Avalanche Analysis from Multielectrode Ensemble Recordings in Cat, Monkey, and Human Cerebral Cortex during Wakefulness and Sleep.

Self-organized critical states are found in many natural systems, from earthquakes to forest fires, they have also been observed in neural systems, particularly, in neuronal cultures. However, the presence of critical states in the awake brain remains controversial. Here, we compared avalanche analyses performed on different in vivo preparations during wakefulness, slow-wave sleep, and REM sleep, using high density electrode arrays in cat motor cortex (96 electrodes), monkey motor cortex and premotor cortex and human temporal cortex (96 electrodes) in epileptic patients. In neuronal avalanches defined from units (up to 160 single units), the size of avalanches never clearly scaled as power-law, but rather scaled exponentially or displayed intermediate scaling. We also analyzed the dynamics of local field potentials (LFPs) and in particular LFP negative peaks (nLFPs) among the different electrodes (up to 96 sites in temporal cortex or up to 128 sites in adjacent motor and premotor cortices). In this case, the avalanches defined from nLFPs displayed power-law scaling in double logarithmic representations, as reported previously in monkey. However, avalanche defined as positive LFP (pLFP) peaks, which are less directly related to neuronal firing, also displayed apparent power-law scaling. Closer examination of this scaling using the more reliable cumulative distribution function (CDF) and other rigorous statistical measures, did not confirm power-law scaling. The same pattern was seen for cats, monkey, and human, as well as for different brain states of wakefulness and sleep. We also tested other alternative distributions. Multiple exponential fitting yielded optimal fits of the avalanche dynamics with bi-exponential distributions. Collectively, these results show no clear evidence for power-law scaling or self-organized critical states in the awake and sleeping brain of mammals, from cat to man.

Bayesian inference of functional connectivity and network structure from spikes.

Current multielectrode techniques enable the simultaneous recording of spikes from hundreds of neurons. To study neural plasticity and network structure it is desirable to infer the underlying functional connectivity between the recorded neurons. Functional connectivity is defined by a large number of parameters, which characterize how each neuron influences the other neurons. A Bayesian approach that combines information from the recorded spikes (likelihood) with prior beliefs about functional connectivity (prior) can improve inference of these parameters and reduce overfitting. Recent studies have used likelihood functions based on the statistics of point-processes and a prior that captures the sparseness of neural connections. Here we include a prior that captures the empirical finding that interactions tend to vary smoothly in time. We show that this method can successfully infer connectivity patterns in simulated data and apply the algorithm to spike data recorded from primary motor (M1) and premotor (PMd) cortices of a monkey. Finally, we present a new approach to studying structure in inferred connections based on a Bayesian clustering algorithm. Groups of neurons in M1 and PMd show common patterns of input and output that may correspond to functional assemblies.

Template-based spike pattern identification with linear convolution and dynamic time warping.

Pattern identification for spiking activity, which is central to neurophysiological analysis, is complicated by variability in spiking at multiple timescales. Incorporating likelihood tests on the variability at two timescales, we developed an approach to identifying segments from continuous neurophysiological recordings that match preselected spike "templates." At smaller timescales, each component of the preselected pattern is represented by a linear filter. Local scores to measure the similarities between short data segments and the pattern components are computed as filter responses. At larger timescales, overall scores to measure the similarities between relatively long data segments and the entire pattern are computed by dynamic time warping, which combines the local similarity scores associated with the pattern components, optimizing over a range of intercomponent time intervals. Occurrences of the pattern are identified by local peaks in the overall similarity scores. This approach is developed for point process representations and binary representations of spiking activity, both deriving from a single underlying statistical model. Point process representations are suitable for highly reliable single-unit responses, whereas binary representations are preferred for more variable single-unit responses and multiunit responses. Testing with single units recorded from individual electrodes within the robust nucleus of the arcopallium of zebra finches and with recordings from an array placed within the motor cortex of macaque monkeys demonstrates that the approach can identify occurrences of specified patterns with good time precision in a broad range of neurophysiological data.

Binaural beat induced theta EEG activity and hypnotic susceptibility: contradictory results and technical considerations.

The present study offered a constructive replication of an earlier study which demonstrated significant increases in theta EEG activity following theta binaural beat (BB) entrainment training and significant increases in hypnotic susceptibility. This study improved upon the earlier small-sample, multiple-baseline investigation by employing a larger sample, by utilizing a double-blind, repeated-measures group experimental design, by investigating only low and moderate susceptible participants, and by providing 4 hours of binaural beat training. With these design improvements, results were not supportive of the specific efficacy of the theta binaural beat training employed in this study in either increasing frontal theta EEG activity or in increasing hypnotic susceptibility. Statistical power analyses indicated the theta binaural beat training to be a very low power phenomenon on theta EEG activity. Furthermore, we found no significant relationship between frontal theta power and hypnotizability, although the more hypnotizable participants showed significantly greater increases in hypnotizability than the less hypnotizables. Results are discussed within the context of participant selection and classification factors, technical considerations in the presentation of TBB training, and theta blocking.