A longitudinal investigation of the relationship between unconditional positive self-regard and posttraumatic growth

The present study investigated whether unconditional positive self-regard (UPSR) is associated with subsequent posttraumatic growth (PTG) following the experience of a traumatic life event. A total of 143 participants completed an online questionnaire to assess the experience of traumatic life events, posttraumatic stress, well-being and UPSR (Time 1). Three months later, 76 of the participants completed measures of well-being and perceived PTG (Time 2). Analyses were conducted to test for association between UPSR at Time 1 and perceptions of PTG at Time 2. Results showed that higher UPSR at T1 was associated with higher perceived PTG at Time 2. To measure actual growth, individual differences in well-being were computed between Time 1 and Time 2. Results showed that higher UPSR at T1 was associated with higher actual PTG. Implications of these findings are discussed and future directions for research in this area considered. Specifically, results are consistent with a person-centered understanding of therapeutic approaches to the facilitation of PT

Preliminary prediction of individual response to electroconvulsive therapy using whole-brain functional magnetic resonance imaging data.

Electroconvulsive therapy (ECT) works rapidly and has been widely used to treat depressive disorders (DEP). However, identifying biomarkers predictive of response to ECT remains a priority to individually tailor treatment and understand treatment mechanisms. This study used a connectome-based predictive modeling (CPM) approach in 122 patients with DEP to determine if pre-ECT whole-brain functional connectivity (FC) predicts depressive rating changes and remission status after ECT (47 of 122 total subjects or 38.5% of sample), and whether pre-ECT and longitudinal changes (pre/post-ECT) in regional brain network biomarkers are associated with treatment-related changes in depression ratings. Results show the networks with the best predictive performance of ECT response were negative (anti-correlated) FC networks, which predict the post-ECT depression severity (continuous measure) with a 76.23% accuracy for remission prediction. FC networks with the greatest predictive power were concentrated in the prefrontal and temporal cortices and subcortical nuclei, and include the inferior frontal (IFG), superior frontal (SFG), superior temporal (STG), inferior temporal gyri (ITG), basal ganglia (BG), and thalamus (Tha). Several of these brain regions were also identified as nodes in the FC networks that show significant change pre-/post-ECT, but these networks were not related to treatment response. This study design has limitations regarding the longitudinal design and the absence of a control group that limit the causal inference regarding mechanism of post-treatment status. Though predictive biomarkers remained below the threshold of those recommended for potential translation, the analysis methods and results demonstrate the promise and generalizability of biomarkers for advancing personalized treatment strategies

A Robust Classifier to Distinguish Noise from fMRI Independent Components

Analyzing Functional Magnetic Resonance Imaging (fMRI) of resting brains to determine the spatial location and activity of intrinsic brain networks–a novel and burgeoning research field–is limited by the lack of ground truth and the tendency of analyses to overfit the data. Independent Component Analysis (ICA) is commonly used to separate the data into signal and Gaussian noise components, and then map these components on to spatial networks. Identifying noise from this data, however, is a tedious process that has proven hard to automate, particularly when data from different institutions, subjects, and scanners is used. Here we present an automated method to delineate noisy independent components in ICA using a data-driven infrastructure that queries a database of 246 spatial and temporal features to discover a computational signature of different types of noise. We evaluated the performance of our method to detect noisy components from healthy control fMRI (sensitivity = 0.91, specificity = 0.82, cross validation accuracy (CVA) = 0.87, area under the curve (AUC) = 0.93), and demonstrate its generalizability by showing equivalent performance on (1) an age- and scanner-matched cohort of schizophrenia patients from the same institution (sensitivity = 0.89, specificity = 0.83, CVA = 0.86), (2) an agematched cohort on an equivalent scanner from a different institution (sensitivity = 0.88, specificity = 0.88, CVA = 0.88), and (3) an age-matched cohort on a different scanner from a different institution (sensitivity = 0.72, specificity = 0.92, CVA = 0.79). We additionally compare our approach with a recently published method [1]. Our results suggest that our method is robust to noise variations due to population as well as scanner differences, thereby making it well suited to the goal of automatically distinguishing noise from functional networks to enable investigation of human brain function

A Robust Classifier to Distinguish Noise from fMRI Independent Components

Analyzing Functional Magnetic Resonance Imaging (fMRI) of resting brains to determine the spatial location and activity of intrinsic brain networks–a novel and burgeoning research field–is limited by the lack of ground truth and the tendency of analyses to overfit the data. Independent Component Analysis (ICA) is commonly used to separate the data into signal and Gaussian noise components, and then map these components on to spatial networks. Identifying noise from this data, however, is a tedious process that has proven hard to automate, particularly when data from different institutions, subjects, and scanners is used. Here we present an automated method to delineate noisy independent components in ICA using a data-driven infrastructure that queries a database of 246 spatial and temporal features to discover a computational signature of different types of noise. We evaluated the performance of our method to detect noisy components from healthy control fMRI (sensitivity = 0.91, specificity = 0.82, cross validation accuracy (CVA) = 0.87, area under the curve (AUC) = 0.93), and demonstrate its generalizability by showing equivalent performance on (1) an age- and scanner-matched cohort of schizophrenia patients from the same institution (sensitivity = 0.89, specificity = 0.83, CVA = 0.86), (2) an agematched cohort on an equivalent scanner from a different institution (sensitivity = 0.88, specificity = 0.88, CVA = 0.88), and (3) an age-matched cohort on a different scanner from a different institution (sensitivity = 0.72, specificity = 0.92, CVA = 0.79). We additionally compare our approach with a recently published method [1]. Our results suggest that our method is robust to noise variations due to population as well as scanner differences, thereby making it well suited to the goal of automatically distinguishing noise from functional networks to enable investigation of human brain function

The role of forest harvesting and subsequent vegetative regrowth

Conservation of forest-dependent amphibians is dependent on finding a balance between timber management and species’ habitat requirements. To examine the effect of short-term vegetative regrowth post-harvesting on amphibian habitat use, we studied the response of eight species (four forest specialists and four habitat generalists) to four forestry treatments (partial harvest, clearcut with coarse woody debris [CWD] removed, clearcut with CWD retained, and uncut control) over a 6-year period, using replicated experimental treatments in Maine, USA. Forest amphibians showed a strong negative response to clearcutting through the duration of the study, regardless of the presence of CWD, but only during the post-breeding season (i.e., summer). The spring breeding migrations of wood frogs and spotted salamanders to experimental pools were not affected by the forestry treatments. The use of partial cut treatments by forest amphibians differed between animals emerging from experimental pools (i.e., juvenile wood frogs and spotted salamanders), and animals originating from outside the experimental arrays (i.e., adults of all forest species, juvenile wood frogs and spotted salamanders). Animals emerging from our experimental pools showed no difference in the use of control and partial cut treatments, while all the other animals preferred control plots. In addition, we found a modest increase in the use of clearcuts over the 6 years following harvesting by juvenile wood frogs from experimental pools (from an 8-fold difference between forest and clearcut treatments in the first year post-clearcutting to a 3-fold difference during years 3–5). However, this increase was not significantly associated with vegetation regrowth. Forest specialists declined in abundance in all treatments beginning 2–3 years post-disturbance. Despite high yearly fluctuations in abundance, there was a shift in relative abundance towards habitat generalist species, most notably green frog juveniles. Most habitat generalist species were not affected by clearcutting or vegetative regrowth; however, we observed a lower use of clearcut treatments by green frogs starting 3 years post-harvesting, perhaps due to an increase in habitat resistance to movements associated with vegetative regrowth. These general patterns of habitat use were overridden at the local scale by site-specific variation in the use of forestry treatments, most evident in emigrating juvenile wood frogs. From a management standpoint, implementing broad silvicultural prescriptions could be a viable strategy in extensively forested landscapes, but local variation in habitat use has to be acknowledged when managers focus on a limited area

Psychopathic traits modulate brain responses to drug cues in incarcerated offenders

Recent neuroscientific evidence indicates that psychopathy is associated with abnormal function and structure in limbic and paralimbic areas. Psychopathy and substance use disorders are highly comorbid, but clinical experience suggests that psychopaths abuse drugs for different reasons than non-psychopaths, and that psychopaths do not typically experience withdrawal and craving upon becoming incarcerated. These neurobiological abnormalities may be related to psychopaths\u27 different motivations for-and symptoms of-drug use. This study examined the modulatory effect of psychopathic traits on the neurobiological craving response to pictorial drug stimuli. Drug-related pictures and neutral pictures were presented and rated by participants while hemodynamic activity was monitored using functional magnetic resonance imaging. These data were collected at two correctional facilities in New Mexico using the Mind Research Network mobile magnetic resonance imaging system. The sample comprised 137 incarcerated adult males and females (93 females) with histories of substance dependence. The outcome of interest was the relation between psychopathy scores (using the Hare Psychopathy Checklist-Revised) and hemodynamic activity associated with viewing drug-related pictures vs. neutral pictures. There was a negative association between psychopathy scores and hemodynamic activity for viewing drug-related cues in the anterior cingulate, posterior cingulate, hippocampus, amygdala, caudate, globus pallidus, and parts of the prefrontal cortex. Psychopathic traits modulate the neurobiological craving response and suggest that individual differences are important for understanding and treating substance abuse

Correction Technique for Raman Water Vapor Lidar Signal-Dependent Bias and Suitability for Water Wapor Trend Monitoring in the Upper Troposphere

The MOHAVE-2009 campaign brought together diverse instrumentation for measuring atmospheric water vapor. We report on the participation of the ALVICE (Atmospheric Laboratory for Validation, Interagency Collaboration and Education) mobile laboratory in the MOHAVE-2009 campaign. In appendices we also report on the performance of the corrected Vaisala RS92 radiosonde measurements during the campaign, on a new radiosonde based calibration algorithm that reduces the influence of atmospheric variability on the derived calibration constant, and on other results of the ALVICE deployment. The MOHAVE-2009 campaign permitted the Raman lidar systems participating to discover and address measurement biases in the upper troposphere and lower stratosphere. The ALVICE lidar system was found to possess a wet bias which was attributed to fluorescence of insect material that was deposited on the telescope early in the mission. Other sources of wet biases are discussed and data from other Raman lidar systems are investigated, revealing that wet biases in upper tropospheric (UT) and lower stratospheric (LS) water vapor measurements appear to be quite common in Raman lidar systems. Lower stratospheric climatology of water vapor is investigated both as a means to check for the existence of these wet biases in Raman lidar data and as a source of correction for the bias. A correction technique is derived and applied to the ALVICE lidar water vapor profiles. Good agreement is found between corrected ALVICE lidar measurments and those of RS92, frost point hygrometer and total column water. The correction is offered as a general method to both quality control Raman water vapor lidar data and to correct those data that have signal-dependent bias. The influence of the correction is shown to be small at regions in the upper troposphere where recent work indicates detection of trends in atmospheric water vapor may be most robust. The correction shown here holds promise for permitting useful upper tropospheric water vapor profiles to be consistently measured by Raman lidar within NDACC (Network for the Detection of Atmospheric Composition Change) and elsewhere, despite the prevalence of instrumental and atmospheric effects that can contaminate the very low signal to noise measurements in the UT

Everyday cosmopolitanism in representations of Europe among young Romanians in Britain

The paper presents an analysis of everyday cosmopolitanism in constructions of Europe among young Romanian nationals living in Britain. Adopting a social representations approach, cosmopolitanism is understood as a cultural symbolic resource that is part of everyday knowledge. Through a discursively-oriented analysis of focus group data, we explore the ways in which notions of cosmopolitanism intersect with images of Europeanness in the accounts of participants. We show that, for our participants, representations of Europe are anchored in an Orientalist schema of West-vs.-East, whereby the West is seen as epitomising European values of modernity and progress, while the East is seen as backward and traditional. Our findings further show that representations of cosmopolitanism reinforce this East/West dichotomy, within a discourse of ‘Occidental cosmopolitanism’. The paper concludes with a critical discussion of the diverse and complex ideological foundations of these constructions of European cosmopolitanism and their implications