The Nature of Attachment Relationships and Grief Responses in Older Adults: An Attachment Path Model of Grief

BACKGROUND: Various researchers have theorized that bereaved adults who report non-secure attachment are at higher risk of pathological grief. Yet past findings on avoidant attachment representations and grief have yielded limited and contradictory outcomes. Little research has been conducted with older adults to identify the psychological processes that mediate between self-reported attachment representations and the patterns of grief. OBJECTIVE: To examine the impacts of avoidant attachment and anxious attachment dimensions on emotion and non-acceptance, in response to the loss of a conjugal partner, and the mediating effect of yearning thoughts. DESIGN: Men (N = 21) and women (N = 68) aged 60 years and above who had lost a partner within the last 12 to 72 months were invited to participate. Participants rated their levels of yearning thoughts about the deceased, emotions and non-acceptance on the Texas Revised Inventory of Grief (TRIG-Present), and their type and level of general romantic attachment on the Experiences In Close Relationship questionnaire (ECR). RESULTS: Structural equation modelling (SEM) indicated that individuals who reported higher levels of avoidant attachment reported less emotional responses and less non-acceptance. SEM also showed that individuals who reported higher levels of anxious attachment reported greater emotional responses and greater non-acceptance. SEM further indicated that these relationships were mediated by yearning thoughts. CONCLUSION: People adopt different grief coping patterns according to their self-reported attachment representations, with the nature of their yearning thoughts influencing the process. Grief therapy may be organized according to individual differences in attachment representations

The latent structure of the adult attachment interview: Large sample evidence from the collaboration on attachment transmission synthesis

The Adult Attachment Interview (AAI) is a widely used measure in developmental science that assesses adults’ current states of mind regarding early attachment-related experiences with their primary caregivers. The standard system for coding the AAI recommends classifying individuals categorically as having an autonomous, dismissing, preoccupied, or unresolved attachment state of mind. However, previous factor and taxometric analyses suggest that: (a) adults’ attachment states of mind are captured by two weakly correlated factors reflecting adults’ dismissing and preoccupied states of mind and (b) individual differences on these factors are continuously rather than categorically distributed. The current study revisited these suggestions about the latent structure of AAI scales by leveraging individual participant data from 40 studies (N = 3,218), with a particular focus on the controversial observation from prior factor analytic work that indicators of preoccupied states of mind and indicators of unresolved states of mind about loss and trauma loaded on a common factor. Confirmatory factor analyses indicated that: (a) a 2-factor model with weakly correlated dismissing and preoccupied factors and (b) a 3-factor model that further distinguished unresolved from preoccupied states of mind were both compatible with the data. The preoccupied and unresolved factors in the 3-factor model were highly correlated. Taxometric analyses suggested that individual differences in dismissing, preoccupied, and unresolved states of mind were more consistent with a continuous than a categorical model. The importance of additional tests of predictive validity of the various models is emphasized

Attachment styles and personal growth following romantic breakups: The mediating roles of distress, rumination, and tendency to rebound

© 2013 Marshall et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.This article has been made available through the Brunel Open Access Publishing Fund.The purpose of this research was to examine the associations of attachment anxiety and avoidance with personal growth following relationship dissolution, and to test breakup distress, rumination, and tendency to rebound with new partners as mediators of these associations. Study 1 (N = 411) and Study 2 (N = 465) measured attachment style, breakup distress, and personal growth; Study 2 additionally measured ruminative reflection, brooding, and proclivity to rebound with new partners. Structural equation modelling revealed in both studies that anxiety was indirectly associated with greater personal growth through heightened breakup distress, whereas avoidance was indirectly associated with lower personal growth through inhibited breakup distress. Study 2 further showed that the positive association of breakup distress with personal growth was accounted for by enhanced reflection and brooding, and that anxious individuals’ greater personal growth was also explained by their proclivity to rebound. These findings suggest that anxious individuals’ hyperactivated breakup distress may act as a catalyst for personal growth by promoting the cognitive processing of breakup-related thoughts and emotions, whereas avoidant individuals’ deactivated distress may inhibit personal growth by suppressing this cognitive work

A novel approach to the clustering of microarray data via nonparametric density estimation

<p>Abstract</p> <p>Background</p> <p>Cluster analysis is a crucial tool in several biological and medical studies dealing with microarray data. Such studies pose challenging statistical problems due to dimensionality issues, since the number of variables can be much higher than the number of observations.</p> <p>Results</p> <p>Here, we present a general framework to deal with the clustering of microarray data, based on a three-step procedure: (i) gene filtering; (ii) dimensionality reduction; (iii) clustering of observations in the reduced space. Via a nonparametric model-based clustering approach we obtain promising results both in simulated and real data.</p> <p>Conclusions</p> <p>The proposed algorithm is a simple and effective tool for the clustering of microarray data, in an unsupervised setting.</p

Development and Validation of the Behavioral Tendencies Questionnaire

At a fundamental level, taxonomy of behavior and behavioral tendencies can be described in terms of approach, avoid, or equivocate (i.e., neither approach nor avoid). While there are numerous theories of personality, temperament, and character, few seem to take advantage of parsimonious taxonomy. The present study sought to implement this taxonomy by creating a questionnaire based on a categorization of behavioral temperaments/tendencies first identified in Buddhist accounts over fifteen hundred years ago. Items were developed using historical and contemporary texts of the behavioral temperaments, described as “Greedy/Faithful”, “Aversive/Discerning”, and “Deluded/Speculative”. To both maintain this categorical typology and benefit from the advantageous properties of forced-choice response format (e.g., reduction of response biases), binary pairwise preferences for items were modeled using Latent Class Analysis (LCA). One sample (n1 = 394) was used to estimate the item parameters, and the second sample (n2 = 504) was used to classify the participants using the established parameters and cross-validate the classification against multiple other measures. The cross-validated measure exhibited good nomothetic span (construct-consistent relationships with related measures) that seemed to corroborate the ideas present in the original Buddhist source documents. The final 13-block questionnaire created from the best performing items (the Behavioral Tendencies Questionnaire or BTQ) is a psychometrically valid questionnaire that is historically consistent, based in behavioral tendencies, and promises practical and clinical utility particularly in settings that teach and study meditation practices such as Mindfulness Based Stress Reduction (MBSR)

Comparison of scores for bimodality of gene expression distributions and genome-wide evaluation of the prognostic relevance of high-scoring genes

<p>Abstract</p> <p>Background</p> <p>A major goal of the analysis of high-dimensional RNA expression data from tumor tissue is to identify prognostic signatures for discriminating patient subgroups. For this purpose genome-wide identification of bimodally expressed genes from gene array data is relevant because distinguishability of high and low expression groups is easier compared to genes with unimodal expression distributions.</p> <p>Recently, several methods for the identification of genes with bimodal distributions have been introduced. A straightforward approach is to cluster the expression values and score the distance between the two distributions. Other scores directly measure properties of the distribution. The kurtosis, e.g., measures divergence from a normal distribution. An alternative is the outlier-sum statistic that identifies genes with extremely high or low expression values in a subset of the samples.</p> <p>Results</p> <p>We compare and discuss scores for bimodality for expression data. For the genome-wide identification of bimodal genes we apply all scores to expression data from 194 patients with node-negative breast cancer. Further, we present the first comprehensive genome-wide evaluation of the prognostic relevance of bimodal genes. We first rank genes according to bimodality scores and define two patient subgroups based on expression values. Then we assess the prognostic significance of the top ranking bimodal genes by comparing the survival functions of the two patient subgroups. We also evaluate the global association between the bimodal shape of expression distributions and survival times with an enrichment type analysis.</p> <p>Various cluster-based methods lead to a significant overrepresentation of prognostic genes. A striking result is obtained with the outlier-sum statistic (<it>p </it>< 10^-12). Many genes with heavy tails generate subgroups of patients with different prognosis.</p> <p>Conclusions</p> <p>Genes with high bimodality scores are promising candidates for defining prognostic patient subgroups from expression data. We discuss advantages and disadvantages of the different scores for prognostic purposes. The outlier-sum statistic may be particularly valuable for the identification of genes to be included in prognostic signatures. Among the genes identified as bimodal in the breast cancer data set several have not yet previously been recognized to be prognostic and bimodally expressed in breast cancer.</p

Codominant scoring of AFLP in association panels

A study on the codominant scoring of AFLP markers in association panels without prior knowledge on genotype probabilities is described. Bands are scored codominantly by fitting normal mixture models to band intensities, illustrating and optimizing existing methodology, which employs the EM-algorithm. We study features that improve the performance of the algorithm, and the unmixing in general, like parameter initialization, restrictions on parameters, data transformation, and outlier removal. Parameter restrictions include equal component variances, equal or nearly equal distances between component means, and mixing probabilities according to Hardy–Weinberg Equilibrium. Histogram visualization of band intensities with superimposed normal densities, and optional classification scores and other grouping information, assists further in the codominant scoring. We find empirical evidence favoring the square root transformation of the band intensity, as was found in segregating populations. Our approach provides posterior genotype probabilities for marker loci. These probabilities can form the basis for association mapping and are more useful than the standard scoring categories A, H, B, C, D. They can also be used to calculate predictors for additive and dominance effects. Diagnostics for data quality of AFLP markers are described: preference for three-component mixture model, good separation between component means, and lack of singletons for the component with highest mean. Software has been developed in R, containing the models for normal mixtures with facilitating features, and visualizations. The methods are applied to an association panel in tomato, comprising 1,175 polymorphic markers on 94 tomato hybrids, as part of a larger study within the Dutch Centre for BioSystems Genomics

Excess Mortality Rate During Adulthood Among Danish Adoptees

BACKGROUND AND OBJECTIVE: Adoption studies have been used to disentangle the influence of genes from shared familial environment on various traits and disease risks. However, both the factors leading to adoption and living as an adoptee may bias the studies with regard to the relative influence of genes and environment compared to the general population. The aim was to investigate whether the cohort of domestic adoptees used for these studies in Denmark is similar to the general population with respect to all-cause mortality and cause-specific mortality rates. METHODS: 13,111 adoptees born in Denmark in 1917, or later, and adopted in 1924 to 1947 were compared to all Danes from the same birth cohorts using standardized mortality ratios (SMR). The 12,729 adoptees alive in 1970 were similarly compared to all Danes using SMR as well as cause-specific SMR. RESULTS: The excess in all-cause mortality before age 65 years in adoptees was estimated to be 1.30 (95% CI 1.26-1.35). Significant excess mortality before age 65 years was also observed for infections, vascular deaths, cancer, alcohol-related deaths and suicide. Analyses including deaths after age 65 generally showed slightly less excess in mortality, but the excess was significant for all-cause mortality, cancer, alcohol-related deaths and suicides. CONCLUSION: Adoptees have an increased all-cause mortality compared to the general population. All major specific causes of death contributed, and the highest excess is seen for alcohol-related deaths

Individual Attachment Style Modulates Human Amygdala and Striatum Activation during Social Appraisal

Adult attachment style refers to individual personality traits that strongly influence emotional bonds and reactions to social partners. Behavioral research has shown that adult attachment style reflects profound differences in sensitivity to social signals of support or conflict, but the neural substrates underlying such differences remain unsettled. Using functional magnetic resonance imaging (fMRI), we examined how the three classic prototypes of attachment style (secure, avoidant, anxious) modulate brain responses to facial expressions conveying either positive or negative feedback about task performance (either supportive or hostile) in a social game context. Activation of striatum and ventral tegmental area was enhanced to positive feedback signaled by a smiling face, but this was reduced in participants with avoidant attachment, indicating relative impassiveness to social reward. Conversely, a left amygdala response was evoked by angry faces associated with negative feedback, and correlated positively with anxious attachment, suggesting an increased sensitivity to social punishment. Secure attachment showed mirror effects in striatum and amygdala, but no other specific correlate. These results reveal a critical role for brain systems implicated in reward and threat processing in the biological underpinnings of adult attachment style, and provide new support to psychological models that have postulated two separate affective dimensions to explain these individual differences, centered on the ventral striatum and amygdala circuits, respectively. These findings also demonstrate that brain responses to face expressions are not driven by facial features alone but determined by the personal significance of expressions in current social context. By linking fundamental psychosocial dimensions of adult attachment with brain function, our results do not only corroborate their biological bases but also help understand their impact on behavior