Co-designing a Physical Activity Service for Refugees and Asylum Seekers Using an Experience-Based Co-design Framework.

People from refugee and asylum seeker backgrounds resettling in Australia often experience intersecting risks for poor mental and physical health. Physical activity can promote better health outcomes, however there are limited programs tailored for this population. Therefore, understanding how to support refugees and asylum seekers to engage in physical activity is crucial. This paper aims to describe how the experience-based co-design (EBCD) process was used to identify priorities for a new physical activity service for refugees and asylum seekers. Using an EBCD framework we conducted qualitative interviews and co-design workshops with service users (refugees and asylum seekers living in the community) and service providers at a community Centre in Sydney, Australia. Sixteen participants, including eight service users and eight service providers engaged in the EBCD process over 12-months. The interviews revealed common themes or 'touchpoints' including barriers and enablers to physical activity participation such as access, safety and competing stressors. Subsequent co-design focus groups resulted in the establishment of five fundamental priorities and actionable strategies; ensuring cultural and psychological safety, promoting accessibility, facilitating support to access basic needs, enhancing physical activity literacy and fostering social connection. Using EBCD methodology, this study used the insights and lived experiences of both service users and providers to co-design a physical activity service for refugees and asylum seekers which is safe, supportive, social and accessible. The results of the implementation and evaluation of the program are ongoing.

Female Cerebellum Seems Sociable; An iTBS Investigation.

The cerebellum has been shown to be engaged in tasks other than motor control, including cognitive and affective functions. Prior neuroimaging studies have documented the role of this area in social cognition and despite these findings, no studies have yet examined the causal relationship between the cerebellum and social cognition. This study aimed to investigate the role of the cerebellum in empathy and theory of mind (ToM) in a randomized, placebo-controlled, double-blind, parallel study. 32 healthy participants were assigned to either a sham or active group. For the active group, an intermittent theta-burst stimulation (iTBS) protocol at 100% of the motor threshold was applied to the cerebellum, while the control group received sham stimulation. An eyes-closed EEG session, the Empathy Quotient (EQ) test, and the Reading the Mind in the Eyes Test (RMET) were administered before and after the iTBS session. The results demonstrated differences in cognitive empathy, ToM, and a decrease in the activity of the default mode network (DMN) between the active and sham groups in females. Females also showed a decrease in the activity of the affective empathy network and connectivity in the DMN. We conclude that cognitive empathy and ToM are associated with cerebellar activity, and due to sex-related differences in the cortical organization of this area which is modulated by sex hormones, the stimulation of the cerebellum in males and females yields different results.

Assessments of variability in cortical and subcortical measurements and within-network connectivity of the brain using test-retest data.

The evaluation and diagnosis of structural changes in brain caused by disease or treatment over time has become one of the important applications of medical imaging methods, in particular MRI, and it is growing. It is critical to evaluate the reliability of the changes in measurements observed in an individual patient for any clinical decision-making. In this paper, we calculated the repeatability coefficient (RC) as a measure of uncertainty for MRI measurements of subcortical volumes and cortical thickness, and within-network connectivity using test-retest data of 20 healthy subjects. We also evaluated changes in 13 patients who received 20 sessions of transcranial magnetic stimulation as a treatment. The most reliable measure seems to be in the thickness of the left occipital with RC% of 3.5 and the least reliable measure is the brain connectivity within visual network using Yeo's atlas with RC% of 29.4. The most sensitive measure to the percentage of true changes in treated patients is the connectivity within subcortical network of AAL with 76.9%.Clinical Relevance- The results of this study can be useful for evaluating changes in the gray matter structures or functional connectivity of the brain due to a neurological disease such as Alzheimer's or Parkinson's. Also, the obtained results can be used to evaluate the changes caused by any intervention or treatment that may have any positive or negative effect on the brain.

Effective Connectivity Estimation by a Hybrid Neural Network, Empirical Wavelet Transform, and Bayesian Optimization.

Accurately measuring nonlinear effective connectivity is a crucial step in investigating brain functions. Brain signals like EEG is nonstationary. Many effective connectivity methods have been proposed but they have drawbacks in their models such as a weakness in proposing a way for hyperparameter and time lag selection as well as dealing with non-stationarity of the time series. This paper proposes an effective connectivity model based on a hybrid neural network model which uses Empirical Wavelet Transform (EWT) and a long short-term memory network (LSTM). The best hyperparameters and time lag are selected using Bayesian Optimization (BO). Due to the importance of generalizability in neural networks and calculating GC, an algorithm was proposed to choose the best generalizable weights. The model was evaluated using simulated and real EEG data consisting of attention deficit hyperactivity disorder (ADHD) and healthy subjects. The proposed model's performance on simulated data was evaluated by comparing it with other neural networks, including LSTM, CNN-LSTM, GRU, RNN, and MLP, using a Blocked cross-validation approach. GC of the simulated data was compared with GRU, linear Granger causality (LGC), Kernel Granger Causality (KGC), Partial Directed Coherence (PDC), and Directed Transfer Function (DTF). Our results demonstrated that the proposed model was superior to the mentioned models. Another advantage of our model is robustness against noise. The results showed that the proposed model can identify the connections in noisy conditions. The comparison of the effective connectivity of ADHD and the healthy group showed that the results are in accordance with previous studies.

Dynamic causal modeling of reorganization of memory and language networks in temporal lobe epilepsy.

OBJECTIVE: To evaluate the alterations of language and memory functions using dynamic causal modeling, in order to identify the epileptogenic hemisphere in temporal lobe epilepsy (TLE). METHODS: Twenty-two patients with left TLE and 13 patients with right TLE underwent functional magnetic resonance imaging (fMRI) during four memory and four language mapping tasks. Dynamic causal modeling (DCM) was employed on fMRI data to examine effective directional connectivity in memory and language networks and the alterations in people with TLE compared to healthy individuals. RESULTS: DCM analysis suggested that TLE can influence the memory network more widely compared to the language network. For memory mapping, it demonstrated overall hyperconnectivity from the left hemisphere to the other cranial regions in the picture encoding, and from the right hemisphere to the other cranial regions in the word encoding tasks. On the contrary, overall hypoconnectivity was seen from the brain hemisphere contralateral to the seizure onset in the retrieval tasks. DCM analysis further manifested hypoconnectivity between the brain's hemispheres in the language network in patients with TLE compared to controls. The CANTAB® neuropsychological test revealed a negative correlation for the left TLE and a positive correlation for the right TLE cohorts for the connections extracted by DCM that were significantly different between the left and right TLE cohorts. INTERPRETATION: In this study, dynamic causal modeling evidenced the reorganization of language and memory networks in TLE that can be used for a better understanding of the effects of TLE on the brain's cognitive functions.

EEG-based classification of individuals with neuropsychiatric disorders using deep neural networks: A systematic review of current status and future directions.

The use of deep neural networks for electroencephalogram (EEG) classification has rapidly progressed and gained popularity in recent years, but automatic feature extraction from EEG signals remains a challenging task. The classification of neuropsychiatric disorders demands the extraction of neuro-markers for use in automated EEG classification. Numerous advanced deep learning algorithms can be used for this purpose. In this article, we present a comprehensive review of the main factors and parameters that affect the performance of deep neural networks in classifying different neuropsychiatric disorders using EEG signals. We also analyze the EEG features used for improving classification performance. Our analysis includes 82 scientific journal papers that applied deep neural networks for subject-wise classification based on EEG signals. We extracted information on the EEG dataset and types of disorders, deep neural network structures, performance, and hyperparameters. The results show that most studies have focused on clinical classification, achieving an average accuracy of 91.83 ± 7.34, with convolutional neural networks (CNNs) being the most frequently used network architecture and resting-state EEG signals being the most commonly used data type. Additionally, the review reveals that depression (N = 18), Alzheimer's (N = 11), and schizophrenia (N = 11) were studied more frequently than other types of neuropsychiatric disorders. Our review provides insight into the performance of deep neural networks in EEG classification and highlights the importance of EEG feature extraction in improving classification accuracy. By identifying the main factors and parameters that affect deep neural network performance in EEG classification, our review can guide future research in this area. We hope that our findings will encourage further exploration of deep learning methods for EEG classification and contribute to the development of more accurate and effective methods for diagnosing and monitoring neuropsychiatric disorders using EEG signals.

Prediction of response to repetitive transcranial magnetic stimulation for major depressive disorder using hybrid Convolutional recurrent neural networks and raw Electroencephalogram Signal.

Major Depressive Disorder (MDD) is a high prevalence disease that needs an effective and timely treatment to prevent its progress and additional costs. Repetitive Transcranial Magnetic Stimulation (rTMS) is an effective treatment option for MDD patients which uses strong magnetic pulses to stimulate specific regions of the brain. However, some patients do not respond to this treatment which causes the waste of multiple weeks as treatment time and clinical resources. Therefore developing an effective way for the prediction of response to the rTMS treatment of depression is necessary. In this work, we proposed a hybrid model created by pre-trained Convolutional Neural Networks (CNN) models and Bidirectional Long Short-Term Memory (BLSTM) cells to predict response to rTMS treatment from raw EEG signal. Three pre-trained CNN models named VGG16, InceptionResNetV2, and EffecientNetB0 were utilized as Transfer Learning (TL) models to construct hybrid TL-BLSTM models. Then an ensemble of these models was created using weighted majority voting which the weights were optimized by Differential Evolution (DE) optimization algorithm. Evaluation of these models shows the superior performance of the ensemble model by the accuracy of 98.51%, sensitivity of 98.64%, specificity of 98.36%, F1-score of 98.6%, and AUC of 98.5%. Therefore, the ensemble of the proposed hybrid convolutional recurrent networks can efficiently predict the treatment outcome of rTMS using raw EEG data.

A convolutional recurrent neural network with attention for response prediction to repetitive transcranial magnetic stimulation in major depressive disorder.

Prediction of response to Repetitive Transcranial Magnetic Stimulation (rTMS) can build a very effective treatment platform that helps Major Depressive Disorder (MDD) patients to receive timely treatment. We proposed a deep learning model powered up by state-of-the-art methods to classify responders (R) and non-responders (NR) to rTMS treatment. Pre-treatment Electro-Encephalogram (EEG) signal of public TDBRAIN dataset and 46 proprietary MDD subjects were utilized to create time-frequency representations using Continuous Wavelet Transform (CWT) to be fed into the two powerful pre-trained Convolutional Neural Networks (CNN) named VGG16 and EfficientNetB0. Equipping these Transfer Learning (TL) models with Bidirectional Long Short-Term Memory (BLSTM) and attention mechanism for the extraction of most discriminative spatiotemporal features from input images, can lead to superior performance in the prediction of rTMS treatment outcome. Five brain regions named Frontal, Central, Parietal, Temporal, and occipital were assessed and the highest evaluated performance in 46 proprietary MDD subjects was acquired for the Frontal region using the TL-LSTM-Attention model based on EfficientNetB0 with accuracy, sensitivity, specificity, and Area Under the Curve (AUC) of 97.1%, 97.3%, 97.0%, and 0.96 respectively. Additionally, to test the generalizability of the proposed models, these TL-BLSTM-Attention models were evaluated on a public dataset called TDBRAIN and the highest accuracy of 82.3%, the sensitivity of 80.2%, the specificity of 81.9% and the AUC of 0.83 were obtained. Therefore, advanced deep learning methods using a time-frequency representation of EEG signals from the frontal brain region and the convolutional recurrent neural networks equipped with the attention mechanism can construct an accurate platform for the prediction of response to the rTMS treatment.

Potential use of the cholesterol transfer inhibitor U18666A as an antiviral drug for research on various viral infections.

Cholesterol plays critical functions in arranging the biophysical attributes of proteins and lipids in the plasma membrane. For various viruses, an association with cholesterol for virus entrance and/or morphogenesis has been demonstrated. Therefore, the lipid metabolic pathways and the combination of membranes could be targeted to selectively suppress the virus replication steps as a basis for antiviral treatment. U18666A is a cationic amphiphilic drug (CAD) that affects intracellular transport and cholesterol production. A robust tool for investigating lysosomal cholesterol transfer and Ebola virus infection is an androstenolone derived termed U18666A that suppresses three enzymes in the cholesterol biosynthesis mechanism. In addition, U18666A inhibited low-density lipoprotein (LDL)-induced downregulation of LDL receptor and triggered lysosomal aggregation of cholesterol. According to reports, U18666A inhibits the reproduction of baculoviruses, filoviruses, hepatitis, coronaviruses, pseudorabies, HIV, influenza, and flaviviruses, as well as chikungunya and flaviviruses. U18666A-treated viral infections may act as a novel in vitro model system to elucidate the cholesterol mechanism of several viral infections. In this article, we discuss the mechanism and function of U18666A as a potent tool for studying cholesterol mechanisms in various viral infections.

Attention-Based Convolutional Recurrent Deep Neural Networks for the Prediction of Response to Repetitive Transcranial Magnetic Stimulation for Major Depressive Disorder.

Repetitive Transcranial Magnetic Stimulation (rTMS) is proposed as an effective treatment for major depressive disorder (MDD). However, because of the suboptimal treatment outcome of rTMS, the prediction of response to this technique is a crucial task. We developed a deep learning (DL) model to classify responders (R) and non-responders (NR). With this aim, we assessed the pre-treatment EEG signal of 34 MDD patients and extracted effective connectivity (EC) among all electrodes in four frequency bands of EEG signal. Two-dimensional EC maps are put together to create a rich connectivity image and a sequence of these images is fed to the DL model. Then, the DL framework was constructed based on transfer learning (TL) models which are pre-trained convolutional neural networks (CNN) named VGG16, Xception, and EfficientNetB0. Then, long short-term memory (LSTM) cells are equipped with an attention mechanism added on top of TL models to fully exploit the spatiotemporal information of EEG signal. Using leave-one subject out cross validation (LOSO CV), Xception-BLSTM-Attention acquired the highest performance with 98.86% of accuracy and 97.73% of specificity. Fusion of these models as an ensemble model based on optimized majority voting gained 99.32% accuracy and 98.34% of specificity. Therefore, the ensemble of TL-LSTM-Attention models can predict accurately the treatment outcome.

An effective brain connectivity technique to predict repetitive transcranial magnetic stimulation outcome for major depressive disorder patients using EEG signals.

One of the most effective treatments for drug-resistant Major depressive disorder (MDD) patients is repetitive transcranial magnetic stimulation (rTMS). To improve treatment efficacy and reduce health care costs, it is necessary to predict the treatment response. In this study, we intend to predict the rTMS treatment response in MDD patients from electroencephalogram (EEG) signals before starting the treatment using machine learning approaches. Effective brain connectivity of 19-channel EEG data of MDD patients was calculated by the direct directed transfer function (dDTF) method. Then, using three feature selection methods, the best features were selected and patients were classified as responders or non-responders to rTMS treatment by using the support vector machine (SVM). Results on the 34 MDD patients indicated that the Fp2 region in the delta and theta frequency bands has a significant difference between the two groups and can be used as a significant brain biomarker to assess the rTMS treatment response. Also, the highest accuracy (89.6%) using the SVM classifier for the best features of the dDTF method based on the area under the receiver operating characteristic curve (AUC-ROC) criteria was obtained by combining the delta and theta frequency bands. Consequently, the proposed method can accurately detect the rTMS treatment response in MDD patients before starting treatment on the EEG signal to avoid financial and time costs to patients and medical centers.

Remember NIBS? tACS improves memory performance in elders with subjective memory complaints.

Subjective memory complaints (SMC), the main cognitive component of which is event memory, is a predictor of Alzheimer's disease in elderly people. The purpose of this trial was to investigate the effect of transcranial alternating current stimulation (tACS) with theta frequency (6 Hz) on the medial prefrontal cortex (mPFC) in the improvement of episodic memory in individuals with SMC in a double blind, randomized, and sham-controlled parallel study. Sixteen participants with SMC received either active or sham theta tACS on the mPFC. EEG was recorded, and Rey Auditory Verbal Learning Test (RAVLT) was administered. tACS resulted in a significant improvement in episodic memory performance as measured by RAVLT. EEG data revealed a decrease in theta power; decrease in theta, alpha, and gamma current source density (CSD) in the postcentral, insula, and cingulate gyrus; and decrease in theta and gamma phase synchronization as a result of active tACS, compared to the sham group. Moreover, a significant correlation between delayed recall score of RAVLT and CSD in left inferior gyrus in theta frequency band was observed. The results of the current study showed that theta tACS of the mPFC can improve event memory in individuals with SMC through modulating the activity in the frontal and temporal regions in the brain and thus can be considered a potential therapeutic intervention for this population.

Direct and Indirect Measures of Attention Indicating a Bias Toward Cues in Methamphetamine Users.

Introduction: This study aims to investigate the attentional bias toward drug-related stimuli along with subjective craving after encountering such stimuli in methamphetamine users. Studies of cue reactivity have confirmed a bias in attention and gaze toward drug-related stimuli for most substances; however, methamphetamine drugs are less studied through a direct measure, such as eye tracking. Methods: A total of 30 male subjects in the case group (methamphetamine users) and 36 subjects in the control group (no prior drug use) participated in this study. The participant's eye movement data were collected while they were viewing pairs of drug-related and non-drug images in a dot-probe paradigm. Craving was assessed via a self-report questionnaire on a scale of 0 to 10 before and after the psychophysical task. Results: The analysis of eye-movement data showed a meaningful gaze bias toward cue images (drug-related) in the case group. Additionally, the gaze duration on cue images was significantly higher in the case group, in contrast to the control group. The same effect was observed in analyzing the dot-probe task; that is, the mean reaction time to a probe that replaced a cue image was significantly lower. The mean of the first-fixation measure in the control group was not significantly higher than chance; however, the percentage of the first-fixation on cue images in the drug users was meaningfully biased. Reported craving was significantly greater after performing the task compared to before. Conclusion: Our results indicated an attentional bias toward drug-related cues in methamphetamine users as well as subjective craving after encountering such cues. Highlights: The gaze duration on cue images was significantly higher in methamphetamine users.The mean reaction time to a probe that replaced a cue image was significantly lower in methamphetamine users compared to the control group.The mean of the first-fixation measure in the case group was significantly better than chance.Craving was reported to be significantly greater after performing the task. Plain Language Summary: Substance users tend to focus on the stimuli associated with substances. This is known as attention bias. Attention bias leads to increased craving. Attention bias for various substances has been previously reported; however, methamphetamine attention bias has not been evaluated so far. In this study, we measured the attention bias toward stimuli related to methamphetamine in methamphetamine users and control subjects with direct (eye tracking) and indirect (dot probe paradigm) methods. In addition, we measured the number of cravings in the case group. Our results confirmed the bias in attention toward methamphetamine-related stimuli in the case group compared to the control group.

Epidemiological Features of Spontaneous Abortion in the North Africa and the Middle East From 1990 to 2019: Results From the Global Burden of Disease Study 2019.

Objective: Spontaneous abortion is one of the most common problems which a woman may encounter during her pregnancy which is one of the most important causes of maternal death. Therefore, the aim of this study was to report the epidemiological features of spontaneous abortion in North Africa and the Middle East (NAME) countries. Materials and methods: The study population included 21 countries in the NAME region with a population of more than 600 million. The Global Burden of Disease (GBD) 2019 database was used. Incidence rates, the prevalence rates, death rates, the disability adjusted life years (DALYs) rates by age-standardized rate (ASR) per 100,000 people were measured. Also, the attributed burden to iron deficiency was reported. Results: In 2019, the highest prevalence 39.44 (95% CI, 24.58_ 59.26) and incidence 4794.16 (95% CI, 3491.77_ 6353.03) rates of spontaneous abortion were in Afghanistan. In 2019, the highest spontaneous abortion related-death 5.88 (95% CI, 3.23_ 8.97) and DALYs 339.12 (95% CI, 184.29_ 516.95) rates by ASR were in Yemen. In MENA, average prevalence (44.7 to 19.82) and incidence (5434.95 to 2409.61) rates have decreased by nearly 56%, and also average death (1990 4.51 to 2019 0.48) and DALYs (263.15 to 29.37) rates have decreased by nearly 89% between 1990 and 2019. The highest spontaneous abortion-related DALYs rate was attributed to iron deficiency. In 2019, Yemen (29%) had the highest attributed burden to iron deficiency. Conclusion: This study on 21 countries in the NAME region with a population of more than 600 million showed that average prevalence and incidence rates of spontaneous abortion have decreased by nearly 56%, and also average and the disability adjusted life years (DALYs) rates have decreased by nearly 89% between 1990 and 2019.

Extraction of discriminative features from EEG signals of dyslexic children; before and after the treatment.

Dyslexia is a neurological disorder manifested as difficulty reading and writing. It can occur despite adequate instruction, intelligence, and intact sensory abilities. Different electroencephalogram (EEG) patterns have been demonstrated between dyslexic and healthy subjects in previous studies. This study focuses on the difference between patients before and after treatment. The main goal is to identify the subset of features that adequately discriminate subjects before and after a specific treatment plan. The treatment consists of Transcranial Direct Current Stimulation (tDCS) and occupational therapy using the BrainWare SAFARI software. The EEG signals of sixteen dyslexic children were recorded during the eyes-closed resting state before and after treatment. The preprocessing step was followed by the extraction of a wide range of features to investigate the differences related to the treatment. An optimal subset of features extracted from recorded EEG signals was determined using Principal Component Analysis (PCA) in conjunction with the Sequential Floating Forward Selection (SFFS) algorithm. The results showed that treatment leads to significant changes in EEG features like spectral and phase-related EEG features, in various regions. It has been demonstrated that the extracted subset of discriminative features can be useful for classification applications in treatment assessment. The most discriminative subset of features could classify the data with an accuracy of 92% with SVM classifier. The above result confirms the efficacy of the treatment plans in improving dyslexic children's cognitive skills.

The Effect of Thyme Essential Oil on Liver Injuries Caused by Renal Ischemia-Reperfusion in Rats.

Liver damage occurs following renal ischemia-reperfusion (RIR) that can cause inflammation and inflammatory cytokines activated after kidney injury. In this study, thyme essential oil (TE) with antioxidant and anti-inflammatory properties was used to reduce liver damage induced by renal IR. 32 male rats were randomly divided into 4 equal groups: (1) control, (2) RIR, (3) RIR+TE, and (4) TE. Rats received TE as a pretreatment at a dose of 0.5 ml/kg for one week. Then, under anesthesia for 45 minutes for ischemia, the kidneys of the animals were closed with clamps, and reperfusion was performed for 24 hours. Animal serum was isolated to evaluate alkaline phosphatase (ALP), aspartate aminotransferase (AST), and alanine aminotransferase (ALT) parameters. The liver of rats was examined for the measurement of malondialdehyde (MDA), nitric oxide (NO), glutathione (GSH), glutathione peroxidase (GPX), catalase (CAT), and expression of genes such as interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), and caspase-3. ALP, AST, ALT, MDA, NO, IL-6, TNF-α, and caspase-3 increased significantly in the RIR group compared to the control group (p < 0.05). GSH, GPX, and CAT decreased significantly in the RIR group compared to the control group (p < 0.05). TE caused a decrease in ALP, AST, ALT, MDA, NO, IL-6, and TNF-α compared to the RIR group and caused an increase in the amount of GSH, GPX, and CAT in the RIR group (p < 0.05). This study showed that TE has antioxidant and anti-inflammatory properties that reduce liver damage induced by RIR.

Electrophysiological and behavioral effects of unilateral and bilateral rTMS; A randomized clinical trial on rumination and depression.

BACKGROUND: Rumination is significantly frequent in major depressive disorder (MDD). However, not a lot of studies have investigated the effects of repetitive transcranial magnetic stimulation (rTMS) on rumination. METHODS: 61 participants with a minimum Hamilton Depression Rating Scale (HAM-D) score of 20 were randomly assigned to sham, bilateral stimulation (BS) or unilateral stimulation (US) groups. EEG, The Ruminative Response Scale (RRS), and HAM-D were administered before and after the 20 sessions of rTMS. Phase locked values (PLV) were calculated as a measure of connectivity. RESULTS: There was a significant decrease in HAM-D scores in both BS and US. In responders, BS and US differed significantly in RRS total scores, with greater reduction in BS. PLV significantly changed in the default mode network (DMN) in delta, theta, alpha, and beta in BS, in responders of which PLV decreased in the DMN in beta and gamma. Positive correlations between PLV and brooding in delta and theta, and negative correlations between PLV and reflection were found in theta, alpha, and beta. In US, connectivity in the DMN increased in beta, and PLV increased in theta and beta, and decreased in alpha and beta in its responders. Positive correlations between PLV and brooding in the delta and theta, as well as negative correlations between PLV and reflection in theta were observed in the DMN. CONCLUSION: US and BS resulted in different modulations in the DMN, however, both could alleviate both rumination and depression. Reductions in the beta and alpha frequency bands in the DMN can be considered as potential EEG-based markers of response to bilateral and unilateral rTMS, respectively.

Cold Cognition as Predictor of Treatment Response to rTMS; A Retrospective Study on Patients With Unipolar and Bipolar Depression.

Background: Cognitive impairments are prevalent in patients with unipolar and bipolar depressive disorder (UDD and BDD, respectively). Considering the fact assessing cognitive functions is increasingly feasible for clinicians and researchers, targeting these problems in treatment and using them at baseline as predictors of response to treatment can be very informative. Method: In a naturalistic, retrospective study, data from 120 patients (Mean age: 33.58) with UDD (n = 56) and BDD (n = 64) were analyzed. Patients received 20 sessions of bilateral rTMS (10 Hz over LDLPFC and 1 HZ over RDLPFC) and were assessed regarding their depressive symptoms, sustained attention, working memory, and executive functions, using the Beck Depression Inventory (BDI-II) and Neuropsychological Test Automated Battery Cambridge, at baseline and after the end of rTMS treatment course. Generalized estimating equations (GEE) and logistic regression were used as the main statistical methods to test the hypotheses. Results: Fifty-three percentage of all patients (n = 64) responded to treatment. In particular, 53.1% of UDD patients (n = 34) and 46.9% of BDD patients (n = 30) responded to treatment. Bilateral rTMS improved all cognitive functions (attention, working memory, and executive function) except for visual memory and resulted in more modulations in the working memory of UDD compared to BDD patients. More improvements in working memory were observed in responded patients and visual memory, age, and sex were determined as treatment response predictors. Working memory, visual memory, and age were identified as treatment response predictors in BDD and UDD patients, respectively. Conclusion: Bilateral rTMS improved cold cognition and depressive symptoms in UDD and BDD patients, possibly by altering cognitive control mechanisms (top-down), and processing negative emotional bias.

Epidemiological status of type 2 diabetes mellitus in the Middle East and North Africa, 1990-2019.

Background: Type 2 diabetes mellitus (T2D) is associated with various complications and imposes significant economic pressures. Aims: The aim of this study was to determine the epidemiological status and the burden of T2D in the Middle East and North Africa (MENA) countries during 1990-2019; to inform targeting of prevention strategies. Methods: The study population included 21 countries, covering a population of about 400 million. The global burden of disease 2019 database was used. Disability-adjusted life years (DALYs) were computed by summing up the years of life lost and the years lived with disability. Prevalence, incidence, death rates and DALY rates per 100 000 people for all locations by age-standardized rates were calculated. Results: In 2019, Qatar had the highest prevalence [16312.4; 95% unit interval (UI): 15050.0-17723.2] and incidence rates (818.0; 95% UI: 773.9-868.7) of T2D Bahrain had the highest death (127.0; 95% UI: 102.5-154.6) and DALYs (3232.5; 95% UI: 2622.4-3929.3) rates In the MENA area, average DALY rates increased by nearly 31% (808.3 to 1060.8) and average death rates increased by 0.2% (24.8 to 25.2) during 1990-2019. The highest increase for T2D-related DALYs (516.5 to 958.1; 85%) and the highest increase for T2D-related deaths (12.5 to 22.0; 76%) was in the Islamic Republic of Iran. Conclusion: Prevalence, incidence, deaths and DALYs rates for T2D have continued to increase in most of the MENA countries. Health care systems must make efforts to control modifiable risk factors.

Impact of methodological variability on EEG responses evoked by transcranial magnetic stimulation: A meta-analysis.

OBJECTIVE: This meta-analysis aimed to synthesize the existing literature on how different parameters of transcranial magnetic stimulation (TMS) and electroencephalogram (EEG) modulate the amplitudes of TMS-evoked potentials (TEPs). METHODS: A comprehensive search was run in PubMed and completed by Google Scholar to find articles studying healthy participants who underwent single pulse TMS-EEG sessions over their left primary motor cortex (M1) or dorsolateral prefrontal cortex (DLPFC). The amplitudes of the most commonly investigated TEP peaks for DLPFC stimulation (positives: 25, 60, 185 ms, negatives: 40, 100 ms) and M1 stimulation (positives: 30, 55,180 ms and negatives: 15, 45, 100, 280 ms) were extracted from studies. RESULTS: Cohen's d effect sizes were obtained in five independent categories that were stratified based on the stimulation, recording, and analyzing parameters. The overall effect sizes and equivalent means and standard deviations were computed within every category. CONCLUSIONS: This meta-analysis spotlights the need to rigorously and systematically control for the critical parameters in recording and analyzing TMS-EEG data to make the outcomes of further studies more comparable to the current body of literature. SIGNIFICANCE: The study demonstrates the possibility of reliably measuring TEPs by offering approximate ranges for every TEP peak in the most commonly targeted areas of DLPFC and M1.