Comprehensive profiling of anaesthetised brain dynamics across phylogeny
Presented During:
Saturday, June 28, 2025: 11:30 AM - 12:45 PM
Brisbane Convention & Exhibition Centre
Room:
P2 (Plaza Level)
Poster No:
2122
Submission Type:
Abstract Submission
Authors:
Andrea Luppi1, Lynn Uhrig2, Jordy Tasserie2, Golia Shafiei3, Kanako Muta4, Junichi Hata4, Hideyuki Okano5, Daniel Golkowski6, Andreas Ranft6, Rudiger Ilg6, Denis Jordan6, Silvia Gini7, Zhen-Qi Liu8, Yohan Yee8, Camilo Signorelli2, Rodrigo Cofre9, Alain Destexhe9, David Menon10, Emmanuel Stamatakis11, Christopher Connor12, Alessandro Gozzi13, Ben Fulcher14, Bechir Jarraya15, Bratislav Misic8
Institutions:
1University of Oxford, Oxford, United Kingdom, 2Université Paris-Saclay, Gif-sur-Yvette, France, 3University of Pennsylvania, Philadelphia, PA, 4Tokyo Metropolitan University, Tokyo, Japan, 5Keio University School of Medicine, Tokyo, Japan, 6Technical University Munich, Munich, Germany, 7Istituto Italiano di Tecnologia, Rovereto, Trentino, 8Montreal Neurological Institute, Montreal, Quebec, 9Paris‑Saclay Institute of Neuroscience, Gif-sur-Yvette, France, 10University of Cambridge, Cambridge, United Kingdom, 11University of Cambridge, Cambridge, Cambridgeshire, 12Brigham and Women’s Hospital, Boston, MA, 13Istituto Italiano di Tecnologia, Rovereto, Trento, 14University of Sydney, Sydney, Australia, 15Paris‑Saclay University, Gif-sur-Yvette, France
First Author:
Co-Author(s):
Introduction:
Anaesthetics act on molecular signalling to suppress behaviour. Combined with neuroimaging, they provide a unique opportunity to investigate how local neural dynamics mediate the link between microscale chemoarchitecture and the organism's functional repertoire. However, most studies focus on single species, single anaesthetics, and hand-picked properties of neural activity (e.g. entropy, power spectrum), providing a fundamentally incomplete picture.
To overcome these challenges, we systematically characterise how diverse anaesthetics perturb the entire dynamical profile of the invertebrate, murine and primate brain cross thousands of time-series features (Fig.1).
To overcome these challenges, we systematically characterise how diverse anaesthetics perturb the entire dynamical profile of the invertebrate, murine and primate brain cross thousands of time-series features (Fig.1).
·Fig. 1. Systematic phenotyping of brain dynamics under anaesthesia
Methods:
We combine FMRI signals from N=15 humans (Ranft et al., 2016), N=7 macaques (Tasserie et al., 2022), N=4 marmosets (Muta et al., 2023) and N=43 mice (Gutierrez-Barragan et al., 2021) and calcium imaging from N=10 nematodes (Awal et al., 2020), during wakefulness and anaesthesia due to intravenous or volatile agents (Fig.1a).
To comprehensively characterise local neural activity, we apply massive feature extraction of >6000 features from the highly-comparative time-series analysis library (Fulcher et al., 2013), the most exhaustive collection of time-series features from neuroscience, physics, engineering and economics (Fig.1b).
We use partial least squares (PLS) regression to integrate anaesthetic-induced changes in regional dynamics, with species-specific cortical transcriptomics from human microarray (Hawrylycz et al., 2012), macaque stereo-seq (Chen et al., 2023), and mouse in situ hybridization (Lein et al., 2007). We use Moran spectral randomisation to control for spatial autocorrelation (Markello and Misic, 2021).
Finally we reproduce our findings in silico by tuning the inter-regional coupling in a biophysical model of excitatory and inhibitory populations, coupled according to the human structural connectome.
To comprehensively characterise local neural activity, we apply massive feature extraction of >6000 features from the highly-comparative time-series analysis library (Fulcher et al., 2013), the most exhaustive collection of time-series features from neuroscience, physics, engineering and economics (Fig.1b).
We use partial least squares (PLS) regression to integrate anaesthetic-induced changes in regional dynamics, with species-specific cortical transcriptomics from human microarray (Hawrylycz et al., 2012), macaque stereo-seq (Chen et al., 2023), and mouse in situ hybridization (Lein et al., 2007). We use Moran spectral randomisation to control for spatial autocorrelation (Markello and Misic, 2021).
Finally we reproduce our findings in silico by tuning the inter-regional coupling in a biophysical model of excitatory and inhibitory populations, coupled according to the human structural connectome.
Results:
We identify a phylogenetically conserved dynamical profile of N=403 dynamical features that are consistently altered by anaesthesia in each species (Fig.2a-b). This dynamical profile is enriched for features of temporal predictability, including symbolic, outlier-related, and (auto)correlation-related properties (all p<0.001), such that brain signals exhibit reduced temporal persistence (Fig.2c). These dynamical signatures are reversed upon reawakening from anaesthesia induced by central thalamic deep-brain stimulation in the macaque (Tasserie et al., 2022).
PLS analysis reveals a significant latent variable (p=0.01 against 10,000 Moran-autocorrelated nulls) linking the dynamical profile of anaesthesia with an anterior-posterior gradient of cortical gene expression that is shared across human, macaque, and mouse (Fig.2d-f). This latent variable co-varies spatially with genes for inhibitory interneurons (PVALB) and receptors regulating sleep-wake transitions: cholinergic (CHRNB2, CHRM4), orexin/hypocretin (HCRTR1), and histamine (HRH2).
Finally we find that anaesthesia consistently decouples the emergence of inter-regional functional connectivity from local dynamics: regions with similar dynamical profiles are no longer synchronised. We provide mechanistic evidence by reducing the strength of monosynaptic coupling between brain regions in our biophysical model, which reproduces the local and global dynamical profile of anaesthesia (Fig.2g).
PLS analysis reveals a significant latent variable (p=0.01 against 10,000 Moran-autocorrelated nulls) linking the dynamical profile of anaesthesia with an anterior-posterior gradient of cortical gene expression that is shared across human, macaque, and mouse (Fig.2d-f). This latent variable co-varies spatially with genes for inhibitory interneurons (PVALB) and receptors regulating sleep-wake transitions: cholinergic (CHRNB2, CHRM4), orexin/hypocretin (HCRTR1), and histamine (HRH2).
Finally we find that anaesthesia consistently decouples the emergence of inter-regional functional connectivity from local dynamics: regions with similar dynamical profiles are no longer synchronised. We provide mechanistic evidence by reducing the strength of monosynaptic coupling between brain regions in our biophysical model, which reproduces the local and global dynamical profile of anaesthesia (Fig.2g).
·Fig. 2. Consistent effects of anaesthesia on neural dynamics
Conclusions:
We systematically characterised >6000 features of local neuronal dynamics, identifying an evolutionarily conserved dynamical signature of anaesthesia across imaging modalities, species, and anaesthetics. Anaesthesia desynchronises regions with similar dynamical profiles and reduces temporal persistence of neural activity. Phylogenetically conserved changes in local neural dynamics are underpinned by corresponding phylogenetically conserved patterns of gene expression regulating arousal. Altogether, under anaesthesia neural activity remains locally confined both spatially and temporally.
Genetics:
Transcriptomics
Modeling and Analysis Methods:
Task-Independent and Resting-State Analysis 2
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Transmitter Receptors
Perception, Attention and Motor Behavior:
Consciousness and Awareness
Physiology, Metabolism and Neurotransmission:
Pharmacology and Neurotransmission 1
Keywords:
ANIMAL STUDIES
Computational Neuroscience
Consciousness
Cross-Species Homologues
Data analysis
FUNCTIONAL MRI
Modeling
Neurotransmitter
Other - Anesthesia; Deep-Brain Stimulation; Time-series Features
1|2Indicates the priority used for review
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Provide references using APA citation style.
Chen, A., (2023). Single-cell spatial transcriptome reveals cell-type organization in the macaque cortex. Cell, 186(17), 3726-3743.
Fulcher, B. D. (2013). Highly comparative time-series analysis: The empirical structure of time series and their methods. Journal of the Royal Society Interface, 10(83), p.20130048.
Gutierrez-Barragan, D. (2021). Unique spatiotemporal fMRI dynamics in the awake mouse brain. Current Biology, 32(2), 1–14.
Hawrylycz, M. J. (2012). An anatomically comprehensive atlas of the adult human brain transcriptome. Nature, 489(7416), 391–399.
Lein, E. S. (2007). Genome-wide atlas of gene expression in the adult mouse brain. Nature, 445(7124).
Markello, R. D., & Misic, B. (2021). Comparing spatial null models for brain maps. NeuroImage, 236, 118052.
Muta, K. (2023). Effect of sedatives or anesthetics on the measurement of resting brain function in common marmosets. Cerebral Cortex, 33(9), 5148–5162.
Ranft, A. (2016). Neural Correlates of Sevoflurane-induced Unconsciousness Identified by Simultaneous Functional Magnetic Resonance Imaging and Electroencephalography. Anesthesiology, 125(5), 861–872.
Tasserie, J. (2022). Deep brain stimulation of the thalamus restores signatures of consciousness in a nonhuman primate model. Science Advances, 8, 5547.