State-Dependent Cortical Unit Activity Reflects Dynamic Brain State Transitions in Anesthesia

Abstract

How anesthesia affects cortical neuronal spiking and information transfer could help understand the neuronal basis of conscious state. Recent investigations suggest that global state of the anesthetized brain is not stationary but changes spontaneously at a fixed level of anesthetic concentration. How cortical unit activity changes with dynamically transitioning brain states under anesthesia is unclear. We hypothesized that distinct cortical states are characterized by distinct neuronal spike patterns.Extracellular unit activity was measured with sixty-four-channel silicon microelectrode arrays in cortical layers 5/6 of primary visual cortex of chronically instrumented, freely moving male rats (N = 7) during stepwise reduction of the anesthetic desflurane (6, 4, 2, and 0%). Unsupervised machine learning applied to multi-unit spike patterns revealed five distinct brain states of which four occurred at various anesthetic concentrations and shifted spontaneously. In deeper anesthesia states, the number of active units and overall spike rate decreased while the remaining active units showed increased bursting (excitatory neurons), spike timing variability, unit-to-population correlation and unit-to-unit transfer entropy, especially among putative excitatory units, despite the overall decrease in transfer entropy. A novel desynchronized brain state with increased spike timing variability, entropy and electromyographic activity that occurred mostly in deep anesthesia was discovered. These results provide evidence for distinct unit activity patterns associated with spontaneous changes in local cortical brain states at stationary anesthetic conditions. The appearance of a paradoxical, desynchronized brain state in deep anesthesia contends the prevailing view of monotonic dose-dependent anesthetic effects on the brain.. CC-BY-NC-ND 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made SIGNIFICANCE STATEMENTRecent studies suggest that spontaneous changes in brain state occur under anesthesia. However, the spiking behavior of cortical neurons associated with such state changes has not been investigated. We found that local brain states defined by multi-unit activity had non-unitary relationship with the current anesthetic level. A paradoxical brain state displaying asynchronous firing pattern and high electromyographic activity was found unexpectedly at high-dose anesthesia. In contrast, the synchronous fragmentation of neuronal spiking appeared to be a robust signature of the state of anesthesia. The findings challenge the assumption of monotonic, anesthetic dose-dependent behavior of cortical neuron populations. They enhance the interpretation of neuroscientific data obtained under anesthesia and understanding of the neuronal basis of anesthetic-induced state of unconsciousness..

Network Model With Reduced Metabolic Rate Predicts Spatial Synchrony of Neuronal Activity

“…Our previous study with the same data discovered that, during desflurane anesthesia, most frequently at a 6% concentration, spontaneous spike activity was occasionally desynchronous while showing a low firing rate (Lee et al, 2020). This unexpected, paradoxical desynchronized brain state has not been reported before and contends with the generally presumed dosedependent effect of anesthesia.…”

“…The study was approved by the Institutional Animal Care and Use Committee in accordance with the Guide for the Care and Use of Laboratory Animals of the Governing Board of the National Research Council (National Academy Press, Washington, DC, 2011). The experimental data used in this study were previously analyzed and published in a different context (Lee et al, 2020(Lee et al, , 2021. A multi-electrode array consisting of 64-contact silicon probes (shank length 2 mm, width 28-60 µm, probe thickness 15 µm, shank spacing 200 µm, row separation 100 µm, contact size 413 µm 2 ; custom design 8 × 8_edge_2 mm 100_200_413; Neuronexus Technologies, Ann Arbor, MI, United States) was chronically implanted in the primary visual cortex of each animal (eight adult male Long-Evans rats).…”

State-Dependent Synchrony and Functional Connectivity in the Primary and Secondary Whisker Somatosensory Cortices

“…Working under anesthesia allowed us to investigate well-controlled and repeated robust activation of whiskers. Nevertheless, both the anesthesia and the circumstance that the rats were not actively performing the task may have influenced the activity and synchronization ( Constantinople and Bruno, 2011 ; Lissek et al, 2016 ; Lee et al, 2020 ). Future experiments are required to corroborate our findings in awake behaving rodents.…”

Slow waves form expanding, memory-rich mesostates steered by local excitability in fading anesthesia

“…In the last two decades, several studies have performed recordings of the neuronal activity during various depths of either natural sleep or anesthesia, as well as during the process of awakening (Barttfeld et al, 2015;Bettinardi et al, 2015;Dasilva et al, 2021;Fischer et al, 2018;Hahn et al, 2012;Hudetz et al, 2015;Hudson et al, 2014;Lee et al, 2020;Li and Mashour, 2019;Liu et al, 2013;Schartner et al, 2017;Tort-Colet et al, 2019). Results from single -or a small number of simultaneously recorded -areas (including primary visual (Hudetz et al, 2015;Lee et al, 2020;Vizuete et al, 2012), cingulate (Hudson et al, 2014), retrospenial (Hudson et al, 2014), temporo-parieto-occipital (Fischer et al, 2018) cortices and the thalamus (Hudson et al, 2014)), as well as from the whole cortex (Barttfeld et al, 2015;Bettinardi et al, 2015;Dasilva et al, 2021;Grandjean et al, 2014;Li and Mashour, 2019;Liu et al, 2013;Schartner et al, 2017), showed that during the emergence from deep anesthesia (NREM sleep) the network activity increases its integration and complexity dynamical properties, possibly starting to wander among so-called micro-states (Brodbeck et al, 2012;Hudson et al, 2014;Lee et al, 2020;Liu et al, 2013). These studies strengthened the hypothesis that the anesthetized (sleeping) brain is not confined in a static dynamical state, but actually explores a more complex landscape, eventually operating a rather progressive state transition to wakefulness (Barttfeld et al, 2015;Dasilva et al, 2021;Li and Mashour, 2019;…”

“…These include cortico-thalamic loops (Crunelli et al, 2015;Crunelli and Hughes, 2010;Destexhe and Sejnowski, 2003;Grenier et al, 1998;Merica and Fortune, 2004;Sheroziya and Timofeev, 2014;Steriade et al, 1993b) and the balance between local dynamical features and global connectivity of the brain network shaping its integration and segregation capabilities (Barttfeld et al, 2015;Deco et al, 2015Deco et al, , 2014Mohajerani et al, 2013). At the cellular and local cortical assembly level, various experimental and modelling studies pointed at two main players playing a critical role in the process of awakening: firing adaptation of excitatory cortical neurons, and neuronal excitability (Compte et al, 2003;Lee et al, 2020;Levenstein et al, 2019;Mattia and Sanchez-Vives, 2012;Muller and Destexhe, 2012;Sanchez-Vives and McCormick, 2000). However, the cellular and network mechanisms underlying this state transitions remain to be conclusively elucidated.…”