bioRxiv Science⌕ Search

bioRxiv · 10.1101/2025.10.13.682019

Dynamic electrocortical states and paradoxical complexity during desflurane anesthesia

Abstract

BackgroundHow general anesthesia alters the dynamics of electrocortical activity is crucial to understand the neural mechanisms of unconsciousness. Local cortical activity undergoes spontaneous transitions at constant anesthetic concentration. The spatial organization and temporal dynamics of state transitions in large-scale electrocortical activity is incompletely understood. MethodsEpidural electrocorticogram was recorded from the right hemisphere in 8 rats (14 experiments) using chronically implanted 32-channel flexible electrode arrays during desflurane anesthesia at 6%, 4%, 2%, 0% inhaled concentrations, each maintained for 1 hour. Cortical states were identified by principal component analysis of power spectrograms followed by density-based clustering simultaneously across all anesthetic conditions. State-specific spatiotemporal complexity was quantified by the normalized Lempel-Ziv algorithm to capture signal variability beyond spectral effects. Temporal dynamics were assessed by state occurrence, dwell times, and transition probabilities. ResultsSeven cortical states were identified. Six states generally tracked anesthetic depth with an increase in delta power and decrease in complexity, but their occurrence was not tied to any anesthetic level (p<0.001). The 7th state was a paradoxical, activated state that mostly occurred during deep anesthesia and was marked by reduced delta (p<0.001) and elevated complexity (p<0.001). Cortical activity was more likely to remain in a given state than to switch (mean dwell time of 136.55 s, persistence likelihood of 99.36%). When transitions occurred, they followed structured, non-random dynamics, primarily within light- or deep-anesthesia states (FDR-corrected p<0.05), with a mild tendency to exit deep states and enter light states (p=0.0039), consistent with anesthetic emergence. ConclusionsElectrocortical activity is not a unitary function of anesthetic concentration but involves spontaneous dynamics and paradoxically activated states with increased global complexity during deep anesthesia. The results suggest ongoing spontaneous reorganization of cortical activity during prolonged anesthetic challenge and provide new insight into anesthesia-induced brain dynamics that may inform future strategies for monitoring and manipulating the state of consciousness and facilitating recovery from general anesthesia.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Li, D., Hudetz, A. G.. 2025-10-14. Dynamic electrocortical states and paradoxical complexity during desflurane anesthesia. https://doi.org/10.1101/2025.10.13.682019

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

The Unreasonable Effectiveness of Cell Types in Describing Neuronal Physiological Features

Single-cell RNA sequencing (scRNA-seq) captures detailed gene expression profiles at scale, while patch-clamp recordings measure intrinsic neuronal electrophysiological properties. Modeling the relations between these two modalities remains a challenge. Here, we compare how well electrophysiological features can be predicted by traditional transcriptomic cell type classification, representations derived from a foundational model (scGPT) pretrained on large-scale scRNA-seq datasets, ion channel-coding genes, and highly variable genes. Using paired transcriptomic and electrophysiological patch-sequencing data from 495 human neurons from neurosurgical tissue, we find that cluster-level cell type representations consistently outperform highly variable gene selection, ion channel gene selection, and context-enriched scGPT embeddings. Notably, performance varies across model architectures and initializations, and the best results are obtained by combining the outputs of separate cell type and scGPT-based models. Together, these findings suggest that traditional discrete cellular classification is highly effective in predicting physiological features. For maximum performance it can be complemented by pretrained transformer models.

neuroscience↗

A nonlinear inhibition pathway underlying cortical responses to tuned holographic optogenetic perturbations

Optogenetics enables causal manipulation of cortical activity. Perturbation responses can be counterintuitive due to network interactions, making theory essential for predicting them. Existing approaches often rely on linear approximations, which fail for many biologically relevant perturbations. Here we develop a nonlinear theory of responses to holographic perturbations in cell-type-specific recurrent networks with structured connectivity. We fit a nonlinear model to mouse V1 data, which shows cotuned-ensemble suppression: perturbing spatially clustered neurons with similar preferred orientations yields markedly stronger short-range suppression than perturbing untuned ensembles. We show that cotuned-ensemble suppression arises from a feature-tuned, nonlinear inhibition pathway implicating somatostatin-positive (SST) interneurons. The theory predicts that cotuned ensembles suppress parvalbumin-positive (PV) neurons but facilitate SST neurons, and links the degree of cotuned-ensemble suppression or facilitation to the variance of the SST response. This framework identifies mechanisms by which nonlinear inhibition sculpts cortical dynamics and establishes a predictive basis for targeted optogenetic interventions.

neuroscience↗

Proteomic signatures of APOE ε4 across human tissues and cell types in Alzheimers disease

The apolipoprotein E {varepsilon}4 (APOE {varepsilon}4) allele is the strongest genetic risk factor for late-onset Alzheimers disease (AD). However, the underlying molecular mechanisms remain unclear. This study included 1691 participants from the Religious Orders Study and Rush Memory and Aging Project (ROSMAP), 1226 participants from the Accelerating Medicines Partnership - Alzheimers Disease (AMP-AD) Diverse Cohorts Study, and 735 participants from the Alzheimers Disease Neuroimaging Initiative (ADNI). To characterise APOE {varepsilon}4 molecular effects, we analysed proteomic data from plasma, cerebrospinal fluid (CSF), and induced pluripotent stem cell (iPSC)-derived astrocytes and neurons, as well as transcriptomic and proteomic data from multiple brain regions. The association of APOE {varepsilon}4 with AD neuropathology was also examined. APOE {varepsilon}4 carriers shared a plasma proteomic signature enriched for immune processes, irrespective of AD diagnosis. A machine learning classifier trained on this signature discriminated APOE {varepsilon}4 carriers from non-carriers in an independent cohort using CSF proteomics. APOE {varepsilon}4 carriage was associated with higher Braak stages and Consortium to Establish a Registry for Alzheimers Disease (CERAD) score. However, only limited APOE {varepsilon}4-associated transcriptomic and proteomic changes were observed in bulk brain tissue, with poor cross-layer concordance. Proteomic analyses of iPSC-derived astrocytes and neurons further revealed cell-type-specific APOE {varepsilon}4-associated changes. APOE {varepsilon}4 is associated with a consistent proteomic signature across plasma and CSF. Its molecular effects in the brain differ across cell types, brain regions and molecular layers. These findings support the need for cell-type-resolved multi-omic studies to elucidate how APOE {varepsilon}4 confers AD risk.

neuroscience↗