Robust Off- and Online Separation of Intracellularly Recorded Up and Down Cortical States

Abstract

BackgroundThe 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 FindingsIntracellular 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/SignificanceWe 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.

Effects and Mechanisms of Wakefulness on Local Cortical Networks

“…To compare neuronal output during up and awake states, up states were algorithmically excerpted from anesthetized data (Seamari et al, 2007). Firing rates were comparable between up and awake states in each layer (Figure 2C) although up states had higher firing rates when data were pooled across all layers (+1.1 +/− 0.37 Hz, paired t-test, p = 0.006).…”

Relationship between individual neuron and network spontaneous activity in developing mouse cortex

“…The region is particularly susceptible to generating epileptiform activity and is believed to play a crucial role in kindling (Löscher and Ebert 1996). Spontaneous rhythmic activity comprised of up and down states has also been observed in vitro under physiological conditions (Seamari et al 2007). Developmentally, the paleocortex, including the piriform cortex, matures slightly earlier than the neocortex.…”

Cortical Up State Activity Is Enhanced After Seizures: A Quantitative Analysis

“…One standard is to place this threshold at a fixed distance between the Up and Down state peaks in a membrane potential histogram constructed post hoc (Anderson et al, 2000; Hasenstaub et al, 2007; Metherate and Ashe, 1993; Reynolds and Wickens, 2003; Sanchez-Vives and McCormick, 2000). More sophisticated methods involve using the intersection times of a narrow-window and a wide-window running mean to identify state transitions (Seamari et al, 2007) or using both the membrane potential mean and variance and segregating according to a fixed threshold in a two-dimensional space (Frohlich et al, 2006). All of these approaches are limited theoretically and practically because (a) they assume stationarity in the data, (b) they fail to account for inhomogeneity in the membrane potential variance, and thus, the probability of spurious threshold crossings, (c) they require computation of thresholds post hoc rather than online, (d) the value of the threshold chosen does not have any simple mathematical relation to the likelihood that it will be crossed, or (e) any or all of the above.…”

“…Because quantitative analysis of state-dependent firing output requires accurate determination of state transitions, we developed a robust algorithm for unsupervised detection of Up and Down states. This method bears similarities to other methods that have been described (Mukovski et al, 2006; Seamari et al, 2007) but is entirely unsupervised and nearly parameter free. We use this method to evaluate seizure-induced changes in Up and Down state characteristics in vitro .…”