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The Identifiability Framework (If) has been shown to improve differentialidentifiability (reliability across-sessions and -sites, and differentiabilityacross-subjects) of functional connectomes for a variety of fMRI tasks. Buthaving a robust single session/subject functional connectome is just thestarting point to subsequently assess network properties for characterizingproperties of integration, segregation and communicability, among others.Naturally, one wonders if uncovering identifiability at the connectome levelalso uncovers identifiability on the derived network properties. This alsoraises the question of where to apply the If framework: on the connectivitydata or directly on each network measurement? Our work answers these questionsby exploring the differential identifiability profiles of network measures whenIf is applied on 1) the functional connectomes, and 2) directly on derivednetwork measurements. Results show that improving across-session reliability ofFCs also improves reliability of derived network measures. We also find that,for specific network properties, application of If directly on networkproperties is more effective. Finally, we discover that applying the framework,either way, increases task sensitivity of network properties. At a time whenthe neuroscientific community is focused on subject-level inferences, thisframework is able to uncover FC fingerprints, which propagates to derivednetwork properties.

Uncovering differential identifiability in network properties of human brain functional connectomes