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script_first_level_localizer.py
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script_first_level_localizer.py
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# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
"""
Script that perform the first-level analysis of a dataset of the FIAC
Last updated by B.Thirion
Author : Lise Favre, Bertrand Thirion, 2008-2010
"""
import os
from configobj import ConfigObj
from numpy import arange
from nipy.neurospin.utils.mask import compute_mask_files
from nipy.neurospin.glm_files_layout import glm_tools, contrast_tools
# -----------------------------------------------------------
# --------- Set the paths -----------------------------------
#-----------------------------------------------------------
DBPath = "/volatile/thirion/Localizer"
Subjects = ["s12069"]#["s12277", "s12300","s12401","s12431","s12508","s12532","s12635","s12636","s12826","s12898","s12913","s12919","s12920"]
Acquisitions = ["acquisition"]
Sessions = ["loc1"]
model_id = "default"
fmri_wc = "S*.nii"
# ---------------------------------------------------------
# -------- General Information ----------------------------
# ---------------------------------------------------------
tr = 2.4
nb_frames = 128
frametimes = tr * arange(nb_frames)
Conditions = [ 'damier_H', 'damier_V', 'clicDaudio', 'clicGaudio',
'clicDvideo', 'clicGvideo', 'calculaudio', 'calculvideo', 'phrasevideo',
'phraseaudio' ]
# ---------------------------------------------------------
# ------ First level analysis parameters ---------------------
# ---------------------------------------------------------
#---------- Masking parameters
infTh = 0.4
supTh = 0.9
#---------- Design Matrix
# Possible choices for hrf_model : "Canonical", \
# "Canonical With Derivative" or "FIR"
hrf_model = "Canonical With Derivative"
# Possible choices for drift_model : "Blank", "Cosine", "Polynomial"
drift_model = "Cosine"
hfcut = 128
#-------------- GLM options
# Possible choices : "Kalman_AR1", "Kalman", "Ordinary Least Squares"
fit_algo = "Kalman_AR1"
def generate_localizer_contrasts(contrast):
"""
This utility appends standard localizer contrasts
to the input contrast structure
Parameters
----------
contrast: configObj
that contains the automatically generated contarsts
Caveat
------
contrast is changed in place
"""
d = contrast.dic
d["audio"] = d["clicDaudio"] + d["clicGaudio"] +\
d["calculaudio"] + d["phraseaudio"]
d["video"] = d["clicDvideo"] + d["clicGvideo"] + \
d["calculvideo"] + d["phrasevideo"]
d["left"] = d["clicGaudio"] + d["clicGvideo"]
d["right"] = d["clicDaudio"] + d["clicDvideo"]
d["computation"] = d["calculaudio"] +d["calculvideo"]
d["sentences"] = d["phraseaudio"] + d["phrasevideo"]
d["H-V"] = d["damier_H"] - d["damier_V"]
d["V-H"] =d["damier_V"] - d["damier_H"]
d["left-right"] = d["left"] - d["right"]
d["right-left"] = d["right"] - d["left"]
d["audio-video"] = d["audio"] - d["video"]
d["video-audio"] = d["video"] - d["audio"]
d["computation-sentences"] = d["computation"] - d["sentences"]
d["reading-visual"] = d["sentences"]*2 - d["damier_H"] - d["damier_V"]
#####################################################################
# Launching Pipeline on all subjects, all acquisitions, all sessions
#####################################################################
# Treat sequentially all subjects & acquisitions
for s in Subjects:
print "Subject : %s" % s
for a in Acquisitions:
# step 1. set all the paths
basePath = os.sep.join((DBPath, s, "fMRI", a))
paths = glm_tools.generate_all_brainvisa_paths( basePath, Sessions,
fmri_wc, model_id)
misc = ConfigObj(paths['misc'])
misc["sessions"] = Sessions
misc["tasks"] = Conditions
misc["mask_url"] = paths['mask']
misc.write()
# step 2. Create one design matrix for each session
design_matrices = {}
for sess in Sessions:
design_matrices[sess] = glm_tools.design_matrix(
paths['misc'], paths['dmtx'][sess], sess, paths['paradigm'],
frametimes, hrf_model=hrf_model, drift_model=drift_model,
hfcut=hfcut, model=model_id)
# step 3. Compute the Mask
# fixme : it should be possible to provide a pre-computed mask
print "Computing the Mask"
mask_array = compute_mask_files( paths['fmri'].values()[0][0],
paths['mask'], True, infTh, supTh)
# step 4. Creating functional contrasts
print "Creating Contrasts"
clist = contrast_tools.ContrastList(misc=ConfigObj(paths['misc']),
model=model_id)
generate_localizer_contrasts(clist)
contrast = clist.save_dic(paths['contrast_file'])
CompletePaths = glm_tools.generate_brainvisa_ouput_paths(
paths["contrasts"], contrast)
# step 5. Fit the glm for each session
glms = {}
for sess in Sessions:
print "Fitting GLM for session : %s" % sess
glms[sess] = glm_tools.glm_fit(
paths['fmri'][sess], design_matrices[sess],
paths['glm_dump'][sess], paths['glm_config'][sess],
fit_algo, paths['mask'])
#step 6. Compute Contrasts
print "Computing contrasts"
glm_tools.compute_contrasts(contrast, misc, CompletePaths,
glms, model=model_id)