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2.2.1 How does fMRI work?

FMRI is a non-invasive neuroimaging technique that enables us to investigate brain activity. FMRI is a type of MRI scan which uses the same physics phenomenon of NMR applied in MRS (e.g. when an external magnetic field is applied to protons, the nuclei align to the magnetic field and the frequency of the spin is determined by the strength of the magnetic field, governed by the Larmor equation (Logothetis, 2002)). Whereas MRS uses the information about the frequency of the hydrogen nuclei spins to differentiate different metabolites, however, fMRI uses this frequency information as one method to determine the spatial location of a signal within the volume scanned. This is

known as frequency encoding, and is used in conjunction with slice selection and phase encoding to measure the fMRI signal from each voxel within a 3D space (Huettel et al., 2009).

FMRI is an indirect measure of neuronal activity, as fMRI works by measuring the amount of oxygen in a brain region, with those regions with more active neurons having greater oxygen needs (Logothetis, 2002). FMRI measures the blood oxygenation level dependent (BOLD) contrast which represents the ratio of the amount of deoxyhaemoglobin to oxyhaemoglobin in the blood (Logothetis, Pauls, Augath, Trinath, & Oeltermann, 2001; Logothetis, 2002). This can be estimated because deoxyhaemoglobin and oxyhaemoglobin have different magnetic properties, where oxyhaemoglobin is diamagnetic and deoxyhaemoglobin is paramagnetic (Pauling & Coryell, 1936). The paramagnetic deoxyhaemoglobin creates distortions in the surrounding magnetic field, causing a more rapid decay in the T2* signal (Brooks et al., 1975). The ratio of oxygenated and deoxygenated blood is altered when neurons are active, thus by measuring this BOLD contrast we can make an inferences as to the underlying neuronal activity. When neurons are depolarised, their metabolic demand for oxygen increases in order to replenish energy stores used during depolarisation, and enable reuptake of neurotransmitters from the synaptic cleft following depolarisation (Logothetis et al., 2001; Logothetis, 2002). Oxygen is extracted from the local capillaries, which changes oxyhaemoglobin to deoxyhaemoglobin, and so causes a decrease in the detected signal. This is only a small signal decrease, lasting for 1-2 seconds, and following this “initial dip” in the signal there is an over-compensation in oxygenated blood flow to the site of neural activity. This latter effect is caused by the vasodilation of capillaries adjacent to this site, which vastly increases the proportion of oxyhaemoglobin to deoxyhaemoglobin, and results, therefore, in a large increase in signal. The peak of this signal occurs approximately 6 seconds after the underlying neural activity, then decreases to below the baseline level after approximately 12 seconds. This latter effect is termed the “post- stimulus undershoot”, which then returns to baseline. This profile of blood flow in response to neuronal activity is termed the haemodynamic response function (HRF, see Figure 2.5) (Huettel et al., 2009), and is the basis of how fMRI is able to (indirectly) measure brain activity.

Figure 2.5: The haemodynamic response function (HRF), which shows the change in BOLD signal detected post stimulus due to an increase in blood flow followed by reduction in this

blood flow back to the baseline (image from http://www.mdpi.com/1996- 1944/4/11/1941/htm)

2.2.2 How do we use fMRI to investigate brain function?

The BOLD contrast is measured from many thousands of voxels (3D pixels) in the brain, which are typically 3mm3 _{in volume, giving fMRI good spatial resolution (Huettel} et al., 2009). In fMRI, participants are asked to view or perform different conditions of a task. By comparing the BOLD contrast across different voxels in the brain in different task conditions, we can make inferences as to which regions have higher neuronal activity than others for different conditions. By comparing the BOLD contrast in the same voxels between conditions, we can also make inferences as to which task demands elicit greater neuronal activity than others in that region (Amaro & Barker, 2006).

Different fMRI study designs can be applied to investigate brain activity during different task conditions. The two types of design that have been used in this thesis are a cognitive subtraction design (applied in Chapter 3), and a factorial design (applied in Chapter 4) (Henson, 2006; Price, Moore, & Friston, 1997). In a cognitive subtraction design, participants are asked to perform a minimum of two different task conditions. For example, an experiment could consist of conditions A and B, where A is the condition of interest and B is the baseline condition. The BOLD response in task condition B is subtracted from that elicited in task condition A, in order to isolate the activity from the

task condition of interest, A, above the baseline condition, B, (Amaro & Barker, 2006; Henson, 2006). Chapter 3 of this thesis has used this cognitive subtraction design. As will be expanded on in the introduction and methods sections of Chapter 3, this design has been used to compare PCC activity during different task conditions of scene, face and object oddity discrimination compared to a baseline condition of size. In a subtraction design there is one factor that is manipulated, i.e. in Chapter 3, the factor is the category of the stimuli presented.

In a factorial design, more than one factor is manipulated within the same experiment. Such a design enables the investigation of whether the effect of manipulating one factor on the BOLD response of a brain region is modulated by another (Henson, 2006; Price et al., 1997). For example, the two factors in an experiment could be stimulus category and working memory load, and there could be two levels of each factor, where the stimulus categories were scenes and shapes, and the working memory load was low or high. To investigate whether the BOLD contrast was modulated by the first factor of stimulus category, we could perform a cognitive subtraction design, as described above (i.e. BOLD response for scenes minus shapes). A factorial design however, would extend this to test whether the BOLD response for scenes minus shapes was the same or different at the two levels of working memory load, thus telling us whether the second factor had an impact on the outcome of the first factor. If there was no difference in the BOLD response to scenes-shapes between the low and high working memory loads, this would suggest that there is a main effect of the factor of stimulus category, whereas if the BOLD response to scenes-shapes was different at each level of working memory load, this would suggest that there is an interaction effect between the two factors (Henson, 2006; Price et al., 1997). Chapter 4 of this thesis applied exactly this type of factorial design to investigate whether there is an interaction between working memory load and the spatial complexity of stimuli on brain activity in the PCC.

In addition to these different experimental approaches, there are different strategies for fMRI experimental design. These are event related and blocked designs (Henson, 2006; Huettel et al., 2009). An event-related design has been applied in Chapter 3 of this thesis while a blocked design has been applied in Chapter 4. Further details of each design and analysis strategy are provided in the methods section in the appropriate Chapter.

The difference in the BOLD contrast between task conditions can be quantified, by calculating the BOLD percentage signal change between conditions (Huettel et al., 2009). This is the method adopted in Chapters 3 and 4, where I obtain a measure of the difference in PCC BOLD between task conditions in order to correlate this with MRS metabolites. Further details will be given in Section 3.2 of this Chapter, and in the methods Sections of Chapters 3 and 4.

2.2.3 Methodological considerations in fMRI

2.2.3.1Correction for multiple comparisons

Given that the voxel size in fMRI is typically 3mm3_{, this means that there are many} thousands of voxels in one brain volume. In an fMRI analysis, therefore, this results in many thousands of statistical comparisons in order to make inferences about differences in activity within and between voxels. Performing this large number of statistical tests means that there is an increased risk of detecting false positives (Huettel et al., 2009). To correct for this problem, two methods have been adopted in this thesis: (1) lower voxelwise level of significance and (2) a clusterwise correction. These strategies are consistent with the fMRI analyses performed in previous studies that have applied the fMRI paradigms adopted in this thesis (A. C. H. Lee & Rudebeck, 2010; Shine et al., 2015). A more detailed explanation of these corrections is given in the methods section of Chapters 3 and 4.