Neuronal avalanches in the neocortex

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We previously described neuronal avalanches as a fundamental synchronization dynamics of the cortex. During the last year, we made two major advances in neuronal avalanche research: 1. We demonstrated in collaboration with the Nicolelis lab at Duke that ongoing activity in awake monkeys is composed of neuronal avalanches. This introduces criticality as a precise, quantitative framework of the awake state that allows cortex to expand during development and evolution without fundamental changes in architecture (Petermann et al, 2009, PNAS). Summary: Spontaneous neuronal activity is an important property of the cerebral cortex but its spatiotemporal organization and dynamical framework remain poorly understood. Studies in reduced systems tissue cultures, acute slices, and anesthetized rats show that spontaneous activity forms characteristic clusters in space and time, called neuronal avalanches. Modeling studies suggest that networks with this property are poised at a critical state that optimizes input processing, information storage, and transfer, but the relevance of avalanches for fully functional cerebral systems has been controversial. Here we show that ongoing cortical synchronization in awake rhesus monkeys carries the signature of neuronal avalanches. Negative LFP deflections (nLFPs) correlate with neuronal spiking and increase in amplitude with increases in local population spike rate and synchrony. These nLFPs form neuronal avalanches that are scale-invariant in space and time and with respect to the threshold of nLFP detection. This new dimension, threshold invariance, describes a fractal organization: smaller nLFPs are embedded in clusters of larger ones without destroying the spatial and temporal scale-invariance of the dynamics. These findings suggest an organization of ongoing cortical synchronization that is scale-invariant in its three fundamental dimensions time, space, and local neuronal group size. Such scale-invariance has ontogenetic and phylogenetic implications because it allows increases in network capacity without a fundamental reorganization of the system. 2. We developed new theory that reconstructs the functional architecture of neuronal networks from the observed network dynamics. This general theory is superior over current attempts to reconstruct functional network graphs from highly parrallel recordings of brain activity (e.g. MEG, EEG, fMRI) using correlation based approaches. This is an important milestone to unravel the organization of individual avalanches (Pajevic and Plenz, 2009, PLoS Comp Biol): Summary: Cascading activity is commonly found in complex systems with directed interactions such as metabolic networks, neuronal networks, or disease spreading in social networks. Substantial insight into a system's organization can be obtained by reconstructing the underlying functional network architecture from the observed activity cascades. Here we focus on Bayesian approaches and reduce their computational demands by introducing the Iterative Bayesian (IB) and Posterior Weighted Averaging (PWA) method. We show that PWA, when applied to near-critical cascading dynamics, can be cast in non-parametric form, which we call the normalized count (NC) algorithm. NC efficiently reconstructs random and small-world functional network topologies and architectures from sub-critical, critical, and super-critical cascading dynamics and yields significant improvements over commonly used correlation methods. Using experimental data, NC identified the functional and structural small-world topologies and corresponding traffic in cortical networks with neuronal avalanche dynamics.