Cross-sectional data

The optimized VBM protocol of Good et al. (2001) extends the VBM procedure by the following ideas:

  1. Improving spatial normalization by use of gray matter images and a gray matter template rather than anatomical (T1) images
  2. Cleaning up of partitions by applying morphological operations
  3. Optional modulation of partitions to preserve the total amount of signal in the images

The segmentation function in VBM2 will normalize and segment your raw images according to the optimized VBM procedure. You are asked for the raw (non-normalized) images to be analyzed and the template and prior images. If you have created a customized template select the new created T1.img image as T1 template for nonlinear normalization and the gray/white/csf.img images as prior images respectively. Pay attention to the right order of your selections. The use of own prior images is recommended if the gray and white matter distribution is very different from a normal population (e.g. children, Alzheimer’s disease).

This function will save the following files:

    w*.img warped raw image
    Gw*.img gray matter of warped image
    Ww*.img white matter of warped image
    Cw*.img CSF of warped image
    m[GWC]w*.img modulated segmented image of warped raw image
    avgT1.img average of warped T1 images

Use of customized prior images

Depending on your dataset it is sometimes useful to use cutomized prior images resulting from the create customized templates function. The use of own priors is recommended if the gray/white matter distribution of your sample differs from a control population (e.g. children, Alzheimer disease…).

Mask raw lesions

There is a hidden function to use masks for lesions or other regions which should not contribute to the estimation of normalization parameters. If you want to use this option call cg_vbm_optimized(1).


optimized-vbm
This figure summarizes the optimized VBM procedure. The initial gray matter segmentation is used for spatial normalization to a gray matter template to improve normalization accuracy. The normalization estimates are applied to the raw T1 image and the normalized T1 image is then finally segmented. Optionally a clean up procedure can be applied to remove non-connected parts of the segmentation. To correct for the volume changes due to spatial normalization we can additionally modulate the segmented data.