fMRI data Preprocessing

This section covers the standard steps that are performed for most fMRI data (functional and anatomical scans). The preprocessing of the functional scans involves slice acquisition time correction, head motion correction, high-pass filter, linear drift removal and spatial smoothing. The processing of anatomical scans involves intensity correction and Brain extraction. All the data in this thesis were preprocessed using the BrainVoyager QX 2.8 software package.

3.2.1 Functional Data preprocessing

Typically, fMRI data consist of a 3D-matrix (volume) of volumetric pixels (voxels) that is repeatedly sampled over time. Each voxel contains a BOLD signal, which changes over time and represents an indirect measurement of the neural activity. An fMRI experiment might have

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an image volume of 70×70×32 voxels, which is sampled every 2 seconds for a total of 5 minutes. Prior to the statistical analysis, a series of computational operations known as a preprocessing are performed on the raw data to reduce artefact and noise-related components of fMRI data, and make it ready for further analysis.

3.2.1.1 Slice time correction

fMRI data are collected in the form of slices selected by radiofrequency excitation pulses, followed by simultaneous data collection throughout the slice. The slices of each volume are selected to have equal spacing in time across one TR. This could be done by collecting the data either in ascending or descending slice order (for instance 1-2-3-4-5-…-31- 32). Most fMRI scans nowadays are using interleaved slice acquisition, where all the odd- numbered slices are collected first, and then all the even-numbered ones are collected, to avoid cross slice excitation. The disadvantage of this technique is that the BOLD signals of contiguous parts of the brain are acquired at non-adjacent time points. Therefore, slice acquisition time correction solves this discrepancy by temporally interpolating the voxels’ time courses so that it is assumed they are being collected simultaneously. The most common forms of interpolation are linear, cubic spline or Sinc (Huettel et al., 2008).

3.2.1.2 Motion correction

The most damaging and frustrating problem in fMRI data acquisition is head motion. For example, small sub-voxel motion may corrupt the data, and result in that subject needing to be excluded from the experiment. The purpose of motion correction is to realign the functional images to a reference image, such that every voxel will have the same coordinates throughout the experiment. This will improve the quality of the images and increase signal-to- noise ratio (SNR). Rigid-body transformation parameters with three rotation parameters (x, y and z) and three translation parameters (roll, pitch and yaw) are calculated to realign all the functional images to the first image (reference image). These parameters are estimated

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iteratively, using an optimisation algorithm to minimise the sum of the square of the differences between the reference image and each subsequent image (Friston et al., 1995; Huettel et al., 2008). The tri-linear sinc Interpolation approach is used to detect motion using linear interpolation, and to correct it using sinc interpolation (Huettel et al., 2008).

3.2.1.3 High pass filter and low frequency drift

fMRI data can be contaminated by low frequency fluctuations caused by different sources varying over time. The most common sources of noise are temperature variation within the subject, or scanner hardware and low frequency physiological artifacts like respiration and heart rate. These artifacts will significantly reduce the power of statistical analysis and invalidate event-related averaging, which assumes stationary time courses, therefore removing low frequencies. Correcting drifts is one of the most important preprocessing steps, and these can be removed by using a high-pass filter, performed by using GLM with Fourier basis. The GLM is used to estimate the presence of low frequencies in a voxel's time course. The projected time course from a GLM based on the predictors (in this case sines and cosines for low frequencies) will then be subtracted from the original data, resulting in a filtered time course (Huettel et al., 2008; Ashby, 2011).

3.2.2 Anatomical Data preprocessing

Intensity inhomogeneities in anatomical images (T1) can substantially reduce the accuracy of segmentation and functional co-registration (figure 3.5A). A well-known method of Intensity inhomogeneities correction (IIC) is based on surface fitting approach, in which low-order polynomials are used to model low frequency intensity variation (known as a bias field, see figure 3.5B) in a subset of selected voxels belonging to the white matter. This field is then removed from the data, producing a homogeneous intensity image (figure 3.5C) (Vovk

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Figure 3.5. Intensity inhomogeneity difference before and after the IIC. (A) T1 image before IIC, (B) low frequency intensity variations, (C) T1 image after IIC.

3.2.3 Co-registration

The differences between the functional and anatomical images are noticeable. Typically, the functional data have low resolution with unidentified and blurry structure (figure 3.6A). In contrast, the anatomical images seem remarkably detailed, with clear outlines of the sulci and gyri, and distinct boundaries between the grey and white matter, as seen in figure 3.6B (Huettel et al., 2008).

Co-registration is necessary to improve the spatial localisation of the functional images. The low resolution functional images are aligned to the high resolution structural images using a rigid body transformation (3 rotation and 3 translations).

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Figure 3.6. Comparison of functional image (A) and anatomical image (B). Structural landmarks that are visible in one structural image may not be distinguishable in functional

images of the same slice.

3.2.4 Spatial Normalisation

Human brains differ in size and shape. These variations extend to every identifiable brain region, meaning that even major landmarks like the calcarine sulcus can have different positions and orientations. Consequently, normalisation is used to stretch, squeeze and warp subjects’ anatomical images into a standard anatomical space or template like Montreal Neurological Institute template (MNI) (Evans et al., 1993) and Talairach space (Talairach and Tournoux, 1988). Normalisation allows for group level statistical analyses to be performed and for these to be compared across subjects and studies at specific anatomical coordinates.