Abstract
Functional connectomes (FCs) are estimations of brain region interaction derived from brain activity, often obtained from functional magnetic resonance imaging recordings. Quantifying the distance between FCs is important for understanding the relation between behavior, disorders, disease, and changes in connectivity. Recently, tangent space projections, which account for the curvature of the mathematical space of FCs, have been proposed for calculating FC distances. We compare the efficacy of this approach relative to the traditional method in the context of subject identification using the Midnight Scan Club dataset in order to study resting-state and task-based subject discriminability. The tangent space method is found to universally outperform the traditional method. We also focus on the subject identification efficacy of subnetworks. Certain subnetworks are found to outperform others, a dichotomy that largely follows the "control" and "processing" categorization of resting-state networks, and relates subnetwork flexibility with subject discriminability. Identification efficacy is also modulated by tasks, though certain subnetworks appear task independent. The uniquely long recordings of the dataset also allow for explorations of resource requirements for effective subject identification. The tangent space method is found to universally require less data, making it well suited when only short recordings are available.