Abstract
The identifiability framework (𝕀f) 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 whether uncovering identifiability at the connectome level also uncovers identifiability on the derived network properties. This also raises the question of where to apply the 𝕀f 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 𝕀f is applied (a) on the functional connectomes, and (b) directly on derived network measurements. Results show that improving across-session reliability of functional connectomes (FCs) also improves reliability of derived network measures. We also find that, for specific network properties, application of 𝕀f 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 focused on subject-level inferences, this framework is able to uncover FC fingerprints, which propagate to derived network properties.
Highlights
The analysis of structural and functional human brain connectivity based on network science has become prevalent for understanding the underlying mechanisms of the human brain
In this work we found that improving the fingerprint of the functional connectome improves the “fingerprint” of its network properties
When using the identifiability framework on the network properties directly, certain network properties like search information and communicability add to the functional connectome (FC) fingerprint
Summary
The analysis of structural and functional human brain connectivity based on network science has become prevalent for understanding the underlying mechanisms of the human brain. Many brain connectivity studies used group-level comparisons, where data from many subjects are collapsed (e.g., group averaging) into a representative sample of clinical and healthy population (Castellanos, Di Martino, Craddock, Mehta, & Milham, 2013; Crossley et al, 2014; Fornito, Zalesky, & Breakspear, 2015). This comes at a price of potentially ignoring intragroup individual variability (Seitzman et al, 2019). These subject-specific fingerprints have been used to track fluctuations in attention at the individual level (Rosenberg et al, 2019)
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