BackgroundIt has been hypothesised that seizure induced neuronal loss and axonal damage in medial temporal lobe epilepsy (MTLE) may lead to the development of aberrant connections between limbic structures and eventually result in the reorganisation of the limbic network. In this study, limbic structural connectivity in patients with MTLE was investigated, using diffusion tensor MRI, probabilistic tractography and graph theory based network analysis.Methods12 patients with unilateral MTLE and hippocampal sclerosis (five left and seven right MTLE) and 26 healthy controls were studied. The connectivity of 10 bilateral limbic regions of interest was mapped with probabilistic tractography, and the probabilistic fibre density between each pair of regions was used as the measure of their weighted structural connectivity. Binary connectivity matrices were then obtained from the weighted connectivity matrix using a range of fixed density thresholds. Graph theory based properties of nodes (degree, local efficiency, clustering coefficient and betweenness centrality) and the network (global efficiency and average clustering coefficient) were calculated from the weight and binary connectivity matrices of each subject and compared between patients and controls.ResultsMTLE was associated with a regional reduction in fibre density compared with controls. Paradoxically, patients exhibited (1) increased limbic network clustering and (2) increased nodal efficiency, degree and clustering coefficient in the ipsilateral insula, superior temporal region and thalamus. There was also a significant reduction in clustering coefficient and efficiency of the ipsilateral hippocampus, accompanied by increased nodal degree.ConclusionsThese results suggest that MTLE is associated with reorganisation of the limbic system. These results corroborate the concept of MTLE as a network disease, and may contribute to the understanding of network excitability dynamics in epilepsy and MTLE.

Despite clinical evidence that thyroid hormone is essential for brain development before birth, effects of thyroid hormone on the fetal brain have been largely unexplored. One mechanism of thyroid hormone action is regulation of gene expression, because thyroid hormone receptors (TRs) are ligand-activated transcription factors. We used differential display to identify genes affected by acute T 4 administration to the dam before the onset of fetal thyroid function. Eight of the 11 genes that we identified were selectively expressed in brain areas known to contain TRs, indicating that these genes were directly regulated by thyroid hormone. Using in situ hybridization, we confirmed that the cortical expression of both neuroendocrine-specific protein (NSP) and Oct-1 was affected by changes in maternal thyroid status. Additionally, we demonstrated that both NSP and Oct-1 were expressed in the adult brain and that their responsiveness to thyroid hormone was retained. These data are the first to identify thyroid hormone-responsive genes in the fetal brain.

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SUMMARYSurgical resection of the hippocampus is the most successful treatment for medication-refractory medial temporal lobe epilepsy (MTLE) due to hippocampal sclerosis. Unfortunately, at least one of four operated patients continue to have disabling seizures after surgery, and there is no existing method to predict individual surgical outcome. Prior to surgery, patients who become seizure free appear identical to those who continue to have seizures after surgery. Interestingly, newly converging presurgical data from magnetic resonance imaging (MRI) and intracranial electroencephalography (EEG) suggest that the entorhinal and perirhinal cortices may play an important role in seizure generation. These areas are not consistently resected with surgery and it is possible that they continue to generate seizures after surgery in some patients. Therefore, subtypes of MTLE patients can be considered according to the degree of extrahippocampal damage and epileptogenicity of the medial temporal cortex. The identification of these subtypes has the potential to drastically improve surgical results via optimized presurgical planning. In this review, we discuss the current data that suggests neural network damage in MTLE, focusing on the medial temporal cortex. We explore how this evidence may be applied to presurgical planning and suggest approaches for future investigation.

Objective This study investigated inter-rater agreement (IRA) among EEG experts for the identification of electrographic seizures and periodic discharges (PDs) in continuous ICU EEG recordings. Methods Eight board-certified EEG experts independently identified seizures and PDs in thirty 1-hour EEG segments which were selected from ICU EEG recordings collected from three medical centers. IRA was compared between seizure and PD identifications, as well as among rater groups that have passed an ICU EEG Certification Test, developed by the Critical Care EEG Monitoring Research Consortium (CCEMRC). Results Both kappa and event-based IRA statistics showed higher mean values in identification of seizures compared to PDs (k = 0.58 vs. 0.38; p < 0.001). The group of rater pairs who had both passed the ICU EEG Certification Test had a significantly higher mean IRA in comparison to rater pairs in which neither had passed the test. Conclusions IRA among experts is significantly higher for identification of electrographic seizures compared to PDs. Additional instruction, such as the training module and certification test developed by the CCEMRC, could enhance this IRA. Significance This study demonstrates more disagreement in the labeling of PDs in comparison to seizures. This may be improved by education about standard EEG nomenclature.

Purpose The goal of the project is to determine characteristics of academic neurophysiologist EEG interpreters (EEGers), which predict good interrater agreement (IRA) and to determine the number of EEGers needed to develop an ideal standardized testing and training data set for epileptiform transient (ET) detection algorithms. Methods A three-phase scoring method was used. In phase 1, 19 EEGers marked the location of ETs in two hundred 30-second segments of EEG from 200 different patients. In phase 2, EEG events marked by at least 2 EEGers were annotated by 18 EEGers on a 5-point scale to indicate whether they were ETs. In phase 3, a third opinion was obtained from EEGers on any inconsistencies between phase 1 and phase 2 scoring. Results The IRA for the 18 EEGers was only fair. A select group of the EEGers had good IRA and the other EEGers had low IRA. Board certification by the American Board of Clinical Neurophysiology was associated with better IRA performance but other board certifications, years of fellowship training, and years of practice were not. As the number of EEGers used for scoring is increased, the amount of change in the consensus opinion decreases steadily and is quite low as the group size approaches 10. Conclusions The IRA among EEGers varies considerably. The EEGers must be tested before use as scorers for ET annotation research projects. The American Board of Clinical Neurophysiology certification is associated with improved performance. The optimal size for a group of experts scoring ETs in EEG is probably in the 6 to 10 range.

Inter-rater agreement among EEGers in both academic and PP settings varies considerably. Practice location, years of EFT, and board certification are associated with significantly higher performance for IED detection in routine scalp EEG. Continued medical education of PP neurologists and neurologists without EFT is needed to improve routine scalp EEG interpretation skills. The performance of automated detection algorithms is approaching that of human experts.