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Alzheimer's disease (AD) is the only major cause of mortality in the worldwithout an effective disease modifying treatment. Evidence supporting the socalled disconnection hypothesis suggests that functional connectivitybiomarkers may have clinical potential for early detection of AD. However,known issues with low test-retest reliability and signal to noise in functionalconnectivity may prevent accuracy and subsequent predictive capacity. Wevalidate the utility of a novel principal component based diagnosticidentifiability framework to increase separation in functional connectivityacross the Alzheimer's spectrum by identifying and reconstructing FC using onlyAD sensitive components or connectivity modes. We show that this framework (1)increases test-retest correspondence and (2) allows for better separation, infunctional connectivity, of diagnostic groups both at the whole brain andindividual resting state network level. Finally, we evaluate a posteriori theassociation between connectivity mode weights with longitudinal neurocognitiveoutcomes.

Towards Subject and Diagnostic Identifiability in the Alzheimer's Disease Spectrum based on Functional Connectomes