4.2. The Data Object

4.2.1. Description

This is the object that traverses the steps of the pipeline. It is indeed a dictionary of dictionaries. The first level of keys are the data types and the values are the corresponding information as dictionaries.

{'BOLD': {...}, 'T1w': {...}}

The second level of keys are the actual data. A special second-level key named meta is present in each step, that contains all the information on the data type including source and previous transformation steps.

The Data Grabber step adds the path second-level key which gives the path to the file containing the data. The meta key in this step only contains information about the DataGrabber used.

{'BOLD': {'meta': {'datagrabber': {'class': 'SPMAuditoryTestingDataGrabber',
                                   'types': ['BOLD', 'T1w']},
                   'dependencies': set(),
                   'element': {'subject': 'sub001'}},
          'path': PosixPath('/var/folders/dv/2lbr8f8j0q12zrx3mz3ll5m40000gp/T/tmpgxcyjfo1/sub001_bold.nii.gz')},
 'T1w': {'meta': {'datagrabber': {'class': 'SPMAuditoryTestingDataGrabber',
                                  'types': ['BOLD', 'T1w']},
                  'dependencies': set(),
                  'element': {'subject': 'sub001'}},
         'path': PosixPath('/var/folders/dv/2lbr8f8j0q12zrx3mz3ll5m40000gp/T/tmpgxcyjfo1/sub001_T1w.nii.gz')}}

The Data Reader step adds the data second-level key which is the actual data loaded into memory. The meta key in this step adds information about the DataReader used to read the data.

{'BOLD': {'data': <nibabel.nifti1.Nifti1Image object at 0x16b5d8910>,
          'meta': {'datagrabber': {'class': 'SPMAuditoryTestingDataGrabber',
                                   'types': ['BOLD', 'T1w']},
                   'datareader': {'class': 'DefaultDataReader'},
                   'dependencies': {'nilearn'},
                   'element': {'subject': 'sub001'}},
          'path': PosixPath('/var/folders/dv/2lbr8f8j0q12zrx3mz3ll5m40000gp/T/tmpe49321ce/sub001_bold.nii.gz')},
 'T1w': {'data': <nibabel.nifti1.Nifti1Image object at 0x16b5d78d0>,
         'meta': {'datagrabber': {'class': 'SPMAuditoryTestingDataGrabber',
                                  'types': ['BOLD', 'T1w']},
                  'datareader': {'class': 'DefaultDataReader'},
                  'dependencies': set(),
                  'element': {'subject': 'sub001'}},
         'path': PosixPath('/var/folders/dv/2lbr8f8j0q12zrx3mz3ll5m40000gp/T/tmpe49321ce/sub001_T1w.nii.gz')}}

The Preprocess step, if used, modifies the data second-level key’s value and appends the meta key with information about the preprocessor.

The Marker step removes the path second-level key, replaces the data second-level key’s value with the marker’s computed value and adds further keys needed for the storage, for example, col_names.

{'BOLD': {'col_names': ['root_sum_of_squares_ets'],
          'data': ...,
          'meta': {'datagrabber': {'class': 'SPMAuditoryTestingDataGrabber',
                                   'types': ['BOLD', 'T1w']},
                   'datareader': {'class': 'DefaultDataReader'},
                   'dependencies': {'nilearn'},
                   'element': {'subject': 'sub001'},
                   'marker': {'agg_method': 'mean',
                              'agg_method_params': None,
                              'class': 'RSSETSMarker',
                              'masks': None,
                              'name': 'RSSETSMarker',
                              'parcellation': 'Schaefer100x17'},
                   'type': 'BOLD'}}}

Note

You never directly interact with the data object but it’s important to know where and how the object is being manipulated to reason about your pipeline.

4.2.2. Data Types

Name	Description	Example
T1w	T1w image (3D)	Preprocessed or Raw T1w image
BOLD	BOLD image (4D)	Preprocessed/Denoised BOLD image (fmriprep output)
BOLD_confounds	BOLD image confounds (CSV/TSV file)	Confounds that can be applied to the BOLD image.
VBM_GM	VBM Gray Matter segmentation (3D)	CAT output (m0wp1 images)
VBM_WM	VBM White Matter segmentation (3D)	CAT output (m0wp2 images)
fALFF	Voxel-wise fALFF image (3D)	fALFF computed with CONN toolbox
GCOR	Global Correlation image (3D)	GCOR computed with CONN toolbox
LCOR	Local Correlation image (3D)	LCOR computed with CONN toolbox