Human Brain Mapping

Foundation models pre-trained on large neuroimaging datasets offer a promising approach to overcome the limited sample sizes typical of mental health imaging studies, yet their generalization across diverse clinical populations remains unclear. We present the first systematic benchmark of four publicly available structural MRI foundation models -- AnatCL, BrainIAC, 3D-Neuro-SimCLR, and SwinBrain -- on tasks relevant to mental health research. Using T1-weighted MRI from Parkin-sons Progression Markers Initiative (PPMI), Healthy Brain Network (HBN), and Nathan Kline Institute (NKI), we evaluate these models on sex classification, brain age prediction, and Parkinsons disease (PD) classification, benchmarking against models trained from FreeSurfer-derived cortical thickness and cortical surface area features, as well as an un-trained CNN baseline. Although some individual foundation models out-performed FreeSurfer on particular tasks and datasets, 3D-Neuro-SimCLR demonstrated the most consistent performance overall, with the notable exception of HBN sex classification, and all models failed to classify early-stage Parkinsons disease above chance. Notably, untrained CNNs achieved performance comparable to or exceeding FreeSurfer in multiple instances, establishing them as computationally efficient reference models. The cross-model feature correlation analysis reveals that foundation model representations correlate differently with traditional cortical measurements. These findings position structural MRI foundation models, particularly 3D-Neuro-SimCLR and AnatCL, as promising avenues to boost the performance of neuroimaging predictive models in mental health.

Big neuroimaging data enable researchers to study subtle structural and functional brain changes and relationships between brain characteristics and genetics, lifestyle, and disease factors. However, substantial effort is needed to minimise technical, non-biological differences between data batches to avoid incorrect inferences. In this study, we address a previously identified bias in UK Biobank FreeSurfer IDPs derived from only the T1 image compared to those using both T1 and T2-FLAIR by treating the bias as a batch effect and using harmonisation approaches. We investigate and characterise this bias through direct within-participant comparison at the image and IDP level, comparing the results with those seen in the wider UKB sample. We then assess different methods of addressing the effect of missing T2-FLAIR, starting from simple linear regression before moving to ComBat, a widely used harmonisation method, testing different approaches for applying ComBat and showing its similarity to simple linear regression. Finally, we examine how ComBat estimates vary with batch and sample size. Our results show clear benefits in using both T1 and T2-FLAIR data in FreeSurfer, as opposed to just the T1, which is more common, with the pial surface fitting being less likely to fail and showing greater biologically plausible inter-subject variability. This is particularly important for cortical thickness IDPs, where T2-FLAIR omission leads to reduced true variability and systematic underestimation, as shown through within-participant repeat testing. We demonstrate that ComBat can address this bias, with its standard use (i.e., applied separately on different IDP categories) showing the best improvement in cortical thickness measures where the bias is strongest, and we find that it is important not to pool ComBat priors across different classes of IDPs. Our proposed version of ComBat with a reference batch (i.e., estimating mean and variance only from data with T2-FLAIR available) performed best in recovering both mean and variance differences between batches across different IDP classes and offers a promising approach for cases where a reference batch is clearly identifiable. While ComBat reliably corrects mean (additive) batch effects with relatively small sample sizes ({approx}30 subjects per batch), we show that its variance (multiplicative) correction is substantially less stable, requiring much larger sample sizes and becoming unreliable when batches are small or imbalanced, or when there is a large variance difference between them.

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.

BrainAge models hold promise as a clinical biomarker for developmental brain health, especially in childhood when there is the potential for early intervention. To distinguish between normative developmental variance and pathological divergence, BrainAge models should reflect the dynamic and diverse neurodevelopmental processes that occur in distinct developmental windows across childhood. We utilized multi-modal neuroimaging data from three pediatric cohorts covering ages 4 to 13 years (n = 1005, 2126 scans), split into Train and Test datasets. Twelve sex-stratified BrainAge models were built stratified by type and different combinations of neuroimaging features. Model types were "Full-Span" models covering the full age range, and "Phase-Specific" models split into early- and late-childhood. We first compared BrainAge estimates in the Test dataset amongst our candidate models, then benchmarked the best-performing model against published pre-trained models and DNA-based biological age measures. Our findings show that a BrainAge model that was phase-specific and consisted of both structural and functional features (cortical thickness, subcortical volumes, and functional network integration measures) showed good prediction of age and best distinguished between healthy and symptomatic subgroups. We present a proof-of-concept for developmental models supporting building BrainAge models of higher temporal resolution that align to different childhood developmental phases.

Normative modeling quantifies individual deviation from population norms by estimating the conditional mean and variance of brain-derived measures as functions of clinically relevant parameters such as age. The rapid growth of multicenter consortia has created an urgent need for normative models that incorporate batch harmonization. Several harmonization methods based on linear mixed models--ComBat, GAMLSS, HBR, and Generalized Normative Modeling (GNM)--offer explicit formulations of the mean and variance, making them natural candidates for batch-harmonized normative modeling; yet the absence of a unified theoretical framework leaves it unclear whether and how these methods support the computation of batch-harmonized z-scores. We bridge this gap by writing existing harmonization methods as special cases of a single location-scale equation, y = m(x, {Theta})+{sigma}(x, {Theta}){varepsilon} , which we term the unified form of batch harmonization equation for normative modeling. The methods differ only in the functional forms of m and{sigma} , how batch parameters enter{Theta} , and how{Theta} is estimated. This unified form yields both harmonized data y* and site-invariant z-scores from the same model, providing a common theoretical language for harmonized normative modeling. Building on this framework, we evaluate the underlying regression engines (parametric, spline, Gaussian process, kernel, deep learning), sensitivity to outliers, computational scalability, and federated decomposability for privacy-preserving multi-center computation. By clarifying what each method assumes, what it delivers, and where the boundaries of current methodology lie, the unified equation establishes a principled foundation for method selection and charts a path toward reliable, scalable, and privacy-aware normative modeling across multi-center neuroimaging.

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.

IntroductionWhile global topological properties of brain networks reach relative maturity early in development, functional reconfigurations at the regional level continue throughout adolescence to support cognitive maturation. However, regional age and sex-specific developmental patterns of functional reconfiguration remain incompletely understood. MethodsWe analyzed resting-state fMRI data from 528 participants aged 5-21 years from the Human Connectome Project in Development. Three regional graph-theory metrics (betweenness centrality, hub score, and local efficiency) were computed for each individuals functional network. Cognition was measured using NIH toolbox. Parallel factor analysis was employed to decompose an individual x region x metric array into factors representing distinct developmental properties in the full sample and separately for males and females. Brain-cognition associations were examined in developmental subgroups (<13, 13-18, >18 years). ResultsThree factors emerged, characterizing visual, multimodal integration, and higher-order factors. Across development, metrics capturing network integration (betweenness centrality and hubness) showed general stability, while metrics capturing segregation (local efficiency) presented distinct peaks, particularly in the visual factor. Females showed earlier peaks and declines in higher-order factor, while males exhibited greater variability and protracted maturation in multimodal and higher-order factors. Brain-cognition associations were modest with early childhood and crystallized cognition composites showed small negative correlations with hub score in entire sample (r=-0.212) and local efficiency in males aged <13 years (r=-0.215). ConclusionFindings highlight nonlinear, sex-specific functional reconfiguration at region-level during childhood and adolescence, underscoring the importance of sex-stratified analyses in developmental and providing a crucial foundation for future investigations of developmental disorders.

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.

Functional magnetic resonance imaging (fMRI) data are inherently complex, characterized by high dimensionality, intricate inter-regional dependencies, and substantial individual variability across experimental paradigms. Traditional linear mixed models (LMMs) provide a principled framework that models population-level fixed effects while estimating variance components arising from subject-level random effects; however, they often fail to adequately capture nonlinear relationships inherent in neuroimaging data. To address these limitations, we introduce the nonlinear mixed model (NMM) approach, an innovative extension of the LMM framework that integrates neural networks to flexibly model complex fixed-effect relationships while preserving the random-effects structure to account for individual differences. NMM advances fMRI analysis by: (1) identifying robust functional connectivity (FC) patterns consistently observed across multiple paradigms; (2) leveraging SHapley Additive exPlanations (SHAP) analysis to provide post-hoc interpretability of the nonlinear fixed effects, quantifying how age, sex, and paradigm contribute to predicted FC and how these effects are distributed across large-scale brain networks; and (3) using subject-specific random effects as neural fingerprints that not only show systematic variability across attention and default mode systems but also predict standardized cognitive scores, demonstrating biological relevance. Applied to the Philadelphia Neurodevelopmental Cohort (PNC) across emotion, n-back, and resting-state paradigms, NMM achieved superior model fit relative to classical LMMs, as evidenced by lower mean squared error (MSE) in predicting FC. This framework offers a statistically rigorous and practically explainable approach for modeling large-scale FC from modest covariates while explicitly separating population-level effects from stable individual variability in functional brain organization.

NeuroMark is a fully automated hybrid independent component analysis (ICA) framework designed to extract functional network features that are individually resolved and comparable across different cohorts. By integrating a reliable spatial template with spatially constrained ICA that adapts to each scan, NeuroMark retains the advantages of data-driven decomposition while avoiding limitations of fixed region-of-interest approaches. NeuroMark typically employs direct spatial normalization of fMRI data to a standardized adult EPI template; it remains unclear whether this approach is optimal for populations whose anatomy differs substantially from that of adults. We evaluated two normalization strategies in three large datasets spanning infancy, development, and aging: (1) direct normalization to the adult EPI template (EPInorm), and (2) normalization using an age-specific anatomical T1 template followed by transformation to the adult EPI template (T1toEPInorm). Across all cohorts, average intrinsic connectivity networks derived from EPInorm and T1toEPInorm exhibited very high spatial correspondence (mean {+/-} SD: 0.9966 {+/-} 0.0012 in infants; 0.9947 {+/-} 0.0019 in development; 0.9963 {+/-} 0.0012 in aging). The individual level also showed high similarity, though time courses showed slightly higher consistency than spatial maps (average correlations for time courses: 0.7990-0.9931; average correlations for spatial maps: 0.6879-0.9131). Functional network connectivity (FNC) measures were extremely well preserved across scans (95% of FNC with r > 0.9374 in infants; r > 0.8670 in developmental cohorts; r > 0.9219 in aging), demonstrating the robustness of NeuroMark features to different normalization strategies. Together, these results indicate that NeuroMark yields highly stable functional network features irrespective of whether an age-specific intermediate registration step is incorporated. NeuroMark, along with direct normalization to the adult EPI template, thus provides a robust, efficient, and harmonizable approach for large-scale, multisite, and lifespan neuroimaging studies, facilitating broad comparability across datasets while avoiding potential biases introduced by using multiple age-specific templates within a single study.

Clinical adoption of new biomedical techniques depends on establishing reference values against which individual patients can be compared. In resting-state functional MRI (rsfMRI), most biomarker research has relied on the case-control paradigm, whose underlying assumptions are often invalid as diseases are frequently heterogeneous, limiting biomarker generalizability. Normative modeling offers a complementary alternative by characterizing individual deviations against a reference population. However, in rsfMRI, normative modeling has been applied almost exclusively to functional connectivity, with limited attention to age trajectories and sex effects. We address these gaps by developing a spatial normative model of four rsfMRI metrics that capture complementary features of the blood-oxygen-level-dependent (BOLD) signal across age and sex. Five publicly available datasets were aggregated to form a sample of 1,978 participants aged 10-30 years. Four metrics were computed for each of 110 grey matter regions: amplitude of low-frequency fluctuations (ALFF), fractional amplitude of low-frequency fluctuations (fALFF), regional homogeneity (ReHo), and Hurst exponent. A machine-learning model based on hierarchical Bayesian regression with a non-Gaussian likelihood was fitted per metric, modeling non-linear age effects, sex, and multi-site acquisition. Models were well calibrated across all four metrics, with fALFF showing the strongest predictive performance and Hurst exponent the weakest. Normative trajectories varied across brain regions for each metric, but on average, the median of each distribution remained bounded across regions, while the spread was more regionally variable. All four metrics showed predominantly negative slopes with age, indicating a decrease in each metric over the age window. This work provides a normative reference across four rsfMRI metrics that capture distinct features of the BOLD signal, complementing the case-control paradigm and supporting individual-level inference.

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.

BackgroundWhile resting-state fMRI demonstrated that brain networks are spatially dynamic (expanding, shrinking, and changing complexity over time), understanding the transient spatial network interactions that remain poorly characterized is critical for revealing the mechanisms underlying brain disorders. MethodsWe introduce DESINE (Dynamic Estimation of Spatially Interactive Networks), a novel framework using joint density distributions (2D histograms) of voxel-wise activity to quantify 4D spatial network interactions across sliding windows. We analysed transient deviations from the average functional state using root-mean-square error (RMSE) and mean absolute deviation (MAD), and characterized recurring interaction patterns using k-means clustering. We applied DESINE to 91 network pairs (14 networks) in a cohort of 508 subjects (315 healthy controls; 193 patients with schizophrenia, SZ). ResultsSZ is characterized by a significantly "constrained dynamic repertoire" of network interactions. SZ patients showed markedly lower means and standard deviations for both RMSE and MAD metrics across network pairs, particularly in regions of high activity, indicating systematic rigidity. Cluster analysis revealed significant alterations in state affinity metrics, suggesting a global breakdown in the brains capacity to preserve diverse, high-fidelity spatial configurations. Critically, these interaction metrics were associated with cognitive performance, symptom scores on the positive and negative syndrome scale, and chlorpromazine equivalent drug scores. ConclusionsThis work introduces DESINE as a global, voxel-agnostic framework for characterizing time-varying spatial interactions. Our findings highlight spatial rigidity as a fundamental feature of psychopathology, suggesting that the inability to express a diverse range of spatial interactions is a factor underlying cognitive deficits in schizophrenia.

The relationship between cortical morphology and intelligence during adolescence has been widely studied, with existing literature reporting varying degrees of association across different modeling approaches. This study provides a comprehensive comparison of model performance in investigating the association between crystallized intelligence and cortical surface area using data from 11,351 subjects in the Adolescent Brain Cognitive Development (ABCD) study. We evaluate ten widely used models ranging from linear regression to graph convolutional networks across three covariate adjustment formulations: full (no adjustment), partial (age and sex adjusted), and total surface area (TSA) partial (age, sex, and TSA adjusted). Using bootstrap resampling with 50 iterations, we estimate the fraction of variance explained (FVE) for each model. Our results suggest that more complex models do not lead to higher FVE, with LASSO having the highest FVE of 15.9% (full formulation), Ridge at 10.5% (partial formulation), and Principal Component Regression (PCR) with 102 PCs at 2.5% (TSA partial formulation). Our results also reveal that the relationship between cortical surface area and crystallized intelligence is predominantly driven by global factors age, sex, and TSA, rather than by localized cortical surface area.

Background Children in low- and middle-income countries (LMICs) face an elevated risk of developmental delay, yet scalable neuroimaging tools to study early brain development in these contexts remain limited. Children who are HIV-exposed but uninfected (CHEU) represent a growing population with evidence of language and motor delays and altered brain development compared with children who are HIV-unexposed (CHU). Ultra-low-field (ULF) MRI offers a more affordable alternative to conventional high-field (HF) MRI, but its application in early childhood remains underexplored. Methods We compared brain volumes derived from ULF (64mT) and HF (3T) MRI in South African CHEU and CHU as part of the DolPHIN-2 PLUS study. Volumetric segmentation was performed using FreeSurfer v7.4.1 and SynthSeg on the Flywheel platform. Agreement between modalities was assessed using Pearsons and Lins concordance correlation coefficients across global and subcortical regions. Associations between ULF-derived brain volumes and developmental outcomes, measured by the Bayley Scales of Infant Development, Third Edition, were evaluated using partial correlations adjusted for sex and age. Results Forty-five children (9 CHEU, 36 CHU; mean age 45.6 months) had paired ULF and HF scans of usable quality. Strong correlations were observed between ULF and HF volumes for global white and grey matter regions (r > 0.92) and larger subcortical grey matter structures such as the thalamus, caudate, and putamen (r = 0.86-0.89). Moderate-to-weak correlations were evident in smaller structures (hippocampus, pallidum, amygdala). ULF underestimated most grey matter volumes, and overestimated total white matter volume relative to HF. ULF-derived global and subcortical volumes were associated with receptive and expressive communication (r = 0.34-0.59, all p < 0.05). Conclusions ULF MRI produces brain volume estimates comparable to HF MRI and captures meaningful associations with early language development. These findings support ULF MRI as a feasible and scalable tool for studying neurodevelopment in vulnerable paediatric populations in LMICs.

IntroductionThe thalamic nuclei play a crucial role in regulating information flow to the cortex and supports diverse cognitive functions. Although previous studies have linked thalamic structural and functional characteristics to cognition, these measures do not fully capture the thalamuss role in dynamic control, which is essential for complex cognitive processes. Moreover, it remains unclear how these different metrics relate to each other in the way they account for cognition. MethodsT1-weighted MRI, diffusion MRI, resting-state fMRI, and neuropsychological data were obtained from 419 unrelated participants in the Human Connectome Project. We measured grey matter volume, white matter integrity, and functional controllability of each thalamic nucleus to examine their associations with cognitive performance across domains identified through clustering analysis of the neuropsychological data. We also assessed the relationships among these structural and functional metrics and evaluated their individual and combined contributions in capturing covariance with performance in various cognitive domains. ResultsSignificant correlations were observed between thalamic grey matter volume and white matter integrity; however, thalamic functional controllability showed no significant association with either structural metric. White matter integrity demonstrated the strongest association with sequence working memory and language processing. In contrast, thalamic controllability metrics accounted more for performance in executive function, reasoning and encoding, visuospatial processing, and impulse control, outperforming the combination of grey and white matter structural metrics. ConclusionThis study highlights the critical role of the thalamus from a dynamic control perspective, demonstrating that thalamic structural and functional metrics provide complementary rather than redundant information related to cognitive performance. These findings underscore a promising new direction for understanding the complex and dynamic contributions of the thalamus to human cognition.

Understanding the pathogenesis of amyloid-{beta} pathology in Alzheimers Disease (AD) proves to be a challenge. In this work, we expand upon the application of network diffusion models (NDM) to study pathophysiological spread of amyloid-{beta} throughout white matter structural brain networks. We found that the NDM successfully recaptures subpopulation-level spatial patterns (Pearsons R=0.45-0.48, PFDR < 0.01) of amyloid-{beta} deposition in the Alzheimers Disease Neuroimaging Cohort at a regional level, but with drawbacks in mechanism interpretability. We then moved to an extended NDM framework (eNDM), including a protein synthesis term to better reflect the role of amyloid-{beta} metabolism, as well as including regional vulnerability using spatial transcriptomics from the Allen Human Brain Atlas to modulate the region-level rate parameters of the synthesis term. The novel gene eNDMs exhibited significant performance increases in Pearsons correlation (Steigers Z, PFDR < 0.10) over baseline NDM performance in mild cognitive impairment and AD groups using APOE, SORL1, and FGL2 for gene modulation. The results were robust and replicable when testing on an external cohort of the Alzheimers Disease Sequencing Project. The study thus demonstrates the importance of regional genetic vulnerability, in conjunction with network diffusion mechanisms, in improving the modelling and prediction of amyloid-{beta} pathophysiological spread.

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.

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 proliferation of gambling advertising has intensified concerns regarding its influence on vulnerable populations, yet the neural mechanisms underlying cue-reactivity to these stimuli remain underexplored in ecologically valid settings. This study protocol proposes a novel methodological framework to investigate prefrontal cortical responses to gambling advertisements in individuals with varying degrees of gambling experience. Materials and methods: This cross-sectional study will recruit 44 participants, divided into a clinical group (individuals with high-frequency gambling or gambling disorder) and a matched control group. Neural activity will be recorded using fNIRS while participants view gambling-related, neutral, violent, and sexual stimuli. Secondary measures include validated scales for gambling severity (SOGS), impulsivity, sensation seeking, and alexithymia. Data analysis will primarily utilize inter-subject correlation (ISC) to quantify neural synchronization and multiband frequency decomposition to capture dynamic affective processing. Advanced preprocessing, including short-channel regression, will be applied to ensure signal robustness. Discussion: By combining portable neuroimaging with a data-driven ISC approach, this study aims to identify objective neural markers of gambling vulnerability. The findings will provide novel insights into the idiosyncratic processing of commercial stimuli, potentially informing public health policies and the development of more effective evidence-based regulations for gambling marketing.