nimare.decode.discrete
.gclda_decode_roi
- gclda_decode_roi(model, roi, topic_priors=None, prior_weight=1.0)[source]
Perform image-to-text decoding for discrete inputs using method from Rubin et al. (2017).
- Parameters
model (
nimare.annotate.topic.GCLDAModel
) – Model object needed for decoding.roi (
nibabel.nifti1.Nifti1Image
orstr
) – Binary image to decode into text. If string, path to a file with the binary image.topic_priors (
numpy.ndarray
offloat
, optional) – A 1d array of size (n_topics) with values for topic weighting. If None, no weighting is done. Default is None.prior_weight (
float
, optional) – The weight by which the prior will affect the decoding. Default is 1.
- Returns
decoded_df (
pandas.DataFrame
) – A DataFrame with the word-tokens and their associated weights.topic_weights (
numpy.ndarray
offloat
) – The weights of the topics used in decoding.
Notes
Notation
Meaning
Voxel
Topic
Word type
Region of interest (ROI)
Probability of topic given voxel (
p_topic_g_voxel
)Topic weight vector (
topic_weights
)Probability of word type given topic (
p_word_g_topic
)Compute .
From
gclda.model.Model.get_spatial_probs()
Compute topic weight vector () by adding across voxels within ROI.
Multiply by .
The resulting vector (
word_weights
) reflects arbitrarily scaled term weights for the ROI.
See also
nimare.annotate.gclda.GCLDAModel
,nimare.decode.continuous.gclda_decode_map()
,nimare.decode.encode.gclda_encode()
References
Rubin, Timothy N., et al. “Decoding brain activity using a large-scale probabilistic functional-anatomical atlas of human cognition.” PLoS computational biology 13.10 (2017): e1005649. https://doi.org/10.1371/journal.pcbi.1005649