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Purpose: The present study was conducted to investigate and classify two groups of healthy children and children with Attention Deficit Hyperactivity Disorder (ADHD) by Effective Connectivity (EC) measure. Since early detection of ADHD can make the treatment process more effective, it is important to diagnose it using new methods.   Materials and Methods: For this purpose, Effective Connectivity Matrices (ECMs) were constructed based on Electroencephalography (EEG) signals of 61 children with ADHD and 60 healthy children of the same age. ECMs of each individual were obtained by the directed Phase Transfer Entropy (dPTE) between each pair of electrodes. ECMs were calculated in five frequency bands including, delta, theta, alpha, beta, and gamma. Based on ECM, an Effective Connectivity Vector (ECV) was constructed as a feature vector for the classification process. Furthermore, ECV of different frequency bands was pooled in one global ECV (gECV). Multilayer Artificial Neural Network (ANN) was used in the steps of classification and feature selection by the Genetic Algorithm (GA). Results: The highest classification accuracy with the selected features of ECV was related to theta frequency band with 89.7%. After that, the delta frequency band had the highest accuracy with 89.2%. The results of ANN classification and GA on the gECV reported 89.1% of accuracy. Conclusion: Our findings show that the dPTE measure, which determines effective connectivity between the brain regions, can be used to classify between ADHD and healthy groups. The results of the classification have improved compared to some studies that used the functional connectivity measures.

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Background:Results of various studies suggest that the hypertrophic and keloid scars are highly prevalent in the general population and are irritating both physically and mentally.Objective:Considering the variety of existing therapies, intense pulsed light (IPL) method along with corticosteroid injection was evaluated in treating these scars.Materials and Methods:86 subjects were included in this clinical trial. Eight sessions of therapeutic intervention were done with IPL along with corticosteroid intralesional injection using 450 to 1200 NM filter, Fluence 30-40 J/cm2, pulse duration of 2.1-10 ms and palsed delay 10-40 ms with an interval of three weeks. To specify the recovery consequences and complication rate and to determine features of the lesion, the criteria specified in the study of Eroll and Vancouver scar scale were used.Results:The level of clinical improvement, color improvement and scar height was 89.1%, 88.8% and 89.1% respectively. The incidence of complications (1 telangiectasia case, 7 hyperpigmentation cases and 2 atrophy cases) following treatment with IPL was 11.6%. Moreover, the participants’ satisfaction with IPL method was 88.8%.Conclusions:This study revealed that a combined therapy (intralesional corticosteroid injection + IPL) increases the recovery level of hypertrophic and keloid scars. It was also demonstrated that this method had no significant side effect and patients were highly satisfied with this method.

Color Vision Deficiency (CVD) is one of the most common types of vision deficiency. People with CVD have difficulty seeing color spectra depending on what types of retina photoreceptors are impaired. In this paper, the Ishihara test with 38 plates was used to examine the Electroencephalogram (EEG) of ten subjects with CVD plus ten healthy individuals. The recording was performed according to the 10–20 international system. The C-based software was programmed so that subjects could select the number or path in each test plate in the software options while recording EEG. Frequency features in different frequency bands were extracted from the EEG signals of the two groups during the Ishihara test. Statistically significant differences (P < 0.05) between features were assessed by independent samples t-test with False Discovery Rate (FDR) correction. Also, the K-nearest neighbor classifier (KNN) was used to classify the two groups. The results revealed that the most significant difference between the two groups in the Ishihara test images occurred for the electrodes located in the right temporoparietal areas (P4 and T6) of the brain in the Delta, Theta, Beta1, and Beta2 frequency bands. The KNN classifier, using the signals that reported the greatest statistical difference between the two groups, showed that the two groups were distinguishable with 85.2% accuracy. In this way, images from the Ishihara test that would provide the most accurate classification were identified. In conclusion, this research provided new insights into EEG signals of subjects with CVD and healthy subjects based on the Ishihara color vision test.

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Research shows that Attention Deficit Hyperactivity Disorder (ADHD) is related to a disorder in brain networks. The purpose of this study is to use an effective connectivity measure and graph theory to examine the impairments of brain connectivity in ADHD. Weighted directed graphs based on electroencephalography (EEG) signals of 61 children with ADHD and 60 healthy children were constructed. The edges between two nodes (electrodes) were calculated by Phase Transfer Entropy (PTE). PTE is calculated for five frequency bands: delta, theta, alpha, beta, and gamma. The graph theory measures were divided into two categories: global and local. Statistical analysis with global measures indicates that in children with ADHD, the segregation of brain connectivity increases while the integration of the brain connectivity decreases compared to healthy children. These brain network differences were identified in the delta and theta frequency bands. The classification accuracy of 89.4% is obtained for both in-degree and strength measures in the theta band. Our result indicated local graph measures classified ADHD and healthy subjects with accuracy of 91.2 and 90% in theta and delta bands, respectively. Our analysis may provide a new understanding of the differences in the EEG brain network of children with ADHD and healthy children.