A review of diffusion tensor imaging studies in schizophrenia

Abstract

Both post-mortem and neuroimaging studies have contributed significantly to what we know about the brain and schizophrenia. MRI studies of volumetric reduction in several brain regions in schizophrenia have confirmed early speculations that the brain is disordered in schizophrenia. There is also a growing body of evidence suggesting that a disturbance in connectivity between different brain regions, rather than abnormalities within the separate regions themselves, are responsible for the clinical symptoms and cognitive dysfunctions observed in this disorder. Thus an interest in white matter fiber tracts, subserving anatomical connections between distant, as well as proximal, brain regions, is emerging. This interest coincides with the recent advent of diffusion tensor imaging (DTI), which makes it possible to evaluate the organization and coherence of white matter fiber tracts. This is an important advance as conventional MRI techniques are insensitive to fiber tract direction and organization, and have not consistently demonstrated white matter abnormalities. DTI may, therefore, provide important new information about neural circuitry, and it is increasingly being used in neuroimaging studies of psychopathological disorders. Of note, in the past five years 18 DTI studies in schizophrenia have been published, most describing white matter abnormalities. Questions still remain, however, regarding what we are measuring that is abnormal in this disease, and how measures obtained using one method correspond to those obtained using other methods? Below we review the basic principles involved in MR-DTI, followed by a review of the different methods used to evaluate diffusion. Finally, we review MR-DTI findings in schizophrenia.

Fig. 1

Schematic of diffusion tensor imaging.

Fig. 2

Influence of the number of excitations (NEX) on the data noise. 1, 4 and 16 acquisitions of single-shot EPI, and 1 acquisition of LSDI. Slice thickness 4 mm, axial orientation.

Fig. 3

Different indices used to represent diffusion. Upper left – FA (the measure of the fraction of the magnitude of the tensor that can be ascribed to the anisotropic diffusion). Upper right – RA (normalized standard deviation representing the ratio of the anisotropic part of the tensor to its isotropic part). Lower left – TR (the sum of the eigenvalues of the tensor, or the sum of diffusion in all three principal directions). Lower right – linear measure of diffusion (representing the coherence of the fibers for each voxel) and spherical measure of diffusion (visualizing fiber crossings).

Fig. 4

Different methods for visualizing diffusion tensor information – left panel – map of fractional anisotropy with higher intensity representing more anisotropic diffusion, middle panel – color map, where different intensities of the three colors indicate the size and the ADC in each of the three Cartesian directions; right panel – blue lines representing the in-plane component of the principal diffusion direction, and a color-coded out-of-plane component (more intuitive approach, where each line represents the main diffusion direction for local neighborhood, rather than for each voxel).

Fig. 5

ROI methods for quantitative analysis of diffusion – upper-left-hand-side panel – Large ROI placed on the axial slice within the frontal lobe, upper right-hand-side panel – small fixed ROI placed on the axial slice within the cingulum bundle, lower left-hand-side panel – small, fixed ROI placed within the cingulum bundle on the coronal plane, and lower right-hand-side – small ROI of the cingulum bundle on the coronal slice based on the directional information. Yellow – right side, blue – left side.

Fig. 6

Example of fiber tracking. High resolution data acquired on 3 Tesla magnet and post-processed using automated tracking procedure. Voxels within fiber bundles are color coded according to their FA values (i.e., blue, low anisotropy; and red, high anisotropy).