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A Computational Anatomy
Toolbox for SPM


This toolbox is a an extension to SPM12 (Wellcome Department of Cognitive Neurology) to provide computational anatomy. This covers diverse morphometric methods such as voxel-based morphometry (VBM), surface-based morphometry (SBM), deformation-based morphometry (DBM), and region- or label-based morphometry (RBM).

It is developed by Christian Gaser and Robert Dahnke (Jena University Hospital, Departments of Psychiatry and Neurology) and free but copyright software, distributed under the terms of the GNU General Public Licence as published by the Free Software Foundation; either version 2 of the Licence, or (at your option) any later version.

Voxel-based morphometry (VBM)

VBM provides the voxel-wise estimation of the local amount or volume of a specific tissue compartment (Ashburner 2005). VBM is most often applied to investigate the local distribution of grey matter, but can also be used to examine white matter. However, the sensitivity for finding effects in white matter is rather low and there exist more appropriate methods (e.g. DTI) for that purpose.

The concept of VBM incorporates different preprocessing steps: (1) spatial registration to a reference brain (template), (2) tissue classification (segmentation) into grey and white matter and CSF, and (3) bias correction of intensity non-uniformities. Finally, segmentations are modulated by scaling with the amount of volume changes due to spatial registration, so that the total amount of grey matter in the modulated image remains the same as it would be in the original image.

Deformation-based morphometry (DBM)

DBM is based on the application of non-linear registration procedures to spatially normalise one brain to another one. The simplest case of spatial registration is to correct the orientation and size of the brains. In addition to these global changes, a non-linear registration is necessary to minimise the remaining regional differences by means of local deformations. If this local adaptation is possible, the deformations now reveal information about the type and localization of the structural differences between the brains and can undergo subsequent analysis.

Differences between both images are minimized and are now coded in the deformations. Finally, a map of local volume changes can be quantified by a mathematical property of these deformations - the Jacobian determinant. This parameter is well known from continuum mechanics and is usually used for the analysis of volume changes in flowing liquids or gases. The Jacobian determinant allows a direct estimation of the percentage change in volume in each voxel and can be statistically analyzed (Gaser et al. 2001). This approach is also known as tensor-based morphometry because the Jacobian determinant represents such a tensor.

A deformation-based analysis can be carried out not only on the local changes in volume but also on the entire information of the deformations, which also includes the direction and strength of the local deformations (Gaser et al. 1999). Since each voxel contains three-dimensional information, a multivariate statistical test is necessary for analysis. A multivariate general linear model or Hotelling’s T2 test is commonly used for this type of analysis (Gaser et al. 1999).

Surface-based morphometry (SBM)

CAT12 additionally includes the estimation of the cortical thickness and central surface of the left and right hemispheres based on the projection-based thickness (PBT) method (Dahnke et al. 2012).

Furthermore, the surface pipeline uses topology correction (Yotter et al. 2011a), spherical mapping (Yotter et al. 2011b) and estimation of local surface complexity (Yotter et al. 2011c) and local gyrification (Luders et al. 2006).

Surface-based morphometry has several advantages over using volumetric data alone. For instance, brain surface meshes have been shown to increase the accuracy of brain registration compared with Talairach registration (Desai et al. 2005). Brain surface meshes also permit new forms of analyses, such as gyrification indices that measure surface complexity in 3D (Yotter et al. 2011b) or cortical thickness. Furthermore, inflation or spherical mapping of the cortical surface mesh raises the buried sulci to the surface so that mapped functional activity in these regions can be easily visualized.

Cortical thickness and central surface estimation

We use a fully automated method that allows for measurement of cortical thickness and reconstructions of the central surface in one step. It uses a tissue segmentation to estimate the white matter (WM) distance, then projects the local maxima (which is equal to the cortical thickness) to other gray matter voxels by using a neighbor relationship described by the WM distance. This projection-based thickness (PBT) allows the handling of partial volume information, sulcal blurring, and sulcal asymmetries without explicit sulcus reconstruction (Dahnke et al. 2012).

Topological correction

In order to repair topological defects we use a novel method that relies on spherical harmonics (Yotter et al. 2011a). First, the original MRI intensity values are used as a basis to select either a ’fill’ or ’cut’ operation for each topological defect. We modify the spherical map of the uncorrected brain surface mesh, such that certain triangles are favored while searching for the bounding triangle during reparameterization. Then, a low-pass filtered alternative reconstruction based on spherical harmonics is patched into the reconstructed surface in areas that previously contained defects.

Spherical mapping

A spherical map of a cortical surface is usually necessary to reparameterize the surface mesh into a common coordinate system to allow inter-subject analysis. We use a fast algorithm to reduce area distortion resulting in an improved reparameterization of the cortical surface mesh (Yotter et al. 2011b).

Spherical registration

We have adapted the volume-based diffeomorphic Dartel algorithm to the surface (Ashburner 2007) to work with spherical maps (Yotter et al. 2011d). We apply a multi-grid approach that uses reparameterized values of sulcal depth and shape index defined on the sphere to estimate a flow field that allows deforming a spherical grid.

Quality assurance (QA)

Preprocessing of magnetic resonance (MR) images strongly depends on the quality of the input data. Especially multi-center studies and data-sharing projects need to take into account varying image properties due to different scanners, sequences and protocols.

CAT introduces a novel retrospective QA framework for empirical quantification of quality differences in different scans or studies. Retrospective QA allows the evaluation of essential image parameters such as noise, inhomogeneities, and image resolution. All these quality measures will be scaled to a rating scale which easily allows to compare measures across different scanners and sequences. Furthermore, all quality measures are summarised to a single quality rating.


The CAT toolbox is available to the scientific community under the terms of the GNU General Public License.


CAT12 is designed to work with SPM12 and Matlab versions 7.4 (R2007a) to 9.0 (R2016a), and will not work with earlier versions. No additional toolboxes are required.