癫痫杂志

In this research, the effect of hippocampal electrical stimulation on brain waves and spatial memory was studied. The rats were anaesthetized and the electrodes were implanted into the CA1 by stereotaxic instrument. Electrical stimulation with (25 μA) and (100 μA) were induced into CA1; then spatial learning and memory was investigated by Morris water maze test, and then EEG was recorded for each rat. Learning increased in the group stimulated with 25 μA frequency compared to the sham group (P<0.05). This effect increased with high intensity (100 μA) of electrical stimulation. (One-way ANOVA, Tukey's; P=0.041). Also, this current intensity electrical stimulation increases frequency waves of delta (53.88 ± 2.03), reducing the frequency waves of alpha (11.96 ± 0.68), beta (19.72 ± 1.03), and theta (14.42 ± 0.85).

Therefore, electrical stimulation strengthened and improved the recall stage (Tukey's: P=0.007). As well as, high-intensity electrical simulation visible impact on brain waves are delta waves, which play important role to consolidation of memory.

Cerebral palsy remains as the most frequent cause of motor delay in the young children. Epilepsy can be found in about one-third of childhood patients with cerebral palsy. All seizure types can be seen. The most common types are partial complex and secondary generalized seizures. Seizures in children associated with cerebral palsy have the tendency of earlier onset, and harder control, correlated with the severity degree of cerebral palsy and the existence of mental impairment. The seizure outcome in patients with cerebral palsy is majorly poor. They usually require long-term medications and polytherapy, with more chance of intractable seizures and/or status epilepticus.

The risk of seizure recurrence after stopping anticonvulsants in those with cerebral palsy remains high. Factors related to longer seizure-free period in epileptic children having cerebral palsy are normal intelligence quotient, one seizure type, mono-therapy, and spastic diplegic pattern.

Epilepsy is known since ancient history and affects the lives of millions. Due to various physiological and ethical reasons, it is extremely difficult to conduct thorough examination of the human brain. As a result, even after millennia of identifying epilepsy and treating it, we know relatively little about what is causing epilepsy and what is the best way to manage it. In order to meet this challenge, we develop an artificial neural network, one that allows us to mimic several aspects of the epileptic brain. Our model is based on a specially designed neuron “cell”, and the network is formed in a manner that offers several degrees of flexibility in its formation. This allows us to control the formation of the brain model in several levels: starting with the neurotransmitter and up to properties of the entire network. We compare the activity of our model to that recorded from real brains of real patients, and demonstrate resemblance in key properties of the neuronal activity. Using this artificial network offers an easier experimental platform that manifests epileptic-like behavior, which allows to investigate the underlying mechanisms causing epilepsy on one hand, and to examine potential treatments on the other hand. The model can be adopted to manifest other physiological properties that can be suitable for modeling other neurological disorders.

Background: Depression among epileptic patients has multiple effects: poor quality of life, increased seizure frequency, risk of suicide, increased health care cost and worsened side effects of anti-epileptic medications. It is often under recognized and untreated among these patients.
Objective: To assess the prevalence of depression and associated factors among epileptic patient on treatment follow up at selected public health facilities of Bench Maji zone, south west Ethiopia, 2017.
Methods: Cross-sectional study was conducted in selected public health facilities of Bench Maji zone from March 3- April 3/2017. Simple random sampling was used. Data was collected through face to face interview and analyzed using frequency, percentage and binary logistic.
Result: In this study a total of 244 participants were involved, and the response rate was 98.8%. The overall prevalence of depression was 51.2%. Of these, 60%, 36%, and 4% of the patients were found to have mild, moderate and severe depression respectively. Low educational status (AOR=2.5, CI (1.32, 4.78)), Seizure frequencies ≥ 3 per month (AOR=3.06, CI (1.412, 6.65)), Age onset of epilepsy ≤ 11 years (AOR=4.58, CI (1.94, 10.82)), low anti-epileptic drug adherence (AOR=4.81, CI (2.32, 9.97)) and poor knowledge about epilepsy (AOR=2.77, CI (1.5,5.12)) were found to be independent predictors of depression among epileptic patients.
Conclusion and recommendation: Considerable amount of epileptic patients had depression that may predispose them to different health related problems. Low educational status, seizure frequencies, age at onset of the epilepsy, low antiepileptic drug adherence and poor knowledge about epilepsy were found to be contributing factor to the depression.

Epilepsy is a well-known affliction characterized by recurrent, unprovoked seizures affecting 1-2% of the population. Other than the actual seizure and the risks involved in it, the sudden and unpredictable nature of the seizure is one of the most disabling aspects of epilepsy. As such, finding a method capable of predicting epileptic seizures would open new therapeutic possibilities.

When using electrocorticography for capturing brain activity of epileptic patients, usually, it is the clear increase in the brain activity that is recorded during the seizure. The data is usually used in order to find the probable focus of the seizure. Using the same data, we have applied a method that was used for another disorder (Ataxia Telangiectasia), for obtaining quantitative information about changes in the network correlation, rather than network activity. We show that this provides insight for the epileptic brain behavior, demonstrating that other locations of the brain are involved in the seizure other than the focus, and that there might be early indications for the seizure. These findings can potentially be used in order to decrease seizure likelihood.