Modulation of cortical slow oscillations and complexity across anesthesia levels

Dasilva, Miguel; Camassa, Alessandra; Navarro-Guzman, Alvaro; Pazienti, Antonio; Perez-Mendez, Lorena; Zamora-López, Gorka; Mattia, Maurizio; Sanchez-Vives, Maria V.

doi:10.1016/j.neuroimage.2020.117415

Cited by 66 publications

(94 citation statements)

References 57 publications

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“…5A) (Sheroziya and Timofeev, 2014;Volgushev et al, 2006). In agreement with our previous results, the average amount of time the neuronal populations fired action potentials increased with the lightening of the anesthesia (due to more frequent Ups), revealing an increasing excitability of the network (Dasilva et al, 2021). However, the maximum MUA amplitude of the Ups decreased with the lightening up of the anesthesia (Fig.…”

Section: Leading and Following Assemblies Differentially Contribute Tsupporting

confidence: 91%

“…Figure 1: Anesthesia level index (ALI) from slow-wave dimensionality and frequency. A. Superficial 32-channel multi-electrode array placed on the cortical surface of anesthetized mice (left) and schematic representation of the recorded cortical areas (right, as in (Dasilva et al, 2021)). B.…”

Section: Resultsmentioning

confidence: 99%

“…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;…”

Section: Introductionmentioning

confidence: 99%

“…What are the relative roles of local and global network features in shaping the SWA as the anesthesia fades out? To this purpose we developed an objective and quantitative measure of the level of anesthesia based on the slow-wave frequency and the complexity of the wavefronts, referred to as the anesthesia level index (ALI) further advancing our previous characterization (Dasilva et al, 2021). This allowed to sort the recordings from different mice and at varying isoflurane doses on a common fine grained frame.…”

Section: Introductionmentioning

confidence: 99%

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

Pazienti

Galluzzi

Dasilva

et al. 2021

Preprint

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During sleep and anesthesia, large groups of neurons throughout the entire cortex activate rhythmically producing wavefronts of activity referred to as slow-wave activity (SWA). In the arousal process, the brain restores its integrative and complex activity. The network mechanisms underlying this global state transition remain however to be elucidated. Here we investigated the network features shaping the SWA under fading anesthesia. Using electrocorticographical recordings of wide cortical areas of the mouse brain, we developed a quantitative measure of the anesthesia level based on slow-wave frequency and complexity. At deep anesthesia, we document a stringent alternation of posterior-anterior-posterior modes of propagation. With fading anesthesia, SWA evolves to produce a plethora of metastable spatiotemporal patterns. A network model of spiking neurons reproduced the data using short-range connectivity, subcortical input and a local activity-dependent adaptation. The emergence from deep anesthesia does not require modifying the connectivity, but small changes in the local excitability of cortical cell assemblies, further supporting the hypothesis of a tight bound between scales in the brain.

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Section: Leading and Following Assemblies Differentially Contribute Tsupporting

confidence: 91%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Pazienti

Galluzzi

Dasilva

et al. 2021

Preprint

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“…The perturbational complexity index (PCI; Casali et al, 2013 ; Comolatti et al, 2019 ), in which neural activity is exogenously perturbed by means of stimulation (transcranial magnetic stimulation or electrical stimulation) has been proposed as one such measure. This method has been validated for different instances such as physiological brain states ( Casali et al, 2013 ), anesthesia levels ( Sarasso et al, 2015 ; Arena et al, 2020 ; Dasilva et al, 2021 ), and disorders of consciousness ( Casarotto et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Impact of GABA_A and GABA_B Inhibition on Cortical Dynamics and Perturbational Complexity during Synchronous and Desynchronized States

et al. 2021

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Quantitative estimations of spatiotemporal complexity of cortical activity patterns are used in the clinic as a measure of consciousness levels, but the cortical mechanisms involved are not fully understood. We used a version of the Perturbational Complexity Index adapted to multisite recordings from the ferret (either sex) cerebral cortex in vitro (sPCI) to investigate the role of GABAergic inhibition in cortical complexity. We studied two dynamical states: slow-wave activity (synchronous state) and desynchronized activity, that express low and high causal complexity respectively.Progressive blockade of GABAergic inhibition during both regimes revealed its impact on the emergent cortical activity and on sPCI. Gradual GABA A receptor blockade resulted in higher synchronization, being able to drive the network from a desynchronized to a synchronous state, with a progressive decrease of complexity (sPCI). Blocking GABA B receptors also resulted in a reduced sPCI, in particular when in a synchronous, slow wave state. Our findings demonstrate that physiological levels of inhibition contribute to the generation of dynamical richness and spatiotemporal complexity. However, if inhibition is diminished or enhanced, cortical complexity decreases. Using a computational model, we explored a larger parameter space in this relationship and demonstrate a link between excitatory/inhibitory balance and the complexity expressed by the cortical network. 3 Significance statementThe spatiotemporal complexity of the activity expressed by the cerebral cortex is a highly revealing feature of the underlying network's state. Complexity varies with physiological brain states: it is higher during awake than during sleep states. But it also informs about pathological states: in disorders of consciousness, complexity is lower in an unresponsive wakefulness syndrome than in a minimally conscious state. What are the network parameters that modulate complexity? Here we investigate how inhibition, mediated by either GABA A or GABA B -Rs, influences cortical complexity. And we do this departing from two extreme functional states: a highly synchronous, slow-wave state, and a desynchronized one that mimics wakefulness. We find that there is an optimal level of inhibition in which complexity is highest.

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Propofol modulates neural dynamics of thalamo-cortical system associated with anesthetic levels in rats

Jiang

Wen

et al. 2022

Cogn Neurodyn

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Modulation of cortical slow oscillations and complexity across anesthesia levels

Cited by 66 publications

References 57 publications

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

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

Impact of GABA_A and GABA_B Inhibition on Cortical Dynamics and Perturbational Complexity during Synchronous and Desynchronized States

Propofol modulates neural dynamics of thalamo-cortical system associated with anesthetic levels in rats

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Modulation of cortical slow oscillations and complexity across anesthesia levels

Cited by 66 publications

References 57 publications

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

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

Impact of GABAA and GABAB Inhibition on Cortical Dynamics and Perturbational Complexity during Synchronous and Desynchronized States

Propofol modulates neural dynamics of thalamo-cortical system associated with anesthetic levels in rats

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Impact of GABA_A and GABA_B Inhibition on Cortical Dynamics and Perturbational Complexity during Synchronous and Desynchronized States