Run a meta-analytic coactivation modeling analysis

Meta-analytic coactivation modeling (MACM) is a common coordinate-based analysis in which task-independent “connectivity” is assessed by selecting studies within a larger database based on locations of report coordinates.

import os

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

import nimare
from nimare.tests.utils import get_test_data_path

Load Dataset

We will assume that the Neurosynth database has already been downloaded and converted to a NiMARE dataset.

dset_file = 'neurosynth_nimare_with_abstracts.pkl.gz'
dset = nimare.dataset.Dataset.load(dset_file)

Define a region of interest

We’ll use the right amygdala from the Harvard-Oxford atlas

atlas = datasets.fetch_atlas_harvard_oxford('sub-maxprob-thr50-2mm')
img = nib.load(atlas['maps'])

roi_idx = atlas['labels'].index('Right Amygdala')
img_vals = np.unique(img.get_fdata())
roi_val = img_vals[roi_idx]
roi_img = image.math_img('img1 == {}'.format(roi_val), img1=img)

Select studies with a reported coordinate in the ROI

roi_ids = dset.get_studies_by_mask(roi_img)
dset_sel = dset.slice(roi_ids)
print('{}/{} studies report at least one coordinate in the '
      'ROI'.format(len(roi_ids), len(dset.ids)))

Select studies with no reported coordinates in the ROI

no_roi_ids = list(set(dset.ids).difference(roi_ids))
dset_unsel = dset.slice(no_roi_ids)
print('{}/{} studies report zero coordinates in the '
      'ROI'.format(len(no_roi_ids), len(dset.ids)))

MKDA Chi2 with FWE correction

mkda = nimare.meta.cbma.mkda.MKDAChi2(kernel__r=10)
mkda.fit(dset_sel, dset_unsel)

corr = nimare.correct.FWECorrector(method='montecarlo', n_iters=10000)
cres = corr.transform(mkda.results)

# We want the "specificity" map (2-way chi-square between sel and unsel)
plotting.plot_stat_map(
    cres.get_map('logp_level-cluster_corr-FWE_method-montecarlo'),
    threshold=3., draw_cross=False, cmap='RdBu_r'
)

SCALE

Another good option for a MACM analysis is the SCALE algorithm, which was designed specifically for MACM. Unfortunately, SCALE does not support multiple-comparisons correction.

# First, we must define our null model of reported coordinates in the literature.
# We will use the IJK coordinates in Neurosynth
ijk = dset.coordinates[['i', 'j', 'k']].values
scale = nimare.meta.cbma.ale.SCALE(ijk=ijk, n_iters=10000, kernel__n=20)
scale.fit(dset_sel)
plotting.plot_stat_map(
    scale.results.get_map('z_vthresh'),
    draw_cross=False, cmap='RdBu_r'
)