During the last year, we made several major advances in neuronal avalanche research: 1. We demonstrated for the first time that cortical networks with avalanche dynamics maintain a balance of excitation and inhibition at which information capacity and information processing is optimized (Shew et al., 2010). Summary: The repertoire of neural activity patterns that a cortical network can produce constrains the networks ability to transfer and process information. Here, we measured activity patterns obtained from multi-site local field potential (LFP) recordings in cortex cultures, urethane anesthetized rats, and awake macaque monkeys. First, we quantified the information capacity of the pattern repertoire of ongoing and stimulus-evoked activity using Shannon entropy. Next, we quantified the efficacy of information transmission between stimulus and response using mutual information. By systematically changing the ratio of excitation/inhibition (E/I) in vitro and in a network model, we discovered that both information capacity and information transmission are maximized at a particular intermediate E/I, at which ongoing activity emerges as neuronal avalanches. Next, we used our in vitro and model results to correctly predict in vivo information capacity and interactions between neuronal groups during ongoing activity. Close agreement between our experiments and model suggest that neuronal avalanches and peak information capacity arise due to criticality and are general properties of cortical networks with balanced E/I. 2. We provided an exhaustive statistical analysis showing that neuronal avalanches are characterized by true power laws indicative of critical state dynamics (Klaus et al., 2010). Summary: The size distribution of neuronal avalanches in cortical networks has been reported to follow a power law distribution with slope close to -1.5, which is a reflection of long-range spatial correlations in spontaneous neuronal activity. However, identifying power law scaling in empirical data can be difficult and sometimes controversial. In the present study, we tested the power law hypothesis for neuronal avalanches by using more stringent statistical analysis. In particular, we performed the following steps: (i) analysis of finite-size scaling to identify scale-free dynamics in neuronal avalanches, (ii) model parameter estimation to determine the specific exponent of the power law, and (iii) comparison of the power law to alternative model distributions. Consistent with critical state dynamics, avalanche size distributions exhibited robust scaling behavior in which the maximum avalanche size was limited only by the spatial extent of sampling (""""""""finite size"""""""" effect). This scale-free dynamics suggests the power law as a model for the distribution of avalanche sizes. Using both the Kolmogorov-Smirnov statistic and a maximum likelihood approach, we found the slope to be close to -1.5, which is in line with previous reports. Finally, the power law model for neuronal avalanches was compared to the exponential and to various heavy-tail distributions based on the Kolmogorov-Smirnov distance and by using a log-likelihood ratio test. Both the power law distribution with and without exponential cutoff provided a significantly better fit to the cluster size distributions in neuronal avalanches than the exponential, the lognormal and the gamma distribution. In summary, our findings strongly support the power law scaling in neuronal avalanches, providing further evidence for critical state dynamics in superficial layers of cortex. 3. In our effort to improve in vitro conditions for systems research, we have now provided a detailed protocol with video support and error analysis for the preparation of our unique cortex-forebrain co-culture systems. Summary: A robust way to study neuronal avalanches, i.e. scale-invariant spatio-temporal activity bursts, indicative of critical state dynamics in cortex. Avalanches emerge spontaneously in developing superficial layers of cultured cortex which allows for long-term measurements of the activity with planar integrated multi-electrode arrays (MEA) under precisely controlled conditions (Plenz, et al., 2011).

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U.S. National Institute of Mental Health
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Seshadri, Saurav; Klaus, Andreas; Winkowski, Daniel E et al. (2018) Altered avalanche dynamics in a developmental NMDAR hypofunction model of cognitive impairment. Transl Psychiatry 8:3
Bai, Ruiliang; Springer Jr, Charles S; Plenz, Dietmar et al. (2018) Fast, Na+ /K+ pump driven, steady-state transcytolemmal water exchange in neuronal tissue: A study of rat brain cortical cultures. Magn Reson Med 79:3207-3217
Meisel, Christian; Klaus, Andreas; Vyazovskiy, Vladyslav V et al. (2017) The Interplay between Long- and Short-Range Temporal Correlations Shapes Cortex Dynamics across Vigilance States. J Neurosci 37:10114-10124
Yu, Shan; Ribeiro, Tiago L; Meisel, Christian et al. (2017) Maintained avalanche dynamics during task-induced changes of neuronal activity in nonhuman primates. Elife 6:
Meisel, Christian; Bailey, Kimberlyn; Achermann, Peter et al. (2017) Decline of long-range temporal correlations in the human brain during sustained wakefulness. Sci Rep 7:11825
Klaus, Andreas; Plenz, Dietmar (2016) A Low-Correlation Resting State of the Striatum during Cortical Avalanches and Its Role in Movement Suppression. PLoS Biol 14:e1002582
Bai, Ruiliang; Stewart, Craig V; Plenz, Dietmar et al. (2016) Assessing the sensitivity of diffusion MRI to detect neuronal activity directly. Proc Natl Acad Sci U S A 113:E1728-37
Lombardi, F; Herrmann, H J; Plenz, D et al. (2016) Temporal correlations in neuronal avalanche occurrence. Sci Rep 6:24690
Meisel, Christian; Plenz, Dietmar; Schulze-Bonhage, Andreas et al. (2016) Quantifying antiepileptic drug effects using intrinsic excitability measures. Epilepsia 57:e210-e215
Bellay, Timothy; Klaus, Andreas; Seshadri, Saurav et al. (2015) Irregular spiking of pyramidal neurons organizes as scale-invariant neuronal avalanches in the awake state. Elife 4:e07224

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