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statistics-surface - Surface-based mass-univariate analysis with SurfStat

This command performs statistical analysis (e.g. group comparison, correlation) on surface-based features using the GLM.

Warning

Prior to release 0.7.3 of Clinica, this pipeline was relying on the Matlab toolbox SurfStat designed for statistical analyses of univariate and multivariate surface and volumetric data using the GLM [Worsley et al., 2009]. SurfStat, as a Matlab toolbox, necessitates a valid Matlab license to be executed, which could be a heavy dependency for users. Moreover, the maintenance of SurfStat ceased a few years ago which posed some issues, especially with newer versions of Matlab. For these reasons, the pipeline was completely rewritten to rely on Brainstat which is a pure Python implementation of SurfStat, offering a very similar API. The different images produced by the pipeline are generated using Nilearn, also a pure Python library. Although the dependencies have been reduced, please be aware that this pipeline has not been extensively tested with the new implementation. Do not hesitate to open a new issue on GitHub to report bugs you might encounter.

Surface-based measurements are analyzed on the FsAverage surface template (from FreeSurfer).

Currently, this pipeline can handle:

Prerequisites

You need to process your data with the t1-freesurfer pipeline for measurements of cortical thickness measurements from T1 images or pet-surface pipeline for measurements of activity map from PET.

Do not hesitate to have a look at the paragraph Specifying what surface data to use if you want to use your own surface feature.

Dependencies

If you only installed the core of Clinica, this pipeline needs the installation of FreeSurfer on your computer.

Running the pipeline

The pipeline can be run with the following command line:

clinica run statistics-surface [OPTIONS] CAPS_DIRECTORY GROUP_LABEL {t1-freesurfer|pet-surface|custom-pipeline}
                               {group_comparison|correlation} SUBJECT_VISITS_WITH_COVARIATES_TSV CONTRAST

where:

  • CAPS_DIRECTORY is the folder containing the results of the t1-freesurfer or pet-surface pipeline and the output of the present command, both in a CAPS hierarchy.
  • GROUP_LABEL is a string defining the group label for the current analysis, which helps you keep track of different analyses.
  • The third positional argument defines the type of surface-based feature. It can be:
  • t1-freesurfer for cortical thickness
  • pet-surface for projected PET data
  • custom-pipeline for you own data in CAPS directory (see below for details)
  • The fourth positional argument is a string defining the type of analysis of your model. It can be either group_comparison or correlation.
  • SUBJECT_VISITS_WITH_COVARIATES_TSV is a TSV file containing a list of subjects with their sessions and all the covariates and factors in your model (the content of the file is explained in the Example subsection).
  • CONTRAST is a string defining the contrast matrix or the variable of interest for the GLM. For example group, sex, or age.

Pipeline options:

  • -c, or --covariates: Covariates must be provided one at the time: -c covariate_1 -c covariate_2, or equivalently --covariates covariate_1 --covariates covariate_2. Each covariate must match the name of the TSV file. By default, no covariate is considered.
  • --full_width_at_half_maximum: FWHM for the surface smoothing. The default value is 20.

Pipeline options if you use inputs from the pet-surface pipeline:

  • --acq_label: Name of the label given to the PET acquisition, specifying the tracer used (trc-<acq_label>).
  • --suvr_reference_region: Reference region used to perform intensity normalization (i.e. dividing each voxel of the image by the average uptake in this region) resulting in a standardized uptake value ratio (SUVR) map. It can be either cerebellumPons (used for amyloid tracers), or pons (used for FDG).

Tip

Check the Example subsection for further clarification.

Outputs

Group comparison analysis

Results are stored in the following folder of the CAPS hierarchy: groups/<group_id>/statistics/surfstat_group_comparison/.

The main outputs for the group comparison are:

  • <group_id>_<group_1>-lt-<group_2>_measure-<label>_fwhm-<label>_correctedPValue.jpg: contains both the cluster level and the vertex level corrected p-value maps, based on the random field theory.
  • <group_id>_<group_1>-lt-<group_2>_measure-<label>_fwhm-<label>_FDR.jpg: contains corrected p-value maps, based on the false discovery rate (FDR).

The <group_1>-lt-<group_2> means that the tested hypothesis is: "the measurement of <group_1> is lower than (lt) the measurement of <group_2>".

The pipeline includes both contrasts so *<group_2>-lt-<group_1>* files are also saved.

The FWHM value corresponds to the size in mm of the kernel used to smooth the surface and can be 5, 10, 15, 20.

Analysis with cortical thickness (respectively PET data) will be saved under the _measure-ct keyword (respectively the _measure-<acq_label> keyword).

Correlations analysis

Results are stored in the following folder of the CAPS hierarchy: groups/<group_id>/statistics/surfstat_correlation/.

The main outputs for the correlation are:

  • <group_id>_correlation-<label>_contrast-<label>_measure-<label>_fwhm-<label>_correctedPValue.jpg: contains p-value maps corrected at both the cluster and vertex levels, based on the random field theory.
  • <group_id>_correlation-<label>_contrast-<label>_measure-<label>_fwhm-<label>_FDR.jpg: contains corrected p-value maps, based on the false discovery rate (FDR).
  • <group_id>_correlation-<label>_contrast-<label>_measure-<label>_fwhm-<label>_T-statistics.jpg: contains the maps of T statistics.
  • <group_id>_correlation-<label>_contrast-<label>_measure-<label>_fwhm-<label>_Uncorrected p-value.jpg: contains the maps of uncorrected p-values.

The correlation-<label> describes the factor of the model, which can be for example age.

The contrast-<label> is the sign of your factor which can be negative or positive.

Analysis with cortical thickness (respectively PET data) will be saved under the _measure-ct keyword (respectively the _measure-<acq_label> keyword).

Note

The full list of output files can be found in the ClinicA Processed Structure (CAPS) specifications.

Example

Comparison analysis

Let's assume that you want to perform a group comparison of cortical thickness between patients with Alzheimer’s disease (group_1 will be called AD) and healthy subjects (group_2 will be called HC). ADvsHC will define the group_label.

The TSV file containing the participants and covariates will look like this:

participant_id    session_id    sex       group    age
sub-CLNC0001      ses-M000       Female    CN       71.1
sub-CLNC0002      ses-M000       Male      CN       81.3
sub-CLNC0003      ses-M000       Male      CN       75.4
sub-CLNC0004      ses-M000       Female    CN       73.9
sub-CLNC0005      ses-M000       Female    AD       64.1
sub-CLNC0006      ses-M000       Male      AD       80.1
sub-CLNC0007      ses-M000       Male      AD       78.3
sub-CLNC0008      ses-M000       Female    AD       73.2

We call this file ADvsHC_participants.tsv.

For this group comparison, we will use age and sex as covariates.

As a result, the command line will be:

clinica run statistics-surface caps_directory ADvsHC t1-freesurfer group_comparison ADvsHC_participants.tsv group -c age -c sex

The results of the group comparison between AD and HC are given by the group-ADvsHC_AD-lt-HC_measure-ct_fwhm-20_correctedPValue.jpg file.

Correlation analysis

Let's now assume that you are interested in knowing whether cortical thickness is correlated with age using the same population as above, namely ADvsHC_participants.tsv.

The contrast will become age and we will choose correlation instead of group_comparison. The command line is:

clinica run statistics-surface caps_directory ADvsHC t1-freesurfer correlation ADvsHC_participants.tsv age -c group -c sex

Describing this pipeline in your paper

Example of paragraph (group comparison):

These results have been obtained using the statistics-surface command of Clinica [Routier et al., 2021]. More precisely, a point-wise, vertex-to-vertex model based on the Brainstat Python library (https://brainstat.readthedocs.io/en/master/) was used to conduct a group comparison of whole brain cortical thickness. The data were smoothed using a Gaussian kernel with a full width at half maximum (FWHM) set to <FWHM> mm. The general linear model was used to control for the effect of <covariate_1>, ... and <covariate_N>. Statistics were corrected for multiple comparisons using the random field theory for non-isotropic images [Worsley et al., 1999]. A statistical threshold of P < <ClusterThreshold> was first applied (height threshold). An extent threshold of P < 0.05 corrected for multiple comparisons was then applied at the cluster level.

Tip

Easily access the papers cited on this page on Zotero.

Support

(Advanced) Specifying what surface data to use

The optional flag --custom_file CUSTOM_FILE allows you to specify yourself what file should be taken in the CAPS/subjects directory.

CUSTOM_FILE is a string describing the folder hierarchy to find the file.

For instance, let's say we want to manually indicate to use the cortical thickness.

Here is the generic link to the surface data files:

CAPS/subjects/sub-*/ses-M*/t1/freesurfer_cross_sectional/sub-*_ses-M*/surf/*h.thickness.fwhm*.fsaverage.mgh

Example: CAPS/subjects/sub-ADNI011S4075/ses-M000/t1/freesurfer_cross_sectional/sub-ADNI011S4075_ses-M000/surf/lh.thickness.fwhm15.fsaverage.mgh

Note that the file must be in the CAPS/subjects directory.

So my CUSTOM_STRING must only describe the path starting after the subjects folder. We need to replace the * by the correct keywords, in order for the pipeline to catch the correct filenames:

  • @subject is the subject
  • @session is the session
  • @hemi is the hemisphere
  • @fwhm is the full width at half maximum

All those variables are already known, you just need to indicate where they are in the filename! As a result, we will get for CUSTOM_FILE of cortical thickness:

@subject/@session/t1/freesurfer_cross_sectional/@subject_@session/surf/@hemi.thickness.fwhm@fwhm.fsaverage.mgh

You will finally need to define the name your surface feature --feature_label FEATURE_LABEL. It will appear in the _measure-<FEATURE_LABEL> of the output files once the pipeline has run.

Appendix

  • For more information about Brainstat, please check here.
  • For more information about the GLM, please check here.
  • The cortical thickness map is obtained from the FreeSurfer segmentation. More precisely, it corresponds to the subject’s map normalized onto FSAverage and smoothed using a Gaussian kernel FWHM of <fwhm> mm (the surf/?h.thickness.fwhm<fwhm>.fsaverage.mgh files).

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