Automated post-processing tools

2.1.2.1

Voxel-based morphometry

Voxel-based morphometry (VBM) is a fully automated, objective image- processing framework to assess regional differences in tissue volume at a voxel level and is one of the most popular computational anatomy tools. Usually, grey matter is examined but it can also be used to assess white matter changes, though with decreased sensitivity. (Ashburner and Friston, 2000)

The software package Statistical Parametric Mapping (SPM), which has been designed for the analysis of brain imaging data trough the assessment of spatially extended statistical processes, offers one option to analyse VBM data.

Preprocessing

There are three basic preprocessing steps, including tissue classification, normalisation to a common space and spatial smoothing.

Tissue classification

Prior to tissue segmentation, non-brain parts are removed via skull-stripping. Inhomogeneities of the magnetic field will cause intensity nonuniformities, resulting in different intensities for the same tissue class in different regions. This is corrected for by using bias correction prior to applying tissue segmentation. The tissue is then segmented into grey matter, white matter and cerebrospinal fluid (CSF). Additional probability maps can be used to guide the segmentation process, i.e. the segmentation process is restricted and driven by a map for each tissue class that indicates how probable it is to be present at a certain voxel in the image. These tissue probability maps are problematic in cohorts that may deviate from these maps, e.g. children.

Normalisation

Images are normalised to a standard space to enable voxel-wise comparisons across subjects. There are two main normalisation methods available in Statistical Parametric Mapping (SPM): a low-dimensional SPM default normalisation, which uses pre-existing symmetric tissue probability maps as a reference atlas, and a high-dimensional DARTEL (Diffeomorphic Anatomical Registration using Exponentiated Lie algebra) normalisation, which creates a study specific reference atlas (Ashburner, 2007). In DARTEL normalisation, registration starts by creating a mean of all images. This is then used as initial template; the images are registered to this template and subsequently averaged again. This procedure is repeated several times and eventually results in a highly accurate mean template and deformation fields, describing how local structures were adjusted to match the mean template. Deformations are finally used to warp the initial images into standard space.

Voxel-wise volume changes due to normalisation can be derived from the deformation fields as Jacobian determinants. This information can be used to apply modulation, which means that the normalised grey matter segments are multiplied with the Jacobian determinants to correct for volume changes due to normalisation. Hence original local volumes will be preserved even in standard space.

Spatial smoothing

The normalised tissue segments are then convolved with a Gaussian function, which is referred to as smoothing. Since statistical analysis will be done with parametric tests, smoothing is done to ensure that random errors have a Gaussian distribution. In addition, smoothing compensates for small registration errors and renders the analysis sensitive to effects that approximately match the

size of the smoothing kernel. Usually, a smoothing kernel of full width at half maximum of 4-12 mm is recommended. After smoothing, each voxel represents a weighted mean of its own and neighbouring voxels’ values.

SPM8 toolbox

The SPM8 toolbox is an extension of the “classical” VBM8 method, but uses a different segmentation approach.

Firstly, the segmentation approach is based on an adaptive Maximum A Posterior (MAP) technique without the need for a priori information about tissue probabilities. The Tissue Probability Maps are only used for spatial normalisation. The resulting MAP estimation is adaptive, as local parameter variations (i.e. means and variance) are modelled as slowly varying spatial functions (Rajapakse et al., 1997), accounting for intensity inhomogeneities and other local variations of intensity.

Secondly, a Partial Volume Estimation (PVE) with a mixed model of at most two tissue types is used during segmentation. Images are initially segmented into grey matter (GM), white matter (WM), and cerebrospinal fluid (CSF) based on the MAP estimation. This is followed by a PVE of two additional mixed classes, namely GM-WM and GM‐CSF. As single voxels may contain more than one tissue type, this will result in an estimation of the fraction of each tissue type present in each voxel.

Thirdly, two denoising methods are applied: A spatially adaptive nonlocal means (SANLM) denoising filter (Manjón et al., 2010) that will remove noise while preserving edges; and a classical Markov Random Field (MRF) approach to include spatial prior information of adjacent voxels into the segmentation estimation (Rajapakse et al., 1997).

Finally, for DARTEL normalisation an already existing DARTEL template in Montreal Neurological Institute (MNI) space is used, which was derived from 550 healthy control subjects of the IXI‐database (http://www.braindevelopment.org) and is provided in MNI space for six different iteration steps of DARTEL normalisation.

(http://dbm.neuro.uni-jena.de/vbm8/VBM8-Manual.pdf)

Statistical analysis

Parametric statistical testing is usually applied in a mass-univariate approach, i.e. the same test is applied to each voxel simultaneously. The general linear model and Gaussian random field theory to account for multiple comparisons are employed as described for fMRI in chapter 2.2.4.