Group analysis

Group statistic was performed once fMRI data had been analysed individually (each run), it is the analysis across subjects, and it is called higher-level analysis in Feat.

FEAT offers both fixed-effects and mixed-effects higher-level modelling: we chose a mixed-effects regression model in order to model the subject variability and to have the possibility to make inference about the wider population from which the subjects were drawn. A Paired Two-Group Difference (Two-Sample Paired T-Test) was performed to compare two groups of data and to find out if means were significantly different from one another or if they were relatively the same. In our case, we compared, for the same subjects, activations and deactivations in the connectivity with each of the four seeds, before (OFF state) and after (ON state) the assumption of L-dopa. Therefore the test was paired since we compare groups that were related as participants in the first group are the same as participants in the second group. There were two copes, i.e. two contrast of parameter estimates (activation and deactivation); for each voxel, the parameter estimate was the regression coefficient that resulted from regressing the intensity time-series on the model. Finally, the z-statistic image was thresholded and underwent a clustering analysis, analogous to the one described in 4.5.3. We took account of both OFF runs and both ON runs of each patient (it’s resting state so differences between the two runs are not expected). The contrasts, i.e. the differences, were performed both for OFF versus ON and ON versus OFF; we were mainly interested in the latter, i.e. in activations (or deactivations) that were present in the ON state (after L-dopa) but not in the OFF state (before L-dopa).

Figure 4.13: Feat GUI Higher-level analysis: paired two-group difference model design

Chapter 5 Results

5.1 Physiological noise correction

Physiological correction was performed using two software packages: RETROICOR (see 4.5.1.3), for cardiac noise, by using the ECG measured contextually with the EEG (see 4.5.1.1), and PESTICA estimation for respiratory noise (see 4.5.1.2). In order to quantify our correction, and to have an idea of brain areas in which it was more effective, we computed TSNR (see 4.5.1.3) percentage variation maps between data before and after the correction, therefore a higher positive value of TSNR percentage variation in a given voxel means that in that voxel a higher correction was performed. To average TSNR maps together, they were all registered to the study specific template we created (see 4.5.1.3), and then they were also registered to the MNI standard space. As far as the cardiac noise correction is concerned, Figure 5.1 shows all the axial views of the TSNR percentage variation map, averaged over 6 subjects, superimposed onto the study specific template, while in Figure 5.2 they are superimposed onto the MNI template (showing additional non-brain structures). In Figure 5.3 there are some orthographic views of the same map and in Figure 5.4 the same views as in Figure 5.3 but relative to just one subject. For the respiratory noise correction analogous images are displayed: in Figure 5.5 all the axial views of the TSNR percentage variation map, averaged over 6 subjects, superimposed onto the study specific template, in Figure 5.6 they are superimposed onto the MNI template (showing additional non-brain structures), in Figure 5.7 the same map but shown in various orthographic views and in Figure 5.8 the same orthographic views for one subject. The results for a single subject are consistent with the averaged results, and the comparison between Figures 5.3 and 5.4, and between 5.7 and 5.8 are an example of this. All the axial views and some orthographic views are also shown, respectively in Figure 5.9 and 5.10, for the combination of cardiac and respiratory correction. In the Figures displaying the TSNR percentage variation maps we showed only variations between 5% (or 3%) and 20% (or 10%) in order to focus attention on the areas on which the correction had a higher effect; anyhow small percentage variations were present also in other

voxels. We also computed (with the AFNI function 3dttest) maps of t-statistics values for % TSNR changes after the application of RETROICOR, with respect to 0% level (see Figures 5.11, 5.12, 5.13, 5.14, 5.15, 5.16). They are thresholded at t values corresponding to p < 0.01 after multiple comparison correction (for all voxels) was performed with the FDR (False Discovery Rate) method. Higher levels of significance are noticeable in the areas where the correction was more effective, and that also small variations (of a few percent) of TSNR were significant, especially when both corrections (cardiac and respiratory, Figure 5.15) were performed.

Figure 5.1: TSNR map (percentage variation) for cardiac noise correction: mean across 6 subjects, axial views. The map is superimposed onto the study specific template, to which single maps were registered.

Figure 5.2: TSNR map (percentage variation) for cardiac noise correction: mean across 6 subjects, axial views. The map is superimposed onto the MNI standard template, to which it was registered.

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Figure 5.3: TSNR map (percentage variation) for cardiac noise correction: mean across 6 subjects, some orthographic views. The map is superimposed onto the study specific template, to which single maps were registered.

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Figure 5.4: TSNR map (percentage variation) for cardiac noise correction: example of a single subject, some orthographic views. The map is superimposed onto a functional volume of the subject.

Figure 5.5: TSNR map (percentage variation) for respiratory noise correction: mean across 6 subjects, axial views. The map is superimposed onto the study specific template, to which single maps were registered.

Figure 5.6: TSNR map (percentage variation) for respiratory noise correction: mean across 6 subjects, axial views. The map is superimposed onto the MNI standard template, to which it was registered.

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Figure 5.7: TSNR map (percentage variation) for respiratory noise correction: mean across 6 subjects, some orthographic views. The map is superimposed onto the study specific template, to which single maps were registered.

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Figure 5.8: TSNR map (percentage variation) for respiratory noise correction: example of a single subject, some orthographic views. The map is superimposed onto a functional volume of the subject.

Figure 5.9: TSNR map (percentage variation) for both cardiac and respiratory noise corrections:

mean across 6 subjects, axial views. The map is superimposed onto the study specific template, to which single maps were registered.

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Figure 5.10: TSNR map (percentage variation) for both cardiac and respiratory noise corrections:

mean across 6 subjects, some orthographic views. The map is superimposed onto the study specific template, to which single maps were registered.

Figure 5.11: Maps of t-statistics values for % TSNR changes after the application of RETROICOR for cardiac noise, with respect to 0% level (axial views, thresholded at t = 2.43, that corresponds to a p-value, FDR correction, of < 0.01).

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Figure 5.12: Maps of t-statistics values for % TSNR changes after the application of RETROICOR for cardiac noise, with respect to 0% level (some orthographic views, thresh-olded at t = 2.43, that corresponds to a p-value, FDR correction, < 0.01).

Figure 5.13: Maps of t-statistics values for % TSNR changes after the application of RETROICOR for respiratory noise, with respect to 0% level (axial views, thresholded at t = 2.37, that corresponds to a p-value, FDR correction, < 0.01).

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Figure 5.14: Maps of t-statistics values for % TSNR changes after the application of RETROICOR for respiratory noise, with respect to 0% level (some orthographic views, thresholded at t = 2.37, that corresponds to a p-value, FDR correction, < 0.01).

Figure 5.15: Maps of t-statistics values for % TSNR changes after the application of RETROICOR for both cardiac and respiratory noise, with respect to 0% level (axial views, thresholded at t = 2.35, that corresponds to a p-value, FDR correction, < 0.01).

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Figure 5.16: Maps of t-statistics values for % TSNR changes after the application of RETROICOR for both cardiac and respiratory noise, with respect to 0% level (some ortho-graphic views, thresholded at t = 2.35, that corresponds to a p-value, FDR correction, < 0.01).

We can compare our maps with images reported in the literature, from studies that deal with the physiological-related signal changes in fMRI, such those by Dagli et al., [52], or Lund et al, [115], as far as cardiac-related noise is concerned.

Dagli et al. presented a topographical description of the regions showing significant contributions of cardiac-related signal variance in resting state fMRI acquisitions.

They measured the cardiac activity during MRI acquisition and estimated the signal intensity changes for each voxel during the cardiac cycle by fitting the data to a Fourier series expanded in terms of cardiac phase (quite similar to what Glover et al.

proposed in [69] to correct physiological noise). They also acquired high-resolution 2D magnetic resonance angiography images to provide information on cerebral vascular anatomy, see Figure 5.18.

Their results, shown in Figure 5.17, were highly consistent across subjects, and showed that reduced sensitivity due to cardiac-induced noise in the BOLD signal is greater in specific areas, typically near major arterial and venous structures, whereas in tissue far from the major vessels the cardiac-induced variation is quite small. A notable variation is present around the sinuses, unsurprisingly as the pulsatile blood flow is unique to the cranial veins and occurs as the result of compression from pulsating brain tissue. Lund et al. [115] investigated various sources of non-white noise in BOLD fMRI, considering RETROICOR nuisance regressors in their analysis, and they also found that cardiac-induced noise is greater near majors vessels, such as the medial cerebral artery and the circle of Willis, see Figure 5.21. A good correspondence between our TSNR maps and their findings can be seen, comparing Figures 5.1, 5.2 and 5.3 with Figures 5.17, 5.18, 5.19.

As for respiratory noise, by comparing Figures 5.5, 5.6 and 5.7 with Figure 5.20 we notice that in this case too, the correction we performed tends to be more effective in areas quite consistent with what is found in literature, i.e. mainly near the edges of the brain and near CSF pools, therefore in areas where there are adjacent tissues with different susceptibility. It is important to remember that we could not directly measure respiration but used just an estimation of it.

As for the effects of physiological correction on functional connectivity results, we did not perform a systematic and detailed analysis or comparison; we can however say that, in general, the effect is that after the correction some of the smaller and less significant voxel clusters are no longer present, while bigger and more significant clusters are confirmed, sometimes with a higher level of significance and with a greater extent. Moreover, edges of active areas are cleaner and some clusters that were in areas where BOLD activation is not expected (such as the ventricles) tend to disappear (see Figures 5.22 and 5.23 for examples). It therefore seems that functional connectivity analysis on physiological-noise corrected data gives somewhat cleaner results.

Figure 5.17: The full volume of a typical subject showing a topographical display of the percent-age signal change during the cardiac cycle. Pixels shown in color indicate regions demonstrating significant cardiac-related signal changes, [52].

Figure 5.18: Comparison of selected functional slices (top slices) from one subject with the cerebral vasculature as examined by magnetic resonance angiography (bottom slices). There is a strong correspondance of tissue areas showing significant cardiac-related signal change to the locations of major blood vessels and CSF pools (A - transverse sinus, B - carotid artery, C - fourth ventricle, D - basilar artery, E - main trunk of middle cerebral artery (MCA), F - circle of Willis (entire region), G posterior cerebral artery, H branch of MCA, I superior sagittal sinus, J

-anterior cerebral artery, K - third ventricle, L - inferior sagittal sinus), [52].

Figure 5.19: Cardiac-induced noise: F-test values of the voxels showing significant (P = 0.05 corrected using GRF) effect of a linear combination of the regressors describing the aliased cardiac oscillation. It is seen that the cardiac-induced noise is dominant near larger vessels (e.g. medial cerebral artery and Circle of Willis, see Figure 5.21), [115].

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Figure 5.20: (a) Respiratory-induced noise: F test values of the voxels showing significant (P = 0.05 corrected using FDR) effect of a linear combination of the regressors describing the aliased respiratory oscillation. It is see that the respiratory-induced noise is dominant near the edges of the brain as well as near in the larger veins and in the ventricles, [115]; (b) coupling power from RETROICOR for respiratory data, [72]

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Figure 5.21: Schematic representation of arterial system (inferior view (a), lateral view (b)) and of venous system (c) in human brain - sources: (a)http://www.mayfieldclinic.com/PE-AnatBrain.htm#.Ui3eDD8atO-, (b) http://newnurseblog.com/2011/03/16/the-neuro-icu-for-beginners/arteries/, (c) http://patientblog.clotconnect.org/2011/02/07/sinus-and-cerebral-vein-thrombosis/

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Figure 5.22: Examples of comparison between the results of FC analysis (with seed in the precentral gyrus) performed on the same dataset before (red) and after (green) the physiological noise correction.

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Figure 5.23: Examples of comparison between the results of FC analysis (with seed in the SMA) performed on the same dataset before (red) and after (green) the physiological noise correction.