Faculty Bibliography

This study examined the impact of a learning collaborative composed of five child mental health agencies which was conducted from November 2005 to November 2006 in a suburban community adjacent to New York City, Data Submitted by each agency (in November/December 2005. and then monthly between April 2006 and November 2006) regarding 1) initial show-rates for first intake appointments for all new evaluations of children and adolescents. and/or; 2) attendance at any scheduled clinic appointment subsequent to the first kept intake appointment. were retrospectively analyzed. Agencies reported an increase in kept initial appointments ranging from 5% to 21% over the previous year. while kept subsequent appointments evidenced an increase between 2% and 16% In contrast. one site that did not administer the engagement strategies noted a decrease in both engagement and subsequent appointment rates during the course of the collaborative between 9% and 13% respectively These findings support the effectiveness of learning collaboratives for improving service use among youth with mental health difficulties and their families (C) 2009 Elsevier Ltd All rights reserved

Analysis of human brain development is a crucial step for improved understanding of neurodevelopmental disorders. We focus on normal brain development as is observed in the multimodal longitudinal MRI/DTI data of neonates to two years of age. We present a spatio-temporal analysis framework using Gompertz function as a population growth model with three different spatial localization strategies: voxel-based, data driven clustering and atlas driven regional analysis. Growth models from multimodal imaging channels collected at each voxel form feature vectors which are clustered using the Dirichlet Process Mixture Models (DPMM). Clustering thus combines growth information from different modalities to subdivide the image into voxel groups with similar properties. The processing generates spatial maps that highlight the dynamic progression of white matter development. These maps show progression of white matter maturation where primarily, central regions mature earlier compared to the periphery, but where more subtle regional differences in growth can be observed. Atlas based analysis allows a quantitative analysis of a specific anatomical region, whereas data driven clustering identifies regions of similar growth patterns. The combination of these two allows us to investigate growth patterns within an anatomical region. Specifically, analysis of anterior and posterior limb of internal capsule show that there are different growth trajectories within these anatomies, and that it may be useful to divide certain anatomies into subregions with distinctive growth patterns.

Deformable image registration in the presence of considerable contrast differences and large-scale size and shape changes represents a significant challenge for image registration. A representative driving application is the study of early brain development in neuroimaging, which requires co-registration of images of the same subject across time or building 4-D population atlases. Growth during the first few years of development involves significant changes in size and shape of anatomical structures but also rapid changes in tissue properties due to myelination and structuring that are reflected in the multi-modal Magnetic Resonance (MR) contrast measurements. We propose a new registration method that generates a mapping between brain anatomies represented as a multi-compartment model of tissue class posterior images and geometries. We transform intensity patterns into combined probabilistic and geometric descriptors that drive the matching in a diffeomorphic framework, where distances between geometries are represented using currents which does not require geometric correspondence. We show preliminary results on the registrations of neonatal brain MRIs to two-year old infant MRIs using class posteriors and surface boundaries of structures undergoing major changes. Quantitative validation demonstrates that our proposed method generates registrations that better preserve the consistency of anatomical structures over time.

The Building Bridges Initiative (BBI) provides a framework for achieving positive outcomes for youth and families served in residential and community programs. Founded on core principles, an emerging evidence base, and acknowledged best practices, the BBI emphasizes collaboration and coordination between providers, families, youth, advocates, and policymakers to achieve its aims. Examples are presented of successful state, community, and provider practice changes, and available tools and resources to support all constituencies in achieving positive outcomes.