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

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 trajectory of early brain development is marked by rapid growth presented by volume but also by tissue property changes. Capturing regional characteristics of axonal structuring and myelination via neuroimaging requires analysis of longitudinal image data with multiple modalities. Complementary to earlier studies of volume and cortical folding analysis, this paper focuses on white matter tissue changes as seen in multimodal MRI and DTI. We propose a new framework for analyzing early maturation in white matter that generates a normative spatiotemporal model and provides 3D maps of absolute and relative indices of maturation. The method, using a continuous model of intensity changes using modified Legendre polynomials, has been applied to a multimodal dataset (T1W, T2W, PD, DTI) with 8 subjects that have been scanned at approximately 2 weeks, 1 year, and 2 years. We demonstrate that spatial maturation maps generated from different modalities capture different properties of white matter growth which might lead to a better understanding of the underlying neurobiology.

Reports an error in "Psychometric properties and U.S. National norms of the Evidence-Based Practice Attitude Scale (EBPAS)" by Gregory A. Aarons, Charles Glisson, Kimberly Hoagwood, Kelly Kelleher, John Landsverk and Guy Cafri (Psychological Assessment, 2010[Jun], Vol 22[2], 356-365). There were three errors in Table 1 on p. 360. In the last row, the row label should be "Overall EBPAS mean," M = 2.73, and SD = 0.49. The revised Table 1 appears in the erratum. (The following abstract of the original article appeared in record 2010-10892-016.) The Evidence-Based Practice Attitude Scale (EBPAS) assesses mental health and social service provider attitudes toward adopting evidence-based practices. Scores on the EBPAS derive from 4 subscales (i.e., Appeal, Requirements, Openness, and Divergence) as well as the total scale, and preliminary studies have linked EBPAS scores to clinic structure and policies, organizational culture and climate, and first-level leadership. EBPAS scores are also related to service provider characteristics, including age, education level, and level of professional development. The present study examined the factor structure, reliability, and norms of EBPAS scores in a sample of 1,089 mental health service providers from a nationwide sample drawn from 100 service institutions in 26 states in the United States. The study also examined associations of provider demographic characteristics with EBPAS subscale and total scores. Confirmatory factor analysis supported a second-order factor model, and reliability coefficients for the subscales ranged from .91 to .67 (total scale = .74). The study establishes national norms for the EBPAS so that comparisons can be drawn for U.S. local as well as international studies of attitudes toward evidence-based practices. The results suggest that the factor structure and reliability are likely generalizable to a variety of service provider contexts and different service settings and that the EBPAS subscales are associated with provider characteristics. Directions for future research are discussed.