{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T03:40:59Z","timestamp":1789530059844,"version":"build-2803163510"},"reference-count":75,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T00:00:00Z","timestamp":1764115200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurosci."],"abstract":"<jats:p>Cognitive deficits affect up to half of the patients with Parkinson\u2019s disease (PD) within a decade of diagnosis, placing an increasing burden on patients, families and caregivers. Therefore, the development of strategies for their early detection is critical to enable timely intervention and management. This study aimed to classify cognitive performance in patients with PD using a binary support vector machine (SVM) model that integrates structural (high-resolution anatomical) and functional connectivity (FC; resting state) MRI data with clinical characteristics. We hypothesized that PD patients with beginning cognitive deficits can be detected through MRI in combination with machine learning. Data from 38 PD patients underwent extensive preprocessing, including large-scale FC and voxel-based analysis. Relevant features were selected using a bootstrapping approach and subsequently trained in an SVM model, with robustness ensured by 10-fold cross-validation. Although clinical parameters were considered during feature selection, the final best-performing model exclusively comprised imaging features\u2014including gray matter volume (e.g., anterior cingulate gyrus, precuneus) and inter-network functional connectivity within the frontoparietal, default mode, and visual networks. This combined model achieved an accuracy of 94.7% and a ROC-AUC of 0.98. However, a model integrating clinical and only functional MRI data reached similar results with an accuracy of 94.7% and a ROC-AUC of 0.90. In conclusion, our findings demonstrate that applying machine learning to multimodal MRI data\u2014integrating structural, functional, and clinical metrics\u2014could advance the early detection of cognitive impairment in PD and could therefore be used to support timely diagnosis.<\/jats:p>","DOI":"10.3389\/fnins.2025.1689302","type":"journal-article","created":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T06:35:39Z","timestamp":1764138939000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["A multimodal MRI framework employing machine learning for detecting beginning cognitive impairment in Parkinson\u2019s disease"],"prefix":"10.3389","volume":"19","author":[{"given":"Kevin","family":"Bal\u00dfuweit","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter","family":"Bublak","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kathrin","family":"Finke","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adriana L.","family":"Ruiz-Rizzo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Franziska","family":"Wagner","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carsten","family":"Klingner","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefan","family":"Brodoehl","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,11,26]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"730","DOI":"10.1212\/WNL.56.6.730","article-title":"Risk of dementia in Parkinson\u2019s disease.","volume":"56","author":"Aarsland","year":"2001","journal-title":"Neurology"},{"key":"B2","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1038\/s41572-021-00280-3","article-title":"Parkinson disease-associated cognitive impairment.","volume":"7","author":"Aarsland","year":"2021","journal-title":"Nat. Rev. Dis. Primers"},{"key":"B3","doi-asserted-by":"publisher","first-page":"392","DOI":"10.1093\/bioinformatics\/btp630","article-title":"Robust biomarker identification for cancer diagnosis with ensemble feature selection methods.","volume":"26","author":"Abeel","year":"2010","journal-title":"Bioinformatics"},{"key":"B4","doi-asserted-by":"publisher","first-page":"45347","DOI":"10.1038\/srep45347","article-title":"Discriminating cognitive status in Parkinson\u2019s disease through functional connectomics and machine learning.","volume":"7","author":"Ab\u00f3s","year":"2017","journal-title":"Sci. Rep."},{"key":"B5","doi-asserted-by":"publisher","first-page":"288","DOI":"10.1212\/WNL.0b013e318225ab66","article-title":"Does vigorous exercise have a neuroprotective effect in Parkinson disease?","volume":"77","author":"Ahlskog","year":"2011","journal-title":"Neurology"},{"key":"B6","doi-asserted-by":"publisher","first-page":"e0303644","DOI":"10.1371\/journal.pone.0303644","article-title":"Machine learning for the detection and diagnosis of cognitive impairment in Parkinson\u2019s disease: A systematic review.","volume":"19","author":"Altham","year":"2024","journal-title":"PLoS One"},{"key":"B7","doi-asserted-by":"publisher","first-page":"491","DOI":"10.1186\/s12859-019-3027-7","article-title":"An application of machine learning with feature selection to improve diagnosis and classification of neurodegenerative disorders.","volume":"20","author":"\u00c1lvarez","year":"2019","journal-title":"BMC Bioinform."},{"key":"B8","doi-asserted-by":"publisher","first-page":"1010147","DOI":"10.3389\/fneur.2022.1010147","article-title":"Machine learning can predict mild cognitive impairment in Parkinson\u2019s disease.","volume":"13","author":"Amboni","year":"2022","journal-title":"Front. Neurol."},{"key":"B9","doi-asserted-by":"publisher","first-page":"425","DOI":"10.1007\/s00415-014-7591-5","article-title":"Resting-state functional connectivity associated with mild cognitive impairment in Parkinson\u2019s disease.","volume":"262","author":"Amboni","year":"2015","journal-title":"J. Neurol."},{"key":"B10","doi-asserted-by":"publisher","first-page":"1238","DOI":"10.1006\/nimg.2001.0961","article-title":"Why voxel-based morphometry should be used.","volume":"14","author":"Ashburner","year":"2001","journal-title":"NeuroImage"},{"key":"B11","doi-asserted-by":"publisher","first-page":"1857","DOI":"10.1007\/s00247-010-1677-8","article-title":"Shifting from region of interest (ROI) to voxel-based analysis in human brain mapping.","volume":"40","author":"Astrakas","year":"2010","journal-title":"Pediatr. Radiol."},{"key":"B12","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1002\/hbm.22622","article-title":"Cognitive impairment and resting-state network connectivity in Parkinson\u2019s disease.","volume":"36","author":"Baggio","year":"2015","journal-title":"Hum. Brain Mapp."},{"key":"B13","doi-asserted-by":"publisher","first-page":"100200","DOI":"10.1016\/j.imu.2019.100200","article-title":"Machine learning in medicine: Performance calculation of dementia prediction by support vector machines (SVM).","volume":"16","author":"Battineni","year":"2019","journal-title":"Inform. Med. Unlocked"},{"key":"B14","doi-asserted-by":"publisher","first-page":"1375395","DOI":"10.3389\/fnins.2024.1375395","article-title":"Predicting the occurrence of mild cognitive impairment in Parkinson\u2019s disease using structural MRI data.","volume":"18","author":"Beheshti","year":"2024","journal-title":"Front. Neurosci."},{"key":"B15","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","article-title":"Controlling the false discovery rate: A practical and powerful approach to multiple testing.","volume":"57","author":"Benjamini","year":"1995","journal-title":"J. R. Statist. Soc. Ser. B"},{"key":"B16","doi-asserted-by":"publisher","first-page":"550","DOI":"10.1016\/j.neuroimage.2013.05.099","article-title":"The effect of scan length on the reliability of resting-state fMRI connectivity estimates.","volume":"83","author":"Birn","year":"2013","journal-title":"NeuroImage"},{"key":"B17","doi-asserted-by":"publisher","first-page":"e64222","DOI":"10.1371\/journal.pone.0064222","article-title":"Anatomical correlates of cognitive functions in early Parkinson\u2019s disease patients.","volume":"8","author":"Biundo","year":"2013","journal-title":"PLoS One"},{"key":"B18","doi-asserted-by":"publisher","first-page":"102468","DOI":"10.1016\/j.nicl.2020.102468","article-title":"Functional connectivity between resting-state networks reflects decline in executive function in Parkinson\u2019s disease: A longitudinal fMRI study.","volume":"28","author":"Boon","year":"2020","journal-title":"Neuroimage Clin."},{"key":"B19","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1002\/gps.4756","article-title":"A re-examination of Montreal Cognitive Assessment (MoCA) cutoff scores.","volume":"33","author":"Carson","year":"2018","journal-title":"Int. J. Geriatr. Psychiatry"},{"key":"B20","doi-asserted-by":"publisher","first-page":"432","DOI":"10.1001\/jamaneurol.2014.4380","article-title":"Effects of repetitive transcranial magnetic stimulation on motor symptoms in Parkinson disease: A systematic review and meta-analysis.","volume":"72","author":"Chou","year":"2015","journal-title":"JAMA Neurol."},{"key":"B21","doi-asserted-by":"publisher","first-page":"681","DOI":"10.1016\/j.tins.2017.10.001","article-title":"Predicting age using neuroimaging: Innovative brain ageing biomarkers.","volume":"40","author":"Cole","year":"2017","journal-title":"Trends Neurosci."},{"key":"B22","doi-asserted-by":"publisher","first-page":"717","DOI":"10.1016\/j.parkreldis.2013.03.009","article-title":"Combined dementia-risk biomarkers in Parkinson\u2019s disease: A prospective longitudinal study.","volume":"19","author":"Compta","year":"2013","journal-title":"Parkinson. Relat. Disord."},{"key":"B23","doi-asserted-by":"publisher","first-page":"103601","DOI":"10.1016\/j.nicl.2024.103601","article-title":"Connecting the dots: Motor and default mode network crossroads in post-stroke motor learning deficits.","volume":"42","author":"Dahms","year":"2024","journal-title":"NeuroImage: Clin."},{"key":"B24","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1002\/mds.23443","article-title":"Ventricular enlargement and mild cognitive impairment in early Parkinson\u2019s disease.","volume":"26","author":"Dalaker","year":"2011","journal-title":"Mov. Disord."},{"key":"B25","doi-asserted-by":"publisher","first-page":"514","DOI":"10.1016\/j.neurobiolaging.2006.11.010","article-title":"Detection of prodromal Alzheimer\u2019s disease via pattern classification of magnetic resonance imaging.","volume":"29","author":"Davatzikos","year":"2008","journal-title":"Neurobiol. Aging"},{"key":"B26","doi-asserted-by":"publisher","first-page":"499","DOI":"10.1002\/mds.27329","article-title":"MoCA for cognitive screening in Parkinson\u2019s disease: Beware of floor effect.","volume":"33","author":"Federico","year":"2018","journal-title":"Mov. Disord."},{"key":"B27","doi-asserted-by":"publisher","first-page":"883","DOI":"10.1016\/j.neuroimage.2010.01.005","article-title":"Estimating the age of healthy subjects from T1-weighted MRI scans using kernel methods: Exploring the influence of various parameters.","volume":"50","author":"Franke","year":"2010","journal-title":"NeuroImage"},{"key":"B28","doi-asserted-by":"publisher","first-page":"giae049","DOI":"10.1093\/gigascience\/giae049","article-title":"CAT: A computational anatomy toolbox for the analysis of structural MRI data.","volume":"13","author":"Gaser","year":"2024","journal-title":"GigaScience"},{"key":"B29","doi-asserted-by":"publisher","first-page":"813","DOI":"10.1002\/gps.3885","article-title":"Applicability of the MoCA-S test in populations with little education in Colombia.","volume":"28","author":"G\u00f3mez","year":"2013","journal-title":"Int. J. Geriatr. Psychiatry"},{"key":"B30","doi-asserted-by":"publisher","first-page":"1727","DOI":"10.1016\/j.neurobiolaging.2014.12.026","article-title":"To rise and to fall: Functional connectivity in cognitively normal and cognitively impaired patients with Parkinson\u2019s disease.","volume":"36","author":"Gorges","year":"2015","journal-title":"Neurobiol. Aging"},{"key":"B31","doi-asserted-by":"publisher","first-page":"2841","DOI":"10.3390\/jcm11102841","article-title":"Predictive value of the third ventricle width for neurological status in multiple sclerosis.","volume":"11","author":"Guenter","year":"2022","journal-title":"J. Clin. Med."},{"key":"B32","doi-asserted-by":"publisher","first-page":"2713","DOI":"10.1007\/s00415-020-09757-9","article-title":"Predictors of cognitive impairment in Parkinson\u2019s disease: A systematic review and meta-analysis of prospective cohort studies.","volume":"268","author":"Guo","year":"2021","journal-title":"J. Neurol."},{"key":"B33","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1037\/neu0000110","article-title":"Hyperconnectivity is a fundamental response to neurological disruption.","volume":"29","author":"Hillary","year":"2015","journal-title":"Neuropsychology"},{"key":"B34","doi-asserted-by":"publisher","first-page":"1056","DOI":"10.1002\/mds.27002","article-title":"Mild cognitive impairment as a risk factor for Parkinson\u2019s disease dementia.","volume":"32","author":"Hoogland","year":"2017","journal-title":"Mov. Disord."},{"key":"B35","doi-asserted-by":"publisher","first-page":"788846","DOI":"10.3389\/fnagi.2022.788846","article-title":"Magnetic resonance imaging markers for cognitive impairment in Parkinson\u2019s disease: Current view.","volume":"14","author":"Hou","year":"2022","journal-title":"Front. Aging Neurosci."},{"key":"B36","doi-asserted-by":"publisher","first-page":"1343","DOI":"10.1002\/mds.20974","article-title":"Subtypes of mild cognitive impairment in parkinson\u2019s disease: Progression to dementia.","volume":"21","author":"Janvin","year":"2006","journal-title":"Mov. Disord."},{"key":"B37","doi-asserted-by":"publisher","first-page":"498","DOI":"10.3390\/ijms25010498","article-title":"Pathobiology of cognitive impairment in Parkinson disease: Challenges and outlooks.","volume":"25","author":"Jellinger","year":"2024","journal-title":"Int. J. Mol. Sci."},{"key":"B38","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1186\/s12918-017-0514-1","article-title":"Hadamard Kernel SVM with applications for breast cancer outcome predictions.","volume":"11","author":"Jiang","year":"2017","journal-title":"BMC Syst. Biol."},{"key":"B39","doi-asserted-by":"publisher","first-page":"139","DOI":"10.3174\/ajnr.A8067","article-title":"Recommended resting-state fMRI acquisition and preprocessing steps for preoperative mapping of language and motor and visual areas in adult and pediatric patients with brain tumors and epilepsy.","volume":"45","author":"Kumar","year":"2024","journal-title":"AJNR: Am. J. Neuroradiol."},{"key":"B40","doi-asserted-by":"publisher","first-page":"21485","DOI":"10.1038\/s41598-022-25942-4","article-title":"Efficient cross-validation traversals in feature subset selection.","volume":"12","author":"Lausser","year":"2022","journal-title":"Sci. Rep."},{"key":"B41","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1177\/0891988712464823","article-title":"Cognitive impairment in Parkinson disease: Impact on quality of life, disability, and caregiver burden.","volume":"25","author":"Leroi","year":"2012","journal-title":"J. Geriatr. Psychiatry Neurol."},{"key":"B42","doi-asserted-by":"publisher","first-page":"1604","DOI":"10.1002\/hbm.23474","article-title":"Cognitive phenotypes in Parkinson\u2019s disease differ in terms of brain-network organization and connectivity.","volume":"38","author":"Lopes","year":"2017","journal-title":"Hum. Brain Mapp."},{"key":"B43","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v088.i02","article-title":"JASP : Graphical statistical software for common statistical designs.","volume":"88","author":"Love","year":"2019","journal-title":"J. Statist. Software"},{"key":"B44","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1007\/s00234-008-0463-x","article-title":"Support vector machine-based classification of Alzheimer\u2019s disease from whole-brain anatomical MRI.","volume":"51","author":"Magnin","year":"2009","journal-title":"Neuroradiology"},{"key":"B45","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1136\/jnnp-2011-300828","article-title":"Grey matter atrophy in cognitively impaired Parkinson\u2019s disease.","volume":"83","author":"Melzer","year":"2012","journal-title":"J. Neurol. Neurosurg. Psychiatry"},{"key":"B46","doi-asserted-by":"publisher","first-page":"695","DOI":"10.1111\/j.1532-5415.2005.53221.x","article-title":"The montreal cognitive assessment, MoCA: A brief screening tool for mild cognitive impairment.","volume":"53","author":"Nasreddine","year":"2005","journal-title":"J. Am. Geriatr. Soc."},{"key":"B47","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1201.0490","article-title":"Scikit-learn: Machine learning in python.","author":"Pedregosa","year":"2018","journal-title":"arXiv [Preprint]"},{"key":"B48","first-page":"1792","article-title":"Computer aided analysis of cognitive disorder in patients with Parkinsonism using machine learning method with multilevel ROI-based features","volume-title":"Proceedings of the 2016 9th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)","author":"Peng","year":"2016"},{"key":"B49","volume-title":"Statistical parametric mapping: The analysis of functional brain images.","author":"Penny","year":"2011"},{"key":"B50","doi-asserted-by":"publisher","first-page":"461","DOI":"10.1007\/s11910-024-01365-8","article-title":"Resting-State functional MRI Approaches to parkinsonisms and related dementia.","volume":"24","author":"Piramide","year":"2024","journal-title":"Curr. Neurol. Neurosci. Rep."},{"key":"B51","doi-asserted-by":"publisher","first-page":"1591","DOI":"10.1002\/mds.26424","article-title":"MDS clinical diagnostic criteria for Parkinson\u2019s disease.","volume":"30","author":"Postuma","year":"2015","journal-title":"Mov. Disord."},{"key":"B52","doi-asserted-by":"publisher","first-page":"635661","DOI":"10.3389\/fbioe.2021.635661","article-title":"Toward predicting motion sickness using virtual reality and a moving platform assessing brain, muscles, and heart signals.","volume":"9","author":"Recenti","year":"2021","journal-title":"Front. Bioeng. Biotechnol."},{"key":"B53","doi-asserted-by":"publisher","first-page":"402","DOI":"10.1016\/j.nbd.2010.05.006","article-title":"Structural imaging in the presymptomatic stage of genetically determined parkinsonism.","volume":"39","author":"Reetz","year":"2010","journal-title":"Neurobiol. Dis."},{"key":"B54","doi-asserted-by":"publisher","first-page":"232","DOI":"10.1159\/000334765","article-title":"Default mode network and extrastriate visual resting state network in patients with Parkinson\u2019s disease dementia.","volume":"10","author":"Rektorova","year":"2012","journal-title":"Neurodegenerat. Dis."},{"key":"B55","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1016\/j.jneumeth.2013.11.016","article-title":"Machine learning on brain MRI data for differential diagnosis of Parkinson\u2019s disease and progressive supranuclear palsy.","volume":"222","author":"Salvatore","year":"2014","journal-title":"J. Neurosci. Methods"},{"key":"B56","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1002\/mdc3.12921","article-title":"Levodopa equivalent dose conversion factors: An Updated Proposal Including Opicapone and Safinamide.","volume":"7","author":"Schade","year":"2020","journal-title":"Mov. Disord. Clin. Pract."},{"key":"B57","doi-asserted-by":"publisher","first-page":"1588027","DOI":"10.3389\/fnagi.2025.1588027","article-title":"Subregional analysis of the amygdala, thalamus, and hypothalamus at the pre-decline stage in Parkinson\u2019s disease with later cognitive impairment.","volume":"17","author":"Seo","year":"2025","journal-title":"Front. Aging Neurosci."},{"key":"B58","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1186\/s43045-020-00076-3","article-title":"Comparing VBM and ROI analyses for detection of gray matter abnormalities in patients with bipolar disorder using MRI.","volume":"27","author":"Seyedi","year":"2020","journal-title":"Middle East Curr. Psychiatry"},{"key":"B59","doi-asserted-by":"publisher","DOI":"10.1101\/2021.08.13.21261662","article-title":"Predicting future cognitive impairment in de novo Parkinson\u2019s disease using clinical data and structural MRI.","author":"Smith","year":"2021","journal-title":"medRxiv [Preprint]"},{"key":"B60","doi-asserted-by":"publisher","first-page":"5101","DOI":"10.1002\/hbm.25606","article-title":"Topologically convergent and divergent morphological gray matter networks in early-stage Parkinson\u2019s disease with and without mild cognitive impairment.","volume":"42","author":"Suo","year":"2021","journal-title":"Hum. Brain Mapp."},{"key":"B61","doi-asserted-by":"publisher","first-page":"4857","DOI":"10.1093\/cercor\/bhab520","article-title":"Brain functional network abnormalities in parkinson\u2019s disease with mild cognitive impairment.","volume":"32","author":"Suo","year":"2022","journal-title":"Cereb. Cortex"},{"key":"B62","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3448302","article-title":"Dimensionality reduction methods for brain imaging data analysis.","volume":"54","author":"Tang","year":"2021","journal-title":"ACM Comput. Surv."},{"key":"B63","doi-asserted-by":"publisher","first-page":"3156","DOI":"10.1111\/jgs.19020","article-title":"A cross-cultural study of the montreal cognitive assessment for people with hearing impairment.","volume":"72","author":"Theocharous","year":"2024","journal-title":"J. Am. Geriatr. Soc."},{"key":"B64","doi-asserted-by":"publisher","first-page":"105633","DOI":"10.1016\/j.cmpb.2020.105633","article-title":"EQUIDopa: A responsive web application for the levodopa equivalent dose calculator.","volume":"196","author":"Verber","year":"2020","journal-title":"Comp. Methods Prog. Biomed."},{"key":"B65","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1016\/j.neuroscience.2015.12.056","article-title":"Dual-task-related neural connectivity changes in patients with Parkinson\u2019 disease.","volume":"317","author":"Vervoort","year":"2016","journal-title":"Neuroscience"},{"key":"B66","doi-asserted-by":"publisher","first-page":"1453","DOI":"10.1016\/S0140-6736(07)61602-X","article-title":"The strengthening the reporting of observational studies in epidemiology (STROBE) statement: Guidelines for reporting observational studies.","volume":"370","author":"von Elm","year":"2007","journal-title":"Lancet"},{"key":"B67","doi-asserted-by":"publisher","first-page":"2827","DOI":"10.1093\/brain\/aww175","article-title":"Visual dysfunction in Parkinson\u2019s disease.","volume":"139","author":"Weil","year":"2016","journal-title":"Brain"},{"key":"B68","doi-asserted-by":"publisher","first-page":"1562","DOI":"10.1001\/archneurol.2011.725","article-title":"Neurodegeneration across stages of cognitive decline in Parkinson disease.","volume":"68","author":"Weintraub","year":"2011","journal-title":"Arch. Neurol."},{"key":"B69","doi-asserted-by":"publisher","first-page":"1258","DOI":"10.1136\/jnnp-2013-305277","article-title":"The CamPaIGN study of Parkinson\u2019s disease: 10-year outlook in an incident population-based cohort.","volume":"84","author":"Williams-Gray","year":"2013","journal-title":"J. Neurol. Neurosurg. Psychiatry"},{"key":"B70","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.parkreldis.2018.12.016","article-title":"Resting-state fMRI in Parkinson\u2019s disease patients with cognitive impairment: A meta-analysis.","volume":"62","author":"Wolters","year":"2019","journal-title":"Parkinson. Related Disord."},{"key":"B71","doi-asserted-by":"publisher","first-page":"308","DOI":"10.1212\/WNL.0000000000000066","article-title":"Characterizing mild cognitive impairment in incident Parkinson disease.","volume":"82","author":"Yarnall","year":"2014","journal-title":"Neurology"},{"key":"B72","doi-asserted-by":"publisher","first-page":"1125","DOI":"10.1152\/jn.00338.2011","article-title":"The organization of the human cerebral cortex estimated by intrinsic functional connectivity.","volume":"106","author":"Yeo","year":"2011","journal-title":"J. Neurophysiol."},{"key":"B73","doi-asserted-by":"publisher","first-page":"106020","DOI":"10.1016\/j.parkreldis.2024.106020","article-title":"Connectome-based predictive modelling estimates individual cognitive status in Parkinson\u2019s disease.","volume":"123","author":"Ysb\u00e6k-Nielsen","year":"2024","journal-title":"Parkinson. Related Disord."},{"key":"B74","doi-asserted-by":"publisher","first-page":"657","DOI":"10.1007\/s13246-022-01128-0","article-title":"The effect of scan parameters on T1, T2 relaxation times measured with multi-dynamic multi-echo sequence: A phantom study.","volume":"45","author":"Zheng","year":"2022","journal-title":"Phys. Eng. Sci. Med."},{"key":"B75","doi-asserted-by":"publisher","first-page":"218","DOI":"10.1038\/s41531-024-00828-6","article-title":"Multimodal neuroimaging-based prediction of Parkinson\u2019s disease with mild cognitive impairment using machine learning technique.","volume":"10","author":"Zhu","year":"2024","journal-title":"NPJ Parkinsons. Dis."}],"container-title":["Frontiers in Neuroscience"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnins.2025.1689302\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T06:35:41Z","timestamp":1764138941000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnins.2025.1689302\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,26]]},"references-count":75,"alternative-id":["10.3389\/fnins.2025.1689302"],"URL":"https:\/\/doi.org\/10.3389\/fnins.2025.1689302","relation":{},"ISSN":["1662-453X"],"issn-type":[{"value":"1662-453X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,26]]},"article-number":"1689302"}}