Unhealthy Gambling Amongst New Zealand Secondary School Students: An Exploration of Risk and Protective Factors

This study sought to determine the prevalence of gambling and unhealthy gambling behaviour and describe risk and protective factors associated with these behaviours amongst a nationally representative sample of New Zealand secondary school students (n = 8,500). Factor analysis and item response theory were used to develop a model to provide a measure of ‘unhealthy gambling’. Logistic regressions and multiple logistic regression models were used to investigate associations between unhealthy gambling behaviour and selected outcomes. Approximately one-quarter (24.2 %) of students had gambled in the last year, and 4.8 % had two or more indicators of unhealthy gambling. Multivariate analyses found that unhealthy gambling was associated with four main factors: more accepting attitudes towards gambling (pp = 0.0061); being worried about and/or trying to cut down on gambling (p p = 0.0009). Unhealthy gambling is a significant health issue for young people in New Zealand. Ethnic and social inequalities were apparent and these disparities need to be addressed

Large Bergman Spaces: invertibility, cyclicity, and subspaces of arbitrary index

In a wide class of weighted Bergman spaces, we construct invertible non-cyclic elements. These are then used to produce z-invariant subspaces of index higher than one. In addition, these elements generate nontrivial bilaterally invariant subspaces in anti-symmetrically weighted Hilbert spaces of sequences

A Combined EMD-ICA Analysis of Simultaneously Registered EEG-fMRI Data

Within a combined EEG-fMRI study of contour integration, we analyze responses to Gabor stimuli with an Empirical Mode Decomposition combined with an Independent Component Analysis. Generally, responses to different stimuli are very similar thus hard to differentiate. EMD and ICA are used intermingled and not simply in a sequential way. This novel combination helps to suppress redundant modes resulting from an application of ensemble EMD alone. The simulation results show an improved mode separation quality. Hence, the proposed method is an efficient data analysis tool to clearly reveal differences between similar response signals and activity distributions

Combining EMD with ICA to analyze combined EEG-fMRI data.

Within a combined EEG-fMRI study of contour integration, we analyze responses to Gabor stimuli with an Empirical Mode Decomposition combined with an Independent Component Analysis. Generally, responses to different stimuli are very similar thus hard to differentiate. EMD and ICA are used intermingled and not simply in a sequential way. This novel combination helps to suppress redundant modes resulting from an application of ensemble EMD alone. The simulation results show an improved mode separation quality. Hence, the proposed method is an efficient data analysis tool to clearly reveal differences between similar response signals and activity distributions

Ensemble Empirical Mode Decomposition Analysis of EEG Data Collected during a Contour Integration Task

We discuss a data-driven analysis of EEG data recorded during a combined EEG/fMRI study of visual processing during a contour integration task. The analysis is based on an ensemble empirical mode decomposition (EEMD) and discusses characteristic features of event related modes (ERMs) resulting from the decomposition. We identify clear differences in certain ERMs in response to contour vs noncontour Gabor stimuli mainly for response amplitudes peaking around 100 [ms] (called P100) and 200 [ms] (called N200) after stimulus onset, respectively. We observe early P100 and N200 responses at electrodes located in the occipital area of the brain, while late P100 and N200 responses appear at electrodes located in frontal brain areas. Signals at electrodes in central brain areas show bimodal early/late response signatures in certain ERMs. Head topographies clearly localize statistically significant response differences to both stimulus conditions. Our findings provide an independent proof of recent models which suggest that contour integration depends on distributed network activity within the brain

Bidimensional ensemble empirical mode decomposition of functional biomedical images taken during a contour integration task

In cognitive neuroscience, extracting characteristic textures and features from functional imaging modalities which could be useful in identifying particular cognitive states across different conditions is still an important field of study. This paper explores the potential of two-dimensional ensemble empirical mode decomposition (2DEEMD) to extract such textures, so-called bidimensional intrinsic mode functions (BIMFs), of functional biomedical images, especially functional magnetic resonance images (fMRI) taken while performing a contour integration task. To identify most informative textures, i.e. BIMFs, a support vector machine (SVM) as well as a random forest (RF) classifier is trained for two different stimulus/response conditions. Classification performance is used to estimate the discriminative power of extracted BIMFs. The latter are then analyzed according to their spatial distribution of brain activations related with contour integration. Results distinctly show the participation of frontal brain areas in contour integration. Employing features generated from textures represented by BIMFs exhibit superior classification performance when compared with a canonical general linear model (GLM) analysis employing statistical parametric mapping (SPM)

Combined EMD-sLORETA Analysis of EEG Data Collected during a Contour Integration Task.

Lately, Ensemble Empirical Mode Decomposition (EEMD) techniques receive growing interest in biomedical data analysis. Event-Related Modes (ERMs) represent features extracted by an EEMD from electroencephalographic (EEG) recordings. We present a new approach for source localization of EEG data based on combining ERMs with inverse models. As the first step, 64 channel EEG recordings are pooled according to six brain areas and decomposed, by applying an EEMD, into their underlying ERMs. Then, based upon the problem at hand, the most closely related ERM, in terms of frequency and amplitude, is combined with inverse modeling techniques for source localization. More specifically, the standardized low resolution brain electromagnetic tomography (sLORETA) procedure is employed in this work. Accuracy and robustness of the results indicate that this approach deems highly promising in source localization techniques for EEG data

Ensemble Empirical Mode Decomposition Analysis of EEG Data collected during a Contour Integration Task

This EEG dataset was taken during a contour integration task for 18 subject

Late Response (120-180 ms) P100 ERP.

<p>Paired t-test values of significant potential amplitude differences at electrodes are illustrated at a significance level as specified. Views are axial, saggital and coronal. The left column shows the distribution on the scalp. All 62 electrodes were used as entries to the data matrix <b>Φ</b>. <i>(Top):</i> Raw ERP P100 with significance level <i>P</i> = 0.001. <i>(Bottom):</i> ERM5 extracted from the ERP P100 with significance level <i>P</i> = 0.001. Red color (positive paired T-test values) indicates that the <i>ERP</i> amplitude for the stimulus condition <i>CT</i> is larger than for condition <i>NCT</i> while blue color (negative paired T-test values) indicates that the <i>ERP</i> amplitude for the stimulus condition <i>NCT</i> is larger than for condition <i>CT</i>.</p