International Clinical Neuroscience Journal
Volume:5 Issue: 4, Autumn 2018

Multiple sclerosis (MS) is a chronic disorder of the central nervous system that affects various parts of the brain and the spinal cord, leading to interruptions of the nervous, defense and movement systems, which usually affect balance and gait. Considering that the diagnosis of MS and its classification is a function of the expertise of the physician, the use of creative methods can help physicians to diagnose and classify different levels of the disease. The primary objective of the present study was to detect different levels of MS disease based on the nonlinear evaluation of body features. To do so, we studied eight MS patients and posture information of these patients such as the center of pressure (COP) were recorded at different levels with various degrees of Expanded Disability Status Scale (EDSS) by a motion analyzer device, while subjects were standing on the force plate in the eyes-opened and eyes-closed modes. After extracting and validating features that are used to assess posture disorders and explain the balancing behavior, the support vector machine (SVM) was employed to classify different levels of disease. Using the Spearman correlation test, each feature evaluated by the EDSS test. The features obtained from Higuchi’s fractal dimensional algorithm in both anterior-posterior and mediolateral directions of the COP, which were significant (P < 0.05) were selected and provided to SVM and neural network for classification of different levels. It found that SVM outperformed neural network and was able to carry out the classification with the accuracy of 90.7%. As an intelligent method, the non-linear evaluation of body features such as dimensional fractal analysis of the COP can help physicians diagnose different levels of MS with greater precision.

Traumatic brain injury (TBI) is one of the main causes of death and disability in both sex, young and old age group population in different countries. This study aimed to estimate effects of sex, age group and intensity level of TBI in neurocognitive dysfunction. The study was done using the mini-mental state examination (MMSE) to estimate cognitive dysfunction that directed presence to the emergency department center with medical cares in the Zahedan city. Individuals were deliberated eligible if they were 18 years of age or older. This investigation covered 6-months. The sample study estimated 85 patients, 73% males with 27% females. The mean age patients reported 32.5 years (range 18-66 year) and SD (12.924) with 95% CI. Two-way between groups analysis of variance test was used to assess the impacts of sex, age and level of TBI as measured by neurocognitive dysfunction. The interaction effect between sex, age group and level of TBI was statistically significant F (0, 85) = 3.96, P = 0.01 however, the effect size was medium (partial eta squared = 0.54). This study supported research hypothesis that sex, age group and severity level of TBI show greater effect in neurocognitive dysfunction. In addition, the greatest amount of improvement in disability was observed among the male youngest group of survivors. These results advocate TBI survivors, especially older patients, may be candidates for neuroprotective therapies after TBI.

One of the challenges today is that the quality of sleep has weakened by many external factors, which we are not even aware of and which directly affect sleep. Sleep quality has an essential role in maintaining the cognitive function and memory consolidation of individuals. So far, various studies have been done to improve the quality of sleep by using external electrical stimulation, vestibular and olfactory system stimulation. In this study, the increase in sleep quality was considered by simultaneous acoustic stimulation in a deep sleep to increase the density of slow oscillations. Slow oscillations are the important events recorded in electroencephalography (EEG) and hallmark deep sleep. Acoustic stimulation of pink noise with random frequency ranging from 0.8 to 1.1 Hz was used to improve sleep quality. Eight healthy adults (mean age: 28.4±7.8 years) studied in 3 nights under 3 conditions: accommodation night, stimulation night (STIM) and no stimulation night (SHAM), in counter-balanced order. Significant characteristics of the objective and subjective quality of sleep have been extracted from the acquired EEG and compared in the last 2 nights. Also, the arousal and cyclic alternating pattern characteristics have been measured to assess sleep stability over 2 nights of STIM and SHAM. Our findings confirm this goal of the study that applying designed acoustic stimulation simultaneously in the slow wave sleep (SWS) stage increases the duration of deep sleep and ultimately improves overall sleep stability and quality.

Emotion recognition, as a subset of affective computing, has received considerable attention in recent years. Emotions are key to human-computer interactions. Electroencephalogram (EEG) is considered a valuable physiological source of information for classifying emotions. However, it has complex and chaotic behavior. In this study, an attempt is made to extract important nonlinear features from EEGs with the aim of emotion recognition. We also take advantage of machine learning methods such as evolutionary feature selection methods and committee machines to enhance the classification performance. Classification performed concerning both arousal and valence factors. Results suggest that the proposed method is successful and comparable to the previous works. A recognition rate equal to 90% achieved, and the most significant features reported. We apply the final classification scheme to 2 different databases including our recorded EEGs and a benchmark dataset to evaluate the suggested approach. Our findings approve of the effectiveness of using nonlinear features and a combination of classifiers. Results are also discussed from different points of view to understand brain dynamics better while emotion changes. This study reveals useful insights about emotion classification and brain-behavior related to emotion elicitation.

Chronopsychology researches claim that cognitive processes performance during learning in the educational environment in times of the day and days of the week fluctuate, and working memory is essential among these cognitive processes. The research aimed to study the rhythm of daily and weekly working memory performance of preschoolers based on their chronotype (morningness and eveningness) orientation. The research method is causal-comparative. The participants are 100 preschool children in Tehran that were selected based on purposive sampling. Their working memory was tested at different time intervals of (8, 11, 13, and 15) and weekly (Saturday, Sunday, Monday, Tuesday and Wednesday). Saturday also considered as the first day of the week. Data collection instrument were children morningness-eveningness preference (CMEP) in the form of questionnaire and working memory test. Data analysis based on a mixed analysis of variance. The results showed that preschoolers working memory performance during different days of the week and time of day was different (P < 0.01). There was a significant difference between children in different groups regarding memory at different hours of the day, but on different days of the week, there was no significant difference in memory performance (P < 0.01). According to the findings, teachers and clinicians are suggested to consider the importance of circadian rhythm parameters in assessing cognitive function in patients and healthy people. Awareness of individual differences of the morningness-eveningness type can be very effective in designing training programs and preventive health associated matters with each type.

The purpose of this study was to compare the effectiveness of cognitive-behavioral therapy (CBT) and acceptance and commitment therapy (ACT) on reducing mood symptoms in patients with substance abuse. The current research was a semi-experimental study with pre-test and post-test with a control group. The participants consisted of all people with substance abuse referred to drug abuse treatment centers in district 4 and 8 of Tehran city in 2016-2017. In this way, 45 subjects selected by purposeful sampling method and randomly divided into 2 groups of experimental and one control group (15 persons for each group). Then, the Mood Disorder Questionnaire and Depression Inventory took from the subjects of each group. CBT and ACT performed in 8 sessions of 90 minutes in 2 experimental groups and control group were also without any training program. After completing the training, the post-test performed for all three groups. Data analysis was done by using the covariance analysis (MANCOVA) and using SPSS-21 software. The findings showed that CBT and ACT were effective in reducing mood syndrome in patients with substance abuse (P<00.001). Moreover, there were no significant differences between the effectiveness of the CBT and ACT on the reduction of mood syndrome in patients with substance abuse. Considering the effect of CBT and ACT on the reduction of mood syndrome among patients with substance abuse, it is worth considering the role of these 2 treatments as one of the educational and therapeutic strategies for substance abuse.

Nurses suffer from sleep disorders. Sleep disorders will lead to listlessness and distractibility, and interfere with people’s normal working state. Effective methods upon nurses sleep quality should identify. The purpose of this study was determining the efficacy of progressive muscular relaxation (PMR) upon nurses sleeping quality. The current research was an experimental study with pre-test and post-test design with the control group. The statistical population consisted of all nurses in Imam Sajad hospital in Ramsar 2017 (N=120). In this way, 40 nurses who were in the test of the quality of sleep score above five were randomly selected and randomly divided into two groups. For data gathering, Pittsburgh sleeps quality questionnaire and PMR instruction used. It was used multivariate covariance analysis (MANCOVA) to analysis data by SPSS-22. The findings of this study showed that PMR instruction is useful for nurses’ sleep quality. Also, there is a significant difference between experimental and control groups after the intervention, so that the mean scores of the experimental group were improved significantly compared to the control group (P < 0.0001). According to results, PMR instruction can improve the sleep quality and other variables related to sleep problems of nurses. Therefore, it can be used in nursing programs to improve their sleep quality.