A Bayesian latent spatial model for mapping the cortical signature of progression to Alzheimer's disease

Prior studies have shown that atrophy in vulnerable cortical regions is associated with an increased risk of progression to clinical dementia. In this work, we utilize the longitudinal structural magnetic resonance imaging (MRI) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to investigate the relationship between the temporally changing spatial topography of cortical thickness and conversion from mild cognitive impairment to Alzheimer's disease (AD). We develop a novel Bayesian latent spatial model that employs the spatial information underlying the thickness effects across the cortical surface. The proposed method facilitates the development of imaging markers by reliably quantifying and mapping the regional vulnerability to AD progression across the cortical surface. Simulation results showed substantial gains in statistical power and estimation performance by accounting for the spatial structure of the association. Using MRI data from ADNI, we examined the topographic patterns of anatomic regions where cortical thinning is associated with an increased risk of developing AD.     

A grouped beta process model for multivariate resting‐state EEG microstate analysis on twins

EEG microstate analysis investigates the collection of distinct temporal blocks that characterize the electrical activity of the brain. Brain activity within each microstate is stable, but activity switches rapidly between different microstates in a nonrandom way. We propose a Bayesian nonparametric model that concurrently estimates the number of microstates and their underlying behaviour. We use a Markov switching vector autoregressive (VAR) framework, where a hidden Markov model (HMM) controls the nonrandom state switching dynamics of the EEG activity and a VAR model defines the behaviour of all time points within a given state. We analyze the resting‐state EEG data from twin pairs collected through the Minnesota Twin Family Study, consisting of 70 epochs per participant, where each epoch corresponds to 2 s of EEG data. We fit our model at the twin pair level, sharing information within epochs from the same participant and within epochs from the same twin pair. We capture within twin‐pair similarity, using an Indian buffet process, to consider an infinite library of microstates, allowing each participant to select a finite number of states from this library. The state spaces of highly similar twins may completely overlap while dissimilar twins could select distinct state spaces. In this way, our Bayesian nonparametric model defines a sparse set of states that describe the EEG data. All epochs from a single participant use the same set of states and are assumed to adhere to the same state switching dynamics in the HMM model, enforcing within‐participant similarity.     

Serving Immigrant Communities: Effectiveness of Nonprofit Legal Aid Organizations in an Age of Heightened Enforcement

VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations - A burgeoning line of research examines nonprofit advocacy, yet few have examined how nonprofits advocate against policy...     

Racial Contexts' Enduring Influence on Attitudes Toward Poverty*

Objective. This article's objective is to reply to Rodgers (2009) and to expand on the claim that living near poor African Americans influences Americans' views of poverty. Methods. It first presents additional analyses of the 2001 Poverty in America Survey demonstrating that respondents' racial contexts are consistent predictors of attitudes toward poverty. Other contextual variables are not. Results. The article then presents results from an exogenous shock—the post‐Katrina migration—to reinforce its claims. Despite the widespread understanding that the evacuees were not to blame for their situation, their arrival in Houston and Baton Rouge led to more individualistic explanations of poverty. Conclusion. This article concludes that even in unlikely cases, racial contexts influence attitudes toward poverty.     

Using an evidence-based CBT group intervention model for adolescents with depressive symptoms: lessons learned from a school-based adaptation

This paper highlights the process of implementing and adapting an evidence-based cognitive–behavioural therapy (CBT) depression group intervention model for adolescents (ages 11–18) in two middle schools and one alternative high school in the USA. The paper describes the process of moving an evidence-based intervention from a clinic-based setting to a school-based setting by engaging in the following steps: (1) reviewing the literature and selecting the evidence-based intervention with the greatest chance of success in the school environment; (2) adapting the evidence-based intervention to address delivery issues in a school setting; (3) preparing social workers to deliver the evidence-based intervention with fidelity; (4) evaluating the fidelity and outcomes of the implementation of the evidence-based intervention; and (5) addressing issues of sustainability or continuous quality improvement of the evidence-based intervention. The paper concludes with a section on lessons learned from piloting an adaptation of an evidence-based intervention in a real world setting.     

Who creates the jobs?

Small business start-ups create most of the new jobs in our economy. It is an attractive argument to those who want governments to support small businesses. But is it true? Or are large firms the ones that generate most jobs? Michael Anyadike-Danes, Mark Hart and Karen Bonner examine a long-running and acrimonious dispute.     

A martingale residual diagnostic for longitudinal and recurrent event data

One method of assessing the fit of an event history model is to plot the empirical standard deviation of standardised martingale residuals. We develop an alternative procedure which is valid also in the presence of measurement error and applicable to both longitudinal and recurrent event data. Since the covariance between martingale residuals at times t ₀ and t > t ₀ is independent of t, a plot of these covariances should, for fixed t ₀, have no time trend. A test statistic is developed from the increments in the estimated covariances, and we investigate its properties under various types of model misspecification. Applications of the approach are presented using two Brazilian studies measuring daily prevalence and incidence of infant diarrhoea and a longitudinal study into treatment of schizophrenia.     

Review Essay: Recent Research on Philanthropy and the Nonprofit Sector in India and South Asia

VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations -