Linking Cortical Morphology and Neurophysiological Dynamics in Parkinson's Disease

Linking Cortical Morphology and Neurophysiological Dynamics in Parkinson's Disease | 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 Linking Cortical Morphology and Neurophysiological Dynamics in Parkinson's Disease Koorosh Mirpour, Amirreza Alijanpourotaghsara, Nader Pouratian This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7992155/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted 13 You are reading this latest preprint version Abstract Parkinson’s disease (PD) involves progressive neurodegeneration and distinctive structural and functional alterations in cortico-basal ganglia circuits. This study proposes bridging the gap between structural and functional biomarkers to uncover fundamental mechanisms underlying PD pathophysiology and support more comprehensive diagnostic and therapeutic approaches. This study analyzed intraoperative electrocorticography (ECoG) and pallidal (GPi) local field potential (LFP) recordings alongside preoperative T1-weighted structural MRI from 50 patients with PD undergoing deep brain stimulation surgery. We extracted 36 morphometric features (thickness, volume, surface area) from nine sensorimotor Brodmann areas using FreeSurfer and 92 neurophysiological features (power, burst dynamics, coherence) across multiple frequency bands. Pairwise analyses revealed only fragmented, though significant, correlations. In contrast, the SPLS analysis identified a robust and significant latent dimension (test set rho = 0.818, p = 0.001) linking the two modalities. This primary latent variable was driven by a strong negative association with cortical thickness in sensorimotor areas (e.g., BA3b, BA1, BA6) and a complex combination of neurophysiological features, most notably altered burst dynamics in the alpha, gamma, and low-beta bands. This structure-function relationship was independent of age, disease duration, and UPDRS-III scores (rho = 0.701 after partialling out confounds). Critically, the SPLS model failed to find a significant correlation when applied to the ET cohort (rho = -0.342, p = 0.102), suggesting the identified relationship is specific to PD pathophysiology. These findings highlight the value of multimodal approaches for uncovering structure–function interactions in PD, and the potential of integrated biomarkers for improving diagnosis and treatment strategies. Health sciences/Biomarkers Health sciences/Neurology Biological sciences/Neuroscience Parkinson’s disease Deep Brain Stimulation DBS Beta Oscillation Beta Burst GPi Motor Cortex Cortical morphometry cortical structure electrophysiological biomarkers neuroimaging structural MRI Gray matter thickness Gray matter volume Local field potential LFP ECoG Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Full Text Additional Declarations Competing interest reported. K.M. and A.A. declare no competing interests. N.P. receives consulting fees from Abbott and Boston Scientific. Tables are available in the Supplementary Files section. Supplementary Files table1.docx table2.docx table3.docx table4.docx tabl5.docx Cite Share Download PDF Status: Published Journal Publication published 05 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 12 Jan, 2026 Reviews received at journal 10 Jan, 2026 Reviewers agreed at journal 09 Dec, 2025 Reviews received at journal 19 Nov, 2025 Reviewers agreed at journal 17 Nov, 2025 Reviewers agreed at journal 13 Nov, 2025 Reviewers agreed at journal 13 Nov, 2025 Reviewers agreed at journal 12 Nov, 2025 Reviewers invited by journal 12 Nov, 2025 Editor invited by journal 10 Nov, 2025 Editor assigned by journal 31 Oct, 2025 Submission checks completed at journal 31 Oct, 2025 First submitted to journal 30 Oct, 2025 You are reading this latest preprint version 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. 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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-7992155","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":549223056,"identity":"dd1efb3d-eed8-4a44-8e32-8b3d3d1d145d","order_by":0,"name":"Koorosh Mirpour","email":"data:image/png;base64,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","orcid":"","institution":"The University of Texas Southwestern Medical Center","correspondingAuthor":true,"prefix":"","firstName":"Koorosh","middleName":"","lastName":"Mirpour","suffix":""},{"id":549223057,"identity":"a430c841-7c72-43d1-836c-39585af93bab","order_by":1,"name":"Amirreza Alijanpourotaghsara","email":"","orcid":"","institution":"The University of Texas Southwestern Medical 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10:27:50","extension":"html","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":185270,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7992155/v1/098c12049b67757690bd653b.html"},{"id":96708411,"identity":"933f4f78-5114-4aa9-bef4-6a7911fa67f7","added_by":"auto","created_at":"2025-11-25 10:01:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":660439,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMethodology of the study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A and B) Schematic illustration of the location of an ECoG strip on the cortical surface, with electrode contacts marked as red dots in (A). The central sulcus (M1, black line in A) serves as the structural boundary. The bipolar contact spanning both sides of the central sulcus corresponds to the M1. The first bipolar contact pair anterior to the M1 is designated as PM (Premotor cortex), while the first bipolar contact pair posterior to the M1 is labeled as S1 (Primary somatosensory cortex). (C) An example of burst analysis. Burst periods are highlighted on the filtered narrowband beta band signal (blue trace), the analytical envelope of the signal, and the 75\u003csup\u003eth\u003c/sup\u003e percentile threshold (green dashed line). The burst periods are marked (orange trace) on the bottom panel's original broadband signal (blue trace). (D \u0026amp; E) High-resolution T1-weighted MRI figures from subject bG10 showcase the anatomical primary motor region, Brodmann Area 4a (BA4a, red line), and the PM region (Brodmann Area 6, BA6, blue line) using FreeSurfer's Recon-all command for parcellation.\u003c/p\u003e","description":"","filename":"fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-7992155/v1/78ba3c2d49ddeb3b5c5052db.png"},{"id":96617518,"identity":"3a9ac7d0-0a26-48ee-ba33-9a148a6c22ab","added_by":"auto","created_at":"2025-11-24 10:27:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":438291,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSchematic pipeline of associative correlation analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) The analysis method illustrated in the figure follows a comprehensive multiple-holdout framework. This approach begins by splitting the original dataset into two parts: an optimization set (comprising 80% of the data) and a holdout set (comprising the remaining 20%) as shown in panel A. The optimization set is divided into a training set (80%) and a validation set (20%). These subsets are used to fit a regularized Partial Least Squares/Canonical Correlation Analysis (PLS/CCA) model and to optimize the regularization parameters through 50 different training and validation iterations. After determining the optimal parameters, the regularized PLS/CCA model is refitted using the entire optimization set and then evaluated on the holdout set with permutation testing to assess its performance. This whole process is repeated 10 times to enhance the robustness and reliability of the model results.\u003c/p\u003e\n\u003cp\u003e(B) The panel B illustrates the PLS/CCA models, which are employed to find weight vectors that maximize the covariance (in the case of PLS) or correlation (in the case of CCA) between linear combinations of brain imaging and behavioral data. The sparse PLS model introduces sparsity constraints, effectively reducing some weights for imaging and electrophysiology variables to zero, thereby selecting the most significant features. The resulting linear combinations or weighted sums of structural and neurophysiological data (derived from matrices 𝑋and 𝑌 with corresponding weights Wx and Wy) yield structure and neurophysiology scores (𝑋Wx and 𝑌Wy) for each participant. These scores are then used to construct an structure-neurophysiology latent space, which represents the relationships between structural and neurophysiological factors across the entire study sample.\u003c/p\u003e","description":"","filename":"fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-7992155/v1/bc7da2dfc420611757f8df55.png"},{"id":96617516,"identity":"20c6e950-18b5-427b-bc7a-e2532ef119eb","added_by":"auto","created_at":"2025-11-24 10:27:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":137570,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCross-Correlation Analysis Between Structural and Electrophysiological Measures in PD Patients.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Heatmap showing the pairwise Pearson correlation coefficients between structural and electrophysiological measures across 50 PD patients. The structural measures include cortical thickness, volume, and surface area for Brodmann areas (BA1, BA2, BA3a, BA3b, BA4a, BA4p, BA6, BA44, BA45), while the electrophysiological measures encompass power, burst duration, burst amplitude, burst rate, cortico-cortical coherence, and cortico-subcortical coherence, calculated across various frequency bands. The color scale represents the strength and direction of the correlation coefficients.\u003c/p\u003e\n\u003cp\u003e(B) The significance map of the correlations is displayed in (A) following permutation testing. Significant correlations (p \u0026lt; 0.05) are highlighted in yellow. Red outlines indicate clusters where the significance exceeds three median absolute deviations from the center of the distribution. The figure emphasizes regions of significant association between structural brain metrics and electrophysiological activity, particularly in the areas related to burst dynamics and cortical thickness.\u003c/p\u003e\n\u003cp\u003eBA = Broadman area, S = surface area, Vol = volume, Tick = average thickness, Pwr = power, LBeta = low beta band, Hbeta = high beta band, BD = burst duration, BA = Burst amplitude, BR = burst rate, M1 = Motor cortex, PM = Premotor cortex, S1 = Primary somatosensory cortex, Sub cort = \u0026nbsp;subcortical region (Pallidum), Coh-CC = cortico-cortical coherence, Coh-M1 = cortico-subcortical coherence.\u003c/p\u003e","description":"","filename":"fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-7992155/v1/e3c6b9cfc28d8cb534feddc1.png"},{"id":96617523,"identity":"5b6abc76-ad94-43b4-9d58-292ed5e630fb","added_by":"auto","created_at":"2025-11-24 10:27:49","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":283936,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDetailed Analysis of Cross-Correlation Between Structural and Electrophysiological Measures.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A-F) Heatmaps illustrate the pairwise Pearson correlation coefficients between structural and electrophysiological measures, grouped by different subcategories across 50 PD patients. The color scale represents the strength and direction of correlation coefficients. Asterisks (*) indicate significant correlations after permutation testing and False Discovery Rate (FDR) control.\u003c/p\u003e\n\u003cp\u003e(A) Correlation of structural measures (BA1 to BA6) with electrophysiological metrics categorized by electrode location: M1, PM and S1, and subcortical (Sub-Cort).\u003c/p\u003e\n\u003cp\u003e(B) Correlation of surface area (Surf), volume (Vol), and average thickness (Thick Avg) with electrophysiological metrics categorized by electrode location.\u003c/p\u003e\n\u003cp\u003e(C) Correlation of structural measures (BA1 to BA6) with detailed electrophysiological metrics: power, burst duration, burst amplitude, burst rate, cortico-cortical coherence (Coherence-CC), and cortico-subcortical coherence (Coherence-M1).\u003c/p\u003e\n\u003cp\u003e(D) Correlation of surface area, volume, and average thickness with detailed electrophysiological metrics.\u003c/p\u003e\n\u003cp\u003e(E) Correlation of structural measures (BA1 through BA6) with electrophysiological metrics across specific frequency bands: alpha, low beta (Lbeta), high beta (Hbeta), and gamma.\u003c/p\u003e\n\u003cp\u003e(F) Correlation of surface area, volume, and average thickness with electrophysiological metrics across specific frequency bands.\u003c/p\u003e\n\u003cp\u003eBA = Broadman area, Surf = surface area, Vol = volume, Tick Avg= average thickness, LBeta = low beta band, Hbeta = high beta band, M1 = Motor cortex , PM = Premotor cortex, S1 = Primary somatosensory cortex, Sub-cort = \u0026nbsp;subcortical region (Pallidum), Coherence-CC = cortico-cortical coherence, Coherence-M1 = cortico-subcortical coherence. Significant correlations are marked with asterisks in all panels.\u003c/p\u003e","description":"","filename":"fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-7992155/v1/14c82d49bdbe086d826dc113.png"},{"id":96617529,"identity":"419e925e-94b0-4013-8daa-2b58593da72f","added_by":"auto","created_at":"2025-11-24 10:27:50","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":527510,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCanonical Correlation Between Structural and Neurophysiological Latent Scores and related weights.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Scatterplot illustrating the relationship between structural and neurophysiological latent scores in both the training (orange) and test (blue) sets. The fit line represents the linear regression, and the shaded area indicates the 95% confidence interval (CI). The strong correlation between these scores in both sets (train: ρ = 0.688, p \u0026lt; 0.001; test: ρ = 0.818, p = 0.001) highlights the robustness of the identified structure-neurophysiology association.\u003c/p\u003e\n\u003cp\u003e(B) Scatterplot showing the relationship between structural and neurophysiological latent scores across all subjects, with color indicating patient age and size representing the Unified Parkinson’s Disease Rating Scale (UPDRS) scores. The correlation coefficient (ρ = 0.69, p \u0026lt; 0.001) demonstrates a significant association between the latent scores.\u003c/p\u003e\n\u003cp\u003e(C) Boxplot of the weights for the structural variables contributing to the first latent dimension identified by SPLS. The structural variables include volume (Vol) and average thickness (Thick Avg) for Brodmann areas (BA) 6, 4a, 3a, 3b, 2, and 1. The negative weights indicate a strong negative association between cortical thickness and the neurophysiology latent dimension, particularly in the sensorimotor regions BA3b, BA1, BA6, BA2, and BA4a, suggesting that thinning in these regions is strongly linked to electrophysiological alterations in PD.\u003c/p\u003e\n\u003cp\u003e(D) Boxplot of the weights for the neurophysiological variables contributing to the first latent dimension. The neurophysiological variables include burst rate (BR), burst duration (BD), and cortico-cortical coherence (CCC) across different frequency bands (alpha, low beta [Lbeta], high beta [Hbeta], gamma) and locations (M1, PM, GPi). Positive weights indicate a positive association with the structure's latent dimension, with M1 alpha burst rate and gamma burst amplitude showing the strongest contributions. The negative weights, particularly in low beta burst duration and alpha burst duration, highlight their negative association with cortical thickness.\u003c/p\u003e\n\u003cp\u003e(E) Scatterplot illustrating the relationship between structural and neurophysiological latent scores in ET control cohort. The fit line represents the linear regression, and the shaded area indicates the 95% confidence interval (CI). No significant correlation was found between structural and neurophysiological latent scores (ρ = -0.342, p = 0.102).\u003c/p\u003e\n\u003cp\u003eBA = Broadman area, Vol = volume, Tick Avg= average thickness, LBeta = low beta band, Hbeta = high beta band.\u003c/p\u003e","description":"","filename":"fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-7992155/v1/ec7309a26bc8637ad617c80f.png"},{"id":104252351,"identity":"3a94b8be-dd79-4147-8767-8c1eee7cb0ff","added_by":"auto","created_at":"2026-03-09 16:18:09","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2645294,"visible":true,"origin":"","legend":"","description":"","filename":"AnatphysioAAv16KMshort.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7992155/v1_covered_277cbbf1-4a4b-4a79-89ef-2023647939a1.pdf"},{"id":96708370,"identity":"978666fa-111b-43e4-b836-90a250f0aa55","added_by":"auto","created_at":"2025-11-25 10:01:28","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":21737,"visible":true,"origin":"","legend":"","description":"","filename":"table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7992155/v1/c45973232f625f55eb190066.docx"},{"id":96617521,"identity":"2e9bcc3b-2a70-41c2-8b0f-d66657b92d6b","added_by":"auto","created_at":"2025-11-24 10:27:49","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16459,"visible":true,"origin":"","legend":"","description":"","filename":"table2.docx","url":"https://assets-eu.researchsquare.com/files/rs-7992155/v1/f107bc96ac595eb1382c6867.docx"},{"id":96709061,"identity":"bee91c77-56c4-4ddb-8cb5-392197f94502","added_by":"auto","created_at":"2025-11-25 10:07:25","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":15409,"visible":true,"origin":"","legend":"","description":"","filename":"table3.docx","url":"https://assets-eu.researchsquare.com/files/rs-7992155/v1/01b3bcdaa02011c270b2af1d.docx"},{"id":96617519,"identity":"9a19c20e-ceca-46bc-b957-d72a6193e453","added_by":"auto","created_at":"2025-11-24 10:27:49","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":16745,"visible":true,"origin":"","legend":"","description":"","filename":"table4.docx","url":"https://assets-eu.researchsquare.com/files/rs-7992155/v1/c9ed6efba9b66d90618115eb.docx"},{"id":96617531,"identity":"4183b3e0-bfce-4c72-b1d3-501a82839765","added_by":"auto","created_at":"2025-11-24 10:27:50","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":15322,"visible":true,"origin":"","legend":"","description":"","filename":"tabl5.docx","url":"https://assets-eu.researchsquare.com/files/rs-7992155/v1/77d4ab2006cf64341f8ca484.docx"}],"financialInterests":"\u003cp\u003eCompeting interest reported. K.M. and A.A. declare no competing interests. N.P. receives consulting fees from Abbott and Boston Scientific.\u003c/p\u003e\n\u003cp\u003eTables are available in the Supplementary Files section.\u003c/p\u003e","formattedTitle":"Linking Cortical Morphology and Neurophysiological Dynamics in Parkinson's Disease","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Parkinson’s disease, Deep Brain Stimulation, DBS, Beta Oscillation, Beta Burst, GPi, Motor Cortex, Cortical morphometry, cortical structure, electrophysiological biomarkers, neuroimaging, structural MRI, Gray matter thickness, Gray matter volume, Local field potential, LFP, ECoG","lastPublishedDoi":"10.21203/rs.3.rs-7992155/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7992155/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eParkinson\u0026rsquo;s disease (PD) involves progressive neurodegeneration and distinctive structural and functional alterations in cortico-basal ganglia circuits. This study proposes bridging the gap between structural and functional biomarkers to uncover fundamental mechanisms underlying PD pathophysiology and support more comprehensive diagnostic and therapeutic approaches.\u003c/p\u003e\u003cp\u003eThis study analyzed intraoperative electrocorticography (ECoG) and pallidal (GPi) local field potential (LFP) recordings alongside preoperative T1-weighted structural MRI from 50 patients with PD undergoing deep brain stimulation surgery. We extracted 36 morphometric features (thickness, volume, surface area) from nine sensorimotor Brodmann areas using FreeSurfer and 92 neurophysiological features (power, burst dynamics, coherence) across multiple frequency bands.\u003c/p\u003e\u003cp\u003ePairwise analyses revealed only fragmented, though significant, correlations. In contrast, the SPLS analysis identified a robust and significant latent dimension (test set rho\u0026thinsp;=\u0026thinsp;0.818, p\u0026thinsp;=\u0026thinsp;0.001) linking the two modalities. This primary latent variable was driven by a strong negative association with cortical thickness in sensorimotor areas (e.g., BA3b, BA1, BA6) and a complex combination of neurophysiological features, most notably altered burst dynamics in the alpha, gamma, and low-beta bands. This structure-function relationship was independent of age, disease duration, and UPDRS-III scores (rho\u0026thinsp;=\u0026thinsp;0.701 after partialling out confounds). Critically, the SPLS model failed to find a significant correlation when applied to the ET cohort (rho = -0.342, p\u0026thinsp;=\u0026thinsp;0.102), suggesting the identified relationship is specific to PD pathophysiology.\u003c/p\u003e\u003cp\u003eThese findings highlight the value of multimodal approaches for uncovering structure\u0026ndash;function interactions in PD, and the potential of integrated biomarkers for improving diagnosis and treatment strategies.\u003c/p\u003e","manuscriptTitle":"Linking Cortical Morphology and Neurophysiological Dynamics in Parkinson's Disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-24 10:27:45","doi":"10.21203/rs.3.rs-7992155/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-12T11:30:33+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-10T14:44:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"6152256352723507660984723625215200763","date":"2025-12-09T11:41:29+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-19T05:06:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"323761865227366612900672979027443131816","date":"2025-11-17T23:25:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"295781557423955763096959089096248836511","date":"2025-11-14T00:39:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"70635379012700582483787592355157785769","date":"2025-11-13T07:32:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"299727302926220741042278752140506522753","date":"2025-11-12T22:40:07+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-12T18:21:05+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-10T16:22:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-01T01:06:20+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-01T01:06:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-10-30T18:47:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1a503bf9-9058-4c9d-8866-875fd388a515","owner":[],"postedDate":"November 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":58442203,"name":"Health sciences/Biomarkers"},{"id":58442204,"name":"Health sciences/Neurology"},{"id":58442205,"name":"Biological sciences/Neuroscience"}],"tags":[],"updatedAt":"2026-03-09T16:17:35+00:00","versionOfRecord":{"articleIdentity":"rs-7992155","link":"https://doi.org/10.1038/s41598-026-41274-z","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-03-05 15:58:09","publishedOnDateReadable":"March 5th, 2026"},"versionCreatedAt":"2025-11-24 10:27:45","video":"","vorDoi":"10.1038/s41598-026-41274-z","vorDoiUrl":"https://doi.org/10.1038/s41598-026-41274-z","workflowStages":[]},"version":"v1","identity":"rs-7992155","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7992155","identity":"rs-7992155","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}