An implementation of integrated information theory in resting-state fMRI
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,...
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| Published in | Communications biology Vol. 6; no. 1; pp. 692 - 14 |
|---|---|
| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
Nature Publishing Group UK
05.07.2023
Nature Publishing Group Nature Portfolio |
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| Online Access | Get full text |
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| 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 Φ
max
, a measure of integrated information, is modulated by the sedative in different resting-state networks. We compare Φ
max
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 Φ
max
presents a variety of sedative-induced behaviours for different networks. Notably, changes to Φ
max
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.
Resting-state fMRI data in conditions of wakefulness and anaesthesia was analyzed using integrated information theory and its key measure, Φmax, which presents a variety of sedative-induced behaviours across different cortical networks. |
|---|---|
| AbstractList | 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 Φ
max
, a measure of integrated information, is modulated by the sedative in different resting-state networks. We compare Φ
max
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 Φ
max
presents a variety of sedative-induced behaviours for different networks. Notably, changes to Φ
max
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.
Resting-state fMRI data in conditions of wakefulness and anaesthesia was analyzed using integrated information theory and its key measure, Φmax, which presents a variety of sedative-induced behaviours across different cortical networks. 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.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. 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 Φ , a measure of integrated information, is modulated by the sedative in different resting-state networks. We compare Φ 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 Φ presents a variety of sedative-induced behaviours for different networks. Notably, changes to Φ 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. 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. 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 Φ max , a measure of integrated information, is modulated by the sedative in different resting-state networks. We compare Φ max 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 Φ max presents a variety of sedative-induced behaviours for different networks. Notably, changes to Φ max 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. 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.Resting-state fMRI data in conditions of wakefulness and anaesthesia was analyzed using integrated information theory and its key measure, Φmax, which presents a variety of sedative-induced behaviours across different cortical networks. |
| ArticleNumber | 692 |
| Author | Caius, Matthew Owen, Adrian M. Rudas, Jorge Soddu, Andrea Popiel, Nicholas J. M. Naci, Lorina Schiff, Nicholas D. Nemirovsky, Idan E. |
| Author_xml | – sequence: 1 givenname: Idan E. orcidid: 0000-0003-3065-0760 surname: Nemirovsky fullname: Nemirovsky, Idan E. email: inemiro@uwo.ca organization: Western Institute for Neuroscience, Department of Physics and Astronomy, University of Western Ontario – sequence: 2 givenname: Nicholas J. M. orcidid: 0000-0002-5563-3476 surname: Popiel fullname: Popiel, Nicholas J. M. organization: Cavendish Laboratory, University of Cambridge – sequence: 3 givenname: Jorge surname: Rudas fullname: Rudas, Jorge organization: Institute of Biotechnology, Universidad Nacional de Colombia – sequence: 4 givenname: Matthew surname: Caius fullname: Caius, Matthew organization: Western Institute for Neuroscience, Department of Physics and Astronomy, University of Western Ontario, Department of Medical Biophysics, University of Western Ontario – sequence: 5 givenname: Lorina orcidid: 0000-0001-9630-3978 surname: Naci fullname: Naci, Lorina organization: Trinity College Institute of Neuroscience, Trinity College Dublin – sequence: 6 givenname: Nicholas D. surname: Schiff fullname: Schiff, Nicholas D. organization: Feil Family Brain Mind Research Institute, Weill Cornell Medical College – sequence: 7 givenname: Adrian M. surname: Owen fullname: Owen, Adrian M. organization: Department of Physiology and Pharmacology and Department of Psychology, University of Western Ontario – sequence: 8 givenname: Andrea surname: Soddu fullname: Soddu, Andrea organization: Western Institute for Neuroscience, Department of Physics and Astronomy, University of Western Ontario |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/37407655$$D View this record in MEDLINE/PubMed |
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| Title | An implementation of integrated information theory in resting-state fMRI |
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