{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,9,27]],"date-time":"2026-09-27T12:50:52Z","timestamp":1790513452023,"version":"4.1.0"},"posted":{"date-parts":[[2026,8,14]]},"group-title":"Neurology","reference-count":54,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T00:00:00Z","timestamp":1786665600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"award":["MR\/W006804\/1"],"award-info":[{"award-number":["MR\/W006804\/1"]}],"id":[{"id":"https:\/\/ror.org\/03x94j517","id-type":"ROR","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000269","name":"Economic and Social Research Council","doi-asserted-by":"crossref","award":["ES\/P000681\/1"],"award-info":[{"award-number":["ES\/P000681\/1"]}],"id":[{"id":"10.13039\/501100000269","id-type":"DOI","asserted-by":"crossref"}]},{"award":["BB\/S006605\/1"],"award-info":[{"award-number":["BB\/S006605\/1"]}],"id":[{"id":"https:\/\/ror.org\/00cwqg982","id-type":"ROR","asserted-by":"publisher"}]},{"name":"Bial Foundation Grants Programme","award":["A-29315"],"award-info":[{"award-number":["A-29315"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,8,14]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:p>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.<\/jats:p>","DOI":"10.64898\/2026.08.13.26359403","type":"posted-content","created":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T19:30:10Z","timestamp":1786735810000},"source":"Crossref","is-referenced-by-count":2,"title":["NeuroMorph: A Unified Morphological Reference Space for Cross-Disease Brain Profiling"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-5705-061X","authenticated-orcid":false,"given":"Connor","family":"Dalby","sequence":"first","affiliation":[{"name":"School of Psychology & Neuroscience, University of Glasgow, Glasgow,UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-3963-041X","authenticated-orcid":false,"given":"Austin","family":"Dibble","sequence":"additional","affiliation":[{"name":"School of Psychology & Neuroscience, University of Glasgow, Glasgow,UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2152-9424","authenticated-orcid":false,"given":"Sergio","family":"Benini","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, University of Brescia, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Damiano","family":"Ferrari","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, University of Brescia, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3850-1487","authenticated-orcid":false,"given":"Donald M.","family":"Lyall","sequence":"additional","affiliation":[{"name":"School of Health & Wellbeing, University of Glasgow, Glasgow, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1694-1174","authenticated-orcid":false,"given":"Monika","family":"Harvey","sequence":"additional","affiliation":[{"name":"School of Psychology & Neuroscience, University of Glasgow, Glasgow,UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Terry","family":"Quinn","sequence":"additional","affiliation":[{"name":"School of Cardiovascular & Metabolic Health, University of Glasgow, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0143-4324","authenticated-orcid":false,"given":"Lars","family":"Muckli","sequence":"additional","affiliation":[{"name":"School of Psychology & Neuroscience, University of Glasgow, Glasgow,UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4542-112X","authenticated-orcid":false,"given":"Alessio","family":"Fracasso","sequence":"additional","affiliation":[{"name":"School of Psychology & Neuroscience, University of Glasgow, Glasgow,UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7828-9209","authenticated-orcid":false,"given":"Michele","family":"Svanera","sequence":"additional","affiliation":[{"name":"School of Psychology & Neuroscience, University of Glasgow, Glasgow,UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"name":"Alzheimer\u2019s Disease Neuroimaging Initiative","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"name":"Frontotemporal Lobar Degeneration Neuroimaging Initiative","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"54368","reference":[{"key":"2026081801101927000_2026.08.13.26359403v1.1","doi-asserted-by":"publisher","DOI":"10.1038\/nn.4478"},{"key":"2026081801101927000_2026.08.13.26359403v1.2","doi-asserted-by":"crossref","unstructured":"Scarpazza, C. et al. Translating research findings into clinical practice: a systematic and critical review of neuroimaging-based clinical tools for brain disorders. Translational Psychiatry 10, 107 (2020). URL https:\/\/www.nature.com\/articles\/s41398-020-0798-6.","DOI":"10.1038\/s41398-020-0798-6"},{"key":"2026081801101927000_2026.08.13.26359403v1.3","unstructured":"Brainstorm Consortium et al. Analysis of shared heritability in common disorders of the brain. Science 360, eaap8757 (2018). Place: New York, N.Y."},{"key":"2026081801101927000_2026.08.13.26359403v1.4","doi-asserted-by":"publisher","DOI":"10.1038\/S41586-022-04554-Y"},{"key":"2026081801101927000_2026.08.13.26359403v1.5","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1038\/s41593-025-02144-5","article-title":"Charting brain morphology in international healthy and neurological populations","volume":"29","year":"2026","journal-title":"Nature Neuroscience"},{"key":"2026081801101927000_2026.08.13.26359403v1.6","doi-asserted-by":"publisher","DOI":"10.7554\/eLife.72904"},{"key":"2026081801101927000_2026.08.13.26359403v1.7","doi-asserted-by":"crossref","first-page":"41","DOI":"10.2463\/mrms.rev.2021-0096","article-title":"Advantages of Using Both Voxel- and Surface-based Morphometry in Cortical Morphology Analysis: A Review of Various Applications","volume":"21","year":"2022","journal-title":"Magnetic Resonance in Medical Sciences"},{"key":"2026081801101927000_2026.08.13.26359403v1.8","first-page":"100","volume-title":"Translational Psychiatry","volume":"10","year":"2020"},{"key":"2026081801101927000_2026.08.13.26359403v1.9","doi-asserted-by":"crossref","first-page":"1711","DOI":"10.1038\/s41596-022-00696-5","article-title":"The Normative Modeling Framework for Computational Psychiatry","volume":"17","year":"2022","journal-title":"Nature protocols"},{"key":"2026081801101927000_2026.08.13.26359403v1.10","doi-asserted-by":"publisher","DOI":"10.1038\/s41380-019-0441-1"},{"key":"2026081801101927000_2026.08.13.26359403v1.11","doi-asserted-by":"crossref","first-page":"106284","DOI":"10.1016\/j.neubiorev.2025.106284","article-title":"The promise of quantifying individual risk for brain disorders through normative modeling, a narrative review","volume":"176","year":"2025","journal-title":"Neuroscience & Biobehavioral Reviews"},{"key":"2026081801101927000_2026.08.13.26359403v1.12","doi-asserted-by":"crossref","first-page":"678","DOI":"10.1016\/j.biopsych.2020.04.027","article-title":"Cross-Disorder Analysis of Brain Structural Abnormalities in Six Major Psychiatric Disorders: A Secondary Analysis of Mega- and Meta-analytical Findings From the ENIGMA Consortium","volume":"88","year":"2020","journal-title":"Biological Psychiatry"},{"key":"2026081801101927000_2026.08.13.26359403v1.13","doi-asserted-by":"crossref","first-page":"6851","DOI":"10.1038\/s41467-022-34367-6","article-title":"Coordinated cortical thickness alterations across six neurodevelopmental and psychiatric disorders","volume":"13","year":"2022","journal-title":"Nature Communications"},{"key":"2026081801101927000_2026.08.13.26359403v1.14","doi-asserted-by":"publisher","DOI":"10.1093\/BRAIN\/AWAB165"},{"key":"2026081801101927000_2026.08.13.26359403v1.15","first-page":"e2442","volume-title":"Neurology","volume":"100","year":"2023"},{"key":"2026081801101927000_2026.08.13.26359403v1.16","doi-asserted-by":"crossref","unstructured":"Correia, M. M. , et al. Towards accurate and unbiased imaging-based differentiation of Parkinson\u2019s disease, progressive supranuclear palsy and corticobasal syndrome. Brain Communications 2, fcaa051 (2020). URL https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC7325838\/.","DOI":"10.1093\/braincomms\/fcaa051"},{"key":"2026081801101927000_2026.08.13.26359403v1.17","doi-asserted-by":"crossref","first-page":"1364","DOI":"10.1212\/CON.0000000000001158","article-title":"Progressive Supranuclear Palsy and Corticobasal Syndrome","volume":"28","year":"2022","journal-title":"Continuum"},{"key":"2026081801101927000_2026.08.13.26359403v1.18","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1007\/s00787-010-0092-x","article-title":"Shared heritability of attention-deficit\/hyperactivity disorder and autism spectrum disorder","volume":"19","year":"2010","journal-title":"European Child & Adolescent Psychiatry"},{"key":"2026081801101927000_2026.08.13.26359403v1.19","doi-asserted-by":"publisher","DOI":"10.1186\/s13195-018-0459-7"},{"key":"2026081801101927000_2026.08.13.26359403v1.20","doi-asserted-by":"publisher","DOI":"10.64898\/2026.03.27.26349489v2"},{"key":"2026081801101927000_2026.08.13.26359403v1.21","doi-asserted-by":"publisher","DOI":"10.1016\/J.NEUROIMAGE.2018.11.024"},{"key":"2026081801101927000_2026.08.13.26359403v1.22","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.dcn.2018.03.001","article-title":"The Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites","volume":"32","year":"2018","journal-title":"Developmental Cognitive Neuroscience"},{"key":"2026081801101927000_2026.08.13.26359403v1.23","unstructured":"Dennis, E. , et al. AIMS-TBI - Automated Identification of Moderate-Severe Traumatic Brain Injury Lesions (2025). URL https:\/\/zenodo.org\/records\/15084120."},{"key":"2026081801101927000_2026.08.13.26359403v1.24","doi-asserted-by":"crossref","first-page":"e080463","DOI":"10.1002\/alz.080463","article-title":"ALLFTD: Identifying and Characterizing Frontotemporal Lobar Degeneration Participants in a Multi-center North American Study","volume":"19","year":"2023","journal-title":"Alzheimer\u2019s & Dementia"},{"key":"2026081801101927000_2026.08.13.26359403v1.25","doi-asserted-by":"crossref","unstructured":"Snoek, L. et al. The Amsterdam Open MRI Collection, a set of multimodal MRI datasets for individual difference analyses. Scientific Data 8, 85 (2021). URL https:\/\/www.nature.com\/articles\/s41597-021-00870-6.","DOI":"10.1038\/s41597-021-00870-6"},{"key":"2026081801101927000_2026.08.13.26359403v1.26","doi-asserted-by":"crossref","unstructured":"Prado, P. et al. The BrainLat project, a multimodal neuroimaging dataset of neurodegeneration from underrepresented backgrounds. Scientific Data 10, 889 (2023). URL https:\/\/www.nature.com\/articles\/s41597-023-02806-8.","DOI":"10.1038\/s41597-023-02806-8"},{"key":"2026081801101927000_2026.08.13.26359403v1.27","doi-asserted-by":"publisher","DOI":"10.1038\/mp.2013.78"},{"key":"2026081801101927000_2026.08.13.26359403v1.28","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/j.neuroimage.2016.06.034","article-title":"The Neuro Bureau ADHD-200 Preprocessed repository","volume":"144","year":"2017","journal-title":"NeuroImage"},{"key":"2026081801101927000_2026.08.13.26359403v1.29","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2013.05.041"},{"key":"2026081801101927000_2026.08.13.26359403v1.30","unstructured":"Taylor, P. N. , et al. The Imaging Database for Epilepsy And Surgery (IDEAS) (2024). URL http:\/\/arxiv.org\/abs\/2406.06731. ArXiv:2406.06731 [q-bio]."},{"key":"2026081801101927000_2026.08.13.26359403v1.31","doi-asserted-by":"crossref","unstructured":"Schwarz, C. G. , et al. A Large Public Release of Clinical and Imaging Data from the Mayo Clinic Study of Aging. Alzheimer\u2019s & Dementia 20, e093966 (2024). URL https:\/\/onlinelibrary.wiley.com\/doi\/abs\/10.1002\/alz.093966. eprint: https:\/\/alz-journals.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/alz.093966.","DOI":"10.1002\/alz.091346"},{"key":"2026081801101927000_2026.08.13.26359403v1.32","doi-asserted-by":"crossref","unstructured":"LaMontagne, P. J. , et al. OASIS-3: Longitudinal Neuroimaging, Clinical, and Cognitive Dataset for Normal Aging and Alzheimer Disease (2019). URL https:\/\/www.medrxiv.org\/content\/10.1101\/2019.12.13.19014902v1. ISSN: 1901-4902 Pages: 2019.12.13.19014902.","DOI":"10.1101\/2019.12.13.19014902"},{"key":"2026081801101927000_2026.08.13.26359403v1.33","unstructured":"Rorden, C. , Absher, J. & Newman-Norlund, R . Stroke Outcome Optimization Project (SOOP) (2024). URL https:\/\/openneuro.org\/datasets\/ds004889\/versions\/1.1.2."},{"key":"2026081801101927000_2026.08.13.26359403v1.34","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1038\/s41597-021-01004-8","article-title":"A multi-site, multi-disorder resting-state magnetic resonance image database","volume":"8","year":"2021","journal-title":"Scientific Data"},{"key":"2026081801101927000_2026.08.13.26359403v1.35","doi-asserted-by":"crossref","first-page":"160110","DOI":"10.1038\/sdata.2016.110","article-title":"A phenome-wide examination of neural and cognitive function","volume":"3","year":"2016","journal-title":"Scientific Data"},{"key":"2026081801101927000_2026.08.13.26359403v1.36","doi-asserted-by":"crossref","unstructured":"Sudlow, C. , et al. UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age. PLOS Medicine 12, e1001779 (2015). URL https:\/\/journals.plos.org\/plosmedicine\/article?id=10.1371\/journal.pmed.1001779.","DOI":"10.1371\/journal.pmed.1001779"},{"key":"2026081801101927000_2026.08.13.26359403v1.37","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1038\/s41597-022-01560-7","article-title":"The University of Pennsylvania glioblastoma (UPenn-GBM) cohort: advanced MRI, clinical, genomics, & radiomics","volume":"9","year":"2022","journal-title":"Scientific Data"},{"key":"2026081801101927000_2026.08.13.26359403v1.38","doi-asserted-by":"crossref","unstructured":"Chang, J. et al. An fMRI dataset for appetite neural correlates in people living with Motor Neuron Disease. Scientific Data 12, 466 (2025). URL https:\/\/www.nature.com\/articles\/s41597-025-04828-w.","DOI":"10.1038\/s41597-025-04828-w"},{"key":"2026081801101927000_2026.08.13.26359403v1.39","doi-asserted-by":"crossref","unstructured":"Schuch, F. et al. An open presurgery MRI dataset of people with epilepsy and focal cortical dysplasia type II. Scientific Data 10, 475 (2023). URL https:\/\/www.nature.com\/articles\/s41597-023-02386-7.","DOI":"10.1038\/s41597-023-02386-7"},{"key":"2026081801101927000_2026.08.13.26359403v1.40","doi-asserted-by":"crossref","unstructured":"Soler-Vidal, J. , et al. Brain correlates of speech perception in schizophrenia patients with and without auditory hallucinations. PLOS ONE 17, e0276975 (2022). URL https:\/\/journals.plos.org\/plosone\/article?id=10.1371\/journal.pone.0276975.","DOI":"10.1371\/journal.pone.0276975"},{"key":"2026081801101927000_2026.08.13.26359403v1.41","doi-asserted-by":"crossref","unstructured":"Svanera, M. , Savardi, M. , Signoroni, A. , Benini, S. & Muckli, L . Fighting the scanner effect in brain MRI segmentation with a progressive level-of-detail network trained on multi-site data. Medical Image Analysis 93, 103090 (2024). URL https:\/\/www.sciencedirect.com\/science\/article\/pii\/S136184152400015X.","DOI":"10.1016\/j.media.2024.103090"},{"key":"2026081801101927000_2026.08.13.26359403v1.42","doi-asserted-by":"crossref","unstructured":"Bontempi, D. , Benini, S. , Signoroni, A. , Svanera, M. & Muckli, L . CEREBRUM: a fast and fully-volumetric Convolutional Encoder-decodeR for weakly-supervised sEgmentation of BRain strUctures from out-of-the-scanner MRI. Medical Image Analysis 62, 101688 (2020). URL https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1361841520300542.","DOI":"10.1016\/j.media.2020.101688"},{"key":"2026081801101927000_2026.08.13.26359403v1.43","doi-asserted-by":"publisher","DOI":"10.1002\/hbm.25636"},{"key":"2026081801101927000_2026.08.13.26359403v1.44","doi-asserted-by":"crossref","unstructured":"Billot, B. et al. SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining. Medical Image Analysis 86, 102789 (2023). URL https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1361841523000506.","DOI":"10.1016\/j.media.2023.102789"},{"key":"2026081801101927000_2026.08.13.26359403v1.45","doi-asserted-by":"publisher","DOI":"10.1007\/s10548-012-0246-x"},{"key":"2026081801101927000_2026.08.13.26359403v1.46","unstructured":"Ronneberger, O. , Fischer, P. & Brox, T. Navab, N. , Hornegger, J. , Wells, W. M. & Frangi, A. F. (eds) U-Net: Convolutional Networks for Biomedical Image Segmentation. (eds Navab, N. , Hornegger, J. , Wells, W. M. & Frangi, A. F. ) Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015, 234\u2013241 (Springer International Publishing, Cham, 2015)."},{"key":"2026081801101927000_2026.08.13.26359403v1.47","unstructured":"Kingma, D. P. & Ba, J . Adam: A Method for Stochastic Optimization (2017). URL http:\/\/arxiv.org\/abs\/1412.6980. ArXiv:1412.6980 [cs]."},{"key":"2026081801101927000_2026.08.13.26359403v1.48","first-page":"347","article-title":"Marching cubes: a high resolution 3D surface construction algorithm","volume":"1","year":"1998","journal-title":"Seminal graphics: pioneering efforts that shaped the field"},{"key":"2026081801101927000_2026.08.13.26359403v1.49","unstructured":"Taubin, G . Curve and surface smoothing without shrinkage (1995). URL https:\/\/ieeexplore.ieee.org\/document\/466848\/."},{"key":"2026081801101927000_2026.08.13.26359403v1.50","doi-asserted-by":"publisher","DOI":"10.2312\/egst.20051044"},{"key":"2026081801101927000_2026.08.13.26359403v1.51","doi-asserted-by":"publisher","DOI":"10.1002\/hbm.25776"},{"key":"2026081801101927000_2026.08.13.26359403v1.52","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2012.01.021"},{"key":"2026081801101927000_2026.08.13.26359403v1.53","unstructured":"Marquand, A. et al. PCNToolkit (2021). URL https:\/\/zenodo.org\/record\/5207839."},{"key":"2026081801101927000_2026.08.13.26359403v1.54","doi-asserted-by":"crossref","unstructured":"McInnes, L. , Healy, J. , Saul, N. & Gro\u00dfberger, L . UMAP: Uniform Manifold Approximation and Projection. Journal of Open Source Software 3, 861 (2018). URL https:\/\/joss.theoj.org\/papers\/10.21105\/joss.00861.","DOI":"10.21105\/joss.00861"}],"container-title":[],"original-title":[],"link":[{"URL":"https:\/\/syndication.highwire.org\/content\/doi\/10.64898\/2026.08.13.26359403","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T08:10:40Z","timestamp":1787040640000},"score":1,"resource":{"primary":{"URL":"http:\/\/medrxiv.org\/lookup\/doi\/10.64898\/2026.08.13.26359403"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,14]]},"references-count":54,"URL":"https:\/\/doi.org\/10.64898\/2026.08.13.26359403","relation":{},"subject":[],"published":{"date-parts":[[2026,8,14]]},"subtype":"preprint"}}