Contents
- Introduction
- User Guide
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. 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 data 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 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.
Examples of the most basic types of call to msm (using these inputs):
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 output name is highlighted in bold, with the example output path used as a prefix.
~/mydirname/L.sphere.LR.reg.surf.gii - this is a downsampled version of the above warp where the resolution of this mesh will be equivalent to the resolution of the final datamesh (see configuration files ). This can be used for warping new meshes through the transformation (see section on transformation).
~/mydirname/L.resampledandprojected.func.gii - the input data passed through the MSM warp and projected onto the target surface (useful for vertex-wise comparison with the target).
which GIFTI outputs are used here only as examples. The program also supports output as ASCII and VTK using the command line token -f.
Template Spaces
For cortical surface alignment it is common practice to align to a population average template space. For adults there are two prominent examples: the FreeSurfer fsaverage mesh and for the HCP the FS_LR164k and FS_LR32k spaces for the HCP. The HCP 164k population average space (see here for more details) is based upon fsaverage but has right and left vertex equivalence (it is symmetric). The FS_LR32k surface is a downsampled version of this for f/dMRI processing.
An important point to note about the HCP average space is that the process used to achieve symmetry generates a mesh space rotated with respect to the fsaverage and native mesh spaces. To account for this the HCP have introduced a rotated native sphere called sphere.rot.native.surf.gii. Therefore, by example for some subject 'BOB' using HCP notation in directory /path/to/mystudy/:
/path/to/mystudy/BOB/MNINonLinear/ - respresents files resampled to 164k average space
/path/to/mystudy/BOB/MNINonLinear/fsaverage_LR32K/ - represents files resampled to the 32k average space;
/path/to/mystudy/BOB/MNINonLinear/Native - represents and all files relating to the subjects original 'native' mesh. In other words the meshes directly extracted from each subjects structural image.
In each subject's Native space there will be several spherical mesh representations. Using left as an example, these three meshes will be important for any further processing:
/path/to/mystudy/BOB/MNINonLinear/Native/BOB.L.sphere.native.surf.gii - this is the mesh obtained from projecting the subject's native white matter mesh to a sphere using FreeSurfer
/path/to/mystudy/BOB/MNINonLinear/Native/BOB.L.sphere.rot.native.surf.gii - this is the result of correcting for the rotation between the FS_LR and native spaces
/path/to/mystudy/BOB/MNINonLinear/Native/BOB.L.sphere.MSMSulc.native.surf.gii - this is the result of aligning the native surface to FS_LR using HCP protocol constrained MSM
The important thing to take away here is that the HCP provides spheres aligned using MSM, with cortical folding as the features that drive the alignment. But, as the goal of the HCP is fMRI alignment this is highly constrained. This means the regularisation is strong (see configuration files ). Therefore users may wish to define their own MSMSulc alignment. For this they must use the L.sphere.rot.native.surf.gii as the input mesh. There are also two more spheres that represent FreeSurfer alignment:
/path/to/mystudy/BOB/MNINonLinear/Native/BOB.L.sphere.reg.native.surf.gii - This is the results of aligning the native sphere to fsaverage using FreeSurfer
/path/to/mystudy/BOB/MNINonLinear/Native/BOB.L.sphere.reg.reg_LR.native.surf.gii - This is the results of transforming BOB.L.sphere.reg.native.surf.gii to the FS_LR space using CARET software
These should not be required, other than for comparisons of results to FreeSurfer-based processing pipelines.
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 that 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 (parameters are described in more detail below). Another very useful feature is the 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 Costfunction weighting (CFw) can be controlled using Two final useful parameters are 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 be stopped after two cycles or registration levels irrespective of the number of levels specified in the configuration file.
To be continued ....
Configuration files modify all tunable parameters of the registration. For a full list of all registration parameters you can enter: msm -p Some parameters require inputs for every stage of the registration, and are input as comma separated lists e.g. --lambda=0,0.1,0.2,0.3 (for four levels). These are: Other parameters need only be specified once: 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 (which also forms the default parameterisation in the absence of any supplied configuration file) is: --simval=1,2,2,2 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, run as an additional level at the beginning. Therefore, the above case 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; Following this discrete optimisation is run over 3 levels with 3 iterations at each level, using control point grid resolutions 162, 642, and 2562, where the sampling grid resolution is 2 subdivisions above this, and the data grids have resolution: 2562, 10242 and 40962 vertices. Smoothing is applied to the source image as 4, 2, then 1mm sigma smoothing kernels, and to the reference image as 2, 1 and 1mmm smoothing. --IN indicates that the source intensity distribution is matched to the target intensity distribution, once at the beginning of the registration. If you choose to edit or optimise the config files then it is important to remember that all multiresolution level parameter lists must have the same length, else the program will throw the following error: MeshREG ERROR:: config file parameter list lengths are inconsistent In addition, as affine registration only implements the following parameters: --opt, --simval, --it, --sigma_in, --sigma_ref, --IN, --VN, --scale, --excl, for all other multi level parameters, it is necessary to supply a zero value should be supplied for the AFFINE stage.
In msm a warp or deformation field is prescribed by two meshes representing the start and end point of a transformation. For example, using the common syntax of this user guide, the warp for the left hemisphere is between the input_mesh.surf.gii and L.sphere.reg.surf.gii. These represent the same mesh, so have the same number of vertices and vertex numbers correspond. Therefore, the change in coordinates from the input_mesh to sphere.reg tells us where each vertex on the input_mesh has to move so as to optimise overlap of the input and reference data. Nevertheless, the warp alone is not sufficient to enable direct comparisons between the alignment of the input and reference featurespace (i.e. sulc/myelin/RSN maps) following alignment. To achieve this it is also necessary to resample the input features onto the reference mesh surface. This will take the features for each input mesh vertex and project them onto to the reference mesh at their Two functions are supplied for the warping and resampling of unseen data: * msmapplywarp input_mesh.surf.gii input_meshLR.sphere.reg.surf.gii input_mesh.sphere.reg.gii -original input_meshLR.surf.gii This can be useful for HCP data where fMRI data is resampled onto the low resolution 32k_FS_LR mesh, but other data lies on the high resolution 164k_FSLR surface. The to-be-deformed mesh will be transformed by first projecting its vertices onto the undeformed surface, and then using nearest points on the undeformed surfaces to resample the to-be-deformed-mesh in the space of the deformed mesh. NOTE *** This means to-be-transformed mesh and undeformed_mesh must already be in alignment.
M.F. Glasser, S.N. Sotiropoulos, J.A. Wilson, T.S. Coalson, B. Fischl, J.L. Andersson, J. Xu, S. Jbabdi, M. Webster, J. R. Polimeni, D.C. Van Essen, M. Jenkinson, The minimal preprocessing pipelines for the Human Connectome Project, NeuroImage, Volume 80, 15 October 2013, Pages 105-124, ISSN 1053-8119, http://dx.doi.org/10.1016/j.neuroimage.2013.04.127.
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. Cost Function Weighting
Configuration Files
--lambda weights the contribution of the regulariser relative to the similarity force. We have found that we typically have to increase regularisation slightly when the 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.
--excl is used to ignore a predefined exclusion region when resampling the data.
--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
Regular Mesh Surfaces
Scripts
Post Processing
Transforming Unseen Data
* msmresamplemetric:
Averaging
Estimating metric distortion
References