A nonsynaptic mechanism underlying interictal discharges in human epileptic neocortex.

Very fast oscillations (VFOs, >80 Hz) are important for physiological brain processes and, in excess, with certain epilepsies. Putative mechanisms for VFO include interneuron spiking and network activity in coupled pyramidal cell axons. It is not known whether either, or both, of these apply in pathophysiological conditions. Spontaneously occurring interictal discharges occur in human tissue in vitro, resected from neocortical epileptic foci. VFO associated with these discharges was manifest in both field potential and, with phase delay, in excitatory synaptic inputs to fast spiking interneurons. Recruitment of somatic pyramidal cell and interneuron spiking was low, with no correlation between VFO power and synaptic inputs to principal cells. Reducing synaptic inhibition failed to affect VFO occurrence, but they were abolished by reduced gap junction conductance. These data suggest a lack of a causal role for interneurons, and favor a nonsynaptic pyramidal cell network origin for VFO in epileptic human neocortex.

Using recurrence quantification analysis determinism for noise removal in cardiac optical mapping.

Selecting signal processing parameters in optical imaging by utilizing the change in Determinism, a measure introduced in Recurrence Quantification Analysis, provides a novel method using the change in residual noise Determinism for improving noise quantification and removal across signals exhibiting disparate underlying tissue pathologies. The method illustrates an improved process for selecting filtering parameters and how using measured signal-to-noise ratio alone can lead to improper parameter selection.

Coherence analysis over the latent period of epileptogenesis reveal that high-frequency communication is increased across hemispheres in an animal model of limbic epilepsy.

A total of 32 microwire electrodes were implanted bilaterally into the hippocampus of Sprague-Dawley rats, which were then stimulated in the manner prescribed for the chronic limbic epilepsy model. After the initial seizure brought on by the stimulation, the animals were recorded at a high sampling rate (approximately 12 kHz) for the entire duration of the latent period. Coherence was calculated across channels in both stimulated (and later seizing) animals and non-stimulated (and thus non-seizing control) animals. Average coherence over time was greatest in intrahemispherical electrode pairs in both stimulated and non-stimulated animals. However, the 200-800 Hz band displays increased coherence interhemispherically and up to 200 Hz band displays decreased coherence interhemispherically: this occurs only in stimulated animals.

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.

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.