OPTIMIZING ELECTROENCEPHALOGRAPHIC ANALYSIS FOR EXPERIMENTAL EPILEPSY MODELS

Document Type

Presentation

Start Date

22-10-2010 3:00 PM

End Date

22-10-2010 4:30 PM

Description

Epileptogenesis is a dynamic process that leads to an altered state of brain activity. One result of these changes is heightened response to electrical stimulation of the dorsal hippocampus. The data collected during hippocampal stimulation via a bipolar electrode implanted in the rat dorsal hippocampus holds a wealth of information and may be of critical importance in understanding the evolution of epileptiform changes in the hippocampus. Meticulous human visual analysis provides arguably the best interpretation of electroencephalography (EEG) data; however large volumes and the detail with which even small data selections may be visually analyzed large scale data analysis a daunting task. Therefore, we seek to automate several aspects of this process and create a data processing algorithm that facilitates a more effective use of human analysis. In the current study, we use a subset of data obtained during a series of stimulations to the right dentate gyrus of untreated, male Wistar rats. Raw EEG recordings were analyzed using NeuroExplorer, and automated processes were created using NeuroNex script software. Using these tools we streamlined the processing of EEG data for analysis in the context of discreet recordings the series of recordings for a single animal. Pre-and post-stimulation activity is isolated and aligned both visually and numerically, making temporal patterns clearly discernable. We automated the isolation of different wave forms for any and all variables. We also automated the task of spike counting so that voltage spikes are chosen based on a threshold obtained from a predefined portion of the EEG recording which excludes the portion affected by the actual stimulation. Using these spikes, the actual after-discharge (AD) intervals are located, marked for observation, and calculations including the AD duration, AD number, spike frequency, spikes per AD are performed. Finally, a power spectral density plot is generated for portions of the recording which may include the entire post stimulation interval or may be set to use the limits of the AD intervals. In conclusion, the data analysis model we created allows EEG data to be analyzed faster, more efficiently and more completely. The data used in this experiment from untreated rats will serve as a control against which rodent epilepsy models and potential anticonvulsive and antiepileptic treatments may be compared.

Comments

See abstract book page 65

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Oct 22nd, 3:00 PM Oct 22nd, 4:30 PM

OPTIMIZING ELECTROENCEPHALOGRAPHIC ANALYSIS FOR EXPERIMENTAL EPILEPSY MODELS

Epileptogenesis is a dynamic process that leads to an altered state of brain activity. One result of these changes is heightened response to electrical stimulation of the dorsal hippocampus. The data collected during hippocampal stimulation via a bipolar electrode implanted in the rat dorsal hippocampus holds a wealth of information and may be of critical importance in understanding the evolution of epileptiform changes in the hippocampus. Meticulous human visual analysis provides arguably the best interpretation of electroencephalography (EEG) data; however large volumes and the detail with which even small data selections may be visually analyzed large scale data analysis a daunting task. Therefore, we seek to automate several aspects of this process and create a data processing algorithm that facilitates a more effective use of human analysis. In the current study, we use a subset of data obtained during a series of stimulations to the right dentate gyrus of untreated, male Wistar rats. Raw EEG recordings were analyzed using NeuroExplorer, and automated processes were created using NeuroNex script software. Using these tools we streamlined the processing of EEG data for analysis in the context of discreet recordings the series of recordings for a single animal. Pre-and post-stimulation activity is isolated and aligned both visually and numerically, making temporal patterns clearly discernable. We automated the isolation of different wave forms for any and all variables. We also automated the task of spike counting so that voltage spikes are chosen based on a threshold obtained from a predefined portion of the EEG recording which excludes the portion affected by the actual stimulation. Using these spikes, the actual after-discharge (AD) intervals are located, marked for observation, and calculations including the AD duration, AD number, spike frequency, spikes per AD are performed. Finally, a power spectral density plot is generated for portions of the recording which may include the entire post stimulation interval or may be set to use the limits of the AD intervals. In conclusion, the data analysis model we created allows EEG data to be analyzed faster, more efficiently and more completely. The data used in this experiment from untreated rats will serve as a control against which rodent epilepsy models and potential anticonvulsive and antiepileptic treatments may be compared.