Epileptic Patient with Mild Malformation of Cortical Development with Oligodendroglial Hyperplasia and Epilepsy (MOGHE): A Case Report and Review of the Literature.

INTRODUCTION: There is an emerging interest in the literature about MOGHE (Mild Malformation of Cortical Development with Oligodendroglial Hyperplasia and Epilepsy). We report the case of an epileptic patient with MOGHE. CASE REPORT: A 33-year-old male patient was suffering from refractory focal epilepsy since adolescence. MRI demonstrated increased T2/FLAIR signal intensity of right frontal lobe. Presurgical evaluation led to definition of epileptogenic network in a specific area of right frontal lobe. The resected specimen revealed MOGHE. Discussion. MOGHE appears to be a brain entity which shares some unique histopathological features. Review of the literature is in accordance with our patient's findings. The major neuropathological finding consists of areas with blurred gray-white matter boundaries due to heterotopic neurons in white matter and increased numbers of subcortical oligodendroglial cells with increased proliferation. MR abnormalities are present in T2/FLAIR sequences. It concerns patients with refractory frontal lobe epilepsy and appears to associate with unfavourable postsurgical outcome in seizure control. CONCLUSION: More cases are needed in order to establish more data about this distinct entity in frontal lobe epilepsy. This could be valuable knowledge to patients and doctors concerning expectations or management of undesirable outcome in frontal lobe epilepsy surgery.

A pilot study and brief overview of rehabilitation via virtual environment in patients suffering from dementia.

Dementia is one of the increasing problems of modern societies. The immediate cure is not a possible solution, at least at the moment, but science has found a number of new ways to retard and under specific conditions to halt its development. A potential, and constantly evolving scientific field is the use of Computerized Cognitive Rehabilitation (CCR) and Virtual Environments (Vr.E). According to the existing literature, subjecting patients to various neuro-rehabilitative conditions within 3D virtual environments, allows them to obtain significant therapeutic benefits in which both transferability and durations over time are observed, in relation to the training period of the intervention. In the present study we examine whether "Serious Games (SGs)" - (learning and rehabilitating games in virtual and augmented reality) - have utilitarian value in the field of cognitive neurorehabilitation, concerned with demented population. For research purposes, we have conducted a number of case studies, based on 10 elderly patients, suffering from moderate or mild severity impairment of higher cortical functions, attributed to various types of dementias (Vascular, Alzheimer's disease, DLB dementia and mixed dementia). Each participant underwent rehabilitative intervention through our SG for a total of 10 hours within 4-5 weeks period. At the end of the cognitive rehabilitation program, patients' performance was assessed based in standard neuropsychological tests (measuring: working memory, memory retention, attention, problem solving, rigid thinking and executive function) and the results were compared with measurements taken before, during, and at the end of the intervention. Our experimental hypothesis states that there will be a significant difference between the results of cognitive performance of the patients between the pre- and post- rehabilitative period, consequential of the Interactive Computer-based Training (ICT). In conclusion, a review and brief analysis of the relevant literature was carried out in order to investigate the specification of potentially beneficial variables and to appreciate as much as possible the multifactorial causes related to this particular rehabilitation method of the corresponding suffering population. The ultimate purpose of our research is the design and creation of a prospective interactive cognitive rehabilitation training SG, able to combine both the neuro-rehabilitative character of the controlled virtual environment, as well as the potential realism that is also attributed to it (factual validity under high experimental realism). The results showed a relative improvement in the total of the cognitive variables under consideration after the completion of the neuro-rehabilitative program, while a parallel review of the literature on the subject revealed methodological considerations similar to those of the present study.

Brain abscess following intracerebral haemorrhage.

A 79-year old male patient developed a brain abscess as infective transformation of a preceding intracerebral haemorrhage after urinary tract infection. The awareness of brain abscess formation at the site of intracerebral haemorrhage is crucial for early diagnosis and treatment of this rare but potentially fatal complication.

Comparison of fractal dimension estimation algorithms for epileptic seizure onset detection.

Fractal dimension (FD) is a natural measure of the irregularity of a curve. In this study the performances of three waveform FD estimation algorithms (i.e. Katz's, Higuchi's and the k-nearest neighbour (k-NN) algorithm) were compared in terms of their ability to detect the onset of epileptic seizures in scalp electroencephalogram (EEG). The selection of parameters involved in FD estimation, evaluation of the accuracy of the different algorithms and assessment of their robustness in the presence of noise were performed based on synthetic signals of known FD. When applied to scalp EEG data, Katz's and Higuchi's algorithms were found to be incapable of producing consistent changes of a single type (either a drop or an increase) during seizures. On the other hand, the k-NN algorithm produced a drop, starting close to the seizure onset, in most seizures of all patients. The k-NN algorithm outperformed both Katz's and Higuchi's algorithms in terms of robustness in the presence of noise and seizure onset detection ability. The seizure detection methodology, based on the k-NN algorithm, yielded in the training data set a sensitivity of 100% with 10.10 s mean detection delay and a false positive rate of 0.27 h(-1), while the corresponding values in the testing data set were 100%, 8.82 s and 0.42 h(-1), respectively. The above detection results compare favourably to those of other seizure onset detection methodologies applied to scalp EEG in the literature. The methodology described, based on the k-NN algorithm, appears to be promising for the detection of the onset of epileptic seizures based on scalp EEG.