Balancing practicality and complexity in neuroimaging models of Parkinson's disease progression

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dc.contributor.authorKaasinen, Valtteri
dc.contributor.authorvan Eimeren, Thilo
dc.contributor.organizationfi=kliiniset neurotieteet|en=Clinical Neurosciences|
dc.contributor.organizationfi=tyks, vsshp|en=tyks, varha|
dc.contributor.organization-code1.2.246.10.2458963.20.74845969893
dc.converis.publication-id500427468
dc.converis.urlhttps://research.utu.fi/converis/portal/Publication/500427468
dc.date.accessioned2026-01-21T12:45:06Z
dc.date.available2026-01-21T12:45:06Z
dc.description.abstract<p>Reliable progression models are essential for clinical decision-making and trial design in Parkinson’s disease. We discuss linear, exponential, and sigmoidal patterns in PET and SPECT data, emphasizing the mismatch between biomarker and clinical trajectories. We propose more adaptable modeling strategies to improve patient stratification, support trial outcomes, and align imaging biomarkers with real-world disease complexity.<br></p>
dc.identifier.eissn2373-8057
dc.identifier.jour-issn2373-8057
dc.identifier.olddbid212934
dc.identifier.oldhandle10024/195952
dc.identifier.urihttps://www.utupub.fi/handle/11111/54119
dc.identifier.urlhttps://www.nature.com/articles/s41531-025-01125-6
dc.identifier.urnURN:NBN:fi-fe202601217281
dc.language.isoen
dc.okm.affiliatedauthorKaasinen, Valtteri
dc.okm.affiliatedauthorDataimport, tyks, vsshp
dc.okm.discipline3112 Neurosciencesen_GB
dc.okm.discipline3112 Neurotieteetfi_FI
dc.okm.internationalcopublicationinternational co-publication
dc.okm.internationalityInternational publication
dc.okm.typeB1 Scientific Journal
dc.publisherNATURE PORTFOLIO
dc.publisher.countryUnited Statesen_GB
dc.publisher.countryYhdysvallat (USA)fi_FI
dc.publisher.country-codeUS
dc.relation.articlenumber262
dc.relation.doi10.1038/s41531-025-01125-6
dc.relation.ispartofjournalNPJ Parkinson's disease
dc.relation.issue1
dc.relation.volume11
dc.source.identifierhttps://www.utupub.fi/handle/10024/195952
dc.titleBalancing practicality and complexity in neuroimaging models of Parkinson's disease progression
dc.year.issued2025

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