New Approaches for Data-mining and Classification of Mental Disorder in Brain Imaging Data

Brain imaging data are incredibly complex and new information is being learned as approaches to mine these data are developed. In addition to studying the healthy brain, new approaches for using this information to provide information about complex mental illness such as schizophrenia are needed. Functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) are two well-known neuroimaging approaches that provide complementary information, both of which provide a huge amount of data that are not easily modelled. Currently, diagnosis of mental disorders is based on a patients self-reported experiences and observed behavior over the longitudinal course of the illness. There is great interest in identifying biologically based marker of illness, rather than relying on symptoms, which are a very indirect manifestation of the illness. The hope is that biological markers will lead to earlier diagnosis and improved treatment as well as reduced costs. Understanding mental disorders is a challenging task due to the complexity of brain structure and function, overlapping features between disorders, small numbers of data sets for training, heterogeneity within disorders, and a very large amount of high dimensional data. This doctoral work proposes machine learning and data mining based algorithms to detect abnormal functional network connectivity patterns of patients with schizophrenia and distinguish them from healthy controls using 1) independent components obtained from task related fMRI data, 2) functional network correlations based on resting-state and a hierarchy of tasks, and 3) functional network correlations in both fMRI and MEG data. The abnormal activation patterns of the functional network correlation of patients are characterized by using a statistical analysis and then used as an input to classification algorithms. The framework presented in this doctoral study is able to achieve good characterization of schizophrenia and provides an initial step towards designing an objective biological marker-based diagnostic test for schizophrenia. The methods we develop can also help us to more fully leverage available imaging technology in order to better understand the mystery of the human brain, the most complex organ in the human body

Coupled nonparametric shape priors for segmentation of multiple basal ganglia structures

This paper presents a new method for multiple structure segmentation, using a maximum a posteriori (MAP) estimation framework, based on prior shape densities involving nonparametric multivariate kernel density estimation of multiple shapes. Our method is motivated by the observation that neighboring or coupling structures in medical images generate configurations and co-dependencies which could potentially aid in segmentation if properly exploited. Our technique allows simultaneous segmentation of multiple objects, where highly contrasted, easy-to-segment structures can help improve the segmentation of weakly contrasted objects. We demonstrate the effectiveness of our method on both synthetic images and real magnetic resonance images (MRI) for segmentation of basal ganglia structures

Comparison of Effects of Smoking and Smokeless Tobacco “Maras Powder” Use on Humoral Immune System Parameters

Background. The aim of this study is to assess the impacts of “Maras powder” and cigarette smoking on the parameters of the humoral immune system. Material and Methods. One hundred seventy seven subjects were included in the study. The IgA, IgG, IgM, C3 and C4 levels were detected via nephelometric method. Results. In 1.4% of the control group IgM levels were below normal where it was 10.8% and 18.6% in Maras powder group and in cigarette smoking group respectively. The IgM levels of both groups were significantly lower compared to the control group (P < .05). Nonetheless, the IgE levels of Maras powder group and smoking group were found to be remarkably higher compared to the control group (P < .01). Conclusion. Effects of Maras powder on humoral immune response were found to be similar to that of smoking

INVESTIGATION OF CLASSROOM TEACHER CANDIDATES’ COGNITIVE STRUCTURES ON SOME BASIC SCIENCE CONCEPTS

In this study, it was aimed to investigate the cognitive structures of classroom teacher candidates on some basic science concepts. Word association test (WAT) technique was used to gather data. Twelve keywords related to basic physics, chemistry, and biology concepts were determined and used in the formation of WAT’s. Forty-three classroom teacher candidates studying at 2nd classes at an education faculty were the participants of this study. Data obtained by WAT were examined by using number of different responses given to each keyword, and by drawing concept maps according to both frequencies and relatedness coefficients. A cut-off point technique was used when drawing the concept maps. Because of this study, it can be said that participants have moderate cognitive structures on the investigated science concepts and their cognitive structure was strongest on chemistry concepts and weakest on biology concepts.   Article visualizations

Volumetric segmentation of multiple basal ganglia structures

We present a new active contour-based, statistical method for simultaneous volumetric segmentation of multiple subcortical structures in the brain. Neighboring anatomical structures in the human brain exhibit co-dependencies which can aid in segmentation, if properly analyzed and modeled. Motivated by this observation, we formulate the segmentation problem as a maximum a posteriori estimation problem, in which we incorporate statistical prior models on the shapes and inter-shape (relative) poses of the structures of interest. This provides a principled mechanism to bring high level information about the shapes and the relationships of anatomical structures into the segmentation problem. For learning the prior densities based on training data, we use a nonparametric multivariate kernel density estimation framework. We combine these priors with data in a variational framework, and develop an active contour-based iterative segmentation algorithm. We test our method on the problem of volumetric segmentation of basal ganglia structures in magnetic resonance (MR) images. We compare our technique with existing methods and demonstrate the improvements it provides in terms of segmentation accuracy

Relation of Epicardial Fat Thickness with Carotid Intima-Media Thickness in Patients with Type 2 Diabetes Mellitus

Aims. The aim of this study was to investigate the relationship of echocardiographic epicardial fat thickness (EFT) with carotid intima-media thickness (CIMT), in patients with type 2 diabetes mellitus (T2DM). Methods and Results. A total of 139 patients with T2DM (mean age 54.3 ± 9.2 and 49.6% male) and 40 age and sex-matched control subjects were evaluated. Echocardiographic EFT and ultrasonographic CIMT were measured in all subjects. Patients with T2DM had significantly increased EFT and CIMT than those of the controls (6.0 ± 1.5 mm versus 4.42 ± 1.0 mm, P<0.001 and 0.76 ± 0.17 mm versus 0.57 ± 0.14 mm, P<0.001, resp.). EFT was correlated with CIMT, waist circumference, BMI, age, duration of T2DM, HbA1c in the type 2 diabetic patients. Linear regression analysis showed that CIMT (β=3.52, t=3.72, P<0.001) and waist circumference (β=0.36, t=2.26, P=0.03) were found to be independent predictors of EFT. A cutoff high risk EFT value of 6.3 mm showed a sensitivity and specificity of 72.5% and 71.7%, respectively, for the prediction of subclinical atherosclerosis. Conclusion. We found that echocardiographic EFT was significantly higher in patients with T2DM. Our study also showed that EFT was strongly correlated with waist circumference and CIMT as being independent of sex

Coupled non-parametric shape and moment-based inter-shape pose priors for multiple basal ganglia structure segmentation

This paper presents a new active contour-based, statistical method for simultaneous volumetric segmentation of multiple subcortical structures in the brain. In biological tissues, such as the human brain, neighboring structures exhibit co-dependencies which can aid in segmentation, if properly analyzed and modeled. Motivated by this observation, we formulate the segmentation problem as a maximum a posteriori estimation problem, in which we incorporate statistical prior models on the shapes and inter-shape (relative) poses of the structures of interest. This provides a principled mechanism to bring high level information about the shapes and the relationships of anatomical structures into the segmentation problem. For learning the prior densities we use a nonparametric multivariate kernel density estimation framework. We combine these priors with data in a variational framework and develop an active contour-based iterative segmentation algorithm. We test our method on the problem of volumetric segmentation of basal ganglia structures in magnetic resonance (MR) images. We present a set of 2D and 3D experiments as well as a quantitative performance analysis. In addition, we perform a comparison to several existent segmentation methods and demonstrate the improvements provided by our approach in terms of segmentation accuracy

Generalized Lymphadenopathy: Unusual Presentation of Prostate Adenocarcinoma

Generalized lymphadenopathy is a rare manifestation of metastatic prostate cancer. Here, we report the case of a 59-year-old male patient with supraclavicular, mediastinal, hilar, and retroperitoneal and inguinal lymphadenopathy, which suggested the diagnosis of lymphoma. There were no urinary symptoms. A biopsy of the inguinal lymph node was compatible with adenocarcinoma, whose prostatic origin was shown by immunohistochemical staining with PSA. The origin of the primary tumor was confirmed by directed prostate biopsy. We emphasize that a suspicion of prostate cancer in men with adenocarcinoma of undetermined origin is important for an adequate diagnostic and therapeutic approach