Discrete functional decoding

Perform meta-analytic functional decoding on regions of interest.

We can use the methods in nimare.decode.discrete to apply functional characterization analysis to regions of interest or subsets of the Dataset.

import os

import nibabel as nib
import numpy as np
from nilearn.plotting import plot_roi

from nimare.dataset import Dataset
from nimare.decode import discrete
from nimare.utils import get_resource_path

Load dataset with abstracts

We’ll load a small dataset composed only of studies in Neurosynth with Angela Laird as a coauthor, for the sake of speed.

dset = Dataset(os.path.join(get_resource_path(), "neurosynth_laird_studies.json"))
dset.annotations.head(5)

	id	study_id	contrast_id	Neurosynth_TFIDF__001	Neurosynth_TFIDF__01	Neurosynth_TFIDF__05	Neurosynth_TFIDF__10	Neurosynth_TFIDF__100	Neurosynth_TFIDF__11	Neurosynth_TFIDF__12	Neurosynth_TFIDF__12 healthy	Neurosynth_TFIDF__13	Neurosynth_TFIDF__14	Neurosynth_TFIDF__14 healthy	Neurosynth_TFIDF__15	Neurosynth_TFIDF__15 healthy	Neurosynth_TFIDF__16	Neurosynth_TFIDF__16 healthy	Neurosynth_TFIDF__17	Neurosynth_TFIDF__18	Neurosynth_TFIDF__18 healthy	Neurosynth_TFIDF__19	Neurosynth_TFIDF__20	Neurosynth_TFIDF__20 healthy	Neurosynth_TFIDF__200	Neurosynth_TFIDF__2014	Neurosynth_TFIDF__21	Neurosynth_TFIDF__22	Neurosynth_TFIDF__23	Neurosynth_TFIDF__24	Neurosynth_TFIDF__25	Neurosynth_TFIDF__26	Neurosynth_TFIDF__27	Neurosynth_TFIDF__28	Neurosynth_TFIDF__29	Neurosynth_TFIDF__30	Neurosynth_TFIDF__31	Neurosynth_TFIDF__32	Neurosynth_TFIDF__33	Neurosynth_TFIDF__34	...	Neurosynth_TFIDF__weight	Neurosynth_TFIDF__weighted	Neurosynth_TFIDF__whilst	Neurosynth_TFIDF__white	Neurosynth_TFIDF__white matter	Neurosynth_TFIDF__wide	Neurosynth_TFIDF__widely	Neurosynth_TFIDF__widespread	Neurosynth_TFIDF__wiley	Neurosynth_TFIDF__wiley periodicals	Neurosynth_TFIDF__window	Neurosynth_TFIDF__wise	Neurosynth_TFIDF__wm	Neurosynth_TFIDF__wm task	Neurosynth_TFIDF__women	Neurosynth_TFIDF__word	Neurosynth_TFIDF__word form	Neurosynth_TFIDF__word pairs	Neurosynth_TFIDF__word recognition	Neurosynth_TFIDF__words	Neurosynth_TFIDF__work	Neurosynth_TFIDF__working	Neurosynth_TFIDF__working memory	Neurosynth_TFIDF__world	Neurosynth_TFIDF__worse	Neurosynth_TFIDF__written	Neurosynth_TFIDF__year	Neurosynth_TFIDF__year old	Neurosynth_TFIDF__years	Neurosynth_TFIDF__years old	Neurosynth_TFIDF__yield	Neurosynth_TFIDF__yielded	Neurosynth_TFIDF__young	Neurosynth_TFIDF__young adults	Neurosynth_TFIDF__young healthy	Neurosynth_TFIDF__young older	Neurosynth_TFIDF__younger	Neurosynth_TFIDF__younger adults	Neurosynth_TFIDF__youth	Neurosynth_TFIDF__zone
0	17029760-1	17029760	1	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
1	18760263-1	18760263	1	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
2	19162389-1	19162389	1	0.0	0.0	0.0	0.0	0.0	0.176321	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
3	19603407-1	19603407	1	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
4	20197097-1	20197097	1	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0

5 rows × 3231 columns

Create a region of interest

# First we'll make an ROI
arr = np.zeros(dset.masker.mask_img.shape, np.int32)
arr[65:75, 50:60, 50:60] = 1
mask_img = nib.Nifti1Image(arr, dset.masker.mask_img.affine)
plot_roi(mask_img, draw_cross=False)

# Get studies with voxels in the mask
ids = dset.get_studies_by_mask(mask_img)

Decode an ROI image using the BrainMap method

# Run the decoder
decoder = discrete.BrainMapDecoder(correction=None)
decoder.fit(dset)
decoded_df = decoder.transform(ids=ids)
decoded_df.sort_values(by="probReverse", ascending=False).head()

	pForward	zForward	likelihoodForward	pReverse	zReverse	probReverse
Term
Neurosynth_TFIDF__gyrus	1.0	0.0	69.8125	1.0	0.0	0.03412
Neurosynth_TFIDF__critical role	1.0	0.0	69.8125	1.0	0.0	0.03412
Neurosynth_TFIDF__stg	1.0	0.0	69.8125	1.0	0.0	0.03412
Neurosynth_TFIDF__communication	1.0	0.0	69.8125	1.0	0.0	0.03412
Neurosynth_TFIDF__inferior frontal	1.0	0.0	69.8125	1.0	0.0	0.03412

Decode an ROI image using the Neurosynth chi-square method

# Run the decoder
decoder = discrete.NeurosynthDecoder(correction=None)
decoder.fit(dset)
decoded_df = decoder.transform(ids=ids)
decoded_df.sort_values(by="probReverse", ascending=False).head()

	pForward	zForward	probForward	pReverse	zReverse	probReverse
Term
Neurosynth_TFIDF__role	5.926024e-08	5.421019	0.250000	0.006644	2.714160	1.000000
Neurosynth_TFIDF__presence	3.596754e-08	5.509588	0.500000	0.000037	4.123106	1.000000
Neurosynth_TFIDF__primary	5.035123e-08	5.450066	0.333333	0.001145	3.252350	1.000000
Neurosynth_TFIDF__motor	6.523915e-08	5.403812	0.200000	0.019681	2.332381	1.000000
Neurosynth_TFIDF__analyze	7.756332e-03	2.662497	0.531250	0.004759	2.822897	0.941176

Decode an ROI image using the Neurosynth ROI association method

# This method decodes the ROI image directly, rather than comparing subsets of the Dataset like the
# other two.
decoder = discrete.ROIAssociationDecoder(mask_img)
decoder.fit(dset)

# The `transform` method doesn't take any parameters.
decoded_df = decoder.transform()

decoded_df.sort_values(by="r", ascending=False).head()

	r
feature
Neurosynth_TFIDF__presence	0.852701
Neurosynth_TFIDF__primary	0.848964
Neurosynth_TFIDF__motor	0.746275
Neurosynth_TFIDF__inferior frontal	0.621745
Neurosynth_TFIDF__communication	0.621745