The Role of Home Practice Engagement in a Mindfulness-Based Intervention

Over the last three decades, there has been a precipitous rise in curiosity regarding the clinical use of mindfulness meditation for the self-management of a broad range of chronic health conditions. Despite the ever-growing body of evidence supporting the use of mindfulness-based therapies for both medical and psychological concerns, data on the active ingredients of these mind-body interventions are relatively scarce. Regular engagement in formal mindfulness practice is considered by many to be requisite for generating therapeutic change; however, previous investigations of at-home practice in MBIs have produced mixed results. The equivocal nature of these findings has been attributed to significant methodological limitations, including the lack of standardized, systematic practice monitoring tools, and a singular focus on practice time, with little attention paid to the nature and quality of one’s practice. The present study used a prospective, observational design to assess the effects of home-based practice on dispositional mindfulness, self-compassion, and psychological functioning in twenty-eight people enrolled in an MBSR or MBCT program. To address some of the aforementioned limitations, the present study collected detailed weekly accounts of participants’ home-based practice engagement, including information about practice time (i.e., frequency and duration), exercise type, perceived effort and barriers to participation, and practice quality. Hierarchical multiple regression was used to examine the relative contribution of practice time and practice quality on treatment outcomes, and to explore possible predictors of adherence to at-home practice recommendations. As anticipated, practice quality and perceived effort improved with time; however, rather unexpectedly, practice quality was not a significant predictor of treatment-related improvements in psychological health. Home practice engagement, however, was predictive of change in dispositional mindfulness, in the expected direction. Results of our secondary analyses demonstrated that employment status was predictive of home practice engagement, with those who were unemployed completing more at-home practice on average. Mindfulness self-efficacy at baseline and previous experience with meditation or other contemplative practices were independently predictive of mean practice quality. The results of this study suggest that home practice helps generate meaningful change in dispositional mindfulness, which is purportedly a key mechanism of action in mindfulness-based interventions.

Machine Learning-based Techniques to Address Spectral Distortions in Photon Counting X-ray Computed Tomography

Spectral CT using a photon counting x-ray detector (PCXD) shows great potential for measuring material composition based on energy dependent x-ray attenuation. Spectral CT is especially suited for imaging with K-edge contrast agents to address the otherwise limited contrast in soft tissues. We have developed a micro-CT system based on a PCXD. This system enables full spectrum CT in which the energy thresholds of the PCXD are swept to sample the full energy spectrum for each detector element and projection angle. Measurements provided by the PCXD, however, are distorted due to undesirable physical eects in the detector and are very noisy due to photon starvation. In this work, we proposed two methods based on machine learning to address the spectral distortion issue and to improve the material decomposition. This rst approach is to model distortions using an articial neural network (ANN) and compensate for the distortion in a statistical reconstruction. The second approach is to directly correct for the distortion in the projections. Both technique can be done as a calibration process where the neural network can be trained using 3D printed phantoms data to learn the distortion model or the correction model of the spectral distortion. This replaces the need for synchrotron measurements required in conventional technique to derive the distortion model parametrically which could be costly and time consuming. The results demonstrate experimental feasibility and potential advantages of ANN-based distortion modeling and correction for more accurate K-edge imaging with a PCXD. Given the computational eciency with which the ANN can be applied to projection data, the proposed scheme can be readily integrated into existing CT reconstruction pipelines.

Risk Prediction for Epithelial Ovarian Cancer in 11 United States-Based Case-Control Studies: Incorporation of Epidemiologic Risk Factors and 17 Confirmed Genetic Loci.

Previously developed models for predicting absolute risk of invasive epithelial ovarian cancer have included a limited number of risk factors and have had low discriminatory power (area under the receiver operating characteristic curve (AUC) < 0.60). Because of this, we developed and internally validated a relative risk prediction model that incorporates 17 established epidemiologic risk factors and 17 genome-wide significant single nucleotide polymorphisms (SNPs) using data from 11 case-control studies in the United States (5,793 cases; 9,512 controls) from the Ovarian Cancer Association Consortium (data accrued from 1992 to 2010). We developed a hierarchical logistic regression model for predicting case-control status that included imputation of missing data. We randomly divided the data into an 80% training sample and used the remaining 20% for model evaluation. The AUC for the full model was 0.664. A reduced model without SNPs performed similarly (AUC = 0.649). Both models performed better than a baseline model that included age and study site only (AUC = 0.563). The best predictive power was obtained in the full model among women younger than 50 years of age (AUC = 0.714); however, the addition of SNPs increased the AUC the most for women older than 50 years of age (AUC = 0.638 vs. 0.616). Adapting this improved model to estimate absolute risk and evaluating it in prospective data sets is warranted.