More than just front or back: Parietal-striatal-thalamic circuits predict consciousness level

Afrasiabi, Mohsen; Redinbaugh, Michelle J.; Phillips, Jessica M.; Kambi, Niranjan A.; Mohanta, Sounak; Raz, Aeyal; Haun, Andrew M.; Saalmann, Yuri B.

doi:10.1101/2020.04.07.030429

Cited by 3 publications

(3 citation statements)

References 51 publications

(36 reference statements)

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“…This highlights the importance of studying not only the change in statistics between brain states but also their sensitivity on network dynamics. Consistent with our findings, a recent study of neuronal activity from several brain regions and in different arousal states (40) shows that parieto-basal ganglia circuits predicted the state of consciousness, while prefrontal activity failed.…”

Section: B-c)supporting

confidence: 93%

Macroscopic Quantities of Collective Brain Activity during Wakefulness and Anesthesia

et al. 2021

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The study of states of arousal is key to understand the principles of consciousness. Yet, how different brain states emerge from the collective activity of brain regions remains unknown. Here, we studied the fMRI brain activity of monkeys during wakefulness and anesthesia-induced loss of consciousness. We showed that the coupling between each brain region and the rest of the cortex provides an efficient statistic to classify the two brain states. Based on this and other statistics, we estimated maximum entropy models to derive collective, macroscopic properties that quantify the system’s capabilities to produce work, to contain information, and to transmit it, which were all maximized in the awake state. The differences in these properties were consistent with a phase transition from critical dynamics in the awake state to supercritical dynamics in the anesthetized state. Moreover, information-theoretic measures identified those parameters that impacted the most the network dynamics. We found that changes in the state of consciousness primarily depended on changes in network couplings of insular, cingulate, and parietal cortices. Our findings suggest that the brain state transition underlying the loss of consciousness is predominantly driven by the uncoupling of specific brain regions from the rest of the network.

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Section: B-c)supporting

confidence: 93%

Macroscopic Quantities of Collective Brain Activity during Wakefulness and Anesthesia

et al. 2021

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Section: Discussionsupporting

confidence: 93%

Macroscopic quantities of collective brain activity during wakefulness and anesthesia

Ponce‐Alvarez

Uhrig²,

Deco

et al. 2021

Preprint

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The study of states of arousal is key to understand the principles of consciousness. Yet, how different brain states emerge from the collective activity of brain regions remains unknown. Here, we studied the fMRI brain activity of monkeys during wakefulness and anesthesia-induced loss of consciousness. Using maximum entropy models, we derived collective, macroscopic properties that quantify the system's capabilities to produce work, to contain information and to transmit it, and that indicate a phase transition from critical awake dynamics to supercritical anesthetized states. Moreover, information-theoretic measures identified those parameters that impacted the most the network dynamics. We found that changes in brain state and in state of consciousness primarily depended on changes in network couplings of insular, cingulate, and parietal cortices. Our findings suggest that the brain state transition underlying the loss of consciousness is predominantly driven by the uncoupling of specific brain regions from the rest of the network.

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“…Examples are Massimini et al (2005) and Casarotto et al ( 2016), which at most provide evidence that a high level of consciousness depends on brain dynamics that (i) explore a large range of states, so that the current state can carry a large amount of information, and that (ii) exhibit integration of information between regions. Aspirational measures such as Afrasiabi et al (2020) and Leung, Cohen, et al (2020), are closer to the definition of Φ. Additionally, spatiotemporal patterns of integrated information have been derived from the fusiform gyrus and superior temporal gyrus and correlated with the quality (or contents) of conscious perception of faces and other objects (Haun, Oizumi, et al (2017)). Exact Φ calculations on simple networks can be performed at http://integratedinformationtheory.org/calculate.html.…”

Section: From Causal Irreducibility To Integrated Informationmentioning

confidence: 99%

When do parts form wholes? Integrated information as the restriction on mereological composition

McQueen¹,

Tsuchiya²

2023

Preprint

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Under what conditions are material objects, such as particles, parts of a whole object? This is the composition question and is a longstanding open question in philosophy. Existing attempts to specify a non-trivial restriction on composition tend to be vague and face serious counterexamples. Consequently, two extreme answers have become mainstream: composition (the forming of a whole by its parts) happens under no or all conditions. In this paper, we provide a self-contained introduction to the integrated information theory of consciousness (IIT). We show that IIT specifies a non-trivial restriction on composition: composition happens when integrated information is maximized. We compare the IIT-restriction to existing proposals and argue that the IIT-restriction has significant advantages, especially in response to the problems of vagueness and counterexamples. An appendix provides an introduction to calculating parts and wholes with a simple system.

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More than just front or back: Parietal-striatal-thalamic circuits predict consciousness level

Cited by 3 publications

References 51 publications

Macroscopic Quantities of Collective Brain Activity during Wakefulness and Anesthesia

Macroscopic Quantities of Collective Brain Activity during Wakefulness and Anesthesia

Macroscopic quantities of collective brain activity during wakefulness and anesthesia

When do parts form wholes? Integrated information as the restriction on mereological composition

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