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Functional connectivity (FC) changes have been shown to dynamically associate with
propagation of AD pathology and with cognitive outcomes, thus showing promise as theragnostic
biomarkers of AD. However, FC-based biomarkers and predictive models lack the reproducibility
and generalizability needed for clinical use. To address this, we propose an approach
combining connectome predictive modeling (CPM) and PCA based differential identifiability
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