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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 numpy import arange
from nipy.externals.configobj import ConfigObj
from nipy.neurospin.utils.mask import compute_mask_files
from nipy.neurospin.glm_files_layout import glm_tools, contrast_tools
# -----------------------------------------------------------
# --------- Set the paths -----------------------------------
#-----------------------------------------------------------
DBPath ="/neurospin/lnao/Panabase/data_fiac/fiac_fsl/"
Subjects = [ "fiac1"]
#Subjects = ["fiac2", "fiac3", "fiac4", "fiac6", "fiac8",\
# "fiac9", "fiac10", "fiac11", "fiac12", "fiac13",\
# "fiac14", "fiac15"]
Acquisitions = ["acquisition"]
Sessions = ["fonc1", "fonc2"]#["fonc3", "fonc4"]
model_id = "default"
fmri_wc = "swra*.nii.gz"
# ---------------------------------------------------------
# -------- General Information ----------------------------
# ---------------------------------------------------------
tr = 2.5
nb_frames = 191
frametimes = tr * arange(nb_frames)
Conditions = ["SSt-SSp", "SSt-DSp", "DSt-SSp", "DSt-DSp", "FirstSt"]
# ---------------------------------------------------------
# ------ First level analysis parameters ---------------------
# ---------------------------------------------------------
#---------- Masking parameters
infTh = 0.4
supTh = 0.9
#---------- Design Matrix
# hrf model, to be chosen among "Canonical",
# "Canonical With Derivative" or "FIR Model"
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"
#####################################################################
# Launching Pipeline on all subjects, all acquisitions, all sessions
#####################################################################
# main loop on subjects
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'], False, infTh, supTh)
# step 4. Create Contrast Files
print "Creating Contrasts"
clist = contrast_tools.ContrastList(misc=misc)
d = clist.dic
d["SStSSp_minus_DStDSp"] = d["SSt-SSp"] - d["DSt-DSp"]
d["DStDSp_minus_SStSSp"] = d["DSt-DSp"] - d["SSt-SSp"]
d["DSt_minus_SSt"] = d["DSt-SSp"] + d["DSt-DSp"]\
- d["SSt-SSp"] - d["SSt-DSp"]
d["DSp_minus_SSp"] = d["DSt-DSp"] - d["DSt-SSp"]\
- d["SSt-SSp"] + d["SSt-DSp"]
d["DSt_minus_SSt_for_DSp"] = d["DSt-DSp"] - d["SSt-DSp"]
d["DSp_minus_SSp_for_DSt"] = d["DSt-DSp"] - d["DSt-SSp"]
if d.has_key("FirstSt"):
d["Deactivation"] = d["FirstSt"] - d["SSt-SSp"]\
- d["DSt-DSp"] - d["DSt-DSp"] - d["SSt-SSp"]
else:
d["Deactivation"] = (d["SSt-SSp"] * -1)\
- d["DSt-DSp"] - d["DSt-DSp"] - d["SSt-SSp"]
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'])
#6. Compute the Contrasts
print "Computing contrasts"
glm_tools.compute_contrasts(contrast, misc, CompletePaths,
glms, model=model_id)
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