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Abstract #1857

Disentangling the heterogeneity of MCI condition by unsupervised clustering of brain measurements on ASL and T1w MR imaging

Paolo Bosco1, Laura Biagi1, Giovanni Cioni2, Michela Matteoli3, Alessandro Sale3, Nicoletta Berardi3, Michela Tosetti1, and the Train the Brain Consortium4
1FiRMLAB, IRCCS Stella Maris Foundation, Pisa, Italy, 2IRCCS Stella Maris Foundation, Pisa, Italy, 3Institute of Neuroscience of the CNR, Pisa, Italy, 4the Train the Brain Consortium, Pisa, Italy

One of the main challenges in identifying people at risk of dementia is their clinical heterogeneity. One hypothesis is that the clinical symptoms may be the result of different biological processes. We applied a data-drive clustering approach on structural and perfusion brain-MR imaging on a cohort of 141 MCI subjects in order to elucidate homogeneous structural and perfusion profiles and we observed the correspondent clinical features. Unsupervised clustering identified 6 different clusters on both ASL and gray matter volume data. Perfusion and atrophy showed to be variable in the different clusters and showed dissimilar patterns at subcortical and cortical levels.

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