For countless aeons did neuroscientists burn with the perverse desire to segment human brains apart in vivo, while the juicy glands still pulsated with life within their unfortunate hosts. Numerous methods were attempted, as crude as they were unnatural - paint scrapers, lint rollers, zesters - but without success. And the neuroscientists did curse and they did rage and they did utter blasphemy of such wickedness as to make the ears of Satan himself bleed. With the terrible advent of FMRI did that all change; now, the tissue of the brain, the seat of consciousness, could be blasted apart while leaving its host intact; now could the grey be separated from the white, the gold from the dross. And then did the neuroscientists go down and slay the Canaanites, thirty thousand in number, and not a man survived as the neuroscientists did wade through swales of blood covered with the skins of their enemies and their eyes burned centroids of murder.
So goes the story of the creation of FAST. The tool is straightforward: Provide a skullstripped brain, decide how many tissue classes you wish to segment, and the rest of the defaults are usually fine. Often a researcher will want three tissue classes: White matter, grey matter, and cerebrospinal fluid (CSF). However, if you are dealing with a subject that presents with a brain abnormality, such as a lesion, you may want to increase the number of classes to four in order to segment the lesion into its own class.
FAST outputs a dataset for each tissue type. For example, if three tissue types have been segmented, there will be three output datasets, one corresponding to each tissue class; each dataset is a mask for each tissue type, and contains a fraction estimate at each voxel. The picture below shows a grey matter mask segmented with FAST. The intensity at the voxel centered at the crosshairs is 0.42, meaning that 42% of that voxel is estimated to be grey matter; presumably, the other 58% is white matter, as the voxel lies at the boundary between the head of the caudate nucleus (a grey matter structure), and the internal capsule (which is composed of white matter).
For some packages such as SPM, tissue masks can be used for normalization. For example, the grey matter and white matter masks will be normalized to mask templates in a standard space, such as MNI, and these warping parameters are then applied to the functional runs. However, the volume of these masks can also be calculated and compared across subjects or across groups. In order to calculate the total grey matter volume within a mask, for example, fslstats can be used:
fslstats s007a1001_brain_pve_1.nii.gz -M -V | awk '{ print $1 * $3 }'
This will return the volume of the mask in cubic millimeters; the same operation can be applied to the other masks by substituting s007a1001_brain_pve_1 with a different class (e.g., either 0 or 2).
However, for more sophisticated voxel-based morphometry comparing volumetric differences between focal cortical areas or specific subcortical structures, I recommend FreeSurfer. Brain segmentation is part of the default processing stream in FreeSurfer, and the volume of each area is output into a formatted table. This will be covered in a later tutorial; for now, use FAST and appreciate its bloody, violent history.
So goes the story of the creation of FAST. The tool is straightforward: Provide a skullstripped brain, decide how many tissue classes you wish to segment, and the rest of the defaults are usually fine. Often a researcher will want three tissue classes: White matter, grey matter, and cerebrospinal fluid (CSF). However, if you are dealing with a subject that presents with a brain abnormality, such as a lesion, you may want to increase the number of classes to four in order to segment the lesion into its own class.
FAST outputs a dataset for each tissue type. For example, if three tissue types have been segmented, there will be three output datasets, one corresponding to each tissue class; each dataset is a mask for each tissue type, and contains a fraction estimate at each voxel. The picture below shows a grey matter mask segmented with FAST. The intensity at the voxel centered at the crosshairs is 0.42, meaning that 42% of that voxel is estimated to be grey matter; presumably, the other 58% is white matter, as the voxel lies at the boundary between the head of the caudate nucleus (a grey matter structure), and the internal capsule (which is composed of white matter).
For some packages such as SPM, tissue masks can be used for normalization. For example, the grey matter and white matter masks will be normalized to mask templates in a standard space, such as MNI, and these warping parameters are then applied to the functional runs. However, the volume of these masks can also be calculated and compared across subjects or across groups. In order to calculate the total grey matter volume within a mask, for example, fslstats can be used:
fslstats s007a1001_brain_pve_1.nii.gz -M -V | awk '{ print $1 * $3 }'
This will return the volume of the mask in cubic millimeters; the same operation can be applied to the other masks by substituting s007a1001_brain_pve_1 with a different class (e.g., either 0 or 2).
However, for more sophisticated voxel-based morphometry comparing volumetric differences between focal cortical areas or specific subcortical structures, I recommend FreeSurfer. Brain segmentation is part of the default processing stream in FreeSurfer, and the volume of each area is output into a formatted table. This will be covered in a later tutorial; for now, use FAST and appreciate its bloody, violent history.