Bayesian longitudinal tensor response regression for modeling neuroplasticity

A major interest in longitudinal neuroimaging studies involves investigating voxel-level neuroplasticity due to treatment and other factors across visits. However, traditional voxel-wise methods are beset with several pitfalls, which can compromise the accuracy of these approaches. We propose a novel Bayesian tensor response regression approach for longitudinal imaging data, which pools information across spatially-distributed voxels to infer significant changes while adjusting for covariates. The proposed method, which is implemented using Markov chain Monte Carlo (MCMC) sampling, utilizes low-rank decomposition to reduce dimensionality and preserve spatial configurations of voxels when estimating coefficients. It also enables feature selection via joint credible regions which respect the shape of the posterior distributions for more accurate inference. In addition to group level inferences, the method is able to infer individual-level neuroplasticity, allowing for examination of personalized disease or recovery trajectories. The advantages of the proposed approach in terms of prediction and feature selection over voxel-wise regression are highlighted via extensive simulation studies. Subsequently, we apply the approach to a longitudinal Aphasia dataset consisting of task functional MRI images from a group of subjects who were administered either a control intervention or intention treatment at baseline and were followed up over subsequent visits. Our analysis revealed that while the control therapy showed long-term increases in brain activity, the intention treatment produced predominantly short-term changes, both of which were concentrated in distinct localized regions. In contrast, the voxel-wise regression failed to detect any significant neuroplasticity after multiplicity adjustments, which is biologically implausible and implies lack of power.Comment: 28 pages, 8 figures, 6 table

Social Determinants of Digital Health Adoption: Pilot Cross-sectional Survey

BackgroundInterest in and funding for digital health interventions have rapidly grown in recent years. Despite the increasing familiarity with mobile health from regulatory bodies, providers, and patients, overarching research on digital health adoption has been primarily limited to morbidity-specific and non-US samples. Consequently, there is a limited understanding of what personal factors hold statistically significant relationships with digital health uptake. Moreover, this limits digital health communities’ knowledge of equity along digital health use patterns. ObjectiveThis study aims to identify the social determinants of digital health tool adoption in Georgia. MethodsWeb-based survey respondents in Georgia 18 years or older were recruited from mTurk to answer primarily closed-ended questions within the following domains: participant demographics and health consumption background, telehealth, digital health education, prescription management tools, digital mental health services, and doctor finder tools. Participants spent around 15 to 20 minutes on a survey to provide demographic and personal health care consumption data. This data was analyzed with multivariate linear and logistic regressions to identify which of these determinants, if any, held statistically significant relationships with the total number of digital health tool categories adopted and which of these determinants had absolute relationships with specific categories. ResultsA total of 362 respondents completed the survey. Private insurance, residence in an urban area, having a primary care provider, fewer urgent emergency room (ER) visits, more ER visits leading to inpatient stays, and chronic condition presence were significantly associated with the number of digital health tool categories adopted. The separate logistic regressions exhibited substantial variability, with 3.5 statistically significant predictors per model, on average. Age, federal poverty level, number of primary care provider visits in the past 12 months, number of nonurgent ER visits in the past 12 months, number of urgent ER visits in the past 12 months, number of ER visits leading to inpatient stays in the past 12 months, race, gender, ethnicity, insurance, education, residential area, access to the internet, difficulty accessing health care, usual source of care, status of primary care provider, and status of chronic condition all had at least one statistically significant relationship with the use of a specific digital health category. ConclusionsThe results demonstrate that persons who are socioeconomically disadvantaged may not adopt digital health tools at disproportionately higher rates. Instead, digital health tools may be adopted along social determinants of health, providing strong evidence for the digital health divide. The variability of digital health adoption necessitates investing in and building a common framework to increase mobile health access. With a common framework and a paradigm shift in the design, evaluation, and implementation strategies around digital health, disparities can be further mitigated and addressed. This likely will begin with a coordinated effort to determine barriers to adopting digital health solutions