BackgroundScale-invariant neuronal avalanches have been observed in cell cultures and slices as well as anesthetized and awake brains, suggesting that the brain operates near criticality, i.e. within a narrow margin between avalanche propagation and extinction. In theory, criticality provides many desirable features for the behaving brain, optimizing computational capabilities, information transmission, sensitivity to sensory stimuli and size of memory repertoires. However, a thorough characterization of neuronal avalanches in freely-behaving (FB) animals is still missing, thus raising doubts about their relevance for brain function.Methodology/Principal FindingsTo address this issue, we employed chronically implanted multielectrode arrays (MEA) to record avalanches of action potentials (spikes) from the cerebral cortex and hippocampus of 14 rats, as they spontaneously traversed the wake-sleep cycle, explored novel objects or were subjected to anesthesia (AN). We then modeled spike avalanches to evaluate the impact of sparse MEA sampling on their statistics. We found that the size distribution of spike avalanches are well fit by lognormal distributions in FB animals, and by truncated power laws in the AN group. FB data surrogation markedly decreases the tail of the distribution, i.e. spike shuffling destroys the largest avalanches. The FB data are also characterized by multiple key features compatible with criticality in the temporal domain, such as 1/f spectra and long-term correlations as measured by detrended fluctuation analysis. These signatures are very stable across waking, slow-wave sleep and rapid-eye-movement sleep, but collapse during anesthesia. Likewise, waiting time distributions obey a single scaling function during all natural behavioral states, but not during anesthesia. Results are equivalent for neuronal ensembles recorded from visual and tactile areas of the cerebral cortex, as well as the hippocampus.Conclusions/SignificanceAltogether, the data provide a comprehensive link between behavior and brain criticality, revealing a unique scale-invariant regime of spike avalanches across all major behaviors.
It was recently proposed that fast gamma oscillations (60-150 Hz) convey spatial information from the medial entorhinal cortex (EC) to the CA1 region of the hippocampus. However, here we describe 2 functionally distinct oscillations within this frequency range, both coupled to the theta rhythm during active exploration and rapid eye movement sleep: an oscillation with peak activity at ∼80 Hz and a faster oscillation centered at ∼140 Hz. The 2 oscillations are differentially modulated by the phase of theta depending on the CA1 layer; theta-80 Hz coupling is strongest at stratum lacunosum-moleculare, while theta-140 Hz coupling is strongest at stratum oriens-alveus. This laminar profile suggests that the ∼80 Hz oscillation originates from EC inputs to deeper CA1 layers, while the ∼140 Hz oscillation reflects CA1 activity in superficial layers. We further show that the ∼140 Hz oscillation differs from sharp wave-associated ripple oscillations in several key characteristics. Our results demonstrate the existence of novel theta-associated high-frequency oscillations and suggest a redefinition of fast gamma oscillations.
Recent reports converge to the idea that high-frequency oscillations in local field potentials (LFPs) represent multiunit activity. In particular, the amplitude of LFP activity above 100 Hz-commonly referred to as "high-gamma" or "epsilon" band-was found to correlate with firing rate. However, other studies suggest the existence of true LFP oscillations at this frequency range that are different from the well established ripple oscillations. Using multisite recordings of the hippocampus of freely moving rats, we show here that high-frequency LFP oscillations can represent either the spectral leakage of spiking activity or a genuine rhythm, depending on recording location. Both spike-leaked, spurious activity and true fast oscillations couple to theta phase; however, the two phenomena can be clearly distinguished by other key features, such as preferred coupling phase and spectral signatures. Our results argue against the idea that all high-frequency LFP activity stems from spike contamination and suggest avoiding defining brain rhythms solely based on frequency range. IntroductionOver the last 5 years, a growing consensus has emerged that highfrequency activity (Ͼ100 Hz) in local field potentials (LFPs) essentially reflects spiking activity (Ray et al., 2008b;Ray et al., 2008c;Jia and Kohn, 2011; Ray and Maunsell, 2011;Belluscio et al., 2012; Buzsáki and Wang, 2012; see also Manning et al., 2009). This upper part of the LFP spectrum has been called "high-gamma" (Canolty et al., 2006;Ray et al., 2008a;Ray and Maunsell, 2011) or "epsilon" band (Freeman, 2007;Belluscio et al., 2012). Some researchers have stressed the broadband nature of the power changes associated with spiking activity (Manning et al., 2009) and advocated avoiding the term "oscillations" when referring to these phenomena (Jacobs et al., 2010). The evidence that high-frequency LFP activity stems from extracellular spikes is several fold: (1) the power of broadband high-gamma activity correlates well with firing rate (Ray et al., 2008c; Ray and Maunsell, 2011); (2) local increases of high-frequency LFP activity are restricted to cortical regions expected to present increased spiking activity (Miller et al., 2009;Miller, 2010); (3) The notion that the upper LFP spectrum may reflect multiunit activity implies that examining broadband changes in LFP power could be a proxy for tracking neuronal activity (Manning et al., 2009; Buzsáki and Wang, 2012;; this is particularly good news to those interested in brain-machine interfaces (Crone et al., 2006;Miller et al., 2009). The purpose of the present work, however, is to challenge the emerging view of highfrequency LFP activity as essentially denoting spiking activity. Recent work of ours has provided evidence for genuine LFP oscillations above 100 Hz in the hippocampus and neocortex of rodents (Tort et al., 2008; Scheffzük et al., 2011; Scheffer-Teixeira et al., 2012), which we refer to as high-frequency oscillations (HFO). We have previously shown that HFOs differ from sharp wave-associated ripple osc...
The power-law size distributions obtained experimentally for neuronal avalanches are an important evidence of criticality in the brain. This evidence is supported by the fact that a critical branching process exhibits the same exponent . Models at criticality have been employed to mimic avalanche propagation and explain the statistics observed experimentally. However, a crucial aspect of neuronal recordings has been almost completely neglected in the models: undersampling. While in a typical multielectrode array hundreds of neurons are recorded, in the same area of neuronal tissue tens of thousands of neurons can be found. Here we investigate the consequences of undersampling in models with three different topologies (two-dimensional, small-world and random network) and three different dynamical regimes (subcritical, critical and supercritical). We found that undersampling modifies avalanche size distributions, extinguishing the power laws observed in critical systems. Distributions from subcritical systems are also modified, but the shape of the undersampled distributions is more similar to that of a fully sampled system. Undersampled supercritical systems can recover the general characteristics of the fully sampled version, provided that enough neurons are measured. Undersampling in two-dimensional and small-world networks leads to similar effects, while the random network is insensitive to sampling density due to the lack of a well-defined neighborhood. We conjecture that neuronal avalanches recorded from local field potentials avoid undersampling effects due to the nature of this signal, but the same does not hold for spike avalanches. We conclude that undersampled branching-process-like models in these topologies fail to reproduce the statistics of spike avalanches.
Cortical areas that directly receive sensory inputs from the thalamus were long thought to be exclusively dedicated to a single modality, originating separate labeled lines. In the past decade, however, several independent lines of research have demonstrated cross-modal responses in primary sensory areas. To investigate whether these responses represent behaviorally relevant information, we carried out neuronal recordings in the primary somatosensory cortex (S1) and primary visual cortex (V1) of rats as they performed whisker-based tasks in the dark. During the free exploration of novel objects, V1 and S1 responses carried comparable amounts of information about object identity. During execution of an aperture tactile discrimination task, tactile recruitment was slower and less robust in V1 than in S1. However, V1 tactile responses correlated significantly with performance across sessions. Altogether, the results support the notion that primary sensory areas have a preference for a given modality but can engage in meaningful cross-modal processing depending on task demand.
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