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. 2018 Feb;12(1):54-63.
doi: 10.1007/s11682-016-9666-7.

Alteration of gray matter microstructure in schizophrenia

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Alteration of gray matter microstructure in schizophrenia

Johanna Seitz et al. Brain Imaging Behav. 2018 Feb.

Abstract

Neuroimaging studies demonstrate gray matter (GM) macrostructural abnormalities in patients with schizophrenia (SCZ). While ex-vivo and genetic studies suggest cellular pathology associated with abnormal neurodevelopmental processes in SCZ, few in-vivo measures have been proposed to target microstructural GM organization. Here, we use diffusion heterogeneity- to study GM microstructure in SCZ. Structural and diffusion magnetic resonance imaging (MRI) were acquired on a 3 Tesla scanner in 46 patients with SCZ and 37 matched healthy controls (HC). After correction for free water, diffusion heterogeneity as well as commonly used diffusion measures FA and MD and volume were calculated for the four cortical lobes on each hemisphere, and compared between groups. Patients with early course SCZ exhibited higher diffusion heterogeneity in the GM of the frontal lobes compared to controls. Diffusion heterogeneity of the frontal lobe showed excellent discrimination between patients and HC, while none of the commonly used diffusion measures such as FA or MD did. Higher diffusion heterogeneity in the frontal lobes in early SCZ may be due to abnormal brain maturation (migration, pruning) before and during adolescence and early adulthood. Further studies are needed to investigate the role of heterogeneity as potential biomarker for SCZ risk.

Keywords: Diffusion MRI; Gray matter; Heterogeneity; Neurodevelopment; Schizophrenia.

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Figures

Fig. 1
Fig. 1
Image processing (A) Diffusion images were motion, distortion and rotation corrected (FLIRT, FSL, Oxford; http://fsl.fmrib.ox.ac.uk/fsl (Jenkinson et al., 2002)) and (B) structural images were realigned and parcellated using FreeSurfer (http://surfer.nmr.mgh.harvard.edu/). (C) This segmentation was non-linearly registered (FNIRT, FSL, Oxford; http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FNIRT) to the diffusion images. A free water correction was applied to separate the signal into a “free water” signal (D) and the tissue tensor map (E). FA and MD were calculated for each voxel of this tissue tensor map. Afterwards heterogeneities of FA and MD were computed for the frontal, parietal, temporal and occipital lobes.
Fig. 2
Fig. 2
Area under the curve analysis HFA of the frontal region demonstrated excellent discrimination between patients with SCZ and HC.

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