Discovering Subtypes with Imaging Signatures in the Motoric Cognitive Risk Syndrome Consortium using Weakly-Supervised Clustering
Nallapu, B.; Ezzati,, A.; Blumen, H. M.; Petersen,, K. K.; Lipton,, R. B.; Ayers, E.; Pradeep Kumar, V. G.; Velandai, S.; Beare, R.; Beauchet, O.; Doi, T.; Shimada, H.; Milman, S.; Aleksic, S.; Verghese, J.
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STRUCTURED ABSTRACTO_ST_ABSINTRODUCTIONC_ST_ABSUnderstanding the heterogeneity of brain structure in individuals with the Motoric Cognitive Risk Syndrome (MCR) may improve the current risk assessments of dementia. METHODSWe used data from 6 cohorts from the MCR consortium (N=1987). A weakly- supervised clustering algorithm called HYDRA was applied to volumetric MRI measures to identify distinct subgroups in the population with gait speeds lower than one standard deviation (1SD) above mean. RESULTSThree subgroups (Groups A, B & C) were identified through MRI-based clustering with significant differences in regional brain volumes, gait speeds, and performance on Trail Making (Part-B) and Free and Cued Selective Reminding Tests. DISCUSSIONBased on structural MRI, our results reflect heterogeneity in the population with moderate and slow gait, including those with MCR. Such a data-driven approach could help pave new pathways toward dementia at-risk stratification and have implications for precision health for patients.
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