https://github.com/xgrg/tbss
FSL TBSS helpers
https://github.com/xgrg/tbss
Last synced: 3 months ago
JSON representation
FSL TBSS helpers
- Host: GitHub
- URL: https://github.com/xgrg/tbss
- Owner: xgrg
- License: gpl-3.0
- Created: 2018-03-19T18:05:30.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2021-07-14T11:58:22.000Z (almost 4 years ago)
- Last Synced: 2023-10-20T23:52:34.846Z (over 1 year ago)
- Language: Python
- Size: 764 KB
- Stars: 2
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Basic helper functions for TBSS
[](https://travis-ci.org/xgrg/tbss)
[](https://coveralls.io/github/xgrg/tbss?branch=master)
Requires prior basic knowledge about _Tract-Based Spatial Statistics_. Requires [nilearn](http://nilearn.github.io) and [nipype](http://nipype.readthedocs.io).
See [this post](http://xgrg.github.io/parallelize-TBSS/) for a few explanations on why and how.
## Parallelize TBSS/Randomise
### Example
The following splits a `.con` contrast file into multiple ones and generates a series of independent commands to run `randomise` on the new contrast files in parallel jobs.
```python
from tbss import contrasts
commands = contrasts.randomise_parallel(in_file = '/path/to/all_FA_skeletonised.nii.gz',
out_basename = '/path/to/tbss_FA',
mask = '/path/to/mean_FA_skeleton_mask.nii.gz',
tcon = '/path/to/contrasts.con',
design_mat = '/path/to/design.mat',
num_perm = 1000,
n_cpus = 6,
email_notify = None,
sleep_interval = 120)
```
```
Temporary contrast files: ['/path/to/tbss_FA_part0.con', '/path/to/tbss_FA_part1.con', '/path/to/tbss_FA_part2.con', '/path/to/tbss_FA_part3.con', '/path/to/tbss_FA_part4.con', '/path/to/tbss_FA_part5.con']
[u'sleep 0 ; randomise -i /path/to/all_FA_skeletonised.nii.gz -d /path/to/design.mat -t /path/to/tbss_FA_part0.con -m /path/to/mean_FA_skeleton_mask.nii.gz -n 1000 -R --T2 -o /path/to/tbss_FA --skipTo=1 -V &> /tmp/tmpHj9gPW.log',
u'sleep 120 ; randomise -i /path/to/all_FA_skeletonised.nii.gz -d /path/to/design.mat -t /path/to/tbss_FA_part1.con -m /path/to/mean_FA_skeleton_mask.nii.gz -n 1000 -R --T2 -o /path/to/tbss_FA --skipTo=2 -V &> /tmp/tmpDP2rM9.log',
u'sleep 240 ; randomise -i /path/to/all_FA_skeletonised.nii.gz -d /path/to/design.mat -t /path/to/tbss_FA_part2.con -m /path/to/mean_FA_skeleton_mask.nii.gz -n 1000 -R --T2 -o /path/to/tbss_FA --skipTo=3 -V &> /tmp/tmp5u74y0.log',
u'sleep 360 ; randomise -i /path/to/all_FA_skeletonised.nii.gz -d /path/to/design.mat -t /path/to/tbss_FA_part3.con -m /path/to/mean_FA_skeleton_mask.nii.gz -n 1000 -R --T2 -o /path/to/tbss_FA --skipTo=4 -V &> /tmp/tmpfIf08c.log',
u'sleep 480 ; randomise -i /path/to/all_FA_skeletonised.nii.gz -d /path/to/design.mat -t /path/to/tbss_FA_part4.con -m /path/to/mean_FA_skeleton_mask.nii.gz -n 1000 -R --T2 -o /path/to/tbss_FA --skipTo=5 -V &> /tmp/tmp0fhKN5.log',
u'sleep 600 ; randomise -i /path/to/all_FA_skeletonised.nii.gz -d /path/to/design.mat -t /path/to/tbss_FA_part5.con -m /path/to/mean_FA_skeleton_mask.nii.gz -n 1000 -R --T2 -o /path/to/tbss_FA --skipTo=6 -V &> /tmp/tmpPaWk5L.log']
```
* `n_cpus` sets the number of available CPU cores that `randomise` will run on simultaneously (consequently the number of `.con` files the contrasts will be split into) (default: 6)
* If `email_notify` is an email address, notifications will be sent to that address before and after every job (default: None)
* `sleep_interval` sets the interval in seconds before running the following job (will avoid RAM exhaustion when `randomise` starts loading the data)
There are a few hard-coded options in the command call such as `--T2` or `-R`. These can be easily modified in the code if necessary.
The produced commands can finally be stored in a shell script e.g. that may be passed to [GNU parallel](https://www.gnu.org/software/parallel/) for execution, as in the following example:
```
cat /tmp/script.sh | parallel -j
```
## Plotting results in Python
Requires [nilearn](http://nilearn.github.io/).
### Example
```python
from tbss import plotting
plotting.plot_stat_map('/path/to/tbss_tstat1.nii.gz',
'/path/to/mean_FA_skeleton.nii.gz',
start=-15,
end=43,
row_l=6,
step=2,
title='Effect of age (-)')
```
