Generalized models for estimating cerebral lateralisation of young children using functional transcranial Doppler ultrasound.

Thompson et al., 2023 (Generalized models for quantifying laterality using functional transcranial Doppler ultrasound. Human Brain Mapping, 44(1), 35-48) introduced generalised model-based analysis methods for determining cerebral lateralisation from functional transcranial Doppler ultrasound (fTCD) data which substantially decreased the uncertainty of individual lateralisation estimates across several large adult samples. We aimed to assess the suitability of these methods for increasing precision in lateralisation estimates for child fTCD data. We applied these methods to adult fTCD data to establish the validity of two child-friendly language and visuospatial tasks. We also applied the methods to fTCD data from 4- to 7-year-old children. For both samples, the laterality estimates from the complex generalised additive model (GAM) approach correlated strongly with the traditional methods while also decreasing individual standard errors compared to the popular period-of-interest averaging method. We recommend future research using fTCD with young children consider using GAMs to reduce the noise in their LI estimates.

Generalized models for quantifying laterality using functional transcranial Doppler ultrasound.

We consider how analysis of brain lateralization using functional transcranial Doppler ultrasound (fTCD) data can be brought in line with modern statistical methods typically used in functional magnetic resonance imaging (fMRI). Conventionally, a laterality index is computed in fTCD from the difference between the averages of each hemisphere's signal within a period of interest (POI) over a series of trials. We demonstrate use of generalized linear models (GLMs) and generalized additive models (GAM) to analyze data from individual participants in three published studies (N = 154, 73 and 31), and compare this with results from the conventional POI averaging approach, and with laterality assessed using fMRI (N = 31). The GLM approach was based on classic fMRI analysis that includes a hemodynamic response function as a predictor; the GAM approach estimated the response function from the data, including a term for time relative to epoch start (simple GAM), plus a categorical index corresponding to individual epochs (complex GAM). Individual estimates of the fTCD laterality index are similar across all methods, but error of measurement is lowest using complex GAM. Reliable identification of cases of bilateral language appears to be more accurate with complex GAM. We also show that the GAM-based approach can be used to efficiently analyze more complex designs that incorporate interactions between tasks.

Identification and utilization of copy number information for correcting Hi-C contact map of cancer cell lines.

BACKGROUND: Hi-C and its variant techniques have been developed to capture the spatial organization of chromatin. Normalization of Hi-C contact map is essential for accurate modeling and interpretation of high-throughput chromatin conformation capture (3C) experiments. Hi-C correction tools were originally developed to normalize systematic biases of karyotypically normal cell lines. However, a vast majority of available Hi-C datasets are derived from cancer cell lines that carry multi-level DNA copy number variations (CNVs). CNV regions display over- or under-representation of interaction frequencies compared to CN-neutral regions. Therefore, it is necessary to remove CNV-driven bias from chromatin interaction data of cancer cell lines to generate a euploid-equivalent contact map. RESULTS: We developed the HiCNAtra framework to compute high-resolution CNV profiles from Hi-C or 3C-seq data of cancer cell lines and to correct chromatin contact maps from systematic biases including CNV-associated bias. First, we introduce a novel 'entire-fragment' counting method for better estimation of the read depth (RD) signal from Hi-C reads that recapitulates the whole-genome sequencing (WGS)-derived coverage signal. Second, HiCNAtra employs a multimodal-based hierarchical CNV calling approach, which outperformed OneD and HiNT tools, to accurately identify CNVs of cancer cell lines. Third, incorporating CNV information with other systematic biases, HiCNAtra simultaneously estimates the contribution of each bias and explicitly corrects the interaction matrix using Poisson regression. HiCNAtra normalization abolishes CNV-induced artifacts from the contact map generating a heatmap with homogeneous signal. When benchmarked against OneD, CAIC, and ICE methods using MCF7 cancer cell line, HiCNAtra-corrected heatmap achieves the least 1D signal variation without deforming the inherent chromatin interaction signal. Additionally, HiCNAtra-corrected contact frequencies have minimum correlations with each of the systematic bias sources compared to OneD's explicit method. Visual inspection of CNV profiles and contact maps of cancer cell lines reveals that HiCNAtra is the most robust Hi-C correction tool for ameliorating CNV-induced bias. CONCLUSIONS: HiCNAtra is a Hi-C-based computational tool that provides an analytical and visualization framework for DNA copy number profiling and chromatin contact map correction of karyotypically abnormal cell lines. HiCNAtra is an open-source software implemented in MATLAB and is available at https://github.com/AISKhalil/HiCNAtra .

Modeling seasonal variations of long-term soil CO2 emissions in an orchard plantation in a semiarid area, SE Turkey.

Emissions of soil CO2 under different management systems have a significant effect on the carbon balance in the atmosphere. Soil CO2 emissions were measured from an apricot orchard at two different locations: under the crown of trees (CO2-UC) and between tree rows (CO2-BR). For comparison, one other measurement was performed on bare soil (CO2-BS) located next to the orchard field. Analytical data were obtained weekly during 8 years from April 2008 to December 2016. Various environmental parameters such as air temperature, soil temperature at different depths, soil moisture, rainfall, and relative humidity were used for modeling and estimating the long-term seasonal variations in soil CO2 emissions using two different methods: generalized linear model (GLM) and artificial neural network (ANN). Before modeling, data were randomly split into two parts, one for calibration and the second for validation, with a varying number of samples in each part. Performances of the models were compared and evaluated using means absolute of estimations (MAE), square root of mean of prediction (RMSEP), and coefficient of determination (R2) values. CO2-UC, CO2-BR, and CO2-BS values ranged from 11 to 3985, from 9 to 2365, and from 8 to 1722 kg ha-1 week-1, respectively. Soil CO2 emissions were significantly correlated (p < 0.05) with some environmental variables. The results showed that GLM and ANN models provided similar accuracies in modeling and estimating soil CO2 emissions, as the number of samples in the validation data set increased. The ANN was more advantageous than GLM models by providing a better fit between actual observations and predictions and lower RMSEP and MAE values. The results suggested that the success of environmental variables for estimations of CO2 emissions using the two methods was moderate.

Enhancing the Biological Relevance of Secretome-Based Proteomics by Linking Tumor Cell Proliferation and Protein Secretion.

Secretome profiling has become a methodology of choice for the identification of tumor biomarkers. We hypothesized that due to the dynamic nature of secretomes cellular perturbations could affect their composition but also change the global amount of protein secreted per cell. We confirmed our hypothesis by measuring the levels of secreted proteins taking into account the amount of proteome produced per cell. Then, we established a correlation between cell proliferation and protein secretion that explained the observed changes in global protein secretion. Next, we implemented a normalization correcting the statistical results of secretome studies by the global protein secretion of cells into a generalized linear model (GLM). The application of the normalization to two biological perturbations on tumor cells resulted in drastic changes in the list of statistically significant proteins. Furthermore, we found that known epithelial-to-mesenchymal transition (EMT) effectors were only statistically significant when the normalization was applied. Therefore, the normalization proposed here increases the sensitivity of statistical tests by increasing the number of true-positives. From an oncology perspective, the correlation between protein secretion and cellular proliferation suggests that slow-growing tumors could have high-protein secretion rates and consequently contribute strongly to tumor paracrine signaling.

Estimating soil N2O emissions induced by organic and inorganic fertilizer inputs using a Tier-2, regression-based meta-analytic approach for U.S. agricultural lands.

Consistent methods are essential for generating country and region-specific estimates of greenhouse gas (GHG) emissions used for reporting and policymaking. The estimates of direct N2O emissions from U.S. agricultural soils have primarily relied on the use of emission factors (EFs, Tier-1) and process-based models (Tier-3). However, Tier-1 estimates are relatively crude while Tier-3 calculations can be costly. This work addressed this gap by developing a Tier-2, regression-based approach by leveraging a meta-database containing 1883 field N2O observations together with environmental and management covariates from 139 studies. Our results estimated higher monthly soil N2O emissions (N2Om, kg N/ha) during the growing season (0.38) than the fallow period (0.15), highlighting the importance of considering measurement periods when utilizing meta-databases for analyzing N2O drivers. Significantly different N2Om were found for tillage practices (conventional > no-till: 0.42 > 0.27), fertilizer type (liquid > solid manure: 0.55 > 0.32), and soil texture (fine > coarse: 0.36 > 0.22). The comparisons of the influence of crop type and rotation, water management, and soil order on N2O emissions are complicated by regional data availability and interactions among different factors. Additionally, the finding that N2O emissions reported based on area (N2Om), N input rate (EF), or yield can alter treatment rankings underscores the need to establish transparent criteria for rewarding or discouraging regionally-based management practices using N2O metrics. Finally, we show how General Linear Models (GLMs) can be used to estimate country and regional Tier-2 N2Om using a suite of covariates. Our GLMs identified tillage, water management, N input type and rate, soil properties, and elevation as the most influential covariates for the conterminous U.S. The limited accuracy of regional-scale GLMs, however, suggests the need to further improve the quality and availability of GHG and covariate data through concerted efforts in data collection.

Modeling dragonfly population data with a Bayesian bivariate geometric mixed-effects model.

We develop a generalized linear mixed model (GLMM) for bivariate count responses for statistically analyzing dragonfly population data from the Northern Netherlands. The populations of the threatened dragonfly species Aeshna viridis were counted in the years 2015-2018 at 17 different locations (ponds and ditches). Two different widely applied population size measures were used to quantify the population sizes, namely the number of found exoskeletons ('exuviae') and the number of spotted egg-laying females were counted. Since both measures (responses) led to many zero counts but also feature very large counts, our GLMM model builds on a zero-inflated bivariate geometric (ZIBGe) distribution, for which we show that it can be easily parameterized in terms of a correlation parameter and its two marginal medians. We model the medians with linear combinations of fixed (environmental covariates) and random (location-specific intercepts) effects. Modeling the medians yields a decreased sensitivity to overly large counts; in particular, in light of growing marginal zero inflation rates. Because of the relatively small sample size (n = 114) we follow a Bayesian modeling approach and use Metropolis-Hastings Markov Chain Monte Carlo (MCMC) simulations for generating posterior samples.

Analysis of self-organizing maps and explainable artificial intelligence to identify hydrochemical factors that drive drinking water quality in Haor region.

Water contamination undermines human survival and economic growth. Water resource protection and management require knowledge of water hydrochemistry and drinking water quality characteristics, mechanisms, and factors. Self-organizing maps (SOM) have been developed using quantization and topographic error approaches to cluster hydrochemistry datasets. The Piper diagram, saturation index (SI), and cation exchange method were used to determine the driving mechanism of hydrochemistry in both surface and groundwater, while the Gibbs diagram was used for surface water. In addition, redundancy analysis (RDA) and a generalized linear model (GLM) were used to determine the key drinking water quality parameters in the study area. Additionally, the study aimed to utilize Explainable Artificial Intelligence (XAI) techniques to gain insights into the relative importance and impact of different parameters on the entropy water quality index (EWQI). The SOM results showed that thirty neurons generated the hydrochemical properties of water and were organized into four clusters. The Piper diagram showed that the primary hydrochemical facies were HCO3--Ca2+ (cluster 4), Cl---Na+ (all clusters), and mixed (clusters 1 and 4). Results from SI and cation exchange show that demineralization and ion exchange are the driving mechanisms of water hydrochemistry. About 45 % of the studied samples are classified as "medium quality"," that could be suitable as drinking water with further refinement. Cl- may pose increased non-carcinogenic risk to adults, with children at double risk. Cluster 4 water is low-risk, supporting EWQI findings. The RDA and GLM observations agree in that Ca2+, Mg2+, Na+, Cl- and HCO3- all have a positive and significant effect on EWQI, with the exception of K+. TDS, EC, Na+, and Ca2+ have been identified as influencing factors based on bagging-based XAI analysis at global and local levels. The analysis also addressed the importance of SO4, HCO3, Cl, Mg2+, K+, and pH at specific locations.

Close association of PFASs exposure with hepatic fibrosis than steatosis: evidences from NHANES 2017-2018.

Multiple animals and in vitro studies have demonstrated that perfluoroalkyl and polyfluoroalkyl substances (PFASs) exposure causes liver damage associated with fat metabolism. However, it is lack of population evidence for the correlation between PFAS exposure and nonalcoholic fatty liver disease (NAFLD). A cross-sectional analysis was performed of 1150 participants aged over 20 from the US. Liver ultrasound transient elastography was to identify the participants with NAFLD and multiple biomarkers were the indicators for hepatic steatosis and hepatic fibrosis. Logistics regression and restricted cubic splines models were used to estimate the association between PFASs and NAFLD. PFASs had not a significant association with NAFLD after adjustment. The hepatic steatosis indicators including fatty liver index, NAFLD liver fat score, and Framingham steatosis index were almost not significantly correlated with PFASs exposure respectively. But fibrosis indicators including fibrosis-4 index (FIB-4), NAFLD fibrosis score, and Hepamet fibrosis score were positively correlated with each type of PFASs exposure. After adjustment by gender, age, race, education, and poverty income rate, there was also a significant correlation between PFOS and FIB-4 with 0.07 (0.01, 0.13). The mixed PFASs were associated with FIB-4, with PFOS contributing the most (PIP = 1.000) by the Bayesian kernel machine regression model. The results suggested PFASs exposure appeared to be more closely associated with hepatic fibrosis than steatosis, and PFOS might be the main cause of PFASs associated with hepatic fibrosis.Key messagesCurrent exposure doses of PFAS did not significantly change the risk of developing NAFLD.PFASs exposure appeared to be more closely associated with hepatic fibrosis than steatosis.PFOS might be the main cause of PFASs associated with hepatic fibrosis.

Factors Affecting the Product Life Cycle of Generic Medicines.

Background: Product life cycle (PLC) refers to the time ranging from when a product is introduced into the market to when it is taken off the shelves. The PLC management can guarantee product survival and prevent its decline. Objectives: This study investigated generic antibiotic PLCs and detected factors affecting them in the competitive pharmaceutical market of Iran to improve the PLC management of such drugs. Methods: To study the PLC of antibiotics, data were collected from 2002 to 2017, and then the PLC curves were analyzed. Accordingly, factors affecting the PLC of antibiotics were illustrated in two sections: all PLC curves and the PLC curves with one sales peak. Using a generalized linear model combined with a machine learning approach, we identified the sales patterns and the effect of the product-related and the competition-related factors on the PLC curves, peak height, and the time to reach peak sales. Results: According to the findings, 16, 11.87, 13.03, and 59% of the antibiotics had linear, binomial, one-peak, and oscillating sales patterns, respectively. The most crucial factors affecting the PLC shape were the quality, microbial spectrum, dosage forms, number of competitors, and entry arrangement. Conclusions: This study examined factors affecting the PLC patterns of generic pharmaceutical products. The findings would provide more insights into the generic pharmaceutical market as one of the less-studied markets in many countries.

Parents' knowledge and attitude and behavior toward autism: a survey of Chinese families having children with autism spectrum disorder.

Background: In recent years, parent-mediated intervention for children with autism spectrum disorder (ASD) has increased. Therefore, implementing effective parent training programs for parents of children with autism is of paramount importance, particularly in low- and middle-income countries. However, little is known about the status of and gaps in parents' knowledge on ASD, which may hinder the development of valid parental training programs. Herein, we aimed at exploring the status of Chinese parents' knowledge, attitude and behavior toward ASD, and potential factors affecting the acquisition of correct knowledge. Methods: This study used a self-designed parental knowledge questionnaire of autism (PKQA) comprising 20 questions alongside another questionnaire comprising additional 17 questions covering the aspects of family demographics, attitudes, and behaviors of parents. In total, we included 394 parents who visited the outpatient department of the Child's Development and Behavior Center of the Third Affiliated Hospital of Sun Yat-Sen University between December 2018 and May 2019, with their children meeting the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) diagnostic criteria for ASD. Results: The median knowledge score in the PKQA was 15 [interquartile range (IQR), 13-17]. Advanced paternal age and longer time interval from diagnosis to enrolling into the parent training program were associated with a lower total knowledge score (all P<0.001). Higher maternal education attainment, higher family income, child being currently under intervention, and family members sharing a common perception of the diagnosis were associated with a higher total knowledge score (all P<0.01). Reading autism-related books (P<0.001) or attending professional lectures (P=0.019) were also associated with a higher total knowledge score. Conclusions: Taken together, this study revealed that family demographics and parents' attitudes and behaviors toward ASD may significantly influence their knowledge about autism, suggesting the need for promoting more targeted parental skills training programs.

Lung Radiomics Features Selection for COPD Stage Classification Based on Auto-Metric Graph Neural Network.

Chronic obstructive pulmonary disease (COPD) is a preventable, treatable, progressive chronic disease characterized by persistent airflow limitation. Patients with COPD deserve special consideration regarding treatment in this fragile population for preclinical health management. Therefore, this paper proposes a novel lung radiomics combination vector generated by a generalized linear model (GLM) and Lasso algorithm for COPD stage classification based on an auto-metric graph neural network (AMGNN) with a meta-learning strategy. Firstly, the parenchyma images were segmented from chest high-resolution computed tomography (HRCT) images by ResU-Net. Second, lung radiomics features are extracted from the parenchyma images by PyRadiomics. Third, a novel lung radiomics combination vector (3 + 106) is constructed by the GLM and Lasso algorithm for determining the radiomics risk factors (K = 3) and radiomics node features (d = 106). Last, the COPD stage is classified based on the AMGNN. The results show that compared with the convolutional neural networks and machine learning models, the AMGNN based on constructed novel lung radiomics combination vector performs best, achieving an accuracy of 0.943, precision of 0.946, recall of 0.943, F1-score of 0.943, and ACU of 0.984. Furthermore, it is found that our method is effective for COPD stage classification.

Association between Perfluoroalkyl and Polyfluoroalkyl Substances and Women's Infertility, NHANES 2013-2016.

Perfluoroalkyl and polyfluoroalkyl substances (PFASs) are widely used in consumer products. However, the role of PFAS in infertility is still poorly understood. A total of 788 women from the 2013-2016 nationally representative NHANES were included to explore the association between PFAS exposure and self-reported infertility. Six PFAS, including PFDE, PFNA, PFHxS, n-PFOA, n-PFOS, and Sm-PFOS, were detected by online SPE-HPLC-TIS-MS/MS. We used the generalized linear regression model (GLM), generalized additive models (GAM), and Bayesian kernel machine regression (BKMR) to assess the single effects, non-linear relationships, and mixed effects on women's infertility, respectively. The prevalence of self-reported infertility was 15.54% in this study. In GLM, n-PFOA showed a negative association with self-reported infertility in women for the Q3 (OR: 0.396, 95% CI: 0.119, 0.788) and Q4 (OR: 0.380, 95% CI: 0.172-0.842) compared with Q1 (p for trend = 0.013). A negative trend was also observed in n-PFOS and ∑PFOS (p for trend < 0.05). In GAM, a non-linear relationship was revealed in Sm-PFOS, which exhibits a U-shaped relationship. The BKMR model indicated that there might be a joint effect between PFAS and women's infertility, to which PFNA contributed the highest effect (PIP = 0.435). Moreover, age stratification analysis showed a different dose-response curve in under and above 35 years old. Women under the age of 35 have a more noticeable U-shaped relationship with infertility. Therefore, the relatively low level of mixed PFAS exposure was negatively associated with self-reported infertility in women in general, and the impact of PFAS on infertility may vary among women of different age groups. Further studies are needed to determine the etiological relationship.

Exposure to a mixture of personal care product and plasticizing chemicals in relation to reproductive hormones and menarche timing among 12-19 years old girls in NHANES 2013-2016.

The effects of co-exposure to personal care product and plasticizing chemicals (PCPPCs) on reproductive hormones and menarche timing has rarely been studied. We used Bayesian kernel machine regression (BKMR), adaptive least absolute shrinkage and selection operator (adaptive LASSO), and generalized linear model (GLM) to explore the mixture effects of 16 PCPPCs on reproductive hormones and early menarche among 297 girls aged 12-19 years in the National Health and Nutrition Examination Survey (NHANES) 2013-2016. In the BKMR, the mixture of PCPPCs was negatively and positively associated with testosterone (TT) and sex hormone-binding globulin (SHBG), respectively. In the adaptive LASSO, 2,5-dichlorophenol (2, 5-DCP), Mono (carboxyisoctyl) phthalate (MCOP), and Mono-benzyl phthalate (MBzP) were positively associated with estrogen (E2), whereas Mono-ethyl phthalate (MEP), Monoisobutyl phthalate (MiBP) and Mono-(2-ethyl-5-oxohexyl) phthalate (MEOHP) were inversely associated with E2. Bisphenol A (BPA) and MBzP were positively associated with SHBG, whereas Mono (carboxyisonony) phthalate (MCNP) and Mono (2-ethyl-5-carboxypentyl) phthalate (MECPP) were inversely associated with SHBG. In GLM, Benzophenone-3 (BZP) and MECPP were negatively associated with TT, whereas 2,4-dichlorophenol (2,4-DCP) revealed a positive association with early menarche onset. In conclusion, PCPPCs in the mixture and individually are associated with reproductive hormones and early menarche onset in girls.

Calculating and Comparing the Annualized Relapse Rate and Estimating the Confidence Interval in Relapsing Neurological Diseases.

Calculating the crude or adjusted annualized relapse rate (ARR) and its confidence interval (CI) is often required in clinical studies to evaluate chronic relapsing diseases, such as multiple sclerosis and neuromyelitis optica spectrum disorders. However, accurately calculating ARR and estimating the 95% CI requires careful application of statistical approaches and basic familiarity with the exponential family of distributions. When the relapse rate can be regarded as constant over time or by individuals, the crude ARR can be calculated using the person-years method, which divides the number of all observed relapses among all participants by the total follow-up period of the study cohort. If the number of relapses can be modeled by the Poisson distribution, the 95% CI of ARR can be obtained by finding the 2.5% upper and lower critical values of the parameter λ as the mean. Basic familiarity with F-statistics is also required when comparing the ARR between two disease groups. It is necessary to distinguish the observed relapse rate ratio (RR) between two sample groups (sample RR) from the unobserved RR between their originating populations (population RR). The ratio of population RR to sample RR roughly follows the F distribution, with degrees of freedom obtained by doubling the number of observed relapses in the two sample groups. Based on this, a 95% CI of the population RR can be estimated. When the count data of the response variable is overdispersed, the negative binomial distribution would be a better fit than the Poisson. Adjusted ARR and the 95% CI can be obtained by using the generalized linear regression models after selecting appropriate error structures (e.g., Poisson, negative binomial, zero-inflated Poisson, and zero-inflated negative binomial) according to the overdispersion and zero-inflation in the response variable.

Effect of non-normality on the monitoring of simple linear profiles in two-stage processes: a remedial measure for gamma-distributed responses.

The relationship between the response variable and one or more independent variables refers to the quality characteristic in some statistical quality control applications, which is called profile. Most research dealt with the monitoring of profiles in single-stage processes considering a basic assumption of normality. However, some processes are made up of several sub-processes; thus, the effect of cascade property in multistage processes should be considered. Moreover, sometimes in practice, the assumption of normally distributed data does not hold. This paper first examines the effect of non-normal data to monitor simple linear profiles in two-stage processes in Phase II. We study non-normal distributions such as the skewed gamma distribution and the heavy-tailed symmetric t-distribution to measure the non-normality effect using the average run length criterion. Next, generalized linear models have been used and a monitoring approach based on generalized likelihood ratio (GLR) has been developed for gamma-distributed responses as a remedial measure to reduce the detrimental effects of non-normality. The results of simulation studies reveal that the performance of the GLR procedure is satisfactory for the multistage non-normal linear profiles. Finally, the simulated and real case studies with gamma-distributed data have been provided to show the application of the competing monitoring approaches.

Integrating a learned probabilistic model with energy functional for ultrasound image segmentation.

The segmentation of ultrasound (US) images is steadily growing in popularity, owing to the necessity of computer-aided diagnosis (CAD) systems and the advantages that this technique shows, such as safety and efficiency. The objective of this work is to separate the lesion from its background in US images. However, most US images contain poor quality, which is affected by the noise, ambiguous boundary, and heterogeneity. Moreover, the lesion region may be not salient amid the other normal tissues, which makes its segmentation a challenging problem. In this paper, an US image segmentation algorithm that combines the learned probabilistic model with energy functionals is proposed. Firstly, a learned probabilistic model based on the generalized linear model (GLM) reduces the false positives and increases the likelihood energy term of the lesion region. It yields a new probability projection that attracts the energy functional toward the desired region of interest. Then, boundary indicator and probability statistical-based energy functional are used to provide a reliable boundary for the lesion. Integrating probabilistic information into the energy functional framework can effectively overcome the impact of poor quality and further improve the accuracy of segmentation. To verify the performance of the proposed algorithm, 40 images are randomly selected in three databases for evaluation. The values of DICE coefficient, the Jaccard distance, root-mean-square error, and mean absolute error are 0.96, 0.91, 0.059, and 0.042, respectively. Besides, the initialization of the segmentation algorithm and the influence of noise are also analyzed. The experiment shows a significant improvement in performance. A. Description of the proposed paper. B. The main steps involved in the proposed method.

Localizing confined epileptic foci in patients with an unclear focus or presumed multifocality using a component-based EEG-fMRI method.

Precise localization of epileptic foci is an unavoidable prerequisite in epilepsy surgery. Simultaneous EEG-fMRI recording has recently created new horizons to locate foci in patients with epilepsy and, in comparison with single-modality methods, has yielded more promising results although it is still subject to limitations such as lack of access to information between interictal events. This study assesses its potential added value in the presurgical evaluation of patients with complex source localization. Adult candidates considered ineligible for surgery on account of an unclear focus and/or presumed multifocality on the basis of EEG underwent EEG-fMRI. Adopting a component-based approach, this study attempts to identify the neural behavior of the epileptic generators and detect the components-of-interest which will later be used as input in the GLM model, substituting the classical linear regressor. Twenty-eight sets interictal epileptiform discharges (IED) from nine patients were analyzed. In eight patients, at least one BOLD response was significant, positive and topographically related to the IEDs. These patients were rejected for surgery because of an unclear focus in four, presumed multifocality in three, and a combination of the two conditions in two. Component-based EEG-fMRI improved localization in five out of six patients with unclear foci. In patients with presumed multifocality, component-based EEG-fMRI advocated one of the foci in five patients and confirmed multifocality in one of the patients. In seven patients, component-based EEG-fMRI opened new prospects for surgery and in two of these patients, intracranial EEG supported the EEG-fMRI results. In these complex cases, component-based EEG-fMRI either improved source localization or corroborated a negative decision regarding surgical candidacy. As supported by the statistical findings, the developed EEG-fMRI method leads to a more realistic estimation of localization compared to the conventional EEG-fMRI approach, making it a tool of high value in pre-surgical evaluation of patients with refractory epilepsy. To ensure proper implementation, we have included guidelines for the application of component-based EEG-fMRI in clinical practice.

Localizing Epileptic Foci Using Simultaneous EEG-fMRI Recording: Template Component Cross-Correlation.

Conventional EEG-fMRI methods have been proven to be of limited use in the sense that they cannot reveal the information existing in between the spikes. To resolve this issue, the current study obtains the epileptic components time series detected on EEG and uses them to fit the Generalized Linear Model (GLM), as a substitution for classical regressors. This approach allows for a more precise localization, and equally importantly, the prediction of the future behavior of the epileptic generators. The proposed method approaches the localization process in the component domain, rather than the electrode domain (EEG), and localizes the generators through investigating the spatial correlation between the candidate components and the spike template, as well as the medical records of the patient. To evaluate the contribution of EEG-fMRI and concordance between fMRI and EEG, this method was applied on the data of 30 patients with refractory epilepsy. The results demonstrated the significant numbers of 29 and 24 for concordance and contribution, respectively, which mark improvement as compared to the existing literature. This study also shows that while conventional methods often fail to properly localize the epileptogenic zones in deep brain structures, the proposed method can be of particular use. For further evaluation, the concordance level between IED-related BOLD clusters and Seizure Onset Zone (SOZ) has been quantitatively investigated by measuring the distance between IED/SOZ locations and the BOLD clusters in all patients. The results showed the superiority of the proposed method in delineating the spike-generating network compared to conventional EEG-fMRI approaches. In all, the proposed method goes beyond the conventional methods by breaking the dependency on spikes and using the outside-the-scanner spike templates and the selected components, achieving an accuracy of 97%. Doing so, this method contributes to improving the yield of EEG-fMRI and creates a more realistic perception of the neural behavior of epileptic generators which is almost without precedent in the literature.

A Longitudinal Analysis of Gambling Predictors among Adolescents.

Although gambling is forbidden for minors, the prevalence of gambling among adolescents is increasing. In order to improve preventive interventions, more evidence on predictors of gambling onset is needed. A longitudinal study was proposed to (1) establish the prevalence of gambling; (2) identify factors associated with gambling behavior the following year; and (3) adjust a model to predict gambling behavior. A cohort of 1074 students (13-18 years old) was followed for 12 months. The prevalence of gambling reached 42.0% in the second measure. Boys gambled 2.7 times more than girls, and the highest percentages of gambling onset showed up between 13 and 14 years old. Gambling onset and maintenance was associated with gender, age, sensation-seeking, risk perception, self-efficacy for not gambling, parents' attitude towards gambling, group pressure (friends), subjective norm, exposure to advertising, accessibility, normative perception, gambling in T1 and parents gambling behavior. Gender, gambling in T1 and risk perception were significant in all three logistic adjusted regression models, with the fourth variable being sensation seeking, peer pressure (friends) and accessibility, respectively. It is suggested that universal prevention should be aimed preferably at children under 15 years old and to alert regulators and public administrations to the directly proportional relationship between accessibility and gambling onset.