nimare.decode.discrete
.BrainMapDecoder¶
-
class
BrainMapDecoder
(feature_group=None, features=None, frequency_threshold=0.001, u=0.05, correction='fdr_bh')[source]¶ Perform image-to-text decoding for discrete image inputs (e.g., regions of interest, significant clusters) according to the BrainMap method.
- Parameters
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.frequency_threshold (
float
, optional) – Threshold to apply to dataset annotations. Values greater than or equal to the threshold as assigned as label+, while values below the threshold are considered label-. Default is 0.001.u (
float
, optional) – Alpha level for multiple comparisons correction. Default is 0.05.correction (
str
or None, optional) – Multiple comparisons correction method to apply. Corresponds to available options forstatsmodels.stats.multitest.multipletests()
. Default is ‘fdr_bh’ (Benjamini-Hochberg FDR correction).
See also
nimare.decode.discrete.brainmap_decode()
The associated function for this method.
References
Amft, Maren, et al. “Definition and characterization of an extended social-affective default network.” Brain Structure and Function 220.2 (2015): 1031-1049. https://doi.org/10.1007/s00429-013-0698-0
-
fit
(dataset)[source]¶ Fit Estimator to Dataset.
- Parameters
dataset (
nimare.dataset.Dataset
) – Dataset object to analyze.- Returns
nimare.results.MetaResult
– Results of Estimator fitting.
Notes
The
fit
method is a light wrapper that runs input validation and preprocessing before fitting the actual model. Estimators’ individual “fitting” methods are implemented as_fit
, although users should callfit
.
-
get_params
(deep=True)[source]¶ Get parameters for this estimator.
- Parameters
deep (boolean, optional) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns
params (mapping of string to any) – Parameter names mapped to their values.
-
set_params
(**params)[source]¶ 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.- Returns
self
-
transform
(ids, ids2=None)[source]¶ Apply the decoding method to a Dataset.
- Parameters
ids (
list
) – Subset of studies in coordinates/annotations dataframes indicating target for decoding. Examples include studies reporting at least one peak in an ROI, or studies selected from a clustering analysis.ids2 (
list
or None, optional) – Second subset of studies, representing “unselected” studies. If None, then all studies in coordinates/annotations dataframes not inids
will be used.
- Returns
results (
pandas.DataFrame
) – Table with each label and the following values associated with each label: ‘pForward’, ‘zForward’, ‘likelihoodForward’, ‘pReverse’, ‘zReverse’, and ‘probReverse’.