Automatic diagnosis of schizophrenia and attention deficit hyperactivity disorder in rs-fMRI modality using convolutional autoencoder model and interval type-2 fuzzy regression.

Nowadays, many people worldwide suffer from brain disorders, and their health is in danger. So far, numerous methods have been proposed for the diagnosis of Schizophrenia (SZ) and attention deficit hyperactivity disorder (ADHD), among which functional magnetic resonance imaging (fMRI) modalities are known as a popular method among physicians. This paper presents an SZ and ADHD intelligent detection method of resting-state fMRI (rs-fMRI) modality using a new deep learning method. The University of California Los Angeles dataset, which contains the rs-fMRI modalities of SZ and ADHD patients, has been used for experiments. The FMRIB software library toolbox first performed preprocessing on rs-fMRI data. Then, a convolutional Autoencoder model with the proposed number of layers is used to extract features from rs-fMRI data. In the classification step, a new fuzzy method called interval type-2 fuzzy regression (IT2FR) is introduced and then optimized by genetic algorithm, particle swarm optimization, and gray wolf optimization (GWO) techniques. Also, the results of IT2FR methods are compared with multilayer perceptron, k-nearest neighbors, support vector machine, random forest, and decision tree, and adaptive neuro-fuzzy inference system methods. The experiment results show that the IT2FR method with the GWO optimization algorithm has achieved satisfactory results compared to other classifier methods. Finally, the proposed classification technique was able to provide 72.71% accuracy.

A sex-dependent computer-aided diagnosis system for autism spectrum disorder using connectivity of resting-state fMRI.

Objective.Autism spectrum disorder (ASD) is a prevalent neurodevelopmental disorder with the main symptoms of social communication disabilities. ASD is more than four times more common among males than females. The diagnosis of ASD is currently a subjective process by experts the same for males and females. Various studies have suggested the use of brain connectivity features for the diagnosis of ASD. Also, sex-related biological factors have been shown to play a role in ASD etiology and influence the brain connectivity. Therefore, proposing an accurate computer-aided diagnosis system (CADS) for ASD which considers the sex of subjects seems necessary. In this study, we present a sex-dependent connectivity-based CADS for ASD using resting-state functional magnetic resonance imaging. The proposed CADS classifies ASD males from normal males, and ASD females from normal females.Approach.After data preprocessing, group independent component analysis (GICA) was applied to obtain the resting-state networks (RSNs) followed by applying dual-regression to obtain the time course of each RSN for each subject. Afterwards, functional connectivity measures of full correlation and partial correlation and the effective connectivity measure of bivariate Granger causality were computed between time series of RSNs. To consider the role of sex differences in the classification process, male, female, and mixed groups were taken into account, and feature selection and classification were designed for each sex group separately. At the end, the classification accuracy was computed for each sex group.Main results.In the female group, a classification accuracy of 93.3% was obtained using full correlation while in the male group, a classification accuracy of 86.7% was achieved using both full correlation and bivariate Granger causality. Also, in the mixed group, a classification accuracy of 83.3% was obtained using full correlation.Significance.This supports the importance of considering sex in diagnosing ASD patients from normal controls.

An overview of artificial intelligence techniques for diagnosis of Schizophrenia based on magnetic resonance imaging modalities: Methods, challenges, and future works.

Schizophrenia (SZ) is a mental disorder that typically emerges in late adolescence or early adulthood. It reduces the life expectancy of patients by 15 years. Abnormal behavior, perception of emotions, social relationships, and reality perception are among its most significant symptoms. Past studies have revealed that SZ affects the temporal and anterior lobes of hippocampus regions of the brain. Also, increased volume of cerebrospinal fluid (CSF) and decreased volume of white and gray matter can be observed due to this disease. Magnetic resonance imaging (MRI) is the popular neuroimaging technique used to explore structural/functional brain abnormalities in SZ disorder, owing to its high spatial resolution. Various artificial intelligence (AI) techniques have been employed with advanced image/signal processing methods to accurately diagnose SZ. This paper presents a comprehensive overview of studies conducted on the automated diagnosis of SZ using MRI modalities. First, an AI-based computer aided-diagnosis system (CADS) for SZ diagnosis and its relevant sections are presented. Then, this section introduces the most important conventional machine learning (ML) and deep learning (DL) techniques in the diagnosis of diagnosing SZ. A comprehensive comparison is also made between ML and DL studies in the discussion section. In the following, the most important challenges in diagnosing SZ are addressed. Future works in diagnosing SZ using AI techniques and MRI modalities are recommended in another section. Results, conclusion, and research findings are also presented at the end.

Normal development of the brain: a survey of joint structural-functional brain studies.

Joint structural-functional (S-F) developmental studies present a novel approach to address the complex neuroscience questions on how the human brain works and how it matures. Joint S-F biomarkers have the inherent potential to model effectively the brain's maturation, fill the information gap in temporal brain atlases, and demonstrate how the brain's performance matures during the lifespan. This review presents the current state of knowledge on heterochronous and heterogeneous development of S-F links during the maturation period. The S-F relationship has been investigated in early-matured unimodal and prolonged-matured transmodal regions of the brain using a variety of structural and functional biomarkers and data acquisition modalities. Joint S-F unimodal studies have employed auditory and visual stimuli, while the main focus of joint S-F transmodal studies has been resting-state and cognitive experiments. However, nonsignificant associations between some structural and functional biomarkers and their maturation show that designing and developing effective S-F biomarkers is still a challenge in the field. Maturational characteristics of brain asymmetries have been poorly investigated by the joint S-F studies, and the results were partially inconsistent with previous nonjoint ones. The inherent complexity of the brain performance can be modeled using multifactorial and nonlinear techniques as promising methods to simulate the impact of age on S-F relations considering their analysis challenges.

Relationship between type 2 cytokine and inflammasome responses in obesity-associated asthma.

BACKGROUND: Obesity is a risk factor for asthma, and obese asthmatic individuals are more likely to have severe, steroid-insensitive disease. How obesity affects the pathogenesis and severity of asthma is poorly understood. Roles for increased inflammasome-mediated neutrophilic responses, type 2 immunity, and eosinophilic inflammation have been described. OBJECTIVE: We investigated how obesity affects the pathogenesis and severity of asthma and identified effective therapies for obesity-associated disease. METHODS: We assessed associations between body mass index and inflammasome responses with type 2 (T2) immune responses in the sputum of 25 subjects with asthma. Functional roles for NLR family, pyrin domain-containing (NLRP) 3 inflammasome and T2 cytokine responses in driving key features of disease were examined in experimental high-fat diet-induced obesity and asthma. RESULTS: Body mass index and inflammasome responses positively correlated with increased IL-5 and IL-13 expression as well as C-C chemokine receptor type 3 expression in the sputum of subjects with asthma. High-fat diet-induced obesity resulted in steroid-insensitive airway hyperresponsiveness in both the presence and absence of experimental asthma. High-fat diet-induced obesity was also associated with increased NLRP3 inflammasome responses and eosinophilic inflammation in airway tissue, but not lumen, in experimental asthma. Inhibition of NLRP3 inflammasome responses reduced steroid-insensitive airway hyperresponsiveness but had no effect on IL-5 or IL-13 responses in experimental asthma. Depletion of IL-5 and IL-13 reduced obesity-induced NLRP3 inflammasome responses and steroid-insensitive airway hyperresponsiveness in experimental asthma. CONCLUSION: We found a relationship between T2 cytokine and NLRP3 inflammasome responses in obesity-associated asthma, highlighting the potential utility of T2 cytokine-targeted biologics and inflammasome inhibitors.

Automatic Diagnosis of Schizophrenia in EEG Signals Using CNN-LSTM Models.

Schizophrenia (SZ) is a mental disorder whereby due to the secretion of specific chemicals in the brain, the function of some brain regions is out of balance, leading to the lack of coordination between thoughts, actions, and emotions. This study provides various intelligent deep learning (DL)-based methods for automated SZ diagnosis via electroencephalography (EEG) signals. The obtained results are compared with those of conventional intelligent methods. To implement the proposed methods, the dataset of the Institute of Psychiatry and Neurology in Warsaw, Poland, has been used. First, EEG signals were divided into 25 s time frames and then were normalized by z-score or norm L2. In the classification step, two different approaches were considered for SZ diagnosis via EEG signals. In this step, the classification of EEG signals was first carried out by conventional machine learning methods, e.g., support vector machine, k-nearest neighbors, decision tree, naïve Bayes, random forest, extremely randomized trees, and bagging. Various proposed DL models, namely, long short-term memories (LSTMs), one-dimensional convolutional networks (1D-CNNs), and 1D-CNN-LSTMs, were used in the following. In this step, the DL models were implemented and compared with different activation functions. Among the proposed DL models, the CNN-LSTM architecture has had the best performance. In this architecture, the ReLU activation function with the z-score and L2-combined normalization was used. The proposed CNN-LSTM model has achieved an accuracy percentage of 99.25%, better than the results of most former studies in this field. It is worth mentioning that to perform all simulations, the k-fold cross-validation method with k = 5 has been used.

Deep learning for neuroimaging-based diagnosis and rehabilitation of Autism Spectrum Disorder: A review.

Accurate diagnosis of Autism Spectrum Disorder (ASD) followed by effective rehabilitation is essential for the management of this disorder. Artificial intelligence (AI) techniques can aid physicians to apply automatic diagnosis and rehabilitation procedures. AI techniques comprise traditional machine learning (ML) approaches and deep learning (DL) techniques. Conventional ML methods employ various feature extraction and classification techniques, but in DL, the process of feature extraction and classification is accomplished intelligently and integrally. DL methods for diagnosis of ASD have been focused on neuroimaging-based approaches. Neuroimaging techniques are non-invasive disease markers potentially useful for ASD diagnosis. Structural and functional neuroimaging techniques provide physicians substantial information about the structure (anatomy and structural connectivity) and function (activity and functional connectivity) of the brain. Due to the intricate structure and function of the brain, proposing optimum procedures for ASD diagnosis with neuroimaging data without exploiting powerful AI techniques like DL may be challenging. In this paper, studies conducted with the aid of DL networks to distinguish ASD are investigated. Rehabilitation tools provided for supporting ASD patients utilizing DL networks are also assessed. Finally, we will present important challenges in the automated detection and rehabilitation of ASD and propose some future works.

Functional Networks Abnormalities in Autism Spectrum Disorder: Age-Related Hypo and Hyper Connectivity.

Autism spectrum disorder (ASD) is a developmental disorder characterized by defects in social interaction. The past functional connectivity studies using resting-state fMRI have found both patterns of hypo-connectivity and hyper-connectivity in ASD and proposed the age as an important factor on functional connectivity disorders. However, this influence is not clearly characterized yet. Previous studies have often examined the functional connectivity disorders in particular brain regions in an age group or a mixture of age groups. The present study compares whole-brain within-connectivity and between-connectivity between ASD individuals and typically developing (TD) controls in three age groups including children (< 11 years), adolescents (11-18 years), and adults (> 18 years), each comprising 21 ASD individuals and 21 TD controls. The age groups were matched for age, Full IQ, and gender. Independent component analysis and dual regression were used to investigate within-connectivity. The full and partial correlations between ICs were used to investigate between-connectivity. Examination of the within-connectivity showed hyper-connectivity, especially in cerebellum and brainstem in ASD children but both hyper/hypo connectivity in adolescents and ASD adults. In ASD children, difference in the between-connectivity among default mode network (DMN), salience-executive network and fronto-parietal network were observed. There was also a negative correlation between DMN and temporal network. Full correlation comparison between ASD adolescents and TD individuals showed significant differences between cerebellum and DMN. Our results supported just the hyper-connectivity in childhood, but both hypo and hyper-connectivity after childhood and hypothesized that abnormal resting connections in ASD exist in the regions of the brain known to be involved in social cognition.

Real diesel engine exhaust emission control: indirect non-thermal plasma and comparison to direct plasma for NOX, THC, CO, and CO2.

Recently, diesel engine exhaust emission control by non-thermal plasma (NTP) technology has been shown to be promising. However, carbon and soot deposition on the inner surface of the NTP reactor for direct plasma processing decreased the efficiency of the plasma process throughout the experiments. In the present work, the feasibility of indirect plasma processing was investigated as an innovative and novel method compared to direct plasma processing. Air was directed through an NTP at an applied voltage of VP-P = 7 kV and a flow rate of 1-4 L/min, and then, it was combined with engine exhaust gas at a flow rate of 5 L/min. In this case, the maximum conversion of NOX was 64.9% at 4 L/min. However, for direct plasma processing at 5 L/min, NO conversion was 58%, which proves that the indirect NTP process can decrease NOX concentration effectively. The maximum conversion for unburned hydrocarbon (UHC), carbon monoxide (CO) and carbon dioxide (CO2) was obtained as 2%, 4% and 0.7% at 4, 2 and 3 L/min in indirect plasma processing; While their remove rate for direct plasma processing was 16.3%, -0.5% and 13.2%, respectively.

Improved dynamic connection detection power in estimated dynamic functional connectivity considering multivariate dependencies between brain regions.

To estimate dynamic functional connectivity (dFC), the conventional method of sliding window correlation (SWC) suffers from poor performance of dynamic connection detection. This stems from the equal weighting of observations, suboptimal time scale, nonsparse output, and the fact that it is bivariate. To overcome these limitations, we exploited the kernel-reweighted logistic regression (KELLER) algorithm, a method that is common in genetic studies, to estimate dFC in resting state functional magnetic resonance imaging (rs-fMRI) data. KELLER can estimate dFC through estimating both spatial and temporal patterns of functional connectivity between brain regions. This paper compares the performance of the proposed KELLER method with current methods (SWC and tapered-SWC (T-SWC) with different window lengths) based on both simulated and real rs-fMRI data. Estimated dFC networks were assessed for detecting dynamically connected brain region pairs with hypothesis testing. Simulation results revealed that KELLER can detect dynamic connections with a statistical power of 87.35% compared with 70.17% and 58.54% associated with T-SWC (p-value = .001) and SWC (p-value <.001), respectively. Results of these different methods applied on real rs-fMRI data were investigated for two aspects: calculating the similarity between identified mean dynamic pattern and identifying dynamic pattern in default mode network (DMN). In 68% of subjects, the results of T-SWC with window length of 100 s, among different window lengths, demonstrated the highest similarity to those of KELLER. With regards to DMN, KELLER estimated previously reported dynamic connection pairs between dorsal and ventral DMN while SWC-based method was unable to detect these dynamic connections.

A cracking complication: Displacement of existing clavicle fracture complicating subcutaneous implantable cardioverter defibrillator implantation.

An 18 year old male with an incompletely healed clavicle fracture presented with unexplained syncope. Subsequent investigations were consistent with a diagnosis of arrhythmogenic right ventricular cardiomyopathy (ARVC). A subcutaneous implantable cardioverter-defibrillator (S-ICD) was successfully implanted and defibrillation threshold (DFT) testing performed as per standard protocol. Shortly following the procedure, the patient complained of pain and swelling over the left clavicle. A radiograph revealed aggravation and displacement of the underlying clavicle fracture. Surgical reduction and internal fixation was performed one week later.

Encapsulation of soybean meal with fats enriched in palmitic and stearic acids: effects on rumen-undegraded protein and in vitro intestinal digestibility.

Fat coating of soybean meal (SBM) can reduce its protein degradability in the rumen, but the encapsulation of SBM with palmitic (PA) and stearic acids (SA) has not yet been investigated, despite both fatty acids are common energy sources in dairy cow diets. This study aimed to evaluate the effects of applying a novel method, using either 400 or 500 g fat/kg (treatments FL40 and FL50, respectively), which was enriched in PA and SA at different ratios (100:0, 75:25, 50:50, 25:75 and 0:100), on physical and chemical characteristics, ruminal degradability, solubility and in vitro intestinal protein digestibility (IVIPD) of the obtained products. Encapsulation of SBM in fat resulted in greater mean particle size and lower bulk density and protein solubility than unprotected SBM (USBM). Treatment FL50 resulted in increased (p < 0.01) rumen-undegraded protein (RUP) compared to USBM. There were no differences in RUP of SBM when different PA: SA ratios were used. The mean RUP content of treatments FL40 and FL50 (306 and 349 g/kg, respectively) was greater compared to USBM (262 g/kg, p < 0.05), but lower than that for a standard heat-treated SBM (431 g/kg). Values of IVIPD did not differ among SBM, heat-treated SBM and FL40 and FL50 samples, all being greater than 97.8%. In conclusion, encapsulation of SBM with fats enriched in PA and SA proved to be effective in reducing protein solubility and increasing RUP without depressing protein digestibility in the intestine. For validation of the method, in vivo research to investigate the effects of these products on the production of dairy cows is warranted.

Assessing the protective effect of bentonite against lead toxicity in growing lambs.

The objective of the present experiment was to investigate the effect of bentonite supplementation in lead (Pb)-exposed lambs on serum Pb, Ca, P, Cu, Zn, and Fe concentrations, blood hematological parameters, and hepatic enzymes. Twenty Zandi male lambs (initial BW, 17.5 ± 1.6 kg) were randomly assigned to one of the four treatments: (1) control (no Pb or bentonite), (2) 15 mg/kg DM Pb as Pb acetate with no bentonite, (3) 15 mg/kg DM Pb as Pb acetate with 1.5% bentonite, and (4) 15 mg/kg DM Pb as Pb acetate with 3% bentonite. The experiment lasted after 90 days. Lead intake resulted in a decrease (P < 0.05) in serum Fe and an increase in serum Pb, aspartate aminotransferase (AST), and alanine aminotransferase (ALT) activities (P < 0.05). Bentonite supplementation at 1.5 or 3% of DM decreased blood Pb concentration (P < 0.01) in lambs fed diets containing Pb and reduced (P < 0.05) blood concentration of Cu and Zn compared to control group (P < 0.01). However, the hematological parameters were not affected by any of the treatments. Our results showed that the dietary supplementation of bentonite could protect lambs against lead toxicity.

Linolenic acid improves oocyte developmental competence and decreases apoptosis of in vitro-produced blastocysts in goat.

The effects of α-linolenic acid (ALA) on developmental competence of oocytes in goats were evaluated in this study. Initially, the level of ALA in small and large antral follicles was determined to be in a range of 0.018-0.028 mg/ml (64.6-100.6 µM, respectively). In vitro maturation was performed in the presence of various concentrations (10, 50, 100, or 200 µM) of ALA. Cumulus expansion, meiotic maturation, levels of intracellular glutathione (GSH), embryonic cleavage, blastocyst formation following parthenogenetic activation (PA) and in vitro fertilization (IVF), number of total and apoptotic cells in blastocyst, and expression of Bax, Bcl-2, and p53 genes in blastocyst cells were determined. Compared with the control, no improvement was observed in cumulus expansion in ALA-treated groups. At 50 µM concentration, ALA increased meiotic maturation rate but had no effect on GSH level. When oocytes treated with 50 µM ALA were subsequently used for PA or IVF, a higher rate of blastocyst formation was observed, and these embryos had a higher total cell number and a lower apoptotic cell number. Expression analyses of genes in blastocysts revealed lesser transcript abundances for Bax gene, and higher transcript abundances for Bcl-2 gene in 50 µM ALA group. Expression of p53 gene was also less observed in ALA-treated blastocysts. Our results show that ALA treatment at 50 µM during in vitro maturation (IVM) had a beneficial effect on maturation of goat oocytes and this, in turn, stimulated embryonic development and regulated apoptotic gene expression.

Long-Range Reduced Predictive Information Transfers of Autistic Youths in EEG Sensor-Space During Face Processing.

The majority of previous functional/effective connectivity studies conducted on the autistic patients converged to the underconnectivity theory of ASD: "long-range underconnectivity and sometimes short-rang overconnectivity". However, to the best of our knowledge the total (linear and nonlinear) predictive information transfers (PITs) of autistic patients have not been investigated yet. Also, EEG data have rarely been used for exploring the information processing deficits in autistic subjects. This study is aimed at comparing the total (linear and nonlinear) PITs of autistic and typically developing healthy youths during human face processing by using EEG data. The ERPs of 12 autistic youths and 19 age-matched healthy control (HC) subjects were recorded while they were watching upright and inverted human face images. The PITs among EEG channels were quantified using two measures separately: transfer entropy with self-prediction optimality (TESPO), and modified transfer entropy with self-prediction optimality (MTESPO). Afterwards, the directed differential connectivity graphs (dDCGs) were constructed to characterize the significant changes in the estimated PITs of autistic subjects compared with HC ones. By using both TESPO and MTESPO, long-range reduction of PITs of ASD group during face processing was revealed (particularly from frontal channels to right temporal channels). Also, it seemed the orientation of face images (upright or upside down) did not modulate the binary pattern of PIT-based dDCGs, significantly. Moreover, compared with TESPO, the results of MTESPO were more compatible with the underconnectivity theory of ASD in the sense that MTESPO showed no long-range increase in PIT. It is also noteworthy that to the best of our knowledge it is the first time that a version of MTE is applied for patients (here ASD) and it is also its first use for EEG data analysis.

Effect of Linolenic acid during in vitro maturation of ovine oocytes: embryonic developmental potential and mRNA abundances of genes involved in apoptosis.

PURPOSE: To study the effect of α-linolenic acid (ALA) on meiotic maturation, mRNA abundance of apoptosis-related (Bax and Bcl-2) molecules, and blastocyst formation in ovine oocytes. METHODS: A preliminary experiment was conducted to analyze the concentration of ALA in "small" (≤2 mm) and "large" (≥6 mm) follicles using gas chromatography/mass spectrometry analysis. The concentration of ALA in small and large follicles was determined to be in a range of 75.4 to 125.7 µM, respectively. In vitro maturation (IVM) of oocyte was then performed in presence of 0 (control), 10 (ALA-10), 50 (ALA-50), 100 (ALA-100), and 200 (ALA-200) µM of ALA. Meiotic maturation and mRNA abundance of Bax, and Bcl-2 genes was evaluated after 24 h of IVM. The embryonic cleavage and blastocyst formation following parthenogenetic activation were also determined for each group. RESULTS: The highest concentration of ALA (ALA-200) decreased the oocyte maturation rate compared with the control group. Analysis of apoptosis-related genes in oocytes after IVM revealed lesser transcript abundances for Bax gene, and higher transcript abundances for Bcl-2 gene in ALA-treated oocytes as compared with the control oocytes. In term of cleavage rate (considered as 2-cell progression), we did not observe any differences among the groups. However, ALA-100 group promoted more blastocyst formation as compared with the control group. CONCLUSION: Our results suggested that ALA treatment during IVM had a beneficial effect on developmental competence of ovine oocytes by increasing the blastocyst formation and this might be due to the altered abundance of apoptosis-regulatory genes.

Quantification of the effects of volume conduction on the EEG/MEG connectivity estimates: an index of sensitivity to brain interactions.

In the context of EEG/MEG, the term 'volume conduction (VC) effects' refers to the recording of an instantaneous linear mixture of multiple brain source activities by each EEG/MEG channel. VC effects may lead to the detection of spurious functional/effective couplings among EEG/MEG channels that are not caused by brain interactions. It is of importance to determine which detected couplings are indicators of brain interactions and which originate from the VC artefacts. In this paper, a quantitative framework is proposed to explore the origin of detected channel couplings by using two types of surrogate datasets. Also, a sensitivity index (called SI) is proposed to compare the power of different connectivity measures to discriminate between the brain interactions and the instantaneous linear mixing effects. We use seven different functional connectivity estimators to evaluate our method on simulation models and resting state EEG data. The error rate of the proposed framework for simulation data by using each of the connectivity estimators is less than 5.2%. Also, SI ranks these connectivity estimators according to their sensitivity to brain interactions in the presence of VC artefacts. As expected, the connectivity measures which are theoretically robust to VC artefacts yield high SI in simulation models and EEG data. In addition, for EEG data in the alpha frequency band the reproducible functional couplings which are indicators of brain interactions are in the back-front directions. This is consistent with the previous studies in this field.

Estimation of direct nonlinear effective connectivity using information theory and multilayer perceptron.

BACKGROUND: Despite the variety of effective connectivity measures, few methods can quantify direct nonlinear causal couplings and most of them are not applicable to high-dimensional datasets. NEW METHOD: In this paper, a novel approach (called ßmRMR-MLP-GC) is proposed to estimate direct nonlinear effective connectivity of high-dimensional datasets. ßmRMR is used to select a suitable subset of candidate regressors for approximating each neural (here EEG) signal. The multilayer perceptron (MLP) is used for multivariate characterization of EEG signals while the optimum MLP structure is selected using an iterative cross-validation scheme. Finally a causality measure is defined based on Granger Causality (GC) concept to quantify the casual relations among EEG channels. RESULTS: Applying ßmRMR-MLP-GC to high-dimensional simulated datasets with different linear and nonlinear structures yields sensitivity and specificity values higher than 95%. Also, applying it to eyes-closed resting state EEG of six normal subjects in the alpha frequency band yields significant net activity propagations from the posterior to anterior brain regions. This is in accordance with the most previous studies in this field. COMPARISON WITH EXISTING METHOD(S): ßmRMR-MLP-GC is compared with Granger Causality Index, Conditional Granger Causality Index, and Transfer Entropy. It outperforms these methods in terms of sensitivity and specificity in simulated datasets. Also, ßmRMR-MLP-GC detects the most number of significant and reproducible Back-to-Front net information flows among the specified brain regions and highlights the posterior brain regions as dominant source of alpha activity propagation. CONCLUSIONS: ßmRMR-MLP-GC provides a novel tool to estimate the direct nonlinear causal networks of high-dimensional datasets.

Antioxidant, antimicrobial, cytotoxic and analgesic activities of ethanolic extract of Mentha arvensis L

Objective: To investigate potential antioxidant, antimicrobial, cytotoxic and analgesic activities of ethanolic extract of Mentha arvensis L. in different in vivo and in vitro experimental models. Methods: In vitro DPPH radical scavenging assay was used to evaluate the antioxidant activity of the plant extract. In vivo analgesic activity was carried out by acetic acid-induced writhing test in Swiss albino mice. All studies in mice were undertaken at the doses of 250 and 500 mg/kg body weight. Antibacterial activity was studied by disk diffusion assay against some Gram-positive and Gram-negative bacterial strains. Brine shrimp lethality assay was used to investigate cytotoxicity effects of the plant extract. Results: The extract showed free radical scavenging activity in the DPPH assay (IC

Cardiac lipofibromatosis.

We present a case of cardiac lipofibromatosis associated with atrial fibrillation and complete heart block requiring permanent pacemaker implantation. Multimodality cardiac imaging including transthoracic echocardiography and cardiac magnetic resonance were useful for tissue characterization of this rare cardiac diagnosis.