Transform images into coordinates

Create a dataset with coordinates derived from peak statistic identification in images.

Why would you want to do this?

  • Compare CBMA and IBMA

  • Add more studies to your existing CBMA dataset

  • Normalize how coordinates were derived (provided the image data is available)

import os

import matplotlib.pyplot as plt
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.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)

# ImagesToCoordinates uses z or p statistical maps
z_transformer = ImageTransformer(target="z")
dset = z_transformer.transform(dset)

study_no_images = "pain_02.nidm-1"
# delete images for study
dset.images = dset.images.query(f"id != '{study_no_images}'")

study_no_coordinates = "pain_03.nidm-1"

# delete coordinates for study
dset.coordinates = dset.coordinates.query(f"id != '{study_no_coordinates}'")

Inspect Dataset

# There is only one study contrast with coordinates, but no images
print(f"studies with only coordinates: {set(dset.coordinates['id']) - set(dset.images['id'])}\n")

print(f"studies with only images: {set(dset.images['id']) - set(dset.coordinates['id'])}\n")

# the images dataframe has z maps as one of the columns
print(f"columns in images dataframe: {dset.images.columns}\n")

# there is no z_stat column in the coordinates dataframe
print(f"columns in coordinates dataframe: {dset.coordinates.columns}\n")

Use different strategies to overwrite existing coordinate data

There are three choices for how to treat existing coordinate data in the dataset which are named: ‘fill’, ‘replace’, and ‘demolish’.

  • ‘fill’ will only create coordinates for study contrasts with images, but no coordinates. With ‘fill’ you trust and want to keep all existing coordinate data and the transformer will help “fill” in the blanks for study contrasts with no coordinates

  • ‘replace’ will create coordinates for study contrasts with images. In addition to filling in the blanks, ‘replace’ will overwrite existing coordinate data if images are available. However, if image data is not available and only coordinates exist for a particular study contrast, those coordinates will be retained in the resulting dataset. With ‘replace’, you prefer to have coordinates generated consistently by NiMARE, but you will keep other coordinate data if that particular study contrast does not have images.

  • ‘demolish’ will create coordinates for study contrasts with images and remove any coordinates from the dataset it cannot overwrite. With ‘demolish’, you only trust coordinates generated by NiMARE.

# create coordinates from statistical maps
coord_fill = ImagesToCoordinates(merge_strategy="fill")
coord_replace = ImagesToCoordinates(merge_strategy="replace")
coord_demolish = ImagesToCoordinates(merge_strategy="demolish")

dset_fill = coord_fill.transform(dset)
dset_replace = coord_replace.transform(dset)
dset_demolish = coord_demolish.transform(dset)

Inspect generated datasets

example_study = "pain_01.nidm-1"

print(f"no coordinate data for {study_no_coordinates}")
assert study_no_coordinates not in dset.coordinates["id"]

# 'fill' will add coordinates for study without coordinates
print(f"'fill' strategy for study {study_no_coordinates}")
print(dset_fill.coordinates.query(f"id == '{study_no_coordinates}'"))

# 'replace' will change the data for studies with images
print(f"original data for study {example_study}")
print(dset.coordinates.query(f"id == '{example_study}'"))
print(f"'replace' strategy for study {example_study}")
print(dset_replace.coordinates.query(f"id == '{example_study}'"))

# 'demolish' will remove studies that do not have images
print(f"'demolish' strategy for study {study_no_images}")
assert study_no_images not in dset.coordinates["id"]

# while studies with only coordinates (no images) are in 'replace',
# they are removed from 'demolish'.
    "studies in 'replace', but not 'demolish': "
    f"{set(dset_replace.coordinates['id']) - set(dset_demolish.coordinates['id'])}"


Run a meta analysis using each of the strategies. The biggest difference is between ‘fill’ and the other two strategies. The difference is because in ‘fill’ most of the original coordinates in the dataset are used, whereas with ‘replace’ and ‘demolish’ the majority/all of the coordinates are generated by NiMARE.

ale = ALE()
res_fill =
res_replace =
res_demolish =
fig, axs = plt.subplots(3, figsize=(6, 8))
for ax, strat, res in zip(
    axs, ["fill", "replace", "demolist"], [res_fill, res_replace, res_demolish]
        cut_coords=[0, 0, -8],
        title=f"'{strat}' strategy",

Tracking positive and negative z-scores

There is a new column in the transformed coordinates, z_stat. This column contains the z-score of the individual peak. Currently, no CBMA algorithm implemented in NiMARE takes advantage of z-scores, but we can still take advantage of whether the peak was positive or negative by running a CBMA on positive and negative z-scores separately, testing the convergence of positive and negative z-scores separately.

coord_two_sided = ImagesToCoordinates(merge_strategy="demolish", two_sided=True)

dset_two_sided = coord_two_sided.transform(dset)

dset_positive = dset_two_sided.copy()
dset_negative = dset_two_sided.copy()
dset_positive.coordinates = dset_two_sided.coordinates.query("z_stat >= 0.0")
dset_negative.coordinates = dset_two_sided.coordinates.query("z_stat < 0.0")

# plot the results
ale = ALE()
res_positive =
res_negative =
fig, axs = plt.subplots(2, figsize=(6, 6))
for ax, sign, res in zip(axs, ["positive", "negative"], [res_positive, res_negative]):
        cut_coords=[0, 0, -8],
        title=f"'{sign}' z-scores",

Total running time of the script: ( 0 minutes 0.000 seconds)

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