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

Pazienti, Antonio; Galluzzi, Andrea; Dasilva, Miguel; Sánchez-Vives, María V.; Mattia, Maurizio

doi:10.1016/j.isci.2022.103918

Cited by 19 publications

(26 citation statements)

References 89 publications

(149 reference statements)

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“…It has been shown that the dynamics of slow waves crucially depend on the level of anesthesia. While the velocity of waves tends to decrease slightly in deeper anesthesia states (Pazienti et al, 2022), the inter-wave intervals become more prolonged, i.e., the frequency of waves decreases (Dasilva et al, 2021;Pazienti et al, 2022). The same trends are visible in the corresponding pipeline output presented in Figure 5A.…”

Section: Dataset Comparisons Quantify the Variability Of Slow Wave Ch...supporting

confidence: 64%

“…The inter-wave interval is defined as the time delay between the occurrence of two consecutive waves at a recording site. The channel-wise velocity is calculated from the derivatives of the delay function of a wave ( , ), which indicates when a wave has reached the position ( , ) in its propagation (Capone et al, 2019b;Greenberg et al, 2018;Pazienti et al, 2022):…”

Section: Exemplary Realization Of the Collaborative Brain Wave Analys...mentioning

confidence: 99%

“…The channel-wise and wave-wise measured directions and velocities, as well as the wave-wise planarities, are summarized for 4 groups of similar waves, i.e., "wave modes". The wave-mode clustering method (implemented as an optional block in stage 4 of the pipeline) applies a k-means clustering on the trigger delay matrix containing the relative trigger times for each channel in each wave (Capone et al, 2019b;Pazienti et al, 2022;Ruiz-Mejias et al, 2011). The number of modes was set by hand to reasonably represent the variability of wave types in the recording.…”

Section: Exemplary Realization Of the Collaborative Brain Wave Analys...mentioning

confidence: 99%

“…The properties that are typically reported are thus often heuristic and include, for example, transition slopes (Chan et al, 2015), b) recorded anesthetized GCaMP6f mice with wide-field fluorescence microscopy (Celotto et al, 2020), c) distributed network of cortical columns of LIF with Spike Frequency Adaptation neurons (Pastorelli et al, 2019), d) one-dimensional multi-layer thalamo-cortical model with one-and two-compartment neuron models using Hodgkin-Huxley kinetics (Bazhenov et al, 2002), e) 2D balanced conductance-based spiking neural network model (Keane and Gong, 2015), f) multi-electrode recording in ferret cortical slices (Capone et al, 2019b), g) human HD-EEG during first sleep episode of the night (Massimini et al, 2004), h) human ECoG recording during sleep (Muller et al, 2016), i) intracranial depth EEG in sleeping human subjects (Nir et al, 2011), j) intracranial depth EEG in humans during sleep (Botella-Soler et al, 2012). (Ruiz-Mejias et al, 2011), phase velocity (Massimini et al, 2004;Muller et al, 2016), wave type classification (Camassa et al, 2021;Denker et al, 2018b;Pazienti et al, 2022;Roberts et al, 2019;Townsend et al, 2015), source/sink location and propagation patterns (Huang et al, 2010;Liang et al, 2021), excitability (De Bonis et al, 2019Mattia and Sanchez-Vives, 2012;Ruiz-Mejias et al, 2016), event frequency (Capone et al, 2022), and others. Thus, we here also focus on such common observables (i.e., planarity, inter-wave intervals, velocity, and direction).…”

Section: Introductionmentioning

confidence: 99%

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Comparing apples to apples -- Using a modular and adaptable analysis pipeline to compare slow cerebral rhythms across heterogeneous datasets

Gutzen¹,

Bonis²,

Luca³

et al. 2022

Preprint

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Neuroscience is moving towards a more integrative discipline, where understanding brain function requires consolidating the accumulated evidence seen across experiments, species, and measurement techniques. A remaining challenge on that path is integrating such heterogeneous data into analysis workflows such that consistent and comparable conclusions can be distilled as an experimental basis for models and theories. Here, we propose a solution in the context of slow wave activity (< 1 Hz), which occurs during unconscious brain states like sleep and general anesthesia, and is observed across diverse experimental approaches. We address the issue of integrating and comparing heterogeneous data by conceptualizing a general pipeline design that is adaptable to a variety of inputs and applications. Furthermore, we present the Collaborative Brain Wave Analysis Pipeline (Cobrawap) as a concrete, reusable software implementation to perform broad, detailed, and rigorous comparisons of slow wave characteristics across multiple, openly available ECoG and calcium imaging datasets.

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Section: Dataset Comparisons Quantify the Variability Of Slow Wave Ch...supporting

confidence: 64%

Section: Exemplary Realization Of the Collaborative Brain Wave Analys...mentioning

confidence: 99%

Section: Exemplary Realization Of the Collaborative Brain Wave Analys...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparing apples to apples -- Using a modular and adaptable analysis pipeline to compare slow cerebral rhythms across heterogeneous datasets

Gutzen¹,

Bonis²,

Luca³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Clearly, the present theoretical framework only represents a first stepping stone upon which more realistic and complex models can be built such as those incorporating the topological organization of cortical networks (Capone et al, 2019; Barbero-Castillo et al, 2021; Pazienti et al, 2022). To be comprehensive such models should also include other subcortical structures like the thalamus whose input is known to play an important role in shaping Off-periods (van Wijngaarden et al, 2016; Zucca et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

Adaptation shapes local cortical reactivity: from bifurcation diagram and simulations to human physiological and pathological responses

Cattani

Galluzzi

Fecchio

et al. 2022

Preprint

Self Cite

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Human studies employing intracerebral and transcranial perturbations suggest that the input-output properties of cortical circuits are dramatically affected during sleep in healthy subjects as well as in awake patients with multifocal and focal brain injury. In all these conditions, cortical circuits react to direct stimulation with an initial activation followed by suppression of activity (Off-period) that disrupts the build-up of sustained causal interactions typically observed in healthy wakefulness. The transition to this stereotypical response is of clinical relevance, being associated with loss of consciousness or loss of function. Here, we provide a mechanistic explanation of these findings by means of mean-field theory and simulations of a cortical-like module endowed with activity-dependent adaptation. First, we show that fundamental aspects of the local responses elicited in humans by direct cortical stimulation can be replicated by systematically varying the relationships between adaptation strength and excitation level in the network. Then, we reveal a region in the adaptation-excitation parameter space of key relevance for both physiological and pathological conditions, where spontaneous activity and responses to perturbation diverge in their ability to reveal Off-periods. Finally, we substantiate through simulations of connected cortical-like modules the role of adaptation mechanisms in preventing cortical neurons from engaging in reciprocal causal interactions, as suggested by empirical studies. These modeling results provide a general theoretical framework and a mechanistic interpretation for a body of neurophysiological measurements that bears key relevance for physiological states as well as for the assessment and rehabilitation of brain-injured patients.

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

Cited by 19 publications

References 89 publications

Comparing apples to apples -- Using a modular and adaptable analysis pipeline to compare slow cerebral rhythms across heterogeneous datasets

Comparing apples to apples -- Using a modular and adaptable analysis pipeline to compare slow cerebral rhythms across heterogeneous datasets

Adaptation shapes local cortical reactivity: from bifurcation diagram and simulations to human physiological and pathological responses

A modular and adaptable analysis pipeline to compare slow cerebral rhythms across heterogeneous datasets

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