FMRI Data Analysis. Introduction. Pre-Processing

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1 FMRI Data Analysis Introduction The experiment used an event-related design to investigate auditory and visual processing of various types of emotional stimuli. During the presentation of each stimuli the participant had to decide if the stimuli was 'happy' or 'sad'. The conditions were as follows: Happy face Happy voice Happy face Angry voice Angry face Happy voice Angry face Angry voice The data given to analyse consisted of a T1-weighted structural high resolution image and 4D functional EPI data for each of the 15 participants. The participants given to analyse were: 006, 007, 008, 009, 014, 019, 021, 023, 026, 028, 032, 043, 044, 045, 046. All processing and analysis of data was carried out using BET (brain extraction tool, version 2.1; Smith 2002) and FEAT (FMRI Expert Analysis Tool version 5.98). Data Acquisition Sagittal slices were acquired in interleaved order with voxel dimensions of 5x5x3. TR(s) = 2.0; 290 3D volumes per run. The parameters allowed for whole-brain coverage. The anatomical T1-weighted image acquired from each participant allowed the functional data to be registered to an anatomical space and then normalised to the Montreal Neurological Institute (MNI) atlas space (Chester, 2013). Pre-Processing Brain Extraction Brain extraction was performed because skull and cerebrospinal fluid (CSF) varies from individual to individual and can confuse the registration template. So as recommended these were removed at the 1

2 start of the analysis, which was carried out using BET v2.1 (Smith, 2002). The default settings on BET2 where -f value (fractional intensity threshold) is 0.5 and -g (gradient threshold) value is 0.0 took away far too much brain matter. As a result the -f value was reduced, to 0.2 which increased the amount of extracted brain matter, but was still not enough. Therefore -R (robust brain centre estimation) setting was used, which proved to be more accurate in extracting brain from non-brain matter. However the -f and -g values were still modified for each participant (see table 1). -R ignores small numbers of voxels that have a widely different values from the majority of the image (Smith, 2002). As well as this a binary brain mask was generated which highlights the area of extracted brain (see figure 1). In regards to participant 006 the final thresholds used were -f = 0.2 and -g = 0.1 there were a few extra voxels around the occipital lobe, but removing these was at the expense of also removing large parts of the cerebellum. Figure 1. Screenshot of FSLView T1-weighted structural image form participant 026, brain extraction with binary brain mask overlayed Number Participant -f value -g value

3 Table 1. Frequency and Gradient threshold values for each of the participants First Level FEAT Analysis A first level feat analysis was carried out (Full Analysis) using FEAT (FMRI Expert Analysis Tool V5.98). In total there were 290 volumes per run with a TR of 2.0 seconds. The Brain Background Threshold setting was kept to the default at 10%. In regards to design efficiency temporal smoothing, noise level % and Z threshold were calculated using the estimate from data option, because this uses the input data to calculate these values, rather than leaving them at the default. This gave the values of: Noise level % ; temporal smoothing ; Z threshold 5.3. No scans were deleted. Pre-Statistics Brain extraction was used to remove non-brain matter from the functional images. This was done automatically using the BET option in FEAT (FMRI Expert Analysis Tool v.5.98). Motion correction Using MCFLIRT any movement from the participant during the scanning session was corrected on the functional images (Jenkinson et al., 2002). 3

4 Spatial Smoothing For spatial smoothing which improves the signal-to-noise ratio a Gaussian kernel full-width-half-maximum (FWHM) of 9.0 mm was chosen because the voxel dimensions of the functional images were 3x3x5 this value should be three times the size of the voxels (Lindquist, 2008) but as 15.0 mm seemed excessive and is not commonly used in fmri data analysis therefore the x and y dimensions were used which gave the value of 9.0 mm. This was deemed acceptable since a fullbrain analysis was to be carried out, as opposed to a regions of interest (ROI) since the Gaussian kernel should not be larger than the area to be analysed (Brett, 2013). However, the caveat in this is that the spatial frequency is reduced (Reimond, 2005). Slice-timing was not used since the images were acquired sagittaly, the images would need to be acquired axially in order to use this function. To remove scanner drifts a high pass filter was used (Chester et al., 2013) but the cut off was brought down from the default at 60.0 (s) to 50.0 (s) as recommended by FMRIB (FMRIB's Software Library, and Smith (2004). Statistics As part of setting up the General Linear Model (GLM) a 'custom (3 column format)' was chosen. This allowed input of the onset times of the stimulus, the duration of the stimulus and the weights of each stimulus in the time course model. See figure 2 for an example. To do this a text file with this information was created for each condition: happy/happy, angry/angry, happy/angry, angry/happy. These modelled the time course of the stimuli. Figure 2. Text file for the Happy face Happy voice condition. Left most column is the stimulus onset times, the other two columns are the duration (s) and weight of each stimulus 4

5 Contrasts of Interest Explanatory variables (EV) were then set for each of the conditions along with temporal derivatives and temporal filtering. From the 4 EVs 8 contrasts were set up as follows: i. happy face happy voice (HfHv) ii. happy face angry voice (HfAv) iii. angry face happy voice (AfHv) iv. angry face angry voice (AfAv) v. incongruous congruent (I/C) vi. congruent incongruous (C/I) vii. happy angry (H-A) viii. angry happy (A-H) Figure 3. The set-up for the General Linear Model with explanatory variables for each of the conditions The 4 EVs are modelled in the design matrix below in figure 4. The time courses of the 4 EVs are modelled in columns 1, 3, 5 and 7. The white horizontal lines across these columns indicates when the onset of the stimulus. The dark bar running downwards on the left (the y-axis) represents time, and the red bar on this axis marks the duration of the high pass filter. The additional waveforms in columns 2, 4, 6 and 8 are the temporal derivatives of the 1, 3, 5 and 7 EVs. The purpose of these is to correct for 5

6 unexplained noise in the data by reducing it; therefore allowing a better fit to the data by increasing the statistical significance (Smith, 2004). Figure 4. Design Matrix for explanatory variables 6

7 Figure 5. Covariance matrix of the design matrix efficiency of the design/contrasts for first level analysis FILM prewhitening option was selected because this makes the statistics valid and maximally efficient for parameter estimation (Woolrich et al., 2004; FMRIB's Software Library, Post-Statistics Cluster-level thresholding was used with the following parameters: Thresholding at Z> 2.3 and the Cluster P threshold = 0.01 (Smith, 2004) to form clusters. Registration 7

8 A two stage registration procedure was used, not three because coplanar images were not part of the data to be analysed, which would have been entered as the 'initial structural image' (Johnstone, 2006). The T1-weighted high resolution images for each participant that had undergone brain extraction were selected as the 'main structural image' and these were then registered to their corresponding functional images using 7 degrees of freedom (DOF). Using 7 DOF allows for a global rescale in all 3 dimensions (x, y and z). Since each of the images comes from a different participant using 7 DOF compensates for any global scale changes. Following this, data was registered to or spatially normalised, to the MNI template. This was done using 12 DOF because the images had a large FOV. Examples of these are given below (see figures 6ad, 7a-d). Results Registration An example of a good registration is participant 006: Figure 6(a) Summary registration FMRI to standard space Figure 6(b) Registration of functional image to high resolution structural image 8

9 Figure 6(c) Registration of high resolution to structural standard image (MNI) Figure 6(d) Registration of functional to standard image (MNI) An example of an extremely poor registration comes from participant 046: Figure 7(a) Summary registration FMRI to standard space Figure 7(b) Registration of functional image to high resolution structural image Figure 7(c) Registration of high resolution to structural standard image (MNI) 9

10 Figure 7(d) Registration of functional to standard image (MNI) Motion Correction An example of a successful motion correction using MCFLIRT (Jenkinson et al., 2002) is evident in participant 043. The mean displacements: absolute=0.1mm, relative=0.07mm. Figure 8(a) Motion plot for head rotation (043) Figure 8(b) Motion plot for head translation (043) Figure 8(c) Motion plot for total displacement (043) An example of poor motion correction is evident in participant 046. The mean displacements: absolute=0.67 mm, relative=0.08 mm. 10

11 Figure 9(a) Motion plot for head rotation (046) Figure 9(b) Motion plot for head translations (046) Figure 9(c) Motion plot for total displacement (046) In the plots representing total displacement (figures 8c, 9c) the y-axis is the relative displacement (mm) on the x-axis is the timepoints specifically the translations between timepoints before and after motion correction; alternatively described as how far the brain is from the reference image (Poldrack et al., 2011). Relative displacement is often more useful as a means to inspect for motion correction than absolute translation (Poldrack et al., 2011). The plots representing head rotation (figures 8b, 9b) plots the first derivative of the data which corresponds to relative displacement at each timepoint; thus how far away the brain is from the previous timepoint. 11

12 The data from participant 046 is problematic as indicated by figure 9(c). The y-axis goes as high as 3.0 mm and the 'absolute' pot is as high as this value, and the 'relative' motion plot is above 1.5 which is also indicative of problematic data. This is because rapid motion through the magnetic field (B0) disrupts the image intensity. Spikes as in figure 9(c) above can be modelled out by using regressors, the alternative is to remove the data from any further analysis. In this data analysis, higher level FEAT analysis was run with and without data from participant 046; the findings from these are explained and discussed in the Higher level analysis section. Activation Maps and Re-thresholding The levels of activation between participants differed greatly. In example participants 007 (see figure 11a-b and 15a-b) and 043 (see figure 10a-b) and 14a-b) exhibited high levels of activation, while participant 006 (see figure 12(a-b) and 16(a-b) and 044 (see figure 13a-b) and 17a-b) exhibited particularly low levels of activation. The data from these participants was chosen; specifically the t-contrasts of: angry face angry voice (AfAv) and happy face angry voice (HfAv). This is because these contrasts were the most prominent in respect to activation/deactivation. Manual thresholding was carried out by re-running FEAT with an altered Z threshold, which was changed from Z>2.3 to Z>3.1. This value was chosen because according to Poldrack et al. (2011) the Z threshold is usually within this range. If the value were to be set too high significant activation could be cut out since higher values of Z increases the stringency of what is considered activation in the clusterlevel thresholding. Displayed below are the activation maps from these participants where the first of the two images is where thresholding is at Z>2.3 and the second is when thresholding is at Z>3.1. These are also ordered by contrast: HfAv followed by AfAv. 12

13 Happy face Angry voice (HfAv) contrast Figure 10(a) Participant 043 (HfAv) Thresholded activation Figure 10(b) Participant 043 (HfAv) Thresholded activation Figure 11(a) Participant 007 (HfAv) Thresholded activation

14 Figure 11(b) Participant 007 (HfAv) Thresholded activation Figure 12(a) Participant 006 HfAv Thresholded activation Figure 12(b) Participant 006 HfAv Thresholded activation

15 Figure 13(a) Participant 044 HfAv Thresholded activation Figure 13(b) Participant 044 HfAv Thresholded activation Angry face Angry voice (AfAv) contrast Figure 14(a) Participant 043 AfAv Thresholded activation

16 Figure 14(b) Participant 043 AfAv Thresholded activation Figure 15(a) Participant 007 AfAv Thresholded activation Figure 15(b) Participant 007 AfAv Thresholded activation

17 Figure 16(a) Participant 006 AfAv Thresholded activation Figure 16(b) Participant 006 AfAv Thresholded activation Figure 17(a) Participant 044 AfAv Thresholded activation

18 Figure 17(b) Participant 044 AfAv Thresholded activation It is evident that the increased Z threshold has decreased the amount of recorded activation. From these results it was decided to keep the more stringent Z value of 3.1 because this resulted in activation blobs without any stray spots of activation and thus making the data easier to analyse. 18

19 Higher Level Analysis The data generated from the first level analysis, the.feat directories, were selected for higher level FEAT analysis. Statistics Mixed effects analysis uses a regression model that takes into consideration variation that is not generalisable to the independent variable. This type of analysis creates group average maps for contrasts of interest (Chester et al., 2013). This also models subject variability therefore the results have higher ecological validity. Since there were more than ten participants to process data for FLAME1 was the setting used on FEAT for the mixed effects analysis. Additionally this is deemed to be more accurate than using fixed effects analysis because although it is more sensitive to activation it does not consider variance between subjects as the mixed effects analysis does, and this is why mixed effects has higher ecological validity. The mixed effects using a simple OLS model has the advantage of being fast but this is at the expense of being the least accurate of all the mixed effects models. FLAME1 is considered accurate (FMRIB's Software Library, The criterion requested that a Single group average be used as the design model; once this was generated it provided the design matrix as shown in figure 18. This shows the design matrix as including participant 046, however without 046 the only difference would be one less '1' in the left hand column. 19

20 Figure 18 Group average design matrix for model in higher level analysis Post Statistics Cluster-level thresholding was used, whereby clusters were formed using the following parameters: Z>3.1 and the Cluster P threshold = 0.01 (Smith, 2004). Results The higher level analysis was run six times, once with and once without participant 046, to see what effect this would have. The reasoning for this was the poor registration (see figure 7(a-d) and the problematic motion plot (see figure 9(c). Higher level analysis was run for a third time because a problem was encountered in the output of the 'cluster list' only 1 cluster was reported from the activation maps for contrasts HfHv; AfAv and AfHv, and the size of these as measured by voxels, ranged from which equates to an area nearly the size of an entire brain hemisphere. From observing the activation maps this amount of activation can be observed, but it is clearly in different regions of the brain. Although viewing 3D clusters on 2D slices has its own inherent problems 20

21 because the contiguity of the clusters were measured using 3 dimensions which will be difficult to accurately judge from 2D slices. This is problematic because (1) this was not representative of the activation maps (2) is very unusual (3) is not supported by any previous research (Johnstone, 2006). As a result it was decided that the Z threshold was too stringent and was not allowing discrimination between activation areas. Thus, following Worsley (2001) a Z threshold of 3.5 was used since the value of P is not particularly sensitive to the shape of the search region (Worsley, 2001) therefore this showed improvement of the high level data but is still not what would be considered decent use to the lack of spatial specificity since the only information this provides is that 1/46070 voxels elicited significant activation (Poldrack et al., 2011). Z>3.8 fourth time was tried but this did not produce satisfying results either since most clusters were within the range of Therefore the Z threshold was increased again, this time to Z>. and this provided more informative results. The activation maps displayed below thus use the values Z>., P=0.01. Z>4.0 fifth time did produce results with recognisable spatial specificity, however this is considered very high, since the default is 2.3, and is not often used in analysis. Group Average Analysis The thresholded activation images for all 8 contrasts are displayed below. The first 4 contrasts show activation while the last 4 do not as illustrated in figures below. In these participant 046 is excluded, however for the HfHv contrast an activation map with and without the data for participant 046 is given to illustrate the impact this had. The most significant difference is activation in the occipital lobe, as with the exclusion of that data there is much higher activation in this brain region. The same cannot be said for the frontal, temporal or parietal lobes. 21

22 Thresholded activation images Figure 19(a) contrast 1 HfHv with 046 (n=15) Cluster Index 1 Voxels Z-MAX Z-MAX X (mm) -70 Z-MAX Y (mm) -18 Z-MAX Z (mm) 10

23 Thresholded activation images Figure -19(b) contrast 1 HfHv without 046 (n=14) Cluster Index 1 Voxels Z-MAX Z-MAX X (mm) 2 Z-MAX Y (mm) -88 Z-MAX Z (mm) -8

24 Thresholded activation images Figure 20 contrast 2 HfAv (n=140) Cluster Index 2 1 Voxels Z-MAX Z-MAX X (mm) Z-MAX Y (mm) Z-MAX Z (mm) 10 46

25 Thresholded activation images Figure 21 contrast 3 AfHv (n=14) Cluster Index 1 Voxels Z-MAX Z-MAX X (mm) -10 Z-MAX Y (mm) -86 Z-MAX Z (mm) -6

26 Thresholded activation images Figure 22 contrast 4 AfAv (n=14) Cluster Index 1 Voxels Z-MAX Z-MAX X (mm) -18 Z-MAX Y (mm) -98 Z-MAX Z (mm) 12

27 Thresholded activation images Figure 23 contrast 5 C/I (n=14) 27

28 Thresholded activation images Figure 24 contrast 6 I/C (n=14) 28

29 Thresholded activation images Figure 25 contrast 7 H-A (n=14) 29

30 Thresholded activation images Figure 26 contrast 8 A-H (n=14) 30

31 Activation Maps Contrast 2, HfAv, from the higher level analysis was used to investigate anatomical regions of activation more in depth. The mean functional data from this contrast was viewed in FSLView, and the Harvard-Oxford Cortical Structural Atlas and the Harvard-Oxford Subcortical Structural Atlas were used to assign anatomical labels to the areas of activation. Cluster 1 Atlas report (figure 27): 32% Paracingulate Gyrus, 9% Superior Frontal Gyrus, 2% Cingulate Gyrus, anterior division (Harvard-Oxford Cortical Structural Atlas). 50% Right Cerebral White Matter, 49% Right Cerebral Cortex (Harvard-Oxford Subcortical Structural Atlas). Cluster 2 Atlas report (figure 28): 13% Superior Temporal Gyrus, posterior division, 3% Planum Temporale, 2% Postcentral Gyrus (Harvard-Oxford Cortical Structural Atlas). 13% Left Cerebral Cortex (Harvard-Oxford Subcortical Structural Atlas). Figure 27 Cluster 1 from contrast 2 HfAv, thresholded at Z>3.1 31

32 Figure 28 Cluster 2 from contrast 2 HfAv, thresholded at Z>3.1 32

33 Reduced Quality Data To reduce the quality of the data, higher level analysis was re-run but this time rather than using mixed effects: FLAME 1 for statistical analysis instead mixed effects: simple OLS (ordinary least squares) was used. This is deemed to be less effective in analysis for reasons that are explained above (see Higher level analysis Statistics). Figure 29 Activation Image for contrast 1 HfHv using fixed effects analysis By using mixed effects: simple OLS less activation is showing up and the activation is more dispersed 33

34 and grainy which can be inferred as having less quality than when mixed effects: FLAME 1 was used. Also, since variance is not the same between participants and the OLS model models it as so, the data output from using this is by its very nature going to be less accurate. Improved Quality Data Following this first level analysis was run again with all participants but this time reducing the FWH? back to its default at 5.0 mm rather than the original 9.0 mm because if the data is smoothed too much it is harder to parcellate the activation. Following this higher level analysis was run for the sixth time using this higher level analysis report excludes participant 046, Z>3.1, P=0.01, FWHM 5.0 mm. Although the overall activation is less it is not grainy, moreover the cluster list reports 4 clusters which are of a more informative size than what was produced with a FHWM of 9.0 mm (see figure 30 below). Therefore the spatial specificity is higher by making this modification. These improvements were seen because smoothing has the effect of reducing the number of individual observations (Brett, 2003). Therefore as the FWHM was decreased the number of clusters increased, and within this the image quality did not deteriorate and the clusters were not as large as was seen earlier on in the analysis. 34

35 Thresholded activation images Figure 30 Activation map of contrast 2 HfAv using FHWM 5.0 mm Cluster Index Voxels Z-MAX Z-MAX X (mm) Z-MAX Y (mm) Z-MAX Z (mm)

36 Conclusion The threshold of Z was stringent enough to exclude false positives with a high probability. Thus setting Z to P = 0.01 means that only 1% of the unactivated parts of the brain will show false positives. Anatomically the data displays activation in the anticipated areas. Since the experiment used visual and auditory stimuli activation in the occipital lobe and temporal lobes would be expected, respectively (Belin et al., 2004; Johnstone et al., 2006). This is evident from the activation maps. In addition prefrontal lobe activation is recorded in the improved data (see figure 30) which correlates with Cluster 1 (see figure 27) and this too is not unexpected since the stimuli was designed to elicit an emotional response, and the Paracingulate Gyrus (32% activation in cluster 1) has been associated with understanding people's intentions during social interaction (Walter et al., 2004) and the superior temporal region has also been associated with understanding social interaction when presented with false-belief stories (Gobbini et al., 2007) which this experiment would have tested with the incongruous conditions. Better quality data would have improved the analysis, for instance participant 046 which had to be excluded from higher level analysis due to the incorrectable motion and registration in first level analysis. Moreover a larger set of participants, would also have improved in the results from the data analysis. On a similar note the absence of activation on the activation maps of contrasts 5 6 indicates there was overlapping activation, but with more participants this may improve because there is a higher chance of their being activation in different brain regions. 36

37 References Belin, P., Fecteau, S., & Bédard, C. (2004). Thinking the voice: neural correlates of voice perception. Trends in cognitive sciences, 8(3), Brett, M. (2013). Principles Smoothing. Retrieved 1st May 2013, from Chester, D. S., Eisenberger, N. I., Pond, R. S., Richman, S. B., Bushman, B. J., & DeWall, C. N. (2013). The Interactive Effect of Social Pain and Executive Functioning on Aggression: An fmri Experiment. Social Cognitive and Affective Neuroscience. FMRIB's Software Library, Friston, K. J., Josephs, O., Zarahn, E., Holmes, A. P., Rouquette, S., & Poline, J. B. (2000). To smooth or not to smooth?: Bias and efficiency in fmri time-series analysis. NeuroImage, 12(2), Gobbini, M. I., Koralek, A. C., Bryan, R. E., Montgomery, K. J., & Haxby, J. V. (2007). Two takes on the social brain: A comparison of theory of mind tasks. Journal of Cognitive Neuroscience, 19(11), Jenkinson, M., Bannister, P., Brady, M., & Smith, S. (2002). Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage, 17(2), Johnstone, T., van Reekum, C. M., Oakes, T. R., & Davidson, R. J. (2006). The voice of emotion: an FMRI study of neural responses to angry and happy vocal expressions. Social Cognitive and Affective Neuroscience, 1(3), Lindquist, M. (2008). The Statistical Analysis of fmri Data. Statistical Science. 23(4), Poldrack, R. A., Mumford, J. A., & Nichols, T. E. (2011). Handbook of functional MRI data analysis. Cambridge University Press. 37

38 Reimold, M., Slifstein, M., Heinz, A., Mueller-Schauenburg, W., & Bares, R. (2005). Effect of spatial smoothing on t-maps: arguments for going back from t-maps to masked contrast images. Journal of Cerebral Blood Flow & Metabolism, 26(6), Smith, S. M. (2002). Fast robust automated brain extraction. Human brain mapping, 17(3), Smith, S. M. (2004). Overview of fmri analysis. British journal of radiology,77 (suppl 2), S167-S175. Walter, H., Adenzato, M., Ciaramidaro, A., Enrici, I., Pia, L., & Bara, B. G. (2004). Understanding intentions in social interaction: the role of the anterior paracingulate cortex. Journal of cognitive neuroscience, 16(10), Woolrich, M. W., Behrens, T. E., Beckmann, C. F., Jenkinson, M., & Smith, S. M. (2004). Multilevel linear modelling for FMRI group analysis using Bayesian inference. Neuroimage, 21(4), Worsley, K. J. (2001). Statistical analysis of activation images. Functional MRI: an introduction to methods, 14,

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