Two-sample ALE meta-analysis

Meta-analytic projects often involve a number of common steps comparing two or more samples.

In this example, we replicate the ALE-based analyses from Enge et al.1.

A common project workflow with two meta-analytic samples involves the following:

  1. Run a within-sample meta-analysis of the first sample.

  2. Characterize/summarize the results of the first meta-analysis.

  3. Run a within-sample meta-analysis of the second sample.

  4. Characterize/summarize the results of the second meta-analysis.

  5. Compare the two samples with a subtraction analysis.

  6. Compare the two within-sample meta-analyses with a conjunction analysis.

import os

import matplotlib.pyplot as plt
from nilearn.plotting import plot_stat_map

Load Sleuth text files into Datasets

The data for this example are a subset of studies from a meta-analysis on semantic cognition in children 1. A first group of studies probed children’s semantic world knowledge (e.g., correctly naming an object after hearing its auditory description) while a second group of studies asked children to decide if two (or more) words were semantically related to one another or not.

Individual group ALEs

Computing separate ALE analyses for each group is not strictly necessary for performing the subtraction analysis but will help the experimenter to appreciate the similarities and differences between the groups.

from nimare.correct import FWECorrector
from nimare.meta.cbma import ALE

ale = ALE(null_method="approximate")
knowledge_results = ale.fit(knowledge_dset)
related_results = ale.fit(related_dset)

corr = FWECorrector(method="montecarlo", voxel_thresh=0.001, n_iters=100, n_cores=2)
knowledge_corrected_results = corr.transform(knowledge_results)
related_corrected_results = corr.transform(related_results)

fig, axes = plt.subplots(figsize=(12, 10), nrows=2)
knowledge_img = knowledge_corrected_results.get_map(
    "z_desc-size_level-cluster_corr-FWE_method-montecarlo"
)
plot_stat_map(
    knowledge_img,
    cut_coords=4,
    display_mode="z",
    title="Semantic knowledge",
    threshold=2.326,  # cluster-level p < .01, one-tailed
    cmap="RdBu_r",
    vmax=4,
    axes=axes[0],
    figure=fig,
)

related_img = related_corrected_results.get_map(
    "z_desc-size_level-cluster_corr-FWE_method-montecarlo"
)
plot_stat_map(
    related_img,
    cut_coords=4,
    display_mode="z",
    title="Semantic relatedness",
    threshold=2.326,  # cluster-level p < .01, one-tailed
    cmap="RdBu_r",
    vmax=4,
    axes=axes[1],
    figure=fig,
)
fig.show()
08 plot cbma subtraction conjunction

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Characterize the relative contributions of experiments in the ALE results

NiMARE contains two methods for this: Jackknife and FocusCounter. We will show both below, but for the sake of speed we will only apply one to each subgroup meta-analysis.

from nimare.diagnostics import FocusCounter

counter = FocusCounter(
    target_image="z_desc-size_level-cluster_corr-FWE_method-montecarlo",
    voxel_thresh=None,
)
knowledge_count_table, _ = counter.transform(knowledge_corrected_results)
knowledge_count_table.head(10)

Out:

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100%|##########| 21/21 [00:01<00:00, 15.85it/s]
1 2 3 4 5 6 7
Cluster ID
Center of Mass (54, -28, 2) (38, 22, -8) (0, 20, 46) (-6, -14, 12) (-34, 20, -2) (-42, 10, 28) (-52, -38, 4)
arnoldussen2006nc- 0 0 0 0 0 1 0
arnoldussen2006rm- 0 0 0 0 0 1 0
backes2002- 0 0 1 0 0 1 0
balsamo2002- 0 0 0 0 0 0 1
balsamo2006- 0 0 1 0 0 1 0
bauer2017- 0 1 0 1 0 0 0
berl2014- 1 1 2 1 0 2 0
brauer2007- 0 0 1 0 0 0 0
gaillard2001- 0 0 0 0 0 1 0


from nimare.diagnostics import Jackknife

jackknife = Jackknife(
    target_image="z_desc-size_level-cluster_corr-FWE_method-montecarlo",
    voxel_thresh=None,
)
related_jackknife_table, _ = jackknife.transform(related_corrected_results)
related_jackknife_table.head(10)

Out:

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1 2 3 4 5
Cluster ID
Center of Mass (38, 22, -8) (-4, 16, 50) (-10, 6, 4) (-46, 22, 8) (-56, -44, 0)
booth2001- 0.0 0.0 0.0 0.029415 0.054821
booth2003- 0.0 0.0 0.0 0.0 0.000008
booth2007- 0.021799 0.068333 0.000019 0.051598 0.000028
cao2008- 0.134026 0.06561 0.0 0.035607 0.094222
chou2006a- 0.211268 0.123099 0.0 0.000075 0.095687
chou2006b- 0.21024 0.117018 0.0 0.004724 0.117981
chou2009- 0.188491 0.1575 0.330668 0.22325 0.212472
chou2019- 0.0 0.061734 0.0 0.0 0.0
fan2020- 0.0 0.070051 0.0 0.002654 0.101595


Subtraction analysis

Typically, one would use at least 10000 iterations for a subtraction analysis. However, we have reduced this to 100 iterations for this example.

from nimare.meta.cbma import ALESubtraction

sub = ALESubtraction(n_iters=100, n_cores=1)
res_sub = sub.fit(knowledge_dset, related_dset)
img_sub = res_sub.get_map("z_desc-group1MinusGroup2")

plot_stat_map(
    img_sub,
    cut_coords=4,
    display_mode="z",
    title="Subtraction",
    cmap="RdBu_r",
    vmax=4,
)
08 plot cbma subtraction conjunction

Out:

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 19%|#9        | 19/100 [00:08<00:36,  2.20it/s]
 20%|##        | 20/100 [00:09<00:35,  2.23it/s]
 21%|##1       | 21/100 [00:09<00:35,  2.21it/s]
 22%|##2       | 22/100 [00:10<00:34,  2.23it/s]
 23%|##3       | 23/100 [00:10<00:34,  2.23it/s]
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 25%|##5       | 25/100 [00:11<00:33,  2.22it/s]
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 30%|###       | 30/100 [00:13<00:31,  2.23it/s]
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 50%|#####     | 50/100 [00:22<00:22,  2.20it/s]
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 58%|#####8    | 58/100 [00:26<00:18,  2.22it/s]
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 60%|######    | 60/100 [00:27<00:18,  2.22it/s]
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 62%|######2   | 62/100 [00:28<00:17,  2.23it/s]
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 78%|#######8  | 78/100 [00:35<00:09,  2.21it/s]
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 80%|########  | 80/100 [00:36<00:08,  2.23it/s]
 81%|########1 | 81/100 [00:36<00:08,  2.24it/s]
 82%|########2 | 82/100 [00:37<00:08,  2.23it/s]
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 89%|########9 | 89/100 [00:40<00:04,  2.21it/s]
 90%|######### | 90/100 [00:40<00:04,  2.21it/s]
 91%|#########1| 91/100 [00:41<00:04,  2.23it/s]
 92%|#########2| 92/100 [00:41<00:03,  2.24it/s]
 93%|#########3| 93/100 [00:42<00:03,  2.25it/s]
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 95%|#########5| 95/100 [00:43<00:02,  2.23it/s]
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 99%|#########9| 99/100 [00:44<00:00,  2.24it/s]
100%|##########| 100/100 [00:45<00:00,  2.24it/s]
100%|##########| 100/100 [00:45<00:00,  2.21it/s]

  0%|          | 0/228483 [00:00<?, ?it/s]
  0%|          | 931/228483 [00:00<00:24, 9304.31it/s]
  1%|          | 1882/228483 [00:00<00:24, 9421.20it/s]
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  3%|2         | 6605/228483 [00:00<00:23, 9393.52it/s]
  3%|3         | 7545/228483 [00:00<00:23, 9385.29it/s]
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<nilearn.plotting.displays._slicers.ZSlicer object at 0x7fba23d45710>

Conjunction analysis

To determine the overlap of the meta-analytic results, a conjunction image can be computed by (a) identifying voxels that were statistically significant in both individual group maps and (b) selecting, for each of these voxels, the smaller of the two group-specific z values Nichols et al.2. Since this is simple arithmetic on images, conjunction is not implemented as a separate method in NiMARE but can easily be achieved with nilearn.image.math_img().

from nilearn.image import math_img

formula = "np.where(img1 * img2 > 0, np.minimum(img1, img2), 0)"
img_conj = math_img(formula, img1=knowledge_img, img2=related_img)

plot_stat_map(
    img_conj,
    cut_coords=4,
    display_mode="z",
    title="Conjunction",
    threshold=2.326,  # cluster-level p < .01, one-tailed
    cmap="RdBu_r",
    vmax=4,
)
08 plot cbma subtraction conjunction

Out:

<nilearn.plotting.displays._slicers.ZSlicer object at 0x7fba49278c90>

References

1(1,2)

Alexander Enge, Rasha Abdel Rahman, and Michael A Skeide. A meta-analysis of fmri studies of semantic cognition in children. NeuroImage, 241:118436, 2021. URL: https://doi.org/10.1016/j.neuroimage.2021.118436, doi:10.1016/j.neuroimage.2021.118436.

2

Thomas Nichols, Matthew Brett, Jesper Andersson, Tor Wager, and Jean-Baptiste Poline. Valid conjunction inference with the minimum statistic. Neuroimage, 25(3):653–660, 2005. URL: https://doi.org/10.1016/j.neuroimage.2004.12.005, doi:10.1016/j.neuroimage.2004.12.005.

Total running time of the script: ( 3 minutes 30.778 seconds)

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