Biomedical subjects

Berj L Bardakjian

Publications and source records attributed to Berj L Bardakjian.

8 recordsLinked to original sources

PubMed2006

Neuronal electrical rhythms described by composite mapped clock oscillators.

The Mapped Clock Oscillator (MCO) model is a representation of omnipresent transmembrane voltage oscillations in excitable cells. We present a generalized version of the MCO that can model neuronal electrical oscillations, both labile and omnipresent, entirely within the framework of a system of ordinary differential equations. The previously described MCO was a second-order system, whereas the model presented here, which we call the composite MCO (cMCO) is a fourth-order system. Furthermore, we show how this cMCO can also be adapted to describe a pair of cells that forms a functional unit, as illustrated here by a model of the CA3 pyramidal cell and its basket cell interneuron feedback loop. The model was able to reproduce the high frequencies (super gamma) and possibly chaotic dynamics observed in the biological system.

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PubMed2006

Online prediction of onsets of seizure-like events in hippocampal neural networks using wavelet artificial neural networks.

It has been previously shown that wavelet artificial neural networks (WANNs) are able to classify the different states of epileptiform activity and predict the onsets of seizure-like events (SLEs) by offline processing (Ann. Biomed. Eng. 33(6):798-810, 2005) of the electrical data from the in-vitro hippocampal slice model of recurrent spontaneous SLEs. The WANN design entailed the assumption that time-varying frequency information from the biological recordings can be used to estimate the times at which onsets of SLEs would most likely occur in the future. Progressions of different frequency components were captured by the artificial neural network (ANN) using selective frequency inputs from the initial wavelet transform of the biological data. The training of the WANN had been established using 184 SLE episodes in 34 slices from 21 rats offline. Nine of these rats also exhibited periods of interictal bursts (IBs). These IBs were included as part of the training to help distinguish the difference in dynamics of bursting activities between the preictal- and interictal type. In this paper, we present the results of an online processing using WANN on 23 in-vitro rat hippocampal slices from 9 rats having 93 spontaneous SLE episodes generated under low magnesium conditions. Over the test cases, three of the nine rats exhibited over 30 min of IB activities. We demonstrated that the WANN was able to classify the different states, namely, interictal, preictal, ictal, and IB activities with an accuracy of 86.6, 72.6, 84.5, and 69.1%, respectively. Prediction of state transitions into ictal events was achieved using regression of initial "normalized time-to-onset" estimates. The SLE onsets can be estimated up to 36.4 s ahead of their actual occurrences, with a mean error of 14.3 +/- 27.0 s. The prediction errors decreased progressively as the actual time-to-onset decreased and more initial "normalized time-to-onset" estimates were used for the regression procedure.

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PubMed2005

The effects of high-frequency oscillations in hippocampal electrical activities on the classification of epileptiform events using artificial neural networks.

The existence of hippocampal high-frequency electrical activities (greater than 100 Hz) during the progression of seizure episodes in both human and animal experimental models of epilepsy has been well documented (Bragin A, Engel J, Wilson C L, Fried I and Buzsáki G 1999 Hippocampus 9 137-42; Khosravani H, Pinnegar C R, Mitchell J R, Bardakjian B L, Federico P and Carlen P L 2005 Epilepsia 46 1-10). However, this information has not been studied between successive seizure episodes or utilized in the application of seizure classification. In this study, we examine the dynamical changes of an in vitro low Mg2+ rat hippocampal slice model of epilepsy at different frequency bands using wavelet transforms and artificial neural networks. By dividing the time-frequency spectrum of each seizure-like event (SLE) into frequency bins, we can analyze their burst-to-burst variations within individual SLEs as well as between successive SLE episodes. Wavelet energy and wavelet entropy are estimated for intracellular and extracellular electrical recordings using sufficiently high sampling rates (10 kHz). We demonstrate that the activities of high-frequency oscillations in the 100-400 Hz range increase as the slice approaches SLE onsets and in later episodes of SLEs. Utilizing the time-dependent relationship between different frequency bands, we can achieve frequency-dependent state classification. We demonstrate that activities in the frequency range 100-400 Hz are critical for the accurate classification of the different states of electrographic seizure-like episodes (containing interictal, preictal and ictal states) in brain slices undergoing recurrent spontaneous SLEs. While preictal activities can be classified with an average accuracy of 77.4 +/- 6.7% utilizing the frequency spectrum in the range 0-400 Hz, we can also achieve a similar level of accuracy by using a nonlinear relationship between 100-400 Hz and <4 Hz frequency bands only.

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PubMed2005

Prediction of seizure onset in an in-vitro hippocampal slice model of epilepsy using Gaussian-based and wavelet-based artificial neural networks.

We propose that artificial neural networks (ANNs) can be used to predict seizure onsets in an in-vitro hippocampal slice model capable of generating spontaneous seizure-like events (SLEs) in their extracellular field recordings. This paper assesses the effectiveness of two ANN prediction schemes: Gaussian-based artificial neural network (GANN) and wavelet-based artificial neural network (WANN). The GANN prediction system consists of a recurrent network having Gaussian radial basis function (RBF) nonlinearities capable of extracting the estimated manifold of the system. It is able to classify the underlying dynamics of spontaneous in-vitro activities into interictal, preictal and ictal modes. It is also able to successfully predict the onsets of SLEs as early as 60 s before. Improvements can be made to the overall seizure predictor design by incorporating time-varying frequency information. Consequently, the idea of WANN is considered. The WANN design entails the assumption that frequency variations in the extracellular field recordings can be used to compute the times at which onsets of SLEs are most likely to occur in the future. Progressions of different frequency components can be captured by the ANN using appropriate frequency band adjustments via pruning, after the initial wavelet transforms. In the off-line processing comprised of 102 spontaneous SLEs generated from 14 in-vitro rat hippocampal slices, with half of them used for training and the other half for testing, the WANN is able to predict the forecoming ictal onsets as early as 2 min prior to SLEs with over 75% accuracy within a 30 s precision window.

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PubMed2005

Increased high-frequency oscillations precede in vitro low-Mg seizures.

PURPOSE: High-frequency oscillations (HFOs) in the range of > or = 80 Hz have been recorded in neocortical and hippocampal brain structures in vitro and in vivo and have been associated with physiologic and epileptiform neuronal population activity. Frequencies in the fast-ripple range (> 200 Hz) are believed to be exclusive to epileptiform activity and have been recorded in vitro, in vivo, and in epilepsy patients. Although the presence of HFOs is well characterized, their temporal evolution in the context of transition to seizure activity is not well understood. METHODS: With an in vitro low-magnesium model of spontaneous seizures, we obtained extracellular field recordings (hippocampal regions CA1 and CA3) of interictal, preictal, and ictal activity. Recordings were subjected to power-frequency analysis, in time, by using a local multiscale Fourier transform. The power spectrum was computed continuously and was quantified for each epileptiform discharge into four frequency ranges spanning subripple, ripple, and two fast-ripple frequency bands. RESULTS: A statistically significant increasing trend was observed in the subripple (0-100 Hz), ripple (100-200 Hz), and fast-ripple 1 (200-300 Hz) frequency bands during the epoch corresponding to the transition to seizure (preictal to ictal). CONCLUSIONS: Temporal patterns of HFOs during epileptiform activity are indicative of dynamic changes in network behavior, and their characterization may offer insights into pathophysiologic processes underlying seizure initiation.

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PubMed2004

A synaptic input portal for a mapped clock oscillator model of neuronal electrical rhythmic activity.

Neuronal electrical oscillations play a central role in a variety of situations, such as epilepsy and learning. The mapped clock oscillator (MCO) model is a general model of transmembrane voltage oscillations in excitable cells. In order to be able to investigate the behaviour of neuronal oscillator populations, we present a neuronal version of the model. The neuronal MCO includes an extra input portal, the synaptic portal, which can reflect the biological relationships in a chemical synapse between the frequency of the presynaptic action potentials and the postsynaptic resting level, which in turn affects the frequency of the postsynaptic potentials. We propose that the synaptic input-output relationship must include a power function in order to be able to reproduce physiological behaviour such as resting level saturation. One linear and two power functions (Butterworth and sigmoidal) are investigated, using the case of an inhibitory synapse. The linear relation was not able to produce physiologically plausible behaviour, whereas both the power function examples were appropriate. The resulting neuronal MCO model can be tailored to a variety of neuronal cell types, and can be used to investigate complex population behaviour, such as the influence of network topology and stochastic resonance.

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PubMed2004

Stochastic and coherence resonance in an in silico neural model.

We show that it is possible for chaotic systems to display the main features of stochastic and coherence resonance. In particular, a model of coupled nonlinear oscillators which emulates the transmembrane voltage activities in CA3 neurons, operating in a chaotic regime and in the presence of noise, can exhibit coherence resonance and stochastic resonance. Certain firing frequencies become more "rhythmic" for some optimal values of noise intensity. The effect of noise in different coupling pathways is investigated. We found that the effect of coherence resonance and stochastic resonance are more prominent if noise is presented in either electric field or gap junction coupling pathways. Frequency sensitivity of the model is investigated as a preliminary step in illustrating the principles of possible epileptic seizure control strategies using "chaos control" concepts. Significant effects of stochastic resonance are observed in the 4-8 Hz range. Weaker effects can be found in the 1-4 Hz and 8-10 Hz ranges whereas 0.5 Hz does not exhibit any resonance phenomenon. Our results suggest that: (a) Stochastic resonance could enhance the intrinsic 4-8 Hz rhythms in CA3 neurons more prominently via field coupling pathways. It could also help explain why some reported seizure control strategies using pulse-trains would only be effective at 0.5 Hz. (b) Stochastic resonance-like behavior can occur in the gamma range only if noise is presented via chemical synaptic pathways.

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PubMed2004

Control of state transitions in an in silico model of epilepsy using small perturbations.

We propose the use of artificial neural networks in an in silico epilepsy model of biological neural networks: 1) to predict the onset of state transitions from higher complexities, possibly chaotic to lower complexity possibly rhythmic activities; and 2) to restore the original higher complexity activity. A coupled nonlinear oscillators model (Bardakjian and Diamant, 1994) was used to represent the spontaneous seizure-like oscillations of CA3 hippocampal neurons (Bardakjian and Aschebrenner-Scheibe, 1995) to illustrate the prediction and control schemes of these state transition onsets. Our prediction scheme consists of a recurrent neural network having Gaussian nonlinearities. When the onset of lower complexity activity is predicted in the in silico model, then our control scheme consists of applying a small perturbation to a system variable (i.e., the transmembrane voltage) when it is sufficiently close to the unstable higher complexity manifold. The system state can be restored back to its higher complexity mode utilizing the forces of the system's vector field.

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