8.1.4. Markers#

Provide imports for markers sub-package.

class junifer.markers.AmplitudeLowFrequencyFluctuationParcels(parcellation, fractional, highpass=0.01, lowpass=0.1, tr=None, use_afni=None, mask=None, method='mean', method_params=None, name=None)#

Class for computing fALFF/ALFF on parcels.

Parameters:

parcellationstr or list of str: The name(s) of the parcellation(s). Check valid options by calling junifer.data.parcellations.list_parcellations().
fractionalbool: Whether to compute fractional ALFF.
highpasspositive float, optional: The highpass cutoff frequency for the bandpass filter (default 0.01).
lowpasspositive float, optional: The lowpass cutoff frequency for the bandpass filter (default 0.1).
trpositive float, optional: The Repetition Time of the BOLD data. If None, will extract the TR from NIFTI header (default None).
use_afnibool, optional: Whether to use AFNI for computing. If None, will use AFNI only if available (default None).
maskstr, optional: The name of the mask to apply to regions before extracting signals. Check valid options by calling junifer.data.masks.list_masks() (default None).
methodstr, optional: The method to perform aggregation using. Check valid options in junifer.stats.get_aggfunc_by_name() (default “mean”).
method_paramsdict, optional: Parameters to pass to the aggregation function. Check valid options in junifer.stats.get_aggfunc_by_name().
namestr, optional: The name of the marker. If None, will use the class name (default None).

Notes

The tr parameter is crucial for the correctness of fALFF/ALFF computation. If a dataset is correctly preprocessed, the TR should be extracted from the NIFTI without any issue. However, it has been reported that some preprocessed data might not have the correct TR in the NIFTI header.

ALFF/fALFF are computed using a bandpass butterworth filter. See scipy.signal.butter() and scipy.signal.filtfilt() for more details.

class junifer.markers.AmplitudeLowFrequencyFluctuationSpheres(coords, fractional, radius=None, highpass=0.01, lowpass=0.1, tr=None, use_afni=None, mask=None, method='mean', method_params=None, name=None)#

Class for computing fALFF/ALFF on spheres.

Parameters:

coordsstr: The name of the coordinates list to use. See junifer.data.coordinates.list_coordinates() for options.
radiusfloat, optional: The radius of the sphere in mm. If None, the signal will be extracted from a single voxel. See nilearn.maskers.NiftiSpheresMasker for more information (default None).
fractionalbool: Whether to compute fractional ALFF.
highpasspositive float, optional: The highpass cutoff frequency for the bandpass filter (default 0.01).
lowpasspositive float, optional: The lowpass cutoff frequency for the bandpass filter (default 0.1).
trpositive float, optional: The Repetition Time of the BOLD data. If None, will extract the TR from NIFTI header (default None).
use_afnibool, optional: Whether to use AFNI for computing. If None, will use AFNI only if available (default None).
maskstr, optional: The name of the mask to apply to regions before extracting signals. Check valid options by calling junifer.data.masks.list_masks() (default None).
methodstr, optional: The method to perform aggregation using. Check valid options in junifer.stats.get_aggfunc_by_name() (default “mean”).
method_paramsdict, optional: Parameters to pass to the aggregation function. Check valid options in junifer.stats.get_aggfunc_by_name().
namestr, optional: The name of the marker. If None, will use the class name (default None).

Notes

ALFF/fALFF are computed using a bandpass butterworth filter. See scipy.signal.butter() and scipy.signal.filtfilt() for more details.

class junifer.markers.BaseMarker(on=None, name=None)#

Abstract base class for all markers.

Parameters:

onstr or list of str: The kind of data to apply the marker to. By default, will work on all available data.
namestr, optional: The name of the marker. By default, it will use the class name as the name of the marker (default None).

abstract compute(input, extra_input=None)#

Compute.

Parameters:

inputdict: A single input from the pipeline data object in which to compute the marker.
extra_inputdict, optional: The other fields in the pipeline data object. Useful for accessing other data kind that needs to be used in the computation. For example, the functional connectivity markers can make use of the confounds if available (default None).

Returns:

dict: The computed result as dictionary. This will be either returned to the user or stored in the storage by calling the store method with this as a parameter.

abstract get_output_type(input_type)#

Get output type.

Parameters:

input_typestr: The data type input to the marker.

Returns:

str: The storage type output by the marker.

get_valid_inputs()#

Get valid data types for input.

Returns:

list of str: The list of data types that can be used as input for this marker.

store(type_, out, storage)#

Store.

Parameters:

type_str: The data type to store.
outdict: The computed result as a dictionary to store.
storagestorage-like: The storage class, for example, SQLiteFeatureStorage.

validate_input(input)#

Validate input.

Parameters:

inputlist of str: The input to the pipeline step. The list must contain the available Junifer Data dictionary keys.

Raises:

ValueError: If the input does not have the required data.

class junifer.markers.CrossParcellationFC(parcellation_one, parcellation_two, aggregation_method='mean', correlation_method='pearson', mask=None, name=None)#

Class for calculating parcel-wise correlations with 2 parcellations.

Parameters:

parcellation_onestr: The name of the first parcellation.
parcellation_twostr: The name of the second parcellation.
aggregation_methodstr, optional: The aggregation method (default “mean”).
correlation_methodstr, optional: Any method that can be passed to pandas.DataFrame.corr (default “pearson”).
maskstr, optional: The name of the mask to apply to regions before extracting signals. Check valid options by calling junifer.data.masks.list_masks() (default None).
namestr, optional: The name of the marker. If None, will use the class name (default None).

compute(input, extra_input=None)#

Compute.

Take a timeseries, parcellate them with two different parcellation schemes, and get parcel-wise correlations between the two different parcellated time series. Shape of output matrix corresponds to number of ROIs in (parcellation_two, parcellation_one).

Parameters:

inputdict: The BOLD data as a dictionary.
extra_inputdict, optional: The other fields in the pipeline data object (default None).

Returns:

dict

The computed result as dictionary. This will be either returned to the user or stored in the storage by calling the store method with this as a parameter. The dictionary has the following keys:

data : the correlation values between the two parcellations as a numpy.ndarray
col_names : the ROIs for first parcellation as a list
row_names : the ROIs for second parcellation as a list

get_output_type(input_type)#

Get output type.

Parameters:

input_typestr: The data type input to the marker.

Returns:

str: The storage type output by the marker.

get_valid_inputs()#

Get valid data types for input.

Returns:

list of str: The list of data types that can be used as input for this marker

class junifer.markers.FunctionalConnectivityParcels(parcellation, agg_method='mean', agg_method_params=None, cor_method='covariance', cor_method_params=None, mask=None, name=None)#

Class for functional connectivity.

Parameters:

parcellationstr or list of str: The name(s) of the parcellation(s). Check valid options by calling junifer.data.parcellations.list_parcellations().
agg_methodstr, optional: The method to perform aggregation using. Check valid options in junifer.stats.get_aggfunc_by_name() (default “mean”).
agg_method_paramsdict, optional: Parameters to pass to the aggregation function. Check valid options in junifer.stats.get_aggfunc_by_name() (default None).
cor_methodstr, optional: The method to perform correlation using. Check valid options in nilearn.connectome.ConnectivityMeasure (default “covariance”).
cor_method_paramsdict, optional: Parameters to pass to the correlation function. Check valid options in nilearn.connectome.ConnectivityMeasure (default None).
maskstr, optional: The name of the mask to apply to regions before extracting signals. Check valid options by calling junifer.data.masks.list_masks() (default None).
namestr, optional: The name of the marker. If None, will use the class name (default None).

compute(input, extra_input=None)#

Compute.

Parameters:

inputdict: A single input from the pipeline data object in which to compute the marker.
extra_inputdict, optional: The other fields in the pipeline data object. Useful for accessing other data kind that needs to be used in the computation. For example, the functional connectivity markers can make use of the confounds if available (default None).

Returns:

dict

The computed result as dictionary. The following data will be included in the dictionary:

data : functional connectivity matrix as a numpy.ndarray.
row_names : row names as a list
col_names : column names as a list
matrix_kind : the kind of matrix (tril, triu or full)

get_output_type(input_type)#

Get output type.

Parameters:

input_typestr: The data type input to the marker.

Returns:

str: The storage type output by the marker.

get_valid_inputs()#

Get valid data types for input.

Returns:

list of str: The list of data types that can be used as input for this marker.

class junifer.markers.FunctionalConnectivitySpheres(coords, radius=None, agg_method='mean', agg_method_params=None, cor_method='covariance', cor_method_params=None, mask=None, name=None)#

Class for functional connectivity using coordinates (spheres).

Parameters:

coordsstr: The name of the coordinates list to use. See junifer.data.coordinates.list_coordinates() for options.
radiusfloat, optional: The radius of the sphere in mm. If None, the signal will be extracted from a single voxel. See nilearn.maskers.NiftiSpheresMasker for more information (default None).
agg_methodstr, optional: The aggregation method to use. See junifer.stats.get_aggfunc_by_name() for more information (default None).
agg_method_paramsdict, optional: The parameters to pass to the aggregation method (default None).
cor_methodstr, optional: The method to perform correlation using. Check valid options in nilearn.connectome.ConnectivityMeasure (default “covariance”).
cor_method_paramsdict, optional: Parameters to pass to the correlation function. Check valid options in nilearn.connectome.ConnectivityMeasure (default None).
maskstr, optional: The name of the mask to apply to regions before extracting signals. Check valid options by calling junifer.data.masks.list_masks() (default None).
namestr, optional: The name of the marker. By default, it will use KIND_FunctionalConnectivitySpheres where KIND is the kind of data it was applied to (default None).

compute(input, extra_input=None)#

Compute.

Parameters:

inputdict: A single input from the pipeline data object in which to compute the marker.
extra_inputdict, optional: The other fields in the pipeline data object. Useful for accessing other data kind that needs to be used in the computation. For example, the functional connectivity markers can make use of the confounds if available (default None).

Returns:

dict

The computed result as dictionary. The following keys will be included in the dictionary:

data : functional connectivity matrix as a numpy.ndarray.
row_names : row names as a list
col_names : column names as a list
matrix_kind : the kind of matrix (tril, triu or full)

get_output_type(input_type)#

Get output type.

Parameters:

input_typestr: The data type input to the marker.

Returns:

str: The storage type output by the marker.

get_valid_inputs()#

Get valid data types for input.

Returns:

list of str: The list of data types that can be used as input for this marker.

class junifer.markers.MarkerCollection(markers, datareader=None, preprocessing=None, storage=None)#

Class for marker collection.

Parameters:

markerslist of marker-like: The markers to compute.
datareaderdatareader-like, optional: The datareader to use (default None).
preprocessingpreprocessing-like, optional: The preprocessing steps to apply.
storagestorage-like, optional: The storage to use (default None).

fit(input)#

Fit the pipeline.

Parameters:

inputdict: The input data to fit the pipeline on. Should be the output of indexing the Data Grabber with one element.

Returns:

dict or None: The output of the pipeline. Each key represents a marker name and the values are the computer marker values. If the pipeline has a storage configured, then the output will be None.

validate(datagrabber)#

Validate the pipeline.

Without doing any computation, check if the marker collection can be fit without problems. That is, the data required for each marker is present and streamed down the steps. Also, if a storage is configured, check that the storage can handle the markers output.

Parameters:

datagrabberdatagrabber-like: The datagrabber to validate.

class junifer.markers.ParcelAggregation(parcellation, method, method_params=None, mask=None, on=None, name=None)#

Class for parcel aggregation.

Parameters:

parcellationstr or list of str: The name(s) of the parcellation(s). Check valid options by calling junifer.data.parcellations.list_parcellations().
methodstr: The method to perform aggregation using. Check valid options in junifer.stats.get_aggfunc_by_name().
method_paramsdict, optional: Parameters to pass to the aggregation function. Check valid options in junifer.stats.get_aggfunc_by_name().
maskstr, optional: The name of the mask to apply to regions before extracting signals. Check valid options by calling junifer.data.masks.list_masks() (default None).
on{“T1w”, “BOLD”, “VBM_GM”, “VBM_WM”, “fALFF”, “GCOR”, “LCOR”} or list of the options, optional: The data types to apply the marker to. If None, will work on all available data (default None).
namestr, optional: The name of the marker. If None, will use the class name (default None).

compute(input, extra_input=None)#

Compute.

Parameters:

inputdict: A single input from the pipeline data object in which to compute the marker.
extra_inputdict, optional: The other fields in the pipeline data object. Useful for accessing other data kind that needs to be used in the computation. For example, the functional connectivity markers can make use of the confounds if available (default None).

Returns:

dict

The computed result as dictionary. This will be either returned to the user or stored in the storage by calling the store method with this as a parameter. The dictionary has the following keys:

data : the actual computed values as a numpy.ndarray
columns : the column labels for the computed values as a list
row_names (if more than one row is present in data): “scan”

get_output_type(input_type)#

Get output type.

Parameters:

input_typestr: The data type input to the marker.

Returns:

str: The storage type output by the marker.

get_valid_inputs()#

Get valid data types for input.

Returns:

list of str: The list of data types that can be used as input for this marker.

class junifer.markers.RSSETSMarker(parcellation, agg_method='mean', agg_method_params=None, mask=None, name=None)#

Class for root sum of squares of edgewise timeseries.

Parameters:

parcellationstr or list of str: The name(s) of the parcellation(s). Check valid options by calling junifer.data.parcellations.list_parcellations().
agg_methodstr, optional: The method to perform aggregation using. Check valid options in junifer.stats.get_aggfunc_by_name() (default “mean”).
agg_method_paramsdict, optional: Parameters to pass to the aggregation function. Check valid options in junifer.stats.get_aggfunc_by_name() (default None).
maskstr, optional: The name of the mask to apply to regions before extracting signals. Check valid options by calling junifer.data.masks.list_masks() (default None).
namestr, optional: The name of the marker. If None, will use the class name (default None).

compute(input, extra_input=None)#

Compute.

Take a timeseries of brain areas, and calculate timeseries for each edge according to the method outlined in [1]. For more information, check https://github.com/brain-networks/edge-ts/blob/master/main.m

Parameters:

inputdict: The BOLD data as dictionary.
extra_inputdict, optional: The other fields in the pipeline data object (default None).

Returns:

dict

The computed result as dictionary. The dictionary has the following keys:

data : the actual computed values as a numpy.ndarray
columns : the column labels for the computed values as a list
row_names (if more than one row is present in data): “scan”

References

[1]

Zamani Esfahlani et al. (2020) High-amplitude cofluctuations in cortical activity drive functional connectivity doi: 10.1073/pnas.2005531117

get_output_type(input_type)#

Get output type.

Parameters:

input_typestr: The data type input to the marker.

Returns:

str: The storage type output by the marker.

get_valid_inputs()#

Get valid data types for input.

Returns:

list of str: The list of data types that can be used as input for this marker.

class junifer.markers.ReHoParcels(parcellation, use_afni=None, reho_params=None, agg_method='mean', agg_method_params=None, mask=None, name=None)#

Class for regional homogeneity on parcels.

Parameters:

parcellationstr

The name of the parcellation. Check valid options by calling junifer.data.parcellations.list_parcellations().

use_afnibool, optional

Whether to use AFNI for computing. If None, will use AFNI only if available (default None).

reho_paramsdict, optional

Extra parameters for computing ReHo map as a dictionary (default None). If use_afni = True, then the valid keys are:

nneigh{7, 19, 27}, optional (default 27)
Number of voxels in the neighbourhood, inclusive. Can be:
- 7 : for facewise neighbours only
- 19 : for face- and edge-wise nieghbours
- 27 : for face-, edge-, and node-wise neighbors
neigh_radpositive float, optional
The radius of a desired neighbourhood (default None).
neigh_xpositive float, optional
The semi-radius for x-axis of ellipsoidal volumes (default None).
neigh_ypositive float, optional
The semi-radius for y-axis of ellipsoidal volumes (default None).
neigh_zpositive float, optional
The semi-radius for z-axis of ellipsoidal volumes (default None).
box_radpositive int, optional
The number of voxels outward in a given cardinal direction for a cubic box centered on a given voxel (default None).
box_xpositive int, optional
The number of voxels for +/- x-axis of cuboidal volumes (default None).
box_ypositive int, optional
The number of voxels for +/- y-axis of cuboidal volumes (default None).
box_zpositive int, optional
The number of voxels for +/- z-axis of cuboidal volumes (default None).

else if use_afni = False, then the valid keys are:

nneigh{7, 19, 27, 125}, optional (default 27)
Number of voxels in the neighbourhood, inclusive. Can be:
- 7 : for facewise neighbours only
- 19 : for face- and edge-wise nieghbours
- 27 : for face-, edge-, and node-wise neighbors
- 125 : for 5x5 cuboidal volume

agg_methodstr, optional

The method to perform aggregation using. Check valid options in junifer.stats.get_aggfunc_by_name() (default “mean”).

agg_method_paramsdict, optional

Parameters to pass to the aggregation function. Check valid options in junifer.stats.get_aggfunc_by_name() (default None).

maskstr, optional

The name of the mask to apply to regions before extracting signals. Check valid options by calling junifer.data.masks.list_masks() (default None).

namestr, optional

The name of the marker. If None, it will use the class name (default None).

compute(input, extra_input=None)#

Compute.

Parameters:

inputdict: The BOLD data as dictionary.
extra_inputdict, optional: The other fields in the pipeline data object (default None).

Returns:

dict

The computed result as dictionary. The dictionary has the following keys:

data : the actual computed values as a 1D numpy.ndarray
columns : the column labels for the parcels as a list
row_names : None

class junifer.markers.ReHoSpheres(coords, radius=None, use_afni=None, reho_params=None, agg_method='mean', agg_method_params=None, mask=None, name=None)#

Class for regional homogeneity on spheres.

Parameters:

coordsstr

The name of the coordinates list to use. See junifer.data.coordinates.list_coordinates() for options.

radiusfloat, optional

The radius of the sphere in millimeters. If None, the signal will be extracted from a single voxel. See nilearn.maskers.NiftiSpheresMasker for more information (default None).

use_afnibool, optional

Whether to use AFNI for computing. If None, will use AFNI only if available (default None).

reho_paramsdict, optional

Extra parameters for computing ReHo map as a dictionary (default None). If use_afni = True, then the valid keys are:

nneigh{7, 19, 27}, optional (default 27)
Number of voxels in the neighbourhood, inclusive. Can be:
- 7 : for facewise neighbours only
- 19 : for face- and edge-wise nieghbours
- 27 : for face-, edge-, and node-wise neighbors
neigh_radpositive float, optional
The radius of a desired neighbourhood (default None).
neigh_xpositive float, optional
The semi-radius for x-axis of ellipsoidal volumes (default None).
neigh_ypositive float, optional
The semi-radius for y-axis of ellipsoidal volumes (default None).
neigh_zpositive float, optional
The semi-radius for z-axis of ellipsoidal volumes (default None).
box_radpositive int, optional
The number of voxels outward in a given cardinal direction for a cubic box centered on a given voxel (default None).
box_xpositive int, optional
The number of voxels for +/- x-axis of cuboidal volumes (default None).
box_ypositive int, optional
The number of voxels for +/- y-axis of cuboidal volumes (default None).
box_zpositive int, optional
The number of voxels for +/- z-axis of cuboidal volumes (default None).

else if use_afni = False, then the valid keys are:

nneigh{7, 19, 27, 125}, optional (default 27)
Number of voxels in the neighbourhood, inclusive. Can be:
- 7 : for facewise neighbours only
- 19 : for face- and edge-wise nieghbours
- 27 : for face-, edge-, and node-wise neighbors
- 125 : for 5x5 cuboidal volume

agg_methodstr, optional

The aggregation method to use. See junifer.stats.get_aggfunc_by_name() for more information (default None).

agg_method_paramsdict, optional

The parameters to pass to the aggregation method (default None).

maskstr, optional

The name of the mask to apply to regions before extracting signals. Check valid options by calling junifer.data.masks.list_masks() (default None).

namestr, optional

The name of the marker. If None, it will use the class name (default None).

compute(input, extra_input=None)#

Compute.

Parameters:

inputdict: The BOLD data as dictionary.
extra_inputdict, optional: The other fields in the pipeline data object (default None).

Returns:

dict

The computed result as dictionary. The dictionary has the following keys:

data : the actual computed values as a 1D numpy.ndarray
columns : the column labels for the spheres as a list
rows_col_name : None

class junifer.markers.SphereAggregation(coords, radius=None, method='mean', method_params=None, mask=None, on=None, name=None)#

Class for sphere aggregation.

Parameters:

coordsstr: The name of the coordinates list to use. See junifer.data.coordinates.list_coordinates() for options.
radiusfloat, optional: The radius of the sphere in millimeters. If None, the signal will be extracted from a single voxel. See nilearn.maskers.NiftiSpheresMasker for more information (default None).
methodstr, optional: The aggregation method to use. See junifer.stats.get_aggfunc_by_name() for more information (default “mean”).
method_paramsdict, optional: The parameters to pass to the aggregation method (default None).
maskstr, optional: The name of the mask to apply to regions before extracting signals. Check valid options by calling junifer.data.masks.list_masks() (default None).
on{“T1w”, “BOLD”, “VBM_GM”, “VBM_WM”, “fALFF”, “GCOR”, “LCOR”} or list of the options, optional: The data types to apply the marker to. If None, will work on all available data (default None).
namestr, optional: The name of the marker. By default, it will use KIND_SphereAggregation where KIND is the kind of data it was applied to (default None).

compute(input, extra_input=None)#

Compute.

Parameters:

inputdict: A single input from the pipeline data object in which to compute the marker.
extra_inputdict, optional: The other fields in the pipeline data object. Useful for accessing other data kind that needs to be used in the computation. For example, the functional connectivity markers can make use of the confounds if available (default None).

Returns:

dict

The computed result as dictionary. This will be either returned to the user or stored in the storage by calling the store method with this as a parameter. The dictionary has the following keys:

data : the actual computed values as a numpy.ndarray
columns : the column labels for the computed values as a list
row_names (if more than one row is present in data): “scan”

get_output_type(input_type)#

Get output type.

Parameters:

input_typestr: The data type input to the marker.

Returns:

str: The storage type output by the marker.

get_valid_inputs()#

Get valid data types for input.

Returns:

list of str: The list of data types that can be used as input for this marker.