NeuroConn.preprocessing package

Submodules

NeuroConn.preprocessing.preprocessing module

class NeuroConn.preprocessing.preprocessing.FmriPreppedDataSet(BIDS_path)

Bases: RawDataset

Attributes:

data_description

name

participant_data

subjects

Methods

clean_signal(subject[, task, parcellation, ...])	Cleans the time series for a given subject using a specified parcellation.
docker_fmriprep(subject, fs_license_path[, ...])	Runs the fMRIprep pipeline in a Docker container for a given subject.
get_confounds(subject, task[, no_nans, ...])	Returns a list of confounds for a given subject and task.
get_conn_matrix(subject[, subject_ts, ...])	Computes the connectivity matrix for a given subject.
get_sessions(subject)	Returns a list of session names for a given subject.
get_ts_paths(subject, task)	param subject: The subject ID.
parcellate(subject[, parcellation, task, ...])	param subject: subject id

clean_signal(subject, task='rest', parcellation='schaefer', n_parcels=1000, gsr=False, save=False, save_to=None)

Cleans the time series for a given subject using a specified parcellation.

Parameters:

• subject (str) – The ID of the subject to clean the time series for.

• task (str, optional) – The name of the task to use. Default is ‘rest’.

• parcellation (str, optional) – The name of the parcellation to use. Default is ‘schaefer’.

• n_parcels (int, optional) – The number of parcels to use. Default is 1000.

• gsr (bool, optional) – Whether to use global signal regression. Default is False.

• save (bool, optional) – Whether to save the cleaned time series. Default is False.

• save_to (str, optional) – The path to save the cleaned time series. If None, the time series will be saved to the default directory. Default is None.

Returns:

The cleaned time series of shape (n_sessions, n_parcels, n_volumes).

Return type:

np.ndarray

get_confounds(subject, task, no_nans=True, pick_confounds=None)

Returns a list of confounds for a given subject and task.

Parameters:

• subject (str) – The ID of the subject.

• task (str) – The name of the task.

• no_nans (bool, optional) – Whether to impute NaNs in the confounds. Default is True.

• pick_confounds (list or numpy.ndarray, optional) – The confounds to be picked from the dataframe. If None, the default confounds will be used. Default is None.

Returns:

A list of confounds.

Return type:

list

get_conn_matrix(subject, subject_ts=None, parcellation='schaefer', task='rest', concat_ts=False, n_parcels=1000, gsr=False, z_transformed=True, save=False, save_to=None)

Computes the connectivity matrix for a given subject.

Parameters:

• subject (str) – The ID of the subject to compute the connectivity matrix for.

• subject_ts (str, optional) – The path to the cleaned time series. If None, the time series will be cleaned using the clean_signal method. Default is None.

• parcellation (str, optional) – The name of the parcellation to use. Default is ‘schaefer’.

• task (str, optional) – The name of the task to use. Default is ‘rest’.

• concat_ts (bool, optional) – Whether to compute the connectivity matrix on concatenated time series (e.g., if several sessions available). Default is False.

• n_parcels (int, optional) – The number of parcels to use. Default is 1000.

• gsr (bool, optional) – Whether to use global signal regression. Default is False.

• z_transformed (bool, optional) – Whether to apply Fisher’s z transform to the connectivity matrix. Default is True.

• save (bool, optional) – Whether to save the connectivity matrix. Default is False.

• save_to (str, optional) – The path to save the connectivity matrix. If None, the matrix will be saved to the default directory. Default is None.

Returns:

The connectivity matrix of shape (n_sessions, n_parcels, n_parcels).

Return type:

np.ndarray

get_sessions(subject)

Returns a list of session names for a given subject. If the subject has no sessions, an empty list is returned.

Parameters:

subject (str) – The label of the subject to retrieve session names for.

Returns:

A list of session names for the given subject.

Return type:

list of str

get_ts_paths(subject, task)

Parameters:

• subject (str) – The subject ID.

• task (str) – The task name.

Returns:

ts_paths – A list of paths to the time series files.

Return type:

list

parcellate(subject, parcellation='schaefer', task='rest', n_parcels=1000, gsr=False)

Parameters:

• subject (str) – subject id

• parcellation (str) – parcellation to use

• task (str) – task to use

• n_parcels (int) – number of parcels to use

• gsr (bool) – whether to use global signal regression

Returns:

parc_ts_list – list of parcellated time series

Return type:

list

class NeuroConn.preprocessing.preprocessing.RawDataset(BIDS_path)

Bases: object

Attributes:

data_description

name

participant_data

subjects

Methods

docker_fmriprep(subject, fs_license_path[, ...])

Runs the fMRIprep pipeline in a Docker container for a given subject.

property data_description

docker_fmriprep(subject, fs_license_path, skip_bids_validation=True, fs_reconall=True, mem=5000, task='rest')

Runs the fMRIprep pipeline in a Docker container for a given subject.

Parameters:

• subject (str) – The label of the participant to process.

• skip_bids_validation (bool, optional) – Whether to skip BIDS validation. Default is True.

• fs_license_path (str, optional) – The path to the (full) FreeSurfer license file

• fs_reconall (bool, optional) – Whether to run FreeSurfer’s recon-all. Default is True.

• mem (int, optional) – The amount of memory to allocate to the Docker container, in MB. Default is 5000.

• task (str, optional) – The ID of the task to preprocess, or None to preprocess all tasks. Default is ‘rest’.

Return type:

None

property name

property participant_data

property subjects

NeuroConn.preprocessing.preprocessing.z_transform_conn_matrix(conn_matrix)

Applies Fisher’s z transform to a connectivity matrix.

Parameters:

conn_matrix (numpy.ndarray) – The connectivity matrix to transform.

Returns:

The transformed connectivity matrix.

Return type:

numpy.ndarray

Module contents