A Dynamic Entropy Approach Reveals Reduced Functional Network Connectivity Trajectory Complexity in Schizophrenia

• Published in: Entropy (Basel, Switzerland) Vol. 26; no. 7; p. 545
• Main Authors: Blair, David Sutherland, Miller, Robyn L., Calhoun, Vince D.
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.07.2024
• Subjects: Brain; Brain research; Communication; Complexity; dynamic functional connectivity; Dynamic systems theory; Dynamical systems; Electroencephalography; Entropy; Entropy (Information theory); functional network connectivity; independent component analysis; Information processing (biology); Information theory; Medical imaging; Medical research; Medicine, Experimental; NeuroMark; Neurosciences; Patients; principal component analysis; Schizophrenia; Shannon entropy; Time series; Trajectory analysis; Trajectory control; Trajectory measurement; dynamic functional connectivity; independent component analysis; functional network connectivity; Shannon entropy; sliding window correlation; NeuroMark; multiple linear regression; principal component analysis
• ISSN: 1099-4300; 1099-4300
• DOI: 10.3390/e26070545

Over the past decade and a half, dynamic functional imaging has revealed low-dimensional brain connectivity measures, identified potential common human spatial connectivity states, tracked the transition patterns of these states, and demonstrated meaningful transition alterations in disorders and over the course of development. Recently, researchers have begun to analyze these data from the perspective of dynamic systems and information theory in the hopes of understanding how these dynamics support less easily quantified processes, such as information processing, cortical hierarchy, and consciousness. Little attention has been paid to the effects of psychiatric disease on these measures, however. We begin to rectify this by examining the complexity of subject trajectories in state space through the lens of information theory. Specifically, we identify a basis for the dynamic functional connectivity state space and track subject trajectories through this space over the course of the scan. The dynamic complexity of these trajectories is assessed along each dimension of the proposed basis space. Using these estimates, we demonstrate that schizophrenia patients display substantially simpler trajectories than demographically matched healthy controls and that this drop in complexity concentrates along specific dimensions. We also demonstrate that entropy generation in at least one of these dimensions is linked to cognitive performance. Overall, the results suggest great value in applying dynamic systems theory to problems of neuroimaging and reveal a substantial drop in the complexity of schizophrenia patients’ brain function.