Effectiveness of virtual reality and computerized training programs for enhancing emotion recognition in people with autism spectrum disorder: a systematic review and meta-analysis.

Background: People with autism spectrum disorder (ASD) have difficulties recognizing emotions. Studies showed that virtual reality (VR) and computerized training programs might be used as potential tools for enhancing emotion recognition in such people. However, some inconsistencies were observed between the studies. Objective: In the current systematic review and meta-analysis, the potential of computerized and VR training programs were evaluated for enhancing emotion recognition in people with ASD. Method: Using PRISMA guidelines and a PICO model, eligible studies were retrieved and the pooled effect size was calculated. Results: This meta-analysis obtained the pooled effect of Cohen's d = 0.69 (95% CI: [0.49, 0.89]) that showed the positive effect of VR and computerized training on emotion recognition in people with ASD. The effectiveness was confirmed for different types of study design, and for both children and adults, while it was larger for non-VR computerized programs compared with VR counterparts. Conclusion-Due to the small sample size of this study and the substantial heterogeneity between studies, the outcomes should be considered with caution in practice. However, these outcomes can be considered for optimizing suitable computerized applications or as the hypothesis for future studies.

The environmental risk factors associated with fetal macrosomia: An umbrella review.

The present umbrella review evaluated the environmental risk factors prior to conception associated with fetal macrosomia based on meta-analyses and systematic reviews. We systematically searched international databases, including PubMed, Scopus, and Web of Science, until April 2023 by using some relevant keywords. The quality of the included studies was assessed using the AMSTAR 2checklist. The risk factor of advanced maternal age 35-39 years compared with <30 years (OR 1·42, 95 % CI: 1·25, 1·60), prepregnancy obesity (OR 1.93, 95 % CI: 1.65, 2.27) and excessive weight gain before and during pregnancy (OR 2.35, 95 % CI: 1.95, 2.85) were graded as suggestive evidence (class III). Two risk factors of advanced maternal age >40 years compared with <30 years (OR 1.40, 95 % CI: 1.02, 1.78) and gestational diabetes mellitus (GDM) without insulin use (OR 1.70, 95 % CI: 1.23, 2.36) were graded as risk factors with weak evidence (class IV). Advanced maternal age, prepregnancy obesity, excessive weight gain before and during pregnancy, and GDM without insulin use were environmental risk factors for macrosomia.

Parkinson's disease tremor prediction using EEG data analysis-A preliminary and feasibility study.

PURPOSE: Tremor is one of the hallmarks of Parkinson's disease (PD) that does not respond effectively to conventional medications. In this regard, as a complementary solution, methods such as deep brain stimulation have been proposed. To apply the intervention with minimal side effects, it is necessary to predict tremor initiation. The purpose of the current study was to propose a novel methodology for predicting resting tremors using analysis of EEG time-series. METHODS: A modified algorithm for tremor onset detection from accelerometer data was proposed. Furthermore, a machine learning methodology for predicting PD hand tremors from EEG time-series was proposed. The most discriminative features extracted from EEG data based on statistical analyses and post-hoc tests were used to train the classifier for distinguishing pre-tremor conditions. RESULTS: Statistical analyses with post-hoc tests showed that features such as form factor and statistical features were the most discriminative features. Furthermore, limited numbers of EEG channels (F3, F7, P4, CP2, FC6, and C4) and EEG bands (Delta and Gamma) were sufficient for an accurate tremor prediction based on EEG data. Based on the selected feature set, a KNN classifier obtained the best pre-tremor prediction performance with an accuracy of 73.67%. CONCLUSION: This feasibility study was the first attempt to show the predicting ability of EEG time-series for PD hand tremor prediction. Considering the limitations of this study, future research with longer data, and different brain dynamics are needed for clinical applications.

The association between post-term births and autism spectrum disorders: an updated systematic review and meta-analysis.

BACKGROUND: This study aimed to conduct a meta-analysis to determine whether post-term birth has an increased risk of ASD. MATERIALS AND METHODS: To retrieve eligible studies regarding the effect of post-term and ASD in children, major databases including PubMed, Scopus, and Web of Science were searched. A random effect model was used for meta-analysis. For assessing the quality of included studies, the GRADE checklist was used. RESULTS: In total, 18 records were included with 1,412,667 sample populations from 12 countries. The pooled estimates of RR and OR showed a significant association between post-term birth and ASD among children, respectively (RR = 1.34, 95% CI 1.10 to 1.58) and (OR = 1.47, 95% CI 1.03 to 1.91). There was no heterogeneity among the studies that reported the risk of ASD among children based on RR (I2 = 6.6%, P = 0.301). There was high heterogeneity in the studies reported risk of ASD based on OR (I2 = 94.1%, P = 0.000). CONCLUSION: Post-term births still occur relatively frequently (up to 5-10%) even in developed countries. Our results showed that post-term birth is an increased risk of ASD, although high heterogeneity was found among the studies reported based on adjusted and crude forms, however, after subgroup analysis by gender, this heterogeneity disappeared among males.

Electromagnetic radiation therapy for Parkinson's disease tremor reduction- systematic reviews and Bayesian meta-analyses for comparing the effectiveness of electric, magnetic and light stimulation methods.

PURPOSE: Tremor is one of the key characteristics of Parkinson's disease (PD), leading to physical disabilities and often showing limited responses to pharmacological treatments. To suppress tremors in PD patients, several types of non-invasive and non-pharmacological methods have been proposed so far. In the current systematic review, three electromagnetic-based radiation strategies including electrical stimulation, magnetic stimulation, and light stimulation methods were reviewed and compared. METHODS: Major databases were searched to retrieve eligible studies. For the meta-analysis, a random-effect Bayesian framework was used. Also, heterogeneity between studies was assessed using I2 statistic, prediction interval, and tau2. Publication bias was assessed using funnel plot, and the effectiveness of methods for reducing tremor was compared using network Bayesian meta-analysis. RESULTS AND CONCLUSION: Thirty-one studies were found for qualitative analysis, and 16 studies were found for quantitative synthesis. Based on the suppression ratio, methods can be ordered as electrical stimulation, light therapy, and magnetic stimulation. Furthermore, the results showed that electrical and magnetic stimulation were more effective for tremor suppression at early stages of PD, while light therapy was found to be more effective during the later stages of PD.

Neonatal risk factors associated with attention-deficit/hyperactivity disorder: an umbrella review.

BACKGROUND: Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder that is being encountered more frequently. PURPOSE: In this study, by compiling the evidence from available meta-analyses, an umbrella systematic review was performed of the neonatal risk factors associated with ADHD. METHODS: The PubMed, Scopus, and Web of Science databases were searched for eligible studies. Only systematic reviews were included. Using a random-effects model, 95% prediction intervals were reported for each risk factor. Three studies were ultimately included in the review. RESULTS: Congenital heart disease, short-duration or incomplete breastfeeding, low birth weight, and 5-minute Apgar scores <7 were significant risk factors for ADHD. However, the quality of the included systematic reviews was low to moderate and the evidence credibility level was suggestive to weak. CONCLUSION: The results of this umbrella review proposed that congenital anomalies, short-duration or incomplete breastfeeding, low birth weight, and low Apgar scores were important factors for the manifestation of ADHD symptoms. However, the inclusion of more high-quality studies is needed to validate our results.

The environmental risk factors related to uterine leiomyoma: An umbrella review.

BACKGROUND: Uterine leiomyoma (UL) is the most common benign tumor of the reproductive period and become a major health problem. The present umbrella review assessed the environmental risk factors associated with UL based on meta-analyses studies. MATERIALS AND METHODS: We searched three Major databases until February 2022. The meta-analyses that had focused on evaluating the environmental risk factors associated with the UL were included. The summary effect estimates, 95% CI, heterogeneity I², 95% prediction interval, small-study effects, excess significance biases, and sensitive analysis were applied. We used A Measurement Tool to Assess Systematic Reviews 2 (AMSTAR 2) for assessing the quality of the meta-analyses. RESULT: The risk factor of chronic psychological stress (OR 1.24, 95% CI: 1.15, 1.34) and obesity (OR 1.19, 95% CI: 1.09, 1.29) was graded as suggestive evidence (class III). The current alcohol intake (OR 1.33, 95% CI: 1.01, 1.76) was graded as the risk factor with weak evidence (class IV). Current oral contraceptive (OCP) use (RR 0.43, 95% CI: 0.25, 0.73) (class IV), and former smokers (OR 0.93, 95% CI: 0.88, 0.99) (class IV) were as the protective factors. CONCLUSION: The current alcohol intake, chronic psychological stress, and obesity were risk factors for the UL, but current OCP use and former smokers were the protective factors.

Association of vitamin A and its organic compounds with stroke - a systematic review and meta-analysis.

PURPOSE: The main purpose of this systematic review was to evaluate the association between the stroke (risk of stroke and the mortality due to stroke) and vitamin A, its organic compounds and its provitamins. METHOD: Major databases including PubMed, Scopus, and Web of Science were searched. Studies with human samples were included for risk assessment. The association was assessed using odds ratio (log(OR)) and a random-effect model. I2 statistic, variance (tau2) and prediction interval were used for heterogeneity assessment. The funnel plot was used for publication bias. RESULTS: Twenty-one studies including 5789 stroke patients were retrieved. Twenty studies had sufficient information for quantitative analyses. The pooled effect showed an inverse association between vitamin A and its organic compound with the risk of stroke (log(OR) = -0.46 95%CI (-0.81;-0.12)) and with the risk of mortality due to stroke (log(OR) = -0.39 95%CI (-0.74;-0.04)). However, according to subgroup analyses, the association was dependent on the compound in a way that retinol and beta-carotene were the most effective compounds. The effects of several confounding factors and the threshold levels for vitamin A and its organic compound on the effectiveness were discussed. CONCLUSION: Insufficiency of retinol and beta-carotene significantly increased the risk of stroke; however, due to heterogeneity between studies more studies are needed for evaluating clinical significance of this outcome.

Effect of Levodopa Medication on Human Brain Connectome in Parkinson's Disease-A Combined Graph Theory and EEG Study.

Background. Levodopa-based drugs are widely used for mitigating the complications induced by Parkinson's disease (PD). Despite the positive effects, several issues regarding the way that levodopa changes brain activities have remained unclear. Methods. A combined strategy using EEG data and graph theory was used for investigating how levodopa changed connectome and processing hubs of the brain during resting-state. Obtained results were subjected to ANOVA test and multiple-comparison post-hoc correction procedure. Results. Outcomes showed that graph topology was not significantly different between PD and healthy groups during the eyes-closed condition, while in the eyes-open condition, statistically significant differences were found. The main effect of levodopa medication was observed for gamma-band activity in which levodopa changed the brain connectome toward a star-like topology. Considering the beta subband of EEG data, graph leaf number increased following levodopa medication in PD patients. Enhanced brain connectivity in the gamma band and reduced beta band connections in the basal ganglia were also observed after levodopa medication. Furthermore, source localization using dipole fitting showed that levodopa suppressed the activity of collateral trigone. Conclusion. Our combined EEG and graph analysis showed that levodopa medication changed the brain connectome, especially in the high-frequency range of brain electrical activities (beta and gamma).

Mobile phone electromagnetic radiation and the risk of headache: a systematic review and meta-analysis.

PURPOSE: The effects of electromagnetic fields of mobile phones on headaches have attracted researchers during the last decades. However, contradictory results have been reported so far. METHODS: In this systematic review and meta-analysis, major databases including PubMed, Scopus and Web of Science were searched using suitable search terms and PRISMA guidelines to retrieve eligible studies for the effect of mobile phone use on headache. After the abstract and full-text screening, 33 studies were retrieved and the effect size in terms of odds ratio (OR) was extracted. Between-study heterogeneity was assessed using I2 statistic and Q test, while publication bias was evaluated by funnel plot and Egger's and Begg's tests. RESULTS: Among 33 eligible studies, 30 eligible studies were included in the meta-analysis. When considering all studies, the pooled effect size of OR = 1.30(95% CI 1.21-1.39) was obtained, while the heterogeneity between studies was significant. Subgroup analyses by considering the age of participants and EMF exposure duration were performed to find the source of heterogeneity. The odds ratios when the age of participants was the variable were 1.33 (95% CI 1.14-1.53) and 1.29 (95% CI 1.20-1.37), for ages > 18 and age ≤ 18 years, respectively. When EMF exposure duration was considered, subgroup analysis obtained the pooled effect size of OR = 1.41(95% CI 1.22-1.61) and 1.23(95% CI 1.12-1.34), for EMF exposure duration > 100 and ≤ 100 minutes per week, respectively. The pooled effect sizes emphasized the effect of mobile phone use on headaches for all ages and exposure durations. CONCLUSION: Results revealed that age and exposure duration (mainly call duration), both were the source of heterogeneity between studies. Furthermore, results showed that increasing call duration and mobile phone use in older individuals increased the risk of headache.

Analysis of the stance phase of the gait cycle in Parkinson's disease and its potency for Parkinson's disease discrimination.

In this study, using vertical ground reaction force (VGRF) data and focusing on the stance phase of the gait cycle, the effect of Parkinson's disease (PD) on gait was investigated. The used dataset consisted of 93 PD and 72 healthy individuals. Multiple comparisons correction ANOVA test and student t-test were used for statistical analyses. Results showed that a longer stance duration with a larger VGRF peak value (p < 0.05) was observed for PD patients during the stance phase. In addition, the VGRF peak value was delayed and blunted in PD cases compared with healthy individuals. These results indicated more time and effort for PD patients for posture stabilization during the stance phase. The time delay for different locations of the foot sole to contact the ground during the stance phase indicated that PD patients might use a different strategy for maintaining their body stability compared with healthy individuals. Although the VGRF time-domain pattern during the stance phase in PD was similar to healthy conditions, its local characteristics like duration and peak value differed significantly. The classification analysis based on the VGRF time-domain extracted features during the stance phase obtained PD recognition with accuracy, sensitivity and specificity of 90.82%, 88.63% and 82.56%, respectively.

Distinctive prosodic features of people with autism spectrum disorder: a systematic review and meta-analysis study.

In this systematic review, we analyzed and evaluated the findings of studies on prosodic features of vocal productions of people with autism spectrum disorder (ASD) in order to recognize the statistically significant, most confirmed and reliable prosodic differences distinguishing people with ASD from typically developing individuals. Using suitable keywords, three major databases including Web of Science, PubMed and Scopus, were searched. The results for prosodic features such as mean pitch, pitch range and variability, speech rate, intensity and voice duration were extracted from eligible studies. The pooled standard mean difference between ASD and control groups was extracted or calculated. Using I2 statistic and Cochrane Q-test, between-study heterogeneity was evaluated. Furthermore, publication bias was assessed using funnel plot and its significance was evaluated using Egger's and Begg's tests. Thirty-nine eligible studies were retrieved (including 910 and 850 participants for ASD and control groups, respectively). This systematic review and meta-analysis showed that ASD group members had a significantly larger mean pitch (SMD = - 0.4, 95% CI [- 0.70, - 0.10]), larger pitch range (SMD = - 0.78, 95% CI [- 1.34, - 0.21]), longer voice duration (SMD = - 0.43, 95% CI [- 0.72, - 0.15]), and larger pitch variability (SMD = - 0.46, 95% CI [- 0.84, - 0.08]), compared with typically developing control group. However, no significant differences in pitch standard deviation, voice intensity and speech rate were found between groups. Chronological age of participants and voice elicitation tasks were two sources of between-study heterogeneity. Furthermore, no publication bias was observed during analyses (p > 0.05). Mean pitch, pitch range, pitch variability and voice duration were recognized as the prosodic features reliably distinguishing people with ASD from TD individuals.

Effect of Exercise on Parkinson's Disease Tremor: A Meta-analysis Study.

Background: Prior studies suggest that exercise may prevent movement disorders in Parkinson's disease (PD) patients. In this meta-analysis, the pooled effect of exercise on PD-induced tremor was investigated. Method: Relevant published studies (n = 7) were retrieved by searching major databases, including Scopus, Web of Science and PubMed from 1985 to November 2020. Studies were eligible if the effect of exercise on PD-induced tremor was studied and sufficient information for calculating the effect size was reported. Furthermore, non-English manuscripts and studies related to non-human species were excluded. The quality of studies was evaluated using the improved Newcastle-Ottawa scale (NOS). In this study, variables such as participant's age and gender, type of exercise, intervention duration and tremor indices were extracted for each study. Between-study heterogeneity and publication bias were calculated using I2 statistic and funnel plot, respectively. Results: Results showed that hand movement and cycling exercises were effective for reducing tremor amplitude or frequency. When all types of exercises (upper, lower or whole-body movement) were considered, an odds ratio (OR) greater than 1 was obtained (log(OR) = 2, 95% CI: 0.88-3.12), while between-study heterogeneity was high (I2 = 78%). By restricting the studies to hand-movement exercises, a lower odds ratio (log(OR) = 1, 95% CI: 0.24-1.77) with small between-study heterogeneity (I2 = 0.0%, p = 0.502) was obtained. Statistical analysis based on Egger's and Begg's tests revealed no significant publication bias. Conclusions: Outcomes of this study suggested that exercises as inexpensive, non-invasive and easy-to-implement strategies could be applied for PD patients alongside medical interventions for reducing tremors. Clinical Highlights: Exercises reduce tremors in Parkinson's disease patients significantly.Hand movement exercises are beneficial for reducing tremors in PD patients.

Stem Cell Behavior at Hypothermia: A Review Article.

Temperature is a fundamental factor that affects many functions and structural aspects of physiological systems. Despite its importance, few studies have been performed so far for investigating the compartments and mechanisms engaged in the response of cellular systems to temperature perturbation. In this review, focusing on stem cells, we tried to perform a literature review for investigating the possible ways through which the temperature reduction (hypothermia) affects stem cell function and behavior. Besides, using the obtained results of this investigation, the possible mechanisms are proposed. The survey indicates that profound hypothermia enhances cell adhesion by increasing the stability of E-cadherins. Furthermore, mild hypothermia increases stem cell survival by reducing oxidative stress and prevents apoptosis via the overexpression of anti-apoptotic heat shock proteins. Mild-hypothermia also promotes cell proliferation by affecting gene expression in several ways. Even though it seems that hypothermia generally reduced stem cell differentiation, some inconsistencies are observed between obtained results from the literature. Based on the obtained results, mechanisms responsible for the temperature effect of hypothermia in profound and mild ranges are given that might help the researcher in real experiments.

Discrimination of ADHD Subtypes Using Decision Tree on Behavioral, Neuropsychological, and Neural Markers.

INTRODUCTION: Attention-Deficit/Hyperactivity Disorder (ADHD) is a well-known neurodevelopmental disorder. Diagnosis and treatment of ADHD can often lead to a developmental trajectory toward positive results. The present study aimed at implementing the decision tree method to recognize children with and without ADHD, as well as ADHD subtypes. METHODS: In the present study, the subjects included 61 children with ADHD (subdivided into ADHD-I (n=25), ADHD-H (n=14), and ADHD-C (n=22) groups) and 43 typically developing controls matched by IQ and age. The Child Behavior Checklist (CBCL), Integrated Visual And Auditory (IVA) test, and quantitative EEG during eyes-closed resting-state were utilized to evaluate the level of behavioral, neuropsychology, and electrophysiology markers using a decision tree algorithm, respectively. RESULTS: Based on the results, excellent classification accuracy (100%) was obtained to discriminate children with ADHD from the control group. Also, the ADHD subtypes, including combined, inattention, and hyperactive/impulsive subtypes were recognized from others with an accuracy of 80.41%, 84.17%, and 71.46%, respectively. CONCLUSION: Our results showed that children with ADHD can be recognized from the healthy controls based on the neuropsychological data (sensory-motor parameters of IVA). Also, subtypes of ADHD can be distinguished from each other using behavioral, neuropsychiatric and electrophysiological parameters. The findings suggested that the decision tree method may present an efficient and accurate diagnostic tool for the clinicians.

EEG based emotion recognition using minimum spanning tree.

Emotion is a fundamental factor that influences human cognition, motivation, decision making and social interactions. This psychological state arises spontaneously and goes with physiological changes that can be recognized by computational methods. In this study, changes in minimum spanning tree (MST) structure of brain functional connectome were used for emotion classification based on EEG data and the obtained results were employed for interpretation about the most informative frequency content of emotional states. For estimation of interaction between different brain regions, several connectivity metrics were applied and interactions were calculated in different frequency bands. Subsequently, the MST graph was extracted from the functional connectivity matrix and its features were used for emotion recognition. The results showed that the accuracy of the proposed method for separating emotions with different arousal levels was 88.28%, while for different valence levels it was 81.25%. Interestingly, the system performance for binary classification of emotions based on quadrants of arousal-valence space was also higher than 80%. The MST approach allowed us to study the change of brain complexity and dynamics in various emotional states. This capability provided us enough knowledge to claim lower-alpha and gamma bands contain the main information for discrimination of emotional states.

The application of EEG power for the prediction and interpretation of consumer decision-making: A neuromarketing study.

The application of biometric data has been the center of attention for neuromarketing researches. Understanding the underlying mechanisms behind consumer shopping behaviors and the way that advertising affects such behavior are the most important issues that need more investigations. In this study, two purposes were focused including (1)the potential of EEG spectral power for prediction of consumers' preferences and (2)interpretation of the alteration of consumers' decision-making in shopping behavior when the content of an advertisement including background color and promotions was changed. For this purpose, advertisements related to different mobile phone brands which were different according to the content were shown to the participants followed by EEG (electroencephalography) recording. The power of the EEG data was used for finding the most important brain regions for distinguishing between preferences and predicting the incidence of decision-making. Furthermore, the results were used for interpretation of the observed participant behavior. The obtained results showed that the extracted features from EEG power could predict consumer's decision-making incidence with relatively high accuracy (>87%) and distinguished between "Like" and "Dislike" preferences with accuracy higher than 63%. Also, the most discriminative channels for predicting the incidence of decision-making about liking/disliking or buying a product were found to be frontal and Centro-parietal locations (Fp1, Cp3, Cpz) while the difference between "Like" and "Dislike" decisions was observed mostly in the frontal electrodes (F4 and Ft8). Furthermore, the results showed that adding the background color to the designed advertisement had a negative impact on the degree of liking a product. In conclusion, EEG data analysis can be used as a useful tool for predicting costumer decision-making, while in order to obtain higher accuracies, other features should be tested for distinguishing between different preferences.

The role of the transient receptor potential melastatin5 (TRPM5) channels in the pancreatic ß-cell electrical activity: A computational modeling study.

The TRPM5 channels are transient receptor potential channels whose presence in the human pancreatic ß-cell has been confirmed. The sensitivity of these channels to membrane voltage, temperature and intracellular calcium concentration and their possible role in insulin secretion has made them a focal point in research in the past decade. While experimental researches have confirmed the role of the TRPM5 channels in insulin secretion, but the underlying mechanism is still unclear. In this study, based on the experimental results of other studies, a mathematical description of the TRPM5 channel activity has been proposed which correlates the TRPM5 electrical activity to the voltage and intracellular calcium concentration. The resulting expression has been added to the existing mathematical model of the human ß-cell and the enhanced model has been used for investigating the effect of the TRPM5 channel on the ß-cell electrical activity. The results of our study show that the TRPM5 influences other ion channel activities through speeding up membrane depolarization. In addition, we have shown that the TRPM5 increases the amplitude and firing rate of action potentials by possibly affecting the Na+ and gamma-Aminobutyric acid related currents. The results also confirm the prominent effect of the TRPM5 channels in glucose-stimulated condition.

Computational modeling of the effect of temperature variations on human pancreatic ß-cell activity.

The effect of temperature variations on the pancreatic ß-cell activity and the role of different model compartments in temperature sensing have been investigated using a computational modeling approach. The results of our study show that temperature variations by several degrees can change the dynamical states of the ß-cell system. In addition, temperature variations can alter the characteristic features of the membrane voltage, which correlates with insulin secretion. Simulation results show that the ion channels such as the L-type calcium, the hERG potassium, sodium channels and the glycolysis pathway are the possible sites for sensing temperature variation. Results indicate that for a small temperature change, even though the frequency and amplitude of electrical activity are altered, the area under the membrane potential curve remains almost unchanged, which implies the existence of a thermoregulatory mechanism for preserving the amount of insulin secretion. Furthermore, the computational analysis shows that the ß-cell electrical activity exhibits a bursting pattern in physiological temperature (37 °C) while in vitro studies reported almost the spiking activity at lower temperatures. Since hormone-secreting systems work more efficient in bursting mode, we propose that the pancreatic ß-cell works better in the physiological temperature compared with the reference temperature (33 °C).