Enhanced gamma band mutual information is associated with impaired consciousness during temporal lobe seizures.

BACKGROUND: Epileptic seizures are characterized by aberrant synchronization. We hypothesized that higher synchronization across the seizure onset zone (SOZ) channels during a temporal lobe seizure contributes to impaired consciousness. NEW METHOD: All symmetric bivariate synchronization measures were extended to multivariate measure by a principal component analysis (PCA) based technique. A novel nonparametric method has been proposed to test the statistical significance between increased synchronization across the seizure onset zone (SOZ) channels and reduced consciousness. RESULTS: Increased synchronization in the gamma band towards seizure termination significantly contributes to impaired consciousness (p < 0.1). Synchronization reaches its peak in the extratemporal region (frontal lobe) ahead of the temporal region (p < 0.05). Synchronization is prominent in beta and gamma bands by most methods and it is more in the second half of seizure duration than in the first (p < 0.05). CONCLUSIONS: Mutual information is the only synchronization measure out of the six that we studied, whose increase can be associated with the loss of consciousness in a statistically significant way.

A mathematical model of the tripartite synapse: astrocyte-induced synaptic plasticity.

In this paper, we present a biologically detailed mathematical model of tripartite synapses, where astrocytes modulate short-term synaptic plasticity. The model consists of a pre-synaptic bouton, a post-synaptic dendritic spine-head, a synaptic cleft and a peri-synaptic astrocyte controlling Ca(2 + ) dynamics inside the synaptic bouton. This in turn controls glutamate release dynamics in the cleft. As a consequence of this, glutamate concentration in the cleft has been modeled, in which glutamate reuptake by astrocytes has also been incorporated. Finally, dendritic spine-head dynamics has been modeled. As an application, this model clearly shows synaptic potentiation in the hippocampal region, i.e., astrocyte Ca(2 + ) mediates synaptic plasticity, which is in conformity with the majority of the recent findings (Perea and Araque (Science 317, 1083-1086, 2007); Henneberger et al. (Nature 463, 232-236, 2010); Navarrete et al. (PLoS Biol. 10, e1001259, 2012)).

Automatic seizure detection in ECoG by differential operator and windowed variance.

Differential operator has long been used in image and signal processing with great success to detect significant changes. In this paper we show that differentiation can enhance certain features of brain electrophysiological signals, contaminated with noise, artifacts, and acquisition defects, leading to efficient detection of those changes. Windowed variance method has been very successful in detecting seizure onset in the brain electrophysiological signals. In this paper we have combined these two powerful methods under the name of differential windowed variance (DWV) algorithm to automatically detect seizure onsets in almost real time, in continuous ECoG (depth-EEG) signals of epileptic patients. The main advantages of the method are simplicity of implementation and speed. We have tested the algorithm on 369 h of nonseizure ECoG as well as 59 h of seizure ECoG of 15 epileptic patients. It detected all but six seizures (91.525% accuracy) with an average delay of 9.2 s after the onset with a maximum false detection of three in 24 h of nonseizure data. Eight novel empirical measures have been introduced to avoid false detections. To ascertain the reliability of the detection method a novel methodology, called quasi-ROC (qROC) curve analysis has been introduced. DWV has been compared with a difference filter based sharp transient (ST) detection algorithm.

A structural and a functional aspect of stable information processing by the brain.

Brain is an expert in producing the same output from a particular set of inputs, even from a very noisy environment. In this article a model of neural circuit in the brain has been proposed which is composed of cyclic sub-circuits. A big loop has been defined to be consisting of a feed forward path from the sensory neurons to the highest processing area of the brain and feed back paths from that region back up to close to the same sensory neurons. It has been mathematically shown how some smaller cycles can amplify signal. A big loop processes information by contrast and amplify principle. How a pair of presynaptic and postsynaptic neurons can be identified by an exact synchronization detection method has also been mentioned. It has been assumed that the spike train coming out of a firing neuron encodes all the information produced by it as output. It is possible to extract this information over a period of time by Fourier transforms. The Fourier coefficients arranged in a vector form will uniquely represent the neural spike train over a period of time. The information emanating out of all the neurons in a given neural circuit over a period of time can be represented by a collection of points in a multidimensional vector space. This cluster of points represents the functional or behavioral form of the neural circuit. It has been proposed that a particular cluster of vectors as the representation of a new behavior is chosen by the brain interactively with respect to the memory stored in that circuit and the amount of emotion involved. It has been proposed that in this situation a Coulomb force like expression governs the dynamics of functioning of the circuit and stability of the system is reached at the minimum of all the minima of a potential function derived from the force like expression. The calculations have been done with respect to a pseudometric defined in a multidimensional vector space.