Longitudinal Processing

FastSurfer has a dedicated pipeline to quantify longitudinal changes in T1-weighted MRI. FastSurfer’s longitudinal pipeline outpeforms independent (cross sectional) processing of individual MRIs across time in both FastSurfer and FreeSurfer, as well as even the longitudional pipeline in FreeSurfer.

What is Longitudinal Processing

In longitudinal studies, MRIs of the same participant are acquired at different time points. Usually the goal is to quantify potentially subtle anatomical changes representing early disease effects or effects of disease modifying therapies or drug studies. In these situations we know that most of the anatomy will be very similar, as compared to cross sectional differences between participants. Longitudinal processing, as opposed to independent processing of each MRI, tries to make use of the joint information to reduce variance across time, leading to more sensitive estimates of longitudinal changes. This methodological approach leads to increased statistical power to detect subtle changes and, therefore, permits to either find smaller effects or to reduce the number of particiants needed to detect such an effect - saving time and money. Our paper for the FreeSurfer longitudinal stream (Reuter et al. 2012) nicley highlights these advantages, such as increased reliability and sensitiviy and describes the general idea.

Generally the idea is to:

  • Align images across time robustly into an unbiased mid-space (Reuter et al. 2010).

  • Construct a template image for each participant (called within-subject template).

  • Process the template image, e.g. to generate initial WM and GM surfaces.

  • Process each time point, initializing or reusing results from the template, yet allowing enough freedom for results to evolve.

This approach is used in FreeSurfer and in FastSurfer and it avoids multiple issues that are inherent to other approaches:

  • It avoids the introduction of processing bias (Reuter, Fischl 2011) by treating all time points the same.

  • It is independent on the number of time points, and independent of the time differences between acquisitions.

  • It is flexible enough to not over-constrain (smooth) longitudinal effects.

  • It does not enforce or encourage directional temporal changes (e.g. atrophy) and can therefore be used in studying cyclic patterns, or cross-over drug studies.

How to Run Your Data

We are providing a new entry script long_fastsurfer.sh to help you process longitudinal data.

# Setup FASTSURFER and FREESURFER
export FASTSURFER_HOME=/path/to/fastsurfer
export FREESURFER_HOME=/path/to/freesurfer
source $FREESURFER_HOME/SetUpFreeSurfer.sh

# Define data directory
export SUBJECTS_DIR=/home/user/my_fastsurfer_analysis

# Run FastSurfer longitudinally
$FASTSURFER_HOME/long_fastsurfer.sh \
    --tid <templateID> \
    --t1s <T1_1> <T1_2> ... \
    --tpids <tID1> <tID2> ...

Here <templateID> is a name you assign to this individual person and will be used in the output directory ($SUBJECTS_DIR) for the directory containing the within-subject template (e.g. “--tid bert”). The <T1_1> <T1_2> etc. are the global paths to the input full head T1w images for each time point (do not need to be bias corrected) in nifti or mgz format. The <tID1> <tID2> etc. are the ID names for each time point. Corresponding directories will be created in the output directory ($SUBJECTS_DIR) , e.g. “--tpids bert_1 bert_2”. These directories will contain the final results for each time point for downstream analysis.

Note, with a few exceptions, you can add additional flags that can be understood by run_fastsurfer.sh, which will be passed through, e.g. the --3T when working with 3T images.

The above command will, of course, be slightly different when using the preferred installation way Singularity (or Docker). For example for Singularity:

singularity exec --nv \
                 --no-home \
                 -B /home/user/my_mri_data:/data \
                 -B /home/user/my_fastsurfer_analysis:/output \
                 -B /home/user/my_fs_license_dir:/fs_license \
                 ./fastsurfer-gpu.sif \
                 /fastsurfer/long_fastsurfer.sh \
                 --fs_license /fs_license/license.txt \
                 --tid <templateID> \
                 --t1s <T1_1> <T1_2> ... \
                 --tpids <tID1> <tID2> ...
                 --sd /output \
                 --parallel --3T

Behind the Scenes:

long_fastsurfer.sh is just a helper script and will perform the following individual steps for you:

  1. It will prepare the subject template by calling long_prepare_template.sh:

    long_prepare_template.sh \
  --tid <templateID> \
  --i1s <T1_1> <T1_2> ... \
  --tpids <tID1> <tID2>

    This will register (align) all time point images into the unbiased mid-space using mri_robust_template, after an initial segmentation and skull stripping. It will also create the template image.

  2. Next, the template image will be segmented via a call to run_fastsurfer.sh --sid <templateID> --base --seg_only ... where the --base flag indicates that the input image will be taken from the already existing template directory.

  3. This is followed by the surface processing of the template run_fastsurfer.sh --sid <templateID> --base --surf_only ..., which can be combined with the previous step.

  4. Next, the segmentations of each time points, which can theoretically run in parallel with the previous two steps, is performed run_fastsurfer.sh --sid <tIDn> --long <templateID> --seg_only ...,

  5. again followed by the surface processing for each time point: run_fastsurfer.sh --<id <tIDn> --long <templateID> --surf_only. This step needs to wait until 3. and 4. are finished.

Internally we use brun_fastsurfer.sh as a helper script to process multiple time points (in 4. and 5.) in parallel (if experimental --parallel_long is passed to long_fastsurfer.sh).

Final Statistics:

The final results will be located in $SUBJECTS_DIR/tID1 … for each time point. These directories will have the same structure as a regular FastSurfer/FreeSurfer output directory. Therefore, you can use the regular downstream analysis tools, e.g. to extract statistics from the stats files. Note, that the surfaces are already in vertex-correspondence across time for each participant. For group analysis one would still need to map thickness estimates to fsaverage spherical template (this is usually done with mris_preproc). For longitudinal statistics using the (recommended) linear mixed effects models see our R toolbox FS LME R, which can also analyze the mass-univariate situation for e.g. cortical thickness maps. Alternatively use this Matlab package: LME Matlab and our matlab tools for time-to-even (survival) analysis: Survival.

References