Cluster analysis of self-awareness levels in adults with traumatic brain injury and relationship to outcome

The purpose of this study was to investigate the relationship between self-awareness, emotional distress, motivation, and outcome in adults with severe traumatic brain injury. A sample of 55 patients were selected from 120 consecutive patients with severe traumatic brain injury admitted to the rehabilitation unit of a large metropolitan public hospital. Subjects received multidisciplinary inpatient rehabilitation and different types of outpatient rehabilitation and community-based services according to availability and need, Measures used in the cluster analysis were the Patient Competency Rating Scale, Self-Awareness of Deficits Interview, Head Injury Behavior Scale, Change Assessment Questionnaire, the Beck Depression Inventory, and Beck Anxiety Inventory; outcome measures were the Disability Rating Scale, Community Integration Questionnaire, and Sickness Impact Profile. A three-cluster solution was selected, with groups labeled as high self-awareness (n = 23), low self-awareness (n = 23), and good recovery (n = 8). The high self-awareness cluster had significantly higher levels of self-awareness, motivation, and emotional distress than the low self-awareness cluster but did not differ significantly in outcome. Self-awareness after brain injury is associated with greater motivation to change behavior and higher levels of depression and anxiety; however, it was not clear that this heightened motivation actually led to any improvement in outcome. Rehabilitation timing and approach may need to be tailored to match the individual's level of self-awareness, motivation, and emotional distress.

Mixture modelling for cluster analysis

Cluster analysis via a finite mixture model approach is considered. With this approach to clustering, the data can be partitioned into a specified number of clusters g by first fitting a mixture model with g components. An outright clustering of the data is then obtained by assigning an observation to the component to which it has the highest estimated posterior probability of belonging; that is, the ith cluster consists of those observations assigned to the ith component (i = 1,..., g). The focus is on the use of mixtures of normal components for the cluster analysis of data that can be regarded as being continuous. But attention is also given to the case of mixed data, where the observations consist of both continuous and discrete variables.

Cluster analysis of high-dimensional data: A case study

Normal mixture models are often used to cluster continuous data. However, conventional approaches for fitting these models will have problems in producing nonsingular estimates of the component-covariance matrices when the dimension of the observations is large relative to the number of observations. In this case, methods such as principal components analysis (PCA) and the mixture of factor analyzers model can be adopted to avoid these estimation problems. We examine these approaches applied to the Cabernet wine data set of Ashenfelter (1999), considering the clustering of both the wines and the judges, and comparing our results with another analysis. The mixture of factor analyzers model proves particularly effective in clustering the wines, accurately classifying many of the wines by location.

A comparison of body-image dissatisfaction and eating disturbance among Australian and Hong Kong women

The current cross-cultural study was designed to test the validity of a biopsychosocial mediation model which hypothesized that a variety of biological, psychological and social variables would have their mode of action upon eating disturbance through the mediation of body-image dissatisfaction. The biopsychosocial variables examined were body mass, self-esteem, weight-related teasing, previous dieting and sociocultural influences. Forty-eight Hong Kong and 100 Australian females aged 17-28 years were assessed. Results revealed no significant difference between the groups of women in levels of body dissatisfaction and eating disturbance; however, different variables in the biopsychosocial model appeared to have contributed to their predisposition to these conditions. The findings suggest that there appear to exist important cultural differences in various aspects of dieting and body image in young women. Implications for prevention, treatment and future research are discussed. Copyright (c) 2005 John Wiley & Sons, Ltd and Eating Disorders Association.

Media influence on the body image of children and adolescents

To study the media messages portrayed to children, 925 students, from 9 to up to 14 years of age, completed “The Sociocultural Influences Questionnaire.” The media section is the focus of this paper, and the responses from three questions were selected to examine the media's influence to be slimmer, increase weight, or increase muscle size. While the girls and boys exhibited different levels of agreement with each media influence, both genders disagreed that media messages were implying they should gain weight. This is in agreement with the belief that the media perpetuates the ideal of thinness and there is a negative stigma associated with being overweight.

On the simultaneous use of clinical and microarray expression data in the cluster analysis of tissue samples

This paper considers a model-based approach to the clustering of tissue samples of a very large number of genes from microarray experiments. It is a nonstandard problem in parametric cluster analysis because the dimension of the feature space (the number of genes) is typically much greater than the number of tissues. Frequently in practice, there are also clinical data available on those cases on which the tissue samples have been obtained. Here we investigate how to use the clinical data in conjunction with the microarray gene expression data to cluster the tissue samples. We propose two mixture model-based approaches in which the number of components in the mixture model corresponds to the number of clusters to be imposed on the tissue samples. One approach specifies the components of the mixture model to be the conditional distributions of the microarray data given the clinical data with the mixing proportions also conditioned on the latter data. Another takes the components of the mixture model to represent the joint distributions of the clinical and microarray data. The approaches are demonstrated on some breast cancer data, as studied recently in van't Veer et al. (2002).

Cluster analysis of the p53 genetic regulatory network: topology and biology

We describe a network module detection approach which combines a rapid and robust clustering algorithm with an objective measure of the coherence of the modules identified. The approach is applied to the network of genetic regulatory interactions surrounding the tumor suppressor gene p53. This algorithm identifies ten clusters in the p53 network, which are visually coherent and biologically plausible.

Issues of robustness and high dimensionality in cluster analysis

Finite mixture models are being increasingly used to model the distributions of a wide variety of random phenomena. While normal mixture models are often used to cluster data sets of continuous multivariate data, a more robust clustering can be obtained by considering the t mixture model-based approach. Mixtures of factor analyzers enable model-based density estimation to be undertaken for high-dimensional data where the number of observations n is very large relative to their dimension p. As the approach using the multivariate normal family of distributions is sensitive to outliers, it is more robust to adopt the multivariate t family for the component error and factor distributions. The computational aspects associated with robustness and high dimensionality in these approaches to cluster analysis are discussed and illustrated.

A rough cluster analysis of shopping orientation data

This paper describes the application of a new technique, rough clustering, to the problem of market segmentation. Rough clustering produces different solutions to k-means analysis because of the possibility of multiple cluster membership of objects. Traditional clustering methods generate extensional descriptions of groups, that show which objects are members of each cluster. Clustering techniques based on rough sets theory generate intensional descriptions, which outline the main characteristics of each cluster. In this study, a rough cluster analysis was conducted on a sample of 437 responses from a larger study of the relationship between shopping orientation (the general predisposition of consumers toward the act of shopping) and intention to purchase products via the Internet. The cluster analysis was based on five measures of shopping orientation: enjoyment, personalization, convenience, loyalty, and price. The rough clusters obtained provide interpretations of different shopping orientations present in the data without the restriction of attempting to fit each object into only one segment. Such descriptions can be an aid to marketers attempting to identify potential segments of consumers.

Selective attention for threatening information in anxious mood states

Attention for threatening information was investigated using a computerised version of the emotional Stroop. The study examined the influence of state and trait anxiety in an unselected student sample assigned to high trait anxious (HTA) or low trait anxious (LTA) groups on the basis questionnaire scores. State anxiety was manipulated within participants through the threat of electric shock. Threatening words that were either unrelated (e.g., cancer, danger) or related to the source of the threat (e.g., electrocute, shock) were presented to participants both within and outside of awareness. In the latter condition, a backward masking procedure was used to prevent awareness of the stimulus material. In the masked condition, despite chance performance in identification of the lexical status of stimulus items, HTA participants showed facilitated colour naming for all threat words relative to control items under threat of shock, but this effect was not evident in the shock safe condition. For unmasked trials the HTA group showed significant interference in colour naming for all threat words relative to controls under the threat of shock, but not in the shock safe condition. Neither valence of the items nor the threat of shock influenced colour naming latencies for the LTA group. [ABSTRACT FROM AUTHOR]

Event-related potentials in the emotional colour naming stroop task and the emotional counting stroop task

This study compared emotional Stroop interference in the emotional colour naming Stroop and the emotional counting Stroop by measuring reaction times and event-related potentials to positive, negative and neutral words. Twenty participants had ERPs recorded at 61 sites while performing both types of emotional Stroop tasks as well as congruent and incongruent conflict conditions. All participants rated stimulus emotionality retrospectively. A robust reaction time Stroop effect was observed in response latency for the traditional ‘‘conflict’’ conditions (congruent vs. incongruent) for the counting Stroop though not the colour naming Stroop task. There was also no evidence of emotional interference for either of the tasks; however, there was trend for positive interference in the colour naming Stroop. The P5 was identified as the event-related potential associated with emotional processing. For the P5 component, significant emotionality effects were evident in the emotional colour naming Stroop for latency (542 ms). There was a significant interaction between valence and hemisphere. The latency of the P5 in the right hemisphere was later for the positive words than negative and neutral. Comparable effects of valence were evident for the emotional counting Stroop for amplitude but not latency.

Analysis of the acute postoperative pain experience following oral surgery: identification of 'unaffected', 'disabled' and 'depressed, anxious and disabled' patient clusters

Background: Pain is defined as both a sensory and an emotional experience. Acute postoperative tooth extraction pain is assessed and treated as a physiological (sensory) pain while chronic pain is a biopsychosocial problem. The purpose of this study was to assess whether psychological and social changes Occur in the acute pain state. Methods: A biopsychosocial pain questionnaire was completed by 438 subjects (165 males, 273 females) with acute postoperative pain at 24 hours following the surgical extraction of teeth and compared with 273 subjects (78 males, 195 females) with chronic orofacial pain. Statistical methods used a k-means cluster analysis. Results: Three clusters were identified in the acute pain group: 'unaffected', 'disabled' and 'depressed, anxious and disabled'. Psychosocial effects showed 24.8 per cent feeling 'distress/suffering' and 15.1 per cent 'sad and depressed'. Females reported higher pain intensity and more distress, depression and inadequate medication for pain relief (p