Information Capacity and Transmission Are Maximized in Balanced Cortical Networks with Neuronal Avalanches

Shew, Woodrow L.; Yang, Hongdian; Yu, Shan; Roy, Rajarshi; Plenz, Dietmar

doi:10.1523/jneurosci.4637-10.2011

Cited by 537 publications

(616 citation statements)

References 43 publications

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“…Although it may be good for epilepsy patients to have a lower R value corresponding to a lower excitability to prevent the likelihood of seizures, the concomitant decrease in the variability of synchronization might be disadvantageous for optimal brain network functioning. Previous studies using in vitro preparations have shown that the IEMs similar to the ones used here are closely related to certain capabilities in information processing (41)(42)(43)(44). IEMs could therefore be potential candidates to quantify the neural correlates of such cognitive deficits in humans.…”

Section: Discussionmentioning

confidence: 72%

Intrinsic excitability measures track antiepileptic drug action and uncover increasing/decreasing excitability over the wake/sleep cycle

Meisel

Schulze‐Bonhage

Freestone

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

126

112

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Pathological changes in excitability of cortical tissue commonly underlie the initiation and spread of seizure activity in patients suffering from epilepsy. Accordingly, monitoring excitability and controlling its degree using antiepileptic drugs (AEDs) is of prime importance for clinical care and treatment. To date, adequate measures of excitability and action of AEDs have been difficult to identify. Recent insights into ongoing cortical activity have identified global levels of phase synchronization as measures that characterize normal levels of excitability and quantify any deviation therefrom. Here, we explore the usefulness of these intrinsic measures to quantify cortical excitability in humans. First, we observe a correlation of such markers with stimulation-evoked responses suggesting them to be viable excitability measures based on ongoing activity. Second, we report a significant covariation with the level of AED load and a wake-dependent modulation. Our results indicate that excitability in epileptic networks is effectively reduced by AEDs and suggest the proposed markers as useful candidates to quantify excitability in routine clinical conditions overcoming the limitations of electrical or magnetic stimulation. The wake-dependent time course of these metrics suggests a homeostatic role of sleep, to rebalance cortical excitability.excitability | epilepsy | sleep N ormal functioning of cortical networks critically depends on a finely tuned level of excitability, the transient or steady-state response in which the brain reacts to a stimulus. Whereas small, local responses indicate a comparably small excitability, large and global responses consequently suggest excitability to be high. The importance of adequate excitability levels is highlighted by the pathological consequences and impaired performance resulting from aberrant network excitability. In epilepsy, changes in cortical network excitability are believed to be an important cause underlying the initiation and spread of seizures, that is, the large nonphysiological neuronal activity events across time and space (1-3). Evidence for changes of excitability in brain networks affected in epilepsy has come from a variety of observations. To assess the level of excitability under in vivo experimental conditions one usually measures the size of the evoked cortical activity to external perturbations, typically either by transcranial magnetic stimulation (TMS) or, in the case of epilepsy patients, by electrical stimulation. Studies of electrical stimulation with subdural electrodes during interictal periods found the evoked potentials to be larger in regions related to the cortical onset region of epileptiform activity (4-6). The enhanced responses to electrical stimulation in epileptogenic cortex were taken as indication of an increased excitability of neural tissue in these areas. Similarly, studies using TMS consistently reported a hyperexcitability in focal epilepsies (7-10). Consequently, changes in excitability have been helpful in identifying the se...

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Section: Discussionmentioning

confidence: 72%

Intrinsic excitability measures track antiepileptic drug action and uncover increasing/decreasing excitability over the wake/sleep cycle

Meisel

Schulze‐Bonhage

Freestone

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

126

112

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“…To address this question, we investigated a parsimonious model network of probabilistic integrate-and-fire neurons with all-to-all connectivity 6,19,20 (Fig. 2a).…”

Section: And Supplementary Information 5)mentioning

confidence: 99%

“…Previous computational studies and cortex slice experiments suggest that the critical regime optimizes several aspects of information processing (reviewed in ref. 3), including dynamic range 20,26,28 and information transmission 5,6 . Our observation that transient response to stimulus onset is not critical suggests that sensory cortex dynamically adapts to gain the functional benefits of critical dynamics during strong sensory input.…”

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confidence: 99%

Adaptation to sensory input tunes visual cortex to criticality

et al. 2015

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A long-standing hypothesis at the interface of physics and neuroscience is that neural networks self-organize to the critical point of a phase transition, thereby optimizing aspects of sensory information processing 1-3 . This idea is partially supported by strong evidence for critical dynamics observed in the cerebral cortex 4-10 , but the impact of sensory input on these dynamics is largely unknown. Thus, the foundations of this hypothesis-the self-organization process and how it manifests during strong sensory input-remain unstudied experimentally. Here we show in visual cortex and in a computational model that strong sensory input initially elicits cortical network dynamics that are not critical, but adaptive changes in the network rapidly tune the system to criticality. This conclusion is based on observations of multifaceted scaling laws predicted to occur at criticality 4,11 . Our findings establish sensory adaptation as a self-organizing mechanism that maintains criticality in visual cortex during sensory information processing.Sensory nervous systems adapt, dynamically tuning interactions among large networks of neurons, to cope with a changing environment 12,13 . The principles governing such adaptation at the macroscopic level of neuronal network dynamics are not well understood. Computational models and theory suggest that such adaptation can maintain critical network dynamics [14][15][16] , but these previous studies did not consider the strongly driven regime that is expected during intense sensory input. Indeed, sufficiently strong input may increase the overall excitability of a network by bringing neurons closer to their firing thresholds and potentially tipping the network into a high firing rate regime that is inconsistent with critical dynamics (Supplementary Information 1). Thus, the question remains: does strong sensory input drive cortical network dynamics away from criticality or can adaptation counteract this tendency and maintain the critical regime?Here we addressed this question in turtle visual cortex and in a companion computational model. In our experiments, we obtained long-duration recordings of population neural activity (local field potential, LFP) using a microelectrode array inserted into the geniculo-recipient dorsal cortex (visual cortex) of the turtle eyeattached whole-brain ex vivo preparation 17 (Fig. 1a and Supplementary Information 2). We measured multi-scale spatiotemporal patterns of neural activity while visually stimulating the retina. Similarly, in our model we studied changes in neural network activity in response to changes in external input. Experimentally, and in the model, we assessed whether the measured dynamics were near or far from criticality. For this, we examined statistics and spatiotemporal scaling laws of 'neuronal avalanches' , which are bouts of elevated population activity with correlations in space and time 5 (Fig. 1b).In brief, a neuronal avalanche is defined as a group of LFP peaks, occurring on any electrode, irrespective of location, and sep...

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“…Greater dynamic range is important for the adaptability and efficiency of neural systems because it allows a greater range of response to a greater range of stimuli. Interestingly, cellular research indicates that dynamic range (and information transfer within networks) is best achieved when cellular excitation and inhibition are balanced, and response variability is optimally high; upon imbalance and a lack of response variability, dynamic range suffers (Shew et al 2009;Shew et al 2011). In the present case, our young and fast performers appeared to exhibit the greatest dynamic range of BOLD signal (i.e., greater signal variability), within and across voxels and when making transitions from rest to task.…”

Section: Why An Increase In Brain Variability During Internal-to-extementioning

confidence: 99%

The Modulation of BOLD Variability between Cognitive States Varies by Age and Processing Speed

et al. 2012

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Increasing evidence suggests that brain variability plays a number of important functional roles for neural systems. However, the relationship between brain variability and changing cognitive demands remains understudied. In the current study, we demonstrate experimental condition-based modulation in brain variability using functional magnetic resonance imaging (fMRI). Within a sample of younger and older healthy adults, we found that BOLD variability was an effective discriminator between fixation and external cognitive demand. Across a number of regions, brain variability increased broadly on task compared to fixation, particularly in younger and faster performing adults. Conversely, older and slower performing adults exhibited fewer changes in brain variability within and across experimental conditions and brain regions, indicating a reduction in variability-based neural specificity. Increases in brain variability on task may represent a more complex neural system capable of greater dynamic range between brain states, as well as an enhanced ability to efficiently process varying and unexpected external stimuli. The current results help establish the developmental and performance correlates of state-to-state brain variability-based transitions, and offer a new line of inquiry in the study of rest vs. task modes in the human brain. Keywords brain variability; default mode; fMRI; noise Increasing evidence suggests that brain variability is a useful attribute for neural systems, perhaps reflecting greater network complexity, connectivity, increased dynamic range, heightened signal detection thresholds, and superior cognitive processing (Li et al.

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Information Capacity and Transmission Are Maximized in Balanced Cortical Networks with Neuronal Avalanches

Cited by 537 publications

References 43 publications

Intrinsic excitability measures track antiepileptic drug action and uncover increasing/decreasing excitability over the wake/sleep cycle

Intrinsic excitability measures track antiepileptic drug action and uncover increasing/decreasing excitability over the wake/sleep cycle

Adaptation to sensory input tunes visual cortex to criticality

The Modulation of BOLD Variability between Cognitive States Varies by Age and Processing Speed

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