{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T03:50:33Z","timestamp":1788925833992,"version":"build-2803163510"},"reference-count":76,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T00:00:00Z","timestamp":1773014400000},"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. Aging Neurosci."],"abstract":"<jats:sec>\n                    <jats:title>Purpose of review<\/jats:title>\n                    <jats:p>Parkinsonian syndromes are a heterogeneous group of neurodegenerative diseases that pose challenges in early diagnosis, differentiation, and pathophysiological understanding. The objective of this review is to summarize recent contributions of computational models combined with neuroimaging data to the differential diagnosis of Parkinsonian syndromes, disease subtyping, and understanding of disease processes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Recent findings<\/jats:title>\n                    <jats:p>Using machine learning algorithms trained with MRI features, diagnostic accuracies above 90% have been achieved for distinguishing patients with Parkinson\u2019s disease from healthy controls and for the differential diagnosis of Parkinsonian syndromes. Computational models, such as hierarchical cluster analysis and Subtype and Stage Inference (SuStaIn), have enabled the identification of distinct disease subtypes within Parkinson\u2019s disease based on imaging-derived brain features. Network models based on structural and functional connectomes have revealed that disease spread in Parkinson\u2019s disease is primarily driven by global connectivity. Additionally, local brain characteristics such as gene expression, cellular composition, and neuroreceptor profiles may contribute to selective vulnerabilities.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Summary<\/jats:title>\n                    <jats:p>Computational approaches enhance the diagnosis of Parkinsonian syndromes, particularly in the early stages, and refine the characterization of disease subtypes, benefiting clinicians, especially in non-expert centers. Such applications hold significant potential for enabling more personalized management and selecting appropriate candidates for clinical trials. Furthermore, a deeper understanding of pathophysiology supports the development of disease-specific therapies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fnagi.2026.1786423","type":"journal-article","created":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T06:46:19Z","timestamp":1773038779000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Novel applications of machine learning and computational neuroscience models to neuroimaging in Parkinson\u2019s disease and related disorders"],"prefix":"10.3389","volume":"18","author":[{"given":"Lydia","family":"Chougar","sequence":"first","affiliation":[{"name":"The Neuro - Montreal Neurological Institute and Hospital, McGill University","place":["Montreal, QC, Canada"]},{"name":"Sorbonne Universit\u00e9, Institut du Cerveau - Paris Brain Institute - ICM, AP-HP, CNRS, Inserm, H\u00f4pital de la Piti\u00e9 Salp\u00eatri\u00e8re, Department of Neuroradiology","place":["Paris, France"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew","family":"Vo","sequence":"additional","affiliation":[{"name":"The Neuro - Montreal Neurological Institute and Hospital, McGill University","place":["Montreal, QC, Canada"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"St\u00e9phane","family":"Leh\u00e9ricy","sequence":"additional","affiliation":[{"name":"Sorbonne Universit\u00e9, Institut du Cerveau - Paris Brain Institute - ICM, AP-HP, CNRS, Inserm, H\u00f4pital de la Piti\u00e9 Salp\u00eatri\u00e8re, Department of Neuroradiology","place":["Paris, France"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alain","family":"Dagher","sequence":"additional","affiliation":[{"name":"The Neuro - Montreal Neurological Institute and Hospital, McGill University","place":["Montreal, QC, Canada"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,3,9]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"906","DOI":"10.1162\/netn_a_00296","article-title":"Predicting longitudinal brain atrophy in Parkinson\u2019s disease using a susceptible-infected-removed agent-based model","volume":"7","author":"Abdelgawad","year":"2023","journal-title":"Netw. Neurosci."},{"key":"ref9001","doi-asserted-by":"publisher","first-page":"16488","DOI":"10.1038\/s41598-019-52829-8","article-title":"Differentiation of multiple system atrophy from Parkinson\u2019s disease by structural connectivity derived from probabilistic tractography","volume":"9","author":"Abos","year":"2019","journal-title":"Sci Rep."},{"key":"ref2","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1016\/j.neuroimage.2016.05.054","article-title":"Joint feature-sample selection and robust diagnosis of Parkinson\u2019s disease from MRI data","volume":"141","author":"Adeli","year":"2016","journal-title":"NeuroImage"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.media.2018.05.004","article-title":"Complex networks reveal early MRI markers of Parkinson\u2019s disease","volume":"48","author":"Amoroso","year":"2018","journal-title":"Med. Image Anal."},{"key":"ref4","doi-asserted-by":"publisher","first-page":"e222","DOI":"10.1016\/s2589-7500(19)30105-0","article-title":"Development and validation of the automated imaging differentiation in parkinsonism (AID-P): a multi-site machine learning study","volume":"1","author":"Archer","year":"2019","journal-title":"Lancet Digit Health"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"4237","DOI":"10.1038\/s41467-021-24306-2","article-title":"Genetic influences on hub connectivity of the human connectome","volume":"12","author":"Arnatkeviciute","year":"2021","journal-title":"Nat. Commun."},{"key":"ref6","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1016\/j.nicl.2017.07.011","article-title":"Diffusion tensor imaging in Parkinson\u2019s disease: review and meta-analysis","volume":"16","author":"Atkinson-Clement","year":"2017","journal-title":"Neuroimage Clin."},{"key":"ref7","doi-asserted-by":"publisher","first-page":"101720","DOI":"10.1016\/j.nicl.2019.101720","article-title":"Cerebellar resting-state functional connectivity in Parkinson\u2019s disease and multiple system atrophy: characterization of abnormalities and potential for differential diagnosis at the single-patient level","volume":"22","author":"Baggio","year":"2019","journal-title":"Neuroimage Clin."},{"key":"ref8","doi-asserted-by":"publisher","first-page":"353","DOI":"10.1038\/nn.4502","article-title":"Network neuroscience","volume":"20","author":"Bassett","year":"2017","journal-title":"Nat. Neurosci."},{"key":"ref9","doi-asserted-by":"publisher","first-page":"747","DOI":"10.1038\/s41583-023-00752-3","article-title":"Towards a biologically annotated brain connectome","volume":"24","author":"Bazinet","year":"2023","journal-title":"Nat. Rev. Neurosci."},{"key":"ref10","doi-asserted-by":"publisher","first-page":"9808","DOI":"10.1523\/jneurosci.1788-16.2017","article-title":"Prying into the prion hypothesis for Parkinson\u2019s disease","volume":"37","author":"Brundin","year":"2017","journal-title":"J. Neurosci."},{"key":"ref11","doi-asserted-by":"publisher","first-page":"709","DOI":"10.1212\/WNL.0000000000002985","article-title":"Functional MRI of disease progression in Parkinson disease and atypical parkinsonian syndromes","volume":"87","author":"Burciu","year":"2016","journal-title":"Neurology"},{"key":"ref12","doi-asserted-by":"publisher","first-page":"2183","DOI":"10.1093\/brain\/awx146","article-title":"Progression marker of Parkinson\u2019s disease: a 4-year multi-site imaging study","volume":"140","author":"Burciu","year":"2017","journal-title":"Brain"},{"key":"ref13","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1097\/WCO.0000000000000838","article-title":"Machine learning for classification and prediction of brain diseases: recent advances and upcoming challenges","volume":"33","author":"Burgos","year":"2020","journal-title":"Curr. Opin. Neurol."},{"key":"ref14","doi-asserted-by":"publisher","first-page":"919081","DOI":"10.3389\/fnhum.2022.919081","article-title":"Identifying and validating subtypes of Parkinson\u2019s disease based on multimodal MRI data via hierarchical clustering analysis","volume":"16","author":"Cao","year":"2022","journal-title":"Front. Hum. Neurosci."},{"key":"ref15","doi-asserted-by":"publisher","first-page":"402","DOI":"10.3390\/diagnostics10060402","article-title":"Detection of Parkinson\u2019s disease from 3T T1 weighted MRI scans using 3D convolutional neural network","volume":"10","author":"Chakraborty","year":"2020","journal-title":"Diagnostics (Basel)"},{"key":"ref16","doi-asserted-by":"publisher","first-page":"586","DOI":"10.1016\/j.nicl.2017.09.010","article-title":"Refining diagnosis of Parkinson\u2019s disease with deep learning-based interpretation of dopamine transporter imaging","volume":"16","author":"Choi","year":"2017","journal-title":"Neuroimage Clin."},{"key":"ref17","doi-asserted-by":"publisher","first-page":"460","DOI":"10.1002\/mds.28348","article-title":"Automated categorization of parkinsonian syndromes using magnetic resonance imaging in a clinical setting","volume":"36","author":"Chougar","year":"2020","journal-title":"Mov. Disord."},{"key":"ref18","doi-asserted-by":"publisher","first-page":"825","DOI":"10.1002\/mds.29760","article-title":"Contribution of MRI for the early diagnosis of parkinsonism in patients with diagnostic uncertainty","volume":"39","author":"Chougar","year":"2024","journal-title":"Mov. Disord."},{"key":"ref20","doi-asserted-by":"publisher","first-page":"514","DOI":"10.1097\/WCO.0000000000000957","article-title":"Update on neuroimaging for categorization of Parkinson\u2019s disease and atypical parkinsonism","volume":"34","author":"Chougar","year":"2021","journal-title":"Curr. Opin. Neurol."},{"key":"ref19","doi-asserted-by":"publisher","first-page":"665","DOI":"10.3389\/fneur.2020.00665","article-title":"The role of magnetic resonance imaging for the diagnosis of atypical parkinsonism","volume":"11","author":"Chougar","year":"2020","journal-title":"Front. Neurol."},{"key":"ref9002","doi-asserted-by":"publisher","first-page":"966","DOI":"10.3174\/ajnr.A5136","article-title":"Combined Diffusion Tensor Imaging and Apparent Transverse Relaxation Rate Differentiate Parkinson Disease and Atypical Parkinsonism","volume":"38","author":"Du","year":"2017","journal-title":"AJNR Am J Neuroradiol."},{"key":"ref21","doi-asserted-by":"publisher","first-page":"2130","DOI":"10.1002\/mds.30002","article-title":"Houston, we have AI problem! Quality issues with neuroimaging-based artificial intelligence in Parkinson\u2019s disease: a systematic review","volume":"39","author":"Dzialas","year":"2024","journal-title":"Mov. Disord."},{"key":"ref23","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1007\/pl00007856","article-title":"The heterogeneity of idiopathic Parkinson\u2019s disease","volume":"249","author":"Foltynie","year":"2002","journal-title":"J. Neurol."},{"key":"ref24","doi-asserted-by":"publisher","first-page":"3072","DOI":"10.1093\/brain\/awz223","article-title":"Regional transcriptional architecture of Parkinson\u2019s disease pathogenesis and network spread","volume":"142","author":"Freeze","year":"2019","journal-title":"Brain"},{"key":"ref25","doi-asserted-by":"publisher","first-page":"103250","DOI":"10.1016\/j.nicl.2022.103250","article-title":"Nigranet: an automatic framework to assess nigral neuromelanin content in early Parkinson\u2019s disease using convolutional neural network","volume":"36","author":"Gaurav","year":"2022","journal-title":"Neuroimage Clin."},{"key":"ref26","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1016\/j.nbd.2019.01.003","article-title":"Integrative analysis of blood metabolomics and PET brain neuroimaging data for Parkinson\u2019s disease","volume":"124","author":"Glaab","year":"2019","journal-title":"Neurobiol. Dis."},{"key":"ref27","volume-title":"Implementation of an algorithm for estimating free-water fraction in diffusion-weighted magnetic resonance imaging","author":"Golub","year":"2018"},{"key":"ref28","doi-asserted-by":"publisher","first-page":"1240","DOI":"10.1038\/s41562-021-01082-z","article-title":"Mapping gene transcription and neurocognition across human neocortex","volume":"5","author":"Hansen","year":"2021","journal-title":"Nat. Hum. Behav."},{"key":"ref29","doi-asserted-by":"publisher","first-page":"3077","DOI":"10.1093\/brain\/awaa238","article-title":"Brain-first versus body-first Parkinson\u2019s disease: a multimodal imaging case-control study","volume":"143","author":"Horsager","year":"2020","journal-title":"Brain"},{"key":"ref30","doi-asserted-by":"publisher","first-page":"1506","DOI":"10.1002\/mds.26715","article-title":"Differentiation of neurodegenerative parkinsonian syndromes by volumetric magnetic resonance imaging analysis and support vector machine classification","volume":"31","author":"Huppertz","year":"2016","journal-title":"Mov. Disord."},{"key":"ref31","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.parkreldis.2020.11.010","article-title":"Hierarchical cluster analysis of multimodal imaging data identifies brain atrophy and cognitive patterns in Parkinson\u2019s disease","volume":"82","author":"Inguanzo","year":"2021","journal-title":"Parkinsonism Relat. Disord."},{"key":"ref32","doi-asserted-by":"publisher","first-page":"e1003956","DOI":"10.1371\/journal.pcbi.1003956","article-title":"Epidemic spreading model to characterize misfolded proteins propagation in aging and associated neurodegenerative disorders","volume":"10","author":"Iturria-Medina","year":"2014","journal-title":"PLoS Comput. Biol."},{"key":"ref33","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1038\/nature12481","article-title":"Self-propagation of pathogenic protein aggregates in neurodegenerative diseases","volume":"501","author":"Jucker","year":"2013","journal-title":"Nature"},{"key":"ref34","doi-asserted-by":"publisher","first-page":"2567","DOI":"10.1007\/s00330-015-4066-8","article-title":"Neurite orientation dispersion and density imaging in the substantia nigra in idiopathic Parkinson disease","volume":"26","author":"Kamagata","year":"2016","journal-title":"Eur. Radiol."},{"key":"ref35","doi-asserted-by":"publisher","first-page":"6891","DOI":"10.1007\/s00330-019-06327-0","article-title":"Deep learning to differentiate parkinsonian disorders separately using single midsagittal MR imaging: a proof of concept study","volume":"29","author":"Kiryu","year":"2019","journal-title":"Eur. Radiol."},{"key":"ref36","doi-asserted-by":"publisher","first-page":"104041","DOI":"10.1016\/j.compbiomed.2020.104041","article-title":"An explainable machine learning model for early detection of Parkinson\u2019s disease using LIME on DaTSCAN imagery","volume":"126","author":"Magesh","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"ref37","doi-asserted-by":"publisher","first-page":"fcaa065","DOI":"10.1093\/braincomms\/fcaa065","article-title":"Origins of atrophy in Parkinson linked to early onset and local transcription patterns","volume":"2","author":"Maia","year":"2020","journal-title":"Brain Commun."},{"key":"ref38","doi-asserted-by":"publisher","first-page":"2139","DOI":"10.1002\/mds.30300","article-title":"Deep learning to differentiate parkinsonian syndromes using multimodal magnetic resonance imaging: a proof-of-concept study","volume":"40","author":"Mattia","year":"2025","journal-title":"Mov. Disord."},{"key":"ref39","doi-asserted-by":"publisher","first-page":"633752","DOI":"10.3389\/fnagi.2021.633752","article-title":"Machine learning for the diagnosis of Parkinson\u2019s disease: a review of literature","volume":"13","author":"Mei","year":"2021","journal-title":"Front. Aging Neurosci."},{"key":"ref40","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1016\/j.parkreldis.2017.11.343","article-title":"Multi-class parkinsonian disorders classification with quantitative MR markers and graph-based features using support vector machines","volume":"47","author":"Morisi","year":"2018","journal-title":"Parkinsonism Relat. Disord."},{"key":"ref41","doi-asserted-by":"publisher","first-page":"e209498","DOI":"10.1212\/wnl.0000000000209498","article-title":"Spatiotemporal progression patterns of dopamine availability and deep gray matter volume in Parkinson disease\u2013related cognitive impairment","volume":"103","author":"Oh","year":"2024","journal-title":"Neurology"},{"key":"ref42","doi-asserted-by":"publisher","first-page":"1052","DOI":"10.1007\/s00259-017-3918-7","article-title":"Extraction, selection and comparison of features for an effective automated computer-aided diagnosis of Parkinson\u2019s disease based on [123I]FP-CIT SPECT images","volume":"45","author":"Oliveira","year":"2018","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"ref43","doi-asserted-by":"publisher","first-page":"48","DOI":"10.3389\/fninf.2019.00048","article-title":"Parkinson\u2019s disease detection using isosurfaces-based features and convolutional neural networks","volume":"13","author":"Ortiz","year":"2019","journal-title":"Front. Neuroinform."},{"key":"ref44","doi-asserted-by":"publisher","first-page":"975","DOI":"10.1093\/brain\/awaa461","article-title":"Sequence of clinical and neurodegeneration events in Parkinson\u2019s disease progression","volume":"144","author":"Oxtoby","year":"2021","journal-title":"Brain"},{"key":"ref45","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1016\/j.neuroimage.2019.03.001","article-title":"Predictive model of spread of Parkinson\u2019s pathology using network diffusion","volume":"192","author":"Pandya","year":"2019","journal-title":"NeuroImage"},{"key":"ref46","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1002\/mds.27307","article-title":"MRI supervised and unsupervised classification of Parkinson\u2019s disease and multiple system atrophy","volume":"33","author":"P\u00e9ran","year":"2018","journal-title":"Mov. Disord."},{"key":"ref47","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1186\/s42466-020-00092-y","article-title":"A machine learning-based classification approach on Parkinson\u2019s disease diffusion tensor imaging datasets","volume":"2","author":"Prasuhn","year":"2020","journal-title":"Neurol. Res. Pract."},{"key":"ref48","doi-asserted-by":"publisher","first-page":"e460","DOI":"10.1212\/WNL.0000000000011155","article-title":"Parkinson disease propagation using MRI biomarkers and partial least squares path modeling","volume":"96","author":"Pyatigorskaya","year":"2021","journal-title":"Neurology"},{"key":"ref49","doi-asserted-by":"publisher","first-page":"3162","DOI":"10.1101\/2021.12.27.21268442","article-title":"Brain atrophy in prodromal synucleinopathy is shaped by structural connectivity and gene expression","volume":"145","author":"Rahayel","year":"2022","journal-title":"Brain"},{"key":"ref50","doi-asserted-by":"publisher","first-page":"1204","DOI":"10.1016\/j.neuron.2011.12.040","article-title":"A network diffusion model of disease progression in dementia","volume":"73","author":"Raj","year":"2012","journal-title":"Neuron"},{"key":"ref51","doi-asserted-by":"publisher","first-page":"3702","DOI":"10.1093\/brain\/awae303","article-title":"Delineating three distinct spatiotemporal patterns of brain atrophy in Parkinson\u2019s disease","volume":"147","author":"Sakato","year":"2024","journal-title":"Brain"},{"key":"ref52","doi-asserted-by":"publisher","first-page":"1242","DOI":"10.1212\/WNL.0000000000002518","article-title":"Diagnostic potential of automated subcortical volume segmentation in atypical parkinsonism","volume":"86","author":"Scherfler","year":"2016","journal-title":"Neurology"},{"key":"ref53","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1016\/j.neuron.2009.03.024","article-title":"Neurodegenerative diseases target large-scale human brain networks","volume":"62","author":"Seeley","year":"2009","journal-title":"Neuron"},{"key":"ref54","doi-asserted-by":"publisher","first-page":"fcaf146","DOI":"10.1093\/braincomms\/fcaf146","article-title":"Neuroimaging-based data-driven subtypes of spatiotemporal atrophy due to Parkinson\u2019s disease","volume":"7","author":"Shawa","year":"2025","journal-title":"Brain Commun."},{"key":"ref55","doi-asserted-by":"publisher","first-page":"396","DOI":"10.3389\/fnins.2019.00396","article-title":"Use of overlapping group LASSO sparse deep belief network to discriminate Parkinson\u2019s disease and normal control","volume":"13","author":"Shen","year":"2019","journal-title":"Front. Neurosci."},{"key":"ref9003","doi-asserted-by":"publisher","first-page":"101748","DOI":"10.1016\/j.nicl.2019.101748","article-title":"Predictive markers for Parkinson\u2019s disease using deep neural nets on neuromelanin sensitive MRI","volume":"22","author":"Shinde","year":"2019","journal-title":"Neuroimage Clin."},{"key":"ref56","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1016\/j.jneumeth.2018.05.009","article-title":"Alzheimer\u2019s Disease Neuroimaging Initiative, Parkinson progression marker initiative. Determination of imaging biomarkers to decipher disease trajectories and differential diagnosis of neurodegenerative diseases (DIsease TreND)","volume":"305","author":"Singh","year":"2018","journal-title":"J. Neurosci. Methods"},{"key":"ref57","doi-asserted-by":"publisher","first-page":"105793","DOI":"10.1016\/j.cmpb.2020.105793","article-title":"Classification of PPMI MRI scans with voxel-based morphometry and machine learning to assist in the diagnosis of Parkinson\u2019s disease","volume":"198","author":"Solana-Lavalle","year":"2021","journal-title":"Comput. Methods Prog. Biomed."},{"key":"ref59","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1186\/s13550-018-0393-5","article-title":"Computer-aided diagnosis for (123I)FP-CIT imaging: impact on clinical reporting","volume":"8","author":"Taylor","year":"2018","journal-title":"EJNMMI Res."},{"key":"ref60","doi-asserted-by":"publisher","first-page":"fcab269","DOI":"10.1093\/braincomms\/fcab269","article-title":"Brain atrophy progression in Parkinson\u2019s disease is shaped by connectivity and local vulnerability","volume":"3","author":"Tremblay","year":"2021","journal-title":"Brain Commun."},{"key":"ref9004","doi-asserted-by":"publisher","first-page":"100541","DOI":"10.1016\/j.bj.2022.05.006","article-title":"Diffusion tensor imaging for the differential diagnosis of Parkinsonism by machine learning","volume":"46","author":"Tsai","year":"2023","journal-title":"Biomed J."},{"key":"ref61","doi-asserted-by":"publisher","first-page":"495","DOI":"10.1001\/jamaneurol.2025.0112","article-title":"Automated imaging differentiation for parkinsonism","volume":"82","author":"Vaillancourt","year":"2025","journal-title":"JAMA Neurol."},{"key":"ref62","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1038\/s41746-022-00592-y","article-title":"Machine learning for medical imaging: methodological failures and recommendations for the future","volume":"5","author":"Varoquaux","year":"2022","journal-title":"NPJ Digit Med."},{"key":"ref63","doi-asserted-by":"publisher","first-page":"awaf432","DOI":"10.1093\/brain\/awaf432","article-title":"Global network and local vulnerabilities underlie brain atrophy across Parkinson\u2019s disease stages","author":"Vo","year":"2025","journal-title":"Brain"},{"key":"ref64","doi-asserted-by":"publisher","first-page":"103523","DOI":"10.1016\/j.nicl.2023.103523","article-title":"Network connectivity and local transcriptomic vulnerability underpin cortical atrophy progression in Parkinson\u2019s disease","volume":"40","author":"Vo","year":"2023","journal-title":"Neuroimage Clin."},{"key":"ref65","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1080\/01621459.1963.10500845","article-title":"Hierarchical grouping to optimize an objective function","volume":"58","author":"Ward","year":"1963","journal-title":"J. Am. Stat. Assoc."},{"key":"ref66","doi-asserted-by":"publisher","first-page":"561","DOI":"10.1016\/j.tins.2013.06.007","article-title":"Molecular nexopathies: a new paradigm of neurodegenerative disease","volume":"36","author":"Warren","year":"2013","journal-title":"Trends Neurosci."},{"key":"ref67","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1162\/netn_a_00057","article-title":"Multiscale examination of cytoarchitectonic similarity and human brain connectivity","volume":"3","author":"Wei","year":"2018","journal-title":"Netw Neurosci."},{"key":"ref68","doi-asserted-by":"publisher","first-page":"773","DOI":"10.21037\/atm.2019.11.26","article-title":"Use of radiomic features and support vector machine to distinguish Parkinson\u2019s disease cases from normal controls","volume":"7","author":"Wu","year":"2019","journal-title":"Ann Transl Med."},{"key":"ref69","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1038\/s41467-017-02416-0","article-title":"Network connectivity determines cortical thinning in early Parkinson\u2019s disease progression","volume":"9","author":"Yau","year":"2018","journal-title":"Nat. Commun."},{"key":"ref70","doi-asserted-by":"publisher","first-page":"4273","DOI":"10.1038\/s41467-018-05892-0","article-title":"Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with subtype and stage inference","volume":"9","author":"Young","year":"2018","journal-title":"Nat. Commun."},{"key":"ref71","doi-asserted-by":"publisher","first-page":"e08440","DOI":"10.7554\/eLife.08440","article-title":"Network structure of brain atrophy in de novo Parkinson\u2019s disease","volume":"4","author":"Zeighami","year":"2015","journal-title":"eLife"},{"key":"ref73","doi-asserted-by":"publisher","first-page":"3531","DOI":"10.1109\/EMBC.2019.8856747","article-title":"A 3D deep residual convolutional neural network for differential diagnosis of parkinsonian syndromes on 18F-FDG PET images","volume":"2019","author":"Zhao","year":"2019","journal-title":"Annu Int Conf IEEE Eng Med Biol Soc"},{"key":"ref74","doi-asserted-by":"publisher","first-page":"e3000495","DOI":"10.1371\/journal.pbio.3000495","article-title":"Local vulnerability and global connectivity jointly shape neurodegenerative disease propagation","volume":"17","author":"Zheng","year":"2019","journal-title":"PLoS Biol."},{"key":"ref75","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1038\/s41531-023-00556-3","article-title":"Two distinct trajectories of clinical and neurodegeneration events in Parkinson\u2019s disease","volume":"9","author":"Zhou","year":"2023","journal-title":"NPJ Parkinsons Dis."}],"container-title":["Frontiers in Aging Neuroscience"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnagi.2026.1786423\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T06:46:20Z","timestamp":1773038780000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnagi.2026.1786423\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,9]]},"references-count":76,"alternative-id":["10.3389\/fnagi.2026.1786423"],"URL":"https:\/\/doi.org\/10.3389\/fnagi.2026.1786423","relation":{},"ISSN":["1663-4365"],"issn-type":[{"value":"1663-4365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,9]]},"article-number":"1786423"}}