Atlas of the Human Brain Imaging-derived Phenotypes and Disease Risk

Atlas of the Human Brain Imaging-derived Phenotypes and Disease Risk | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Atlas of the Human Brain Imaging-derived Phenotypes and Disease Risk Jian Yu, Qidong Liu, Junrong Guo, Jinfeng Yan, Siqi Yu, Ping Li, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8140747/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract The brain plays a central role in coordinating physiological processes across organ systems, yet population-scale evidence linking brain structure and function alterations, as captured by neuroimaging, to multisystem disease risk remains limited. Leveraging multimodal magnetic resonance imaging (MRI) and linked health records from 64,785 participants, we assessed associations between 505 brain imaging-derived phenotypes (IDPs)—including T1-weighted, diffusion, and resting-state functional MRI measures—and 738 incident diseases spanning 15 organ systems, establishing the largest atlas of brain–disease risk to date. Across approximately 370,000 tests, 1,491 significant IDP–disease pairs were identified, revealing that brain alterations relate not only to neurological but also to peripheral conditions such as circulatory, digestive, and metabolic disorders. Clustering and network analyses highlighted white matter-related IDPs as a central hub linking brain and multisystem health. Prediction models combining IDPs with clinical covariates improved disease discrimination, and Mendelian randomization suggested causal roles of white matter-related IDPs in cerebrovascular disorders. Together, these findings advance population-level understanding of brain–body associations and support the translation of neuroimaging insights into cross-system disease risk assessment and therapeutic strategies. All associations are available through an open-access Brain Imaging–Disease Risk Atlas ( www.brainphewas.com ). Biological sciences/Neuroscience Biological sciences/Computational biology and bioinformatics/Computational neuroscience Health sciences/Risk factors Brain imaging-derived phenotypes Disease risk phenome-wide association study Brain Imaging–Disease Risk Atlas Figures Figure 1 Figure 2 Figure 3 Figure 4 Full Text Additional Declarations The authors declare no competing interests. Supplementary Files 20260121Supplymentarytables.xlsx 20260121.Suppfigures.pdf Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8140747","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":562827077,"identity":"89468a79-3b97-408a-b5ef-23503019a128","order_by":0,"name":"Jian Yu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvElEQVRIiWNgGAWjYDCCA0AsYcDAwM/MfPgBaVok29nSDIjXAgIG53kUJIjSwXf7jJmERYFdnvFhHgYDhhqbaIJaJM/lmElIGCQXmx3mPfCA4VhabgMhLQZneEBamBO3HeZLMGBsOEy0lvrEzc08BhKkaDmcuIGZWC2SZ9iKLSQMjifOOAwM5ARi/MJ3hnnjbYk/1Yn9/YcPP/hQY0NYCwMDhwEzPD4SCCsHAfYHjB+IUzkKRsEoGAUjFQAARXk7Eld2BjMAAAAASUVORK5CYII=","orcid":"https://orcid.org/0009-0001-8377-3046","institution":"School of Medicine, Tongji University, Shanghai, P.R. China","correspondingAuthor":true,"prefix":"","firstName":"Jian","middleName":"","lastName":"Yu","suffix":""},{"id":562827078,"identity":"4d2ce285-9790-4875-9220-dd530927d641","order_by":1,"name":"Qidong Liu","email":"","orcid":"","institution":"School of Medicine, Tongji University, Shanghai, P.R. China","correspondingAuthor":false,"prefix":"","firstName":"Qidong","middleName":"","lastName":"Liu","suffix":""},{"id":562827079,"identity":"78329b71-e284-4542-ae83-6a1bf41bc873","order_by":2,"name":"Junrong Guo","email":"","orcid":"","institution":"Department of Medical Genetics/Prenatal Diagnostic Center, West China Second University Hospital, Sichuan University, Chengdu, P.R. China.","correspondingAuthor":false,"prefix":"","firstName":"Junrong","middleName":"","lastName":"Guo","suffix":""},{"id":562827080,"identity":"a381d882-7b6c-4c24-84b9-65daef1fddbd","order_by":3,"name":"Jinfeng Yan","email":"","orcid":"","institution":"Shanghai Engineering Research Center of Tooth Restoration and Regeneration \u0026 Tongji Research Institute of Stomatology \u0026 Shanghai Tongji Stomatological Hospital and Dental School, Tongji University,","correspondingAuthor":false,"prefix":"","firstName":"Jinfeng","middleName":"","lastName":"Yan","suffix":""},{"id":562827081,"identity":"a96114a1-7e24-4ef4-959c-ee41bc7060b6","order_by":4,"name":"Siqi Yu","email":"","orcid":"","institution":"Shanghai Engineering Research Center of Tooth Restoration and Regeneration \u0026 Tongji Research Institute of Stomatology \u0026 Shanghai Tongji Stomatological Hospital and Dental School, Tongji University,","correspondingAuthor":false,"prefix":"","firstName":"Siqi","middleName":"","lastName":"Yu","suffix":""},{"id":562827082,"identity":"86c9c38b-f6fd-451c-96e5-5e5b03e38c04","order_by":5,"name":"Ping Li","email":"","orcid":"","institution":"Department of Anesthesiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, P.R. China.","correspondingAuthor":false,"prefix":"","firstName":"Ping","middleName":"","lastName":"Li","suffix":""},{"id":562827083,"identity":"73eb1e38-cb96-4e16-9159-875c2ae734e5","order_by":6,"name":"Jiajing Cai","email":"","orcid":"","institution":"Independent Researcher","correspondingAuthor":false,"prefix":"","firstName":"Jiajing","middleName":"","lastName":"Cai","suffix":""},{"id":562827084,"identity":"c5c3d2b8-2d6e-4860-8bac-d90cbd6a1397","order_by":7,"name":"Zhenghao Deng","email":"","orcid":"","institution":"Independent Researcher","correspondingAuthor":false,"prefix":"","firstName":"Zhenghao","middleName":"","lastName":"Deng","suffix":""}],"badges":[],"createdAt":"2025-11-18 04:20:41","currentVersionCode":2,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8140747/v2","doiUrl":"https://doi.org/10.21203/rs.3.rs-8140747/v2","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102295158,"identity":"3f6a04e3-e65a-4695-bc0d-5dded9fdd373","added_by":"auto","created_at":"2026-02-10 10:09:23","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1117105,"visible":true,"origin":"","legend":"\u003cp\u003eSummary of brain IDP-disease association analysis results\u003c/p\u003e\n\u003cp\u003e(A) Overall study design. A total of 64,785 UK Biobank participants with\u003c/p\u003e\n\u003cp\u003emultimodal brain MRI data were analyzed. Eight categories of brain imaging-\u003c/p\u003e\n\u003cp\u003ederived phenotypes (IDPs), including cortical thickness, cortical surface area,\u003c/p\u003e\n\u003cp\u003ecortical volume, subcortical volume, white matter fractional anisotropy (FA), mean\u003c/p\u003e\n\u003cp\u003ediffusivity (MD), functional network nodes, and functional connectivity edges, were\u003c/p\u003e\n\u003cp\u003eexamined in relation to overall incident diseases across multiple organ systems.\u003c/p\u003e\n\u003cp\u003eAnalyses included phenome-wide association screening using Cox proportional\u003c/p\u003e\n\u003cp\u003ehazards models, hierarchical clustering of significant IDP–disease pairs, network\u003c/p\u003e\n\u003cp\u003eanalysis to identify hub IDPs and diseases, LightGBM-based prediction models, and\u003c/p\u003e\n\u003cp\u003eMendelian randomization for causal inference. All findings are integrated in the\u003c/p\u003e\n\u003cp\u003eAtlas of Brain Imaging and Diseases (www.brainphewas.com). Created in\u003c/p\u003e\n\u003cp\u003eBioRender (BioRender.com/qco0yuv).\u003c/p\u003e\n\u003cp\u003e(B) Distribution of significant associations by IDP category. The Manhattan-style\u003c/p\u003e\n\u003cp\u003eplot illustrates the number of significant IDP–disease pairs within each imaging\u003c/p\u003e\n\u003cp\u003emodality (Bonferroni-corrected threshold P \u0026lt; 1.34 × 10⁻⁷).\u003c/p\u003e\n\u003cp\u003e(C) Stacked bar plot of significant associations by IDP category. Colors denote\u003c/p\u003e\n\u003cp\u003edisease categories.\u003c/p\u003e\n\u003cp\u003e(D) Distribution of significant associations by disease category. The Manhattan-style\u003c/p\u003e\n\u003cp\u003eplot shows the number of brain IDPs associated with each disease system.\u003c/p\u003e\n\u003cp\u003e(E) Stacked bar plot of significant associations by disease category. Colors denote\u003c/p\u003e\n\u003cp\u003eIDP categories.\u003c/p\u003e\n\u003cp\u003e(F–H) Representative examples of disease-related IDPs include (F) dementia, (G)\u003c/p\u003e\n\u003cp\u003eAlzheimer’s disease and (H) multiple sclerosis. Forest plots show hazard ratios (HRs)\u003c/p\u003e\n\u003cp\u003eand 95% confidence intervals (CIs) from Cox models.\u003c/p\u003e","description":"","filename":"20260121.Mainfigures1.png","url":"https://assets-eu.researchsquare.com/files/rs-8140747/v2/65ac3c06b23e0729c9acd835.png"},{"id":102295294,"identity":"b60ac401-25d8-4370-b9a9-71640d76e5fd","added_by":"auto","created_at":"2026-02-10 10:10:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1824452,"visible":true,"origin":"","legend":"\u003cp\u003eClustering of brain IDPs based on patterns of significant disease associations\u003c/p\u003e\n\u003cp\u003e(A) Clustered heatmap of significant IDP–disease associations identified in the phenome-\u003c/p\u003e\n\u003cp\u003ewide analysis (P \u0026lt; 1.34 × 10⁻⁷). Rows represent 177 brain IDPs and columns represent 98\u003c/p\u003e\n\u003cp\u003ediseases. IDPs are grouped into 10 clusters based on shared disease association profiles.\u003c/p\u003e\n\u003cp\u003eColor indicates the direction and strength of associations, with red denoting higher IDP\u003c/p\u003e\n\u003cp\u003evalues associated with increased disease risk and blue denoting higher IDP values\u003c/p\u003e\n\u003cp\u003eassociated with decreased disease risk. Darker colors indicate smaller P values. * indicates\u003c/p\u003e\n\u003cp\u003estatistically significant results.\u003c/p\u003e\n\u003cp\u003e(B) Principal component analysis (PCA) plot illustrating the overall distribution of IDPs\u003c/p\u003e\n\u003cp\u003eacross eight imaging modalities. Each color represents an IDP category.\u003c/p\u003e\n\u003cp\u003e(C-D) Representative subcluster heatmaps for brain IDP clusters 2 (C) and 6 (D),\u003c/p\u003e\n\u003cp\u003edisplaying associations between IDPs within each cluster and diseases showing ≥8\u003c/p\u003e\n\u003cp\u003esignificant associations. * indicate statistically significant associations. Comprehensive\u003c/p\u003e\n\u003cp\u003eheatmaps for all clusters are provided in the Supplementary Figure 3-5.\u003c/p\u003e","description":"","filename":"20260121.Mainfigures3.png","url":"https://assets-eu.researchsquare.com/files/rs-8140747/v2/30298568bd3ed9b802aaf3d5.png"},{"id":102295420,"identity":"66c4bce0-3e85-4632-99ba-4172473d9cf4","added_by":"auto","created_at":"2026-02-10 10:11:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1359969,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork analysis of significant brain IDP–disease associations\u003c/p\u003e\n\u003cp\u003e(A) Network visualization of hub brain IDP–disease associations identified from the\u003c/p\u003e\n\u003cp\u003ephenome-wide analysis. The network includes hub brain IDPs and hub diseases, defined\u003c/p\u003e\n\u003cp\u003eas the top 10 nodes ranked by the number of significant connections (degree). Node size\u003c/p\u003e\n\u003cp\u003eis proportional to node degree, and edge width reflects the strength of statistical evidence,\u003c/p\u003e\n\u003cp\u003equantified as −log10(P) for each association.\u003c/p\u003e\n\u003cp\u003e(B–E) Top 10 rankings of (B) brain IDPs and (C) diseases by degree, and brain–disease\u003c/p\u003e\n\u003cp\u003epairs by (D) effect size and (E) statistical significance.\u003c/p\u003e\n\u003cp\u003e(F–G) Manhattan-style plots showing disease categories significantly associated with key\u003c/p\u003e\n\u003cp\u003ewhite-matter microstructural IDPs, including (F) MD in the anterior thalamic radiation\u003c/p\u003e\n\u003cp\u003e(ATR) (left and right) and (G) MD in the superior thalamic radiation (STR) (left and\u003c/p\u003e\n\u003cp\u003eright).\u003c/p\u003e\n\u003cp\u003e(H–I) Forest plots presenting the top five disease associations for each key white-matter\u003c/p\u003e\n\u003cp\u003emicrostructural IDPs, including (H) MD in the ATR (left and right) and (I) MD in the\u003c/p\u003e\n\u003cp\u003eSTR (left and right).\u003c/p\u003e","description":"","filename":"20260121.Mainfigures5.png","url":"https://assets-eu.researchsquare.com/files/rs-8140747/v2/2c50fe1cb4d3a6bbe249cf1e.png"},{"id":101989338,"identity":"b1ada5eb-671f-411b-b90e-2a36107fe76c","added_by":"auto","created_at":"2026-02-05 19:14:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":798047,"visible":true,"origin":"","legend":"\u003cp\u003eMendelian randomization evidence for causal relationships between\u003c/p\u003e\n\u003cp\u003ebrain IDPs and diseases\u003c/p\u003e\n\u003cp\u003eMendelian randomization (MR) was performed for 1,491 brain IDP–disease pairs\u003c/p\u003e\n\u003cp\u003eidentified as significant in the phenome-wide analysis. Statistical significance was\u003c/p\u003e\n\u003cp\u003edefined as P \u0026lt; 0.05/1,491 (3.35 × 10⁻5) and nominal significance as P \u0026lt; 0.001. The plot\u003c/p\u003e\n\u003cp\u003epresents only IDP–disease pairs reaching statistical or nominal significance, with\u003c/p\u003e\n\u003cp\u003econsistent directions of effect between MR and Cox models. Forest plots display MR\u003c/p\u003e\n\u003cp\u003eodds ratios (ORs) with 95% confidence intervals (CIs); * indicates statistically\u003c/p\u003e\n\u003cp\u003esignificant results.\u003c/p\u003e\n\u003cp\u003eATR, Anterior thalamic radiation; SLF, Superior longitudinal fasciculus; STR, Superior\u003c/p\u003e\n\u003cp\u003ethalamic radiation; UF, Uncinate fasciculus.\u003c/p\u003e","description":"","filename":"20260121.Mainfigures7.png","url":"https://assets-eu.researchsquare.com/files/rs-8140747/v2/bd07c5d68a78cf040a08537a.png"},{"id":102298588,"identity":"b1cff32a-eb38-4ea3-a665-33ae44246b19","added_by":"auto","created_at":"2026-02-10 10:50:33","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1512485,"visible":true,"origin":"","legend":"","description":"","filename":"PreprintManuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8140747/v2_covered_2941f1c9-9d6e-4a44-b65e-6efef206b890.pdf"},{"id":101989342,"identity":"1ce64f82-d40f-4759-abaf-031a3c364593","added_by":"auto","created_at":"2026-02-05 19:14:28","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":5949398,"visible":true,"origin":"","legend":"","description":"","filename":"20260121Supplymentarytables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8140747/v2/3ee420937ae2d231ca41af05.xlsx"},{"id":101989343,"identity":"c467828f-92aa-4b58-b560-8d9fb3260003","added_by":"auto","created_at":"2026-02-05 19:14:28","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":11433812,"visible":true,"origin":"","legend":"","description":"","filename":"20260121.Suppfigures.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8140747/v2/8dae689a4d31d89eb054b388.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"Atlas of the Human Brain Imaging-derived Phenotypes and Disease Risk","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Brain imaging-derived phenotypes, Disease risk, phenome-wide association study, Brain Imaging–Disease Risk Atlas","lastPublishedDoi":"10.21203/rs.3.rs-8140747/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8140747/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe brain plays a central role in coordinating physiological processes across organ systems, yet population-scale evidence linking brain structure and function alterations, as captured by neuroimaging, to multisystem disease risk remains limited. Leveraging multimodal magnetic resonance imaging (MRI) and linked health records from 64,785 participants, we assessed associations between 505 brain imaging-derived phenotypes (IDPs)—including T1-weighted, diffusion, and resting-state functional MRI measures—and 738 incident diseases spanning 15 organ systems, establishing the largest atlas of brain–disease risk to date. Across approximately 370,000 tests, 1,491 significant IDP–disease pairs were identified, revealing that brain alterations relate not only to neurological but also to peripheral conditions such as circulatory, digestive, and metabolic disorders. Clustering and network analyses highlighted white matter-related IDPs as a central hub linking brain and multisystem health. Prediction models combining IDPs with clinical covariates improved disease discrimination, and Mendelian randomization suggested causal roles of white matter-related IDPs in cerebrovascular disorders. Together, these findings advance population-level understanding of brain–body associations and support the translation of neuroimaging insights into cross-system disease risk assessment and therapeutic strategies. All associations are available through an open-access Brain Imaging–Disease Risk Atlas (\u003ca href=\"http://www.brainphewas.com/\"\u003ewww.brainphewas.com\u003c/a\u003e).\u003c/p\u003e","manuscriptTitle":"Atlas of the Human Brain Imaging-derived Phenotypes and Disease Risk","msid":"","msnumber":"","nonDraftVersions":[{"code":2,"date":"2026-02-05 19:14:19","doi":"10.21203/rs.3.rs-8140747/v2","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}},{"code":1,"date":"2025-12-04 08:43:35","doi":"10.21203/rs.3.rs-8140747/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bf7a33ef-df7f-4ef2-8e08-1006899c1741","owner":[],"postedDate":"February 5th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":59934678,"name":"Biological sciences/Neuroscience"},{"id":59934679,"name":"Biological sciences/Computational biology and bioinformatics/Computational neuroscience"},{"id":59934680,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-01-27T15:55:24+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-05 19:14:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v2","identity":"rs-8140747","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8140747","identity":"rs-8140747","version":["v2"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}