Impact of Acoustic Radiation Force Excitation Geometry on Shear Wave Dispersion and Attenuation Estimates

Shear wave elasticity imaging (SWEI) characterizes the mechanical properties of human tissues to differentiate healthy from diseased tissue. Commercial scanners tend to reconstruct shear wave speeds for a region of interest using time-of-flight methods reporting a single shear wave speed (or elastic modulus) to the end user under the assumptions that tissue is elastic and shear wave speeds are not dependent on the frequency content of the shear waves. Human tissues, however, are known to be viscoelastic, resulting in dispersion and attenuation. Shear wave spectroscopy and spectral methods have been previously reported in the literature to quantify shear wave dispersion and attenuation, commonly making an assumption that the acoustic radiation force excitation acts as a cylindrical source with a known geometric shear wave amplitude decay. This work quantifies the bias in shear dispersion and attenuation estimates associated with making this cylindrical wave assumption when applied to shear wave sources with finite depth extents, as commonly occurs with realistic focal geometries, in elastic and viscoelastic media. Bias is quantified using analytically derived shear wave data and shear wave data generated using finite-element method models. Shear wave dispersion and attenuation bias (up to 15% for dispersion and 41% for attenuation) is greater for more tightly focused acoustic radiation force sources with smaller depths of field relative to their lateral extent (height-to-width ratios <16). Dispersion and attenuation errors associated with assuming a cylindrical geometric shear wave decay in SWEI can be appreciable and should be considered when analyzing the viscoelastic properties of tissues with acoustic radiation force source distributions with limited depths of field.     

Prospective Associations Between Peer Victimization and Dispositional Mindfulness in Early Adolescence

Peer victimization is associated with several mental health and behavioral problems during childhood and adolescence. Identifying prospective associations between victimization and factors known to protect against these problems may ultimately contribute to more precise developmental models for victimization’s role in behavioral and mental health. This study tested prospective associations between peer victimization and dispositional mindfulness, defined by non-judgmental and accepting awareness of the constant stream of lived experience, during early adolescence. It was hypothesized that victimization would predict lower levels of mindfulness over a 4-month period. Study participants were 152 seventh and eighth grade students (female?=?51%, Caucasian?=?35%, Hispanic/Latino?=?34%, African-American?=?13%, and multi-ethnic or other?=?18%) participating in a social-emotional learning intervention feasibility trial. A structural equation model tested associations between mindfulness, victimization, and covariates at baseline, and mindfulness and victimization at 4-month posttest. As hypothesized, baseline victimization predicted significantly lower levels of mindfulness at 4-month posttest. Baseline mindfulness did not predict victimization. Results may reflect victimized youths’ mindful awareness being recurrently diverted away from the present moment due to thoughts of prior and/or impending victimization. Study implications may include implementing mindful awareness practices as an intervention strategy for victimized youth to enhance and/or restore this promotive factor.     

Severity Scales for Use in Primary Health Care to Assess Chronic Fatigue Syndrome/Myalgic Encephalomyelitis

Chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME) is a physical and cognitive disabling illness, characterized by severe fatigue and a range of physiological symptoms, that primarily affects women. The immense variation in clinical presentation suggests differences in severity based on symptomology and physical and cognitive functional capacities. In this article, we examine a number of severity scales used in assessing severity of patients with CFS/ME and the clinical aspects of CFS/ME severity subgroups. The use of severity scales may be important in CFS/ME because it permits the establishment of subgroups that may improve accuracy in both clinical and research settings.     

Addressing Methodologic Challenges and Minimizing Threats to Validity in Synthesizing Findings from Individual-Level Data Across Longitudinal Randomized Trials

Integrative Data Analysis (IDA) encompasses a collection of methods for data synthesis that pools participant-level data across multiple studies. Compared with single-study analyses, IDA provides larger sample sizes, better representation of participant characteristics, and often increased statistical power. Many of the methods currently available for IDA have focused on examining developmental changes using longitudinal observational studies employing different measures across time and study. However, IDA can also be useful in synthesizing across multiple randomized clinical trials to improve our understanding of the comprehensive effectiveness of interventions, as well as mediators and moderators of those effects. The pooling of data from randomized clinical trials presents a number of methodological challenges, and we discuss ways to examine potential threats to internal and external validity. Using as an illustration a synthesis of 19 randomized clinical trials on the prevention of adolescent depression, we articulate IDA methods that can be used to minimize threats to internal validity, including (1) heterogeneity in the outcome measures across trials, (2) heterogeneity in the follow-up assessments across trials, (3) heterogeneity in the sample characteristics across trials, (4) heterogeneity in the comparison conditions across trials, and (5) heterogeneity in the impact trajectories. We also demonstrate a technique for minimizing threats to external validity in synthesis analysis that may result from non-availability of some trial datasets. The proposed methods rely heavily on latent variable modeling extensions of the latent growth curve model, as well as missing data procedures. The goal is to provide strategies for researchers considering IDA.