Back

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.

2024-10-13 neurology
10.1101/2024.10.11.24315328 medRxiv
Show abstract

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.

Published in Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring · not in our set (fewer than 10 published preprints to learn from) · training set

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.