The SUIT toolbox is written to support a number of different analysis techniques, such as fMRI group analysis, lesion-symptom mapping, and VBM. All techniques share a number of initial similar steps, including the isolation of the cerebellum from the rest of the brain, normalization to the atlas template, and reslicing of the data into atlas space.

New Functions (SPM12)

Isolate (suit_isolate_seg)
Normalize using Dartel (suit_normalize_dartel)
Normalize with dentate ROI (suit_normalize_dentate)
Reslice images into SUIT space using dartel (suit_reslice_dartel)
Summarize data in lobular ROIs (suit_lobuli_summarize)

Functions for compatibility with older versions of SPM

Isolate (suit_isolate)
Normalize (suit_normalize)
Reslice images into SUIT space (suit_reslice)
Reslice images into subject space (suit_reslice_inv)

New Functions (SPM12)

Isolate (suit_isolate_seg)

The Isolation algorithm works best on a whole-brain high-resolution (~1 mm isotropic) T1 scan with good gray-white matter contrast. This new version can also combine the information of the T1 scan with other MRI contrast (Channels e.g. T1, T2, PD etc.) to improve the isolation process. The algorithm works best if the T1 image is brought into LPI-orientation, and the origin of the image is set to the anterior commissure.

To isolate the cerebellum simply type suit_isolate_seg in the matlab command window and then select the appropriate scan to be analysed using the SPM interface (all steps of analyses require that SPM is also running). If you want to use different channels, then select all images to be used. The T1 must be selected first and the following images must have the same dimensions and be coregistered to the first channel. To select more than one subject, click in create new subject and then select the corresponding image(s). Alternatively one can also call suit_isolate_seg({'<name>.nii'}) at the matlab command window. The input must be a cell array for one subject containing the name of one image per field. The isolation procedure:

Suit_isolate_seg has higher performance that its predecesor. By taking advantage of the new segmentation implemented in SPM 12, it can better identify the cerebellar boundaries avoiding misclassification in problematic regions that might result when using the previous version. Isolation takes about 3 min on a normal desktop PC. Even if hand correction is necessary, it does not take much more than 5-10 min per individual. For full options of the isolation algorithms from the command line type: help suit_isolate_seg .


Comparison between cerebellar isolation maps generated by suit_isolate and suit_isolate_seg

Normalize using Dartel (suit_normalize_dartel)

The next step is to normalize an individual cerebellum into the SUIT atlas template. Human brains differ both in size and shape, the goal of normalization is to deform each person's cerebellum to find the best correspondence with the SUIT template. Compared to normalization to the MNI whole-brain template, the new method greatly improves the alignment of individual fissures, reducing their spatial spread by 60%, and improves the overlap of the deep cerebellar nuclei.

Dartel engine written by John Ashburner uses the tissue segmentation maps, the white and gray matter segmentation maps produced by suit_isolate_seg (which uses the segmentation algorithm in SPM12). The non-linear deformation is then found in form of a flowfield, based on Large Deformation Diffeomorphic Metric Mapping (LDDMM, Michael I. Miller).

To normalize, please specify for each Subject:

The normalization will produce:

No resliced image will be produced at this point. For this, please use suit_reslice_dartel.

Normalize with dentate ROI (suit_normalize_dentate)

To investigate activity in the deep cerebellar nuclei, we have developed a version of the SUIT-normalization that uses an ROI from the dentate nucleus to further improve the overlap of the deep cerebellar nuclei (Diedrichsen et al., 2011). As can be seen in the Figure below, this methods leads to exact overlap of the dentate nucleus. This is important for two reasons: First, the dentate nucleus receives input from many functional cerebellar-cortical loops (Dum & Strick, 2003). Thus, when not ensuring good overlap, the activity in these regions will be mixed across participants. Secondly, the raw T2* signal in the dentate nucleus is about 1/2 the size of the BOLD signal of the surrounding gray matter structures (due to the high iron content of the nuclei). Without forcing the dentate nuclei to superimpose during normalization (and without masking the images), cerebellar cortical activation will likely be smoothed into the dentate nucleus.


Overlap of dentate nucleus using different normalization methods

To normalize a the cerebellum and the dentate nucleus


ROI drawing of the dentate "Hull" ROI on the mean EPI image

Reslice images into SUIT space using dartel (suit_reslice_dartel)

The function suit_reslice_dartel uses the flowfield and affine transformation found by suit_normalize_dartel to bring images into Atlas space.

The Inputs to the function for every subject are:

Further options can be specified:

For full options of the function see: help suit_reslice_dartel.

Summarize data in lobular ROIs (suit_lobuli_summarize)

Sometimes a quick summary of cerebellar data in terms of the lobules is very handy. You can use suit_lobuli_summarize to automatically produce such a table. You are asked to select the images over which you want the summary. Note that these images need to be resliced into SUIT space (see above). The function then finds for each image all voxels within each of 28 cerebellar compartments, split by lobule and vermis/hemisphere. These compartments are defined by the probabilistic cerebellar atlas. The function then computes certain summary statistics (by default only mean and max) on the data for each lobule from each image and will generate a text file that can then be used for further analysis (see right).

You can also use this function on data normalized into MNI-space by providing the function with the atlas image for the corresponding MNI-space probabilistic atlas. For full options see help suit_lobuli_summarize.

image region regionname mean max
1 3 Left_V 20.1 70.7
1 4 Right_V 10.3 35.4
1 5 Left_VI 17.4 63.2
1 6 Vermis_VI 10.20 40.4
... ... ... ... ...

Functions for compatibility with older versions of SPM

Isolate (suit_isolate)

The Isolation algorithm works best on a whole-brain high-resolution (~1 mm isotropic) T1 scan with good gray-white matter contrast. The algorithm works best if the image is brought into LPI- or RPI-orientation, and the origin of the image is set to the anterior commissure.

To isolate the cerebellum simply type suit_isolate in the matlab command window and then select the appropriate scan to be analysed using the SPM interface (all steps of analyses require that SPM is also running). Alternatively one can also call suit_isolate('c_<name>.nii') at the matlab command window. The isolation procedure:

Isolation takes about 2 min on a normal desktop PC. Even if hand correction is necessary, it does not take much more than 5-10 min per individual. For full options of the isolation algorithms from the command line type: help suit_isolate .


Cerebellar isolation map generated by the isolation algorithm

Normalize (suit_normalize)

To normalize a cerebellum to the SUIT atlas template type suit_normalize in the matlab command window, you should then select the cropped image (c_<name>.nii) and the (possibly handcorrected) isolation map (c_<name>_pcereb_corr.nii) as a mask using the SPM interface. Both of these files are generated in the cerebellum isolation stage listed directly above.

For full options of the normalization process type: help suit_normalize.

Reslice images into SUIT space (suit_reslice)

The function suit_reslice uses the deformation map found in the normalization step to resample images into the new atlas space. To reslice an image into atlas space simply type suit_reslice in the matlab command window and then select the appropriate image to be resliced, the deformation map (mc_<name>_snc.mat).

Images can be masked with the isolation map c_<name>_pcereb_corr.nii before reslicing. This is useful in avoiding contamination problems by data from the adjacent visual cortex when smoothing.

For full options of the function see: help suit_reslice.

Reslice images into subject space (suit_reslice_inv)

The function suit_reslice_inv takes an image in SUIT space and reslices it into the space of the individual subject. This can be especially useful in connection with the probabilistic cerebellar atlas, which provides a lobular map (Cerebellum-SUIT.nii) that can be resliced in to the original space (see image). This map can then be used to define anatomical ROIs in the space of the individual participant, or it can be further refined according to the individual anatomy.

To reslice an image into atlas space simply type suit_reslice_inv. Then select the appropriate image in SUIT space to as well as the deformation map ( mc_<name>_snc.mat). For full options of the function see: help suit_reslice_inv.


Cropped anatomical image


Overlayed with lobular map in anatomical space