  Functional connectivity, as estimated using resting state fMRI, has shown
potential in bridging the gap between pathophysiology and cognition. However,
clinical use of functional connectivity biomarkers is impeded by unreliable
estimates of individual functional connectomes and lack of generalizability of
models predicting cognitive outcomes from connectivity. To address these
issues, we combine the frameworks of connectome predictive modeling and
differential identifiability. Using the combined framework, we show that
enhancing the individual fingerprint of resting state functional connectomes
leads to robust identification of functional networks associated to cognitive
outcomes and also improves prediction of cognitive outcomes from functional
connectomes. Using a comprehensive spectrum of cognitive outcomes associated to
Alzheimer's disease, we identify and characterize functional networks
associated to specific cognitive deficits exhibited in Alzheimer's disease.
This combined framework is an important step in making individual level
predictions of cognition from resting state functional connectomes and in
understanding the relationship between cognition and connectivity.
