bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.64898/2026.10.18.760000

Isoflurane anesthesia CNS recovery state: ultra-slow oscillations and cellular-stress-responsive gene expression

Dehnad, M.·Bjorness, T.·Kulkarni, A.·Thai, T.·Pandey, R.·Loer, S. A.·Konopka, G.·Greene, R.
View original source ↗Export metadata ↓

Abstract

General anesthesia and NREM sleep share slow-wave EEG features, yet whether anesthesia substitutes for physiological sleep remains unclear. We compared recovery from 4h of isoflurane anesthesia (IA) with time-matched sleep and sleep deprivation (SD) in male C57BL/6J mice using continuous EEG/EMG recordings and frontal cortex RNA sequencing. IA recovery produced transient, rebound arousal, reduced early NREM and REM sleep, and, fragmented subsequent NREM bouts, and persistant, ultra-slow EEG-oscillations that normalized within two weeks. Whereas, SD induced consolidated rebound sleep and slow wave activity. Transcriptomic profiling at ZT6 showed that IA and SD induced largely distinct cortical gene-expression programs. IA preferentially enriched integrated stress response and unfolded protein response genes, whereas SD showed a comparatively orthogonal, growth-permissive, mTOR-associated profile. Although IA-induced gene-expression suggests cellular stress, corticosterone measurements argued against systemic stress as the primary driver. These findings indicate that isoflurane recovery is neurophysiologically and molecularly distinct from restorative sleep.

Explore related subjects

neurosciencePreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Dehnad, M., Bjorness, T., Kulkarni, A., Thai, T., Pandey, R., Loer, S. A., Konopka, G., Greene, R.. 2026-10-19. Isoflurane anesthesia CNS recovery state: ultra-slow oscillations and cellular-stress-responsive gene expression. https://doi.org/10.64898/2026.10.18.760000

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

KEEP EXPLORING

Related preprints

bioRxiv2026

Three-dimensional object-based quantification of nicotinic acetylcholine receptor subunit colocalization using Imaris

Neural circuit function depends not only on synaptic connectivity but also on the molecular composition and spatial organization of neurotransmitter receptors. Determining whether receptor subunits occupy shared or distinct domains requires spatial measurements capable of resolving sparse receptor puncta across neural tissue and, when needed, restricting analysis to defined cellular compartments. Conventional intensity-based colocalization metrics can be difficult to interpret for sparse, punctate signals and do not inherently provide object-level classification or cell-specific analysis. Here, we describe a three-dimensional object-based colocalization workflow for endogenously tagged nicotinic acetylcholine receptor (nAChR) subunits imaged by Airyscan super-resolution confocal microscopy and analyzed in Imaris. Individual receptor puncta are segmented as 3D surface objects and classified as colocalized or non-colocalized using a control-defined spatial overlap criterion. The workflow supports analysis within a defined region of the ventral nerve cord neuropil or within genetically labeled motor-neuron dendritic compartments. For dendrite-restricted analysis, receptor puncta are first selected by their overlap with the dendritic surface before receptor-receptor overlap is quantified. The overlap criterion is empirically calibrated using positive and negative biological controls processed under the same imaging and analysis conditions, providing a reproducible alternative to arbitrary threshold selection. This approach enables receptor organization to be quantified within the neuropil and genetically labeled motor-neuron dendritic compartments and can be adapted to other epitope-tagged receptors, ion channels, and synaptic proteins.

neuroscience↗
bioRxiv2026

Hidden Markov Modeling Reveals Altered Functional Network Dynamics in Multiple Sclerosis

Purpose: Multiple sclerosis (MS) is a neurodegenerative and demyelinating disease of the central nervous system. Recent evidence suggests that localized gray matter atrophy in MS may be involved in distributed functional brain networks. However, the dynamic trajectory of functional network alterations in MS remains unclear. The current study extends prior network-mediated findings and aims to investigate the dynamic functional connectivity (dFC) and energetic landscape across an atrophy-prone network in MS. Methods: Twenty MS participants and twenty healthy controls (HC) underwent resting-state fMRI. The atrophy-based functional network (AFN) model was implemented, and time series from atrophy-prone regions were fit to a 12-state hidden Markov model. Each functional network state, a recurring pattern of activity across the AFN, was characterized by the AFN regions with the strongest activation. The states were then grouped into two metastates. Dynamic functional network metrics were compared between groups by permutation testing and correlated with disability and cognition. The energetic landscape of the network was mapped with a pairwise maximum-entropy model. Temporal irreversibility was also measured, which defines the arrow-of-time asymmetry of network dynamics. Results: People with MS showed lower occupancy of state 1, which peaked on the right middle frontal and anterior cingulate cortex. Occupancy and mean lifetime were higher in states 3 and 10 (p < 0.05). In state 3, cortical activity was below the network average, and state 10 peaked on the postcentral gyrus. At the metastate level, MS participants spent more time in metastate M1 and less in M2 (p = 0.04). Within the MS group, a longer interval between visits to state 3 was associated with greater disability (r = 0.57), and a higher switching rate in state 10 with poorer verbal fluency (r = -0.50). The energetic landscape was similar between groups. Temporal irreversibility was lower in MS, mainly across subcortical connections. Conclusion: MS participants dwelled longer in specific dFC states of the AFN, while the rate of transition between states did not differ from healthy controls. Dynamic functional network metrics were associated with disability and cognitive performance and may serve as functional correlates of clinical impairment. The states were similar between groups, whereas the directed drive between states was lower across subcortical connections. This pattern suggests altered network dynamics rather than altered network architecture in MS. Findings from this study characterize the functional network dynamics of neurodegeneration in MS, which may inform future development of network-based approaches for monitoring disease progression.

neuroscience↗
bioRxiv2026

LATS2 modulates synaptic organization and function by regulating actin dynamics

Recent work has highlighted the importance of core Hippo pathway kinases in cycling cells. Many of these kinases are also expressed in post-mitotic neurons, where they have been linked to synaptic function, and more recently a number have been shown to be dysregulated in neurodegenerative diseases such as Alzheimers disease. Despite this, little is known about the specific role or subcellular localization of the Large Tumor Suppressor kinases, LATS1 and LATS2, in postmitotic neurons. Previously, we demonstrated that LATS1 plays an important role in synapse development; however, the role of LATS2 remains poorly understood. Here, we examined the distribution and function of LATS2 and found that it is highly expressed in CA3/CA4 neurons of the hippocampus and localized throughout dendrites and dendritic spines. In contrast to LATS1, LATS2 expression remains stable throughout neuronal development. Furthermore, ectopic expression of LATS2 induces kinase-dependent structural spine defects, alters actin dynamics and reduces activity-dependent synaptic SEP-GluR1 fluorescence. Collectively, these findings reveal a unique distribution LATS2 in the hippocampus and demonstrate that LATS2 directly regulates actin dynamics and GluR1-containing AMPAR trafficking.

neuroscience↗