Faculty Bibliography

The increased number of challenges for comparative evaluation of biomedical image analysis procedures clearly reflects a need for unbiased assessment of the state-of-the-art methodological advances. Moreover, the ultimate translation of novel image analysis procedures to the clinic requires rigorous validation and evaluation of alternative schemes, a task that is best outsourced to the international research community. We commonly see an increase of the number of metrics to be used in parallel, reflecting alternative ways to measure similarity. Since different measures come with different scales and distributions, these are often normalized or converted into an individual rank ordering, leaving the problem of combining the set of multiple rankings into a final score. Proposed solutions are averaging or accumulation of rankings, raising the question if different metrics are to be treated the same or if all metrics would be needed to assess closeness to truth. We address this issue with a data-driven method for automatic estimation of weights for a set of metrics based on unsupervised rank aggregation. Our method requires no normalization procedures and makes no assumptions about metric distributions. We explore the sensitivity of metrics to small changes in input data with an iterative perturbation scheme, to prioritize the contribution of the most robust metrics in the overall ranking. We show on real anatomical data that our weighting scheme can dramatically change the ranking

Glaucoma, a progressive and degenerative disease of the optic nerve, is the second leading cause of blindness worldwide. Mechanical deformation of the lamina cribrosa (LC) under high intraocular pressure (IOP) can lead to axonal death of optic nerve fibers. To explore the effect of pressure on the LC, we utilize an experimental setup where longitudinal 3D optical coherence tomography (OCT) images are acquired at different levels of IOP administered via a well-controlled external force. Structural changes are measured via image deformations which map all observed images simultaneously into a common coordinate space. These deformations encode local patterns of structural and volume change across the image sequence, resulting in quantification of the spatiotemporal deformation pattern of the LC due to variation of pressure. We also describe a 3D segmentation algorithm to restrict our deformation analysis separately to the beams or pores of the LC. A single case study demonstrates the potential of the proposed methodology for non-invasive in-vivo analysis of LC dynamics in individual subjects

The brain undergoes rapid development during early childhood as a series of biophysical and chemical processes occur, which can be observed in magnetic resonance (MR) images as a change over time of white matter intensity relative to gray matter. Such a contrast change manifests in specific patterns in different imaging modalities, suggesting that brain maturation is encoded by appearance changes in multi-modal MRI. In this paper, we explore the patterns of early brain growth encoded by multi-modal contrast changes in a longitudinal study of children. For a given modality, contrast is measured by comparing histograms of intensity distributions between white and gray matter. Multivariate non-linear mixed effects (NLME) modeling provides subject-specific as well as population growth trajectories which accounts for contrast from multiple modalities. The multivariate NLME procedure and resulting non-linear contrast functions enable the study of maturation in various regions of interest. Our analysis of several brain regions in a study of 70 healthy children reveals a posterior to anterior pattern of timing of maturation in the major lobes of the cerebral cortex, with posterior regions maturing earlier than anterior regions. Furthermore, we find significant differences between maturation rates between males and females

BACKGROUND: ParentCorps is a family-centered enhancement to pre-kindergarten programming in elementary schools and early education centers. When implemented in high-poverty, urban elementary schools serving primarily Black and Latino children, it has been found to yield benefits in childhood across domains of academic achievement, behavior problems, and obesity. However, its long-term cost-effectiveness is unknown. METHODS: We determined the cost-effectiveness of ParentCorps in high-poverty, urban schools using a Markov Model projecting the long-term impact of ParentCorps compared to standard pre-kindergarten programming. We measured costs and quality adjusted life years (QALYs) resulting from the development of three disease states (i.e., drug abuse, obesity, and diabetes); from the health sequelae of these disease states; from graduation from high school; from interaction with the judiciary system; and opportunity costs of unemployment with a lifetime time horizon. The model was built, and analyses were performed in 2015-2016. RESULTS: ParentCorps was estimated to save $4387 per individual and increase each individual's quality adjusted life expectancy by 0.27 QALYs. These benefits were primarily due to the impact of ParentCorps on childhood obesity and the subsequent predicted prevention of diabetes, and ParentCorps' impact on childhood behavior problems and the subsequent predicted prevention of interaction with the judiciary system and unemployment. Results were robust on sensitivity analyses, with ParentCorps remaining cost saving and health generating under nearly all assumptions, except when schools had very small pre-kindergarten programs. CONCLUSIONS: Effective family-centered interventions early in life such as ParentCorps that impact academic, behavioral and health outcomes among children attending high-poverty, urban schools have the potential to result in longer-term health benefits and substantial cost savings.

Modeling subject-specific shape change is one of the most important challenges in longitudinal shape analysis of disease progression. Whereas anatomical change over time can be a function of normal aging; anatomy can also be impacted by disease related degeneration. Shape changes to anatomy may also be affected by external structural changes from neighboring structures, which may cause non-linear pose variations. In this paper, we propose a framework to analyze disease related shape changes by coupling extrinsic modeling of the ambient anatomical space via spatiotemporal deformations with intrinsic shape properties from medial surface analysis. We compare intrinsic shape properties of a subject-specific shape trajectory to a normative 4D shape atlas representing normal aging to separately quantify shape changes related to disease. The spatiotemporal shape modeling establishes inter/intra subject anatomical correspondence, which in turn enables comparisons between subjects and the 4D shape atlas, and also quantitative analysis of disease related shape change. The medial surface analysis captures intrinsic shape properties related to local patterns of deformation. The proposed framework simultaneously models extrinsic longitudinal shape changes in the ambient anatomical space, as well as intrinsic shape properties to give localized measurements of degeneration. Six high risk subjects and six controls are randomly sampled from a Huntington's disease image database for quantitative and qualitative comparison.

We review recent findings related to the neurobiology of infant attachment, emphasizing the role of parenting quality in attachment formation and emotional development. Current findings suggest that the development of brain structures important for emotional expression and regulation (amygdala, prefrontal cortex, hippocampus) is deeply associated with the quality of care received in infancy, with sensitive caregiving providing regulation vital for programming these structures, ultimately shaping the development of emotion into adulthood. Evidence indicates that without sensitive caregiving, infants fail to develop mechanisms needed for later-life emotion and emotion regulation. Research suggests that a sensitive period exists in early life for parental shaping of emotional development, although further cross-species research is needed to discern its age limits, and thus inform interventions.

INTRODUCTION: Attention-deficit hyperactivity disorder (ADHD) has been related to increased rates of unintentional injuries. However, the magnitude of the effect and to which extent variables such as sex, age or comorbidity can influence this relationship is unknown. Additionally, and importantly, it is unclear if, and to which degree, ADHD medications can decrease the number of unintentional injuries. Due to the amount of economic and social resources invested in the treatment of injuries, filling these gaps in the literature is highly relevant from a public health standpoint. Here, we present a protocol for a systematic review and meta-analysis to estimate the relationship between ADHD and unintentional injuries and assess the impact of pharmacological treatment for ADHD METHODS AND ANALYSIS: We will combine results from 114 bibliographic databases for studies relating ADHD and risk of injuries. Bibliographic searches and data extraction will be carried out independently by two researchers. The studies' risk of bias will be assessed using the Newcastle-Ottawa Scale. Articles reporting ORs or HRs of suffering an injury in ADHD compared with controls (or enough data to calculate them) will be combined using Robust Variance Estimation, a method that permits to include multiple non-independent outcomes in the analysis. All analyses will be carried out in Stata. Age, sex and comorbid conduct disorders will be considered as potential causes of variance and their effect analysed through meta-regression and subgroup analysis. Sensitivity analyses will exclude articles with longer follow-ups, non-stringent definitions of ADHD or controls and statistically uncontrolled/controlled outcomes. Studies implementing a self-controlled case series methodology to investigate if ADHD drugs reduce the risk of injuries will be combined with a generalised linear mixed model using the Poisson distribution and a log link function. REGISTRATION DETAILS: PROSPERO-Prospective Register of Systematic Reviews (CRD42017064967).