Train a GCLDA model and use it

This example trains a generalized correspondence latent Dirichlet allocation model using abstracts from Neurosynth and then uses it for decoding.

Warning

The model in this example is trained using (1) a very small, nonrepresentative dataset and (2) very few iterations. As such, it will not provide useful results. If you are interested in using GCLDA, we recommend using a large dataset like Neurosynth, and training with at least 10k iterations.

import os

import nibabel as nib
import numpy as np
from nilearn import image, masking, plotting

import nimare
from nimare import annotate, decode
from nimare.tests.utils import get_test_data_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 = nimare.dataset.Dataset.load(
    os.path.join(get_test_data_path(), "neurosynth_laird_studies.pkl.gz")
)
dset.texts.head(2)

	id	study_id	contrast_id	abstract
0	17029760-1	17029760	1	Repetitive transcranial magnetic stimulation (...
1	18760263-1	18760263	1	In an effort to clarify how deductive reasonin...

Generate term counts

GCLDA uses raw word counts instead of the tf-idf values generated by Neurosynth.

counts_df = annotate.text.generate_counts(
    dset.texts,
    text_column="abstract",
    tfidf=False,
    max_df=0.99,
    min_df=0.01,
)
counts_df.head(5)

	10	10 brains	11	11 published	11 showing	17	17 sca17	2005	2005 major	2012	2012 evidence	aberrant	aberrant hotspots	abilities	abilities action	abnormal	abnormal sexual	abnormal structure	accessible	accessible ensuing	accomplished	accomplished substrates	account	account common	accurate	accurate robust	acetylcholine	acetylcholine receptor	acquired	acquired standard	action	action cognition	action selection	activating	activating cerebellar	activations	activations invariably	activations orofacial	active	active calculation	...	ventromedial	ventromedial prefrontal	versus	versus baseline	vi	vi extent	vi ix	viewed	viewed problem	viib	viib viiia	viiia	viiia viiib	viiib	viiib cerebellar	vmpfc	vmpfc pcc	vmpfc posterior	vocalization	vocalization altered	voice	voice control	voice network	voice perturbation	vowel	vowel phonation	voxel	voxel applying	voxel morphometry	voxel syllable	way	way disrupted	weaknesses	weaknesses conventional	wernicke	wernicke responded	widespread	widespread functional	working	working memory
id
17029760-1	0	0	0	0	0	0	0	1	1	0	0	0	0	0	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	0	...	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
18760263-1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	2	1	...	0	0	0	0	0	0	0	1	1	0	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	1	0	0	0	0
19162389-1	0	0	2	1	1	0	0	0	0	0	0	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	1	1	0	0	...	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	1	1	0	0	1	0	0	0	0	0	0	0	0	0	0
19603407-1	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	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	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	1	0	0	0	0	0	0
20197097-1	0	0	0	0	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	1	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	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 × 1953 columns

Run model

Five iterations will take ~10 minutes with the full Neurosynth dataset. It’s much faster with this reduced example dataset. Note that we’re using only 10 topics here. This is because there are only 13 studies in the dataset. If the number of topics is higher than the number of studies in the dataset, errors can occur during training.

model = annotate.gclda.GCLDAModel(
    counts_df,
    dset.coordinates,
    mask=dset.masker.mask_img,
    n_topics=10,
    n_regions=4,
    symmetric=True,
)
model.fit(n_iters=100, loglikely_freq=20)
model.save("gclda_model.pkl.gz")

# Let's remove the model now that you know how to generate it.
os.remove("gclda_model.pkl.gz")

Look at topics

topic_img_4d = masking.unmask(model.p_voxel_g_topic_.T, model.mask)
for i_topic in range(5):
    topic_img_3d = image.index_img(topic_img_4d, i_topic)
    plotting.plot_stat_map(
        topic_img_3d,
        draw_cross=False,
        colorbar=False,
        annotate=False,
        title=f"Topic {i_topic + 1}",
    )

• 
• 
• 
• 
• 

Generate a pseudo-statistic image from text

text = "dorsal anterior cingulate cortex"
encoded_img, _ = decode.encode.gclda_encode(model, text)
plotting.plot_stat_map(encoded_img, draw_cross=False)

Out:

<nilearn.plotting.displays.OrthoSlicer object at 0x7f4fa73405d0>

Decode an unthresholded statistical map

For the sake of simplicity, we will use the pseudo-statistic map generated in the previous step.

# Run the decoder
decoded_df, _ = decode.continuous.gclda_decode_map(model, encoded_img)
decoded_df.sort_values(by="Weight", ascending=False).head(10)

	Weight
Term
cortex	0.110903
connectivity	0.107221
nachr	0.058124
anterior	0.044651
nachr agonists	0.038750
agonists	0.038750
parietal	0.038750
macm	0.032744
posterior	0.029767
cingulate cortex	0.029062

Decode an ROI image

First we’ll make an ROI

arr = np.zeros(dset.masker.mask_img.shape, int)
arr[65:75, 50:60, 50:60] = 1
mask_img = nib.Nifti1Image(arr, dset.masker.mask_img.affine)
plotting.plot_roi(mask_img, draw_cross=False)

Out:

<nilearn.plotting.displays.OrthoSlicer object at 0x7f4fd5143b50>

Run the decoder

decoded_df, _ = decode.discrete.gclda_decode_roi(model, mask_img)
decoded_df.sort_values(by="Weight", ascending=False).head(10)

	Weight
Term
connectivity	18.984603
cortex	18.337031
nachr	10.002017
anterior	8.573086
nachr agonists	6.668011
parietal	6.668011
agonists	6.668011
macm	6.286930
posterior	5.715391
insula	5.143852