Coordinate-based meta-analysis algorithms

A tour of CBMA algorithms in NiMARE.

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

Load Dataset

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.

Creation of the Dataset from the NIDM-Results packs was done with custom code. The Results packs for collection 1425 are not completely NIDM-Results-compliant, so the nidmresults library could not be used to facilitate data extraction.

import os
from pprint import pprint

from nilearn.plotting import plot_stat_map

from nimare.correct import FWECorrector
from nimare.dataset import Dataset
from nimare.utils import get_resource_path

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

# Some of the CBMA algorithms compare two Datasets,
# so we'll split this example Dataset in half.
dset1 = dset.slice(dset.ids[:10])
dset2 = dset.slice(dset.ids[10:])

Multilevel Kernel Density Analysis

from nimare.meta.cbma.mkda import MKDADensity

meta = MKDADensity()
results = meta.fit(dset)

corr = FWECorrector(method="montecarlo", n_iters=10, n_cores=1)
cres = corr.transform(results)

plot_stat_map(
    results.get_map("z"),
    cut_coords=[0, 0, -8],
    draw_cross=False,
    cmap="RdBu_r",
    threshold=0.1,
)
plot_stat_map(
    cres.get_map("z_level-voxel_corr-FWE_method-montecarlo"),
    cut_coords=[0, 0, -8],
    draw_cross=False,
    cmap="RdBu_r",
    threshold=0.1,
)

print("Description:")
pprint(results.description_)
print("References:")
pprint(results.bibtex_)

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Description:
('A multilevel kernel density (MKDA) meta-analysis \\citep{wager2007meta} was '
 'performed was performed with NiMARE 0.2.2+2.gbf7df33.dirty (RRID:SCR_017398; '
 '\\citealt{Salo2023}), using a(n) MKDA kernel. An MKDA kernel '
 '\\citep{wager2007meta} was used to generate study-wise modeled activation '
 'maps from coordinates. In this kernel method, each coordinate is convolved '
 'with a sphere with a radius of 10.0 and a value of 1. For voxels with '
 'overlapping spheres, the maximum value was retained. Summary statistics (OF '
 'values) were converted to p-values using an approximate null distribution. '
 'The input dataset included 267 foci from 21 experiments.')
References:
('@article{Salo2023,\n'
 '  doi = {10.52294/001c.87681},\n'
 '  url = {https://doi.org/10.52294/001c.87681},\n'
 '  year = {2023},\n'
 '  volume = {3},\n'
 '  pages = {1 - 32},\n'
 '  author = {Taylor Salo and Tal Yarkoni and Thomas E. Nichols and '
 'Jean-Baptiste Poline and Murat Bilgel and Katherine L. Bottenhorn and Dorota '
 'Jarecka and James D. Kent and Adam Kimbler and Dylan M. Nielson and Kendra '
 'M. Oudyk and Julio A. Peraza and Alexandre Pérez and Puck C. Reeders and '
 'Julio A. Yanes and Angela R. Laird},\n'
 '  title = {NiMARE: Neuroimaging Meta-Analysis Research Environment},\n'
 '  journal = {Aperture Neuro}\n'
 '}\n'
 '@article{wager2007meta,\n'
 '  title={Meta-analysis of functional neuroimaging data: current and future '
 'directions},\n'
 '  author={Wager, Tor D and Lindquist, Martin and Kaplan, Lauren},\n'
 '  journal={Social cognitive and affective neuroscience},\n'
 '  volume={2},\n'
 '  number={2},\n'
 '  pages={150--158},\n'
 '  year={2007},\n'
 '  publisher={Oxford University Press},\n'
 '  url={https://doi.org/10.1093/scan/nsm015},\n'
 '  doi={10.1093/scan/nsm015}\n'
 '}')

MKDA Chi-Squared

from nimare.meta.cbma.mkda import MKDAChi2

meta = MKDAChi2(kernel__r=10)
results = meta.fit(dset1, dset2)

corr = FWECorrector(method="montecarlo", n_iters=10, n_cores=1)
cres = corr.transform(results)

plot_stat_map(
    results.get_map("z_desc-uniformity"),
    draw_cross=False,
    cmap="RdBu_r",
    threshold=0.1,
)
plot_stat_map(
    cres.get_map("z_desc-uniformitySize_level-cluster_corr-FWE_method-montecarlo"),
    draw_cross=False,
    cmap="RdBu_r",
    threshold=0.1,
)

print("Description:")
pprint(results.description_)
print("References:")
pprint(results.bibtex_)

/home/docs/checkouts/readthedocs.org/user_builds/nimare/checkouts/latest/nimare/meta/cbma/mkda.py:473: RuntimeWarning: invalid value encountered in divide
  pFgA = pAgF * pF / pA
/home/docs/checkouts/readthedocs.org/user_builds/nimare/checkouts/latest/nimare/meta/cbma/mkda.py:480: RuntimeWarning: invalid value encountered in divide
  pFgA_prior = pAgF * self.prior / pAgF_prior

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Description:
('A multilevel kernel density chi-squared analysis \\citep{wager2007meta} was '
 'performed according to the same procedure as implemented in Neurosynth with '
 'NiMARE 0.2.2+2.gbf7df33.dirty (RRID:SCR_017398; \\citealt{Salo2023}), using '
 'a(n) MKDA kernel. An MKDA kernel \\citep{wager2007meta} was used to generate '
 'study-wise modeled activation maps from coordinates. In this kernel method, '
 'each coordinate is convolved with a sphere with a radius of 10.0 and a value '
 'of 1. For voxels with overlapping spheres, the maximum value was retained. '
 'This analysis calculated several measures. The first dataset was evaluated '
 'for uniformity of activation via a one-way chi-square test. The first input '
 'dataset included 147 foci from 10 experiments. The second input dataset '
 'included 120 foci from 11 experiments.')
References:
('@article{Salo2023,\n'
 '  doi = {10.52294/001c.87681},\n'
 '  url = {https://doi.org/10.52294/001c.87681},\n'
 '  year = {2023},\n'
 '  volume = {3},\n'
 '  pages = {1 - 32},\n'
 '  author = {Taylor Salo and Tal Yarkoni and Thomas E. Nichols and '
 'Jean-Baptiste Poline and Murat Bilgel and Katherine L. Bottenhorn and Dorota '
 'Jarecka and James D. Kent and Adam Kimbler and Dylan M. Nielson and Kendra '
 'M. Oudyk and Julio A. Peraza and Alexandre Pérez and Puck C. Reeders and '
 'Julio A. Yanes and Angela R. Laird},\n'
 '  title = {NiMARE: Neuroimaging Meta-Analysis Research Environment},\n'
 '  journal = {Aperture Neuro}\n'
 '}\n'
 '@article{wager2007meta,\n'
 '  title={Meta-analysis of functional neuroimaging data: current and future '
 'directions},\n'
 '  author={Wager, Tor D and Lindquist, Martin and Kaplan, Lauren},\n'
 '  journal={Social cognitive and affective neuroscience},\n'
 '  volume={2},\n'
 '  number={2},\n'
 '  pages={150--158},\n'
 '  year={2007},\n'
 '  publisher={Oxford University Press},\n'
 '  url={https://doi.org/10.1093/scan/nsm015},\n'
 '  doi={10.1093/scan/nsm015}\n'
 '}')

Kernel Density Analysis

from nimare.meta.cbma.mkda import KDA

meta = KDA()
results = meta.fit(dset)

corr = FWECorrector(method="montecarlo", n_iters=10, n_cores=1)
cres = corr.transform(results)

plot_stat_map(
    results.get_map("z"),
    cut_coords=[0, 0, -8],
    draw_cross=False,
    cmap="RdBu_r",
    threshold=0.1,
)
plot_stat_map(
    cres.get_map("z_desc-size_level-cluster_corr-FWE_method-montecarlo"),
    cut_coords=[0, 0, -8],
    draw_cross=False,
    cmap="RdBu_r",
    threshold=0.1,
)

print("Description:")
pprint(results.description_)
print("References:")
pprint(results.bibtex_)

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  return new_img_like(mask_img, unmasked, affine)

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  return new_img_like(mask_img, unmasked, affine)

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  return new_img_like(mask_img, unmasked, affine)

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  return new_img_like(mask_img, unmasked, affine)

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  return new_img_like(mask_img, unmasked, affine)

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  return new_img_like(mask_img, unmasked, affine)

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  return new_img_like(mask_img, unmasked, affine)

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  return new_img_like(mask_img, unmasked, affine)

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  return new_img_like(mask_img, unmasked, affine)

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  return new_img_like(mask_img, unmasked, affine)

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Description:
('A kernel density (KDA) meta-analysis \\citep{wager2007meta} was performed '
 'was performed with NiMARE 0.2.2+2.gbf7df33.dirty (RRID:SCR_017398; '
 '\\citealt{Salo2023}), using a(n) KDA kernel. A KDA kernel '
 '\\citep{wager2003valence,wager2004neuroimaging} was used to generate '
 'study-wise modeled activation maps from coordinates. In this kernel method, '
 'each coordinate is convolved with a sphere with a radius of 10.0 and a value '
 "of 1. These spheres are then summed within each study to produce the study's "
 'MA map. Summary statistics (OF values) were converted to p-values using an '
 'approximate null distribution. The input dataset included 267 foci from 21 '
 'experiments.')
References:
('@article{Salo2023,\n'
 '  doi = {10.52294/001c.87681},\n'
 '  url = {https://doi.org/10.52294/001c.87681},\n'
 '  year = {2023},\n'
 '  volume = {3},\n'
 '  pages = {1 - 32},\n'
 '  author = {Taylor Salo and Tal Yarkoni and Thomas E. Nichols and '
 'Jean-Baptiste Poline and Murat Bilgel and Katherine L. Bottenhorn and Dorota '
 'Jarecka and James D. Kent and Adam Kimbler and Dylan M. Nielson and Kendra '
 'M. Oudyk and Julio A. Peraza and Alexandre Pérez and Puck C. Reeders and '
 'Julio A. Yanes and Angela R. Laird},\n'
 '  title = {NiMARE: Neuroimaging Meta-Analysis Research Environment},\n'
 '  journal = {Aperture Neuro}\n'
 '}\n'
 '@article{wager2003valence,\n'
 '  title={Valence, gender, and lateralization of functional brain anatomy in '
 'emotion: a meta-analysis of findings from neuroimaging},\n'
 '  author={Wager, Tor D and Phan, K Luan and Liberzon, Israel and Taylor, '
 'Stephan F},\n'
 '  journal={Neuroimage},\n'
 '  volume={19},\n'
 '  number={3},\n'
 '  pages={513--531},\n'
 '  year={2003},\n'
 '  publisher={Elsevier},\n'
 '  url={https://doi.org/10.1016/S1053-8119(03)00078-8},\n'
 '  doi={10.1016/S1053-8119(03)00078-8}\n'
 '}\n'
 '@article{wager2004neuroimaging,\n'
 '  title={Neuroimaging studies of shifting attention: a meta-analysis},\n'
 '  author={Wager, Tor D and Jonides, John and Reading, Susan},\n'
 '  journal={Neuroimage},\n'
 '  volume={22},\n'
 '  number={4},\n'
 '  pages={1679--1693},\n'
 '  year={2004},\n'
 '  publisher={Elsevier},\n'
 '  url={https://doi.org/10.1016/j.neuroimage.2004.03.052},\n'
 '  doi={10.1016/j.neuroimage.2004.03.052}\n'
 '}\n'
 '@article{wager2007meta,\n'
 '  title={Meta-analysis of functional neuroimaging data: current and future '
 'directions},\n'
 '  author={Wager, Tor D and Lindquist, Martin and Kaplan, Lauren},\n'
 '  journal={Social cognitive and affective neuroscience},\n'
 '  volume={2},\n'
 '  number={2},\n'
 '  pages={150--158},\n'
 '  year={2007},\n'
 '  publisher={Oxford University Press},\n'
 '  url={https://doi.org/10.1093/scan/nsm015},\n'
 '  doi={10.1093/scan/nsm015}\n'
 '}')

Activation Likelihood Estimation

from nimare.meta.cbma.ale import ALE

meta = ALE()
results = meta.fit(dset)

corr = FWECorrector(method="montecarlo", n_iters=10, n_cores=1)
cres = corr.transform(results)

plot_stat_map(
    results.get_map("z"),
    cut_coords=[0, 0, -8],
    draw_cross=False,
    cmap="RdBu_r",
    threshold=0.1,
)
plot_stat_map(
    cres.get_map("z_desc-size_level-cluster_corr-FWE_method-montecarlo"),
    cut_coords=[0, 0, -8],
    draw_cross=False,
    cmap="RdBu_r",
    threshold=0.1,
)

print("Description:")
pprint(results.description_)
print("References:")
pprint(results.bibtex_)

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Description:
('An activation likelihood estimation (ALE) meta-analysis '
 '\\citep{turkeltaub2002meta,turkeltaub2012minimizing,eickhoff2012activation} '
 'was performed with NiMARE 0.2.2+2.gbf7df33.dirty (RRID:SCR_017398; '
 '\\citealt{Salo2023}), using a(n) ALE kernel. An ALE kernel '
 '\\citep{eickhoff2012activation} was used to generate study-wise modeled '
 'activation maps from coordinates. In this kernel method, each coordinate is '
 'convolved with a Gaussian kernel with full-width at half max values '
 'determined on a study-wise basis based on the study sample sizes according '
 'to the formulae provided in \\cite{eickhoff2012activation}. For voxels with '
 'overlapping kernels, the maximum value was retained. ALE values were '
 'converted to p-values using an approximate null distribution '
 '\\citep{eickhoff2012activation}. The input dataset included 267 foci from 21 '
 'experiments, with a total of 334 participants.')
References:
('@article{Salo2023,\n'
 '  doi = {10.52294/001c.87681},\n'
 '  url = {https://doi.org/10.52294/001c.87681},\n'
 '  year = {2023},\n'
 '  volume = {3},\n'
 '  pages = {1 - 32},\n'
 '  author = {Taylor Salo and Tal Yarkoni and Thomas E. Nichols and '
 'Jean-Baptiste Poline and Murat Bilgel and Katherine L. Bottenhorn and Dorota '
 'Jarecka and James D. Kent and Adam Kimbler and Dylan M. Nielson and Kendra '
 'M. Oudyk and Julio A. Peraza and Alexandre Pérez and Puck C. Reeders and '
 'Julio A. Yanes and Angela R. Laird},\n'
 '  title = {NiMARE: Neuroimaging Meta-Analysis Research Environment},\n'
 '  journal = {Aperture Neuro}\n'
 '}\n'
 '@article{eickhoff2012activation,\n'
 '  title={Activation likelihood estimation meta-analysis revisited},\n'
 '  author={Eickhoff, Simon B and Bzdok, Danilo and Laird, Angela R and Kurth, '
 'Florian and Fox, Peter T},\n'
 '  journal={Neuroimage},\n'
 '  volume={59},\n'
 '  number={3},\n'
 '  pages={2349--2361},\n'
 '  year={2012},\n'
 '  publisher={Elsevier},\n'
 '  url={https://doi.org/10.1016/j.neuroimage.2011.09.017},\n'
 '  doi={10.1016/j.neuroimage.2011.09.017}\n'
 '}\n'
 '@article{turkeltaub2002meta,\n'
 '  title={Meta-analysis of the functional neuroanatomy of single-word '
 'reading: method and validation},\n'
 '  author={Turkeltaub, Peter E and Eden, Guinevere F and Jones, Karen M and '
 'Zeffiro, Thomas A},\n'
 '  journal={Neuroimage},\n'
 '  volume={16},\n'
 '  number={3},\n'
 '  pages={765--780},\n'
 '  year={2002},\n'
 '  publisher={Elsevier},\n'
 '  url={https://doi.org/10.1006/nimg.2002.1131},\n'
 '  doi={10.1006/nimg.2002.1131}\n'
 '}\n'
 '@article{turkeltaub2012minimizing,\n'
 '  title={Minimizing within-experiment and within-group effects in activation '
 'likelihood estimation meta-analyses},\n'
 '  author={Turkeltaub, Peter E and Eickhoff, Simon B and Laird, Angela R and '
 'Fox, Mick and Wiener, Martin and Fox, Peter},\n'
 '  journal={Human brain mapping},\n'
 '  volume={33},\n'
 '  number={1},\n'
 '  pages={1--13},\n'
 '  year={2012},\n'
 '  publisher={Wiley Online Library},\n'
 '  url={https://doi.org/10.1002/hbm.21186},\n'
 '  doi={10.1002/hbm.21186}\n'
 '}')

Specific Co-Activation Likelihood Estimation

Important

The SCALE algorithm is very memory intensive, so we don’t run it within the documentation.

import numpy as np

from nimare.meta.cbma.ale import SCALE
from nimare.utils import vox2mm

xyz = vox2mm(
    np.vstack(np.where(dset.masker.mask_img.get_fdata())).T,
    dset.masker.mask_img.affine,
)

meta = SCALE(xyz=xyz, n_iters=10)
results = meta.fit(dset)

ALE-Based Subtraction Analysis

from nimare.meta.cbma.ale import ALESubtraction

meta = ALESubtraction(n_iters=10, n_cores=1)
results = meta.fit(dset1, dset2)

plot_stat_map(
    results.get_map("z_desc-group1MinusGroup2"),
    cut_coords=[0, 0, -8],
    draw_cross=False,
    cmap="RdBu_r",
    threshold=0.1,
)

print("Description:")
pprint(results.description_)
print("References:")
pprint(results.bibtex_)

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Description:
('An activation likelihood estimation (ALE) subtraction analysis '
 '\\citep{laird2005ale,eickhoff2012activation} was performed with NiMARE '
 'v0.2.2+2.gbf7df33.dirty (RRID:SCR_017398; \\citealt{Salo2023}), using a(n) '
 'ALE kernel. An ALE kernel \\citep{eickhoff2012activation} was used to '
 'generate study-wise modeled activation maps from coordinates. In this kernel '
 'method, each coordinate is convolved with a Gaussian kernel with full-width '
 'at half max values determined on a study-wise basis based on the study '
 'sample sizes according to the formulae provided in '
 '\\cite{eickhoff2012activation}. For voxels with overlapping kernels, the '
 'maximum value was retained. The subtraction analysis was implemented '
 "according to NiMARE's \\citep{Salo2023} approach, which differs from the "
 'original version. In this version, ALE-difference scores are calculated '
 'between the two datasets, for all voxels in the mask, rather than for voxels '
 'significant in the main effects analyses of the two datasets. Next, '
 'voxel-wise null distributions of ALE-difference scores were generated via a '
 'randomized group assignment procedure, in which the studies in the two '
 'datasets were randomly reassigned and ALE-difference scores were calculated '
 'for the randomized datasets. This randomization procedure was repeated 10 '
 'times to build the null distributions. The significance of the original '
 'ALE-difference scores was assessed using a two-sided statistical test. The '
 'null distributions were assumed to be asymmetric, as ALE-difference scores '
 'will be skewed based on the sample sizes of the two datasets. The first '
 'input dataset (group1) included 147 foci from 10 experiments, with a total '
 'of 153 participants. The second input dataset (group2) included 120 foci '
 'from 11 experiments, with a total of 181 participants. ')
References:
('@article{Salo2023,\n'
 '  doi = {10.52294/001c.87681},\n'
 '  url = {https://doi.org/10.52294/001c.87681},\n'
 '  year = {2023},\n'
 '  volume = {3},\n'
 '  pages = {1 - 32},\n'
 '  author = {Taylor Salo and Tal Yarkoni and Thomas E. Nichols and '
 'Jean-Baptiste Poline and Murat Bilgel and Katherine L. Bottenhorn and Dorota '
 'Jarecka and James D. Kent and Adam Kimbler and Dylan M. Nielson and Kendra '
 'M. Oudyk and Julio A. Peraza and Alexandre Pérez and Puck C. Reeders and '
 'Julio A. Yanes and Angela R. Laird},\n'
 '  title = {NiMARE: Neuroimaging Meta-Analysis Research Environment},\n'
 '  journal = {Aperture Neuro}\n'
 '}\n'
 '@article{eickhoff2012activation,\n'
 '  title={Activation likelihood estimation meta-analysis revisited},\n'
 '  author={Eickhoff, Simon B and Bzdok, Danilo and Laird, Angela R and Kurth, '
 'Florian and Fox, Peter T},\n'
 '  journal={Neuroimage},\n'
 '  volume={59},\n'
 '  number={3},\n'
 '  pages={2349--2361},\n'
 '  year={2012},\n'
 '  publisher={Elsevier},\n'
 '  url={https://doi.org/10.1016/j.neuroimage.2011.09.017},\n'
 '  doi={10.1016/j.neuroimage.2011.09.017}\n'
 '}\n'
 '@article{laird2005ale,\n'
 '  title={ALE meta-analysis: Controlling the false discovery rate and '
 'performing statistical contrasts},\n'
 '  author={Laird, Angela R and Fox, P Mickle and Price, Cathy J and Glahn, '
 'David C and Uecker, Angela M and Lancaster, Jack L and Turkeltaub, Peter E '
 'and Kochunov, Peter and Fox, Peter T},\n'
 '  journal={Human brain mapping},\n'
 '  volume={25},\n'
 '  number={1},\n'
 '  pages={155--164},\n'
 '  year={2005},\n'
 '  publisher={Wiley Online Library},\n'
 '  url={https://doi.org/10.1002/hbm.20136},\n'
 '  doi={10.1002/hbm.20136}\n'
 '}')

Total running time of the script: (1 minutes 16.278 seconds)

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