Analyzing the connectivity between regions of interest: An approach based on cluster Granger causality for NRI data analysis

The identification, modeling, and analysis of interactions between nodes of neural systems in the human brain have become the aim of interest of many studies in neuroscience. The complex neural network structure and its correlations with brain functions have played a role in all areas of neuroscience, including the comprehension of cognitive and emotional processing. Indeed, understanding how information is stored, retrieved, processed, and transmitted is one of the ultimate challenges in brain research. In this context, in functional neuroimaging, connectivity analysis is a major tool for the exploration and characterization of the information flow between specialized brain regions. In most functional magnetic resonance imaging (fMRI) studies, connectivity analysis is carried out by first selecting regions of interest (ROI) and then calculating an average BOLD time series (across the voxels in each cluster). Some studies have shown that the average may not be a good choice and have suggested, as an alternative, the use of principal component analysis (PCA) to extract the principal eigen-time series from the ROI(s). In this paper, we introduce a novel approach called cluster Granger analysis (CGA) to study connectivity between ROIs. The main aim of this method was to employ multiple eigen-time series in each ROI to avoid temporal information loss during identification of Granger causality. Such information loss is inherent in averaging (e.g., to yield a single ""representative"" time series per ROI). This, in turn, may lead to a lack of power in detecting connections. The proposed approach is based on multivariate statistical analysis and integrates PCA and partial canonical correlation in a framework of Granger causality for clusters (sets) of time series. We also describe an algorithm for statistical significance testing based on bootstrapping. By using Monte Carlo simulations, we show that the proposed approach outperforms conventional Granger causality analysis (i.e., using representative time series extracted by signal averaging or first principal components estimation from ROIs). The usefulness of the CGA approach in real fMRI data is illustrated in an experiment using human faces expressing emotions. With this data set, the proposed approach suggested the presence of significantly more connections between the ROIs than were detected using a single representative time series in each ROI. (c) 2010 Elsevier Inc. All rights reserved.

Characterizing QALYs under a general rank dependent utility model

This paper provides a characterization of QALYs, the most important outcome measure in medical decision making, in the context of a general rank dependent utility model. We show that both for chronic and for nonchronic health states the characterization of QALYs depends on intuitive conditions. This facilitates the assessment of the validity of QALYs in rank dependent non-expected utility theories and a comparison with other utility based measures of health.

Comparison of Quantitative Imaging Devices and Subjective Optic Nerve Head Assessment by General Ophthalmologists to Differentiate Normal From Glaucomatous Eyes

Purpose: To compare the ability of Subjective assessment of optic nerve head (ONH) and retinal nerve fiber layer (RNFL) by general ophthalmologists and by a glaucoma expert with objective measurements by optical coherence tomography (Stratus OCT, Carl Zeiss Meditec Inc), confocal scanning laser ophthalmoscope (HRT III; Heidelberg Engineering, Heidelberg. Germany), and scanning laser polarimetry (GDx enhanced corneal compensation; Carl Zeiss Meditec Inc, Dublin, CA) in discriminating glaucomatous and normal eyes. Methods: Sixty-one glaucomatous and 57 normal eyes or 118 subjects Were included in the study. Three independent general ophthalmologists and I glaucoma expert evaluated ONH stereo-photographs. Receiver operating characteristic curves were constructed for each imaging technique and sensitivity at fixed specificity was estimated. Comparisons or areas under these curves (aROCs) and agreement (k) were determined between stereophoto grading and best parameter from each technique. Results: Best parameter from each technique showed larger aROC (Stratus OCT RNFL 0.92; Stratus OCT ONH vertical integrated area = 0.86; Stratus OCT macular thickness = 0.82; GDx enhanced corneal compensation = 0.91, HRT3 global cup-to-disc ratio = 0.83; HRT3 glaucoma probability score numeric area score 0.83) compared with stereophotograph grading by general ophthalmologists (0.80) in separating glaucomatous and normal eyes. Glaucoma expert stereophoto grading provided equal or larger aROC (0.92) than best parameter of each computerized imaging device. Stereophoto evaluated by a glaucoma expert showed better agreement with best parameter of each quantitative imaging technique in classifying eyes either as glaucomatous or normal compared with stereophoto grading by general ophthalmologists, The combination Of Subjective assessment of the optic disc by general ophthalmologists with RNFL objective parameters improved identification of glaucoma patients in a larger proportion than the combination of these objective parameters with Subjective assessment of the optic disc by a glaucoma expert (29.5% vs. 19.7%, respectively). Conclusions: Diagnostic ability of all imaging techniques showed better performance than subjective assessment of the ONH by general ophthalmologists, but not by It glaucoma expert, Objective RNFL measurements may provide improvement in glaucoma detection when combined with subjective assessment of the optic disc by general ophthalmologists or by a glaucoma expert.

PROFILE OF PUBLISHED PAPERS ON ORTHOPEDICS IN GENERAL JOURNALS

The authors study the profile of published papers on orthopedics in general journals, not specific to orthopedics, registered in PUBMED, in a period of two years. There were selected 67 papers with heterogeneous distribution among the magazines studied. It was found the presence of 26.47% of articles with interventional design and 38% with observational one. The data are discussed

Classification System of Raman Spectra using Cluster Analysis to Diagnose Coronary Artery Lesions

The traditional methods employed to detect atherosclerotic lesions allow for the identification of lesions; however, they do not provide specific characterization of the lesion`s biochemistry. Currently, Raman spectroscopy techniques are widely used as a characterization method for unknown substances, which makes this technique very important for detecting atherosclerotic lesions. The spectral interpretation is based on the analysis of frequency peaks present in the signal; however, spectra obtained from the same substance can show peaks slightly different and these differences make difficult the creation of an automatic method for spectral signal analysis. This paper presents a signal analysis method based on a clustering technique that allows for the classification of spectra as well as the inference of a diagnosis about the arterial wall condition. The objective is to develop a computational tool that is able to create clusters of spectra according to the arterial wall state and, after data collection, to allow for the classification of a specific spectrum into its correct cluster.

APOE polymorphism is associated with lipid profile, but not with arterial stiffness in the general population

Background: Cardiovascular diseases (CVD) are the main cause of death and disability in developed countries. In most cases, the progress of CVD is influenced by environmental factors and multifactorial inheritance. The purpose of this study was to investigate the association between APOE genotypes, cardiovascular risk factors, and a noninvasive measure of arterial stiffness in the Brazilian population. Methods: A total of 1493 urban Brazilian individuals were randomly selected from the general population of the Vitoria City Metropolitan area. Genetic analysis of the APOE polymorphism was conducted by PCR-RFLP and pulse wave velocity analyzed with a noninvasive automatic device. Results: Age, gender, body mass index, triglycerides, creatinine, uric acid, blood glucose, blood pressure phenotypes were no different between epsilon 2, epsilon 3 and epsilon 4 alleles. The epsilon 4 allele was associated with higher total-cholesterol (p < 0.001), LDL-C (p < 0.001), total-cholesterol/HDL-C ratio (p < 0.001), LDL/HDL-C ratio (p < 0.001), lower HDL-C values (p < 0.001) and higher risk to obesity (OR = 1.358, 95% CI = 1.019-1.811) and hyperuricemia (OR = 1.748, 95% CI = 1.170-2.611). Nevertheless, pulse wave velocity (p = 0.66) measures were no different between genotypes. The significant association between APOE genotypes and lipid levels persisted after a 5-year follow-up interval, but no interaction between time and genotype was observed for lipids longitudinal behavior. Conclusion: The epsilon 4 allele of the APOE gene is associated with a worse lipid profile in the Brazilian urban population. In our relatively young sample, the observed effect of APOE genotype on lipid levels was not translated into significant effects in arterial wall stiffness.

Screening for serious mental illness in the general population with the K6 screening scale: results from the WHO World Mental Health (WMH) survey initiative

Data are reported on the background and performance of the K6 screening scale for serious mental illness (SMI) in the World Health Organization (WHO) World Mental Health (WMH) surveys. The K6 is a six-item scale developed to provide a brief valid screen for Diagnostic and Statistical Manual of Mental Disorders 4th edition (DSM-IV) SMI based on the criteria in the US ADAMHA Reorganization Act. Although methodological studies have documented good K6 validity in a number of countries, optimal scoring rules have never been proposed. Such rules are presented here based on analysis of K6 data in nationally or regionally representative WMH surveys in 14 countries (combined N = 41,770 respondents). Twelve-month prevalence of DSM-IV SMI was assessed with the fully-structured WHO Composite International Diagnostic Interview. Nested logistic regression analysis was used to generate estimates of the predicted probability of SMI for each respondent from K6 scores, taking into consideration the possibility of variable concordance as a function of respondent age, gender, education, and country. Concordance, assessed by calculating the area under the receiver operating characteristic curve, was generally substantial (median 0.83; range 0.76-0.89; inter-quartile range 0.81-0.85). Based on this result, optimal scaling rules are presented for use by investigators working with the K6 scale in the countries studied. Copyright (c) 2010 John Wiley & Sons, Ltd.

A general hazard model for lifetime data in the presence of cure rate

Historically, the cure rate model has been used for modeling time-to-event data within which a significant proportion of patients are assumed to be cured of illnesses, including breast cancer, non-Hodgkin lymphoma, leukemia, prostate cancer, melanoma, and head and neck cancer. Perhaps the most popular type of cure rate model is the mixture model introduced by Berkson and Gage [1]. In this model, it is assumed that a certain proportion of the patients are cured, in the sense that they do not present the event of interest during a long period of time and can found to be immune to the cause of failure under study. In this paper, we propose a general hazard model which accommodates comprehensive families of cure rate models as particular cases, including the model proposed by Berkson and Gage. The maximum-likelihood-estimation procedure is discussed. A simulation study analyzes the coverage probabilities of the asymptotic confidence intervals for the parameters. A real data set on children exposed to HIV by vertical transmission illustrates the methodology.