fNIR

The analysis of the fNIR data involved analysing the changes in blood oxygenation values throughout the different tasks. Blood oxygenation was calculated by subtracting the Hb values from the HbO2 values at each recording time point (i.e. every half second). For the fNIR analysis, the 16 channels (voxels) were divided into four regions (each comprising four voxels) in an attempt to see of there were any differences in haemodynamic activity as a function of laterality or superiority. The four regions were: superior left, inferior left, superior right and inferior right.

5.5.4.1 WCST

Before the analysis began, two of the participants were omitted from the WCST analysis due to missing data. The HLB tasks for tasks A and B were treated separately in the analysis. The HLB will subsequently be referred to as HLB(A) and HLB(B). All of the tasks were analysed using a number of different 2x2x2 (task x superiority x laterality) repeated-measures ANOVAs.

A 2x2x2 repeated-measures ANOVA for task A and HLB(A) revealed a significant main effect of task F (1,24) = 4.34, p <.05, MSE = 4.571 pη² =.15, with significantly higher blood oxygenation levels during task A than HLB(A). There was also a significant main effect of superiority F (1,24) = 8.50, MSE =4.569, p

<.01, pη² =.26 with higher levels of blood oxygenation in the lower voxels.

However there was no significant main effect of lateralisation [F (1,24) = 0.442, MSE =0.760, p =.51, pη² =.01]. A significant interaction between task and superiority was found F (1,24) = 6.99, MSE =0.211, p <.05, pη² =.23, with task A producing higher levels of blood oxygenation in the inferior voxels compared to HLB(A). This can be seen in Figure 5.3.

A 2x2x2 repeated-measures ANOVA for task B and HLB(B) revealed a significant main effect of lateralisation F (1,24) = 4.54, p <.05, MSE = 1.91, pη²

=.16, with significantly higher oxygenation in the voxels on the left. There were no significant main effects of task [F (1,24) = 0.320, MSE =0.136, p =.57, pη²

=.04] or superiority [F (1,24) = 2.45, MSE = 1.949, p =.13, pη² =.09] and no significant interactions.

A 2x2x2 repeated-measures ANOVA comparing task HLB(A) and HLB(B) revealed a significant main effect of superiority F (1,24) = 5.11, p <.05, MSE

=2.329, pη² =.17, with significantly higher levels of blood oxygenation in the inferior voxels. There were no significant main effects of task [F (1,24) = 0.000, MSE =0.003, p =.98, pη² =.000] or laterality [F (1,24) = 1.268, MSE =0.854, p

=.27, pη² =.05] and no significant interactions.

A 2x2x2 repeated-measures ANOVA for task A and task B revealed a significant main effect superiority F (1,24) = 7.87, MSE = 4.03, p <.001, pη² =.25, with significantly higher oxygenation in inferior voxels. Although not significant, [F (1,24) = 3.227, p =.09, pη² =.1], an interaction between superiority and lateralisation trended towards higher levels of oxygenation in the left inferior voxels. No significant difference in task was found between tasks A and B [F (1,24) = 0.082, MSE = 1.806, p =.77, pη² =.003]. No significant interactions were found either.

The mean level of oxygenation for each participant in tasks A and B was correlated with the mean cognitive costs and the accuracy measures. However no significant correlations were found for any of the measures.

Figure 5.3. Interaction between change in blood oxygenation and voxel region for the HLB(A) and WCST A. Error bars display +/- 1 standard error.

5.5.4.2 CBT

0   0.2   0.4   0.6   0.8   1   1.2  

Δ  HbO2  Concentration  (µM)    

Inferior                                                                        Superior      

HLB(A)   WCST  A    

The fNIR data was analysed using 3x4x2x2 (task x sequence length x superiority x laterality) repeated-measures ANOVA on the encoding and response phases. No significant main effects or interactions were found. Following the suggestions of Toepper et al. (2010) a further analysis was carried out using a reduced time window for the encoding phase only, however this still failed to produce any significant main effects or interactions (possibly due to increased variance and noise in the data due to decreasing the time window). There was a trend towards increased oxygenation during the BST but this was not significant [F (2,52) = 0.770, p =.51, pη² =.02]. A summary of the changes in oxygenation during the Corsi tasks can be found in Table 5.3. These values make it clear that there was a very large amount of variability in the data in the three tasks.

Table 5.3. Changes in blood oxygenation for the three Corsi tasks (measured in micro mols). Values in parentheses are standard deviations.

_________________________________________________________________________

3 Blocks 4 Blocks 5 Blocks 6 Blocks _________________________________________________________________________

Baseline 0.04 (0.6) 0.07 (0.5) 0.1 (0.5) 0.1 (0.55) CBT 0.075 (0.55) 0.1 (0.46) 0.1 (0.55) 0.08 (0.6) BST 0.15 (0.5) 0.13 (0.5) 0.15 (0.38) 0.12 (0.55) ________________________________________________________________________

5.5.4.3 COWAT

The fNIR data was analysed using a 2x2x2 (task x superiority x laterality) repeated-measures ANOVA. The results revealed a significant main effect of task F (1,26) = 18.31, MSE =45.4, p <.001, pη² =.41, with increased oxygenation levels during the generation task compared to the reading task. The results also revealed a significant main effect of superiority F (1,26) = 4.79, MSE = 1.036, p <.05, pη²

=.15, with inferior voxels displaying increased oxygenation levels that superior voxels. No effect of lateralisation was found [F (1,26) = 0.115, MSE = 0.019, p=.73, pη² =.0.003]. No significant interactions were found either. Figure 5.4 details the mean change in blood oxygenation across the voxel regions for the generation and reading tasks.

A bivariate correlation revealed that there was a significant correlation in oxygenation levels between the voxel regions and mean cluster size: superior left r(25) = 0.43, p<.05, inferior left r(25) = 0.41, p<.05, superior right r(25) = 0.44, p<.05 and inferior right r(25) = 0.41, p<.05. No significant correlations were found between voxel groups and any of the other behavioural measures for the COWAT.

Figure 5.4. Mean change in blood oxygenation for the generation and reading tasks across the voxel regions. Error bars represent +/- 1 S.E.

Changes in blood oxygenation throughout the duration of the word generation and reading tasks were averaged across all participants and can bee seen in Figure 5.5. This demonstrates that blood oxygenation decreased throughout the duration of the reading task while it increased throughout the duration of the word generation task.

-­‐0.2   0   0.2   0.4   0.6   0.8   1   1.2   1.4  

Δ  Blood  Oxygenation  (µ  mols  )  

TL                                                                                  BL                                                                                    TR                                                                          BR                    Region                                  

Generation   Reading    

Figure 5.5. Mean changes in blood oxygenation for all participants during the word generation and reading tasks for the COWAT. Error bars display +/- 1 standard error. The black line represents the generation condition and the grey line represents the reading condition.

5.6 Discussion

5.6.1 WCST

The behavioural results of the WCST demonstrate that the design of the task was effective, with the behavioural results falling in line with the difficulty hierarchy (A>B>HLB). Task A was associated with higher cognitive costs, more errors, fewer correct sets and slower reaction times in comparison to task B.

Meanwhile the HLB produced more correct sets and faster reaction times than tasks A and B. This shows that as expected the HLB was the easiest task of all.

This demonstrates that the participants found task A more significantly more difficult than task B, as during task A they had to deduce the sorting rule for themselves whereas in task B the sorting criterion was provided to them for every set of four trials. This means that whereas task A required complex executive functioning in order to select the correct sorting criteria, task B simply required the participants to maintain the sorting criterion provided to them in working memory.

The executive functioning required by the different tasks is based upon the levels

-­‐0.3  

of cognitive switching required: uninstructed (task A), instructed (task B) and no set-shifting (HLB). Cognitive switching entails a greater level of cognitive demand, which would explain the effect on task performance.

The slower reaction times for task A were indicative of the participants needing extra time to deduce the sorting criteria through the use of executive functions. The greater difficulty of task A also manifested itself in the increased number of errors. The most common form of error in task A was classed under the

‘other errors’ (i.e. not preservative or set-loss errors). This indicates that the participants were engaging in a process of trial and error in order to establish what the correct sorting criterion was. The second most common form of error was the preservative error, which also occurred quite frequently. This indicates that the participants failed to adapt when the sorting criteria changed and stuck to the previous rule, despite being informed that they were incorrect. Very few set loss errors were committed, indicating that understood when they chose the correct criterion and were able to maintain that rule until the criterion changed. Correct reaction times of participants also decreased through the trials in task A, indicating that the participants were developing a better understanding of the task.

These behavioural results are largely in line with those of Lie et al. (2006).

They also found that the ‘cognitive costs’ were highest for A, with a decrease in task complexity associated with a fall in cognitive costs. The level of errors in this study also match with those of Lie et al. (2006), who reported that task A was associated with the highest level of errors, with the number of errors falling in tandem with task complexity. Also in line with the findings of Lie et al. (2006), the largest number of errors for task A was ‘other’ errors rather than preservative or set-loss errors. This demonstrates that the participants effectively engaged in the tasks.

Inspection of the fNIR data further supports the design of the task and the difficulty hierarchy. Task A produced significantly higher blood oxygenation values that the HLB(A). This indicates that the full WCST resulted in increased neural activity in the PFC. This is in line with neuroimaging and lesion studies that have demonstrated the importance of the PFC for the WCST and gives us confidence that our fNIR device is successfully measuring blood oxygenation in the DLPFC. The fact that task B did not produce significantly higher levels of blood oxygenation than HLB(B) was to be expected as the participants found task

B to be relatively easy. The fNIR results also support previous neuroimaging and lesion studies, which have reported that areas of the PFC, including the DLPFC are utilised during the WCST (Haines, 1994; Milner, 1963; Rezai et al., 1993). One difference between the behavioural and fNIR results is that the significant differences found between tasks A and B in the behavioural data were not replicated in the fNIR data. A possible explanation is that the fNIR device is dependent upon relatively large differences in task difficulty in order to record significant differences in the change in blood oxygenation. The fact that significant differences in blood oxygenation were found when there were large differences in task difficulty (A compared to the HLBA) but not when task difficulty was closer (A compared to B and B compared to the HLB) would support this explanation. Overall, the fNIR data and behavioural data suggest that increasing task complexity, due to cognitive switching, had a significant effect on task performance and DLPFC blood oxygenation.

One consistent effect found in the fNIR results is that the superiority of voxels was related to differences in the bloody oxygenation, with inferior voxels displaying greater levels of oxygenation. An effect of laterality was also found, with greater levels of blood oxygenation in voxels on the left. Previous research on the WCST has reported that the WCST activates the left PFC (Haines, 1994), whereas other research has reported that it activates that right PFC (e.g. Volz, 1997). As previous research has been very inconsistent with regards to the lateralisation of WCST neural activity, it is hard to know how to interpret these results.  

5.6.2 CBT  

The behavioural results are largely in line with predictions, showing that the participants found the BST most difficult, followed by the CBT and then the baseline task. This gives support for the design of the study that was designed to have a difficulty hierarchy of BST>CBT>Baseline. The proportion of correct responses for the BST was significantly worse than for the CBT, which were in turn significantly worse than for the baseline. Sequence length also had an affect on the proportion of correct responses, with a significant decrease in the proportion of correct responses for the BST and CBT as the sequence length increased from

3-6 blocks. An interaction revealed that at sequences lengths of 5 and 6, performance on the BST and CBT was significantly worse than for the baseline task as well as performance for the previous sequence lengths. This demonstrates that the differences between the tasks emerged when the sequence lengths increased. This is presumably due to the CBT and BST becoming more difficult with increasing sequence length, as the tasks require more complex spatial working memory and inhibitory functions. At a sequence length of 5 the BST and CBT were significantly different, with fewer correct responses for the BST.

Reaction times for correct responses largely mirror the results from the proportion of correct responses. Correct reaction times were slower for the CBT and BST than the baseline, indicating that the participants found the CBT and BST more difficult than the baseline. Correct reaction times were also affected by sequence length, with increasing reaction times due to increasing sequence length.

This indicates that increased sequence length affected task difficulty and performance. An interaction between task and sequence length revealed that the CBT and the BST were significantly different from the baseline at sequence lengths 5 and 6, with significantly slower reaction times at these sequence lengths.

This indicates that increasing sequence length increased task difficulty for the CBT and BST but not the baseline. Increasing sequence length also revealed differences between CBT and BST, with slower reaction times for the CBT at sequence lengths of 3 and 4. Overall, the reaction times again indicate that the BST was the most difficult task, followed by the CBT, with the baseline task being the easiest.

The behavioural results from this study are in line with those of Toepper et al. (2010) on which this study is based. They found that as task difficulty (BST>CBT>Baseline) and sequence length increased, the number of errors and reaction times increased. Given that the average spatial span of individuals is 5 (Kessels et al., 2000), it is not surprising that task performance began to fall significantly at this point. When dealing with sequence lengths of 5 or more, maintaining this spatial information in working memory becomes increasingly difficult, especially for the BST where the participants also have to inhibit supress the irrelevant spatial information from the distractor blocks. Combined, the behavioural data suggests that increased spatial working memory and inhibitory demands from the tasks impacted on performance.

The behavioural results from the CBT, BST and baseline tasks are in line with those of Topper et al. (2010), who found that reaction times increased from the baseline, to the CBT and then the BST. Just like in this study, Topper et al.

(2010) also found that reaction times increased as the sequence length increased.

The finding that increasing sequence length affected the BST and CBT but not the baseline and that the difference between the CBT and BST varied with sequence length also replicated the findings of Toepper et al. (2010). The accuracy findings of this study mirror those of Toepper et al. (2010). As in the Toepper et al. (2010) study, errors were significantly higher in the CBT and BST than in the baseline, while increasing sequence length was associated with an increase in errors for the CBT and BST but not the baseline task. The behavioural results from the CBT condition are in agreement with the standard literature on the CBT, with accuracy falling as the sequence length is increased (Kessels, Zandvoort, Postma et al., 2000).

Despite the significant differences in the behavioural measures, no such differences were found in the fNIR data. The behavioural results suggest that the study was designed and executed in a proper manner. The design of the CBT study as based on that of Toepper et al. (2010), and while the behavioural results a largely in line with theirs, the neuroimaging results are not. Previous neuroimaging the lesion studies have strongly indicated that the CBT, as well as the BST, activate areas of the PFC, in particular the DLPFC. One possible explanation for this discrepancy is relates to the nature of VSMW. While the PFC has been found to be involved in VSWM, it is not specific to this area. Research has shown that areas such as the parietal cortex and the hippocampus are also involved in VSWM (Toepper et al., 2010). Meanwhile neural activity related to the WCST and COWAT appears to be more localised (Alvarez and Emory, 2006).

This links to a further issue, which is the different neuroimaging methods used. While this study used fNIR, many of the previous neuroimaging studies on the CBT have used fMRI or PET. One of the advantages techniques such as fMRI have over fNIR (the system used in this study specifically) is the ability to record neural activity over larger areas and greater depths of the brain. Not only is the fNIR device incapable of recording activity in areas such as the parietal cortex, it may also struggle to record activity in areas such as the VLPFC due to the positioning of the sensor pad on the forehead and the limited depth sensitivity of

the device (this is why the device is best used for recording DLPFC activity). This may be a problem given that research has shown that the VLPFC is activated during the CBT (Owens et al., 1993, 1996; Toepper et al., 2010).

Another possible explanation relates to the recording periods used in the CBT tasks. The window of activation recorded for each task and sequence length is smaller than those used in the WCST and the COWAT, with a 21 second window of activation for a sequence length of 6 (the maximum sequence length used). This includes both the encoding and response phases. However, Toepper et al. (2010) suggested that frontal lobe activity during the CBT and BST is associated with the encoding phase, meaning that the response phase may be a confounding factor. Reducing the window to only include the encoding phase introduces increased noise and variance into the signal, meaning that the fNIR device may not have had the required signal-to-noise ratio to record the haemodynamic activity. When looking at the fNIR results for the CBT, it does appear that the results are heading in the predicted direction, with an increase in blood oxygenation from the baseline to the CBT and then the BST. However the effect was not pronounced enough to be significant. This may be due to the physiological or methodological issues mentioned above.

5.6.3 COWAT

When examining the number of correct words generated in the generation condition, it is clear that there was a wide range in the number of words produced by the participants, with one participant managing only nine correct words while another managed to produce fifty-seven correct words. A possible explanation for this result is that the participant who generated the fifty-seven correct words was studying for an Undergraduate degree in English Literature. As a result that participant may have had a more extensive lexicon, allowing them to generate such a large number of words on sixty seconds. This is in line with research that has suggested that a person’s reading ability affects their verbal fluency performance (Borkowski et al. (1967).

Finding results in the literature with which to compare the behavioural results from the COWAT is difficult due to the COWAT predominantly being used to test clinical populations or specific demographics, as well as the fact that

multiple sets of letters have been used in previous studies. Tombaugh, Kozak and Rees (1999) reported that mean number of correct words produced in a COWAT was 38 to 44 for 16-59 year olds depending on their level of education. Ruff, Light and Parker, (1996) found that the average number of words produced by people of all education levels was 40.1. Meanwhile Ross (2003) found the average number of words produced by undergraduates was 38.16. Ross (2003) also found that the mean number of clusters was 7.7, the mean cluster size was 0.42 and the total number of switches was 24.8. Ross, Calhoun, Cox et al. (2007) reported that average number of words produced by undergraduates in a COWAT was 37.5.

They also reported that mean number of clusters was 7.7, the mean number of switches was 22.8 and the mean cluster size was 0.41. The results from this study

They also reported that mean number of clusters was 7.7, the mean number of switches was 22.8 and the mean cluster size was 0.41. The results from this study