Uncinate Fasciculus Findings in Schizophrenia: A Magnetic Resonance Diffusion Tensor Imaging Study

Department of Psychiatry, Harvard Medical School and VA Boston Healthcare System-Brockton Division, MA 02301, USA.
American Journal of Psychiatry (Impact Factor: 13.56). 06/2002; 159(5):813-20. DOI: 10.1176/appi.ajp.159.5.813
Source: PubMed

ABSTRACT 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.
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.
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.
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.

  • [Show abstract] [Hide abstract]
    ABSTRACT: Whereas language processing in neurotypical brains is left lateralized, individuals with schizophrenia (SZ) display a bilateral or reversed pattern of lateralization. We used MEG to investigate the implications of this atypicality on fine (left hemisphere) versus coarse (right hemisphere) semantic processing. Ten SZ and 14 controls were presented with fine (conventional metaphor, literal, and unrelated expressions) and coarse (novel metaphor) linguistic stimuli. Results showed greater activation of the right hemisphere for novel metaphors and greater bilateral activation for unrelated expressions at the M170 window in SZ. Moreover, at the M350, SZ showed reduced bilateral activation. We conclude that SZ are overreliant on early-stage coarse semantic processing. As a result, they jump too quickly to remote conclusions, with limited control over the meanings they form. This may explain one of the core symptoms of the disorder-loose associations. © 2015 Society for Psychophysiological Research.
    Psychophysiology 01/2015; DOI:10.1111/psyp.12408 · 3.18 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Diffusion tensor imaging is a variation of magnetic resonance imaging that measures the diffusion of water in tissues. This can help measure and quantify a tissue's orientation and structure, making it an ideal tool for examining cerebral white matter and neural fiber tracts. It is only beginning to be utilized in psychiatric research. This article reviews the theory behind diffusion tensor imaging, its potential to map fiber tracts in the brain, and its recent use in psychiatric research.
    Biological Psychiatry 01/2003; DOI:10.1016/S0006-3223(03)00813-8 · 9.47 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Diffusion Tensor MRI (DT-MRI) can provide important in vivo information for the detection of brain abnormalities in diseases characterized by compromised neural connectivity. To quantify diffusion tensor abnormalities based on voxel-based statistical analysis, spatial normalization is required to minimize the anatomical variability between studied brain structures. In this article, we used a multiple input channel registration algorithm based on a demons algorithm and evaluated the spatial normalization of diffusion tensor image in terms of the input information used for registration. Registration was performed on 16 DT-MRI data sets using different combinations of the channels, including a channel of T2-weighted intensity, a channel of the fractional anisotropy, a channel of the difference of the first and second eigenvalues, two channels of the fractional anisotropy and the trace of tensor, three channels of the eigenvalues of the tensor, and the six channel tensor components. To evaluate the registration of tensor data, we defined two similarity measures, i.e., the endpoint divergence and the mean square error, which we applied to the fiber bundles of target images and registered images at the same seed points in white matter segmentation. We also evaluated the tensor registration by examining the voxel-by-voxel alignment of tensors in a sample of 15 normalized DT-MRIs. In all evaluations, nonlinear warping using six independent tensor components as input channels showed the best performance in effectively normalizing the tract morphology and tensor orientation. We also present a nonlinear method for creating a group diffusion tensor atlas using the average tensor field and the average deformation field, which we believe is a better approach than a strict linear one for representing both tensor distribution and morphological distribution of the population.
    NeuroImage 12/2003; 20(4):1995-2009. DOI:10.1016/j.neuroimage.2003.08.008 · 6.13 Impact Factor

Full-text (2 Sources)

Available from
May 22, 2014