Biomedical subjects

Piotr J Franaszczuk

Publications and source records attributed to Piotr J Franaszczuk.

8 recordsLinked to original sources

PubMed2005

Signal complexity and synchrony of epileptic seizures: is there an identifiable preictal period?

OBJECTIVE: Epileptic seizures are characterized by increases in synchronized activity and increased signal complexity. Prediction of seizures depends upon detectable preictal changes before the actual ictal event. The studies reported here test whether two methods designed to detect changes in synchrony and complexity can identify any changes in a preictal period before visual EEG changes or clinical manifestations. METHODS: Two methods are used to characterize different, but linked, properties of the signal-complexity and synchrony. The Gabor atom density (GAD) method allows for quantification of the time-frequency components of the EEG and characterizes the complexity of the EEG signal. The measure S, based on the goodness of fit of a multivariable autoregressive model, allows for characterization of the degree of synchrony of the EEG signal. RESULTS: Complex partial seizures produce very specific patterns of increased signal complexity and subsequent postictal low complexity states. The measure S shows increased synchronization later including a prolonged period of increased synchrony in the postictal period. No significant preictal changes were seen unless contaminated by residual postictal changes in closely clustered seizures. CONCLUSIONS: Both GAD and S measures reveal ictal and prolonged postictal changes; however, there were no significant preictal changes in either complexity or synchrony. Any application of methods to detect preictal changes must be tested on seizures sufficiently separated to avoid residual postictal changes in the potential preictal period.

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PubMed2004

Inhibition modifies the effects of slow calcium-activated potassium channels on epileptiform activity in a neuronal network model.

Generation of epileptiform activity typically results from a change in the balance between network excitation and inhibition. Experimental evidence indicates that alterations of either synaptic activity or intrinsic membrane properties can produce increased network excitation. The slow Ca(2+)-activated K(+) currents (sI(AHP)) are important modulators of neuronal firing rate and excitability and have important established and potential roles in epileptogenesis. While the effects of changes in sI(AHP) on individual neuronal excitability are readily studied and well established, the effects of such changes on network behavior are less well known. The experiments here utilize a defined small network model of multicompartment pyramidal cells and an inhibitory interneuron to study the effects of changes in sI(AHP) on network behavior. The benefits of this model system include the ability to observe activity in all cells in a network and the effects of interactions of multiple simultaneous influences. In the model with no inhibitory interneuron, increasing sI(AHP) results in progressively decreasing burst activity. Adding an inhibitory interneuron changes the observed effects; at modest inhibitory strengths, increasing sI(AHP) in all network neurons actually results in increased network bursting (except at very high values). The duration of the burst activity is influenced by the length of delay in a feedback loop, with longer loops resulting in more prolonged bursting. These observations illustrate that the study of potential antiepileptogenic membrane effects must be extended to realistic networks. Network inhibition can dramatically alter the observations seen in pure excitatory networks.

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PubMed2003

The influences of somatic and dendritic inhibition on bursting patterns in a neuronal circuit model.

The balance between inhibition and excitation plays a crucial role in the generation of synchronous bursting activity in neuronal circuits. In human and animal models of epilepsy, changes in both excitatory and inhibitory synaptic inputs are known to occur. Locations and distribution of these excitatory and inhibitory synaptic inputs on pyramidal cells play a role in the integrative properties of neuronal activity, e.g., epileptiform activity. Thus the location and distribution of the inputs onto pyramidal cells are important parameters that influence neuronal activity in epilepsy. However, the location and distribution of inhibitory synapses converging onto pyramidal cells have not been fully studied. The objectives of this study are to investigate the roles of the relative location of inhibitory synapses on the dendritic tree and soma in the generation of bursting activity. We investigate influences of somatic and dendritic inhibition on bursting activity patterns in several paradigms of potential connections using a simplified multicompartmental model. We also investigate the effects of distribution of fast and slow components of GABAergic inhibition in pyramidal cells. Interspike interval (ISI) analysis is used for examination of bursting patterns. Simulations show that the inhibitory interneuron regulates neuronal bursting activity. Bursting behavior patterns depend on the synaptic weight and delay of the inhibitory connection as well as the location of the synapse. When the inhibitory interneuron synapses on the pyramidal neuron, inhibitory action is stronger if the inhibitory synapse is close to the soma. Alterations of synaptic weight of the interneuron can be compensatory for changes in the location of synaptic input. The relative changes in these parameters exert a considerable influence on whether synchronous bursting activity is facilitated or reduced. Additional simulations show that the slow GABAergic inhibitory component is more effective than the fast component in distal dendrites. Taken together, these findings illustrate the potential for GABAergic inhibition in the soma and dendritic tree to play an important modulatory role in bursting activity patterns.

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PubMed2003

Changing excitation and inhibition in simulated neural networks: effects on induced bursting behavior.

The development of synchronous bursting in neuronal ensembles represents an important change in network behavior. To determine the influences on development of such synchronous bursting behavior we study the dynamics of small networks of sparsely connected excitatory and inhibitory neurons using numerical simulations. The synchronized bursting activities in networks evoked by background spikes are investigated. Specifically, patterns of bursting activity are examined when the balance between excitation and inhibition on neuronal inputs is varied and the fraction of inhibitory neurons in the network is changed. For quantitative comparison of bursting activities in networks, measures of the degree of synchrony are used. We demonstrate how changes in the strength of excitation on inputs of neurons can be compensated by changes in the strength of inhibition without changing the degree of synchrony in the network. The effects of changing several network parameters on the network activity are analyzed and discussed. These changes may underlie the transition of network activity from normal to potentially pathologic (e.g., epileptic) states.

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PubMed2003

Reduction of intracellular calcium removal rate can explain changes in seizure dynamics: studies in neuronal network models.

Complex partial seizures originating from mesial temporal structures are characterized by relatively short durations of organized rhythmic activity (ORA) of 5-8 Hz, typically lasting less than 60s. Previous investigations into seizure dynamics have revealed that this ORA undergoes a monotonic decline prior to seizure evolution into intermittent bursting and subsequent seizure termination. Large neural network models of simplified single-compartment neurons were employed to address the hypothesis that changes in the free intracellular calcium ([Ca2+]i) removal rate during network bursting can result in the alterations of rhythmic seizure activity similar to that observed in recordings from humans. Both exponential and linear models of decreasing calcium removal rates resulted in changes in the predominant frequency of network bursting very similar in frequency and time course to those seen in human intracranial recordings. This supports the concept that changes in [Ca2+]i removal can explain this important network behavior, while not excluding alternative hypotheses. Identifying potential mechanisms underlying the dynamic changes seen in epileptogenic activity in large neural networks can provide important insights into seizure evolution and termination. Model neural network ensembles are attractive systems to address these questions that are difficult to investigate in biological preparations.

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PubMed2003

External excitatory stimuli can terminate bursting in neural network models.

The concept of modulating or terminating seizure activity by brain stimulation is attracting considerable attention. The ability of such external excitatory stimuli to terminate repetitive bursting may depend upon identifiable parameters. We investigate the ability of external stimuli to terminate bursting under various conditions in defined neural network models. Networks of multiple neurons (n=90), with both inhibitory and excitatory synaptic connections were modeled using conductance-based models with a reduced number of variables. Each neuron in the network has synaptic connections from randomly chosen excitatory and inhibitory neurons. The type and number of connections were kept constant. The initial parameters of the networks were chosen to simulate synchronized repetitive bursting activity. Two basic models of repetitive bursting activity were developed. The first model is a single network with constant random excitatory input, the second incorporates two networks with no random background input, but with a feedback loop with a delay of 600-900ms connecting two networks. The ability of external excitatory stimuli to terminate repetitive bursting in each model was studied. External excitatory stimulation terminates repetitive bursting in both models; however, only in the models with a feedback loop, is the burst termination long-lasting. In the single network model with constant random excitatory input, termination is of short duration and recurrent bursting resumes. Timing of the application of the stimulus to the dual network model is critical; long-lasting termination occurs only when the stimuli to the first network are during a time when the connected network is still relatively refractory. The delay in the loop determines the exact timing of the stimulus. Synaptic inhibition is not required for burst termination. Neural network models provide systems for the study of burst termination and can help define the requirements for such stimuli to successfully terminate bursts. Studies in these models can guide the development and application of such stimuli in intact systems.

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PubMed2003

Characterization of epileptic seizure dynamics using Gabor atom density.

OBJECTIVE: The study of epileptic electroencephalograph (EEG) dynamics can potentially provide insights into seizure onset, evolution and termination. We propose a new synthetic measure based on time-frequency decomposition to provide detailed characterization of these dynamic changes. METHODS: The matching pursuit (MP) method allows for continuous time-frequency decomposition. We have developed a derivative of the MP method, the Gabor atom density method (GAD) that facilitates interpretation during the dynamic ictal period. The GAD analysis was applied to intracranial recordings of complex partial seizures (n = 43) of mesial temporal origin in 7 patients. RESULTS: Complex partial seizure occurrence is systematically associated with a GAD increase of 400 +/- 150%. The GAD increase coincides with the electrographical evidence of seizure onset. The similarity between seizures in a given patient is very high with uniform onset slope, maximum level and termination pattern. Global GAD responses over all channels can reveal detailed seizure propagation patterns including secondary independent foci and secondary generalization. CONCLUSIONS: The GAD measure based on the MP decomposition is a reliable tool to detect seizure occurrence in long-term recordings, to differentiate seizures from artifacts on a multi-channel basis and to examine patterns of seizure propagation. The reproducible GAD pattern suggests consistent changes in signal inner structure and may provide new clues about seizure dynamics and evolution. SIGNIFICANCE: The GAD method can provide information about seizure dynamics that can contribute to methods of seizure detection. These analyses may lead to better understanding of seizure termination and help facilitate application of responsive seizure control devices in humans.

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PubMed2003

Human ECoG analysis during speech perception using matching pursuit: a comparison between stochastic and dyadic dictionaries.

We use the matching pursuit (MP) algorithm to detect induced gamma activity in human EEG during speech perception. We show that the MP algorithm is particularly useful for detecting small power changes at high gamma frequencies (> 70 Hz). We also compare the performance of the MP using a stochastic versus a dyadic dictionary and show that despite the frequency bias the time-frequency power plot (averaged over 100 trials) generated by the dyadic MP is almost identical (> 98.5%) to the one generated by the stochastic MP. However, the dyadic MP is computationally much faster than the stochastic MP.

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