Spontaneous cortical activity is transiently poised close to criticality

Hahn, Gerald J.; Ponce‐Alvarez, Adrián; Monier, Cyril; Benvenuti, Giacomo; Kumar, Arvind; Chavane, Frédéric; Deco, Gustavo; Frégnac, Yves

doi:10.1371/journal.pcbi.1005543

Cited by 104 publications

(106 citation statements)

References 87 publications

(157 reference statements)

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“…These insights allow us to address the question posed in our Introduction: are there computational advantages for the brain to operate in a near-critical state? In alignment with these results from other systems, and hypothesised as discussed earlier [13,15,16], the balance between these operations exhibited near the critical state could be expected to support a wide range of general purpose cognitive tasks (requiring both types of operations), as well as in allowing flexibility for rapid transitions to either sub-or supercritical behaviour in order to alter the computational structure and dynamics as required. Indeed it is straightforward to identify situations that would require rapid transitions away from criticality toward more segregated or integrated operation.…”

Section: Discussionsupporting

confidence: 76%

Transitions in brain-network level information processing dynamics are driven by alterations in neural gain

Han

Aburn

et al. 2019

Preprint

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A key component of the flexibility and complexity of the brain is its ability to dynamically adapt its functional network structure between integrated and segregated brain states depending on the demands of different cognitive tasks. Integrated states are prevalent when performing tasks of high complexity, such as maintaining items in working memory, consistent with models of a global workspace architecture. Recent work has suggested that the balance between integration and segregation is under the control of ascending neuromodulatory systems, such as the noradrenergic system. In a previous large-scale nonlinear oscillator model of neuronal network dynamics, we showed that manipulating neural gain led to a 'critical' transition in phase synchrony that was associated with a shift from segregated to integrated topology, thus confirming our original prediction. In this study, we advance these results by demonstrating that the gain-mediated phase transition is characterized by a shift in the underlying dynamics of neural information processing. Specifically, the dynamics of the subcritical (segregated) regime are dominated by information storage, whereas the supercritical (integrated) regime is associated with increased information transfer (measured via transfer entropy). Operating near to the critical regime with respect to modulating neural gain would thus appear to provide computational advantages, offering flexibility in the information processing that can be performed with only subtle changes in gain control. Our results thus link studies of whole-brain network topology and the ascending arousal system with information processing dynamics, and suggest that the constraints imposed by the ascending arousal system constrain low-dimensional modes of information processing within the brain. Author summaryHigher brain function relies on a dynamic balance between functional integration and segregation. Previous work has shown that this balance is mediated in part by alterations in neural gain, which are thought to relate to projections from ascending neuromodulatory nuclei, such as the locus coeruleus. Here, we extend this work by demonstrating that the modulation of neural gain alters the information processing dynamics of the neural components of a biophysical neural model. Specifically, we find March 11, 2019 1/26 that low levels of neural gain are characterized by high Active Information Storage, whereas higher levels of neural gain are associated with an increase in inter-regional Transfer Entropy. Our results suggest that the modulation of neural gain via the ascending arousal system may fundamentally alter the information processing mode of the brain, which in turn has important implications for understanding the biophysical basis of cognition. IntroductionAlthough there is a long history relating individual brain regions to specific and specialized functions, regions in isolation cannot perform meaningful physiological or cognitive processes [1]. Instead, starting at a lower scale, a few prominent features of ...

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Section: Discussionsupporting

confidence: 76%

Transitions in brain-network level information processing dynamics are driven by alterations in neural gain

Han

Aburn

et al. 2019

Preprint

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“…Opening the eyes produced a reduction in beta DFA values in healthy participants. The higher DFA values in the EC state are likely to reflect brain activity operating near criticality, where information handling and capacity to respond to varied inputs are optimised (Gautam et al, 2015;Hahn et al, 2017;Shew et al, 2011). In contrast, the lower DFA values in the EO state may reflect a system operating closer to sub-critical levels, meaning that activity has less temporal complexity and can dwell more consistently close to a particular configuration (Irrmischer et al, 2018b).…”

Section: Discussionmentioning

confidence: 99%

“…This prior work suggests that the brain displays variability around a critical state depending upon the current behavioural condition (Bellay et al, 2015;Hahn et al, 2017). One such behavioural change is the switch from having closed eyes to having them open (Yan et al, 2009), resulting in a change from an internally oriented state to one that is oriented towards the external environment (Marx et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

“…This eyes-open (EO) effect is known to influence the activity in multiple brain networks, impacting upon neural oscillations within different frequency bands (Barry et al, 2007;Barry and De Blasio, 2017). Activity in the eyes-closed (EC) condition appears to be closer to criticality, moving towards sub-critical dynamics in the eyes-open condition (Hahn et al, 2017;Jao et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

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Intrinsic activity temporal structure reactivity to behavioural state change is correlated with depressive symptoms

Duncan

Hsu

Cheng

et al. 2019

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Patients with major depressive disorder (MDD) tend to focus their thoughts on their personal problems and negative self-reflection. This negative self-focus has a major impact on patient quality of life and is associated with a range of cognitive deficits. Self-related thoughts, such as those seen in rumination, have been associated with intrinsic activity within cortical midline structures. In normal conditions this intrinsic activity is responsive to behavioural state, changing as an individual switches from an internally to externally oriented context. It was hypothesised that this responsiveness would be blunted in patients with MDD (n = 26; control n = 37) and that the degree to which an individual was fixed in a particular intrinsic state would be correlated with reported ruminatory behaviours. This was tested by measuring intrinsic EEG activity temporal structure, quantified with detrended fluctuation analysis (DFA), in eyes-closed and eyes-open task-free states and contrasting between the conditions. The difference between the internally oriented eyes-closed and externally oriented eyes-open states was then correlated with ruminatory behaviour, measured with the Rumination Response Scale. Healthy controls showed a robust beta band DFA change between states, moving from a critical to sub-critical activity state with the opening of the eyes. This change was not seen in MDD patients, with the eyes-closed DFA remaining similar to that in eyes-open. A negative correlation was seen between the DFA difference and rumination scores over the midline electrodes. These results identify a reduced reactivity of intrinsic activity properties in MDD that is related to greater ruminative symptoms.

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“…In particular, the reverberating regime covers a specific range ideal baseline [25] from which brain areas or neural circuits can 357 adapt to meet task demands [35,[46][47][48][49][50][51][52].…”

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confidence: 99%

A unified picture of neuronal avalanches arises from the understanding of sampling effects

Neto

Spitzner

Priesemann

2019

Preprint

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To date, it is still impossible to sample the entire mammalian brain with single-neuron precision. This forces one to either use spikes (focusing on few neurons) or to use coarse-sampled activity (averaging over many neurons, e.g. LFP). Naturally, the sampling technique impacts inference about collective properties. Here, we emulate both sampling techniques on a spiking model to quantify how they alter observed correlations and signatures of criticality. We discover a general effect: when the inter-electrode distance is small, electrodes sample overlapping regions in space, which increases the correlation between the signals. For coarse-sampled activity, this can produce power-law distributions even for non-critical systems. In contrast, spikes enable one to distinguish the underlying dynamics. This explains why coarse measures and spikes have produced contradicting results in the past -that are now all consistent with a slightly subcritical regime. Introduction 1For more than two decades, it has been argued that the cor-2 tex might operate at a critical point [1][2][3][4][5][6]. The criticality hy-3 pothesis states that by operating at a critical point, neuronal 4 networks could benefit from optimal information-processing 5 properties. Properties maximized at criticality include the cor-6 relation length [7], the autocorrelation time [6], the dynamic 7 range [8] and the richness of spatio-temporal patterns [9, 10]. 8 Evidence for criticality in the brain often derives from mea-9 surements of neuronal avalanches. Neuronal avalanches are 10 cascades of neuronal activity that spread in space and time. If a 11 system is critical, the probability distribution of avalanche size 12 ( ) follows a power law ( ) ∼ − [7, 11]. Such power-13 law distributions have been observed repeatedly in experiments 14 since they were first reported by Beggs & Plenz in 2003 [1]. 15 However, not all experiments have produced power laws 16 and the criticality hypothesis remains controversial. It turns 17 out that results for cortical recordings in vivo differ systemati-18 cally: 19 Studies that use what we here call coarse-sampled activity 20 typically produce power-law distributions [1, 12-21]. In con-21 trast, studies that use sub-sampled activity typically do not [14, 22 22-26]. Coarse-sampled activity include LFP, M/EEG, fMRI 23 and potentially calcium imaging, while sub-sampled activity is 24 front-most spike recordings. We hypothesize that the apparent 25 contradiction between coarse-sampled (LFP-like) data and sub-26 sampled (spike) data can be explained by the differences in the 27 recording and analysis procedures.28In general, the analysis of neuronal avalanches is not 29 straightforward. In order to obtain avalanches, one needs to de-30 fine discrete events. While spikes are discrete events by nature, 31 a coarse-sampled signal has to be converted into a binary form. 32This conversion hinges on thresholding the signal, which can be 33 problematic [27][28][29][30]. Furthermore, events have to be grouped 34 into avalanches, and th...

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Spontaneous cortical activity is transiently poised close to criticality

Cited by 104 publications

References 87 publications

Transitions in brain-network level information processing dynamics are driven by alterations in neural gain

Transitions in brain-network level information processing dynamics are driven by alterations in neural gain

Intrinsic activity temporal structure reactivity to behavioural state change is correlated with depressive symptoms

A unified picture of neuronal avalanches arises from the understanding of sampling effects

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