Abstract Neighbourhood deprivation is one of the few potential policy-modifiable risk factors for psychiatric and neurological disorders, but the neurobiological pathways underlying these associations remain unclear. We investigated these relationships across three cohorts spanning the life span: the Healthy Brain and Child Development (HBCD) Study (n = 84, aged 0–4 weeks postnatal), the Adolescent Brain Cognitive Development (ABCD) Study (n = 4,792, aged 9–10 years), and the UK Biobank (UKB; ∼ 500,000 adults, aged 44–87 years). Neighbourhood deprivation was associated with elevated disease risk, and individual lifestyle factors accounted for only a small fraction of this burden, indicating that the much larger residual effect reflects broader contextual characteristics of deprived environments rather than individual behaviours alone. Across all cohorts, greater deprivation consistently predicted lower cortical and subcortical brain volume, with effects detectable in early development and substantially stronger in adulthood. Across disorders, regional brain volume emerged as a consistent neuroanatomical mediator linking neighbourhood deprivation to neuropsychiatric disease. We further showed that deprivation preferentially affects brain regions intrinsically vulnerable to neuropsychiatric disorders. Spatial decoding analyses implicated dopaminergic and serotonergic neurotransmitter systems together with specific excitatory and inhibitory neuronal classes. Importantly, both the deprivation–disease associations and their neuroanatomical mediation patterns replicated across independent populations. Our study delivers a translational framework linking neighbourhood deprivation to brain health which could inform public health policies and preventive interventions.

Schizophrenia is often conceptualized as a brain network disorder, yet the organizational principles and heterogeneity underlying widespread cortical abnormalities remain poorly understood. Leveraging multisite MRI data from 3,958 individuals diagnosed with schizophrenia and 5,489 neurotypical individuals, we studied the cortical organization and its subtyping by analyzing individualized cortical network similarity. We used eigenvector decompositions to study spatial patterning of the gradients and graph theory to study small-world topology. Individuals with schizophrenia showed widespread alterations of gradient loadings, which followed inferior-superior and frontal-temporal axes. Alterations in small-world topology were localized in key network hubs, including the insula and anterior cingulate cortex. Brain-symptom association analyses identified a latent dimension linking disorganization symptoms to topological alterations. Finally, clustering cortical alterations identified two robust subtypes, characterized by divergent anterior cingulate (S1) versus temporoparietal (S2) thickness differences aligned with the intrinsic gradient-topology patterns. Both subtypes were present early in the illness and stable across disease stages and age groups. These findings reveal systematic disruptions of cortical organization in schizophrenia, providing a network-level framework for macroscale brain organization and inter-individual heterogeneity.

Abstract The genetic architecture of human brain networks is central to understanding cortical organisation and evolution, the causal links between brain structure and function, and the pathogenesis of neuropsychiatric disorders. Using N > 48,000 subjects, we investigated common genetic effects on Morphometric INverse Divergence (MIND), a heritable, multi-modal structural MRI metric of inter-areal similarity and connectivity. Genetic correlations between MIND network edges were largely reducible to two gradients, each aligned with distance from one of the two phylogenetically primitive areas (paleocortex and archicortex) predicted by the dual origin theory of cortical evolution. MIND was more heritable than comparable measures of functional (f)MRI connectivity, and the paleocortically-aligned MIND gradient was genetically correlated with, and causally predictive of, fMRI connectivity. Finally, we identified genetic overlaps between MIND gradients and neuropsychiatric and biomedical traits. These results provide fresh insight into the dual origins of the cortex and their implications for brain function and health.

Background:Essential tremor (ET) is the most common movement disorder. Its etiology and neuropathology remain controversial, but could be clarified with neuroimaging. Objectives:This study investigated brain structures derived from T1-weighted MRI associated with tremor severity in analyses focusing on the motor circuit and whole-brain to test competing hypotheses. Methods:Structural MRI at 3T was acquired from 50 ET participants and 50 healthy controls (HC). Voxel-based morphometry and surface-based processing generated measures of brain structure. Three two-group comparisons were undertaken: 1) HC versus ET participants; 2) HC versus ET participants with low tremor; and 3) HC versus ET participants with high tremor. For ET participants, correlations with tremor severity were also explored. Results:Within the motor circuit and across the whole brain, no significant case-control differences were observed after correction. Likewise, vertex-wise analyses of cortical volume, cortical thickness, and gyrification did not reveal significant differences. Uncorrected voxel-wise analyses suggested subtle reductions in the cerebellum and the motor circuit, particularly in ET participants with greater tremor severity. However, these effects were generally small and not consistently observed across analyses. Conclusion:The present analysis showed that ET severity was associated with weak structural differences, mainly within the motor circuit, although these findings did not survive correction. Consistent with previous literature, structural MRI abnormalities in ET appeared subtle and heterogeneous across studies, potentially becoming more detectable in patients with more severe symptoms. Although not designed to assess disease progression directly and noting that no finding survived correction for multiple comparisons, the direction of uncorrected exploratory trends within the motor circuit is consistent with the hypothesis of progressive structural alterations; however, this interpretation remains speculative and requires replication in larger, longitudinal cohorts.

Abstract Elucidating the neurobiological basis of neurodevelopmental and psychiatric conditions (NDPCs) remains challenging because brain alterations vary within diagnoses and overlap across them. Whether diverse alterations follow a systematic organization that may reflect shared vulnerabilities remains unknown. Here, we assembled 10,135 individuals with schizophrenia, autism, bipolar, obsessive-compulsive, generalized anxiety, and major depressive disorders, and 11,998 reference participants across six continents through the ENIGMA consortium. Using normative modeling, we quantified individual deviations in cortical thickness, surface area, and subcortical volumes relative to lifespan reference trajectories (5 to 80 years). We show that structural deviations converged along cortical axes reflecting connectome organization, maturation, and cytoarchitectonic diversity. These axes mirrored typical population variation, but their expression differed across diagnoses and partly scaled with symptom severity. Even rare and highly individualized extreme deviations followed this organization, concentrating in densely connected regions. Finally, brain structural deviations overlapped substantially across diagnoses, while differences between them increased toward the association cortex. Together, we provide large-scale evidence that structural deviations across NDPCs are systematically constrained by the brain’s intrinsic architecture. This shared organization provides a framework for reconciling individual variability with transdiagnostic similarities and motivates an integrative, systems-level understanding of mental health.

Schizophrenia spectrum disorders (SSD) are characterized by altered brain structure, reflecting widespread dysconnectivity across brain-specific networks. However, the role of hierarchical organization on cortical morphometric networks in shaping clinical outcomes over the course of the disease remains unclear. Connectome-derived gradients have increasingly been used to investigate spatial transitions in brain organization. Here, we computed cortical and subcortical Morphometric INverse Divergence (MIND) similarity networks from 1293 structural MRI data of 193 healthy controls (HC) and 350 individuals with SSD followed for up to 20 years. MIND features were calculated for each subject-specific network by computing regional averages and performing gradient decomposition. We found that MIND was longitudinally associated with treatment duration and medication in SSD. These associations were co-localized with hierarchical axes of cortical organization and schizophrenia epicenters. Moreover, psychiatric symptoms were associated with these alterations in structural similarity, which were also related to treatment duration. Collectively, these findings advance our understanding of how brain organization, treatment duration, and medication shape clinical symptoms throughout the course of SSD. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement RRG is funded by the Plan de Generacion de Conocimiento from the Spanish Ministry of Science (PID2021-122853OA-I00), and ERANET Neuron JTC 2023 (ERP-2023-23684211). Both RRG and NGS are funded by the Plan de Consolidacion (CNS2023-143647). JS is funded by the Psychosis Immune Mechanism Stratified Medicine Study (PIMS), UK Medical Research Council, MR/S037675/1. All research at the Department of Psychiatry in the University of Cambridge is supported by the NIHR Cambridge Biomedical Research Centre (NIHR203312) and the NIHR Applied Research Collaboration East of England. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: This program has been approved by the CEIC-Cantabria Ethics Committee for Clinical Research (NCT0235832) and the University of Seville Ethics Committee (SICEIA 2024-2534) in accordance with international standards. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The brain maps of cortical hierarchy were obtained using neuromaps toolbox35, available at https://github.com/netneurolab/neuromaps. The brain maps of functional and structural epicenters in SCZ, available at https://enigma-toolbox.readthedocs.io/en/latest/pages/07.epicenter/index.html, were obtained from an ENIGMA study.

In schizophrenia spectrum disorders (SSD), structural alterations of the gray matter (GM) and white matter (WM) have been widely described. However, the complex interplay between early disease-related changes and ongoing brain maturation challenges our ability to identify early biomarkers. In this study, we investigated structural abnormalities and their association with symptoms in a drug-naïve or minimally medicated sample comprising 113 patients with SSD and 112 neurotypical controls. Specifically, we derived centile scores using normative modelling from cortical thickness (CT), and subcortical volumes derived from structural MRI images, and diffusion tensor imaging-derived (DTI) WM tract fractional anisotropy (FA). In addition, we derived raw cortical mean diffusivity (cMD) metrics from DTI. Compared to controls, SSD participants showed reduced CT centiles, ventricular enlargement, and subcortical centile reductions in the hippocampus, thalamus, amygdala, and nucleus accumbens. SSD was also associated with widespread increases in cMD. We also explored associations among these structural markers, identifying significant relationships between CT centiles and raw cMD, as well as between subcortical centiles and FA tract-based centiles. No interaction with SSD diagnosis was found. Furthermore, positive symptoms correlated negatively with CT and FA tract-based centiles, and showed widespread positive associations with raw cMD, whereas negative symptoms showed no associations. These findings underscore multimodal brain abnormalities that originate early in the course of SSD and their distinct associations with symptoms. Our results support the potential of both normative modelling and diffusion imaging markers to identify individualized early brain changes in SSD Among these, cMD emerges as a potential marker of SSD-related microstructural alterations.

The cerebellum contains most of the brain's neurons and supports many functions, yet how it changes with age remains unclear. Here we used three brain imaging studies spanning 47,000 adults and examined how different parts of the cerebellum age and their relation to cognition. We characterized cerebellar aging using volumetry and the T1-weighted/T2-weighted ratio, and corroborated these findings with quantitative magnetic resonance imaging in an independent sample. We show a spatially heterogeneous pattern of aging in which specific association and motor-related regions show steeper relationships with age than other lobules. Greater cerebellar volume was associated with higher cognitive scores with increasing age, suggesting that cerebellar structure may provide brain reserve that helps maintain function despite aging. In patients with Alzheimer's disease, cerebellar volume was linked to cognition in individuals with lower amyloid burden, especially in those carrying two copies of the APOE-ε4 risk gene. This supports a threshold-reserve model, in which the cerebellum helps sustain cognition until pathology becomes widespread. These results show that the cerebellum has an active role in healthy cognitive aging and resilience.

Background Menopause is a natural physiological process, but its effects on the brain remain poorly understood. In England, approximately 15% of women use hormone-replacement therapy (HRT) to manage menopausal symptoms. However, the psychological benefits of HRT are not well established. This study aims to investigate the impact of menopause and HRT on mental health, cognitive function, and brain structure.Methods We analyzed data from nearly 125,000 participants in the UK Biobank to assess associations between menopause, HRT use, and outcomes related to mental health, cognition, and brain morphology. Specifically, we focused on gray matter volumes in the medial temporal lobe (MTL) and anterior cingulate cortex (ACC).Results Menopause was associated with increased levels of anxiety, depression, and sleep difficulties. Women using HRT reported greater mental health challenges than post-menopausal women not using HRT. Post-hoc analyses revealed that women prescribed HRT had higher levels of pre-existing mental health symptoms. In terms of brain structure, MTL and ACC volumes were smaller in post-menopausal women compared to pre-menopausal women, with the lowest volumes observed in the HRT group.Conclusions Our findings suggest that menopause is linked to adverse mental health outcomes and reductions in gray matter volume in key brain regions. The use of HRT does not appear to mitigate these effects and may be associated with more pronounced mental health challenges, potentially due to underlying baseline differences. These results have important implications for understanding the neurobiological effects of HRT and highlighting the unmet need for addressing mental health problems during menopause.

Studies of brain morphology in mental illness often focus on a few neuroimaging phenotypes. Here we present a comprehensive morphological characterization in obsessive-compulsive disorder (OCD) in a large sample (2255 OCD, 2264 controls) using nine cortical and four subcortical phenotypes, including several not previously examined in OCD, among them a subcortical structural similarity network phenotype developed here. Spatially distinct regional alterations emerged across structural phenotypes: cortical curvature alterations in default mode and frontoparietal networks, increased structural similarity network node degree in sensorimotor regions, widespread volume reductions associated with medication use, and localized subcortical shape alterations. In brain-behavior predictive models, curvature phenotypes showed the strongest associations with clinical features. Cortical alterations, especially in structural similarity networks, were associated with specific gene expression patterns, implicating dysregulation of excitatory neurons. RNA-sequencing data from tissue collected during functional neurosurgery revealed that genes downregulated in the dorsolateral prefrontal cortex in OCD contributed to the gene expression patterns linked to cortical alterations. Previously reported differentially expressed genes from postmortem brain studies of OCD also contributed. These findings support the importance of a comprehensive approach to characterizing brain morphology and suggest that cortical curvature and structural similarity alterations reflect key pathophysiological processes in OCD.

Environmental exposures influence the risk of psychiatric disorders, yet the biological mechanisms by which such experiences become embedded in brain structure remain poorly understood. The human cerebral cortex is crucial for cognition and emotional regulation, and variation in cortical thickness (CT) and surface area (SA) is linked to various behavioural and psychiatric traits. Here, we present a large-scale epigenome-wide association study that combines peripheral blood DNA methylation (DNAm) with MRI-derived cortical measures in over 7,400 individuals across 20 cohorts within the ENIGMA consortium. We identify mostly non-overlapping DNAm signatures associated with CT and SA, consistent with their distinct developmental and regulatory architectures. CT-associated CpGs are replicated across independent cohorts and are enriched for environmentally responsive regulatory elements and pathways associated with stress, metabolism, and immune signalling. In contrast, SA-associated CpGs cluster within chromatin-regulatory regions involved in early cortical development. Phenome-wide and Mendelian randomisation analyses reveal pleiotropic associations between DNAm, cortical structure, and psychiatric and cognitive traits. These findings suggest that peripheral DNAm captures environmentally sensitive biological processes that link exposure, cortical organisation, and behavioural vulnerability.

Schizophrenia spectrum disorders (SSD) are characterized by atypical brain maturation, including alterations in structural similarity between regions. Using structural MRI data from 195 healthy controls (HC) and 352 individuals with SSD, we construct individual Morphometric INverse Divergence (MIND) networks. Compared to HC, individuals with SSD mainly exhibit reduced structural similarity in the temporal, cingulate, and insular lobes, being more pronounced in individuals exhibiting a 'poor' clinical status (more impaired cognitive functioning and more severe symptomatology). These alterations are associated with cortical hierarchy and maturational events, locating MIND reductions in higher-order association areas that mature later. Finally, we map 46 neurobiological features onto MIND networks, revealing a high presence of neurotransmitters and astrocytes, along with decreased metabolism and microstructure, in regions with reduced similarity in SSD. These findings provide evidence on the complex interplay between structural similarity, maturational events, and the underlying neurobiology in determining clinical status of individuals with SSD.

Lateralization is a fundamental principle of structural brain organization. In vivo imaging of brain asymmetry is essential for deciphering lateralized brain functions and their disruption in neurodevelopmental and neurodegenerative disorders. Here, we present a normative framework for benchmarking brain asymmetry across the lifespan, developed from an aggregated sample of 128 primary neuroimaging studies, including 177,701 scans from 138,231 individuals, jointly spanning the age range from 20 post menstrual weeks to 102 years. This resource includes comprehensive, hemisphere-specific brain growth charts for multiple neuroimaging phenotypes: regional cortical grey matter volume, thickness, surface area, and subcortical volumes. Our findings reveal distinct spatial patterns of asymmetry, with early leftward asymmetry observed in association cortices and late rightward asymmetry in sensory regions. These trajectories support theories of the neuroplasticity of asymmetry and the role of both genetic and environmental factors in shaping brain lateralization. Additionally, we provide tools to generate asymmetry centile scores, which allow the quantification of individual deviations from typical asymmetry throughout the lifespan and can be applied to unseen data or clinical populations. We demonstrate the utility of these models by highlighting group-level differences in asymmetry in autism spectrum disorder, schizophrenia, and Alzheimer's disease, and exploring genetic correlations with hemispheric specialization. To facilitate further research, we have made this normative framework freely available as an interactive open-access resource (upon publication), offering an essential tool to advance both basic and clinical neuroscience.

The cerebral cortex is topographically organized to integrate and segregate unimodal (e.g. sensorimotor) and transmodal brain networks to scaffold cognition. Cortical gradient mapping provides a framework to examine the relationship between connectivity patterns of macroscale functional brain networks within a low-dimensional (manifold) space. Using this technique, we longitudinally examine how diffuse gliomas, their neurosurgical resection, and subsequent cognitive rehabilitation impact the topographic organization of brain networks. First, using UKBioBank data (n=4000), we validate the general assembly of cortical gradients in healthy individuals. Next, using CamCan data (n=620), we found that gradient dispersion relates to executive functions (EFs) across the lifespan. Finally, in diffuse glioma patients undergoing neurosurgery (n=17, 59 unique scans), we observed that gliomas integrate into the cortical manifold by reducing gradient dispersion compared to healthy controls. This finding was replicated in an independent cohort and contrasted with meningioma patients. Finally, long-term cognitive improvement after surgery was linked to increases in gradient dispersion, while long-term deficits were associated with decreases in gradient dispersion; longitudinal analyses revealed month 3 (not month 12) as the crucial window for gradient reconstitution. Overall, diffuse gliomas minimally disrupt the assembly of cortical manifolds, but the ability to reorganize the cortical manifold within 3 months post-surgery is predictive of long-term cognitive outcomes. By investigating neurosurgical patients with atypical neuroanatomy, this study contributes to the expanding literature on how aging, disease, and pharmacological interventions impact cortical gradients. Future studies are warranted to assess the utility of mapping cortical manifolds in neurosurgical patients. ### Competing Interest Statement MES is the co-founder of Omniscient Neurotechnology. ### Funding Statement This research was supported by the Alan Turing Institute, NSERC, Guarantors of Brain, Cancer Research UK Cambridge Centre, The Brain Tumour Charity and the EMERGIA Junta de Andalucia program. Y.E. is funded by a Royal Society Dorothy Hodgkin Research Fellowship (DHF130100). This research was also supported by the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014). SJP (NIHR Career Development Fellowship, CDF-2018-11-ST2-003) is funded by the National Institute for Health Research (NIHR) for this research project. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The study was approved by the Cambridge Central Research Ethics Committee (Reference number 16/EE/0151) and all patients provided written informed consent. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors

INTRODUCTION:Alzheimer's disease (AD) is characterized by inter-individual heterogeneity in brain degeneration, limiting diagnostic and prognostic precision. We present a novel framework integrating Morphometric Inverse Divergence (MIND) networks with hierarchical Bayesian large-scale population modeling to identify individual-level neuroanatomical deviations. METHODS:MIND networks quantify similarity between brain regions using multivariate magnetic resonance imaging (MRI) features. A normative model of regional MIND values trained on UK Biobank (N = 35,133) was applied to the National Alzheimer's Coordinating Center cohort (N = 3,567). We examined brain deviations across clinical stages, apolipoprotein E (APOE) genotypes, mortality risk, and neuropathological burden. RESULTS:Negative deviations (reduced MIND) stratified disease stages (p < 0.01) and were concentrated in specific functional networks in AD. Greater negative deviations characterized APOE ε4 homozygotes and correlated with post mortem neuropathological severity (p = 0.032). Spatially, deviation patterns were associated with maps of neurotransmitter receptor density. DISCUSSION:This population neuroimaging modeling enables individualized brain mapping with direct utility for diagnosis, prognosis, and understanding of biological mechanisms. HIGHLIGHTS:MIND networks were systematically integrated with normative modeling in AD. Negative deviations stratify clinical stages and correlate with neuropathology. Negative deviation count distinguishes APOE genotypes, highest in ε4 homozygotes. Deviations align with neurotransmitter maps. Individual brain maps enable precision medicine approaches in dementia.

BACKGROUND:Predicting long-term outcome trajectories in psychosis remains a crucial and challenging goal in clinical practice. The identification of reliable neuroimaging markers has often been hindered by the clinical and biological heterogeneity of psychotic disorders and the limitations of traditional case-control methodologies, which often mask individual variability. Recently, normative brain charts derived from extensive magnetic resonance imaging (MRI) data-sets covering the human lifespan have emerged as a promising biologically driven solution, offering a more individualised approach. AIMS:To examine how deviations from normative cortical and subcortical grey matter volume (GMV) at first-episode psychosis (FEP) onset relate to symptom and functional trajectories. METHOD:We leveraged the largest available brain normative model (N > 100 000) to explore normative deviations in a sample of over 240 patients with schizophrenia spectrum disorders who underwent MRI scans at the onset of FEP and received clinical follow-up at 1, 3 and 10 years. RESULTS:Our findings reveal that deviations in regional normative GMV at FEP onset are significantly linked to overall long-term clinical trajectories, modulating the effect of time on both symptom and functional outcome. Specifically, negative deviations in the left superior temporal gyrus and Broca’s area at FEP onset were notably associated with a more severe progression of positive and negative symptoms, as well as with functioning trajectories over time. CONCLUSIONS:These results underscore the potential of brain developmental normative approaches for the early prediction of disorder progression, and provide valuable insights for the development of preventive and personalised therapeutic strategies.

Awake surgery with intraoperative direct electrical stimulation (DES) is the gold-standard to maximize the extent of resection in diffuse cerebral gliomas (Duffau et al. 2023). While this approach is effective in testing for simple motor and language functions, it is inadequate for mapping higher-order cognitive functions such as attention, working memory, and cognitive control. Given that systems neuroscience is moving away from a localizationist to a connectomic perspective of human brain function, ideally, we could better understand how gliomas integrate within the connectome and how performing surgery on the brains mesoscale hub architecture affects long-term cognitive outcomes. To address problem, we combined cellular, connectomic, and cognitive data from healthy individuals (n=629) across the lifespan, cross-sectional glioma imaging (n=98), the Allan Human Brain Atlas (n=6), and a rare cohort of diffuse glioma patients (n=17) followed longitudinally as they underwent neurosurgery. First, we validate that meta-analytic cognitive activation maps co-localize with the Multiple Demand (MD) system and show that diffuse gliomas preferentially localize to the core of this brain network. Second, cellular decoding of the MD core network reveals that it is uniquely enriched with oligodendrocyte precursor cells, glioma proto-oncogenes, and 5HT2-serotonergic neurotransmission. Third, the MD system is preferentially enriched for connector hubs to scaffolding the brains mesoscale hub architecture and that diffuse gliomas induce reorganization in this architecture thereby minimizing cognitive deficits. Lastly, surgical resection of connector, rather than provincial, hubs leads to long-term cognitive deficits while maintenance or dissolution of interhemispheric modularity predicted long-term cognitive outcomes. With the recent demonstration of the high concordance between DES and functional brain mapping (Saurrubo et al. 2024), this study provides new insight into how gliomas integrate within the connectome and that mapping the mesoscale hub architecture in each patient may improve presurgical mapping and postsurgical rehabilitation. Given the small but deeply sampled neurosurgical cohort, additional studies are now warranted to assess the value of mapping mesoscale connectivity for presurgical mapping and interventional neurorehabilitation (Poologaindran et al. 2022). ### Competing Interest Statement MES is the co-founder of Omniscient Neurotechnology ### Funding Statement This research was supported by the Alan Turing Institute and NSERC grants. It was also supported by a Guarantors of Brain, Cancer Research UK Cambridge Centre, The Brain Tumour Charity and the EMERGIA Junta de Andalucia program. Y.E. is funded by a Royal Society Dorothy Hodgkin Research Fellowship (DHF130100). MA was funded by a Cambridge Trust Yousef Jameel Scholarship. This research was also supported by the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014). SJP (NIHR Career Development Fellowship, CDF-2018-11-ST2-003) is funded by the National Institute for Health Research (NIHR) for this research project. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The study was approved by the Cambridge Central Research Ethics Committee (Reference number 16/EE/0151) and all patients provided written informed consent I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors

Recent study has leveraged massive datasets and advanced harmonization methods to construct normative models of neuroanatomical features and benchmark individuals' morphology. However, current harmonization tools do not preserve the effects of biological covariates including sex and age on features' variances; this failure may induce error in normative scores, particularly when such factors are distributed unequally across sites. Here, we introduce a new extension of the popular ComBat harmonization method, ComBatLS, that preserves biological variance in features' locations and scales. We use UK Biobank data to show that ComBatLS robustly replicates individuals' normative scores better than other ComBat methods when subjects are assigned to sex-imbalanced synthetic "sites." Additionally, we demonstrate that ComBatLS significantly reduces sex biases in normative scores compared to traditional methods. Finally, we show that ComBatLS successfully harmonizes consortium data collected across over 50 studies. R implementation of ComBatLS is available at https://github.com/andy1764/ComBatFamily.

Background Neuropsychiatric conditions have long been hypothesised to emerge from alterations to brain structure and function. Clinical neuroimaging studies have identified numerous imaging signatures for different conditions, but success has been limited in biomarker development and therapeutic translation. Hence, we aimed to propose better candidate phenotypes by identifying shared genetics between imaging-derived phenotypes (IDPs) and neuropsychiatric conditions and discerning the causal direction. Methods T1-weighted and diffusion MRI images for 53709 individuals were obtained from UK Biobank, pre-processed with FreeSurfer and AMICO, and registered to the Glasser (2016) parcellation atlas. Measures of macrostructural cortical expansion, cortical folding, and microstructural neurite orientation dispersion and density indices were derived at global and regional levels, totalling 2198 IDPs. GWAS was conducted for these IDPs in subjects of European-like ancestry.We investigated the shared genetics between IDPs and six well-powered neuropsychiatric conditions (ADHD, autism, anxiety, depression, bipolar disorder and schizophrenia). We first conducted LDSC and polygenic score-based regression to identify significant genetic correlations. For significant IDPs, we conducted Mendelian randomisation to identify causal relationships. Finally, we used genomic structural equation modelling (SEM) to account for high-dimensional collinearity across IDPs and different conditions. Results LDSC regression identified negative genetic correlations between cortical expansion IDPs (volume, surface area, folding index and intrinsic curvature) and ADHD, anxiety and autism, both at global level (rg = -0.107 to -0.180) and at regional level (101, 54 and 50 regions respectively), with no other significant correlations. These results were confirmed with correlations between these IDPs with polygenic scores of these conditions (beta = -0.023 to -0.047, p < 2.5e-7). Using GWAS by subtraction, we confirmed that no regional IDP had significant genetic correlation adjusting for the corresponding global IDP. Hence, we focused subsequent analyses on global IDPs only.We used genomic SEM to further account for genetic correlation between IDPs and neuropsychiatric conditions. We previously showed the cortical expansion IDPs share a common genetic factor (Warrier et al. 2023) which also showed negative correlation with these conditions (LDSC rg = -0.125 to -0.191). By recursively conducting GWAS by subtraction between different conditions, we identified no significant genetic correlation for depression and anxiety when adjusting for ADHD (p > 0.17), whereas ADHD remains significantly correlated with cortical expansion after adjusting for depression and anxiety (standardised beta = -0.144, p < .001). Mendelian randomisation showed that each cortical expansion IDP (beta = -0.231 to -0.769, p < 3.4e-5) and the common factor (beta = -0.252, p = 5.8e-12) were causal to ADHD and not vice versa (failed directionality test). Discussion In this study, we identified shared common variant genetics between cortical expansion and ADHD, which drives the genetic correlation of cortical expansion with depression and anxiety. This suggests that the genetic aetiology of ADHD, depression and anxiety may be deeply rooted in neurodevelopment. The lack of genetic correlation between IDPs and other conditions also calls for caution in interpretation of imaging signatures.