Compare image and coordinate based meta-analyses

Run IBMAs and CBMAs on a toy dataset, then compare the results qualitatively.

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

import os

import pandas as pd
from nilearn.plotting import plot_stat_map

from nimare.dataset import Dataset
from nimare.extract import download_nidm_pain
from nimare.meta.cbma import ALE
from nimare.meta.ibma import DerSimonianLaird
from nimare.transforms import ImagesToCoordinates, ImageTransformer
from nimare.utils import get_resource_path

Download data

dset_dir = download_nidm_pain()

Load Dataset

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

# Calculate missing statistical images from the available stats.
xformer = ImageTransformer(target=["varcope"])
dset = xformer.transform(dset)

# create coordinates from statistical maps
coord_gen = ImagesToCoordinates(merge_strategy="fill")
dset = coord_gen.transform(dset)

ALE (CBMA)

meta_cbma = ALE()
cbma_results = meta_cbma.fit(dset)
plot_stat_map(
    cbma_results.get_map("z"),
    cut_coords=[0, 0, -8],
    draw_cross=False,
    cmap="RdBu_r",
)

Out:

<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7f1855484dd0>

DerSimonian-Laird (IBMA)

We must resample the image data to the same MNI template as the Dataset.

meta_ibma = DerSimonianLaird(resample=True)
ibma_results = meta_ibma.fit(dset)
plot_stat_map(
    ibma_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/0.0.12rc7/lib/python3.7/site-packages/nilearn/_utils/niimg.py:62: UserWarning: Non-finite values detected. These values will be replaced with zeros.
  "Non-finite values detected. "

<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7f183ac6f610>

Compare CBMA and IBMA Z-maps

stat_df = pd.DataFrame(
    {
        "CBMA": cbma_results.get_map("z", return_type="array"),
        "IBMA": ibma_results.get_map("z", return_type="array"),
    }
)
print(stat_df.corr())

Out:

          CBMA      IBMA
CBMA  1.000000  0.443177
IBMA  0.443177  1.000000