An implementation of integrated information theory in resting-state fMRI.
Idan E Nemirovsky, Nicholas J M Popiel, Jorge Rudas, Matthew Caius, Lorina Naci, Nicholas D. Schiff, Adrian M. Owen, Andrea Soddu
Communications Biology July 5, 2023 DOI: 10.1038/s42003-023-05063-y (opens in new tab) via PubMed
Summary
AI-generated from the abstractIntegrated Information Theory aims to explain and measure consciousness by quantifying how integrated a system's causal properties are. This work implemented version 3.0 of the theory on functional MRI data from 17 healthy volunteers sedated with propofol. Using the PyPhi software, the measure Φmax was computed and compared with other proposed consciousness metrics: an earlier integrated information version, Granger causality, and correlation-based functional connectivity. Φmax showed varied responses to sedation across different brain networks. Changes in Φmax closely tracked changes in conscious level within the frontoparietal and dorsal attention networks, which support higher-order cognition. The findings offer guidance for future use of these measures in neuroimaging.
Study at a glance
| Characteristics | Observational cohort Peer reviewed |
|---|---|
| Sample size | 17 |
| Population | Healthy subjects |
| Intervention | propofol |
| Key finding | Changes in Φmax closely reflected changes in subjects' conscious level in the frontoparietal and dorsal attention networks. |
Abstract
Integrated Information Theory was developed to explain and quantify consciousness, arguing that conscious systems consist of elements that are integrated through their causal properties. This study presents an implementation of Integrated Information Theory 3.0, the latest version of this framework, to functional MRI data. Data were acquired from 17 healthy subjects who underwent sedation with propofol, a short-acting anaesthetic. Using the PyPhi software package, we systematically analyze how Φmax, a measure of integrated information, is modulated by the sedative in different resting-state networks. We compare Φmax to other proposed measures of conscious level, including the previous version of integrated information, Granger causality, and correlation-based functional connectivity. Our results indicate that Φmax presents a variety of sedative-induced behaviours for different networks. Notably, changes to Φmax closely reflect changes to subjects' conscious level in the frontoparietal and dorsal attention networks, which are responsible for higher-order cognitive functions. In conclusion, our findings present important insight into different measures of conscious level that will be useful in future implementations to functional MRI and other forms of neuroimaging.