get_fmriprep_timeseries

nideconv.utils.roi.get_fmriprep_timeseries(fmriprep_folder, sourcedata_folder, atlas, atlas_type=None, low_pass=None, high_pass=0.0078125, confounds_to_include=None, *args, **kwargs)

Extract time series for each subject, task and run in a preprocessed dataset in BIDS format, given all the ROIs in atlas.

Currently only fmriprep outputs are supported. The sourcedata_folder is necessary to look up the TRs of the functional runs.

Parameters:

fmriprep_folder: string

Path to the folder that contains fmriprep’ed functional MRI data.

sourcedata_folder: string

Path to BIDS folder that has been used as input for fmriprep

atlas: sklearn.datasets.base.Bunch

This Bunch should contain at least a maps-attribute containing a label (3D) or probabilistic atlas (4D), as well as an label attribute, with one label for every ROI in the atlas. The function automatically detects which of the two is provided. It extracts a (weighted) time course per ROI. In the case of the probabilistic atlas, the voxels are weighted by their probability (see also the Mappers in nilearn).

atlas_type: str, optional

Can be ‘labels’ or ‘probabilistic’. A label atlas should be 3D and contains one unique number per ROI. A Probabilistic atlas contains as many volume as ROIs. Usually, atlas_type can be detected automatically.

low_pass: None or float, optional

This parameter is passed to signal.clean. Please see the related documentation for details

high_pass: None or float, optional

This parameter is passed to signal.clean. Please see the related documentation for details

confounds_to_include: list of strings

List of confounds that should be regressed out. By default a limited list of confounds is regressed out: Namely, FramewiseDisplacement, aCompCor00, aCompCor01, aCompCor02, aCompCor03, aCompCor04, aCompCor05, X, Y, Z, RotX, RotY, and RotZ

Examples

>>> source_data = '/data/ds001/sourcedata'
>>> fmriprep_data = '/data/ds001/derivatives/fmriprep'
>>> from nilearn import datasets 
>>> atlas = datasets.fetch_atlas_pauli_2017()
>>> from nideconv.utils.roi import get_fmriprep_timeseries
>>> ts = get_fmriprep_timeseries(fmriprep_data,
                                 source_data,
                                 atlas)
>>> ts.head()
roi                        Pu        Ca
subject task   time                    
001     stroop 0.0  -0.023651 -0.000767
               1.5  -0.362429 -0.012455
               3.0   0.087955 -0.062127
               4.5  -0.099711  0.146744
               6.0  -0.443499  0.093190