Criticality supports cross-frequency cortical-thalamic information transfer during conscious states
- University of California, Los Angeles, United States
- University of Sydney, Australia
- University of Tokyo, Japan
- Andalusian Center for Molecular Biology and Regenerative Medicine, Spain
- Universitat de Vic-Universitat Central de Catalunya, Spain
- Nagoya City University, Japan
- Institut d'Investigacions biomèdiques de Barcelona, Spain
- Veterans Affairs Greater Los Angeles Healthcare System, United States
Abstract
Consciousness is thought to be regulated by bidirectional information transfer between the cortex and thalamus, but the nature of this bidirectional communication - and its possible disruption in unconsciousness - remains poorly understood. Here, we present two main findings elucidating mechanisms of corticothalamic information transfer during conscious states. First, we identify a highly preserved spectral channel of cortical-thalamic communication that is present during conscious states, but which is diminished during the loss of consciousness and enhanced during psychedelic states. Specifically, we show that in humans, mice, and rats, information sent from either the cortex or thalamus via 𝛿/𝜃/𝛼 waves (∼1-13 Hz) is consistently encoded by the other brain region by high 𝛾 waves (52-104 Hz); moreover, unconsciousness induced by propofol anesthesia or generalized spike-and-wave seizures diminishes this cross-frequency communication, whereas the psychedelic 5-methoxy-N,N-dimethyltryptamine (5-MeO-DMT) enhances this low-to-high frequency interregional communication. Second, we leverage numerical simulations and neural electrophysiology recordings from the thalamus and cortex of human patients, rats, and mice to show that these changes in cross-frequency cortical-thalamic information transfer may be mediated by excursions of low-frequency thalamocortical electrodynamics toward/away from edge-of-chaos criticality, or the phase transition from stability to chaos. Overall, our findings link thalamic-cortical communication to consciousness, and further offer a novel, mathematically well-defined framework to explain the disruption to thalamic-cortical information transfer during unconscious states.
Data availability
The source data underlying Figures 2-5 and 8-9, and code necessary to run the mean-field simulations of waking, seizure, and anesthesi states are available at https://doi.org/10.6084/m9.figshare.24777081.v2. The raw electrophysiology recordings from Long-Evans rats are available at the Harvard Dataverse Network, with the following DOI: doi:10.7910/DVN/29366.
Article and author information
Author details
Laia Lladó-Pelfort
Funding
National Institutes of Health (5R01GM135420-04)
- Nader Pouratian
Tiny Blue Dot Foundation (n/a)
- Martin M Monti
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Animal experimentation: Animal data from previously published studies were re-analyzed in this paper. The following ethics statements are quoted from the relevant papers:GAERS rats (from Miyamoto et al, 2019): "All animal experimental protocols were approved by the Animal Experiment Committee of the RIKEN Center for Brain Science. Mice and rats were handled in accordance with the guidelines of the RIKEN Center for Brain Science Animal Experiment Committee."C57BL/6 mice (from Riga et al 2018): "Animal care followed the European Union regulations (directive 2010/63 of 22/09/2010) and was approved by the Institutional Animal Care and Use Committee."Long-Evans rats (from Reed and Plourde 2015): "This study was carried out in strict accordance with the guidelines of the Canadian Council on Animal Care. The protocol was approved by the Montreal Neurological Institute Animal Care Committee. All surgery was performed under general anesthesia with ketamine and xylazine. All efforts were made to minimize suffering."
Human subjects: Ten subjects with essential tremor undergoing surgery for implantation of deep brain stimulation (DBS) leads in the ventral intermediate nucleus of the thalamus, provided written informed consent according to the Declaration of Helsinki. The institutional review board of the University of California, Los Angeles approved the study protocol.
Copyright
This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
Metrics
-
- 2,171
- views
-
- 388
- downloads
-
- 8
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Criticality supports cross-frequency cortical-thalamic information transfer during conscious states
eLife 13:e86547.
https://doi.org/10.7554/eLife.86547
Further reading
-
- Neuroscience
Spiny projection neurons (SPNs) in dorsal striatum are often proposed as a locus of reinforcement learning in the basal ganglia. Here, we identify and resolve a fundamental inconsistency between striatal reinforcement learning models and known SPN synaptic plasticity rules. Direct-pathway (dSPN) and indirect-pathway (iSPN) neurons, which promote and suppress actions, respectively, exhibit synaptic plasticity that reinforces activity associated with elevated or suppressed dopamine release. We show that iSPN plasticity prevents successful learning, as it reinforces activity patterns associated with negative outcomes. However, this pathological behavior is reversed if functionally opponent dSPNs and iSPNs, which promote and suppress the current behavior, are simultaneously activated by efferent input following action selection. This prediction is supported by striatal recordings and contrasts with prior models of SPN representations. In our model, learning and action selection signals can be multiplexed without interference, enabling learning algorithms beyond those of standard temporal difference models.
-
- Neuroscience
Deep learning-based methods have advanced animal pose estimation, enhancing accuracy, and efficiency in quantifying animal behavior. However, these methods frequently experience tracking drift, where noise-induced jumps in body point estimates compromise reliability. Here, we present the anti-drift pose tracker (ADPT), a transformer-based tool that mitigates tracking drift in behavioral analysis. Extensive experiments across cross-species datasets—including proprietary mouse and monkey recordings and public Drosophila and macaque datasets—demonstrate that ADPT significantly reduces drift and surpasses existing models like DeepLabCut and SLEAP in accuracy. Moreover, ADPT achieved 93.16% identification accuracy for 10 unmarked mice and 90.36% accuracy for freely interacting unmarked mice, which can be further refined to 99.72%, enhancing both anti-drift performance and pose estimation accuracy in social interactions. With its end-to-end design, ADPT is computationally efficient and suitable for real-time analysis, offering a robust solution for reproducible animal behavior studies. The ADPT code is available at https://github.com/tangguoling/ADPT.
