BrainVoyager QX v2.8
Cortex-Based Alignment of Sulci and Gyri
A good match between corresponding brain regions is important for many applications including group-level statistical data analysis. Group analysis in Talairach space suffers, however, from a coarse alignment between subjects' brains producing suboptimal, and sometimes even misleading group maps due to extensive spatial smoothing. While functional areas do not precisely follow cortical landmarks, it has been shown for areas V1 and motor cortex (Fischl et al., 1999) and for many other areas (Frost & Goebel, 2012) that a cortical matching approach substantially improves statistical group results by reducing anatomical variability. BrainVoyager QX offers an advanced, high-resolution, cortical mapping approach (Goebel et al., 2002; 2004; Frost & Goebel, 2012) to align brains using curvature information of the cortex. Since the curvature of the cortex reflects the gyral/sulcal folding pattern of the brain, this brain matching approach essentially aligns gyri and sulci across brains when initialized with an approximate (e.g. Talairach-based) pre-alignment step.
Cortex-based alignment operates in several stages. The reconstructed folded cortical representations of each subject and hemisphere constitutes the input of the alignment procedure. In the first step, these folded representations are morphed into a spherical representation, which provides a parameterizable surface well-suited for across-subject non-rigid alignment. Each vertex on the sphere (spherical coordinate system) corresponds to a vertex of the folded cortex (Cartesian coordinate system) and vice versa. The curvature information computed in the folded representation is preserved as a curvature map on the spherical representation. The curvature information (folding pattern) is smoothed along the surface to provide spatially extended gradient information driving intercortex alignment minimizing the mean squared differences between the curvature of a source and a target sphere. The essential step of the alignment is an iterative procedure following a coarse-to-fine matching strategy. Alignment starts with highly smoothed curvature maps and progresses to only slightly smoothed representations. Starting with a coarse alignment as provided by Talairach space, this method ensures that the smoothed curvature of the two cortices possess enough overlap for a locally operating gradient-descent procedure to converge without user intervention (Goebel et al., 2002, 2004, 2006; Frost & Goebel, 2012). Visual inspection and a measure of the averaged mean squared curvature difference reveal that the alignment of major gyri and sulci can be achieved reliably by this method. Smaller structures, visible in the curvature maps with minimal smoothing, are not completely aligned and reflect idiosyncratic differences between the subject's brains, such as continuous versus broken characteristics.
Note that the alignment requires the specification of a target brain to which source brains are aligned. The program offers two approaches to define the target brain. In the explicit target approach, one sphere is selected as a target to which all other spheres are subsequently aligned. The target sphere can be derived from one of the brains of the investigated group or from a special reference brain, such as the MNI template brain. Although tests have shown that alignment results are very similar when using different target spheres, the selection of a specific target brain might lead to sub-optimal results, if the selected brain contains many regions with a non-typical folding pattern. In the moving target group averaging approach, the selection of a target sphere is not required. In this approach, the goal function is specified as a moving target computed repeatedly during the alignment process as the average curvature across all hemispheres at a given alignment stage. The procedure starts with the most coarse curvature maps. Then the next finer curvature maps are used and averaged with the obtained alignment result of the previous level.
The established correspondence mapping between vertices of the cortices can be subsequently used to align the subject's functional data (mesh time courses). The fixed and random-effects GLM procedures have been adapted to take as input these cortically aligned mesh time courses. Furthermore, surface maps (SMPs) and Patches-Of-Interest (POIs) defined on individual subjects' cortex meshes can be transformed into group-aligned cortex space.
Cortex-based alignment is performed using the Cortex-Based Alignment dialog. This dialog is opened via the Cortex-Based Alignment item in the Meshes menu. To use the cortex-based matching approach for inter-subject alignment, follow these steps:
- Prepare a cortex mesh
- Morph a prepared cortex hemisphere mesh into a sphere
- Map vertices of standard sphere to those of morphed sphere
- Create curvature maps for each resampled cortex mesh at different resolutions
- Align source spheres to a moving target group average or to a selected target sphere
After these essential steps, you can perform further procedures to quantify and visualize the achieved alignment. You can also proceed with cortex-based statistical data analyses using the created alignment files with created mesh time course (MTC) files.
In case that information about corresponding regions is available from independent functional localizer runs, it is possible to integrate that information. For more details, see topic Functionally Informed Cortex-Based Alignment.
Fischl B, Sereno, M.I., Tootel, R.B.H., and Dale, A.M. (1999). High-resolution intersubject averaging and a coordinate system for the cortical surface. Human Brain Mapping, 8, 272-284.
Frost, M. & Goebel, R. (2011). Measuring structural-functional correspondence: Spatial variability of specialised brain regions after macro-anatomical alignment. NeuroImage.
Goebel, R., Staedtler, E., Munk, M.H.J., Muckli, L. (2002). Cortex-based alignment using functional and structural constraints. NeuroImage Supplement.
Goebel, R., Hasson, U., Harel, M., Levy, I., Malach, R. (2004). Statistical analyses across aligned cortical hemispheres reveal high-resolution population maps of human visual cortex. NeuroImage Supplement (Human Brain Mapping, Budapest).
Goebel, R., Esposito, F. & Formisano, E. (2006). Analysis of functional image analysis contest (FIAC) data with Brainvoyager QX: From single-subject to cortically aligned group general linear model analysis and self-organizing group independent component analysis. Human Brain Mapping, 27, 392-401.
Frost, M. & Goebel, R. (2012). Measuring structural-functional correspondence: Spatial variability of specialised brain regions after macro-anatomical alignment. NeuroImage, 59, 1369-1381.
Copyright © 2014 Rainer Goebel. All rights reserved.