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IMPROVING PREDICTION OF COGNITIVE OUTCOMES FROM FUNCTIONAL CONNECTIVITY IN ALZHEIMER’S DISEASE

      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 optimization.
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