bioRxiv Science⌕ Search

bioRxiv · 10.1101/2025.10.09.681481

Neural Activity dynamic in Primate Cortex Across Consciousness Levels: Insights from High-Density Neuropixel Recording

Abstract

This study investigates the anesthesia mechanisms induced by sevoflurane and how it modulates neural activity in the posterior parietal cortex (PPC) and prefrontal cortex (PFC) in Non Human Primates (NHPs) using high density Neuropixel probes. Spiking and local field potentials (LFPs) were recorded in two macaque monkeys under going four sevoflurane concentrations (2%, 3%, 4%, and 6%). We aimed to (i) quantify the emergence of anesthesia-induced Up/Down state dynamics, (ii) track changes in oscillatory power and inter-regional synchrony, and (iii) determine whether frontal and parietal areas exhibit differential sensitivity to rising and falling anesthetic depth. Across different anesthetic levels, we observed characteristic slow oscillations in delta range in both PFC and PPC, with neurons transitioning between high-firing "Up" states and near-silent "Down" states. Deeper anesthesia extended Down states, suppressed mean firing rates, and reduced the frequency and duration of Up states. In M1, no single units were detected in PFC, and only a few were recorded in M2. We suspect misalignment of the probe with PFC pyramidal cells and extensive suppression in PFC as the main reasons. Recurrent Neural Networks (RNN) was used to extract Up/Down states from LFP activities, based one the pattern observed in the PPC. The PPC [->] PFC information flow observed in Transfer Entropy analysis suggests that even under anesthesia, some level of feedforward-like interactions may persist. Up states originate in deep cortical layers and propagate toward superficial layers, following a bottom-up progression, indicating that deep-layer pyramidal neurons, which receive strong thalamic input, may be the primary drivers of Up states. The short Up states under deep anesthesia might represent a failed ignition attempt, where the brain momentarily tries to reactivate but cannot sustain functional activity due to global inhibition. LFP analyses revealed that although the absolute delta power remains high at different anesthetic levels, the relative delta band power is anti correlated with anesthetic depth, due to sporadic short (20ms to 40ms) burst in gamma (30-100 Hz) that appeared transient in nature. Lower sensitivity to anesthesia dose changes were observed in PFC as compared to PPC. This could explain why anesthesia first impairs cognitive function before affecting basic sensory responses. These results indicates that the traditional Up/Down state models might oversimplify anesthetic brain dynamics. While anesthesia is often described as a state of simple global slow-wave oscillations, the observed Up/Down state durations are not uniform, they fluctuate, follow non-trivial transition patterns, and differ between PFC and PPC.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Khalili-Ardali, M., Dwarakanath, A., Roustan, M., Jarraya, B., Panagiotaropoulos, T.. 2025-10-10. Neural Activity dynamic in Primate Cortex Across Consciousness Levels: Insights from High-Density Neuropixel Recording. https://doi.org/10.1101/2025.10.09.681481

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↗