Wendy M. Norman

Early MR diffusion and relaxation changes in the parahippocampal gyrus precede the onset of spontaneous seizures in an animal model of chronic limbic epilepsy

Structural changes in limbic regions are often observed in individuals with temporal lobe epilepsy (TLE) and in animal models. However, the brain structural changes during the evolution into epilepsy remain largely unknown. Therefore, the purpose of this study was to define the temporal changes in limbic structures after experimental status epilepticus (SE) during the latency period of epileptogenesis in vivo, with quantitative diffusion tensor imaging (DTI) and T2 relaxometry in an animal model of chronic TLE. A pair of fifty micron electrodes was implanted into the ventral hippocampus in twelve male adult rats. Self-sustaining SE was induced with electrical stimulation in eleven rats. Three rats served as age-matched controls. In vivo diffusion tensor and T2 magnetic resonance imaging (MRI) was performed at 11.1 Tesla, pre- and post-implantation of electrodes and 3, 5, 7, 10, 20, 40 and 60 days post-SE to assess structural changes. Spontaneous seizures were identified with continuous time-locked video-monitoring. Following imaging in vivo, fixed, excised brains were MR imaged at 17.6 Tesla. Subsequently, histological analysis was correlated with MRI results. Following SE, 8/11 injured rats developed spontaneous seizures. Unique to these 8 rats, early T2, diffusivity and anisotropy changes were observed in vivo within the parahippocampal gyrus (contralateral) and fimbria (bilateral). In excised brains, bilateral increase in anisotropy was observed in the dentate gyrus, corresponding to mossy fiber sprouting as determined by Timm staining. Using T2 relaxometry and DTI, specific transient and long-term structural changes were observed only in rats that developed spontaneous limbic seizures.

Evolving into epilepsy: Multiscale electrophysiological analysis and imaging in an animal model

Epilepsy research for the design of seizure detection/prediction neuroprosthetics has been faced with the search for electrophysiologic control parameters that can be used to infer the epileptic state of the animal and be leveraged at a later time to deliver neurotherapeutic feedback. The analysis presented here uses multi-microelectrode array technology to provide an electrophysiologic quantification of a hippocampal neural ensemble during the latent period of epileptogenesis. Through the use of signal processing system identification methodologies, we were able to assess the spatial and temporal interrelations of ensembles of hippocampal neurons and relate them to the evolution of the epileptic condition. High-field magnetic resonance (MR) imaging was used to determine the location of electrode placement and to evaluate hippocampal pyramidal cell structural damage. Long-term single unit activity analysis suggests that hippocampal neurons in both CA1-2 and dentate regions increase the number of occurrences and duration of their bursting activity after injury to the contra-lateral hippocampus. The trends inferred from both single neuron and ensemble analysis suggests that the evolution into epilepsy is not abrupt but modulates gradually from the time of injury.

An investigation of EEG dynamics in an animal model of temporal lobe epilepsy using the maximum Lyapunov exponent.

Analysis of intracranial electroencephalographic (iEEG) recordings in patients with temporal lobe epilepsy (TLE) has revealed characteristic dynamical features that distinguish the interictal, ictal, and postictal states and inter-state transitions. Experimental investigations into the mechanisms underlying these observations require the use of an animal model. A rat TLE model was used to test for differences in iEEG dynamics between well-defined states and to test specific hypotheses: 1) the short-term maximum Lyapunov exponent (STL(max)), a measure of signal order, is lowest and closest in value among cortical sites during the ictal state, and highest and most divergent during the postictal state; 2) STL(max) values estimated from the stimulated hippocampus are the lowest among all cortical sites; and 3) the transition from the interictal to ictal state is associated with a convergence in STL(max) values among cortical sites. iEEGs were recorded from bilateral frontal cortices and hippocampi. STL(max) and T-index (a measure of convergence/divergence of STL(max) between recorded brain areas) were compared among the four different periods. Statistical tests (ANOVA and multiple comparisons) revealed that ictal STL(max) was lower (p<0.05) than other periods, STL(max) values corresponding to the stimulated hippocampus were lower than those estimated from other cortical regions, and T-index values were highest during the postictal period and lowest during the ictal period. Also, the T-index values corresponding to the preictal period were lower than those during the interictal period (p<0.05). These results indicate that a rat TLE model demonstrates several important dynamical signal characteristics similar to those found in human TLE and support future use of the model to study epileptic state transitions.

Epilepsy in Phenylketonuria: A Complex Dependence on Serum Phenylalanine Levels

Summary:  Purpose: Phenylketonuria (PKU) is a disorder of phenylalanine (Phe) metabolism that frequently results in epilepsy if a low Phe diet was not implemented at birth. The mechanisms by which Phe affects the brain are poorly understood.

Detection of High Frequency Oscillations with Teager Energy in an Animal Model of Limbic Epilepsy

High frequency oscillations (HFO) in limbic epilepsy represent a marked difference between abnormal and normal brain activity. Faced with the difficult of visually detecting HFOs in large amounts of intracranial EEG data, it is necessary to develop an automated process. This paper presents Teager Energy as a method of finding HFOs. Teager energy is an ideal measure because unlike conventional energy it takes into account the frequency component of the signal as well as signal amplitude. This greatly aids in the dissection of HFOs out of the noise and other signals contained in the EEG. Therein, Teager energy analysis is able to detect high-frequency, low-amplitude components that conventional energy measurements would miss

Seizure Predictability in an Experimental Model of Epilepsy

We have previously reported preictal spatiotemporal transitions in human mesial temporal lobe epilepsy (MTLE) using short term Lyapunov exponent (STL max ) and average angular frequency (\( \Omega \) ). These results have prompted us to apply the quantitative nonlinear methods to a limbic epilepsy rat (CLE), as this model has several important features of human MTLE. The present study tests the hypothesis that preictal dynamical changes similar to those seen in human MTLE exist in the CLE model. Forty-two, 2-hr epoch data sets from 4 CLE rats (mean seizure duration 74±20 sec) are analyzed, each containing a focal onset seizure and intracranial data beginning 1 hr before the seizure onset. Three nonlinear measures, correlation integral, short-term largest Lyapunov exponent and average angular frequency are used in the current study. Data analyses show multiple transient drops in STL max values during the preictal period followed by a significant drop during the ictal period. Average angular frequency values demonstrate transient peaks during the preictal period followed by a significant peak during the ictal period. Convergence among electrode sites is also observed in both STL max and \( \Omega \) values before seizure onset. Results suggest that dynamical changes precede and accompany seizures in rat CLE. Thus, it may be possible to use the rat CLE model as a tool to refine and test real-time seizure prediction, and closed-loop intervention techniques.

Pre-ictal entropy analysis of microwire data from an animal model of limbic epilepsy.

Epilepsy is a common neurological disorder that can have damaging effects in the brain including over 50% loss of neuronal activity in the hippocampal regions of the CA1 and CA3. The pre-ictal period was studied in an animal model of limbic epilepsy using Shannon entropy and correlation analysis. The primary aim was to uncover underlying relative changes in signals between the Dentate Gyrus and CA1 areas of the bilateral hippocampus. Preliminary entropy analysis results included dynamical changes between channels in the Dentate Gyrus and channels in the CA1 region at and around the time of the seizure

High frequency oscillations in limbic rat model for temporal lobe epilepsy

Recently a number of groups [1,2] have reported on the existence of pathological High frequency oscillations (HFOs) (oscillations in the frequency range of 80–200 Hz, termed as Ripple band and oscillations in the frequency range of 200 Hz and above, termed as Fast Ripple band) in the epileptic brain both in in-vivo and in-vitro experiments. Our goal in this study is to study the statistical modulation of HFOs during epileptogenesis in order to characterize their function in progression to seizures in the epileptic brain. In this study we define a HFO event as a subset of wave having significant high frequency component with low wave amplitude. HFO are detected from data recorded at a sampling rate of 12000 Hz for the entire duration of epileptogenesis which lasts anywhere from about 3–6 weeks. Statistical analysis on the HFO suggest that occurrence of HFOs occur primarily during the 12 hour dark cycle whereas the HFOs primarily seem to occur during the 12 hour day cycle in the control rat The video recording shows that the rat is primarily in active and exploratory state during the dark cycle. These observations suggest that HFO in epileptic rats are correlated with the state of arousal. Spatial correlation of HFOs in different regions of the brain is also investigated with cross-correlogram. Comparison of cross-correlogram of the post-stimulus HFO in the epileptic rat to the pre stimulus HFO (control) suggests modification in the circuitry in the hippocampus, evidence for which in in-vitro experiments were provided by [3].