nimare.decode.discrete.ROIAssociationDecoder

class ROIAssociationDecoder(masker, kernel_transformer=<class 'nimare.meta.kernel.MKDAKernel'>, feature_group=None, features=None, **kwargs)

Bases: Decoder

Perform discrete functional decoding according to Neurosynth’s ROI association method.

Neurosynth was described in Yarkoni et al.[1].

Parameters:

• masker (NiftiMasker, img_like, or similar) – Masker for region of interest.

• kernel_transformer (KernelTransformer, optional) – Kernel with which to create modeled activation maps. Default is MKDAKernel.

• feature_group (str, optional) – Feature group name used to select labels from a specific source. Feature groups are stored as prefixes to feature name columns in Dataset.annotations, with the format [source]_[valuetype]__. Input may or may not include the trailing underscore. Default is None, which uses all feature groups available.

• features (list, optional) – List of features in dataset annotations to use for decoding. If feature_group is provided, then features should not include the feature group prefix. If feature_group is not provided, then features should include the prefix. Default is None, which uses all features available.

Notes

The general approach in this method is:

1. Define ROI.

2. Generate MA maps for all studies in Dataset.

3. Average MA values within ROI to get study-wise MA regressor.

4. Correlate MA regressor with study-wise annotation values (e.g., tf-idf values).

References

[1]

Tal Yarkoni, Russell A Poldrack, Thomas E Nichols, David C Van Essen, and Tor D Wager. Large-scale automated synthesis of human functional neuroimaging data. Nature methods, 8(8):665–670, 2011. URL: https://doi.org/10.1038/nmeth.1635, doi:10.1038/nmeth.1635.

Methods

fit(dataset[, drop_invalid])	Fit Decoder to Dataset.
get_params([deep])	Get parameters for this estimator.
load(filename[, compressed])	Load a pickled class instance from file.
save(filename[, compress])	Pickle the class instance to the provided file.
set_params(**params)	Set the parameters of this estimator.
transform()	Apply the decoding method to a Dataset.

fit(dataset, drop_invalid=True)

Fit Decoder to Dataset.

Parameters:

• dataset (Dataset) – Dataset object to analyze.

• drop_invalid (bool, optional) – Whether to automatically ignore any studies without the required data or not. Default is True.

Notes

The fit method is a light wrapper that runs input validation and preprocessing before fitting the actual model. Decoders’ individual “fitting” methods are implemented as _fit, although users should call fit.

Selection of features based on requested features and feature group is performed in Decoder._preprocess_input.

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deep (bool, optional) – If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

params – Parameter names mapped to their values.

Return type:

dict

classmethod load(filename, compressed=True)

Load a pickled class instance from file.

Parameters:

• filename (str) – Name of file containing object.

• compressed (bool, optional) – If True, the file is assumed to be compressed and gzip will be used to load it. Otherwise, it will assume that the file is not compressed. Default = True.

Returns:

obj – Loaded class object.

Return type:

class object

save(filename, compress=True)

Pickle the class instance to the provided file.

Parameters:

• filename (str) – File to which object will be saved.

• compress (bool, optional) – If True, the file will be compressed with gzip. Otherwise, the uncompressed version will be saved. Default = True.

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Return type:

self

transform()

Apply the decoding method to a Dataset.

Returns:

results – Table with each label and the following values associated with each label: ‘r’.

Return type:

pandas.DataFrame