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.

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.

Addressing overtreatment in patients with refractory epilepsy at a tertiary referral centre in Brazil

Background. Patients with refractory epilepsy often have impaired quality of life (QOL) as a consequence of seizures and adverse effects of antiepileptic drugs. We assessed the impact of adverse effects on QOL and the utility of a structured instrument to help the physician manage adverse effects in patients with refractory epilepsy. Methods. Clinical characteristics, drug treatment and adverse effects were evaluated in 102 patients with refractory epilepsy at a single tertiary referral centre. The Adverse Events Profile (AEP) and Quality of Life in Epilepsy-31 (QOLIE-31) questionnaires were completed at baseline and after six months. At baseline, patients with a high burden of adverse effects (AEP scores >= 45) were randomized to an intervention or control group. AEP scores in the intervention group were available to the physician as an instrument to help to reduce adverse effects. Results. Ninety-five patients (93.1%) were on polytherapy. Sixty-six completed the questionnaires and, of these, 43 (65.1%) had a high AE burden and were randomized to the intervention and control group. QOLIE-31 scores were inversely correlated with AEP scores at both visits. Among randomized patients, AEP scores tended to decrease between the baseline and the final visit without significant differences between groups (intervention group: 54.1 +/- 6.1 vs 51.1 +/- 9.1; control group: 55.8 +/- 5.8 vs 50.5 +/- 12.2). QOLIE-31 scores did not change substantially between visits (intervention group: 45.9 +/- 17.4 vs 48.4 +/- 14; control group: 47.5 +/- 15.7 vs 45.2 +/- 18.9). Conclusion. A significant proportion of patients had a high toxicity burden which had an impact on their QOL. Reduction of over-treatment is a difficult challenge which cannot be addressed solely by providing a structured assessment of adverse effects, but requires a more comprehensive approach aimed at optimizing the many components of the management strategy.

Risk factors for dental erosion in a group of 12- and 16-year-old Brazilian schoolchildren

Background. Dental erosion is a multifactorial disease and is associated with dietary habits in infancy and adolescence. Aim. To investigate possible associations among dental erosion and diet, medical history and lifestyle habits in Brazilian schoolchildren. Design. The sample consisted of a random single centre cluster of 414 adolescents (12- and 16-years old) of both genders from private and public schools in Bauru (Brazil). The O`Brien [Children`s Dental Health in the United Kingdom, 1993 (1994) HMSO, London] index was used for dental erosion assessment. Data on medical history, rate and frequency of food and drinks consumption, and lifestyle habits were collected by a self-reported questionnaire. Odds ratios with 95% confidence intervals were used to assess the univariate relationships between variables. Analysis of questionnaire items was performed by multiple logistic regression analysis. The statistical significance level was set at 5%. Results. The erosion present group comprised 83 subjects and the erosion absent group 331. There were no statistically significant correlations among dental erosion and the consumption of food and drinks, medical history, or lifestyle habits. Conclusion. The results indicate that there was no correlation between dental erosion and the risk factors analysed among adolescents in Bauru/Brazil and further investigations are necessary to clarify the multifactorial etiology of this condition.

Long-term survivor mixture model with random effects: application to a multi-centre clinical trial of carcinoma

A mixture model incorporating long-term survivors has been adopted in the field of biostatistics where some individuals may never experience the failure event under study. The surviving fractions may be considered as cured. In most applications, the survival times are assumed to be independent. However, when the survival data are obtained from a multi-centre clinical trial, it is conceived that the environ mental conditions and facilities shared within clinic affects the proportion cured as well as the failure risk for the uncured individuals. It necessitates a long-term survivor mixture model with random effects. In this paper, the long-term survivor mixture model is extended for the analysis of multivariate failure time data using the generalized linear mixed model (GLMM) approach. The proposed model is applied to analyse a numerical data set from a multi-centre clinical trial of carcinoma as an illustration. Some simulation experiments are performed to assess the applicability of the model based on the average biases of the estimates formed. Copyright (C) 2001 John Wiley & Sons, Ltd.

Closure of the Challinor Centre II: An extended report on 95 individuals after 12 months of community living

As part of an institutional closure programme, 95 individuals with an intellectual disability were relocated to community-based group homes. Each individual was assessed 6 months prior to the relocation and then again after 1, 6, and 12 months of community living. Assessments involved ratings of adaptive and maladaptive behaviour, choice-making, and life circumstances. The group means comparing institution to community ratings showed improvements in adaptive functioning but no significant change in maladaptive behaviour. There were also improvements in life circumstances and increased opportunities for choice-making following relocation to the community. These outcomes suggest that relocation to the community was associated with a more active and normalised lifestyle than experienced in the institutional setting.