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

Abstract

Over the past decade and a half, dynamic functional imaging has revolutionized the neuroimaging field. Since 2009, it has revealed low dimensional brain connectivity measures, has identified potential common human spatial connectivity states, has tracked the transition patterns of these states, and has demonstrated meaningful alterations in these transition and spatial patterns in neurological disorders, psychiatric disorders, and over the course of development. More recently, researchers have begun to analyze this data from the perspective of dynamic system and information theory in the hopes of understanding the constraints within which these dynamics occur and how they may support less easily quantified processes, such as information processing, cortical hierarchy, and consciousness. Progress has begun to accelerate in this area, particularly around consciousness, which appears to be strongly linked to entropy production in the brain. Outside of disorders of consciousness, however, little attention has been paid to the effects of psychiatric disease on entropy production in the human brain. Even disorders characterized by substantial changes in conscious experience have not been widely analyzed from this perspective. Here, we begin to rectify this gap by examining the complexity of subject trajectories in this 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 state space over the course of the scan. The dynamic complexity of these trajectories is estimated using a Kozachenko-Leonenko entropy estimator, which assesses the rate of Shannon entropy production 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 of projected basis space. We also demonstrate that entropy generation in at least one of these dimensions is linked to cognitive performance. Overall, results suggest great value in applying dynamic systems theory to problems of neuroimaging and reveal a substantial drop in the complexity of brain function in schizophrenia patients.

Static and Dynamic Cross-Network Functional Connectivity Shows Elevated Entropy in Schizophrenia Patients

“…The temporal profiles of each subject’s ICNs were detrended, orthogonally aligned with motion parameters, and despiked. Detailed description of data collection, estimation of the functional networks, their functional connectivity and number of temporally independent sources are provided in (Blair et al, 2024; Du et al, 2020).…”

“…Metrics originating from information theory, particularly those linked with entropy, have shown their ability in extracting meaningful information from underlying brain networks, in both healthy and mental disorder state (Poza et al, 2021). Thus, current study (Blair et al, 2024) tracked subject trajectories in dynamic functional connectivity state space during brain scans evaluating entropy production along each dimension of the proposed basis space. Authors found that schizophrenia patients demonstrate lower entropy, suggesting simpler trajectories compared to healthy controls.…”