Stereotypical spatiotemporal activity patterns during slow-wave activity in the neocortex.

Alternating epochs of activity and silence are a characteristic feature of neocortical networks during certain sleep cycles and deep states of anesthesia. The mechanism and functional role of these slow oscillations (<1 Hz) have not yet been fully characterized. Experimental and theoretical studies show that slow-wave oscillations can be generated autonomously by neocortical tissue but become more regular through a thalamo-cortical feedback loop. Evidence for a functional role of slow-wave activity comes from EEG recordings in humans during sleep, which show that activity travels as stereotypical waves over the entire brain, thought to play a role in memory consolidation. We used an animal model to investigate activity wave propagation on a smaller scale, namely within the rat somatosensory cortex. Signals from multiple extracellular microelectrodes in combination with one intracellular recording in the anesthetized animal in vivo were utilized to monitor the spreading of activity. We found that activity propagation in most animals showed a clear preferred direction, suggesting that it often originated from a similar location in the cortex. In addition, the breakdown of active states followed a similar pattern with slightly weaker direction preference but a clear correlation to the direction of activity spreading, supporting the notion of a wave-like phenomenon similar to that observed after strong sensory stimulation in sensory areas. Taken together, our findings support the idea that activity waves during slow-wave sleep do not occur spontaneously at random locations within the network, as was suggested previously, but follow preferred synaptic pathways on a small spatial scale.

Robust off- and online separation of intracellularly recorded up and down cortical states.

BACKGROUND: The neuronal cortical network generates slow (<1 Hz) spontaneous rhythmic activity that emerges from the recurrent connectivity. This activity occurs during slow wave sleep or anesthesia and also in cortical slices, consisting of alternating up (active, depolarized) and down (silent, hyperpolarized) states. The search for the underlying mechanisms and the possibility of analyzing network dynamics in vitro has been subject of numerous studies. This exposes the need for a detailed quantitative analysis of the membrane fluctuating behavior and computerized tools to automatically characterize the occurrence of up and down states. METHODOLOGY/PRINCIPAL FINDINGS: Intracellular recordings from different areas of the cerebral cortex were obtained from both in vitro and in vivo preparations during slow oscillations. A method that separates up and down states recorded intracellularly is defined and analyzed here. The method exploits the crossover of moving averages, such that transitions between up and down membrane regimes can be anticipated based on recent and past voltage dynamics. We demonstrate experimentally the utility and performance of this method both offline and online, the online use allowing to trigger stimulation or other events in the desired period of the rhythm. This technique is compared with a histogram-based approach that separates the states by establishing one or two discriminating membrane potential levels. The robustness of the method presented here is tested on data that departs from highly regular alternating up and down states. CONCLUSIONS/SIGNIFICANCE: We define a simple method to detect cortical states that can be applied in real time for offline processing of large amounts of recorded data on conventional computers. Also, the online detection of up and down states will facilitate the study of cortical dynamics. An open-source MATLAB toolbox, and Spike 2-compatible version are made freely available.