LAUSR

Functional connectivity analysis techniques have broadly applied to capture phenomenological aspects of the brain, e.g., by identifying characteristic network topologies for healthy and disease-affected populations, by highlighting several areas important for the global efficiency of the brain during some cognitive processing and at rest. However, most of the known methods for quantifying functional coupling between fMRI time series are focused on linear correlation metrics. In this work, we propose a multidimensional framework to extract multiple descriptors of the dynamic interaction among BOLD signals in their phase space. A set of metrics is extracted from the cross recurrence plots of each couple of signals to form a multilayer connectivity matrix in which each layer is related to a specific complex dynamic phenomenon. The proposed framework is used to characterize functional abnormalities during a working memory task in patients with schizophrenia. Some topological descriptors are then extracted from both multilayer connectivity matrices and the most used Pearson-based connectivity networks to perform a binary classification task of normal controls and patients. The results show that the proposed connectivity model outperforms the statistical correlation-based connectivity in accuracy, sensitivity and specificity. Moreover, the statistical analysis of the selected features highlights that several dynamic metrics could better identify disease-related dynamic states in brain activity than the statistical correlation among physiological signals.