Central Nervous System Changes in Pediatric Heart Failure: A Volumetric Study

Autonomic dysfunction, mood disturbances, and memory deficits appear in pediatric and adult heart failure (HF). Brain areas controlling these functions show injury in adult HF patients, many of whom have comorbid cerebrovascular disease. We examined whether similar brain pathology develops in pediatric subjects without such comorbidities. In this study, high-resolution T1 brain magnetic resonance images were collected from seven severe HF subjects age (age 8–18 years [mean 13]; left ventricular shortening 9 to 19% [median 14%]) and seven age-matched healthy controls (age 8–18 years [mean 13]). After segmentation into gray matter (GM), white matter, and cerebrospinal fluid (CSF), regional volume loss between groups was determined by voxel-based morphometry. GM volume loss appeared on all HF scans, but ischemic changes and infarcts were absent. HF subjects showed greater CSF volume than controls (mean ± SD 0.30 ± 0.04 vs. 0.25 ± 0.04 l, P = 0.03), but total intracranial volume was identical (1.39 ± 0.11 vs. 1.39 ± 0.09 l, P = NS). Regional GM volume reduction appeared in the right and left posterior hippocampus, bilateral mid-insulae, and the superior medial frontal gyrus and mid-cingulate cortex of HF subjects (threshold P < 0.001). No volume-loss sites appeared in control brains. We conclude that pediatric HF patients show brain GM loss in areas similar to those of adult HF subjects. Substantial changes emerged in sites that regulate autonomic function as well as mood, personality and short-term memory. In the absence of thromboembolic disease and many comorbid conditions found in adult HF patients, pediatric HF patients show significant, focal GM volume loss, which may coincide with the multiple neurologic and psychological changes observed in patients with HF

Co-rumination buffers the link between social anxiety and depressive symptoms in early adolescence

Objectives: We examined whether co-rumination with online friends buffered the link between social anxiety and depressive symptoms over time in a community sample. Methods: In a sample of 526 participants (358 girls; Mage = 14.05) followed at three time points, we conducted a latent cross-lagged model with social anxiety, depressive symptoms, and co-rumination, controlling for friendship stability and friendship quality, and adding a latent interaction between social anxiety and co-rumination predicting depressive symptoms. Results: Social anxiety predicted depressive symptoms, but no direct links between social anxiety and co-rumination emerged. Instead, co-rumination buffered the link between social anxiety and depressive symptoms for adolescents with higher but not lower levels of social anxiety. Conclusions: These findings indicate that co-rumination exerted a positive influence on interpersonal relationships by diminishing the influence from social anxiety on depressive symptoms over time

Social Relationships and Mortality Risk: A Meta-analytic Review

In a meta-analysis, Julianne Holt-Lunstad and colleagues find that individuals' social relationships have as much influence on mortality risk as other well-established risk factors for mortality, such as smoking

Algorithm for the detection of congestive heart failure index

his study documents our efforts to provide computer support for the diagnosis of congestive heart failure (CHF). That computer support takes the form of an index value. A high index value indicates a low probability of CHF, and an index value below a threshold of 25.6 suggests a high probability of CHF. To create that index, we have designed a sophisticated algorithm chain which takes electrocardiogram signals as input. The signals are pre-processed before they are sent to a range of nonlinear feature extraction algorithms. The top 10 feature extraction methods were used to create the CHF index. By using objective feature extraction algorithms, we avoid the problem of inter- and intra-observer variability. We observed that the nonlinear feature extraction methods reflect the nature of the human heart very well. That observation is based on the fact that the nonlinear features achieved low pp-values and high feature ranking criterion scores