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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 localizer
Here the analysis is perfomed on the cortical of one hemisphere.
Author : Lise Favre, Bertrand Thirion, 2008-2010
"""
import os
from numpy import arange
from nipy.externals.configobj import ConfigObj
from nipy.neurospin.glm_files_layout import (glm_tools,
contrast_tools,
cortical_glm)
# -----------------------------------------------------------
# --------- Set the paths -----------------------------------
#-----------------------------------------------------------
DBPath = "/neurospin/lnao/Pmad/alan/subjfreesurfer"
Subjects = ['s12069']#['s12300', 's12401', 's12431', 's12508', 's12532', 's12539', 's12562','s12590', 's12635', 's12636', 's12898', 's12913', 's12919', 's12920']
Acquisitions = [""]
Sessions = ["loc1"]
model_id = "default"
side = 'right'
fmri_wc = "rh*.tex"
if side=='left':
fmri_wc = "lh*.tex"
# ---------------------------------------------------------
# -------- General Information ----------------------------
# ---------------------------------------------------------
tr = 2.4
nb_frames = 128
frametimes = arange(nb_frames) * tr
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 hrfType : "Canonical", \
# "Canonical With Derivative" or "FIR"
hrf_model = "Canonical With Derivative"
# Possible choices for drift : "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, "fct", a))
paths = cortical_glm.generate_all_brainvisa_paths(
basePath, Sessions, fmri_wc, model_id)
misc = ConfigObj(paths['misc'])
misc["sessions"] = Sessions
misc["tasks"] = Conditions
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 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 = cortical_glm.generate_brainvisa_ouput_paths(
paths["contrasts"], contrast, side)
# step 5. Fit the glm for each session
glms = {}
for sess in Sessions:
print "Fitting GLM for session : %s" % sess
glms[sess] = cortical_glm.glm_fit(
paths['fmri'][sess], design_matrices[sess],
paths['glm_dump'][sess], paths['glm_config'][sess], fit_algo)
#step 6. Compute Contrasts
print "Computing contrasts"
cortical_glm.compute_contrasts(
contrast, misc, CompletePaths, glms, model=model_id)
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