• MSM
• UserGuide

Getting started

Prior to running MSM you will need to have passed your data through a surface extraction and inflation pipeline such as FreeSurfer, or the HCP minimal processing pipeline. This is because, if MSM is to work for your data you must have cortical surface meshes that have been mapped to the sphere. If you use the HCP pipeline you must additionally run the preprocessing script PreSulc. In addition, you will require a data file for each mesh, where the data may be scalar (such as sulcal depth, curvature or myelin features) or multivariate (RSNs or fMRI task maps).

Data can be supplied as GIFTI (.func.gii or .shape.gii), ASCII (.asc) files, or as a simple text file (with '.txt' extension) provided the text file has as many columns as there are mesh vertices. Surface files may be supplied as GIFTI (surf.gii) or ASCII (.asc). In general the key files required to run MSM are the:

• input mesh - otherwise known as the "source" or "moving" mesh. This will be the mesh that is deformed during the registration.

• reference mesh - otherwise the "target" or "fixed" mesh. This represents the surface you would like the source mesh to be deformed to most resemble. In some cases (for example if source and target data have both been resampled onto a population average surface (such as the HCP FS_LR164k or FS_LR32k average surface) it will be sufficient to supply just supply an input mesh.

• input data - the data file associated with the input mesh. This must therefore have as many data points as there are surface mesh vertices.

• reference mesh - the data file associated with the referenced mesh.

Example of the most basic call to msm (using these inputs) or:

• A. msm --inmesh=input_mesh.surf.gii --refmesh=ref_mesh.surf.gii --indata=in_data.func.gii --refdata=ref_data.func.gii -o ~/mydirname/L.

  B. msm --inmesh=average.sphere.FSLR32K.surf.gii --indata=in_data.func.gii --refdata=ref_data.func.gii -o ~/mydirname/L.

Where this assumes you are calling msm from the directory where the data exists. The final option (-o) is the stem of the path where you wish to output your data; we suggest ~/mydirname/L. or ~/mydirname/R as an example of how you can input left and right hemisphere results into the same directory. Case B shows an example of how, when both datasets have been resampled to a population average surface (such as the HCP's 32k FS_LR surface), it is possible to enter just the average sphere as the input mesh.

MSM Output

The most relevant outputs of MSM are:

• ~/mydirname/L.sphere.reg.surf.gii - the warped input mesh. Note that the true output basename is highlighted in bold, with the example output path prefixed to it.

• ~/mydirname/L.resampledandprojected.func.gii - the input data passed through the MSM warp and reprojected down onto the target surface for comparison.

The program also spits out a downsampled copy of the initial native mesh ~/mydirname/L.LOWRES.surf.gii and final warp ~/mydirname/L.LOWRES_transformed.surf.gii, which can be used for warping new meshes through the transformation (see section on transformation). GIFTI outputs are used here only as examples. The program also supports output as ASCII and VTK using the command line token -f.

Advanced Command Line Features

In addition to the required inputs to msm, there are several useful options. The most important of these is the --conf call which allows users to supply a configuration file which modifies key parameters of the registration. For optimal running of the registration a configuration file should be supplied, the parameters of which are described in more detail below.

Another very useful feature is the --trans option. This allows users to specify the output mesh from a previous registration stage. For example, if you wished to initialise registration of some resting state network maps (RSNs) by first aligning course folding structure using sulcal maps (as performed in our NeuroImage paper), you could run registration in two stages as:

• Step 1: msm --inmesh=input_mesh.surf.gii --refmesh=ref_mesh.surf.gii --indata=in_SULC_data.func.gii --refdata=ref_SULC_data.func.gii --conf=myconfigSULC -o ~/mySULCdirname/L. 
  Step 2: msm --inmesh=input_mesh.surf.gii --trans ~/mySULCdirname/L.sphere.reg.surf.gii --refmesh=ref_mesh.surf.gii --indata=in_RSN_data.func.gii --refdata=ref_RSN_data.func.gii --conf=myconfRSN -o ~/myRSNdirname/L.

Running registration in this way, rather than simply taking the output from the sulc registration and using it as an input mesh for the RSN registration, allows distortions for the full sulc + RSN registration to be penalised during alignment.

Output file formatting is controlled by the -f/--format option. The options are: GIFTI (surfaces are saved as .surf.gii and data as .func.gii); ASCII (surfaces are saved as .asc and data is saved as .dpv); ASCII_MAT (surfaces are saved as .asc and data is saved as a simple matrix in a textfile .txt); VTK (surfaces as .vtk and data as .txt). For more details on the .dpv format (which is FreeSurfer compatible, but differentiates surface from data files) please see the following blog post: http://brainder.org/2011/09/25/braindering-with-ascii-files/

Costfunction weighting (CFw) can be controlled using --inweight and --refweight options. This allows you to supply a weighting mask for each of your source and reference meshes, although it is possible to run msm with only one. The CFw masks can be multivariate which allows you to vary the contribution different features. For example in the "Multimodal alignment" section of our paper we use a single, multivariate CFw mask created on our template image (and therefore passed as a --refweight option) to vary the contribution of our different modalities to the registration.

Two final useful parameters are --levels which allows you to control the number of resolution levels run during the course of the registration e.g. --levels=2. This supersedes the influence of the configuration file. Finally --smoothout will allow the user to smooth the data after projection to the template image. By default the registration uses adaptive barycentric resampling (reference). However, this option will allow the user to smooth using a gaussian kernel with an input parameter equal to the standard deviation.

Therefore, with all command line parameters used an msm call might look like this:

• msm --inmesh=input_mesh.surf.gii --trans ~/mySULCdirname/L.sphere.reg.surf.gii --refmesh=ref_mesh.surf.gii --indata=in_RSN_data.func.gii --refdata=ref_RSN_data.func.gii --inweight=in_weight_RSN.func.gii --refweight=ref_weight_RSN.gii --levels=2 --smoothout=2 --conf=myconfRSN -o ~/myRSNdirname/L.

This will repeat Step 2 above, but this time each of the meshes will have a corresponding weighting function supplied in the form of a GIFTI func.gii (but this could also be shape.gii, .asc or as matrix in a text file); the output of the registration will also be smoothed using a kernel of standard deviation 2. The registration will also be stopped after two cycles or registration levels irrespective of the number of levels specified in the configuration file.

Configuration Files

Configuration files modify all tunable parameters of the registration. For a full list of all registration parameters you can enter:

• msm -p

With a full list of parameters:

• --lambda Lambda weights the contribution of regulariser relative to the similarity force. We have found that we typically have to increase regularisation slightly when control point grid resolution increases. This is due to the sensitivity of the regularisation method to the spacing of the end points of discrete transformations which is not fully corrected for.

• --opt optimisation approach. Choice of: AFFINE,DISCRETE (default)

• --simvalchoice of similarity measure for each stage of the registration. There is a choice of 1) SSD; 2) correlation (default); 3) NMI; 5) alpha entropy (only for multivariate data). SSD is enforced for affine alignment. For discrete optimisation we strongly recommend correlation for all datasets. The current implementations of SSD and NMI do not in general work well in the discrete case, and we do not advise using them.

• --it controls the number of iterations at each resolution. In general affine registration will require in excess of 30 iterations. Discrete optimisation will work well with only 3-5 iterations.

We supply a series of configuration files, each tuned to work with different (sulcal depth, myelin, and RSN) data. An example of the sulcal depth config file (allparameterssulcDRconf) is:

• --simval=1,2,2,2
  --sigma_in=4,4,2,1
  --sigma_ref=2,2,1,1
  --lambda=0,0.1,0.2,0.3
  --it=50,3,3,3
  --opt=AFFINE,DISCRETE,DISCRETE,DISCRETE
  --CPgrid=0,2,3,4
  --SGgrid=0,4,5,6
  --datagrid=4,4,5,6
  --IN
  

MSM performs registration by serially optimising the alignment over a hierarchy of resolution levels. Therefore, the comma separated lists above represent parameters per level, and the number of resolution levels run by msm can be controlled by the length of the lists specified here. Registration may also be initialised using an affine alignment step, as used here. This should be specified as an additional level at the beginning, so that in the above case the configuration file is stating that the registration should run one affine step using: SSD as a similarity measure, 50 iterations, input mesh smoothing 4mm, reference mesh smoothing 2mm, on a data grid of resolution 2562 vertices; this is followed by discrete optimisation levels, over 3 control point grid resolutions (162,642,2562)

Note all multiresolution parameters must have the same length lists or the programme will throw an error in the initialisation stages. There are additionally a series of fixed parameters (that need be specified only once) such as the requirement to intensity normalise the data (--IN).

Transforming Unseen Data

References