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Patients with schizophrenia show a number of cognitive deficits that may be related to abnormal hippocampal physiology and function. One such cognitive deficit is in transitive inference. Simply stated, transitive inference is the ability to infer A > C after directly learning A > B and B > C. The hippocampus has been implicated in transitive inference as lesions of the hippocampus in C57BL/6 mice after initial training and testing impairs transitive inference. Likewise, lesions of the hippocampus in rats prior to training also impair transitive inference. However, lesions of the whole hippocampus are not able to specifically examine the role of the dorsal versus ventral hippocampus in this task. This is important because studies suggest that the dorsal and ventral poles of the hippocampus may be functionally different. The present experiment used reversible inactivation of the dorsal and ventral hippocampus to examine the role of these structures in transitive inference. Mice were trained to learn that A>B, B>C, C>D, and D>E during training phases and then were tested to show if they learned that A>E (the novel control pairing) and that B>D (the novel pairing which requires transitive inference) during test sessions. Following these test sessions, cannulae were inserted into the hippocampus and the mice were allowed 5 days to recover. After the recovery period, mice underwent 4 more test sessions. The GABAA agonist muscimol or saline was infused into the dorsal or ventral hippocampus thirty minutes before each test session. The mice which received muscimol infusion into the dorsal hippocampus performed similarly to controls on the novel control pairing (A>E) but were significantly impaired on the novel pairing (B>D) which required transitive inference. The DBA/2 strain of mice have altered hippocampal function and has been used to model schizophrenia. The study also compared performance of DBA/2J and C57BL/6J inbred mice in TI, and foreground and background fear conditioning, which both involve the hippocampus. Separate mice were then trained with two different fear conditioning paradigms. For background fear conditioning, mice are trained with two paired presentations of a conditioned stimulus (CS, 30 second, 85 dB white noise) and an unconditioned stimulus (US, 2 second, 0.57 mA foot shock). Mice are then tested the next day for both freezing to the training context. Foreground fear conditioning differed in that the mice were presented with only the shocks during training. DBA/2J mice performed significantly worse than the C57BL/6J in both foreground and background fear conditioning and transitive inference. These results provide further support for the role of the dorsal hippocampus in transitive inference. Moreover, these results may help provide a better understanding of the cognitive deficits associated with schizophrenia. / Psychology

Prognostic scores, and more specifically comorbidity scores, are important and widely used measures in the health care field and in health services research. A comorbidity is an existing disease an individual has in addition to a primary condition of interest, such as cancer. A comorbidity score is a summary score that can be created from these individual comorbidities for prognostic purposes, as well as for confounding adjustment. Despite their widespread use, the properties of and conditions under which comorbidity scores are valid dimension reduction tools in statistical models is largely unknown. This dissertation explores the use of summary comorbidity measures in statistical models. Three particular aspects are examined. First, it is shown that, under standard conditions, the predictive ability of these summary comorbidity measures remains as accurate as the individual comorbidities in regression models, which can include factors such as treatment variables and additional covariates. However, these results are only true when no interaction exists between the individual comorbidities and any additional covariate. The use of summary comorbidity measures in the presence of such interactions leads to biased results. Second, it is shown that these measures are also valid in the causal inference framework through confounding adjustment in estimating treatment effects. Lastly, we introduce a time dependent extension of summary comorbidity scores. This time dependent score can account for changes in patients' health over time and is shown to be a more accurate predictor of patient outcomes. A data example using breast cancer data from the SEER Medicare Database is used throughout this dissertation to illustrate the application of these results to the health care field. / Statistics

For many fundamental problems of computer vision, adopting a graph-based framework can be straight-forward and very effective. In this thesis, I propose several graph-based inference methods tailored for different computer vision applications. It starts from studying contour-based object detection methods. In particular, We propose a novel framework for contour based object detection, by replacing the hough-voting framework with finding dense subgraph inference. Compared to previous work, we propose a novel shape matching scheme suitable for partial matching of edge fragments. The shape descriptor has the same geometric units as shape context but our shape representation is not histogram based. The key contribution is that we formulate the grouping of partial matching hypotheses to object detection hypotheses is expressed as maximum clique inference on a weighted graph. Consequently, each detection result not only identifies the location of the target object in the image, but also provides a precise location of its contours, since we transform a complete model contour to the image. We achieve very competitive results on ETHZ dataset, obtained in a pure shape-based framework, demonstrate that our method achieves not only accurate object detection but also precise contour localization on cluttered background. Similar to the task of grouping of partial matches in the contour-based method, in many computer vision problems, we would like to discover certain pattern among a large amount of data. For instance, in the application of unsupervised video object segmentation, where we need automatically identify the primary object and segment the object out in every frame. We propose a novel formulation of selecting object region candidates simultaneously in all frames as finding a maximum weight clique in a weighted region graph. The selected regions are expected to have high objectness score (unary potential) as well as share similar appearance (binary potential). Since both unary and binary potentials are unreliable, we introduce two types of mutex (mutual exclusion) constraints on regions in the same clique: intra-frame and inter-frame constraints. Both types of constraints are expressed in a single quadratic form. An efficient algorithm is applied to compute the maximal weight cliques that satisfy the constraints. We apply our method to challenging benchmark videos and obtain very competitive results that outperform state-of-the-art methods. We also show that the same maximum weight subgraph with mutex constraints formulation can be used to solve various computer vision problems, such as points matching, solving image jigsaw puzzle, and detecting object using 3D contours. / Computer and Information Science