lesion location and injury burden index separable contributions to cognitive outcome, with number sequencing linked to focal prefrontal damage and switching and motor speed to overall lesion volume, underscoring the value of precise lesion characterization for large-scale multi-site neuroimaging.

The human subcortex integrates information from distributed cortical regions through functionally organized cortico-subcortical systems. Using the publicly available MICA-MICs multimodal MRI dataset of 50 healthy adults, we characterized the microstructural organization of functionally converging cortical regions. First, using resting-state fMRI., we identified cortical regions converging on subcortical targets, including thalamic, basal ganglia, and subcortical limbic structures. Then, using depth-resolved quantitative T1 profiles, we constructed a convergence-restricted microstructural profile covariance matrix, followed by diffusion-map embedding. The first microstructural gradient predominantly differentiated primary and association cortex, whereas the second gradient showed target-related differentiation beyond differences in primary versus association cortex. Cytoarchitectural analysis further revealed target-related differences in cortical composition. Together, these findings reveal complementary dimensions of cortico-subcortical functional organization, spanning broad cortical hierarchy, target-related microstructural differentiation, and cytoarchitectural composition.

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 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.

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.

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.

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.

Collision and contact sport athletes are repeatedly exposed to subconcussive head impacts or subclinical head acceleration events (HAEs), which are impacts that do not result in a clinical diagnosis of mild traumatic brain injury but may induce subtle neurological alterations. These effects may include structural, functional, and metabolic changes within the brain. Various exposure quantification techniques have been developed to characterise HAE exposure, while diffusion MRI enables assessment of brain microstructural organisation, with changes in diffusion-derived metrics associated with pathophysiological processes. This systematic review was conducted in accordance with PRISMA guidelines to evaluate the use of various HAE exposure quantification techniques, exposure weighting approaches, and diffusion MRI in characterising the relationship between HAE exposure and brain microstructure in athletes. Across studies, longitudinal findings for fractional anisotropy (FA), mean (MD), axial (AD), and radial diffusivity (RD) were inconsistent, and substantial heterogeneity in exposure quantification methods and analyses was observed. However, quantitative analysis demonstrated a significant negative association between total subclinical HAE count across the season and FA, and a positive association with MD, suggesting cumulative microstructural alteration. Exposure metrics, including risk-weighted, time-weighted, and finite-element-derived strain measures, showed stronger associations with diffusion MRI alterations than simple impact counts or peak acceleration measures. However, HAE exposure risk models are sport-specific and require broader validation for applicability across sporting cohorts. Future studies should reduce methodological heterogeneity, control for confounding variables, and refine exposure quantification and weighting approaches to better understand the relationship between repetitive subclinical HAE exposure and WM microstructural integrity.

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.