Basic study for constructing the statistical patient analysis system by using multi-modality of MRI

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The voxel-based group analysis(VBA) is one of the most effective examination methods of the entire white matter(WM) of brain. However, the VBA often suffers from low statistical power(high false discovery rate), which caused by embedded noise in voxels. To ameliorate this problem, the combination of the VBA with a pre-segmented WM has already been proposed. On the other hand, 3D whole brain WM atlas(ABA : atlas-based analysis) was proposed to gain statistical power on the examination of the WM analysis. In this report, we attempted to further extend the ABA to obtain statistically stronger detection power than the VBA. We proposed a sub-atlas-based analysis(SBA), which uses 3D plane made from the fitting curves to the WM atlas. From the results of outlier test, the detection power of SBA was clearly affected by both abnormal area and its value. On the other hand, the detection power of VBA was not affected abnormal area and it was only affected by abnormal values. In the case of cluster abnormality, the detection power of SBA was affected by both abnormal area and value. The affection of abnormal area was opposite from random abnormality due to the difference of their arrangement. The detection power of VBA was also affected by both abnormal area and value. From the results, these two methods showed better detection power on the cluster abnormal arrangement than the random abnormal arrangement. This means that the distribution of abnormal values affects the detection power of VBM-style examination methods. We proposed new method"SBA"to analyse abnormality within the white matter in brain. We compared our proposed method with conventional ABA. The SBA had a larger abnormality detection power than the ABA in the case of obsessive-compulsive disorder(OCD) patients, even if the method is naturally including false discovery rate.