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Here, we show that the biophysical properties and neurotoxicity of \u03b1-syn strains change as PD cognitive status transitions from NC to mild cognitive impairment (PD-MCI) and dementia (PD-D). Both cross-sectional and longitudinal analyses reveal distinct \u03b1-syn strains in PD patients correlating to their level of cognitive impairment. Machine learning (ML) was employed to achieve high classification accuracy. The combination of thioflavin T (ThT) maximal fluorescence intensity (mfi), max slope of rise curve (forming rate), lag time (t\n                  <jats:sub>lag<\/jats:sub>\n                  ), 20% time (t\n                  <jats:sub>20<\/jats:sub>\n                  ), and half-time (t\n                  <jats:sub>50<\/jats:sub>\n                  ), dynamic light scattering (DLS) (peak number, \u00bd peak size, \u00bd peak intensity) and neurotoxicity together with demographic variables for model training yielded superior performance (89\u223c99% accuracy in the 4- and 2- classification schema) compared to individual features alone in classifying cognitive status. For the longitudinal study, DLS peak number emerged as the strongest predictor of cognitive transition (HR = 0.12,\n                  <jats:italic>P<\/jats:italic>\n                  = 0.002), with the optimal predictive model combining DLS peak number, sex, education, DLS peak 1 size, and DLS peak 2 polydispersity achieving high accuracy (C-index of \u223c93%). This study presents evidence that individuals with PD have different \u03b1-syn strains correlating to their cognitive status and highlights the potential of \u03b1-syn strain dynamics to guide future diagnosis, management, and stratification of PD patients.\n                <\/jats:p>\n                <jats:sec>\n                  <jats:title>One Sentence Summary<\/jats:title>\n                  <jats:p>Distinct features of \u03b1-syn strains change with cognitive decline in Parkinson\u2019s disease and AI-based analysis incorporating these combined characteristics serves as a powerful tool for PD clinical stratification.<\/jats:p>\n                <\/jats:sec>","DOI":"10.1101\/2024.10.22.619694","type":"posted-content","created":{"date-parts":[[2024,10,24]],"date-time":"2024-10-24T05:15:13Z","timestamp":1729746913000},"source":"Crossref","is-referenced-by-count":4,"title":["\u03b1-Synuclein strain dynamics predict cognitive transitions in Parkinson\u2019s disease"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4530-4772","authenticated-orcid":false,"given":"Kundlik","family":"Gadhave","sequence":"first","affiliation":[{"name":"Neuroregeneration and Stem Cell Programs, Institute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA"},{"name":"Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3410-8509","authenticated-orcid":false,"given":"Ning","family":"Wang","sequence":"additional","affiliation":[{"name":"Neuroregeneration and Stem Cell Programs, Institute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA"},{"name":"Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, 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