Identification of Neuroinflammation and Neuroimaging Biomarkers Through Data Driven Artificial Intelligence Techniques for Unraveling the Heterogeneity of Aged Subjects at Risk of Dementia and to Better Inform Prevention Strategies

SCD and MCI are very heterogeneous conditions, which can be prodromal to different types of dementia. The application of data driven clustering methods on neuroimaging and inflammatory data aims at identifying the features characterizing subgroups of subjects at high risk of developing overt dementia. This approach promises to develop tailored early interventions for subjects with profiles of cognitive decline correlating with high risk of progression. To this end in this project, we propose to examine in these subjects both brain structure and cerebral blood flow (CBF) through MR T1-weighted and pCASL (pseudo-continuous arterial spin labeling) imaging and the neuroinflammatory status. The latter will be done by measuring the populations of innate and adaptive immune cells, the chemokines that are required to attract these cells to their potential sites of action in the CNS, and the cytokines by means they exert their function. The analyses performed in aged subjects with MCI and in SCD, will allow identifying common mechanisms predisposing to an increased susceptibility to progression to overt cognitive decline.