Faculty Bibliography

Diffusion Tenser Imaging (DTI) is becoming a routine magnetic resonance technique to study white matter properties and alterations of fiber integrity due to pathology. The advanced MRI technique needs postprocessing by adequate image analysis and visualization tools. Whereas such tools have been developed at various research centers to drive methodological and clinical research, they have not become widely available as software freely distributed to the community. We have developed an integrated software package for efficient processing, fiber tracking, and interactive visualization of DTI data. This allows even non-experts to explore DTI data and to obtain results that so far were exclusive to reseach teams with strong computer science support. This report describes our effort to combine common, well-established processing methods for DTI data, a recently developed powerful fiber tracking method and a modern image analysis and visualization environment into an integrated tool.

Diffusion Tenser Imaging (DTI) is becoming a routine magnetic resonance technique to study white matter properties and alterations of fiber integrity due to pathology. The advanced MRI technique needs postprocessing by adequate image processing and visualization tools. Analysis of DTI in clinical studies so far use manual definition of regions or interest or image matching followed by voxel-based analysis. This paper presents a novel concept that extracts major fiber bundles by tractography and provides a statistical analysis of diffusion properties along fibers, i.e. geodesic paths within the three-dimensional brain image. Fiber tracing thus serves as a sophisticated, efficient method for defining complex regions of interests along major fiber tracts not accessible otherwise. Fiber bundles extracted from a set of subjects are parametrized by arc-length and mapped to a common coordinate system centered at well-defined anatomical landmarks. The description of the methodology is guided by the example of measuring diffusion properties along the left and right cingulate. We also present preliminary results from an ongoing clinical neonatal study that studies early brain development.

Evidence suggests that some structural brain abnormalities in schizophrenia are neurodevelopmental in origin. There is also growing evidence to suggest that shape deformations in brain structure may reflect abnormalities in neurodevelopment. While many magnetic resonance (MR) imaging studies have investigated brain area and volume measures in schizophrenia, fewer have focused on shape deformations. In this MR study we used a 3D shape representation technique, based on spherical harmonic functions, to analyze left and right amygdala-hippocampus shapes in each of 15 patients with schizophrenia and 15 healthy controls matched for age, gender, handedness and parental socioeconomic status. Left/right asymmetry was also measured for both shape and volume differences. Additionally, shape and volume measurements were combined in a composite analysis. There were no differences between groups in overall volume or shape. Left/right amygdala-hippocampal asymmetry, however, was significantly larger in patients than controls for both relative volume and shape. The local brain regions responsible for the left/right asymmetry differences in patients with schizophrenia were in the tail of the hippocampus (including both the inferior aspect adjacent to parahippocampal gyrus and the superior aspect adjacent to the lateral geniculate nucleus and more anteriorly to the cerebral peduncles) and in portions of the amygdala body (including the anterior-superior aspect adjacent to the basal nucleus). Also, in patients, increased volumetric asymmetry tended to be correlated with increased left/right shape asymmetry. Furthermore, a combined analysis of volume and shape asymmetry resulted in improved differentiation between groups. Classification function analyses correctly classified 70% of cases using volume, 73.3% using shape, and 87% using combined volume and shape measures. These findings suggest that shape provides important new information toward characterizing the pathophysiology of schizophrenia, and that combining volume and shape measures provides improved group discrimination in studies investigating brain abnormalities in schizophrenia. An evaluation of shape deformations also suggests local abnormalities in the amygdala-hippocampal complex in schizophrenia.