Regional deficits in brain volume in schizophrenia: a meta-analysis of voxel-based morphometry studies.
OBJECTIVE
Voxel-based morphometry is a method for detecting group differences in the density or volume of brain matter. The authors reviewed the literature on use of voxel-based morphometry in schizophrenia imaging research to examine the capabilities of this method for clearly identifying specific structural differences in patients with schizophrenia, compared with healthy subjects. The authors looked for consistently reported results of relative deficits in gray and white matter in schizophrenia and evaluated voxel-based morphometry methods in order to propose a future strategy for using voxel-based morphometry in schizophrenia research.


METHODS
The authors reviewed all voxel-based morphometry studies of schizophrenia that were published to May 2004 (15 studies). The studies included a total of 390 patients with a diagnosis of schizophrenia and 364 healthy volunteers.


RESULTS
Gray and white matter deficits in patients with schizophrenia, relative to healthy comparison subjects, were reported in a total of 50 brain regions. Deficits were reported in two of the 50 regions in more than 50% of the studies and in nine of the 50 regions in one study only. The most consistent findings were of relative deficits in the left superior temporal gyrus and the left medial temporal lobe. Use of a smaller smoothing kernel (4-8 mm) led to detection of a greater number of regions implicated in schizophrenia.


CONCLUSIONS
This review implicates the left superior temporal gyrus and the left medial temporal lobe as key regions of structural difference in patients with schizophrenia, compared to healthy subjects. The diversity of regions reported in voxel-based morphometry studies is in part related to the choice of variables in the automated process, such as smoothing kernel size and linear versus affine transformation, as well as to differences in patient groups. Voxel-based morphometry can be used as an exploratory whole-brain approach to identify abnormal brain regions in schizophrenia, which should then be validated by using region-of-interest analyses.
Honea
Robyn
R
Prince of Wales International Centre for SANE Research, Department of Psychiatry, University of Oxford, Warneford Hospital, Headington, Oxford, UK OX3 7JX. robyn.honea@psy.ox.ac.uk
Crow
Tim J
TJ
Passingham
Dick
D
Mackay
Clare E
CE
D000679:Amygdala / Q000033:anatomy & histology / Q000473:pathology; D001284:Atrophy; D001921:Brain / Q000033:anatomy & histology / Q000473:pathology*; D001931:Brain Mapping / Q000379:methods; D003627:Data Interpretation, Statistical; D005260:Female; D007839:Functional Laterality; D006624:Hippocampus / Q000033:anatomy & histology / Q000473:pathology; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D007091:Image Processing, Computer-Assisted / Q000379:methods* / Q000706:statistics & numerical data; D021621:Imaging, Three-Dimensional / Q000706:statistics & numerical data; D008279:Magnetic Resonance Imaging / Q000379:methods* / Q000706:statistics & numerical data; D008297:Male; D012559:Schizophrenia / Q000473:pathology*; D013702:Temporal Lobe / Q000033:anatomy & histology / Q000473:pathology