NCC scores were computed for the entire brain volume as well as the specified ROIs as defined in Section 2.8.2 for each of 17 subjects considering five different registration conditions. NCC results are shown in the form of mean±std in . One-way ANOVA analysis on whole-brain NCC scores (five levels: HAMMER, SPM2c, SPM2i, SPM5, and DARTEL), p < 0.05, was performed using SPSS software (Statistical Package for the Social Sciences). Results showed a significant main effect of the normalization method; HAMMER and DAR-TEL slightly outperformed SPM2 and unified segmentation-based normalizations (i.e., less than 3% improvement). There was no significant difference in performance between SPM5-based normalization and SPM2-based normalization using the Colin27 template (SPM2c). In fact, SPM2c performed worse than other methods. Pairwise comparisons using Sidak-correction between SPM2c and SPM2i revealed no significant difference in performance. Therefore, it can be concluded that using a high-resolution template such as Colin27 does not improve registration accuracy for SPM2-based registration. A second ANOVA was performed on NCC scores for the left and right ROIs with two factors: normalization method (five levels) and hemisphere (Left/Right), p < 0.05. There was a significant main effect of hemisphere (i.e., a greater correlation (higher NCC score) for the right hemisphere compared to left hemisphere). There was also a significant main effect of the normalization method. Pairwise comparisons using Sidak-correction showed that HAMMER significantly outperformed other techniques (i.e., over 11% improvement) and SPM2 with the Colin27 template yielded the lowest NCC scores among all methods. DARTEL slightly outperformed the other SPM-based normalization methods; however, unlike the full volume case, DARTEL did not match up HAMMER’s performance at ROI level. Such difference in performance is due to the diffeomorphic nature of DARTEL registration, in which the method is given enough freedom to estimate quite large deformations. Such freedom of deformation may result in unrealistic shrinkage/expansion of some structures in the brain image volume. Consequently, diffeomorphic normalization can not capture small deformations required for matching areas with small residual anatomical details such as the selected ROI in this study. There was no significant difference in performance between SPM2 using the ICBM152 template and SPM5 using the probabilistic tissue maps. Comparing SPM2c and SPM2i results, it was reconfirmed that SPM2-based normalization cannot take advantage of the high-resolution template. There was also a significant interaction between the two factors; NCC scores were higher for the right hemisphere than the left hemisphere for SPM2c, SPM2i, SPM5, and DARTEL but not for HAMMER, which yielded no difference between the two hemispheres ().
Table 2 Comparing mean and std. (%) of normalized cross-correlation values (17 subjects) among different registration techniques; HAMMER, cosine basis function of SPM2 using two templates: SPM2c (Colin27), SPM2i (ICBM152), unified segmentation (SPM5), and diffeomorphic (more ...)
Significance test of NCC score differences between left and right hemispheres (R - L) among five types of normalization. (*) indicates a significant difference in NCC score between left and right hemispheres.
illustrates the region of activation produced in the contrast of ‘four sound conditions vs. rest’, across five registration conditions. Activation resulting from the HAMMER-based normalization is more intense (lighter color) and more tightly localized over auditory cortex, compared to other techniques. The increase in t-values comparing smoothed vs. unsmoothed data for HAMMER-based, SPM2c-based (using Colin27), SPM2i-based (using ICBM152), unified segmentation-based, and finally, DARTEL-based normalized data are 1.5%, 22.6%, 13.5%, 85.7% and 20.3%, respectively. This implies that smoothing unnecessarily expands the region of activation for the condition using HAMMER since there is no significant increase in t-values for smoothed vs. unsmoothed HAMMER-normalized data; however, for analyses conducted on SPM2, DAR-TEL, and specifically unified segmentation normalized data, smoothing substantially improved fSNR.
Figure 7 Comparing activation maps (axial view) corresponding to an auditory-related fMRI task for conditions given in . The colormap depicts the activation maps resulting from group analysis of 17 subjects for the contrast of ‘four sound conditions (more ...)
The statistical analysis of fMRI data was performed in MATLAB® using SPM functions. Analysis of the ‘four sound conditions vs. rest’ contrast revealed significant activation in the superior temporal region bilaterally using False Discovery Rate (FDR) correction for multiple comparisons, p < 0.05. Highest activation peaks (i.e., t-value + 3D coordinates in MNI space) for two contrasts of ‘four sound conditions vs. rest’ and ‘listening vs. rest’ observed in each hemisphere in the group analyses (in which the subject was treated as a random effect) are listed in .
Coordinates of peak activation (in MNI space) for 10 different processed fMRI datasets and for two different contrasts; (1) listening vs. rest, and (2) four sound conditions vs. rest.
The following can be observed from the data presented in : (1) Group analysis of HAMMER-normalized data, with or without smoothing, yields higher t-values in both hemispheres compared to normalized data using other techniques. Considering NCC comparison results (i.e., higher NCC scores for higher-d registration), one can conclude that increased fSNR is due to increased overlap across subjects; (2) Analysis conducted using SPM2 and Colin27 as the template, without smoothing, yields higher t-values compared to the ICBM152 template without smoothing; however, the opposite is true if data are smoothed; (3) Smoothed fMRI data yields higher t-values compared to unsmoothed data (except for one case; HAMMER, listening vs. rest, Right Hemisphere); however, the smoothing-related increase in t-values is more substantial for SPM2-, SPM5-, and DARTEL-based normalizations. One should note that one application of spatial smoothing kernels in functional group analysis, as mentioned in Section 2.6, is to render the data more normally distributed and therefore, suitable for parametric tests. To check the validity of the normality condition for the non-smoothed normalized data, we checked the effective smoothing of our data using SPM software tools - even without any smoothing applied, our data has an average effective smoothness of 6 mm in all directions, which is approximately twice the voxel size (3 mm3). We judge this to be sufficient to render the data appropriate for parametric tests.
Average Euclidean Distances (A.E.D.) between the highest activation peak obtained from group analysis and the ‘listening vs. rest’ contrast and the closest activation peak observed in individual analyses for the same contrast are shown in . ANOVA on Euclidean distances with the two factors: normalization method (four levels) and smoothing (two levels) revealed a significant main effect of smoothing (F(1, 16) = 17.52, p < 0.05). Smoothing yielded significantly higher distance values than no smoothing. We also observed a significant main effect of normalization method (F(4, 64) = 7.16, p < 0.05), which we followed up using Sidak-corrected pairwise comparisons (see ); HAMMER registration yielded significantly smaller distances between group location estimates and peaks in individuals compared to SPM2i, SPM2c, unified segmentation, and DARTEL. There was no significant difference between SPM2c, SPM2i and SPM5; however, DARTEL yielded significantly higher distances compared to the rest. Further, there was no significant difference between smoothed and un-smoothed data when normalized using HAMMER or SPM2c registration. On the other hand, smoothing yielded higher distance values when the normalization method is either SPM2i, SPM5’s unified segmentation, or DARTEL. One may conclude that using a high-resolution template for normalization (such as in HAMMER and SPM2c-based normalization) results in more accurate alignment of the functional activation foci and therefore suppresses the impact of spatial smoothing which is applied for increasing overlap among subject data; however, validation of such conclusion requires further investigation by adding an extra factor for template type within the statistical analysis. Finally, there was no significant interaction between normalization method and smoothing.
Average Euclidean Distances (A.E.D.) between the highest activation peak obtained from the group analysis and the closest activation peak in each individual from the ‘listening vs. rest’ contrast.
Average Euclidean distance MANOVA results from the ‘listening vs. rest’ contrast for five normalization methods with and without smoothing. (*) indicates the significant difference between smoothed and unsmoothed data.
Based on NCC scores, t-values, and analysis of Euclidean distances, it can be concluded that higher t-test values resulting from HAMMER registration are due to an increased activation overlap among all subjects. The use of the high-resolution template with the low-dimensional SPM normalization procedure neither increased the activation peak value nor improved the localization of activation foci. Clearly, the SPM2 normalization technique cannot take advantage of the spatial detail in a high-resolution template to allow matching of morphologically variable regions. Spatial smoothing of fMRI data prior to group analysis does increase the magnitude of peak activation in most cases. However, such smoothing degrades spatial resolution, so that activation foci cannot be localized as precisely.