Borderline personality disorder (BPD) and schizotypal personality disorder (SPD) are both characterized by inflexible and pervasive behavioral patterns that frequently lead to significant functional impairment. Although considerable research has been conducted on the biological and phenotypic aspects of these disorders, researching, diagnosing, and treating them remains a challenge, primarily due to the difficulties associated with the categorical nature of current diagnostic methods (Skodol and Bender, 2009) which, in turn, results in significant within-group heterogeneity and between-group co-occurrence. Given the relative paucity of research comparing aspects of these disorders with one another, the current study aimed to evaluate overlapping and differentiating aspects of BPD and SPD by examining the integrity of a brain region frequently implicated in both disorders, the cingulum. The current study used a 3T Siemens scanner to acquire structural and diffusion tensor imaging in age-, sex-, and education-matched groups of 28 adults with BPD, 32 adults with SPD, and 36 healthy control participants (HC). The anterior and posterior cingulate were manually traced on all participants and then volume and fractional anisotropy (FA) comparisons were conducted across the groups for the left and right anterior and posterior cingulate. Compared with HC, SPD patients had smaller relative cingulate white matter volume and BPD patients had marginally significantly smaller relative cingulate white matter volume, and the two patient groups did not differ from one another. With regard to FA findings, a spectrum pattern emerged, such that the BPD group had significantly lower FA in the posterior cingulum relative to controls, whereas the SPD group also had lower FA in this region but did not differ from HC. The BPD group had marginally lower FA in dorsal aspects of the anterior cingulum when compared with HC, and the SPD patients did not differ from HC or BPD individuals. In summary, the current study provides evidence of aberrant connectivity of the cingulum in BPD patients, but not SPD patients, compared with HC individuals. Consistent with prior work, overall results suggest potential involvement of cingulum in BPD symptomatology. / Psychology

Medical imagery is increasingly evolving towards higher resolution and throughput. The increasing volume of data and the usage of multiple and often novel imaging modalities necessitates the use of mathematical and computational techniques for quicker, more accurate and more robust analysis of medical imagery. The fields of computer vision and machine learning provide a rich set of techniques that are useful in medical image analysis, in tasks ranging from segmentation to classification and population analysis, notably by integrating the qualitative knowledge of experts in anatomy and the pathologies of various disorders and making it applicable to the analysis of medical imagery going forward. The object of the proposed research is exactly to explore various computer vision and machine learning methods with a view to the improved analysis of multiple modalities of brain and cardiac imagery, towards enabling the clinical goals of studying schizophrenia, brain tumors (meningiomas and gliomas in particular) and cardiovascular disorders. In the first project, a framework is proposed for the segmentation of tubular, branched anatomical structures. The framework uses the tubular surface model which yields computational advantages and further incorporates a novel automatic branch detection algorithm. It is successfully applied to the segmentation of neural fiber bundles and blood vessels. In the second project, a novel population analysis framework is built using the shape model proposed as part of the first project. This framework is applied to the analysis of neural fiber bundles towards the detection and understanding of schizophrenia. In the third and final project, the use of mass spectrometry imaging for the analysis of brain tumors is motivated on two fronts, towards the offline classification analysis of the data, as well as the end application of intraoperative detection of tumor boundaries. SVMs are applied for the classification of gliomas into one of four subtypes towards application in building appropriate treatment plans, and multiple statistical measures are studied with a view to feature extraction (or biomarker detection). The problem of intraoperative tumor boundary detection is formulated as a detection of local minima of the spatial map of tumor cell concentration which in turn is modeled as a function of the mass spectra, via regression techniques.