OBJECTIVE: The corpus callosum, the largest white matter tract in the brain, is a midline structure associated with the formation of the hippocampus, septum pellucidum, and cingulate cortex, which have been implicated in the pathogenesis of schizophrenia. Corpus callosum shape deformation, therefore, may reflect a midline neurodevelopmental abnormality. METHOD: Corpus callosum area and shape were analyzed in 14 first-episode psychotic patients with schizophrenia, 19 first-episode psychotic patients with affective disorder, and 18 normal comparison subjects. RESULTS: No statistically significant corpus callosum area differences between groups were found, but there were differences in the structure’s shape between the patients with schizophrenia and the comparison subjects. A correlation between width and angle of the corpus callosum was found in patients with affective disorder. CONCLUSIONS: Corpus callosum shape abnormalities in first-episode psychotic patients with schizophrenia may reflect a midline neurodevelopmental abnormality.

OBJECTIVE: Disruptions in connectivity between the frontal and temporal lobes may explain some of the symptoms observed in schizophrenia. Conventional magnetic resonance imaging (MRI) studies, however, have not shown compelling evidence for white matter abnormalities, because white matter fiber tracts cannot be visualized by conventional MRI. Diffusion tensor imaging is a relatively new technique that can detect subtle white matter abnormalities in vivo by assessing the degree to which directionally organized fibers have lost their normal integrity. The first three diffusion tensor imaging studies in schizophrenia showed lower anisotropic diffusion, relative to comparison subjects, in whole-brain white matter, prefrontal and temporal white matter, and the corpus callosum, respectively. Here the authors focus on fiber tracts forming temporal-frontal connections. METHOD: Anisotropic diffusion was assessed in the uncinate fasciculus, the most prominent white matter tract connecting temporal and frontal brain regions, in 15 patients with chronic schizophrenia and 18 normal comparison subjects. A 1.5-T GE Echospeed system was used to acquire 4-mm-thick coronal line-scan diffusion tensor images. Maps of the fractional anisotropy were generated to quantify the water diffusion within the uncinate fasciculus. RESULTS: Findings revealed a group-by-side interaction for fractional anisotropy and for uncinate fasciculus area, derived from automatic segmentation. The patients with schizophrenia showed a lack of normal left-greater-than-right asymmetry seen in the comparison subjects. CONCLUSIONS: These findings demonstrate the importance of investigating white matter tracts in vivo in schizophrenia and support the hypothesis of a disruption in the normal pattern of connectivity between temporal and frontal brain regions in schizophrenia.

PURPOSE: To assess the reproducibility and accuracy compared to radiologists of three automated segmentation pipelines for quantitative magnetic resonance imaging (MRI) measurement of brain white matter signal abnormalities (WMSA). MATERIALS AND METHODS: WMSA segmentation was performed on pairs of whole brain scans from 20 patients with multiple sclerosis (MS) and 10 older subjects who were positioned and imaged twice within 30 minutes. Radiologist outlines of WMSA on 20 sections from 16 patients were compared with the corresponding results of each segmentation method. RESULTS: The segmentation method combining expectation-maximization (EM) tissue segmentation, template-driven segmentation (TDS), and partial volume effect correction (PVEC) demonstrated the highest accuracy (the absolute value of the Z-score was 0.99 for both groups of subjects), as well as high interscan reproducibility (repeatability coefficient was 0.68 mL in MS patients and 1.49 mL in aging subjects). CONCLUSION: The addition of TDS to the EM segmentation and PVEC algorithms significantly improved the accuracy of WMSA volume measurements, while also improving measurement reproducibility.

This paper presents processing and visualization techniques for Diffusion Tensor Magnetic Resonance Imaging (DT-MRI). In DT-MRI, each voxel is assigned a tensor that describes local water diffusion. The geometric nature of diffusion tensors enables us to quantitatively characterize the local structure in tissues such as bone, muscle, and white matter of the brain. This makes DT-MRI an interesting modality for image analysis. In this paper we present a novel analytical solution to the Stejskal-Tanner diffusion equation system whereby a dual tensor basis, derived from the diffusion sensitizing gradient configuration, eliminates the need to solve this equation for each voxel. We further describe decomposition of the diffusion tensor based on its symmetrical properties, which in turn describe the geometry of the diffusion ellipsoid. A simple anisotropy measure follows naturally from this analysis. We describe how the geometry or shape of the tensor can be visualized using a coloring scheme based on the derived shape measures. In addition, we demonstrate that human brain tensor data when filtered can effectively describe macrostructural diffusion, which is important in the assessment of fiber-tract organization. We also describe how white matter pathways can be monitored with the methods introduced in this paper. DT-MRI tractography is useful for demonstrating neural connectivity (in vivo) in healthy and diseased brain tissue.

BACKGROUND: Recent investigations using MRI suggest that older persons with mobility impairment have a greater volume of abnormal cerebral white matter compared with persons with normal mobility, thus raising the possibility that those with impairment have lesions in areas critical for the control of mobility. OBJECTIVE: To utilize automated image analysis methods to localize the specific regions of abnormal white matter that distinguish subjects with lower mobility from subjects with higher mobility. METHODS: Tissue classification was performed on subjects’ dual-echo long repetition time spin-echo MRI using computer algorithms operating on intensity criteria integrated with anatomic information. Statistical analysis of group differences was obtained after spatially normalizing each brain to a standard reference brain. RESULTS: Four discrete periventricular regions, including bilaterally symmetric frontal and bilateral occipitoparietal regions, were identified as being sensitive (frontal) or specific (occipitoparietal) in discriminating the subjects with lower mobility from subjects with higher mobility. The symmetry of these lesions in individual subjects suggested pathology other than arteriolar infarction. CONCLUSIONS: These results suggest that damage to discrete frontal and occipitoparietal periventricular white matter locations may be associated with a mobility disorder of aging.