Uncovering Differential Identifiability in Network Properties of Human Brain Functional Connectomes RajapandianMeenusree 2020 <div>The Identifiability Framework (<b>I</b><i>f</i>) has been shown to improve differential identifiability (reliability across-sessions and -sites, and differentiability across-subjects) of functional connectomes for a variety of fMRI tasks. But having a robust single session/subject functional connectome is just the starting point to subsequently assess network properties for characterizing properties of integration, segregation and communicability, among others. Naturally, one wonders if uncovering identifiability at the connectome level also uncovers identifiability on the derived network properties. This also raises the question of where to apply the <b>I</b><i>f</i> framework: on the connectivity data or directly on each network measurement? Our work answers these questions by exploring the differential identifiability profiles of network measures when <b>I</b><i>f</i> is applied on 1) the functional connectomes, and 2) directly on derived network measurements.</div><div><br></div><div>Results show that improving across-session reliability of FCs also improves reliability of derived network measures. We also find that, for specific network properties, application of <b>I</b><i>f </i>directly on network properties is more effective. Finally, we discover that applying the framework, either way, increases task sensitivity of network properties. At a time when the neuroscientific community is moving away from group-average statistics towards subject-level inferences, we have shown that <b>I</b><i>f </i>is a useful tool to enhance robustness in FC fingerprints, which permeates to derived network properties as well.</div>