Attention,Externalizing, and Internalizing Problems of Youth Exposed to Parental Incarceration

Few studies have examined the effects of parental incarceration (PI) on outcomes above and beyond other risk and adverse childhood experiences (ACEs). The objectives of this study were to (1) the associations between PI and mental health problems (attention, externalizing, internalizing, and total behavioral problems) and (2) the mediating role of current socioeconomic status and cumulative ACEs. An observational and cross-sectional design was employed. Analyses included hierarchical multivariable linear regression modeling. The analytic sample included 613 adolescents (11–17?years). On average, youth exposed to PI experienced three times as many ACEs compared with youth unexposed. Youth exposed to PI were more likely to have behavioral problems than their unexposed peers. The main effect for all models was attenuated by current economic hardship as well as exposure to increasing numbers of ACEs. Exposure to PI can be viewed as a marker of accumulative risk for intervention since youth impacted by PI are more likely to experience behavioral difficulties and associated adverse childhood experiences. Due to the associated adversity that impact youth exposed to PI, mental health providers need to be able to identify and screen for symptoms associated with trauma.     

Sample size re-estimation for clinical trials with longitudinal negative binomial counts including time trends

In some diseases, such as multiple sclerosis, lesion counts obtained from magnetic resonance imaging (MRI) are used as markers of disease progression. This leads to longitudinal, and typically overdispersed, count data outcomes in clinical trials. Models for such data invariably include a number of nuisance parameters, which can be difficult to specify at the planning stage, leading to considerable uncertainty in sample size specification. Consequently, blinded sample size re-estimation procedures are used, allowing for an adjustment of the sample size within an ongoing trial by estimating relevant nuisance parameters at an interim point, without compromising trial integrity. To date, the methods available for re-estimation have required an assumption that the mean count is time-constant within patients. We propose a new modeling approach that maintains the advantages of established procedures but allows for general underlying and treatment-specific time trends in the mean response. A simulation study is conducted to assess the effectiveness of blinded sample size re-estimation methods over fixed designs. Sample sizes attained through blinded sample size re-estimation procedures are shown to maintain the desired study power without inflating the Type I error rate and the procedure is demonstrated on MRI data from a recent study in multiple sclerosis.     

Detecting white matter alterations in multiple sclerosis using advanced diffusion magnetic resonance imaging

Multiple sclerosis is a neurodegenerative and inflammatory disease, a hallmark of which is demyelinating lesions in the white matter. We hypothesized that alterations in white matter microstructures can be non-invasively characterized by advanced diffusion magnetic resonance imaging. Seven diffusion metrics were extracted from hybrid diffusion imaging acquisitions via classic diffusion tensor imaging, neurite orientation dispersion and density imaging, and q-space imaging. We investigated the sensitivity of the diffusion metrics in 36 sets of regions of interest in the brain white matter of six female patients(age 52.8 ± 4.3 years) with multiple sclerosis. Each region of interest set included a conventional T2-defined lesion, a matched perilesion area, and normal-appearing white matter. Six patients with multiple sclerosis(n = 5) or clinically isolated syndrome(n = 1) at a mild to moderate disability level were recruited. The patients exhibited microstructural alterations from normal-appearing white matter transitioning to perilesion areas and lesions, consistent with decreased tissue restriction, decreased axonal density, and increased classic diffusion tensor imaging diffusivity. The findings suggest that diffusion compartment modeling and q-spa ce analysis appeared to be sensitive for detecting subtle microstructural alterations between perilesion areas and normal-appearing white matter.