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t1-freesurfer-* – FreeSurfer-based pre-processing of T1-weighted MRI images

This pipeline performs cortical surface extraction, segmentation of subcortical structures [Dale et al., 1999], cortical thickness estimation [Fischl and Dale, 2000], spatial normalization onto the FreeSurfer surface template (FsAverage) [Fischl et al., 1999a], and parcellation of cortical regions based on different atlases [Fischl et al., 2004], using the FreeSurfer recon-all command. Additionally, from the FreeSurfer outputs, we generate TSV files containing a summary of the regional statistics (e.g. regional volume, mean cortical thickness) to ease subsequent statistical analysis.


If you only installed the core of Clinica, this pipeline needs the installation of FreeSurfer on your computer. You can find how to install this software on the installation page.

Running the pipeline

The pipeline can be run with the following command line:

clinica run t1-freesurfer-cross-sectional bids_directory caps_directory

  • bids_directory is the input folder containing the dataset in a BIDS hierarchy.
  • caps_directory is the output folder containing the results in a CAPS hierarchy.

If you want to run the pipeline on a subset of your BIDS dataset, you can use the -tsv flag to specify in a TSV file the participants belonging to your subset.


The computational time for one subject is around 10–15 hours depending on your CPU and the quality of your input T1. Please be aware that even though the pipeline runs in parallel, processing many subjects (e.g. ADNI dataset) is time consuming.


For people familiar with FreeSurfer, we compute the normalized data on the FreeSurfer atlas (FsAverage) with the qcache option from recon-all. If you want to add some custom flags, you can do it in Clinica with the --recon_all_args flag (e.g. --recon_all_args "-bigventricles -qcache").


Results are stored in the following folder of the CAPS hierarchy: subjects/sub-<participant_label>/ses-<session_label>/t1/freesurfer_cross_sectional. This folder contains the standard output structure of the recon-all command, i.e. folders such as label/, mri/, surf/, etc.

Among the files generated by FreeSurfer, you may be interested in the following outputs:

  • */mri/aseg.mgz: subcortical segmentation volume
  • */mri/wm.mgz: white matter mask
  • */mri/brainmask.mgz: skull-stripped volume
  • */surf/?h.white: white surface between white matter and gray matter (where ?h stands for lh or rh)
  • */surf/?h.pial: pial surface between gray matter and CSF (where * stands for sub-<participant_label>_ses-<session_label>)

More details regarding the recon-all output files can be found on the FreeSurfer website.

Visualization of recon-all outputs.

TSV files summarizing the regional statistics are also created for each subject.

The full list of features extracted from the FreeSurfer pipeline can be found in the The ClinicA Processed Structure (CAPS) Specification.

Describing this pipeline in your paper

Example of paragraph (short version):

These results have been obtained using the t1-freesurfer-cross-sectional pipeline of Clinica. This pipeline is a wrapper of different tools of the FreeSurfer software ( [Fischl et al, 2012] . This processing includes segmentation of subcortical structures, extraction of cortical surfaces, cortical thickness estimation, spatial normalization onto the FreeSurfer surface template (FsAverage), and parcellation of cortical regions.

Example of paragraph (long version):

These results have been obtained using the t1-freesurfer-cross-sectional pipeline of Clinica. This pipeline is a wrapper of different tools of the FreeSurfer software, which is documented and freely available for download online ( The technical details of these procedures are described in prior publications [Dale et al., 1999; Dale and Sereno, 1993; Fischl and Dale, 2000; Fischl et al., 2001; Fischl et al., 2002; Fischl et al., 2004a; Fischl et al., 1999a; Fischl et al., 1999b; Fischl et al., 2004b; Han et al., 2006; Jovicich et al., 2006; Segonne et al., 2004; Reuter and Fischl, 2010; Reuter et al., 2012]. Briefly, this processing includes removal of non-brain tissue using a hybrid watershed/surface deformation procedure [Segonne et al., 2004], automated Talairach transformation, segmentation of the subcortical white matter and deep gray matter volumetric structures (including hippocampus, amygdala, caudate, putamen, thalamus, ventricles) [Fischl et al., 2002; Fischl et al., 2004a], intensity normalization [Sled et al., 1998], tessellation of the gray matter/white matter boundary, automated topology correction [Fischl et al., 2001; Segonne et al., 2007], and surface deformation following intensity gradients to optimally place the gray/white and gray/cerebrospinal fluid borders at the location where the greatest shift in intensity defines the transition to the other tissue class [Dale et al., 1999; Dale and Sereno, 1993; Fischl and Dale, 2000], surface inflation [Fischl et al., 1999a], registration to a spherical atlas which is based on individual cortical folding patterns to match cortical geometry across subjects [Fischl et al., 1999b], parcellation of the cerebral cortex into units with respect to gyral and sulcal structures [Desikan et al., 2006; Fischl et al., 2004b], computation of maps of cortical thickness, calculated as the closest distance from the gray/white boundary to the gray/CSF boundary at each vertex on the tessellated surface [Fischl and Dale, 2000] and creation of a variety of surface based data including maps of curvature and sulcal depth. Procedures for the measurement of cortical thickness have been validated against histological analysis [Rosas et al., 2002] and manual measurements [Kuperberg et al., 2003; Salat et al., 2004]. FreeSurfer morphometric procedures have been demonstrated to show good test-retest reliability across scanner manufacturers and across field strengths [Han et al., 2006; Reuter et al., 2012].


Easily access the papers cited on this page on Zotero.

Appendix: Main steps of the t1-freesurfer-cross-sectional pipeline

Following the links to visualize diagrams summarizing the main steps of the t1-freesurfer-cross-sectional pipeline: pre-processing, voxel-based processing, surface-based processing. For a detailed explanation of the FreeSurfer recon-all pipeline, click here.