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The following section refers to general experimental methods, which were implemented during this thesis. These include the computational steps performed in order to segment and flatten cortical surfaces, the technique for delineating the borders of retinotopic visual areas, and the fMRI scanning settings. Further experimental details are described within the relevant chapters.

2.1

Cortical segmentation and flattening

Although the spatial resolution of functional EPI images is quite high in terms of spatial localisation, the images themselves do not have sufficient resolution in order to overlay functional data on them. It has become typical in fMRI studies to acquire a set of high-resolution anatomical images and overlay functional data onto these images showing cortical structure. These structural images are usually T1-weighted, with good grey-white matter contrast, and are acquired during a separate scanning session.

Figure 2.1 Anatomical T1-weighted images are processed so that the grey/white matter surface is segmented and inflated. The area surrounding the occipital sulcus is then extracted and flattened. The inflated and flattened representations help to better appreciate spatial locations in the brain.

However, much of the cortical surface in the structural images is obscured from view by a complex pattern of folds, making hard to interpret spatial locations and visualise functional data. This section describes the computational processing performed to extract the surface between the grey and white matter, inflate this surface and flatten the occipital cortex. Figure 2.1 summarises the analysis stream. These steps help to better appreciate spatial locations in the brain and are particularly useful for visualising retinotopic maps and delineating the borders of visual areas.

2.1.1

Analysis Stream

The most common representation of MR images is as 2-dimensional slices throughout the brain volume. However, using this format it is difficult to appreciate the spatial relationships between different points in the brain. Furthermore, when seen as separate slices, the position of any but the most familiar anatomic features is hard to infer. The method for surface extraction involves a series of steps starting with a T1-weighted anatomical image of the brain and resulting in two cortical surfaces for each hemisphere, one corresponding to the boundary between grey and white matter, and the other to the boundary between grey matter and CSF. The computational analysis was performed using the freely distributed software tool, SurfRelax, developed by Jonas Larsson (Larsson 2001) based on the FSL software library.

The analysis begins with a number of preprocessing steps, which include: intensity normalisation, non-brain tissue removal, filling of the ventricles and subcortical nuclei and segmenting the hemispheres from each other and from the brain stem. After prepocessing a template generated for each hemisphere is deformed onto the surface of white matter. Finally, the surface is extracted and refined after which may be inflated and flattened.

Preprocessing

MR images are susceptible to intensity variations due to magnetic field inhomogeneities. These inhomogeneities result in the same tissue having different intensities at different points. This might be a problem when image intensities are used to delineate tissues (for example finding the boundary between grey and white matter). Thus, in the first step of preprocessing image intensities are normalised using a non-parametric heuristic approach (Larsson 2001). Next, non- brain tissues (e.g. skull) are removed by means of a deformable surface algorithm similar to that described by Dale et al. (1999).

The segmentation procedure extracts the cortical surface at the boundary between grey and white matter. However, the medial white mater surface is connected to subcortical structures such as basal ganglia, ventricles and the thalamus. These structures are of no interest when generating the cortical surface. Hence, they are automatically identified and assigned the mean white matter intensity.

Figure 2.2 (a) A slice of an anatomical T1-weighted image in horizontal view, and (b) the result of the initial preprocessing steps. During these steps the non-brain tissues (e.g. skull) are removed and the left and right hemispheres are segmented from each other.

Then the two cortical hemispheres and subcortical structures are initially separated using an automated template fit. Predefined templates of the

hemispheres and the cerebellum are computationally deformed to match the shape of the target brain. Once the deformation process is completed the templates are smoothed by a series of closing operations and the surfaces of the templates are extracted (the result is shown in Figure 2.2b). These surfaces are then used to segment the grey/white matter boundary.

Figure 2.3 The extracted surface between the grey and white matter for the left hemisphere. This representation helps to appreciate the spatial relation between different locations, but much of the surface is hidden from view.

Although the templates generated in the previous step could be potentially used as a starting point for white matter extraction, they are not suitable for this purpose as they deviate substantially from the actual shape of the target brain white matter. The performance of the surface extraction algorithm is highly dependent on the shape of the initial template; therefore, a template volume with spherical topology is generated directly from the filled white matter of the target brain. An anisotropic diffusion filter smoothes the template volume and the final template is extracted by thresholding. The template generated in the previous step is deformed onto the filled white matter volume by a series of topologically constrained erosions and dilations. These steps ensure that the deformation converges in such a way that the final volume is anatomically correct. Finally, the surface of the white matter is extracted from this volume using a surface extraction algorithm (Larsson 2001). The resulting surface is a good approximation of the grey/white matter boundary (the result is shown in Figure 2.3) and can be used directly as it is. However, to further improve the fit in regions

of high curvature and to generate a surface representation of the outer grey matter, an optimisation step is applied to the surface as described in the following section.