The effect of template selection on diffusion tensor voxel-based analysis results Wim Van Hecke a,b,c, ⁎, Alexander Leemans d , Caroline A. Sage b , Louise Emsell e,f , Jelle Veraart c , Jan Sijbers c , Stefan Sunaert b , Paul M. Parizel a a Department of Radiology, University Hospital Antwerp, Edegem (Antwerp), Belgium b Department of Radiology, University Hospital Leuven, Leuven, Belgium c Department of Physics, Visionlab, University of Antwerp, Wilrijk (Antwerp), Belgium d Image Sciences Institute, University Medical Center Utrecht, Utrecht, the Netherlands e Department of Developmental and Functional Brain Imaging, Murdoch Children's Research Institute, Melbourne, Australia f Department of Psychiatry, National University of Ireland Galway, Galway, Ireland abstract article info Article history: Received 17 June 2010 Revised 3 November 2010 Accepted 2 December 2010 Available online 10 December 2010 Keywords: Diffusion tensor imaging Voxel-based analysis Atlas Template Diffusion tensor imaging (DTI) is increasingly being used to study white matter (WM) degeneration in patients with psychiatric and neurological disorders. In order to compare diffusion measures across subjects in an automated way, voxel-based analysis (VBA) methods were introduced. In VBA, all DTI data are transformed to a template, after which the diffusion measures of control subjects and patients are compared quantitatively in each voxel. Although VBA has many advantages compared to other post-processing approaches, such as region of interest analysis or tractography, VBA results need to be interpreted cautiously, since it has been demonstrated that they depend on the different parameter settings that are applied in the VBA processing pipeline. In this paper, we examine the effect of the template selection on the VBA results of DTI data. We hypothesized that the choice of template to which all data are transformed would also affect the VBA results. To this end, simulated DTI data sets as well as DTI data from control subjects and multiple sclerosis patients were aligned to (i) a population-specific DTI template, (ii) a subject-based DTI atlas in MNI space, and (iii) the ICBM-81 DTI atlas. Our results suggest that the highest sensitivity and specificity to detect WM abnormalities in a VBA setting was achieved using the population-specific DTI atlas, presumably due to the better spatial image alignment to this template. © 2010 Elsevier Inc. All rights reserved. Introduction Recently, voxel-based analysis (VBA) studies have demonstrated the potential of diffusion tensor magnetic resonance imaging (DT-MRI or DTI) to detect white matter (WM) changes in patients with various neurological or psychiatric disorders (White et al., 2007; Sundgren et al., 2004). In VBA, all DTI data sets are first transformed to an atlas or template (Mori et al., 2009). Subsequently, the diffusion measures of control subjects and patients are compared in each voxel (Ashburner and Friston, 2000). Although this VBA approach has many advantages compared to other post-processing methods, such as the region of interest (ROI) analysis, VBA results should be interpreted cautiously, since VBA results have been shown to depend on the selection of different settings in the VBA processing pipeline, such as the coregistration method, smoothing kernel width, statistics, and post- hoc analysis (Jones et al., 2005; Smith et al., 2006; Zhang et al., 2007; Hsu et al., 2008, 2010; Sage et al., 2009; Van Hecke et al., 2010a). For example, since in VBA, the statistical tests are performed on a voxel level, it is important that spatially overlapping voxels of different subjects correspond to the same anatomical structure after image alignment to the atlas. In this context, it has already been reported that the VBA results depend on the image coregistration algorithm that is used in the analysis (Zhang et al., 2007; Sage et al., 2009). In most VBA studies of DTI data, a standard template, such as the Montreal Neurological Institute (MNI) atlas, is used as a reference space for the alignment of all DTI data sets. The advantage of this template is that it contains anatomic and cytoarchitectonic labels in a standard coordinate space, allowing standardized reporting and comparison across studies. However, since this atlas is not study- specific, it might fail to provide a good representation of the group that is examined (e.g. brain structures of neonatal or older subjects can differ from the structures in the MNI atlas), thereby potentially resulting in considerable residual image misalignment after coregis- tration of data sets to MNI space. Furthermore, as the original MNI template only contains anatomical MR information, many studies use the T1 or T2 image intensities as input information for the coregistration algorithm. In other studies, the deformation field that was obtained from the coregistration of the anatomical MR image to NeuroImage 55 (2011) 566–573 ⁎ Corresponding author. Department of Radiology, Antwerp University Hospital, Wilrijkstraat 10, B-2650 Antwerp, Belgium. E-mail address: wim.vanhecke@ua.ac.be (W. Van Hecke). 1053-8119/$ – see front matter © 2010 Elsevier Inc. All rights reserved. doi:10.1016/j.neuroimage.2010.12.005 Contents lists available at ScienceDirect NeuroImage journal homepage: www.elsevier.com/locate/ynimg