Entropy in Brain Networks

Poza, Jesús; García, María M.; Gómez‐Pilar, Javier

doi:10.3390/e23091157

Cited by 2 publications

(1 citation statement)

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“…In recent years, there has been a notable increase in empirical studies with a focus on integration of both structural and functional connectivity analyses with information theory offering a powerful framework for advancing our understanding of brain organization (Poza et al, 2021). 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.…”

Section: Introductionmentioning

confidence: 99%

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

Maksymchuk,

Bustillo,

Mathalon

et al. 2024

Preprint

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Schizophrenia (SZ) patients exhibit abnormal static and dynamic functional connectivity across various brain domains. We present a novel approach based on static and dynamic inter-network connectivity entropy (ICE), which represents the entropy of a given network's connectivity to all the other brain networks. This novel approach enables the investigation of how connectivity strength is heterogeneously distributed across available targets in both SZ patients and healthy controls. We analyzed fMRI data from 151 schizophrenia patients and demographically matched 160 healthy controls. Our assessment encompassed both static and dynamic ICE, revealing significant differences in the heterogeneity of connectivity levels across available brain networks between SZ patients and healthy controls (HC). These networks are associated with subcortical (SC), auditory (AUD), sensorimotor (SM), visual (VIS), cognitive control (CC), default mode network (DMN) and cerebellar (CB) functional brain domains. Elevated ICE observed in individuals with SZ suggests that patients exhibit significantly higher randomness in the distribution of time-varying connectivity strength across functional regions from each source network, compared to healthy control group. C-means fuzzy clustering analysis of functional ICE correlation matrices revealed that SZ patients exhibit significantly higher occupancy weights in clusters with weak, low-scale functional entropy correlation, while the control group shows greater occupancy weights in clusters with strong, large-scale functional entropy correlation. k-means clustering analysis on time-indexed ICE vectors revealed that cluster with highest ICE have higher occupancy rates in SZ patients whereas clusters characterized by lowest ICE have larger occupancy rates for control group. Furthermore, our dynamic ICE approach revealed that it appears healthy for a brain to primarily circulate through complex, less structured connectivity patterns, with occasional transitions into more focused patterns. However, individuals with SZ seem to struggle with transiently attaining these more focused and structured connectivity patterns. Proposed ICE measure presents a novel framework for gaining deeper insights into understanding mechanisms of healthy and disease brain states and a substantial step forward in the developing advanced methods of diagnostics of mental health conditions.

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Section: Introductionmentioning

confidence: 99%