Uncovering differential identifiability in network properties of human brain functional connectomes

TL;DR

This study investigates whether applying the Identifiability Framework to functional connectomes or directly to network properties enhances the reliability and sensitivity of brain network measurements across sessions and tasks.

Contribution

It demonstrates that applying the framework at the connectome level or directly on network measures can improve reliability and task sensitivity of brain network properties.

Findings

Improving connectome reliability enhances network measure reliability.

Applying the framework directly on network properties can be more effective for specific measures.

Both approaches increase task sensitivity of network properties.

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 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 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 on 1) the functional connectomes, and 2) directly on derived network measurements. 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 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 propagates to derived network properties.