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pipeline

This is a workflow for preparing and using TVB brain network models, comprised of three main components

Table of contents

Functionality

  • Automatic generation of surface & connectivity datasets usuable in TVB
  • Forward models for MEG, EEG, sEEG with OpenMEEG
  • Data fitting & parameter tuning via sensitivity analysis & Bayesian inversion

Many aspects are currently works in progress, but the dataflow currently implemented can be seen in the following diagram: dag

Dependencies

  • make
  • Python w/ NumPy, SciPy, NiBabel, MNE
  • FreeSurfer 6
  • FSL
  • MRtrix3 (>= v0.3.15)
  • OpenMEEG
  • DCMTK (optional, for decompressing JPEG DICOMs, see above)

It's likely easier to use our prebuilt Docker image. Install Docker, and run commands with the Docker container (examples below).

The main caveat is the Docker image doesn't do fancy graphics, so you'll still want a native copy of Mrview, Freeview etc for visualization.

Usage

Basic usage requires invoked make with a subject name and your dataset,

make SUBJECT=tvb T1=data/t1 DWI=data/dwi fs-recon conn

where arguments are provided in ARG=value form, and outputs are given as names like fs-recon to perform the FreeSurfer recon-all -all reconstruction. See the following Targets section for a list of available outputs.

Targets

  • fs-recon: FreeSurfer reconstruction. Consists mainly of running recon-all -all. Uses T1.

  • resamp-anat: Lower resolution cortical surfaces & annotations Uses T1.

  • conn: Connectivity matrices in text format. Uses T1 and DWI.

  • tvb: TVB zipfile, cortical and subcortical surfaces in TVB formats, region mappings. Uses T1 and DWI.

  • elec: Positions of the contacts of depth electrodes and gain matrices. Uses T1, DWI, ELEC, and either ELEC_ENDPOINTS or ELEC_POS_GARDEL.

  • seeg: Conversion of SEEG recordings to FIF format and plotting the recordings. Uses SEEGRECDIR, XLSX and everything that elec uses.

  • ez: Extraction of the epileptogenic zone from the patient Excel file. Uses XLSX and everything that elec uses.

TODO more details & help on this

Config file

As an added convenience, a file in Make format can be provided via the CONFIG variable, with the desired values or even extra rules, overriding the defaults. For example, the line

make SUBJECT=tvb T1=data/t1 fs-recon conn

could be replaced by a file tvb.config.mk

SUBJECT := tvb
T1 := data/t1
.DEFAULT: fs-recon conn

and the invocation

make CONFIG=tvb.config.mk

Docker

The docker/run script facilitates invoking the pipeline in a virtual machine, so that no installation is required:

docker/run arguments...

The data folder in the current folder is available to the container under the same name; place input data there and provide corresponding paths to the pipeline. For example, if you use /work/project1 as a working directory, create /work/project1/data, place a T1 at work/project1/data/T1.nii.gz and invoke as follows

~ $ cd /work/project1
/work/project1 $ /path/to/tvb-make/docker/run SUBJECT=tvb T1=data/T1.nii.gz fs-recon
...

The mapped directory can be customized with the TVB_MAKE_DATA environment variable.

Marseille Cluster

For quick introduction look at the basic step-by-step tutorial.

There are two options for running the pipeline on the cluster: non-interactive and interactive. For running the full pipeline, non-interactive mode is recommended due to the large time requirements. For small updates and testing the interactive mode might be more suitable.

Non-interactive mode

In the non-interactive regime, you prepare the data and submit the job(s), and the scheduler takes cares of the execution. The cluster/run script assists in running the pipeline on the Marseille cluster through two modes. First, invoke with typical arguments

<PIPELINE_DIR>/cluster/run SUBJECTS_DIR=fs SUBJECT=foo T1=data/T1.nii.gz fs-recon

for a single run in a single SLURM job. If you have many subjects, create a file params.txt with multiple lines of arguments, e.g.

SUBJECTS_DIR=fs SUBJECT=foo T1=data/T1.nii.gz fs-recon
SUBJECTS_DIR=fs SUBJECT=bar T1=data/T2.nii.gz fs-recon conn
SUBJECTS_DIR=fs SUBJECT=baz T1=data/T3.nii.gz conn

then

<PIPELINE_DIR>/cluster/run params.txt

Each line will result in the pipeline running a SLURM job for every line. You can comment out a line if you prepend it with a # sign,

 # SUBJECTS_DIR=fs SUBJECT=foo T1=data/T1.nii.gz fs-recon

NB You need to provide a custom, valid FreeSurfer SUBJECTS_DIR, since the default directories on the cluster (/soft/freesurfer*/subjects) are not writeable by users.

Interactive mode

First, request a computational node in the interactive mode

srun --pty bash

which should give you the interactive node if there is one available.

If you need to run the reconstruction and tractography in the interactive mode (although that is discouraged), you need to request full node with enough memory:

srun -N 1 -n 1 --exclusive --mem=60G --pty bash

Then setup your working environment by loading the environment file,

source <PIPELINE_DIR>/cluster/env

and run make by hand:

make -f <PIPELINE_DIR>/Makefile  SUBJECTS_DIR=fs SUBJECT=foo T1=data/T1.nii.gz fs-recon

Special Cases

JPEG encoded images

If you DICOM files are encoded with lossless JPEG compression, most of the software used will fail to read them. You can have the pipeline decompress those images prior to processing them by placing the .dcmdjpeg.dir suffix on the DICOM directory. For example, if your T1 DICOM files are in data/t1, you can specify

make T1=data/t1.dcmdjpeg.dir

and the files will be decompressed into the data/t1.dcmdjpeg.dir directory prior to processing.

ADNI data

Diffusion data from the ADNI project require stack the Nifti files and extract the gradient scheme from XML files, which can be automated by renaming the DWI data directory with the .adni.dir suffix and converting to Mrtrix image format via

mv dti_dir dti.adni.dir
make dti.mif

Alternatively, dti.mif can be provided as the DWI argument directly and conversion is performed automatically,

mv dti_dir dti.adni.dir
make DWI=dti.mif T1=... fs-recon conn

Stan support

Generic support for Stan models is implemented in make/Stan.mk with the following conventions: for each Stan model, there are three files to provide in this repository:

  • stan/{model_name}.stan - the Stan code for the model
  • stan/{model_name}.dat.py - Python script to generate input data
  • stan/{model_name}.vis.py - Python script to visualize results

which generate or use files in the stan subfolder of the subjects' folder ($(sd) in the following):

  • $(sd)/stan/{model_name}.stan.pkl - compiled Stan model in PyStan pickle format
  • $(sd)/stan/{model_name}.dat.pkl - input data generated by stan/{model_name}.dat.py
  • $(sd)/stan/{model_name}.opt.pkl - posterior mode found in initial optimization
  • $(sd)/stan/{model_name}.samp.pkl - posterior samples found during fit
  • $(sd)/stan/{model_name}.png - visualization produced by stan/{model_name}.vis.py

See the stan folder for an example, to be completed.