Note
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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 nibabel as nib
import numpy as np
from nilearn import datasets, image, plotting
import nimare
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.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=3.0,
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.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")
Total running time of the script: ( 0 minutes 0.000 seconds)