Image-based meta-analysis algorithms

A tour of IBMA algorithms in NiMARE.

This tutorial is intended to provide a brief description and example of each of the IBMA algorithms implemented in NiMARE. For a more detailed introduction to the elements of an image-based meta-analysis, see other stuff.

from nilearn.plotting import plot_stat_map

Download data

Note

The data used in this example come from a collection of NIDM-Results packs downloaded from Neurovault collection 1425, uploaded by Dr. Camille Maumet.

from nimare.extract import download_nidm_pain

dset_dir = download_nidm_pain()

Load Dataset

import os

from nimare.dataset import Dataset
from nimare.transforms import ImageTransformer
from nimare.utils import get_resource_path

dset_file = os.path.join(get_resource_path(), "nidm_pain_dset.json")
dset = Dataset(dset_file)
dset.update_path(dset_dir)

# Calculate missing images
xformer = ImageTransformer(target=["varcope", "z"])
dset = xformer.transform(dset)

Stouffer’s

from nimare.meta.ibma import Stouffers

meta = Stouffers(use_sample_size=False, resample=True)
results = meta.fit(dset)

plot_stat_map(
    results.get_map("z"),
    cut_coords=[0, 0, -8],
    draw_cross=False,
    cmap="RdBu_r",
)

Out:

/home/docs/checkouts/readthedocs.org/user_builds/nimare/envs/744/lib/python3.7/site-packages/nilearn/_utils/niimg.py:64: UserWarning: Non-finite values detected. These values will be replaced with zeros.
  "Non-finite values detected. "

<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7f59dd7ad310>

Stouffer’s with weighting by sample size

meta = Stouffers(use_sample_size=True, resample=True)
results = meta.fit(dset)

plot_stat_map(
    results.get_map("z"),
    cut_coords=[0, 0, -8],
    draw_cross=False,
    cmap="RdBu_r",
)

Out:

/home/docs/checkouts/readthedocs.org/user_builds/nimare/envs/744/lib/python3.7/site-packages/nilearn/_utils/niimg.py:64: UserWarning: Non-finite values detected. These values will be replaced with zeros.
  "Non-finite values detected. "

<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7f59f6a9b3d0>

Fisher’s

from nimare.meta.ibma import Fishers

meta = Fishers(resample=True)
results = meta.fit(dset)

plot_stat_map(
    results.get_map("z"),
    cut_coords=[0, 0, -8],
    draw_cross=False,
    cmap="RdBu_r",
)

Out:

/home/docs/checkouts/readthedocs.org/user_builds/nimare/envs/744/lib/python3.7/site-packages/nilearn/_utils/niimg.py:64: UserWarning: Non-finite values detected. These values will be replaced with zeros.
  "Non-finite values detected. "

<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7f59de9fd050>

Permuted OLS

from nimare.correct import FWECorrector
from nimare.meta.ibma import PermutedOLS

meta = PermutedOLS(two_sided=True, resample=True)
results = meta.fit(dset)

plot_stat_map(
    results.get_map("z"),
    cut_coords=[0, 0, -8],
    draw_cross=False,
    cmap="RdBu_r",
)

corrector = FWECorrector(method="montecarlo", n_iters=100, n_cores=1)
cresult = corrector.transform(results)

plot_stat_map(
    cresult.get_map("z_level-voxel_corr-FWE_method-montecarlo"),
    cut_coords=[0, 0, -8],
    draw_cross=False,
    cmap="RdBu_r",
)

• 
• 

Out:

/home/docs/checkouts/readthedocs.org/user_builds/nimare/envs/744/lib/python3.7/site-packages/nilearn/_utils/niimg.py:64: UserWarning: Non-finite values detected. These values will be replaced with zeros.
  "Non-finite values detected. "

<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7f59deacd9d0>

Weighted Least Squares

from nimare.meta.ibma import WeightedLeastSquares

meta = WeightedLeastSquares(tau2=0, resample=True)
results = meta.fit(dset)

plot_stat_map(
    results.get_map("z"),
    cut_coords=[0, 0, -8],
    draw_cross=False,
    cmap="RdBu_r",
)

Out:

/home/docs/checkouts/readthedocs.org/user_builds/nimare/envs/744/lib/python3.7/site-packages/nilearn/_utils/niimg.py:64: UserWarning: Non-finite values detected. These values will be replaced with zeros.
  "Non-finite values detected. "

<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7f59f501db90>

DerSimonian-Laird

from nimare.meta.ibma import DerSimonianLaird

meta = DerSimonianLaird(resample=True)
results = meta.fit(dset)

plot_stat_map(
    results.get_map("z"),
    cut_coords=[0, 0, -8],
    draw_cross=False,
    cmap="RdBu_r",
)

Out:

/home/docs/checkouts/readthedocs.org/user_builds/nimare/envs/744/lib/python3.7/site-packages/nilearn/_utils/niimg.py:64: UserWarning: Non-finite values detected. These values will be replaced with zeros.
  "Non-finite values detected. "

<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7f59dec24b90>

Hedges

from nimare.meta.ibma import Hedges

meta = Hedges(resample=True)
results = meta.fit(dset)

plot_stat_map(
    results.get_map("z"),
    cut_coords=[0, 0, -8],
    draw_cross=False,
    cmap="RdBu_r",
)

Out:

/home/docs/checkouts/readthedocs.org/user_builds/nimare/envs/744/lib/python3.7/site-packages/nilearn/_utils/niimg.py:64: UserWarning: Non-finite values detected. These values will be replaced with zeros.
  "Non-finite values detected. "

<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7f59de8b5810>