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https://github.com/dfsp-spirit/freesurfer_parallel_scripts

Shell scripts to run FreeSurfer's recon-all in parallel on multi-core machines.
https://github.com/dfsp-spirit/freesurfer_parallel_scripts

bash freesurfer neuroimaging parallel-computing workstation

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Shell scripts to run FreeSurfer's recon-all in parallel on multi-core machines.

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README 

        
# freesurfer_parallel_scripts

Shell scripts to run FreeSurfer's recon-all in parallel on multi-core machines.



## About



[FreeSurfer](https://freesurfer.net/) is a state-of-the-art neuroimaging software suite, and its `recon-all` pipeline supports the fully automated reconstruction of mesh representations of the human cortex from raw MRI scans.



However, running `recon-all` takes very long (12 - 20 h per subject on current hardware) and typically needs to be run for hundreds of subjects for a medium-sized neuroimaging study. This repo contains bash shell scripts which allow one to run the time-consuming FreeSurfer pre-processing **in parallel** on multi-core systems. The parallelization is done on the subject level, meaning that a single subject still takes a long time, but each core of your computer runs one subject. The main tool behind this is the [GNU parallel](https://www.gnu.org/software/parallel/) software.



These scripts are mainly intended for running FreeSurfer on powerful multi-core workstation computers in a lab.



The tools in the [slurm folder](./slurm/) are suitable for running FreeSurfer on high performance computing (HPC) clusters which use the [Slurm job scheduler](https://slurm.schedmd.com). The scripts implement a method to combine `Slurm` for parallelization across nodes with `GNU Parallel` for parallelization on the nodes.



![Vis](https://github.com/dfsp-spirit/freesurfer_parallel_scripts/blob/main/web/freesurfer_parallel_scripts.png?raw=true "freesurfer_parallel_scripts")



## Hardware requirements



No special hardware is required. You will obviously need a multi-core computer (almost all modern machines are multi-core, even laptops) that can run FreeSurfer and about 1.5 - 2 GB of free RAM per CPU core you want to use.



An example would be a quad-core computer with 12 GB of RAM (4x2 GB for FreeSurfer, leaving another 4 for the operating system and all other processes).



We run this on an AMD Threadripper Linux workstation with 48 cores, 128 GB of RAM and a fast SSD. I typically do not use all of the cores but leave 2 or 3 idle so that I can still use the computer for basic tasks while the pre-processing is running in the background.



## Software requirements



* Any Linux or MacOS operating system that can run FreeSurfer

* A working [FreeSurfer](https://freesurfer.net/) 6 or 7 installation (including proper setup for the bash shell)

* [GNU parallel](https://www.gnu.org/software/parallel/), which is used for parallelization in all of the scripts



If you start the commands on a remote workstation, e.g., via SSH, we strongly recommend to also install and use a terminal multiplexer like [GNU screen](https://www.gnu.org/software/screen/) on the remote computer so that the computations, which will take several days for large samples, will not abort if the network connection from your computer to the workstation gets lost for a second or you accidentaly close the terminal app on your local computer. This is optional though.



## The scripts



### Running the FreeSurfer cross-sectional pipeline (1 time point per participant)



This involves running the `recon-all` pipeline, which takes 12 - 20h per subject. Typically `qcache` is also run as part of the pipeline, to map the resulting data (like per-vertex cortical thickness in native space) to standard space (fsaverage).



We recommend the following steps:



* First convert the raw DICOM files to NIFTI format using [dcm2nii](https://www.nitrc.org/plugins/mwiki/index.php/dcm2nii:MainPage).

  - Note: If you have `.nii.gz` files, make sure to extract them to get `.nii` files. E.g., in bash: `for nf in *.nii.gz; do gunzip "$nf"; done`

  - Note: You may have more than one structural scan per subject at this time (e.g., if the first one was aborted or looked bad during scanning). That is fine, you have to pre-process both with FreeSurfer first to decide which one to use later.

* Use the script [cross-sectional/preproc_reconall_parallel.bash](./cross-sectional/preproc_reconall_parallel.bash) from this repository to run recon-all for all NIFTI files in parallel.

  - Note: If you have TSE (or t2-weighted) files in addition to t1-weighted files, please adapt the [cross-sectional/preproc_reconall_single_subject.bash](https://github.com/dfsp-spirit/freesurfer_parallel_scripts/blob/main/cross-sectional/preproc_reconall_single_subject.bash) script to make sure they are used during pre-processing.

* If you want to add a global brain measure (like total brain volume) as a covariate during modeling later, run the script [tools/extract_total_brain_measures.bash](./tools/extract_total_brain_measures.bash) to compute the measures for all subjects.



Note: If you will get data for a second wave later and intend to run a longitudinal analysis then, please read about the longitudinal pipeline now to avoid duplicate work later.



### Running the FreeSurfer longitudinal pipeline (several time points / scans per participant)



This requires first running the cross-sectional `recon-all` pipeline for all time points you have. If a subject is named `subject1` and you have MRI scans from two time points, the NIFTI input files should be called `subject1_MR1.nii` and `subject1_MR2.nii` before you run the cross-sectional pipeline, so that you get two output directories named `subject1_MR1` and `subject1_MR2`.



Then, you can run the longitudinal pipeline using the script [./longitudinal/fs_longitudinal_pipeline.bash](./longitudinal/fs_longitudinal_pipeline.bash) from this repo. The script performs 3 tasks which can be run independently, and we recommend to run one after the other and check whether everything finished successfully before moving on to the next step. The 3 steps are:



1) Creating an inter-subject template brain from all scans / time points for each subject. Creates a directory named `subject1`.

2) Mapping the data from the time points (directories `subject1_MR1` and `subject1_MR2`) to the template, creating directories `subject1_MR1.long.subject1` and `subject1_MR2.long.subject1`.

3) Computing the change between the timepoints for one or several descriptors (cortical thickness, surface area, ...).



The first 2 steps use a subjects file, but part 3 requires a QDEC table in longitudinal format that holds information on the age of each subject at each timepoint, so that the inter-scan interval can be computed. If you have a more typical demographics table instead and need to create the QDEC file from that format, I recommend to use [R](https://www.r-project.org/). There is a function named `demographics.to.qdec.table.dat` in the [fsbrain package](https://github.com/dfsp-spirit/fsbrain) that makes the tedious and error-prone conversion process a lot easier.



### Tools



The [tools directory](./tools/) contains many scripts to perform various tasks that frequently come up in computational neuroimaging. Have a look and see what's available. It includes scripts to:



* [map data from native to standard space](./tools/map_to_fsaverage_parallel.bash) in parallel for many subjects

* [downsample meshes](./tools/downsample_mesh_subject.bash) for individuals

* compute local gyrification index (lGI) in parallel, both for [cross-sectional](./tools/parallel_lgi_native.bash) and [longitudinal](./tools/parallel_lgi_native_longitudinal.bash) data sets

* [smooth per-vertex data in parallel](./tools/smooth_stddata_custom_parallel.bash)

* [generate mid surfaces (between the white and pial surface) in parallel](./tools/parallel_gen_mid_surface.bash)

* [apply an atlas](./tools/apply_atlas_fs.bash) available as an fsaverage parcellation to a subject (in native space)

* [compute covariates / global brain measures](./tools/extract_total_brain_measures.bash) like the *total brain volume* for your subjects and save them to a CSV table

* [copy only certain files from a FreeSurfer SUBJECTS_DIR to a new directory tree](./tools/deepcopy_brain_measures_only.bash) while keeping the directory layout

* ...



See [all scripts](./tools/).



## Detailed usage instructions



Usage instructions can be found in the individual scripts.



This does not mean that there are no good instructions. It's just that duplicating the docs on this website does not make any sense: it is very likely that I would forget updating the website when I change the scripts. I'm much more likely to adapt the instructions in the scripts when editing them.