Human Brain Mapping

Analytical variability in neuroimaging pipelines contributes to concerns about reproducibility in the field. In structural MRI, different segmentation tools produce discrepant morphometric estimates that may influence downstream analyses, such as predictive modeling. We tested whether integrating several segmentation pipelines improves brain age prediction, and characterized the spatial and demographic structure of pipeline differences across several datasets. T1-weighted scans from five open-access datasets were processed with four widely-used structural segmentation pipelines. Brain age models were trained using single-pipeline features and compared with multi-pipeline aggregation strategies. Inter-pipeline variability was assessed across shared subcortical structures and examined in relation to age and sex. Integrating features across distinct segmentation frameworks improved predictive performance relative to individual pipelines, whereas aggregation within closely related software versions provided limited benefit. Variability was spatially structured and volumetric measures were often systematically associated with age and sex. These results suggest that segmentation differences reflect structured, demographically sensitive variation rather than random noise, and that multi-pipeline feature integration can enhance robustness in neuroimaging-based prediction.

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.

Objective: Brain network controllability provides a framework for understanding how structural organization shapes brain dynamics, yet current models mainly rely on white-matter connectivity and may overlook the contribution of gray-matter architecture. Approach: We constructed a fusion network combining diffusion tensor imaging-derived white-matter connectivity with gray-matter morphological similarity and investigated its controllability, biological associations, heritability, phenotype prediction, and control energy. Main results: Controllability derived from the fusion network preserved key topological properties of the white-matter network and was associated with neurotransmitter systems and cerebral metabolism. Compared with the white-matter connectivity-based network, fusion-based controllability showed a systematic shift toward higher heritability, improved prediction of several individual characteristics and cognitive functions, and lower modeled control energy for activating resting-state networks. Significance: These findings suggest that incorporating gray-matter morphological information into a DTI-supported network provides a complementary structural representation for studying brain network controllability and state transitions. The lower control energy represents a model-derived transition cost and should not be interpreted as a direct measure of physiological energy expenditure.

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.

BACKGROUND AND PURPOSENeighborhood-level socioeconomic disadvantage is associated with adverse brain morphometry, yet whether these associations differ by biological sex remain opaque. Here, we investigated sex-specific associations between the area deprivation index and brain morphometry derived from routine clinical MRI in a real-world clinical population. MATERIALS AND METHODSIntracranial volume-normalized regional brain volumes were extracted from T1-weighted MRI examinations performed in 2,863 consecutive clinical patients (median age 54 years [IQR 38-68]; 61.2% female) at a single academic medical center and associated community partners using an automated atlas-based segmentation pipeline. Exploratory factor analysis was applied to 131 regional brain volumes to identify latent neuroanatomical morphometric networks. Sex-stratified linear regression models examined associations between area deprivation index national percentile rank and each factor score, adjusting for age, with correction for multiple comparisons. RESULTSFactor analysis identified five neuroanatomical morphometric networks: cerebellar (ML1), frontal/executive (ML2), subcortical-ventricular (ML3), medial temporal/limbic (ML4), and posterior cortical/visual (ML5). In male patients (n = 1,112), linear regressions revealed that greater neighborhood-level socioeconomic disadvantage was significantly associated with lower factor scores on the cerebellar ({beta} = -0.006, 95% CI [-0.009, -0.003], P < .001), medial temporal/limbic ({beta} = -0.004, 95% CI [-0.007, -0.001], P = .01), and frontal/executive ({beta} = -0.004, 95% CI [-0.007, -0.0004], P = .04) networks. No significant associations were observed in female patients (all Ps [&ge;] .61). CONCLUSIONSIn a real-world clinical population, neighborhood-level socioeconomic disadvantage was associated with lower regional brain volumes across cerebellar, frontal/executive, and medial temporal/limbic neuroanatomical morphometric networks in male but not female patients. These findings suggest that the neuroanatomical correlates of neighborhood disadvantage may be sex-specific, and that sex-stratified analyses may be necessary to fully characterize the relationship between the social exposome and brain morphometry in clinical neuroimaging research.

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.

Objective: Accurate volumetric analysis of the brain and cerebrospinal fluid (CSF) is essential for monitoring hydrocephalus, a significant pediatric neurological condition. While computed tomography (CT) provides high-quality volumetric assessment, its associated ionizing radiation poses risks, especially for children. Low-field magnetic resonance imaging (LF-MRI) offers a safer and more accessible alternative, particularly in resource-constrained settings. However, its lower resolution and increased susceptibility to structural distortions make accurate segmentation challenging. This study aims to demonstrate that reliable volumetric measurements can be obtained from LF-MRI that are comparable to CT, enabling safer and more frequent monitoring of infants with hydrocephalus. Approach: We propose EnSegNet-Cross, a cross-modality, enhancement-aware segmentation network for brain volume analysis using LF-MRI. The framework leverages high-fidelity CT data during training but requires only LF-MRI during inference. At the core of the framework is a novel cross-modal topological penalty designed to minimize discrepancies between predicted LF-MRI and CT structures. A central contribution is the integration of a three-dimensional topological loss based on persistent homology, which penalizes topological discrepancies in CSF regions, specifically CSF holes formed by enclosed brain parenchyma, between CT and LF-MRI segmentations. Incorporating these structural priors facilitates generalization across heterogeneous clinical cases while eliminating the need for CT data during inference, resulting in more anatomically coherent and topologically faithful segmentations. Main Results: On a curated cohort of infants with hydrocephalus who had paired LF-MRI and CT scans, including cases with infectious and non-infectious causes, EnSegNet-Cross consistently outperformed state-of-the-art machine learning alternatives. It achieved the highest Dice score of 0.8532 plus/minus 0.03 and Volume Score of 0.9318 plus/minus 0.03. The method also demonstrated robust performance in challenging cases with confounding factors, achieving a Dice score of 0.8340 plus/minus 0.03 and a Volume Score of 0.9111 plus/minus 0.05. By leveraging CT-derived topological priors, EnSegNet-Cross successfully handled anatomically complex scenarios in which conventional models failed. Significance: EnSegNet-Cross provides a reliable and interpretable solution for brain and CSF segmentation, particularly in complex cases of hydrocephalus. This study demonstrates that high-fidelity volumetric estimates can be achieved using only LF-MRI, facilitating frequent, radiation-free monitoring. By bridging the fidelity gap between low-quality LF-MRI and high-resolution CT through clinically grounded enhancement and topological supervision, EnSegNet-Cross offers a robust clinical tool for brain volumetric analysis in infants with hydrocephalus using LF-MRI.

Resting-state functional MRI (rs-fMRI) studies in schizophrenia commonly rely on normalization to volumetric templates and fixed atlas parcellations derived from neurotypical populations. While these approaches enable group-level comparisons, they may obscure individual variation in cortical organization and intrinsic brain dynamics. In this study, we compared four preprocessing and parcellation strategies across two independent schizophrenia cohorts (MRI site 1, n=159; MRI site 2, n=255) to evaluate how analytic choices affect static functional connectivity and dynamic quasi-periodic pattern (QPP) measures, including default mode-dorsal attention network opposition, QPP component rank, explained variance, event rate, and associations with PANSS symptom severity. Across datasets, individualized surface-based parcellation (IndiPar) consistently detected more pronounced QPP dynamics, stronger default mode / dorsal attention network opposition, and greater explained variance of QPPs relative to atlas-based pipelines. IndiPar also produced larger and more reproducible patient-control differences in functional connectivity and QPP event-rate measures, suggesting improved sensitivity through preservation of subject-specific organization. IndiPar additionally detected a significantly increased QPP event rate and more symptom associations in patients in the larger dataset. However, associations between fMRI measures and symptom severity showed limited stability across cohorts. These findings extend previous reports of altered resting-state activity in schizophrenia, and demonstrate that preprocessing and parcellation choices substantially influence both static and dynamic rs-fMRI results. Individualized surface-based parcellation appears to better preserve subject-specific variability and improves detection of intrinsic brain dynamics. At the same time, the limited cross-dataset replication of symptom associations highlights the challenges of deriving stable brain-symptom relationships from heterogeneous psychiatric cohorts.

Healthy ageing reorganises large-scale functional brain networks. Using resting-state fMRI from 615 adults aged 18 to 88 years in Cam-CAN. We combined raw functional connectivity analyses, graph-theoretical topology, Ridge and MLP age prediction and their attributions in order to understand how functional brain organisation varies with age and relates to cognitive performance. Both models predicted age accurately, with a modest advantage for the MLP. Within-network FC decreased with age, while between-network FC and participation increased. Topological analyses showed increased participation and a redistribution of nodes across connector, provincial, satellite, and peripheral roles. The MLP SmoothGrad attributions emphasised high-participation and weakly specialised nodes, whereas counterfactual tests showed that replacing CON-SMN, CON-DMN and DMN-FPN connections with a young-adult template produced the largest reductions in predicted age. Brain-cognition PLS linked these mechanisms to a broad cognitive-performance axis and a secondary semantic/connectomic axis. Together, these findings suggest that healthy ageing involves a redistribution of functional organisation that extends beyond a single DMN-FPN axis towards a broader DMN-CON-SMN-FPN configuration.

Background. The neonatal period is characterized by neurodevelopmental processes coinciding with rapid physiological transitions, however, spatial patterns of brain expansion in early life remain incompletely characterized. Here, we leverage grey matter structural covariance in term- and preterm-born neonates to study the interregional synchrony of neonatal brain maturation along principal brain organizational axes. Methods. We used structural MRI data from the developing Human Connectome Project (n=525; 435 term-born) scanned at 37-44 weeks postmenstrual age (PMA)), stratifying the sample by term status and PMA. Based on cortical thickness (CT) and surface area (SA) metrics, we computed age group- (CVg) and individual-level (CVi) structural covariance matrices. Applying surface-based linear models, we identified changes in interregional covariance with postmenstrual age (PMA) and gestational age at birth (GAB), and contextualized alterations to neonatal functional networks. Using gradient decomposition, we identified major trajectories of cortical expansion on individual and group levels, and determined their correlation to archetypal geometric, evolutionary, and functional axes. Results. We found age-related changes reflecting cortical expansion and an increased structural differentiation with age. Gradient decomposition of group-level covariance revealed that primary components followed both an posterior-anterior distribution, anchored in the visual cortex, and a sensorimotor-association gradient. Individual-level covariance gradients adhered most to geometric axes, and diverged from a sensorimotor-association pattern with increasing PMA. Term-born neonates exhibited a higher spatial correlation to functional and geometric axes than preterm-born neonates, overall pointing to different expansion patterns in neonates of the same PMA. Conclusion. We found trajectories of structural maturation in the neonatal period that appear coordinated along geometric and functional axes. On an age group level, term-born neonates appear to follow archetypal axes more than preterm-borns at term-equivalent age, underlining differential expansion patterns in preterm-born neonates.

Structural MRI is routinely acquired in clinical practice, yet quantitative morphometry has had limited impact on clinical decision-making. Overlapping symptoms, trajectories and comorbidities remain difficult to interpret within disease-specific frameworks, leaving it unclear how individual patients relate to the broader organization of brain disease. Here, we construct a cross-disease morphological reference space from 110,591 T1w MRI scans of 78,794 participants, spanning four disease families, 19 diagnoses, and seven subtypes. To construct this space, we developed NeuroMorph, an AI framework deriving thirteen interpretable morphological descriptors and individual normative deviation profiles. The reference space reveals shared and distinct morphological signatures that distinguish conditions within a hierarchy of disease families, diagnoses, subtypes and individual profiles. It identifies overlapping and comorbid morphological profiles and captures longitudinal deviations that precede clinical diagnosis and track progression. Together, these findings establish a unified framework for mapping brain disease organization and positioning individual patients within its morphological landscape.

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.

Background: MRI has increasingly been explored as a biomarker for detecting structural and functional brain changes. For clinical decision-making, it is crucial to validate observed changes in MRI indices at the individual level. The uncertainty in longitudinal MRI indices can be quantified using the repeatability coefficient (RC). Methods: Twenty healthy controls (10 males, 10 females) underwent two test-retest sessions of structural magnetic resonance imaging (MRI) and resting-state functional MRI (rs-fMRI) on the same day, separated by a 30-minute interval. RC values and their 95% confidence intervals (CI) were estimated for subcortical volumes, cortical thickness, and within-network functional connectivity. Additionally, 33 patients with mental health disorders underwent MRI before and after 20 sessions of transcranial magnetic stimulation (TMS). Percentage changes in MRI-derived indices were assessed at the individual level, with changes exceeding the RC threshold considered indicative of true change beyond measurement uncertainty. Results: The RC showed measurement variability in subcortical volumetric in the range of 8% to 17.5% for caudate and left amygdala, respectively. For cortical thickness, the RC was measured between 3.5% and 16.5% for the left occipital pole and the left temporal pole, respectively. The RC% for fractional anisotropy (FA) measures were variable between 11.3% (the left middle cingulum) and 62.9% (the right anterior cingulum). For within-network connectivity, the RC was measured in a range of 9.7% and 29.4% for sensorimotor and visual networks, respectively. TMS-treated patients exhibited no changes beyond the RC in almost all subcortical volumes and within-network connectivity. The most frequent changes beyond the uncertainty were observed in FA measures, particularly in the posterior cingulum, where 17 out of 23 patients exhibited clinically meaningful alterations. Conclusion: Structural brain features extracted from MRI demonstrated high reliability. Among all measures, FA, reflecting white matter integrity, was most sensitive in detecting neural changes following TMS, highlighting its potential utility as a treatment-responsive biomarker.

Background: Quantitative MRI (qMRI) provides voxel-wise measurements of tissue properties related to myelin, iron and water content, making it a powerful tool for studying brain aging and microstructural alterations in vivo. However, conventional spatial smoothing can introduce partial-volume effects and blur tissue boundaries, potentially affecting both statistical sensitivity and anatomical specificity. Several tissue-specific smoothing strategies have been proposed to address these limitations, yet their relative impact on voxel-wise statistical analyses remains insufficiently characterized. The present study aims (i) to systematically compare three tissue-specific smoothing strategies: a linear tissue-weighted compensated approach (TWS), a generalized version of nonlinear tissue-masked compensated smoothing approach (gTSPOON), and an intensity-weighted edge-preserving approach based on the Smallest Univalue Segment Assimilating Nucleus smoothing (SUSANs), and (ii) to investigate how smoothing approaches interact with statistical inference frameworks by comparing parametric and non-parametric voxel-wise analyse. Methods: Analyses were performed on a publicly available lifespan qMRI dataset comprising 138 healthy participants (19-75 years) and quantitative maps of MTsat, PD, R1, and R2*. The generalized TSPOON (gTSPOON) method was implemented using tissue-specific masks derived from probabilistic tissue segmentation. All three smoothing approaches (TWS, gTSPOON and SUSANs) were parameterized to achieve comparable nominal spatial smoothing. Age-related effects were investigated separately in GM and WM using voxel-wise general linear models following a previously published framework. Statistical inference was assessed using multiple complementary approaches, including parametric Random Field Theory (RFT), under both stationarity and non-stationarity assumptions, as well as non-parametric permutation-based inference. In addition to conventional thresholded statistical parametric maps, voxel-wise log-likelihood (LL) maps were computed to quantify general linear model (GLM) goodness-of-fit independently of statistical thresholding. Bland-Altman analyses and spatial agreement metrics were subsequently used to compare smoothing strategies. Results: TWS and gTSPOON produced highly similar spatial distributions of age-related effects across all qMRI parameters and tissue classes. However, TWS consistently yielded a larger number of significant voxels and clusters, reflecting slightly higher sensitivity, from slightly wider effective smoothness and reduced RESEL counts. By contrast, SUSANs generated substantially fewer significant voxels and clusters, associated with approximately half the effective smoothness and a markedly larger number of RESELs. Despite these differences in statistical sensitivity, voxel-wise LL analyses revealed distinct anatomical preferences for each smoothing strategy. TWS provided the best model fit predominantly within GM, whereas gTSPOON showed superior performance in homogeneous WM regions. Conversely, SUSANs achieved the highest LL values at GM-WM interfaces, particularly within sulcal and gyral transitions, indicating improved preservation of sharp anatomical gradients. These spatial patterns were consistently observed across MTsat, PD, R1 and R2* maps. Comparisons across stationary and non-stationary RFT assumptions revealed only minor differences, while non-parametric inference produced highly concordant results, indicating that the primary source of variability originated from the smoothing procedure itself rather than the inference framework. Conclusions: Tissue-specific smoothing strategies substantially influence both statistical sensitivity and voxel-wise model fitting in qMRI analyses. While TWS and gTSPOON provide highly consistent results, the edge-preserving SUSANs approach preferentially enhances model fit at tissue boundaries. Importantly, voxel-wise log-likelihood mapping revealed that no smoothing strategy is uniformly optimal throughout the brain; instead, each method exhibits anatomically preferential regions where model fit is maximized. These findings suggest that smoothing should be viewed as a region-dependent optimization problem and highlight voxel-wise LL mapping as a principled framework for selecting or developing adaptive smoothing strategies tailored to specific neuroanatomical structures and biological processes, including age-related brain changes.

Multimodal neuroimaging studies often involve comparisons between brain maps. Recently, statistical methods have been proposed to quantify and assess spatial correspondence between two modalities. The simple permutation-based inter-modal correspondence (SPICE) test evaluates whether within-subject correspondence exceeds chance, where the null distribution is constructed by permuting subject labels for one modality. Despite its easy implementation and minimal spatial assumptions, the critical assumption underlying permutation analysis, that subjects are exchangeable under the null, may be violated when covariates such as age, sex, or disease status systematically alter brain map distributions. This violation can produce results analogous to Simpson's paradox, where apparent population-level correspondence reflects between-group differences rather than genuine within-subject coupling. We propose a formal U-statistic-based test for such covariate effects, enabling both diagnostic evaluation of assumption violations and scientific discovery. Using synthetic and semi-synthetic neuroimaging data, we demonstrate well-controlled Type I error and high statistical power. We apply our method to test for confounding effects due to age and sex using real data from two pairs of imaging modalities in the Philadelphia Neurodevelopmental Cohort. Our framework increases the rigor and interpretability of intermodal coupling analyses, with broad implications for neuroimaging studies in heterogeneous populations, especially in developmental, aging, and disease-focused research.

Youth can exhibit signs of delayed or accelerated neurodevelopment, measurable via brain magnetic resonance imaging (MRI). Brain age prediction seeks to estimate brain age at an individual level using machine learning models fitted on healthy individuals. The brain age gap (BAG): the difference between brain age and chronological age, has been studied as a potential biomarker. However, BAG suffers from known limitations, including dependence on chronological age, regression to the mean, and challenges in interpretability. As an alternative framework, we introduce brain age intervals (BAIs) as a normative interval on an individual's chronological age, based on MRI measures. BAIs can be derived using a recent statistical framework called conformal prediction that yields prediction intervals with guaranteed coverage. We train several brain age prediction models on structural and functional MRI scans from the Reproducible Brain Charts dataset (aged 6-22) and estimated BAIs. The BAIs demonstrated stable empirical coverage close to the nominal 90% level (median coverage=91%, width=7.36 years, root mean square error (RMSE)=2.20 years across 100 repeated train-test splits). Associations between interval coverage and clinical measures (p-factor, parental education) were weak, with only a modest directional signal for parental education. Together, these findings demonstrate the feasibility of uncertainty-aware brain age modeling in youth populations while highlighting the need for larger and more diverse samples to detect meaningful clinical and environmental associations.

Background: White matter hyperintensities (WMH) represent the most visible manifestation of cerebral small vessel disease and of white matter pathology more broadly, yet empirical evidence points to a brain tissue injury extending beyond radiologically detectable lesions on fluid-attenuated inversion recovery (FLAIR) MRI. We present RADAR-WMH (Relaxometry And Diffusion Analysis for Radiological WMH), a multicontrast MRI machine learning framework that characterises white matter pathology through tissue microstructural information rather than lesion contrast alone. Methods: RADAR-WMH was trained on quantitative relaxometry and diffusion-weighted MRI acquired in community-dwelling participants (mean age 59.6 years [SD 22.4], 60.8% women, n=148) using a LightGBM classifier integrating local, textural, and anatomical features at the voxel level. Biological validity was assessed through longitudinal analyses and associations with age, cardio-vascular risk, and cognitive performance in independent cohorts. Results: RADAR-WMH achieved segmentation performance comparable to state-of-the-art FLAIR-based approaches without requiring FLAIR or T1-weighted data. Mean diffusivity was the most influential feature for lesion classification. Beyond FLAIR-defined WMH, RADAR-WMH identified tissue pathology extending outside lesion borders characterised by myelin loss, axonal injury, and increased extracellular water. These microstructural signatures persisted over follow-up and showed stronger association with age, systolic blood pressure, and cognitive performance than corresponding tissue properties restricted to FLAIR-defined WMH extent. Conclusions: RADAR-WMH reveals a significant burden of biologically meaningful white matter injury that remains invisible to FLAIR-defined WMH segmentation. By capturing microstructural pathology linked to vascular risk, cognitive decline, and lesion evolution, RADAR-WMH may provide more sensitive markers of cerebral small vessel disease than FLAIR-visible WMH alone.

Altered attention network activity has been consistently reported in recent neuroimaging literature of smartphone addiction and problematic smartphone use. However, many functional imaging studies have relied on traditional static functional connectivity analyses, which cannot capture time-varying or recurrent dynamics of attention networks. Given the importance of temporal and spatial dynamics for attentional processing, we applied two complementary dynamic functional connectivity approaches: functional connectivity-based attractor networks (fcANN), which characterize transitions between discrete attractor states related to internal-external context and action-perception dynamics, and cPCA-based quasi-periodic pattern (QPP) analysis, which captures phase relationships between cortical networks including the default mode network, dorsal attention network, and frontoparietal control network. In a resting-state fMRI dataset comparing participants with High and Low SAS-M scores (SAS-M: Smartphone addiction scale-Malay version questionnaire), we observed reduced DAN participation within subject-specific cPCA-derived QPP-like components, along with context-dependent differences in DMN-attention-network relationships across attractor states. The most consistent effects emerged during transitions between internal and external attractor states, where subjects with high smartphone addiction (SPA) scores showed greater DMN dominance relative to attention-related networks. Overall, these findings provide preliminary evidence that smartphone addiction may be associated with altered large-scale attentional dynamics at rest

Brain-age estimates from grey matter structure provide a promising tool to examine the neurobiology of mental health. However, whether existing models generalise to longitudinal adolescent data remains unclear. The CentileBrain Global-BrainAGE Lifespan Model, was applied to N=138 participants (74 females, 64 males) from the Longitudinal Adolescent Brain Study. Participants completed 2-14 MRI scans between the ages of 12-17 years (752 datapoints). Model fit, prediction accuracy and longitudinal consistency were examined. Results showed moderate-to-good longitudinal consistency and reliability, consistent with high-performing cross-sectional age-to-brain-age correlations in youth cohorts. However, the model systematically overestimated brain-age changes relative to chronological changes, and prediction accuracy was unstable, with the mean absolute error increasing with age. Despite sex-specific brain-age calculations, on average, females showed older brain-ages compared with males. Further, younger adolescents were underpredicted, while older adolescents were overpredicted, indicating that standard model adjustments and assumptions may not be applicable.

Optically pumped magnetometer magnetoencephalography (OPM-MEG) offers a wearable, movement-tolerant alternative to conventional cryogenic MEG, placing sensors closer to the scalp and, in principle, improving sensitivity to deep sources. This is advantageous for examining subcortical structures that are affected by ageing, disorders and disease, such as the hippocampus. However, it remains unclear whether well-established activity (such as the attenuation of theta oscillations during the imagination of novel scenes) can be recovered from the medial temporal lobe (MTL) with OPM-MEG, and whether an individual structural MRI is required. Here, fifteen adults completed a scene imagination task. Initially we applied a 12-parameter template warping coregistration pipeline to the full sample. Following source reconstruction, we recovered the expected attenuation of theta (4-8 Hz) power during scene imagination compared to a counting baseline, with a significant cluster of activity peaking in the left parahippocampal gyrus. The clusters centre of mass was localised to the left hippocampus (t = -3.55, p = 0.048, whole-brain FWE-corrected) and was mostly confined to the left medial temporal lobe. We further supported our findings by using an individual T1-weighted MRI reconstruction pipeline in six participants who had these scans available. The two approaches produced similar whole-brain topographies and localised the peak MTL theta effect to left hippocampus, with temporal-lobe conjunction centroids 3-mm apart. These findings provide evidence that the theta attenuation of the scene construction network can be recovered at the group level with OPM-MEG, without an individual MRI.