Faculty Bibliography

Fidelity measurement methods have traditionally been used to develop and evaluate the effects of psychosocial treatments and, more recently, their implementation in practice. The fidelity measurement process can also be used to operationally define and specify components of emerging but untested practices outside the realm of conventional treatment. Achieving optimal fidelity measurement effectiveness (scientific validity and reliability) and efficiency (feasibility and relevance in routine care contexts) is challenging. The purpose of this paper is to identify strategies to address these challenges in child welfare system practices. To illustrate the challenges, and operational steps to address them, we present a case example using the "Team Decisionmaking" (TDM; Annie E. Casey Foundation) intervention. This intervention has potential utility for decreasing initial entry into and time spent in foster care and increasing rates of reunification and relative care. While promising, the model requires rigorous research to refine knowledge regarding the relationship between intervention components and outcomes-research that requires fidelity measurement. The intent of this paper is to illustrate how potentially generalizable steps for developing effective and efficient fidelity measurement methods can be used to more clearly define and test the effects of child welfare system practices.

Recent progress in neurobiology and genetics is beginning to revolutionise our thinking about the developmental origins of children's mental health problems. Such advances, for instance in relation to neural plasticity and programming, and epigenetics, are moving us away from reductionist models of development and motivating a new enthusiasm to incorporate social factors within biological models of developmental psychopathology. As Burt (2014)(1) convincingly argues in the current issue of the JC

OBJECTIVE: We examined the perceived impact of child anxiety disorders on family functioning, because such impact is a key predictor of mental health service receipt. In addition, we examined the relative impact of preschool anxiety compared to that of other early childhood disorders, and whether this impact persisted after accounting for the effects of comorbidity, or varied by child age and sex. METHOD: Drawing from a pediatric primary-care clinic and oversampling for children at risk for anxiety, 917 parents of preschoolers (aged 2-5 years) completed a diagnostic interview and reported on child psychiatric symptom impact on family finances, relationships, activities, and well-being. RESULTS: After accounting for comorbid disorders, families of children with anxiety were 3.5 times more likely to report a negative impact of their child's behavior on the family relative to nondisordered children. Generalized and separation anxiety had an impact on family functioning similar to that of attention-deficit/hyperactivity disorder and disruptive disorders. There was a significant family impact for girls with social phobia, whereas there was no impact for boys. CONCLUSIONS: Preschool anxiety has a significant, unique impact on family functioning, particularly parental adjustment, highlighting the family impairment linked with early anxiety, and the need for further research on barriers to care for these disorders.

The target article argues that developmental processes are key to understanding the mirror neuron system, yet neglects several bodies of developmental research that are informative for doing so. Infants' actions and action understanding are structured by goals, and the former lends structure to the latter. Evaluating the origins and functions of mirror neurons depends on integrating investigations of neural, social-cognitive and motor development.

Post-acquisition motion correction is widely performed in diffusion-weighted imaging (DWI) to guarantee voxel-wise correspondence between DWIs. Whereas this is primarily motivated to save as many scans as possible if corrupted by motion, users do not fully understand the consequences of different types of interpolation schemes on the final analysis. Nonetheless, interpolation might increase the partial volume effect while not preserving the volume of the diffusion profile, whereas excluding poor DWIs may affect the ability to resolve crossing fibers especially with small separation angles. In this paper, we investigate the effect of interpolating diffusion measurements as well as the elimination of bad directions on the reconstructed fiber orientation diffusion functions and on the estimated fiber orientations. We demonstrate such an effect on synthetic and real HARDI datasets. Our experiments demonstrate that the effect of interpolation is more significant with small fibers separation angles where the exclusion of motion-corrupted directions decreases the ability to resolve such crossing fibers.

Temporal modeling frameworks often operate on scalar variables by summarizing data at initial stages as statistical summaries of the underlying distributions. For instance, DTI analysis often employs summary statistics, like mean, for regions of interest and properties along fiber tracts for population studies and hypothesis testing. This reduction via discarding of variability information may introduce significant errors which propagate through the procedures. We propose a novel framework which uses distribution-valued variables to retain and utilize the local variability information. Classic linear regression is adapted to employ these variables for model estimation. The increased stability and reliability of our proposed method when compared with regression using single-valued statistical summaries, is demonstrated in a validation experiment with synthetic data. Our driving application is the modeling of age-related changes along DTI white matter tracts. Results are shown for the spatiotemporal population trajectory of genu tract estimated from 45 healthy infants and compared with a Krabbe's patient.

4D pathological anatomy modeling is key to understanding complex pathological brain images. It is a challenging problem due to the difficulties in detecting multiple appearing and disappearing lesions across time points and estimating dynamic changes and deformations between them. We propose a novel semi-supervised method, called 4D active cut, for lesion recognition and deformation estimation. Existing interactive segmentation methods passively wait for user to refine the segmentations which is a difficult task in 3D images that change over time. 4D active cut instead actively selects candidate regions for querying the user, and obtains the most informative user feedback. A user simply answers 'yes' or 'no' to a candidate object without having to refine the segmentation slice by slice. Compared to single-object detection of the existing methods, our method also detects multiple lesions with spatial coherence using Markov random fields constraints. Results show improvement on the lesion detection, which subsequently improves deformation estimation.

A variety of regression schemes have been proposed on images or shapes, although available methods do not handle them jointly. In this paper, we present a framework for joint image and shape regression which incorporates images as well as anatomical shape information in a consistent manner. Evolution is described by a generative model that is the analog of linear regression, which is fully characterized by baseline images and shapes (intercept) and initial momenta vectors (slope). Further, our framework adopts a control point parameterization of deformations, where the dimensionality of the deformation is determined by the complexity of anatomical changes in time rather than the sampling of the image and/or the geometric data. We derive a gradient descent algorithm which simultaneously estimates baseline images and shapes, location of control points, and momenta. Experiments on real medical data demonstrate that our framework effectively combines image and shape information, resulting in improved modeling of 4D (3D space + time) trajectories.

OBJECTIVE:To describe cumulative radiation exposure in a large single-center cohort of children with congenital heart disease (CHD) and identify risk factors for greater exposure. STUDY DESIGN/METHODS:A detailed medical radiation exposure history was collected retrospectively for patients aged <18 years who underwent surgery for CHD between January 1, 2001, and July 22, 2009. Cumulative per patient exposure was quantified as the effective dose in millisieverts (mSv) and annualized (mSv/year). RESULTS:A total of 4132 patients were subjected to 134,715 radiation examinations at a median follow-up of 4.3 years (range, 0-8.6 years). Exposure clustered around the time of surgery. The median exposure was 14 radiologic tests (the majority of which were plain film radiographs) at an effective dose of 0.96 mSv (the majority of which was from cardiac catheterization), although this distribution had a very wide range. Almost three-quarters (73.7%) were exposed to <3 mSv/year, and 5.3% were exposed to >20 mSv/year. Neonates, children with genetic syndromes, and children requiring surgery for cardiomyopathy, pulmonary valve, single ventricle, or tricuspid valve diseases were more likely to have higher exposure levels, and those requiring surgery for aortic arch anomalies or atrioventricular septal defects were more likely to have lower levels. CONCLUSION/CONCLUSIONS:Children with CHD requiring surgery are exposed to numerous medical forms of ionizing radiation. Although the majority of patients receive <3 mSv/year, there are identifiable risk factors for higher exposure levels. This may have important health implications as these patients age.