Faculty Bibliography

Increased access to antiretroviral treatment worldwide makes it more possible for children diagnosed with HIV before their 15th birthday to age into adolescence and beyond. Many HIV+ youth navigate stressors including poverty and resource scarcity, which may converge to produce emotional distress. For over a decade, CHAMP (Collaborative HIV Prevention and Adolescent Mental Health Project) investigators partnered with youth, caregivers, providers and community stakeholders to address the health, mental health and risk taking behaviors of perinatally HIV-infected youth. This paper explores the mental health needs of aging cohorts of HIV+ youth, across three global contexts, New York (U.S.), Buenos Aires (Argentina), and KwaZulu-Natal (South Africa), to inform the development and implementation of combination HIV care and prevention supports for HIV+ youth. METHODS: Analysis of data pooled across three countries involving HIV+ early adolescents and their caregivers over time (baseline and three month follow-up) was conducted. Univariate and multivariate analyses were applied to data from standardized measures used across sites to identify mental health needs of youth participants. The impact of the site specific versions of a family-strengthening intervention, CHAMP+U.S., CHAMP+Argentina, CHAMP+SA, was also examined relative to a randomized standard of care (SOC) comparison condition. RESULTS: Analyses revealed mental health resilience in a large proportion of HIV+ youth, particularly behavioral functioning and overall mental health. Yet, significant numbers of caregivers across country contexts reported impaired child emotional and prosocial wellbeing. Significant site differences emerged at baseline. Involvement in the CHAMP+ Family Program was related to significant improvement in emotional wellbeing and a trend towards enhanced prosocial behavior relative to SOC across global sites. CONCLUSIONS: Ongoing partnerships with youth, family and provider stakeholders across global sites helped to tailor programs like CHAMP+ to specific contextual needs. This has global intervention research and care implications as cohorts of HIV+ children age into adolescence.

An increasing number of adolescents born with HIV in South Africa are on antiretroviral treatment and have to confront complex issues related to coping with a chronic, stigmatizing and transmittable illness. Very few evidence-based mental health and health promotion programs for this population exist in South Africa. This study builds on a previous collaboratively designed and developmentally timed family-based intervention for early adolescents (CHAMP). The study uses community-based participatory approach as part of formative research to evaluate a pilot randomized control trial at two hospitals. The paper reports on the development, feasibility, and acceptability of the VUKA family-based program and its short-term impact on a range of psychosocial variables for HIV + preadolescents and their caregivers. A 10-session intervention of approximately 3-month duration was delivered to 65 preadolescents aged 10-13 years and their families. VUKA participants were noted to improve on all dimensions, including mental health, youth behavior, HIV treatment knowledge, stigma, communication, and adherence to medication. VUKA shows promise as a family-based mental and HIV prevention program for HIV + preadolescents and which could be delivered by trained lay staff.

The VUKA family program is one of the only evidence-based interventions to promote positive psychosocial outcomes in South African HIV-infected pre- and early adolescents and their families. In this paper, we discuss the collaborative process by which a multidisciplinary team of clinicians, researchers, counselors, and artists/educators and families adapted and developed VUKA for this population using community-based participatory research methods. We describe the intervention and explore lessons learned that may be applicable across contexts related to international collaboration and adapting evidence-based interventions so that they are likely to be acceptable, feasible, and effective in a given setting and country context.

Autism spectrum disorder (ASD) is a developmental disorder defined by behavioral symptoms that emerge during the first years of life. Associated with these symptoms are differences in the structure of a wide array of brain regions, and in the connectivity between these regions. However, the use of cohorts with large age variability and participants past the generally recognized age of onset of the defining behaviors means that many of the reported abnormalities may be a result of cascade effects of developmentally earlier deviations. This study assessed differences in connectivity in ASD at the age at which the defining behaviors first become clear. There were 113 24-month-old participants at high risk for ASD, 31 of whom were classified as ASD, and 23 typically developing 24-month-old participants at low risk for ASD. Utilizing diffusion data to obtain measures of the length and strength of connections between anatomical regions, we performed an analysis of network efficiency. Our results showed significantly decreased local and global efficiency over temporal, parietal and occipital lobes in high-risk infants classified as ASD, relative to both low- and high-risk infants not classified as ASD. The frontal lobes showed only a reduction in global efficiency in Broca's area. In addition, these same regions showed an inverse relation between efficiency and symptom severity across the high-risk infants. The results suggest delay or deficits in infants with ASD in the optimization of both local and global aspects of network structure in regions involved in processing auditory and visual stimuli, language and nonlinguistic social stimuli.

In the last decade, diffusion MRI (dMRI) studies of the human and animal brain have been used to investigate a multitude of pathologies and drug-related effects in neuroscience research. Study after study identifies white matter (WM) degeneration as a crucial biomarker for all these diseases. The tool of choice for studying WM is dMRI. However, dMRI has inherently low signal-to-noise ratio and its acquisition requires a relatively long scan time; in fact, the high loads required occasionally stress scanner hardware past the point of physical failure. As a result, many types of artifacts implicate the quality of diffusion imagery. Using these complex scans containing artifacts without quality control (QC) can result in considerable error and bias in the subsequent analysis, negatively affecting the results of research studies using them. However, dMRI QC remains an under-recognized issue in the dMRI community as there are no user-friendly tools commonly available to comprehensively address the issue of dMRI QC. As a result, current dMRI studies often perform a poor job at dMRI QC. Thorough QC of dMRI will reduce measurement noise and improve reproducibility, and sensitivity in neuroimaging studies; this will allow researchers to more fully exploit the power of the dMRI technique and will ultimately advance neuroscience. Therefore, in this manuscript, we present our open-source software, DTIPrep, as a unified, user friendly platform for thorough QC of dMRI data. These include artifacts caused by eddy-currents, head motion, bed vibration and pulsation, venetian blind artifacts, as well as slice-wise and gradient-wise intensity inconsistencies. This paper summarizes a basic set of features of DTIPrep described earlier and focuses on newly added capabilities related to directional artifacts and bias analysis.

We propose a generic method for the statistical analysis of collections of anatomical shape complexes, namely sets of surfaces that were previously segmented and labeled in a group of subjects. The method estimates an anatomical model, the template complex, that is representative of the population under study. Its shape reflects anatomical invariants within the dataset. In addition, the method automatically places control points near the most variable parts of the template complex. Vectors attached to these points are parameters of deformations of the ambient 3D space. These deformations warp the template to each subject's complex in a way that preserves the organization of the anatomical structures. Multivariate statistical analysis is applied to these deformation parameters to test for group differences. Results of the statistical analysis are then expressed in terms of deformation patterns of the template complex, and can be visualized and interpreted. The user needs only to specify the topology of the template complex and the number of control points. The method then automatically estimates the shape of the template complex, the optimal position of control points and deformation parameters. The proposed approach is completely generic with respect to any type of application and well adapted to efficient use in clinical studies, in that it does not require point correspondence across surfaces and is robust to mesh imperfections such as holes, spikes, inconsistent orientation or irregular meshing. The approach is illustrated with a neuroimaging study of Down syndrome (DS). The results demonstrate that the complex of deep brain structures shows a statistically significant shape difference between control and DS subjects. The deformation-based modelingis able to classify subjects with very high specificity and sensitivity, thus showing important generalization capability even given a low sample size. We show that the results remain significant even if the number of control points, and hence the dimension of variables in the statistical model, are drastically reduced. The analysis may even suggest that parsimonious models have an increased statistical performance. The method has been implemented in the software Deformetrica, which is publicly available at www.deformetrica.org.

Medical imaging studies increasingly use longitudinal images of individual subjects in order to follow-up changes due to development, degeneration, disease progression or efficacy of therapeutic intervention. Repeated image data of individuals are highly correlated, and the strong causality of information over time lead to the development of procedures for joint segmentation of the series of scans, called 4D segmentation. A main aim was improved consistency of quantitative analysis, most often solved via patient-specific atlases. Challenging open problems are contrast changes and occurance of subclasses within tissue as observed in multimodal MRI of infant development, neurodegeneration and disease. This paper proposes a new 4D segmentation framework that enforces continuous dynamic changes of tissue contrast patterns over time as observed in such data. Moreover, our model includes the capability to segment different contrast patterns within a specific tissue class, for example as seen in myelinated and unmyelinated white matter regions in early brain development. Proof of concept is shown with validation on synthetic image data and with 4D segmentation of longitudinal, multimodal pediatric MRI taken at 6, 12 and 24 months of age, but the methodology is generic w.r.t. different application domains using serial imaging.

Diffusion tensor imaging has become an important modality in the field of neuroimaging to capture changes in micro-organization and to assess white matter integrity or development. While there exists a number of tractography toolsets, these usually lack tools for preprocessing or to analyze diffusion properties along the fiber tracts. Currently, the field is in critical need of a coherent end-to-end toolset for performing an along-fiber tract analysis, accessible to non-technical neuroimaging researchers. The UNC-Utah NA-MIC DTI framework represents a coherent, open source, end-to-end toolset for atlas fiber tract based DTI analysis encompassing DICOM data conversion, quality control, atlas building, fiber tractography, fiber parameterization, and statistical analysis of diffusion properties. Most steps utilize graphical user interfaces (GUI) to simplify interaction and provide an extensive DTI analysis framework for non-technical researchers/investigators. We illustrate the use of our framework on a small sample, cross sectional neuroimaging study of eight healthy 1-year-old children from the Infant Brain Imaging Study (IBIS) Network. In this limited test study, we illustrate the power of our method by quantifying the diffusion properties at 1 year of age on the genu and splenium fiber tracts.