Human Brain Mapping

Source-based morphometry (SBM) is a powerful multivariate method for identifying covarying structural brain networks. However, standard SBM provides only a single loading value per component for each subject, which limits the characterization of relationships between these components. We propose a novel technical co-modulation approach to derive an individualized, network-like measure of structural brain organization. This method transforms the subject-specific SBM loading vector into a symmetric co-modulation matrix by computing the vectors outer product. Each element of this matrix quantifies the pairwise interaction between structural components, creating a subject-specific fingerprint. Similar to functional connectivity that maps the temporal synchronization between networks, this matrix maps their joint structural prominence, reflecting how strongly two networks co-occur within an individual. To demonstrate the utility of this method, we applied it to structural MRI data from 210 patients with schizophrenia (SZ) and 195 healthy controls (HC) from the fBIRN psychosis dataset using functional networks as priors for SBM. We observed widespread reductions in structural co-modulation in the SZ group, particularly within and between visual, default-mode, and cognitive control networks. Furthermore, co-modulation patterns were significantly correlated with cognitive performance and clinical symptom severity in patients. Structural co-modulation provides a robust framework for quantifying individualized relationships between structural brain features, overcoming key limitations of standard SBM and offering a new avenue for integrating structural and functional brain analyses.

Single photon emission computed tomography (SPECT) is a highly specialized imaging modality that enables measurement of regional cerebral perfusion and, in particular, resting cerebral blood flow (rCBF). Recent technological advances have improved SPECT quantification and reliability, making it increasingly useful for studying rCBF abnormalities and perfusion network alterations in psychiatric and neurological disorders. To characterize large scale functional organization in SPECT data, data driven decomposition methods such as independent component analysis (ICA) have been used to extract covarying perfusion patterns that map onto interpretable brain networks. Blind ICA provides a data driven approach to estimate these networks without strong prior assumptions. More recently, a hybrid approach that leverages spatial priors to guide a spatially constrained ICA (sc ICA) have been used to fully automate the ICA analysis while also providing participant-specific network estimates. While this has been reliably demonstrated in fMRI with the NeuroMark template, there is currently no comparable SPECT template. A SPECT template would enable automatic estimation of functional SPECT networks with participant-specific expressions that correspond across participants and studies. The current study introduces a new replicable NeuroMark SPECT template for estimating canonical perfusion covariance patterns (networks). We first identify replicable SPECT networks using blind ICA applied to two large sample SPECT datasets. We then demonstrate the use of the resulting template by applying sc-ICA to an independent schizophrenia dataset. In sum, this work presents and shares the first NeuroMark SPECT template and demonstrating its utility in an independent cohort, providing a scalable and robust framework for network-based analyses.

Brain charts, or normative models of quantitative neuroimaging measures, can identify trajectories of brain development and abnormalities in groups and individuals by leveraging large populations. Recent work has extended these brain charts to model microstructural and macrostructural features of white matter. Assessments of variance for these brain charts are necessary to determine whether the models being used for these data are stable. We implement an analytic approach to characterize variability of the parameters in previously released brain charts created using the generalized additive models for location, scale, and shape (GAMLSS) framework. Additionally, we empirically validate the accuracy of each analytic model through a comparison to a bootstrapping approach from 0.2 to 90 years of age. We find that across all models, the analytic coefficient of variation (COV) remains below 5% for ages greater than 0.25 years, with the maximum empirical observed COV reaching 7% at 0.2 years of age. Further, the empirical assessment shows high agreement with the analytic assessment, with COV estimates averaged across the lifespan for all models having a Pearson correlation coefficient of 0.776 and a mean difference of 4 x 10-4. Both methods exhibit volume and surface area as the features with the largest average COV for the majority of tracts. However, the analytic assessment yields axial diffusivity as the feature most frequently having the smallest COV, whereas the corresponding feature for the empirical assessment is average length. These results suggest that the analytic approach overestimates model stability for WM brain charts when the COV is low and that the validation method is suitable for assessing whether GAMLSS models are unstable.

Though diffusion MRI (dMRI) is the gold standard for white matter tractography, fundamental questions remain about whether captured patterns reflect diffusion-specific phenomena or general structural properties accessible through alternative imaging approaches. This work investigates structural probabilities within the human brain as a complex manifold and examines structural-functional relationships of anatomical bundles to clarify what dMRI specifically captures in white matter architecture. We introduce a framework to extract white matter pathways from FLAIR images without additional subject-specific anatomical context. Using a teacher-student model, we capture systemic information from dMRI-based tractography to guide FLAIR-based tractogram creation. The teacher model trains on dMRI features to generate diffusion tractography, while the student utilizes frozen teacher layers to extract tractography features using only FLAIR input. In our pilot analysis of 14 randomly selected subjects from the Baltimore Longitudinal Study of Aging (BLSA), we performed additional inference on 9 withheld subjects to evaluate robustness. We assessed FLAIR-template generated streamlines using bundle adjacency and Dice coefficient at the voxel level across 39 white matter bundles compared to gold standard diffusion streamlines. Statistical evaluations compared our method against other non-diffusion tractography algorithms using T1-weighted and FLAIR images with subject-specific anatomical context. Results demonstrate our proposed method offers statistically similar performance to other non-diffusion methods when compared to diffusion streamlines These findings suggest that without diffusion data, our method captures unconditional subject-specific prior probabilities of tractography, indicating that tractography patterns may sample from a shared latent space of structural information not unique to any single imaging sequence.

Early childhood development is scaffolded by rapid maturation of brain white matter structure, believed to support the emergence of cognitive and socioemotional functions. Previous whole-tract studies have suggested patterns of white matter development occurring along posterior-anterior, deep-superficial and inferior-superior axes. However, little is known as to whether these patterns are evident within tracts. Using longitudinal diffusion imaging data from 133 children (4-8 years; 76 females), the present work characterizes along-tract patterns of white matter development across association, commissural and projection bundles using fixel-based analyses of microstructure and macrostructure. Within long range association bundles, faster age-related changes were observed for segments adjacent to the visual cortices relative to segments located near association regions, supporting a sensorimotor-association axis of brain development. An inferior-superior pattern was found for projection tracts, with faster age-effects observed for segments near the brainstem. Lastly, while several association and commissural bundles exhibited faster maturation within central segments; indicative of a deep-superficial axis, effects were mixed between micro- and macrostructure, underscoring the unique developmental timing of these different fiber properties. Our findings provide evidence that within-tract white matter maturation unfolds along key spatiotemporal axes and suggests that increased spatial precision can advance our understanding of early childhood brain development.

Resting-state functional MRI has been widely used to study brain connectivity, yet the test-retest reliability of commonly used metrics remains a concern. To improve reliability, extended scan lengths and larger subject cohorts are often recommended. However, these solutions can be impractical and pose challenges, particularly in studies of clinical populations. Here, we systematically assess the reliability of two main types of functional connectivity measures: node-based connectome metrics (edge-level intraclass correlation coefficient [ICC], connectome-level ICC, functional connectivity fingerprinting, and discriminability); and voxel-based resting-state networks (RSNs) (spatial similarity of independent component analysis [ICA]-derived RSN maps quantified using the Dice coefficient). Using data from the Human Connectome Project, we evaluated the effects of scan length (3.6, 7.2, 10.8, and 14.4 minutes) and number of participants (n = 10, 20, 50, and 100), on both within-session and between-session reliability. We found that multivariate connectome metrics demonstrated greater reliability than edge-level measures, and that scan length had stronger influence on test-retest reliability than the number of participants. For connectome metrics, 14 minutes of scanning and a cohort of approximately 20 participants were sufficient to achieve reliable estimates. In contrast, RSN measures benefited from larger cohort sizes. Our findings provide practical guidelines for designing resting-state fMRI studies in terms of scan length and number of participants, balancing reliability and feasibility. Ultimately, protocol choices should be guided by the specific study objectives and the functional connectivity metric of interest.

Normative modeling (NM) is a powerful framework for quantifying individual deviations in brain structure and function relative to a population reference. However, its clinical utility depends on well-calibrated models trained on heterogeneous datasets such as those found in neuroimaging. Here, we systematically examine the effect of training sample size on the distributional and centile calibration of hierarchical Bayesian regression (HBR)-based NMs. Using multisite 3D diffusion MRI scans of the brain from 54,583 subjects, spanning almost the entire lifespan (age: 4-91 years), we trained NMs of white matter fractional anisotropy, a key microstructural metric, on subsamples ranging from 5,000 to 40,000 subjects. HBR was modeled with a Sinh-Arcsinh likelihood. Model calibration was evaluated using Kernelized Stein Discrepancy (KSD) to assess distributional agreement of Z-scores with the standard normal distribution; we also used Mean Absolute Centile Error (MACE) to quantify centile accuracy. Both metrics showed consistent and substantial improvements as the training sample size increased, indicating reduced posterior uncertainty and improved estimation of distributional parameters, particularly at the centile extremes. These results demonstrate that large training cohorts are essential for well-calibrated NMs derived from heterogeneous neuroimaging data and highlight the importance of large-scale data aggregation for reliable individual-level inference.

Identifying robust neuroimaging markers associated with schizophrenia is essential for advancing research and informing clinical understanding. However, a major obstacle to clinical translation is the limited ability of neuroimaging-based classification models to generalise across scanning sites. In this study, we first establish best performing within-site models, and then systematically investigate cross-site generalisation in first-episode schizophrenia (FES) classification and evaluate strategies for mitigating site-related distribution shifts. Using data from two acquisition sites (n = 389 in total), we perform train-on-site/test-on-site experiments to analyze performance degradation under domain shift and examine the effectiveness of ComBat, optimal transport, and adversarial adaptation strategies. Across functional, structural, and diffusion-based features, both traditional machine learning (TML) and neural network (NN) models achieve comparable performance in within-site classification, with resting state fMRI functional connectivity providing the most robust unimodal features. When models are transferred across sites, performance degrades substantially across all approaches, highlighting the impact of site-related variability. Distribution-alignment methods partially mitigate this degradation, with ComBat and optimal transport yielding more consistent cross-site improvements than adversarial adaptation. Increasing model complexity alone does not result in systematic performance gains, and simple models combined with effective alignment strategies often perform comparably to more complex neural architectures, while multimodal feature fusion does not consistently outperform functional connectivity alone. Overall, our findings indicate that controlling for site effects is more critical than model complexity for achieving generalisable classification in FES, underscoring the importance of rigorous evaluation designs and explicit distribution-alignment strategies for neuroimaging-based predictive models with potential clinical utility.

Executive dysfunction affects nearly 50% of individuals with traumatic brain injuries (TBI), yet interventions targeting the underlying neural mechanisms remain limited. This study examined whether aerobic exercise modulates functional connectivity to improve executive function in individuals with mild TBI and identified the neural pathways mediating these improvements. In this secondary analysis of a 12-week pilot randomized controlled trial, participants with mild TBI (n=24) were randomized to aerobic exercise (n=12) or active balance control (n=12). Resting-state fMRI with multivariate pattern analysis revealed that aerobic exercise selectively altered functional connectivity patterns of the anterior cingulate cortex (ACC) compared to balance control. Post-hoc seed-to-voxel analyses identified widespread ACC connectivity differences between groups post-intervention while controlling for baseline, across 19 cortical regions spanning default mode, frontoparietal control, and salience networks. Critically, greater anticorrelation between the ACC and insula following aerobic exercise was associated with improved Trail Making Test B-A performance in the aerobic group ({beta}=46.92, p=0.04) but not the balance group, indicating that participants who developed stronger ACC-insula functional segregation showed greater reductions in executive function completion times. These findings establish the ACC-insula circuit as a critical neural substrate mediating exercise-induced executive function recovery after TBI and identify this pathway as a promising therapeutic target for exercise-based rehabilitation interventions.

PurposeCerebral glucose metabolism and cortical morphology are known to undergo significant changes across the lifespan, yet their network-level coordination remains poorly understood. This study aimed to investigate whether individual-level metabolic connectivity (MC) reflects underlying inter-areal morphometric similarity, and to determine how this metabolic-morphometric coupling evolves across the adult lifespan. MethodsDynamic [18F]FDG-PET and structural MRI data were acquired from 67 healthy adults (age range: 38-86 years). Individual MC networks were estimated based on the similarity between regional time-activity curves. Corresponding structural similarity networks were generated using the morphometric inverse divergence (MIND) framework, which integrates multiple vertex-wise features of cortical morphology. The correspondence between metabolic and structural networks was quantified at both global and local scales using Spearman correlations. General linear models were employed to assess age-related effects on MC-MIND similarity. ResultsMC demonstrated a robust positive association with cortical morphometric similarity ({rho} = 0.32, p < 0.0001), an association that persisted after distance correction and was replicated at the individual level. Regional coupling followed a topographic gradient, peaking in heteromodal association cortices and reaching its minimum in paralimbic areas. Crucially, morphology-metabolism alignment systematically strengthened with age at the global level ({beta} = 0.59, p < 0.001). Local age-related increases were spatially heterogeneous, predominantly affecting visual, dorsal parietal, and premotor cortices alongside adjacent multimodal regions. ConclusionIndividual-level MC captures the morphometric organisation of the brain. The age-related increase in morphology-metabolism coupling indicates that metabolic coordination becomes progressively more aligned with cortical architecture, consistent with reduced neuroenergetic flexibility in the ageing brain.

In-scanner head motion is a recognized source of bias in structural magnetic resonance imaging (sMRI), yet it remains under-addressed in psychiatric neuroimaging where structural difference in patient populations are considered foundational. We examined motion-related bias in grey matter volume estimates across eight independent cohorts comprising 9,664 individuals, including 8,979 neurotypical controls (NC), 497 patients with schizophrenia (SCZ), and 188 patients with bipolar disorder (BD). Motion estimates were derived from multiple fMRI scans acquired within the same scanning session and summarized using principal component analysis. In NC, motion accounted for 1-6% of regional grey matter variance, a magnitude comparable to reported psychiatric case-control effect sizes. Adjusting for motion attenuated SCZ-NC group differences, reducing effect sizes in 85% of brain regions and yielding 5% fewer significant ROIs (pFDR<0.05). In BD, motion correction reduced effect sizes in 97% of regions, with a 24% reduction in significant ROIs. Cross-diagnostic spatial patterns were significantly correlated (r=0.63, p=3x10-{superscript 1}3), explaining a sizable portion of SCZ-BD commonalities. Critically, a falsification analysis in UK Biobank (N=5,123) showed that stratifying NC by motion alone produced grey matter differences accounting for 45-62% of SCZ case-control effect magnitude, underscoring how difficult it is to interpret SCZ-like morphometric differences as tissue properties rather than as motion-driven patterns. These findings urge caution in interpretations of sMRIdifferences in patient-control comparisons and use of systematic fMRI based motion control as standard practice in sMRI analyses.

PurposeWe investigate whether bilingual versus monolingual language environments in early infancy are associated with differences in intrinsic functional organization measured from resting-state fNIRS connectivity. ApproachUsing the RS4 infant resting-state fNIRS cohort (HbO), we studied two complementary subject-level representations of resting-state connectivity: correlation-based symmetric positive definite (SPD) operators and learned-graph Laplacian operators. Correlation matrices were estimated over fixed non-overlapping temporal windows, regularized by shrinkage, and aggregated at the subject level using a Jensen- Bregman LogDet (JBLD/Stein) barycentric mean. Dominant eigenspaces were used as compact descriptors of functional organization and compared across subjects through principal angles augmented with spectral jump features. In parallel, learned functional graphs provided a complementary Laplacian-based representation of network structure. All analyses followed a strict leave-one-subject-out protocol on a common subject set (N = 94), with all templates and model parameters estimated from the training fold only. ResultsThe strongest individual branch was the correlation-based spectral-subspace representation (CORR-ANGLES: ROC-AUC = 0.811), while the learned-graph spectral branch also showed clear above-chance performance (LAP-ANGLES: ROC-AUC = 0.785). Fusion improved performance both within representation families and across them. Within-family fusion yielded ROC-AUC = 0.836 for the correlation branch and ROC-AUC = 0.805 for the Laplacian branch, whereas fusion of the two spectral branches reached ROC-AUC = 0.883, supporting the view that covariance-based and learned-graph representations capture complementary aspects of infant functional connectivity. The best overall performance was achieved by the main reported hierarchical four-branch fusion, with balanced accuracy = 0.826, F1 score = 0.781, and ROC-AUC = 0.900. ConclusionsResting-state infant fNIRS contains subtle spectral-geometric structure associated with bilingual exposure. Correlation-based and learned-graph representations provide complementary information, and their hierarchical fusion improves separability under strict cross-subject evaluation.

Large-scale neuroimaging studies increasingly pool data across multiple cohorts, scanners, and acquisition protocols, introducing technical between-cohort variation that must be addressed before meaningful biological inference can be drawn. Existing harmonisation methods, particularly ComBat-based approaches, have been widely adopted for this purpose. However, they remain limited by Gaussian assumptions and by their focus on location or location-scale correction. In this study, we propose a unified hierarchical Generalised Additive Models for Location, Scale and Shape (GAMLSS) framework for multi-cohort harmonisation and normative modelling of structural neuroimaging data. The framework models cohort effects directly within all fitted distributional parameters, accommodates any parametric family for which exact inverse mapping is available, and returns harmonised values on the original measurement scale through centile-based quantile mapping. Normative deviation scores are obtained as a direct by-product of the same fitted model, enabling harmonisation and normative inference to be conducted jointly. The method was evaluated in a pooled longitudinal dataset comprising 88,126 observations across 237 structural neuroimaging features from six cohorts spanning childhood to late life: ABCD, IMAGEN, NCANDA, LIFE, UK Biobank, and MAS. Harmonisation performance was compared with ComBat, ComBat-GAM, and ComBat-LS using complementary criteria assessing data retention, residual batch effects, preservation of age-related and sex-related biological signal, and coherence of post-harmonisation lifespan trajectories. GAMLSS achieved near-complete removal of residual cohort effects, retained almost all valid observations post-harmonisation, and showed the strongest overall preservation of biological signal across validation metrics. In particular, it better preserved biologically plausible age trajectories for distributionally complex features such as white matter hypointensity volume, while simultaneously providing harmonised native-scale values and normative deviation scores within a single framework. These findings suggest that hierarchical GAMLSS offers a flexible and practical alternative to existing ComBat-based methods for large-scale neuroimaging harmonisation, particularly for features with non-Gaussian residual distributions and settings where cohort effects extend beyond differences in mean and variance.

Research on the relationship between digital media and neurocognitive function has blossomed with the rising digital age and advent of social media, producing a growing literature focused on how technological developments may be affecting users brains. Much of the science has focused on the involvement of specific brain systems that support reward (e.g., nucleus accumbens, orbitofrontal cortex), cognitive control (e.g., lateral prefrontal, anterior cingulate), and socio-emotional processes (e.g., temporo-parietal junction) and why they might be especially relevant to digital media engagement. However, a broad and systematic analysis of the consistency of findings across neuroimaging studies has not yet been published. Here, we conducted a coordinate-based meta-analysis based on published structural and functional MRI studies exploring habitual digital media engagement. Adopting a granular approach to summation of this literature, we use Activation Likelihood Estimation (ALE) and find that the most consistent effects arise in the anterior insular cortex, a region implicated in the integration of social and emotional information that has not been frequently highlighted in the prior literature on digital media effects in the brain. This discovery encourages reconsideration of how the brain is likely to affect, and be affected by, digital media engagement and online behavior.

AO_SCPLOWBSTRACTC_SCPLOWMachine learning models using functional magnetic resonance imaging (fMRI) are becoming increasingly popular - these models often rely on training data from multiple, large, and publicly available datasets. It is often necessary to harmonize these data across sites and sequences, and algorithms like ComBat are frequently applied to correct for these differences. This has been shown to improve model performance and generalizability. However, applying traditional ComBat necessitates harmonizing all data (train, validation, test, and other unseen external test sets) simultaneously, which leads to potential data leakage and limits application to new unseen data. We introduce Consistent Reference External Batch (CREB) harmonization, a novel extension of ComBat that learns the prior distribution of site effects exclusively from a designated training set. This learned prior serves as a consistent, easily deployable reference point that employs the empirical Bayes framework to update the site effect for any new, external unseen data. This approach enables training, validation, and test sets to be harmonized separately, thereby preventing data leakage, ensuring the integrity of downstream analyses, and application to new unseen data. CREB is different from traditional ComBat in which each sites prior distribution is estimated at once, but this cannot be applied to unseen data or data from sites not included in the original set of data. We tested CREB with train data from 2846 participants (ages 18-97 years) across 9 different studies and test data from 1113 participants (ages 18-88 years) from 3 studies. We evaluated the performance of harmonization with functional connectivity and gray matter volume. We show that CREB can effectively harmonize the test data to the train data, and have comparable performance to ComBat. CREB is able to conduct this harmonization in a two-step procedure that prevents leakage and is deployable to new unseen data. Finally, we tested whether CREB could similarly preserve biological variance (e.g., whether age associations were preserved after harmonization). We found that CREB, like ComBat could preserve age associations with both functional connectivity and gray matter volume measures. CREB provides an easily deployable, robust harmonization method to standardize data to a common reference distribution, making it uniquely suitable for training generalizable machine learning models.

Harmonisation is widely used to mitigate site- and scanner-related batch variability in multisite neuroimaging studies and is particularly critical in longitudinal clinical trials, where detection of subtle biological or treatment-related changes depends on reliable measurement across scanners and timepoints. However, the effectiveness of harmonisation in small, heterogeneous clinical datasets remains insufficiently understood, particularly in relation to subject-level variability and consistency across acquisition settings, and its impact on both removal of technical variability and preservation of biological variation in pooled multisite analyses. We systematically evaluated a range of image-based and statistical harmonisation methods using a clinically realistic multisite, multiscanner structural T1-weighted (T1w) MRI test-retest dataset comprising three controlled acquisition scenarios: repeatability, intra-scanner reproducibility and inter-scanner reproducibility. Methods were applied under different batch specifications (site, scanner, or both) and performance was assessed within each scenario and in pooled data using a multi-metric framework capturing both technical and biological variability in volumetric imaging-derived phenotypes (IDPs) relevant to aging and dementia research. Across IDPs, before harmonisation variability was lowest in the repeatability scenario (median variability=0.6 to 2.7%, rank consistency {rho} [&ge;]0.9), with modest increases under intra-scanner reproducibility (0.5 to 3.2%, {rho}=0.5 to 1.0) and substantially greater variability under inter-scanner reproducibility conditions (1.7 to 19.2%, {rho} =-0.1 to 0.9). These results offer important information to consider for multisite study design, including sample size calculation in clinical trials. Harmonisation performance was strongly context dependent, with clearer benefits emerged in inter-scanner scenarios where both variability reduction and improvements in subject-level consistency were observed. In pooled data, approaches that explicitly modelled site as batch and accounted for repeated-measure structure showed greater consistency across IDPs in batch effect mitigation and more accurately reflected underlying biological variation. Our evaluation metrics enabled disentangling the removal of global batch effect while highlighting residual variability at the phenotype-specific or multivariate levels. These findings demonstrate that harmonisation cannot be treated as a one-size-fits-all solution and must be interpreted relative to the acquisition context, dataset structure, and downstream analytic goals. Multi-metric evaluation under realistic clinical constraints is essential to support reliable and translatable neuroimaging inference by ensuring appropriate correction of batch effects while preserving longitudinal biological signals and sensitivity to clinically meaningful change in multisite studies.

Tractometry enables detailed mapping of white matter microstructure along individual tracts and is widely used to study disease effects such as those seen in Alzheimers disease (AD). However, how different tractography algorithms influence tractometry outcomes remains unclear. Here, we compared whole-brain deterministic and probabilistic tractography using the BUndle ANalytics (BUAN) framework in the Alzheimers Disease Neuroimaging Initiative (ADNI) dataset, including 118 AD and 728 cognitively normal (CN) participants. Both approaches revealed the expected pattern of lower fractional anisotropy (FA) and higher mean, radial, and axial diffusivity (MD, RD, AxD) in AD, consistent with white matter degeneration. Despite broadly similar global trends, substantial bundle-level differences emerged between the two tractography methods. Probabilistic tracking produced stronger and more spatially extended effects in the fornix, a small and highly curved limbic pathway vulnerable to AD-related degeneration, whereas deterministic tracking showed greater sensitivity in the posterior segments of the right superior longitudinal fasciculus (SLF R). These discrepancies highlight that the choice of tractography algorithm can alter detecting disease effects, emphasizing the need for cross-method validation to ensure the robustness and interpretability of along-tract measures.

IntroductionPreterm birth is a major risk factor for disrupted brain development and subsequent neurodevelopmental disorders, yet the underlying mechanisms remain poorly understood. Further, typical neuroimaging analyses are particularly challenging in the neonatal brain: data is frequently low quality and a lack of cellular development violates the assumptions relied on by many commonly-used techniques. In this study, we develop and present an advanced diffusion magnetic resonance imaging method to examine the microstructural organization of white matter in a clinically-acquired cohort of premature neonates. MethodsUsing a novel approach that resolves multiple tissue compartments within the brain, we provide highly detailed orientation and quantification of white matter fibers and tissue signal fraction. We also utilize a series of automated segmentation algorithms to identify and measure these metrics across key tracts and subcortical regions. We investigate how these measures relate to postmenstrual age, as well as to clinical factors reflecting neonatal illness severity. ResultsWe report successful segmentation and reconstruction of numerous white matter tracts throughout the neonatal brain. We further demonstrate the utility and functionality of microstructural analysis in a variety of pathologies commonly encountered in the neonatal clinical environment. Our results demonstrate tract-specific developmental trajectories, with early-maturing pathways showing higher microstructural organization. Exploratory analyses suggest that neonatal illness severity has modest, tissue-specific associations with microstructural properties. DiscussionThis work demonstrates that advanced microstructural imaging methods can extract meaningful white matter measurements from clinically-acquired scans, providing a practical framework for studying neonatal brain development in real-world hospital settings. These metrics are able to be calculated at extremely young ages, potentially allowing non-invasive study of vulnerable populations before detailed behavioral or neurological assessments are feasible.

The cerebellum undergoes substantial maturation with regionally distinct developmental trajectories. This study examined cerebellar gray matter volume (GMV) in healthy children, adolescents, and adults, using voxel-based morphometry, the ACAPULCO algorithm, and the SUIT toolbox for cerebellum-optimized analyses. A total of 104 typically developing children (n=31, 6-9 years), adolescents (n=35, 13-17 years), and adults (n=38, 30-40 years) were included. We hypothesized age group differences in cerebellar GMV, with adolescents showing the greatest volume, specifically in posterolateral regions. Results revealed significant group differences in GMV. We observed region-specific volumetric patterns, with some areas (e.g., Crus II, lobule X) increasing from childhood to adolescence followed by stabilization, whereas other areas (e.g., lobules I-IV and VI, Crus I, vermis VI and VIIb) exhibited peak GMV during adolescence, with lower volumes in both children and adults. These patterns were partly consistent with our hypothesis. Notably, no regions had greater GMV in adults than adolescents, suggesting that cerebellar growth peaks in adolescence before stabilizing. Our findings indicate differential developmental patterns both between and within lobules of the cerebellum, and highlight adolescence as a peak period of cerebellar growth, with potential implications for the development of cerebellar-supported cognitive and emotional functions that undergo significant changes during this period.

The MNI152 template is widely regarded as a representative average brain in neuroimaging, computational modeling, and neuromodulation research; however, its fidelity as a true population mean has not been systematically evaluated. In this study, we compared the MNI152 6th generation nonlinear template to 436 individual MRI from a publicly available dataset, including Asian, Black, and White participants. We quantified the gross brain dimensions and extracted the mean scaling, shear, and voxel-wise Jacobian determinants from the linear and nonlinear registrations between the template and each subject. Across all racial groups, the MNI152 brain exhibited substantially larger radii than the population means, with z-scores frequently exceeding 1.0. The linear scaling factors indicated consistent contraction of the template, and voxel-wise Jacobian fields revealed spatially heterogeneous deformations, demonstrating that the template differs from real brains in both size and shape. These findings suggest that the MNI152 template does not reflect the average morphology of contemporary population samples and that linear registration alone cannot resolve these discrepancies. Therefore, more robust and unbiased template-generation pipelines may be necessary for applications requiring anatomically accurate head models.