Run image-based meta-analyses on 21 pain studies

Collection of NIDM-Results packs downloaded from Neurovault collection 1425, uploaded by Dr. Camille Maumet.

Note

This will likely change as we work to shift database querying to a remote database, rather than handling it locally with NiMARE.

Start with the necessary imports

import os

import numpy as np
import pandas as pd
import nibabel as nib
from nilearn.masking import apply_mask, unmask
from nilearn.plotting import plot_stat_map

import nimare
from nimare.tests.utils import get_test_data_path
from nimare.meta.esma import fishers
from nimare.meta.ibma import (Fishers, Stouffers, WeightedStouffers,
                              RFX_GLM, FFX_GLM, ffx_glm)

Download data

dset_dir = nimare.extract.download_nidm_pain()

Load Dataset

dset_file = os.path.join(get_test_data_path(), 'nidm_pain_dset.json')
dset = nimare.dataset.Dataset(dset_file)
dset.update_path(dset_dir)

mask_img = dset.masker.mask_img

logp_thresh = -np.log(.05)

Fisher’s (using functions)

Get images for analysis

files = dset.get_images(imtype='z')
files = [f for f in files if f]
z_imgs = [nib.load(f) for f in files]
z_data = apply_mask(z_imgs, mask_img)
print('{0} studies found.'.format(z_data.shape[0]))

result = fishers(z_data, mask_img)
fishers_result = unmask(result['z'], mask_img)
plot_stat_map(fishers_result, cut_coords=[0, 0, -8],
              draw_cross=False, cmap='RdBu_r')

Fisher’s (using Estimators)

Here is the object-oriented approach

meta = Fishers()
meta.fit(dset)
plot_stat_map(meta.results.get_map('z'), cut_coords=[0, 0, -8],
              draw_cross=False, cmap='RdBu_r')

Stouffer’s with fixed-effects inference

meta = Stouffers(inference='ffx', null='theoretical', n_iters=None)
meta.fit(dset)
plot_stat_map(meta.results.get_map('z'), cut_coords=[0, 0, -8],
              draw_cross=False, cmap='RdBu_r')

Stouffer’s with random-effects inference using theoretical null distribution

meta = Stouffers(inference='rfx', null='theoretical', n_iters=None)
meta.fit(dset)
plot_stat_map(meta.results.get_map('z'), cut_coords=[0, 0, -8],
              draw_cross=False, cmap='RdBu_r')

Stouffer’s with random-effects inference using empirical null distribution

meta = Stouffers(inference='rfx', null='empirical', n_iters=1000)
meta.fit(dset)
plot_stat_map(meta.results.get_map('z'), cut_coords=[0, 0, -8],
              draw_cross=False, cmap='RdBu_r')

Weighted Stouffer’s

meta = WeightedStouffers()
meta.fit(dset)
plot_stat_map(meta.results.get_map('z'), cut_coords=[0, 0, -8],
              draw_cross=False, cmap='RdBu_r')

RFX GLM with theoretical null distribution

meta = RFX_GLM(null='theoretical', n_iters=None)
meta.fit(dset)
plot_stat_map(meta.results.get_map('z'), cut_coords=[0, 0, -8],
              draw_cross=False, cmap='RdBu_r')

RFX GLM with empirical null distribution

meta = RFX_GLM(null='empirical', n_iters=1000)
meta.fit(dset)
plot_stat_map(meta.results.get_map('z'), cut_coords=[0, 0, -8],
              draw_cross=False, cmap='RdBu_r')