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% TUTORIAL LOCALIZER

Here a tutorial to start in fMRI with the very known localizer data. The goal is intented to show you how to import, to process and to visualize data by using python scripts and the standalone SPM version (compiled version, without licence). In fact the standalone SPM is used for the preprocessing part, but the statistic levels are based on nipy. We hope these scripts could cover a lot of cases. Other pipelines are available at NeuroSpin, in particular in Capsul http://nsap.intra.cea.fr/capsul-doc/index.html.

CONFIGURATION

What python modules do you need ?

######UnicogFmri UnicogFmri is a git repository located at neurospin. You can use them like template with your own data. So first, don't forgot to clone or to update the repository if needed.

#create a directory <my_repository>
mkdir <my_repository>
cd <my_repository>
git clone   /neurospin/unicog/resources/git_server/UnicogFmri.git

And to install it too !

cd <my_repository>/UnicogFmri
python setup.py install --user

######pypreprocess pypreprocess is an external module. To ensure the pypreprocess module is available:

ipython
import pypreprocess
#if you want to locate where is pypreprocess:
pypreprocess.__file__

If pypreprocess module is not installed, please refer to https://github.com/neurospin/pypreprocess. Some additional python modules are available (numpy, nibabel ...). All information are available on https://github.com/neurospin/pypreprocess page.

###STEPS FOR PROCESSING

Before to launch scripts, please check paths.

STEP1: DATA IMPORTATION

To import data, 2 formats of description are possible:

In order either to use xsl or txt format, just use init_xls or init_txt in the following script [<my_repository>/UnicogFmri/unicogfmri/localizer/1_import_data_txt.py](<my_repository>/importation_data/1_import_data_txt.py) In this example, we are going to use the txt format. You can indicate in the same script where data will be imported in the main_dir variable.

Importation of subjects

cd <my_repository>/UnicogFmri/unicogfmri/localizer
python ./volume_glm/1_import_data_txt.py

Note : if a message such as "ImportError: No module named xlrd", it means the xlrd module is missing for python. This module is needed to read excel files. To install this module:

sudo pip install xlrd

Another possibility is :

#download the package on https://pypi.python.org/pypi/xlrd
tar zxvf <package>
cd <package>
python setup.py install --user

STEP2: DATA PROCESSING

For this part, we are going to use the pypreprocess module. The data processing includes the preprocessing and the first-level.

Configuration of config.ini

Before launching the processing, please take a look at the configuration file, in order to check paths and options:

<my_repository>/UnicogFmri/unicogfmri/volume_glm/Step1_config.ini

Run the preprocessing and first-level

In this section, we launch the following python script and we indicate at the beginning of line the path in order to use the standalone SPM.

cd <my_repository>/UnicogFmri/unicogfmri/localizer
SPM_MCR=/i2bm/local/bin/spm8 python ./volume_glm/Step2_preprocess_1st_level.py

Take a look at the report for preprocessing:

<output_dir>/results/report_preproc.html

And the maps are located:

  • <output_dir>/results/<name_subject>/res_stats/t_maps
  • <output_dir>/results/<name_subject>/res_stats/z_maps
  • <output_dir>/results/<name_subject>/res_stats/variance_maps
  • <output_dir>/results/<name_subject>/res_stats/effects_maps

Note: pypreprocess use a cached system, in other words, if you run again your script the steps which have already been processed, will be skip. Even if this system is very interesting, it takes a lot space.

Note: the <output_dir> is indicated in <my_repository>/UnicogFmri/unicogfmri/volume_glm/Step1_config.ini.

READ THE REPORT

Thanks to pypreprocess, we have an automatic report on processing. We have a lot of information such as design matrix, onsets, name of conditions, snapshots ... and so on.

Please refer to <output_dir>/<name_subj>/res_stats/report_stats.html

STEP 3: VIEW CONTRASTS

It is now time to visualize the maps. Here we use Anatomist Software, but other solutions are possible. In our example, we filter for each subject, all maps (z or t) with a list of contrasts of interest. See the python script : ../volume_glm/Step3_view_maps.py. Note that before launching the script, it is necessary to initialize correctly path for Anatomist with the first line.

source /i2bm/local/Ubuntu-12.04-x86_64/brainvisa/bin/bv_env.sh
cd <my_repository>/UnicogFmri/unicogfmri/localizer
python ./volume_glm/Step3_view_maps.py

ADDITIONAL STEPS:

######How to set up $PYTHONPATH variable in order to find modules? Normally, the installation of python module is set up by using a setup.py or a pip install. But sometimes, you have to add python modules directly in the $PYTHONPATH variable. To update the $PYTHONPATH variable, change your ~/.bashrc:

#add path for a python MODULE 
export PYTHONPATH=$PYTHONPATH:<path_where_is_the_MODULE>