VBM vs SBM
Voxel-Based Morphometric (VBM)
- How do the sizes of gray/white matter and CSF structures change between subjects/populations?
- How to define a volume without defining a boundary?
- How to compare regions without defining a region?
: a neuroimaging technique that investigates focal differences in brain anatomy. The core process of VBM is segmenting the brain into gray matter, white matter, and cerebrospinal fluid, warping the segmented images to template space and smoothing.
: At its simplest, VBM involves a voxelwise comparison of regional gray matter 'density' between two groups of subjects. Density here refers to the relative amount of gray matter and should not be confused with cell packing density (number of cells per unit volume of neuropil). The procedure is relatively straightforward and involves spatially normalizing and segmenting high-resolution magnetic resonance images into the same stereotaxic space. These gray matter segments are then smoothed to a spatial scale at which differences are expressed (usually approximately 8 mm). Voxelwise parametric statistical tests are performed, which compare the smoothed gray matter images from the groups using statistical parametric mapping. Corrections for multiple comparisons are generally made using the theory of random fields.
: MRI 영상의 뇌 전체를 작은 복셀(voxel) 단위로 통계분석을 함으로 뇌의 부피의 감소나 증가를 분석하는 방법.
VBM provides the voxel-wise estimation of the local amount or volume of a specific tissue compartment.
VBM is most often applied to investigate the local distribution of grey matter, but can also be used to examine white matter. However, the sensitivity for finding effects in white matter is rather low and there exist more appropriate methods (e.g. DTI) for that purpose.
The concept of VBM incorporates different preprocessing steps:
(1) spatial registration to a reference brain (template)
(2) tissue classification (segmentation) into gray and white matter and CSF,
(3) bias correction of intensity non-uniformities.
(4) Finally, segmentations are modulated by scaling with the amount of volume changes due to spatial registration, so that the total amount of grey matter in the modulated image remains the same as it would be in the original image.
*Statistical parametric mapping: is an image analysis tool that assesses the significance of cerebral blood flow changes on a voxel-by-voxel basis by automated statistical comparison to a group of normal subjects.
*Parametric mapping: a pixel-wise map of magnetic relaxation parameters, allows evaluation of MR relaxation times of every pixel in the image.
*WARP: summarizes methods to minimize the impact of metal implants on MR image quality.
Surface-Based Morphometry (SBM)
: MRI 영상으로부터 뇌 표면을 계측해 피질 두께 (cortical thickness), 뇌 피질 주름 정도 (gyrification indices), 뇌피질 고랑의 깊이 (sulcus depth), 뇌피질 복잡성 (cortical complexity)와 같은 지표를 구하는 방법.
뇌 표면의 꼭지점 (vertex)를 찾고, 그로부터 mesh를 만들어 뇌의 표면적인 특징을 계측하는 분석.
the estimation of the cortical thickness and central surface of the left and right hemispheres based on the projectio-based thickness (PBT) method.
Furthermore, the surface pipeline uses topology correction and spherical mapping.
Surface-based morphometry has several advantages over using volumetric data alone. For instance, brain surface meshes have been shown to increase the accuracy of brain registration compared with Talairach registration.
Brain surface meshes also permit new forms of analyses, such as gyrification indices that measure surface complexity in 3D, local gyrification, or cortical thickness.
Furthermore, inflation or spherical mapping of the cortical surface mesh raises the buried sulci to the surface so that mapped functional activity in these regions can be easily visualized.
Cortical thickness and central surface estimation
- we use a fully automated method that allows for measurement of cortical thickness and reconstructions of the central surface in one step. It uses a tissue segmentation to estimate the white matter (WM) distance, then projects the local maxima (which is equal to the cortical thickness) to other gray matter voxels by using a neighbor relationship described by the WM distance. This projection-based thickness (PBT) allows the handling of partial volume
information, sulcal blurring, and sulcal asymmetries without explicit sulcus reconstruction
