White matter tracts in first-episode psychosis: A DTI tractography study of the uncinate fasciculus

Price, G; Cercignani, M; Parker, GJM; Altmann, DR; Barnes, TRE; Barker, GJ; Joyce, EM; Ron, MA; (2008) White matter tracts in first-episode psychosis: A DTI tractography study of the uncinate fasciculus. NeuroImage, 39 (3). pp. 949-955. ISSN 1053-8119 DOI: https://doi.org/10.1016/j.neuroimage.2007.09.012

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A model of disconnectivity involving abnormalities in the cortex and connecting white matter pathways may explain the symptoms and cognitive abnormalities of schizophrenia. Recently, diffusion imaging tractography has made it possible to study white matter pathways in detail, and we present here a study of patients with first-episode psychosis using this technique. We studied the uncinate fasciculus (UF), the largest white matter tract that connects the frontal and temporal lobes, two brain regions significantly implicated in schizophrenia. Nineteen patients with first-episode schizophrenia and 23 controls were studied using a probabilistic tractography algorithm (PICo). Fractional anisotropy (FA) and probability of connection were obtained for every voxel in the tract, and the group means and distributions of these variables were compared. The spread of the FA distribution in the upper tail, as measured by the squared coefficient of variance (SCV), was reduced in the left UF in the patient group, indicating that the number of voxels with high FA values was reduced in the core of the tract and suggesting the presence of changes in fibre alignment and tract coherence in the patient group. The SCV of FA was lower in females across both groups and there was no correlation between the SCV of FA and clinical ratings. (c) 2007 Elsevier Inc. All rights reserved.

Item Type: Article
Keywords: Adolescent, Adult, Aging, physiology, Algorithms, Cerebral Cortex, pathology, Diffusion Magnetic Resonance Imaging, Female, Humans, Image Processing, Computer-Assisted, Longitudinal Studies, Male, Neural Pathways, pathology, Psychiatric Status Rating Scales, Psychotic Disorders, pathology, Regression Analysis
Faculty and Department: Faculty of Epidemiology and Population Health > Dept of Medical Statistics
PubMed ID: 17988894
Web of Science ID: 252691800004
URI: http://researchonline.lshtm.ac.uk/id/eprint/7962


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