Shaping the Default Activity Pattern of the Cortical Network
Authors: Maria V. Sanchez-Vives, Marcello Massimini, Maurizio Mattia
Journal: Neuron (2017)

Slow oscillations have been suggested as the default emergent activity of the cortical network. This is a low complexity state that integrates neuronal, synaptic, and connectivity properties of the cortex. Shaped by variations of physiological parameters, slow oscillations provide information about the underlying healthy or pathological network. We review how this default activity is shaped, how it acts as a powerful attractor, and how getting out of it is necessary for the brain to recover the levels of complexity associated with conscious states. We propose that slow oscillations provide a robust unifying paradigm for the study of cortical function. Slow oscillations have been suggested as the default emergent activity of the cortical network. This is a low complexity state that integrates neuronal, synaptic, and connectivity properties of the cortex. Shaped by variations of physiological parameters, slow oscillations provide information about the underlying healthy or pathological network. We review how this default activity is shaped, how it acts as a powerful attractor, and how getting out of it is necessary for the brain to recover the levels of complexity associated with conscious states. We propose that slow oscillations provide a robust unifying paradigm for the study of cortical function. Slow oscillations (SO) constitute a cortical state consisting of periods of activity or neuronal firing (Up or active states) and periods of silence (Down or silent states) that alternate at a frequency of around 1 Hz. SO generated during slow-wave sleep and anesthesia are rather prominent and have been observed since the early times of electroencephalography (for a review, see Andersen and Andersson, 1968Andersen P. Andersson S.A. Physiological Basis of the Alpha Rhythm. Appleton-Century-Crofts, 1968Google Scholar). For decades, little attention was paid to the mechanisms generating SO or to the information that SO provided about the underlying network. The detailed intracellular and network-level description of SO during sleep and anesthesia carried out by Mircea Steriade and collaborators (Steriade et al., 1993Steriade M. Nuñez A. Amzica F. A novel slow (< 1 Hz) oscillation of neocortical neurons in vivo: depolarizing and hyperpolarizing components.J. Neurosci. 1993; 13: 3252-3265Crossref PubMed Google Scholar) was pivotal in increasing the interest in spontaneously generated brain activity (Fox et al., 2005Fox M.D. Snyder A.Z. Vincent J.L. Corbetta M. Van Essen D.C. Raichle M.E. The human brain is intrinsically organized into dynamic, anticorrelated functional networks.Proc. Natl. Acad. Sci. USA. 2005; 102: 9673-9678Crossref PubMed Scopus (6171) Google Scholar, Greicius et al., 2003Greicius M.D. Krasnow B. Reiss A.L. Menon V. 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Perilesional pathological oscillatory activity in the magnetoencephalogram of patients with cortical brain lesions.Neurosci. Lett. 2004; 355: 93-96Crossref PubMed Scopus (60) Google Scholar). Furthermore, SO are spontaneously expressed in cortical slices in the absence of any chemical or electrical stimulation (Sanchez-Vives and McCormick, 2000Sanchez-Vives M.V. McCormick D.A. Cellular and network mechanisms of rhythmic recurrent activity in neocortex.Nat. Neurosci. 2000; 3: 1027-1034Crossref PubMed Scopus (1092) Google Scholar). (3) SO are observed in all tested neocortical areas and express similar features under anesthesia (Chauvette et al., 2011Chauvette S. Crochet S. Volgushev M. Timofeev I. Properties of slow oscillation during slow-wave sleep and anesthesia in cats.J. Neurosci. 2011; 31: 14998-15008Crossref PubMed Scopus (155) Google Scholar, Ruiz-Mejias et al., 2011Ruiz-Mejias M. Ciria-Suarez L. Mattia M. Sanchez-Vives M.V. Slow and fast rhythms generated in the cerebral cortex of the anesthetized mouse.J. Neurophysiol. 2011; 106: 2910-2921Crossref PubMed Scopus (80) Google Scholar), although a larger heterogeneity has been reported during natural sleep (Chauvette et al., 2011Chauvette S. Crochet S. Volgushev M. Timofeev I. Properties of slow oscillation during slow-wave sleep and anesthesia in cats.J. Neurosci. 2011; 31: 14998-15008Crossref PubMed Scopus (155) Google Scholar). SO are thus a collective phenomenon with a dynamical origin not only rooted in the features of single neurons but also determined by the synaptic reverberation of the spiking activity at the mesoscopic (cell assemblies/cortical columns) and macroscopic (cortical areas/whole brain) levels. Computer models aimed at reproducing the features of SO derived from experimental findings range from networks of neurons with Hodgking-Huxley-like ionic currents (Bazhenov et al., 2002Bazhenov M. Timofeev I. Steriade M. Sejnowski T.J. Model of thalamocortical slow-wave sleep oscillations and transitions to activated States.J. Neurosci. 2002; 22: 8691-8704Crossref PubMed Google Scholar, Compte et al., 2003Compte A. Sanchez-Vives M.V. McCormick D.A. Wang X.J. Cellular and network mechanisms of slow oscillatory activity (<1 Hz) and wave propagations in a cortical network model.J. Neurophysiol. 2003; 89: 2707-2725Crossref PubMed Scopus (372) Google Scholar, Hill and Tononi, 2005Hill S. Tononi G. Modeling sleep and wakefulness in the thalamocortical system.J. Neurophysiol. 2005; 93: 1671-1698Crossref PubMed Scopus (259) Google Scholar) to assemblies of simplified point-like, integrate-and-fire neurons (Destexhe, 2009Destexhe A. Self-sustained asynchronous irregular states and Up-Down states in thalamic, cortical and thalamocortical networks of nonlinear integrate-and-fire neurons.J. Comput. Neurosci. 2009; 27: 493-506Crossref PubMed Scopus (130) Google Scholar, Giugliano et al., 2004Giugliano M. Darbon P. Arsiero M. 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Arsever S. et al.Reconstruction and simulation of neocortical microcircuitry.Cell. 2015; 163: 456-492Abstract Full Text Full Text PDF PubMed Scopus (747) Google Scholar). Remarkably, SO also emerged spontaneously in this accurate large-scale model of the cortical network without parameter tuning, strengthening the default mode hypothesis. Notwithstanding the huge amount of detail a cortical model can nowadays integrate, it is important to understand theoretically the key elements determining the onset and the modulation of SO. Mean-field theories—where the activity of a cell assembly characterizes the network dynamics— are powerful descriptors for point-like neuron ensembles (Gigante et al., 2007Gigante G. Mattia M. Del Giudice P. Diverse population-bursting modes of adapting spiking neurons.Phys. Rev. Lett. 2007; 98: 148101Crossref PubMed Scopus (53) Google Scholar, Latham et al., 2000Latham P.E. Richmond B.J. Nelson P.G. Nirenberg S. Intrinsic dynamics in neuronal networks. I. Theory.J. Neurophysiol. 2000; 83: 808-827Crossref PubMed Scopus (241) Google Scholar, Mattia and Sanchez-Vives, 2012Mattia M. Sanchez-Vives M.V. Exploring the spectrum of dynamical regimes and timescales in spontaneous cortical activity.Cogn. Neurodyn. 2012; 6: 239-250Crossref PubMed Scopus (55) Google Scholar). As a result, the coexistence of only a few dynamical elements (bistable dynamics, activity-dependent adaptation, and endogenous noise) is required to fully describe the experimental findings. First, the network activity should be "attracted" into both a high-firing (Up) and an almost silent (Down) state, i.e., it should display "bistable dynamics." Strong synaptic coupling allows sustained spike reverberation in the network through the nonlinear amplification operated by single neurons. A quasi-regular alternation between Up and Down states requires also an activity-dependent adaptation, i.e., a mechanism by which Up states can end and hence give rise to Down states. Such adaptation may include not only that mediated by ionic channels (largely potassium channels) but also short-term synaptic depression and GABAergic activation. In all cases, the net input current received by the neurons decreases with adaptation, consequently reducing neuronal excitability. This activity-dependent adaptation accumulates during Up states, destabilizes synaptic reverberation, and eventually switches the system toward the Down state (Figure 2A). Due to the low neuronal firing in this phase, the adaptation strength relaxes and the network recovers its excitability, eliciting the next sudden transition to the active Up state. This depicts a "relaxation oscillator" (Figure 2B) in which Up and Down state durations are mainly determined by the time course of adaptation to relax or to reach a threshold value and where the state transitions are elicited at a rather regular pace (Mattia and Sanchez-Vives, 2012Mattia M. Sanchez-Vives M.V. Exploring the spectrum of dynamical regimes and timescales in spontaneous cortical activity.Cogn. Neurodyn. 2012; 6: 239-250Crossref PubMed Scopus (55) Google Scholar). Endogenous noise resulting from local firing rates and from fluctuations in the inputs (Braun and Mattia, 2010Braun J. Mattia M. Attractors and noise: twin drivers of decisions and multistability.Neuroimage. 2010; 52: 740-751Crossref PubMed Scopus (89) Google Scholar, Destexhe and Contreras, 2006Destexhe A. Contreras D. Neuronal computations with stochastic network states.Science. 2006; 314: 85-90Crossref PubMed Scopus (179) Google Scholar) is a key factor necessary to cross the barrier that primes the switch toward the Up state. SO activity therefore emerges in the cortical network as a result of synaptic reverberation in the local circuit and its interplay with activity-dependent adaptation mechanisms. Different features of the network (e.g., membrane properties, synaptic properties, and connectivity) and the medium in which the network is bathed (with its ionic concentrations and neurotransmitter levels) shape the emergent pattern. This shaping consists in the modulation of amplitude and/or spatial and temporal properties of the SO even though, as we will see, such bistable pattern is quite resilient to changes. We next discuss the shaping of this default activity regime with selected examples of variations in various physiological parameters. The first example of how the SO regime is resilient to the alteration of homeostatic parameters, such that bistability is not easily lost, concerns the excitatory/inhibitory balance. The increase in neuronal firing rate during Up states comprises spikes from both excitatory and inhibitory neurons (Steriade et al., 1993Steriade M. Nuñez A. Amzica F. A novel slow (< 1 Hz) oscillation of neocortical neurons in vivo: depolarizing and hyperpolarizing components.J. Neurosci. 1993; 13: 3252-3265Crossref PubMed Google Scholar). The balance between excitation and inhibition (Haider et al., 2006Haider B. Duque A. Hasenstaub A.R. McCormick D.A. Neocortical network activity in vivo is generated through a dynamic balance of excitation and inhibition.J. Neurosci. 2006; 26: 4535-4545Crossref PubMed Scopus (653) Google Scholar, Okun and Lampl, 2008Okun M. Lampl I. Instantaneous correlation of excitation and inhibition during ongoing and sensory-evoked activities.Nat. Neurosci. 2008; 11: 535-537Crossref PubMed Scopus (422) Google Scholar) is a critical force shaping SO. If this balance is altered by decreasing inhibition, excitatory recurrency results in reverberatory activity that leads to epileptiform discharges (Sanchez-Vives et al., 2010Sanchez-Vives M.V. Mattia M. Compte A. Perez-Zabalza M. Winograd M. Descalzo V.F. Reig R. Inhibitory modulation of cortical up states.J. Neurophysiol. 2010; 104: 1314-1324Crossref PubMed Scopus (126) Google Scholar). However, there are compensating mechanisms that dampen the effect of reduced inhibition and prevent the immediate occurrence of epileptiform activity. When fast inhibition is progressively blocked in cortical slices, SO are gradually transformed such that Up states become shorter while the multiunit firing rate during Up states increases (Figure 1C). This progressive transformation results from the activation of afterhyperpolarizing currents of increasing amplitude and duration following Up states, which compensate the reduced inhibition (Sanchez-Vives et al., 2010Sanchez-Vives M.V. Mattia M. Compte A. Perez-Zabalza M. Winograd M. Descalzo V.F. Reig R. Inhibitory modulation of cortical up states.J. Neurophysiol. 2010; 104: 1314-1324Crossref PubMed Scopus (126) Google Scholar). As a result, longer Down states decrease the frequency of the oscillation (Figure 1B). Only when a critical point of inhibition blockade is reached, does the network enter an epileptiform regime (Figures 2C and 2D) (Mattia and Sanchez-Vives, 2012Mattia M. Sanchez-Vives M.V. Exploring the spectrum of dynamical regimes and timescales in spontaneous cortical activity.Cogn. Neurodyn. 2012; 6: 239-250Crossref PubMed Scopus (55) Google Scholar). Temperature is another homeostatically controlled physiological parameter. It varies across physiological and pathological conditions and it has a shaping effect over SO. Increasing temperature of oscillating cortical slices from 32°C to 42°C results in progressively shorter and more synchronized Up states, although the cortical network maintains its bistable activity across this wide range of temperatures (Figure 1D) (Reig et al., 2010Reig R. Mattia M. Compte A. Belmonte C. Sanchez-Vives M.V. Temperature modulation of slow and fast cortical rhythms.J. Neurophysiol. 2010; 103: 1253-1261Crossref PubMed Scopus (58) Google Scholar). Above 41°C, Up states become very synchronized and the network evolves into an epileptiform state. The increased synchronization (higher firing rates during shorter Up states) observed at 41°C may underlie febrile seizures in children. On the other extreme, at low (32°C) temperatures, the excitability of individual neurons increases (Volgushev et al., 2000Volgushev M. Vidyasagar T.R. Chistiakova M. Yousef T. Eysel U.T. Membrane properties and spike generation in rat visual cortical cells during reversible cooling.J. Physiol. 2000; 522: 59-76Crossref PubMed Scopus (121) Google Scholar) and with it the firing rate during Down states, blurring the distinction between Up and Down states and leading to continuous firing (Figure 1C). Bistability persists but is modulated across the 32°C to 42°C range, being lost only at the extremes of this range (Figure 2D) (Reig et al., 2010Reig R. Mattia M. Compte A. Belmonte C. Sanchez-Vives M.V. Temperature modulation of slow and fast cortical rhythms.J. Neurophysiol. 2010; 103: 1253-1261Crossref PubMed Scopus (58) Google Scholar). Another physiological parameter that modulates SO when modifying its homeostatic levels is extracellular potassium. Extracellular potassium levels are critical for the neuronal membrane potential due to passive and active potassium permeability in all neuronal types. Extracellular potassium levels ([K+]o) vary with physiological neuronal activity, locally increasing with neuronal discharges. In fact, increases are large in pathological conditions such as massive neuronal firing in ictal episodes (Moody et al., 1974Moody W.J. Futamachi K.J. Prince D.A. Extracellular potassium activity during epileptogenesis.Exp. 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This could occur given that increases in [K+]o effectively result in increasing synaptic noise, the variation in regularity thus corresponding to an expression of collective stochastic coherence (Sancristobal et al., 2016Sancristobal B. Rebollo B. Boada P. Sanchez-Vives M.V. Garcia-Ojalvo J. Collective stochastic coherence in recurrent neuronal networks.Nat. Phys. 2016; 12: 881-887Crossref Scopus (36) Google Scholar). The main mechanism causing physiological modulation or switching of brain states—and not just of SO—are subcortical neuromodulators (reviewed in Lee and Dan, 2012Lee S.-H. Dan Y. Neuromodulation of brain states.Neuron. 2012; 76: 209-222Abstract Full Text Full Text PDF PubMed Scopus (353) Google Scholar). However, the variations in cortical emergent activity can derive not only from endogenous changes, but also from exogenous interventions. 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Boosting slow oscillations during sleep potentiates memory.Nature. 2006; 444: 610-613Crossref PubMed Scopus (1272) Google Scholar). Varying anesthesia levels also modulates the generation of SO, a paradigm that has been often used to control the state of the cortical network (Bettinardi et al., 2015Bettinardi R.G. Tort-Colet N. Ruiz-Mejias M. Sanchez-Vives M.V. Deco G. Gradual emergence of spontaneous correlated brain activity during fading of general anesthesia in rats: Evidences from fMRI and local field potentials.Neuroimage. 2015; 114: 185-198Crossref PubMed Scopus (50) Google Scholar, Deco et al., 2009Deco G. Martí D. Ledberg A. Reig R. Sanchez Vives M.V. Effective reduced diffusion-models: a data driven approach to the analysis of neuronal dynamics.PLoS Comput. Biol. 2009; 5: e1000587Crossref PubMed Scopus (36) Google Scholar) and the levels of consciousness (Chauvette et al., 2011Chauvette S. Crochet S. Volgushev M. Timofeev I. 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In conclusion, the bistability associated with SO shows robustness in spite of relatively large variations of endogenous and exogenous parameters. SO can be shaped by these parameters without being completely disrupted, thus remaining in a nonstationary bistable regime. From the perspective provided by the mean-field theory of SO, changes in the abovementioned parameters can be represented as a shift of the system position into its bifurcation diagram. This is a low-dimensional representation where only a few relevant features of the cortical network are taken into account. This is depicted in Figure 2C, which shows the effect on network behavior of varying inhibitory feedback due to activity-dependent adaptation and the excitability level modulated by an external excitatory current. Network activity corresponding to SO can be found within a wide region (yellow) of the diagram (Gigante et al., 2007Gigante G. Mattia M. Del Giudice P. Diverse population-bursting modes of adapting spiking neurons.Phys. Rev. Lett. 2007; 98: 148101Crossref PubMed Scopus (53) Google Scholar, Latham et al., 2000Latham P.E. Richmond B.J. Nelson P.G. Nirenberg S. Intrinsic dynamics in neuronal networks. I. Theory.J. Neurophysiol. 2000; 83: 808-827Crossref PubMed Scopus (241) Google Scholar, Mattia and Sanchez-Vives, 2012Mattia M. Sanchez-Vives M.V. Exploring the spectrum of dynamical regimes and timescales in spontaneous cortical activity.Cogn. Neurodyn. 2012; 6: 239-250Crossref PubMed Scopus (55) Google Scholar). The size of this region supports the hypothesis that this default activity pattern can be expressed by multiple network configurations, highlighting the robustness of SO to parameter perturbations (Mattia and Sanchez-Vives, 2012Mattia M. Sanchez-Vives M.V. Exploring the spectrum of dynamical regimes and timescales in spontaneous cortical activity.Cogn. Neurodyn. 2012; 6: 239-250Crossref PubMed Scopus (55) Google Scholar). For example, by changing the adaptation strength (parameter g in Figure 2), Up and Down state durations can be compressed or elongated without the network losing its ability to oscillate (Figure 2D). When the border of the SO region is crossed, the system switches to a different dynamical regime. In Figure 2C, this transition can result in a low- or a high-frequency asynchronous state (a continuous Down or Up state, respectively), both having a physiological correlate. Indeed, continuous Down states are reminiscent of those observed in coma (Brown et al., 2010Brown E.N. Lydic R. Schiff N.D. General anesthesia, sleep, and coma.N. Engl. J. Med. 2010; 363: 2638-2650Crossref PubMed Scopus (738) Google Scholar) or under barbiturate administration (Reig et al., 2006Reig R. Gallego R. Nowak L.G. Sanchez-Vives M.V. Impact