Reducing inter-subject anatomical variation: effect of normalization method on sensitivity of functional magnetic resonance imaging data analysis in auditory cortex and the superior temporal region

Abstract

Conventional group analysis of functional MRI (fMRI) data usually involves spatial alignment of anatomy across participants by registering every brain image to an anatomical reference image. Due to the high degree of inter-subject anatomical variability, a low-resolution average anatomical model is typically used as the target template, and/or smoothing kernels are applied to the fMRI data to increase the overlap among subjects' image data. However, such smoothing can make it difficult to resolve small regions such as subregions of auditory cortex when anatomical morphology varies among subjects. Here, we use data from an auditory fMRI study to show that using a high-dimensional registration technique (HAMMER) results in an enhanced functional signal-to-noise ratio (fSNR) for functional data analysis within auditory regions, with more localized activation patterns. The technique is validated against DARTEL, a high-dimensional diffeomorphic registration, as well as against commonly used low-dimensional normalization techniques such as the techniques provided with SPM2 (cosine basis functions) and SPM5 (unified segmentation) software packages. We also systematically examine how spatial resolution of the template image and spatial smoothing of the functional data affect the results. Only the high-dimensional technique (HAMMER) appears to be able to capitalize on the excellent anatomical resolution of a single-subject reference template, and, as expected, smoothing increased fSNR, but at the cost of spatial resolution. In general, results demonstrate significant improvement in fSNR using HAMMER compared to analysis after normalization using DARTEL, or conventional normalization such as cosine basis function and unified segmentation in SPM, with more precisely localized activation foci, at least for activation in the region of auditory cortex.

Figure 1

Preprocessing: The SPM package was used for fMRI data realignment (motion correction), and structural-to-functional coregistration. Skull-stripping and volume trimming were performed using FSL’s BET.

Figure 2

Overlapping functional activation map shown for the anatomically neighboring regions of Heschl’s gyrus (HG) and planum temporale (PT). Right: The labeled regions in blue and red are extracted from HG and PT probabilistic maps, respectively, which are thresholded at 40%. Left: The colormap depicts the activation map resulting from statistical group analysis of an auditory functional task using spatially smoothed fMRI data.

Figure 3

From top to bottom: axial (z = +4), sagittal (x = +50), and coronal (y = −16) sections of (from left to right) Colin27 template, inter-subject average volumes computed for HAMMER-based registration, and SPM2-based normalization using Colin27 as template (SPM2c), SPM2-based normalization using ICBM152 template (SPM2i), unified segmentation-based normalization using ICBM452 tissue probability maps, and DARTEL normalization using the custom-built template. Coordinates are in ICBM152 frame.

Figure 4

Zoomed window over Heschl’s gyrus region on average brain volumes generated using five different registration conditions; HAMMER, SPM2 using two templates: ICBM152 (upper row) and Colin27 (lower row), SPM5 unified segmentation, and DARTEL.

Figure 5

Euclidean distances between the highest activation peak obtained from the group analysis and the ‘listening vs. rest’ contrast and the closest activation peak in each individual from single-subject analysis for the same contrast were calculated for all 10 different processing conditions.

Figure 6

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.

Figure 7

Comparing activation maps (axial view) corresponding to an auditory-related fMRI task for conditions given in Table 1. The colormap depicts the activation maps resulting from group analysis of 17 subjects for the contrast of ‘four sound conditions vs. rest’.

Figure 8

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.