The current package contains PyHRF 1.0 and all its dependencies. A python module that implements a joint detection-estimation approach of brain activity from fMRI datasets described in:
VINCENT, Thomas (CEA), CIUCIU, Philippe (CEA), IDIER,
Jérôme (CNRS) Application and Validation of Spatial Mixture Modelling for the Joint Detection-Estimation of Brain Activity in Fmri 29th IEEE EMBS Annual International Conference, August 23-26, 2007, Cité Internationale, Lyon, France |
VINCENT, Thomas (CEA), CIUCIU, Philippe (CEA), IDIER,
Jérôme (CNRS) Spatial mixture modelling for the joint detection-estimation of brain activity in fMRI International Conference on Acoustics, Speech, and Signal Procession, April 15-20, 2007, Hawai'i Convention Center - Honolulu, Hawaii, USA. |
MAKNI, Salima (CEA), IDIER, Jérôme (CNRS), VINCENT, Thomas (CEA), THIRION, Bertrand (INRIA), DEHAENE-LAMBERTZ, Ghislaine (CNRS) and CIUCIU, Philippe (CEA), A fully Bayesian approach to the parcel-based detection-estimation of brain activity in fMRI NeuroImage, published on line:doi:10.1016/j.neuroimage.2008.02.017 |
Here is an example of what kind of results our algorithm is able to provide:
This software is delivered is on pre-release version and is not
intended to be distributed.
It is mainly written in Python with some extension in C.
The installation is semi-automatic and relies on setuptools which
is an enhancement of setuptools.
Pyhrf requires 2 main dependencies to be installed by the user:
The following softwares are required prior to the installation:
The utilisation of pyhrf is composed of 4 steps :
clustering.xml
):
Information needed by a Pyhrf treatment for one session are: the BOLD 4D data, a 3D volume of labels defining the ROIs, a set of stimuli with their time arrivals. Configuration of pyhrf is made through the edition of a xml file. At first, generate a template of this file by issuing:
detectestim.xml
is then created. By default, it
contains the definition of a treatment over data included in the Pyhrf
distribution (i.e. localizer experiment). This serves as example to build
a customized treatment.
SessionsParams
section describes all fMRI sessions.
Each child tag stands for each session name, here only one session
named session0
.
Down one level, the definition of one session is made of:
onsets
, which stores the different stimuli types
with their time arrivals. Every stimulus designation must be unique.
The sequence associated to each stimulus designation is the set of
time
arrivals in seconds, separated by one space delimiter.treatedParcellationFile
, the file to write the
treated functional mask. May be different from the input mask
if some BOLD signals have NAN values or null variance, for example.
regionIds
, a list of integers being the list of
parcel label to limit the analysis to
RepetitionTime
(expressed in seconds) defines
the temporal resolution of the BOLD signal (number of scans).
IRMfDataFile
, which indicates the location of the
BOLD data.
Supported format: only nifti
parcellationFile
, which indicates the location of
the mask
defining ROIs: a volume of labels (integers) where 0 is the
background.
stimulusLength
, which should have the same structure as
onsets
and codes for length of stimuli. If empty, then
peaked stimuli are considered.
SessionsParams
,
one can define the minimal number of voxels in a ROI. ROIs which do not
verify this condition are discarded.analyser
groups all parameters for the actual
analysis. See contextual comments within the xml for more information.
To run the pyhrf treatment defined in detectestim.xml:
Here -v1
stands for the level of verbosity (from 0: quiet,
to 6: everything - debug only purpose)
fMRITreatmentParameters/analyser/BoldEstimationModel/responseLevels/contrasts/
in the xml file. Output file format is:
<session_name>_pmNRL_con_<contrast_name>.nii
<session_name>_pmNRL_convar_<contrast_name>.nii
<session_name>_pm_hrf.nii
<session_name>_pmNRL_condition_<condition_name>.nii