Comparison of Normalized DTI Analytical Methods: Detection Powers of Voxel-Based Analysis and Sub-Atlas Based Analysis

Koji Sakai^1,2, Susumu Mori², Kenichi Oishi², Andreia Faria²

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. We attempted to further extend the ABA to obtain statistically stronger detection power than the VBA. We propose a sub-atlas-based analysis (SBA), which uses 3D plane made from the fitting curves to the WM atlas. We compared detection power among the VBA and the SBA by using ICBM-152 normal brain artificially embedded abnormal values.