Consciousness & Brain Functional Complexity in Propofol Anaesthesia

Journal article

Dynamic functional connectivity (dFC) in resting-state fMRI holds promise to deliver candidate biomarkers for clinical applications. However, the reliability and interpretability of dFC metrics remain contested. Despite a myriad of methodologies and resulting measures, few studies have combined metrics derived from different conceptualizations of brain functioning within the same analysis - perhaps missing an opportunity for improved interpretability. Using a complexity-science approach, we assessed the reliability and interrelationships of a battery of phase-based dFC metrics including tools originating from dynamical systems, stochastic processes, and information dynamics approaches. Our analysis revealed novel relationships between these metrics, which allowed us to build a predictive model for integrated information using metrics from dynamical systems and information theory. Furthermore, global metastability - a metric reflecting simultaneous tendencies for coupling and decoupling - was found to be the most representative and stable metric in brain parcellations that included cerebellar regions. Additionally, spatiotemporal patterns of phase-locking were found to change in a slow, non-random, continuous manner over time. Taken together, our findings show that the majority of characteristics of resting-state fMRI dynamics reflect an interrelated dynamical and informational complexity profile, which is unique to each acquisition. This finding challenges the interpretation of results from cross-sectional designs for brain neuromarker discovery, suggesting that individual life-trajectories may be more informative than sample means

Authors: Varley, TF; Luppi, AI; Pappas, I; Naci, L; Adapa, R

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Nature Research
• Journal: Scientific Reports
• Volume: 10
• Issue: 1
• Pages: 1018-1018
• Publication date: 2020-01-23
• DOI: 10.1038/s41598-020-57695-3
• EISSN: 2045-2322
• ISSN: 2045-2322
• Language: English
• Keywords: Medicine; Construct (python library); Sedation; Propofol; Principal component analysis; Variance (accounting); Anesthesia; Consciousness; Computer science; Cognitive psychology; Artificial intelligence; Psychology; Neuroscience