Criticality as a signature of healthy neural systems

Massobrio, Paolo; Arcangelis, L. de; Pasquale, Valentina; Plenz, Dietmar

doi:10.3389/fnsys.2015.00022

Cited by 111 publications

(101 citation statements)

References 34 publications

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“…Firing avalanches in neural networks have attracted significant interest because of their possible connection to efficient information processing34579. Through simulations, we studied the critical point W C = 1, Γ C = 1 (with μ = 0) in search for neuronal avalanches39 (Fig.…”

Section: Resultsmentioning

confidence: 99%

Phase transitions and self-organized criticality in networks of stochastic spiking neurons

Brochini

Costa

Abadi

et al. 2016

Sci Rep

102

139

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Phase transitions and critical behavior are crucial issues both in theoretical and experimental neuroscience. We report analytic and computational results about phase transitions and self-organized criticality (SOC) in networks with general stochastic neurons. The stochastic neuron has a firing probability given by a smooth monotonic function Φ(V) of the membrane potential V, rather than a sharp firing threshold. We find that such networks can operate in several dynamic regimes (phases) depending on the average synaptic weight and the shape of the firing function Φ. In particular, we encounter both continuous and discontinuous phase transitions to absorbing states. At the continuous transition critical boundary, neuronal avalanches occur whose distributions of size and duration are given by power laws, as observed in biological neural networks. We also propose and test a new mechanism to produce SOC: the use of dynamic neuronal gains – a form of short-term plasticity probably located at the axon initial segment (AIS) – instead of depressing synapses at the dendrites (as previously studied in the literature). The new self-organization mechanism produces a slightly supercritical state, that we called SOSC, in accord to some intuitions of Alan Turing.

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Section: Resultsmentioning

confidence: 99%

Phase transitions and self-organized criticality in networks of stochastic spiking neurons

Brochini

Costa

Abadi

et al. 2016

Sci Rep

102

139

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show abstract

“…Accumulating evidence supports the hypothesis that healthy brain dynamics maintain proximity to a critical state (for review, see Massobrio et al, 2015). To examine the sensitivity of metrics of criticality to changes in the balance of excitation and inhibition in epilepsy, we evaluated deviations from critical brain dynamics in patients with refractory epilepsy outside of seizures.…”

Section: Discussionmentioning

confidence: 99%

“…In the past decade, numerous studies have presented supporting evidence that a healthy cortex displays near-critical dynamics; namely, it operates on the border between premature termination and runaway explosive growth of neuronal activity, thereby enabling efficient information propagation (Beggs and Timme, 2012;Shew and Plenz, 2013;Massobrio et al, 2015). A critical state is accompanied by scale-free cascades of activity, termed neuronal avalanches (Plenz, 2012).…”

Section: Introductionmentioning

confidence: 99%

Deviations from Critical Dynamics in Interictal Epileptiform Activity

et al. 2016

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The framework of criticality provides a unifying perspective on neuronal dynamics from in vitro cortical cultures to functioning human brains. Recent findings suggest that a healthy cortex displays critical dynamics, giving rise to scale-free spatiotemporal cascades of activity, termed neuronal avalanches. Pharmacological manipulations of the excitation-inhibition balance (EIB) in cortical cultures were previously shown to result in deviations from criticality and from the power law scaling of avalanche size distribution. To examine the sensitivity of neuronal avalanche metrics to altered EIB in humans, we focused on epilepsy, a neurological disorder characterized by hyperexcitable networks. Using magnetoencephalography, we quantitatively assessed deviations from criticality in the brain dynamics of patients with epilepsy during interictal (between-seizures) activity. Compared with healthy control subjects, epilepsy patients tended to exhibit a higher neural gain and larger avalanches, particularly during interictal epileptiform activity. Moreover, deviations from scalefree behavior were exclusively connected to brief intervals at epileptiform discharges, strengthening the association between deviations from criticality and the instantaneous changes in EIB. The avalanches collected during interictal epileptiform activity had not only a stereotypical size range but also involved particular spatial patterns of activations, as expected for periods of epileptic network dominance. Overall, the neuronal avalanche metrics provide a quantitative novel description of interictal brain activity of patients with epilepsy.

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“…Criticality estimated with LRTCs is the universal signature of healthy brain systems by analysing multichannel recordings from various neuroimaging methods (Massobrio et al, 2015). Experimental observations of LRTCs estimated over phase synchronization in EEG/MEG signals suggest that the main driving mechanism of the observed avalanche activity is global where all temporal scales contributing to the characteristic system behavior (Botcharova et al, 2014).…”

Section: Neurofeedback Technologies Have Attracted Growing Interest Fmentioning

confidence: 99%

Modulation of brain criticality via suppression of EEG long-range temporal correlations (LRTCs) in a closed-loop neurofeedback stimulation

Dimitriadis

Linden

2016

Clinical Neurophysiology

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Criticality as a signature of healthy neural systems

Cited by 111 publications

References 34 publications

Phase transitions and self-organized criticality in networks of stochastic spiking neurons

Phase transitions and self-organized criticality in networks of stochastic spiking neurons

Deviations from Critical Dynamics in Interictal Epileptiform Activity

Modulation of brain criticality via suppression of EEG long-range temporal correlations (LRTCs) in a closed-loop neurofeedback stimulation

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