Poster No:
2036
Submission Type:
Abstract Submission
Authors:
Kenneth Shinozuka1, Fernando Rosas2, Joana Cabral3, Robin Carhart-Harris4, Morten Kringelbach1
Institutions:
1University of Oxford, Oxford, Oxfordshire, 2University of Sussex, Falmer, East Sussex, 3ICVS - University of Minho, Braga, Braga, 4University of California San Francisco, San Francisco, CA
First Author:
Co-Author(s):
Introduction:
Many researchers believe that consciousness is an emergent property of brain activity. Emergence is a hotly debated philosophical issue, yet to date there have been relatively few quantitative measures of emergence. Recently, Rosas, Mediano, and colleagues developed an information-theoretic framework for measuring two types of emergence, including downward causation: the irreducible causal power of macroscopic features over the individual parts of a system (Rosas, Mediano et al., 2020).
Methods:
Here, we apply the framework to previously-acquired magnetoencephalography (MEG) data of healthy human participants who were administered lysergic acid diethylamide (LSD), a psychedelic drug that induces strongly altered states of consciousness (Carhart-Harris et al., 2016). In the MEG scanner, participants listened to music during the acute effects of LSD or placebo. We aimed to determine whether experiences of audio-visual synesthesia on LSD can be explained as emergent phenomena, according to Rosas and Mediano's framework. We measured emergence on the synchrony of binarized, bandpass-filtered, source-reconstructed timeseries of several brain regions of interest, including areas in auditory cortex, visual cortex, and other cortices mediating the emotional response to music.
Results:
We find that synchrony between primary auditory cortex and the orbitofrontal cortex, a region implicated in mystical experiences, exerts downward causation onto a higher-order visual area (the fusiform gyrus). Furthermore, this emergent activity is correlated with subjective ratings of the influence of sound on visual perception.
Conclusions:
Our results suggest that information-theoretic emergence in the brain can account for phenomenological features of psychedelics. Future work may consider applying this framework to explain other subjective experiences on these drugs.
Higher Cognitive Functions:
Music
Modeling and Analysis Methods:
EEG/MEG Modeling and Analysis 2
Perception, Attention and Motor Behavior:
Attention: Auditory/Tactile/Motor
Perception: Multisensory and Crossmodal 1
Perception: Visual
Keywords:
Computational Neuroscience
Consciousness
MEG
Perception
Seretonin
1|2Indicates the priority used for review
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Please indicate below if your study was a "resting state" or "task-activation” study.
Resting state
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Healthy subjects
Was this research conducted in the United States?
No
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Yes
Were any animal research approved by the relevant IACUC or other animal research panel?
NOTE: Any animal studies without IACUC approval will be automatically rejected.
Not applicable
Please indicate which methods were used in your research:
MEG
Provide references using APA citation style.
Carhart-Harris, R. L., Muthukumaraswamy, S., Roseman, L., Kaelen, M., Droog, W., Murphy, K., Tagliazucchi, E., Schenberg, E. E., Nest, T., Orban, C., Leech, R., Williams, L. T., Williams, T. M., Bolstridge, M., Sessa, B., McGonigle, J., Sereno, M. I., Nichols, D., Hellyer, P. J., … Nutt, D. J. (2016). Neural correlates of the LSD experience revealed by multimodal neuroimaging. Proceedings of the National Academy of Sciences of the United States of America, 113(17), 4853–4858. https://doi.org/10.1073/pnas.1518377113
Rosas, F. E., Mediano, P. A. M., Jensen, H. J., Seth, A. K., Barrett, A. B., Carhart-Harris, R. L., Bor, D. (2020). Reconciling emergences: An information-theoretic approach to identify causal emergence in multivariate data. PLoS Computational Biology, 16(12): e1008289. https://doi.org/10.1371/journal.pcbi.1008289
No