International Clinical Neuroscience Journal
Volume:5 Issue: 1, Winter 2018

Autism spectrum disorders (ASD) is the name for a group of developmental disorders including a wide range of signs, symptoms and disability. Landau kleffner syndrome (LKS) or acquired epileptic aphasia is a pediatric disorder characterized by the association of epileptiform electroencephalographic (EEG) abnormalities and acquired aphasia. The early stages of the LKS may be manifested by the symptoms of the autism leading to misdiagnosis. Since LKS is a progressive disease, its misdiagnosis leads to a greater neurocognitive deterioration which may result in seizure in the final stages. The purpose of this review was to provide an overview of available researchs on ASD population and patients with LKS and relationship between these two diseases.

Bipolar disorder (BD) is a major public health problem. In time series count data there may be over dispersion, and serial dependency. In such situation some models that can consider the dependency are needed. The purpose current research was to use Poisson hidden Markov model to forecast new monthly BD instances.
In current study the dataset including the frequency of new instances of BD from October 2008 to March 2015 in Hamadan Province, the west of Iran were used. We used Poisson hidden Markov with different number of conditions to determine the best model according to Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). Then we used final model to forecast for the next 24 months.
Poisson hidden Markov with two states were chosen as the final model. Each component of dependent mixture model explained one of the states. The results showed that the new BD cases is increase over time and due to forecasting results number of patients for the next 24 months comforted in state two with mean 85.15. The forecast interval was approximately (56, 100).
As the Poisson hidden Markov models was not used to forecast the future states in other prior researches, the findings of this study set forward a forecasting strategy as an alternative to common methods, by considering its deficiencies.

Sleep disorder or sleep deprivation (SD) is a common issue in today’s society. Numerous evidences show that sleep is essential for proper brain performance and cognitive processes; on the other hand, cognitive functions have a broad range with learning and long-term memory as the most important ones related to attention. Since many studies show that cholinergic system has a significant role in sleep, learning, and memory, this study aims to investigate the impacts of CA1 Cholinergic Nicotinic Receptors on memory acquisition deficit which is stimulated by total sleep deprivation (TSD) and REM sleep deprivation (RSD).
In this study a water box or a multi-platform apparatus was used in order to induce total sleep deprivation (TSD) or REM sleep deprivation (RSD). In order to investigate interactions of cholinergic system and hippocampus-dependent memory, nicotinic receptor agonist (nicotine) or nicotinic receptor antagonist (mecamylamine) was injected in hippocampal CA1.
According to the results of this study, 24 hours TSD or RSD decreased memory acquisition and injection of nicotine (0.0001 or mecamylamine (0.001 in TSD and RSD sham groups didn’t change memory acquisition. However, injection of sub-threshold dose of nicotine (0.0001 and mecamylamine (0.001 could reduce negative effects of SD in both TSD and RSD.
Discussion; According to the present study, cholinergic nicotinic receptors are effective in learning and memory improvement.

Epilepsy is a common neurological disorder with a prevalence of 1% of the world population. Absence epilepsy is a form of generalized seizures with Spike wave discharge in EEG. Epileptic patients have frequent absence seizures that cause immediate loss of consciousness.
In this study, it has been tried to explore whether EEG changes can effectively detect epilepsy in animal model applying non-linear features. To predict the occurrence of absence epilepsy, a long-term EEG signal has been recorded from frontal cortex in seven Wag/Rij rats. After preprocessing, the data was transferred to the phase space to extract the brain system dynamic and geometric properties of this space. Finally, the ability of each features to predict and detect absence epilepsy with two criteria of predictive time and the accuracy of detection and its results were compared with previous studies.
The results indicate that the brain system dynamic changes during the transition from free-seizure to pre-seizure and then seizure. Proposed approach diagnostic characteristics yielded 97% accuracy of absence epilepsy diagnosis indicating that due to the nonlinear and complex nature of the system and the brain signal, the use of methods consistent with this nature is important in understanding the dynamic transfer between different epileptic seizures.
By changing the state of the absence Seizures, the dynamics are changing, and the results of this research can be useful in real-time applications such as predicting epileptic seizures.

External control of the function of the central pattern generators (CPGs), exist in the spinal cord, is possible by electrical or chemical stimulation of some of the spinal Afferents. After restarting the activity of spinal cord CPG, the dynamics of movements such as gait can be changed through the time by controlling the rhythm of the CPG.
The purpose of this study was provision of closed loop control algorithm based on the Takagi-Sugeno fuzzy controller in order to adjust the weight of the spinal cord afferents in a neuro-mechanical model with the aim of controlling the rhythm of the CPGs. Rhythm control of CPGs has been done with the aim of implementing the process of resetting the phase during gait in order to stabilize the movement against external disturbances. In this paper, the efficiency of a continuous Takagi-Sugeno fuzzy controller with the efficiency of 2 fuzzy Takagi-Sugeno chaotic controllers has been compared.
It was shown by the results obtained from the simulation that the process of resetting the motion phase of the skeletal angle in face of applying disturbance in method of chaotic Takagi-Sugeno fuzzy controller is done with good features in the presence of delayed feedback in decreasing overshoot and undershoot to the amount of 1.982 and 0.17 radians, respectively so that the best amount of afferent to reset the phase and return to the desired angle is provided at the shortest possible time.
In this paper, the efficiency of a continuous Takagi-Sugeno fuzzy controller with the efficiency of 2 fuzzy Takagi-Sugeno chaotic controllers has been compared for movement stabilization using spinal cord afferent control. According to the results, the best performance was observed when the chaotic fuzzy Takagi-Sugenocontroller in the presence of delayed feedback was used.

Brain responds in a short timeframe (with certain delay) after the request for doing a motor imagery task and therefore it is most likely that the individual not focus continuously on the task at entire interval of data acquisition time or even think about other things in a very short time slice. In this paper, an effective brain-computer interface system is presented based on the optimal timeframe selection of brain signals.
To prove the stated claim, various timeframes with different durations and delays selected based on a specific rule from EEG signals recorded during right/left hand motor imagery task and subsequently, feature extraction and classification are done.
Implementation results on the two well-known datasets termed Graz 2003 and Graz 2005; shows that the smallest systematically created timeframe of data acquisition interval have had the best results of classification. Using this smallest timeframe, the classification accuracy increased up to 91.43% for Graz 2003 and 88.96, 83.64 and 84.86 percent for O3, S4 and X11 subjects of Graz 2005 database respectively.
Removing the additional information in which the individual does not focus on the motor imagery task and utilizing the most distinguishing timeframe of EEG signals that correctly interpret individual intentions improves the BCI system performance.

Intraorbital foreign body with intracranial extension is a potentially devastating condition leading to blindness and even death in certain circumstances. Lot of controversies still exist regarding the most appropriate approach for removal of orbital foreign body with intracranial extension. In this paper we have discussed a series of 3 cases where the foreign body was removed through the anterior (transorbital) approach.