Personalized Deep Brain Stimulation: AI-Driven Fusion of Multi-Modal Imaging and Finite Element Analysis for Predictive Electrode Field Modeling

Personalized Deep Brain Stimulation: AI-Driven Fusion of Multi-Modal Imaging and Finite Element Analysis for Predictive Electrode Field Modeling | 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 Data Note Personalized Deep Brain Stimulation: AI-Driven Fusion of Multi-Modal Imaging and Finite Element Analysis for Predictive Electrode Field Modeling Yinghao Zhu, Yuchun Wang, Narasimha M. Beeraka, Minyan Ge, Virak Sorn, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7288430/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 Precise targeting in deep brain stimulation (DBS) is challenged by individual neuroanatomical variability and postoperative brain shift, often compromising therapeutic efficacy in movement disorders like Parkinson's disease. Conventional atlas-based approaches lack patient-specific models to predict stimulation field interactions with target nuclei (e.g., STN, GPi). Here we present an integrative computational pipeline combining multi-modal imaging with biophysical simulation to enable personalized DBS planning. Our framework leverages: 1) multi-modal registration (advanced normalization tools, ANTs; or statistical parametric mapping, SPM) with subcortical brain shift correction, significantly reducing electrode placement error; 2) AI-driven electrode reconstruction (PaCER) achieving 0.4 ± 0.1 mm contact localization accuracy; and 3) patient-specific finite element modelling (iso2mesh/TetGen) predicting confined stimulation fields. Validated on clinical imaging data (pre-op T1/T2 MRI; post-op CT), the pipeline generated anatomically grounded electrophysiological models in < 35 min per patient, demonstrating computational accessibility. The resulting 1.3 ± 0.4 mm STN targeting precision and field confinement predictions establish a foundation for physics-informed DBS programming. This work bridges surgical planning with adaptive neuromodulation by translating patient anatomy into dynamically queryable stimulation profiles, paving the way for closed-loop systems responsive to individual neuroelectric landscapes. Biological sciences/Computational biology and bioinformatics Physical sciences/Engineering Full Text Additional Declarations The authors declare no competing interests. Supplementary Files Supplementaryassessmentscaledata.xlsx 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. 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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-7288430","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Data Note","associatedPublications":[],"authors":[{"id":497153434,"identity":"6a1d8bfe-b92a-479a-bc3b-ba77b53fcb43","order_by":0,"name":"Yinghao Zhu","email":"","orcid":"","institution":"Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Yinghao","middleName":"","lastName":"Zhu","suffix":""},{"id":497153435,"identity":"6c129fb7-347b-4aff-a960-83f96941af5c","order_by":1,"name":"Yuchun Wang","email":"","orcid":"","institution":"Huashan Hospital, Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Yuchun","middleName":"","lastName":"Wang","suffix":""},{"id":497153436,"identity":"412e6613-7c80-4754-9b9b-713d776db1b5","order_by":2,"name":"Narasimha M. 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Conventional atlas-based approaches lack patient-specific models to predict stimulation field interactions with target nuclei (e.g., STN, GPi). Here we present an integrative computational pipeline combining multi-modal imaging with biophysical simulation to enable personalized DBS planning. Our framework leverages: 1) multi-modal registration (advanced normalization tools, ANTs; or statistical parametric mapping, SPM) with subcortical brain shift correction, significantly reducing electrode placement error; 2) AI-driven electrode reconstruction (PaCER) achieving 0.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1 mm contact localization accuracy; and 3) patient-specific finite element modelling (iso2mesh/TetGen) predicting confined stimulation fields. Validated on clinical imaging data (pre-op T1/T2 MRI; post-op CT), the pipeline generated anatomically grounded electrophysiological models in \u0026lt;\u0026thinsp;35 min per patient, demonstrating computational accessibility. The resulting 1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4 mm STN targeting precision and field confinement predictions establish a foundation for physics-informed DBS programming. This work bridges surgical planning with adaptive neuromodulation by translating patient anatomy into dynamically queryable stimulation profiles, paving the way for closed-loop systems responsive to individual neuroelectric landscapes.\u003c/p\u003e","manuscriptTitle":"Personalized Deep Brain Stimulation: AI-Driven Fusion of Multi-Modal Imaging and Finite Element Analysis for Predictive Electrode Field Modeling","msid":"","msnumber":"","nonDraftVersions":[{"code":2,"date":"2026-01-30 22:29:00","doi":"10.21203/rs.3.rs-7288430/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":"2025-08-07 12:13:31","doi":"10.21203/rs.3.rs-7288430/v1","editorialEvents":[{"type":"communityComments","content":1}],"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":"a18153f4-afc0-47e8-b609-eaa05973052f","owner":[],"postedDate":"January 30th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":61936029,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":61936030,"name":"Physical sciences/Engineering"}],"tags":[],"updatedAt":"2025-08-26T06:53:31+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-30 22:29:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v2","identity":"rs-7288430","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7288430","identity":"rs-7288430","version":["v2"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}