Elsevier

NeuroImage

Volume 47, Issue 4, 1 October 2009, Pages 1522-1531
NeuroImage

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

https://doi.org/10.1016/j.neuroimage.2009.05.047Get rights and content

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.

Introduction

Inter-subject variability in the spatial location of activation foci in functional neuroimaging studies can result from variability in anatomical structure, variability in functional organization, or both. To the extent that variability in functional localization reflects variability in anatomical structure, it may be decreased by improving anatomical registration across subjects. The conventional approach for functional localization is to find the correspondence among brain volumes of all participants within the study by registering every brain image to a given template brain. Such inter-subject registration in group studies is referred to as “spatial normalization”. Minimizing the contribution of variable anatomical structure to variability in the spatial location of activation foci has several advantages: First, it increases experimental power, so that small, focal functional activations can be more easily detected. Second, it reduces the need for smoothing in group studies, and improves spatial resolution, permitting activation foci to be localized to specific anatomical locations with greater precision.

Several brain normalization techniques have been proposed to register anatomy across subjects. Registration techniques vary from linear transformations of rigid-body registration that have few parameters and match size and shape (Fox et al., 1985, Cox, 1996), to high degrees-of-freedom deformable registration methods that match residual details on the cortical surface and internal brain structures (Klein et al., 2009, Davatzikos, 1996, Fischl et al., 1999, Thompson et al., 2000). An overview of different normalization techniques proposed for brain functional data analysis can be found in Gholipour et al. (2007).

The accuracy of inter-subject registration using normalization methods has been assessed using both landmark-based (Ardekani et al., 2005, Yassa and Stark, 2009) and intensity-based (Hellier et al., 2003) measures. Both types of studies confirm the effectiveness of spatial normalization for reducing inter-subject anatomical variability. In addition, the effect of inter-subject registration on the accuracy of functional group analysis has received some attention. Gee et al. (1997) evaluated three different registration techniques (Bayesian volumetric warping proposed by him, SPM96 (Ashburner and Friston, 1996) and a 9-parameter affine registration) using t-statistics from a functional group analysis. Ardekani et al. (2004) presented a quantitative comparison between three registration techniques (SPM'99, AFNI (Cox, 1996) and ART (Ardekani, 2003)) and examined the effect of registration method on the reproducibility of the fMRI activation maps. Both Gee and Ardekani concluded that increased accuracy in inter-subject registration results in a significant increase in the sensitivity of activation detection. Recently, Wu et al. (2006) compared the performance of AIR (Woods et al., 1997), SPM95 (Friston et al., 1995b), and their custom-developed demons-based registration in a region-of-interest (ROI)-based functional analysis. Similarly, they concluded that improving the normalization step in fMRI data analysis improves the reliability of the colocalized fMRI results, but at a cost of increased complexity of registration and computation time.

However, these published studies suffer from a number of limitations including: 1) the selected registration techniques are relatively low-dimensional and the impact of using a high-dimensional registration method in functional analysis has not been evaluated thoroughly; 2) the use of low-resolution anatomical templates and spatial filtering (smoothing) in current techniques may, in any case, compromise the effectiveness of using a high-dimensional inter-subject registration in group analysis; and 3) the cognitive tasks investigated in previous studies appear to activate large, distributed brain networks. To assess improvements in spatial resolution, it would be better to choose a task that is known to activate an anatomically circumscribed region, so that improvements in structural anatomical registration and in functional signal-to-noise ratio (fSNR) can be assessed concurrently. Here, we assess activity in auditory and speech regions of the temporal cortex in response to auditory and speech stimuli. The fSNR is defined as the ratio between the intensity of a signal associated with changes in brain function and the variability in the data due to all sources of noise. fSNR is conceptually very similar to t-statistics as calculated by SPM (Statistical Parametric Mapping: Wellcome Department of Cognitive Neurology, London, UK) software, which we shall use as an index of fSNR.

In this study, we evaluate and compare the effectiveness of several registration techniques. We compare a high-dimensional technique known as HAMMER (Hierarchical Attribute Matching Mechanism for Elastic Registration) (Shen and Davatzikos, 2002) to DARTEL (Ashburner, 2007), a high-dimensional inverse-consistent diffeomorphic image registration method and also to commonly used low-dimensional normalizations, such as the normalization methods provided with SPM software (version 2 (Ashburner and Friston, 1999): deformable modeling using discrete cosine transform basis functions, and version 5 (Ashburner and Friston, 2005): unified segmentation). We evaluate: (a) the effects of the normalization technique; (b) the effects of the normalization template; and (c) the effects of conventional isotropic spatial smoothing of functional data, on fSNR. We assess the accuracy of the registration in reducing macroanatomical differences among subjects both qualitatively (i.e., visually inspecting the average of the registered volumes resulting from the application of each of the registration techniques) and quantitatively (i.e., comparing the average of the normalized cross-correlation (NCC) values calculated between the normalization template image and the warped image data for all registration methods). Cross-correlation is a simple but effective way to assess similarity between a registered volume and a reference template. This metric is intended to be used in images of the same modality where the relationship between the intensities of the two images is given by a linear equation. For applications in which the brightness of the image and the template can vary due to lighting and exposure conditions, normalized cross-correlation is used. Moreover, we compare peak activation values (t-statistics) resulting from statistical analysis on the group (treating the subject's variable as a random effect) in both smoothed and unsmoothed fMRI data that has been normalized using different registration techniques.

We also evaluate the effect of the normalization template. Standard normalization techniques use a low-resolution average anatomical model as the target template. In this work, we have selected four well-known templates: (1) ICBM152 (Mazziotta et al., 2001), a population-based template; (2) Colin27 (Holmes et al., 1998), a high-resolution anatomical reference; (3) ICBM452 Tissue Probabilistic Atlas (ICBM452, 2009); and finally, (4) A custom-built group template, generated using the DARTEL tool of the SPM5 package.

It is standard in conventional whole-brain fMRI analysis to apply isotropic three-dimensional filtering kernels of 6–10 mm typically to the functional data (Mikl et al., 2008). The spatial smoothing is done for many reasons one of which is to reduce the effect of inter-subject variability in group analysis. Although often helpful and necessary, smoothing has the undesirable effect of reducing the spatial resolution, blurring and/or shifting activations and merging adjacent peaks of activation. In this work, we examine whether using a high-dimensional normalization will reduce or eliminate the need for spatial smoothing to increase anatomical overlap among subjects, while maintaining a similar fSNR to that obtained with spatial smoothing.

This paper is further evidence that high-dimensional non-rigid registration methods are needed for group analysis of functional activation in the auditory cortex, as previously demonstrated by Desai et al. (2005), and Kang et al. (2004), which dealt with flattened 2D cortical surfaces and furthermore, by Viceic et al. (2008), which dealt with 3D volumes. Moreover, this paper explores the effects of the normalization template and spatial smoothing on subsequent group analysis of functional data from an auditory imaging experiment. This experiment examined whether functional networks supporting production of speech and perception of speech overlap (Zheng et al., submitted for publication). We wished specifically to determine whether a subset of regions in the superior temporal region is particularly sensitive to a mismatch between the actual auditory consequences of speaking and the predicted consequences based on the motor speech command. A more complete description of the experiment can be found in Zheng et al. (submitted). Only aspects of the experimental design relevant to the methodological question are described here.

Section snippets

Image acquisition

Seventeen normal healthy volunteer subjects (13 female, 4 male, ages 23 ± 3 years (mean ± std), right-handed, native English speakers) participated in this study. All subjects gave written informed consent for their participation. The experimental protocol was cleared by the Queen's University Health Sciences Research Ethics Board.

MR imaging was performed on the 3.0 Tesla Siemens Trio MRI system in the Queen's University Centre for Neuroscience Studies, MRI Facility, Kingston, Ontario. T2-weighted

Results and discussion

NCC scores were computed for the entire brain volume as well as the specified ROIs as defined in Similarity measure: normalized cross-correlation section for each of 17 subjects considering five different registration conditions. NCC results are shown in the form of mean ± std in Table 2. 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

General discussion

In this work, we compared the effect of a high-dimensional elastic registration technique (i.e., HAMMER) to other normalization methods on group-level statistics in an auditory fMRI experiment. The accuracy of the registration techniques was assessed using normalized cross-correlation. Moreover, the functional contrast-to-noise estimates, and measures of distance between estimates of the location of peak activation for the group and estimates for each individual, were also assessed and compared

Acknowledgments

The authors would like to thank Drs. J. Ashburner, D. Shen, X. Wu, and G. Ridgeway for their help. This work is supported by the Canadian Institutes of Health Research (CIHR) through an operating grant to I.S.J., the Natural Sciences and Engineering Research Council of Canada (NSERC) discovery grant to I.S.J. and P.A., Ontario Graduate Scholarship (OGS) to A.M.T. and an early research award to I.S.J.

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