Seasonality of suicide in the city of Sao Paulo, Brazil, 1979-2003.

OBJECTIVE: To evaluate suicide seasonality in the city of São Paulo within an urban area and tropical zone. METHOD: Suicides were evaluated using the chi-square test and analysis of variance (ANOVA) by comparing monthly, quarterly and half-yearly variations, differentiating by gender. Analyses of time series were carried out using the autocorrelation function and periodogram, while the significance level for seasonality was confirmed with the Fisher's test. RESULTS: The suicides of the period between 1979 and 2003 numbered 11,434 cases. Differences were observed in suicides occurring in Spring and Autumn for the total sample (ANOVA: p-value = 0.01), and in the male sample (ANOVA: p-value = 0.02). For the analysis of time series, seasonality was significant only for the period of 7 months in the male sample (p-value = 0.04). DISCUSSION: In this study, no significant seasonal differences were observed in the occurrences of suicides, with the exception of the male sample. The differences observed did not correspond with the pattern described in studies carried out in temperate zones. Some of the climatic particularities of the tropical zone might explain the atypical pattern of seasonality of suicides found in large populations within an urban area and tropical zone.

Seasonality of suicide in the city of Sao Paulo, Brazil, 1979-2003 / Sazonalidade do suicídio na cidade de São Paulo, Brasil, 1979-2003

OBJECTIVE: To evaluate suicide seasonality in the city of São Paulo within an urban area and tropical zone. METHOD: Suicides were evaluated using the chi-square test and analysis of variance (ANOVA) by comparing monthly, quarterly and half-yearly variations, differentiating by gender. Analyses of time series were carried out using the autocorrelation function and periodogram, while the significance level for seasonality was confirmed with the Fisher's test. RESULTS: The suicides of the period between 1979 and 2003 numbered 11,434 cases. Differences were observed in suicides occurring in Spring and Autumn for the total sample (ANOVA: p-value = 0.01), and in the male sample (ANOVA: p-value = 0.02). For the analysis of time series, seasonality was significant only for the period of 7 months in the male sample (p-value = 0.04). DISCUSSION: In this study, no significant seasonal differences were observed in the occurrences of suicides, with the exception of the male sample. The differences observed did not correspond with the pattern described in studies carried out in temperate zones. Some of the climatic particularities of the tropical zone might explain the atypical pattern of seasonality of suicides found in large populations within an urban area and tropical zone.

OBJETIVO: Avaliar a sazonalidade do suicídio na cidade de São Paulo, uma área urbana em zona tropical. MÉTODO: Os suicídios foram avaliados pelo teste de qui-quadrado e análise de variância (ANOVA), comparando variações mensais, trimestrais e semestrais, diferenciando por gênero. Também foi realizada a análise de séries temporais, utilizando a função de autocorrelação e periodograma, além da confirmação, com o teste de Fisher de significância para sazonalidade. RESULTADOS: Os suicídios do período entre 1979 e 2003 totalizaram 11.434 casos. Foram observadas diferenças apenas nos suicídios ocorridos na primavera e outono na amostra total (ANOVA: p-valor = 0,01), e na amostra para o sexo masculino (ANOVA: p-valor = 0,02). Pela análise de séries temporais, a sazonalidade foi significativa apenas para o período de sete meses, na amostra para o sexo masculino (p-valor = 0,04). DISCUSSÃO: Neste estudo não foram observadas diferenças sazonais significativas na ocorrência de suicídios, com exceção da amostra masculina. Tais diferenças não correspondem ao padrão descrito nos estudos realizados em zona temperada. Algumas das particularidades climáticas da zona tropical poderiam explicar o padrão atípico de sazonalidades de suicídios em uma grande população de área urbana e zona tropical.

The impact of functional connectivity changes on support vector machines mapping of fMRI data.

Functional magnetic resonance imaging (fMRI) is currently one of the most widely used methods for studying human brain function in vivo. Although many different approaches to fMRI analysis are available, the most widely used methods employ so called "mass-univariate" modeling of responses in a voxel-by-voxel fashion to construct activation maps. However, it is well known that many brain processes involve networks of interacting regions and for this reason multivariate analyses might seem to be attractive alternatives to univariate approaches. The current paper focuses on one multivariate application of statistical learning theory: the statistical discrimination maps (SDM) based on support vector machine, and seeks to establish some possible interpretations when the results differ from univariate approaches. In fact, when there are changes not only on the activation level of two conditions but also on functional connectivity, SDM seems more informative. We addressed this question using both simulations and applications to real data. We have shown that the combined use of univariate approaches and SDM yields significant new insights into brain activations not available using univariate methods alone. In the application to a visual working memory fMRI data, we demonstrated that the interaction among brain regions play a role in SDM's power to detect discriminative voxels.

DWT-CEM: an algorithm for scale-temporal clustering in fMRI.

The number of studies using functional magnetic resonance imaging (fMRI) has grown very rapidly since the first description of the technique in the early 1990s. Most published studies have utilized data analysis methods based on voxel-wise application of general linear models (GLM). On the other hand, temporal clustering analysis (TCA) focuses on the identification of relationships between cortical areas by measuring temporal common properties. In its most general form, TCA is sensitive to the low signal-to-noise ratio of BOLD and is dependent on subjective choices of filtering parameters. In this paper, we introduce a method for wavelet-based clustering of time-series data and show that it may be useful in data sets with low signal-to-noise ratios, allowing the automatic selection of the optimum number of clusters. We also provide examples of the technique applied to simulated and real fMRI datasets.

A method to produce evolving functional connectivity maps during the course of an fMRI experiment using wavelet-based time-varying Granger causality.

Functional magnetic resonance imaging (fMRI) is widely used to identify neural correlates of cognitive tasks. However, the analysis of functional connectivity is crucial to understanding neural dynamics. Although many studies of cerebral circuitry have revealed adaptative behavior, which can change during the course of the experiment, most of contemporary connectivity studies are based on correlational analysis or structural equations analysis, assuming a time-invariant connectivity structure. In this paper, a novel method of continuous time-varying connectivity analysis is proposed, based on the wavelet expansion of functions and vector autoregressive model (wavelet dynamic vector autoregressive-DVAR). The model also allows identification of the direction of information flow between brain areas, extending the Granger causality concept to locally stationary processes. Simulation results show a good performance of this approach even using short time intervals. The application of this new approach is illustrated with fMRI data from a simple AB motor task experiment.

Intervention models in functional connectivity identification applied to FMRI.

Recent advances in neuroimaging techniques have provided precise spatial localization of brain activation applied in several neuroscience subareas. The development of functional magnetic resonance imaging (fMRI), based on the BOLD signal, is one of the most popular techniques related to the detection of neuronal activation. However, understanding the interactions between several neuronal modules is also an important task, providing a better comprehension about brain dynamics. Nevertheless, most connectivity studies in fMRI are based on a simple correlation analysis, which is only an association measure and does not provide the direction of information flow between brain areas. Other proposed methods like structural equation modeling (SEM) seem to be attractive alternatives. However, this approach assumes prior information about the causality direction and stationarity conditions, which may not be satisfied in fMRI experiments. Generally, the fMRI experiments are related to an activation task; hence, the stimulus conditions should also be included in the model. In this paper, we suggest an intervention analysis, which includes stimulus condition, allowing a nonstationary modeling. Furthermore, an illustrative application to real fMRI dataset from a simple motor task is presented.