Note
Click here to download the full example code
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_)
Out:
0%| | 0/10 [00:00<?, ?it/s]
10%|# | 1/10 [00:00<00:02, 3.09it/s]
20%|## | 2/10 [00:00<00:02, 3.18it/s]
30%|### | 3/10 [00:00<00:02, 3.19it/s]
40%|#### | 4/10 [00:01<00:01, 3.16it/s]
50%|##### | 5/10 [00:01<00:01, 3.14it/s]
60%|###### | 6/10 [00:01<00:01, 3.16it/s]
70%|####### | 7/10 [00:02<00:00, 3.15it/s]
80%|######## | 8/10 [00:02<00:00, 3.14it/s]
90%|######### | 9/10 [00:02<00:00, 3.16it/s]
100%|##########| 10/10 [00:03<00:00, 3.16it/s]
100%|##########| 10/10 [00:03<00:00, 3.15it/s]
Description:
('A multilevel kernel density (MKDA) meta-analysis \\citep{wager2007meta} was '
'performed was performed with NiMARE 0.1.1+0.g2b73f3b.dirty (RRID:SCR_017398; '
'\\citealt{Salo2022}), 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{Salo2022,\n'
' doi = {10.55458/neurolibre.00007},\n'
' url = {https://doi.org/10.55458/neurolibre.00007},\n'
' year = {2022},\n'
' publisher = {The Open Journal},\n'
' volume = {1},\n'
' number = {1},\n'
' pages = {7},\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 = {NeuroLibre}\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-consistency"),
draw_cross=False,
cmap="RdBu_r",
threshold=0.1,
)
plot_stat_map(
cres.get_map("z_desc-consistencySize_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_)
Out:
/home/docs/checkouts/readthedocs.org/user_builds/nimare/checkouts/0.1.1/nimare/meta/cbma/mkda.py:444: RuntimeWarning: invalid value encountered in divide
pFgA = pAgF * pF / pA
/home/docs/checkouts/readthedocs.org/user_builds/nimare/checkouts/0.1.1/nimare/meta/cbma/mkda.py:450: RuntimeWarning: invalid value encountered in divide
pFgA_prior = pAgF * self.prior / pAgF_prior
0%| | 0/10 [00:00<?, ?it/s]
10%|# | 1/10 [00:00<00:06, 1.42it/s]
20%|## | 2/10 [00:01<00:05, 1.45it/s]
30%|### | 3/10 [00:02<00:04, 1.48it/s]
40%|#### | 4/10 [00:02<00:04, 1.48it/s]
50%|##### | 5/10 [00:03<00:03, 1.50it/s]
60%|###### | 6/10 [00:04<00:02, 1.51it/s]
70%|####### | 7/10 [00:04<00:01, 1.52it/s]
80%|######## | 8/10 [00:05<00:01, 1.52it/s]
90%|######### | 9/10 [00:05<00:00, 1.51it/s]
100%|##########| 10/10 [00:06<00:00, 1.51it/s]
100%|##########| 10/10 [00:06<00:00, 1.50it/s]
Description:
('A multilevel kernel density chi-squared analysis \\citep{wager2007meta} was '
'performed according to the same procedure as implemented in Neurosynth with '
'NiMARE 0.1.1+0.g2b73f3b.dirty (RRID:SCR_017398; \\citealt{Salo2022}), 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 consistency 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{Salo2022,\n'
' doi = {10.55458/neurolibre.00007},\n'
' url = {https://doi.org/10.55458/neurolibre.00007},\n'
' year = {2022},\n'
' publisher = {The Open Journal},\n'
' volume = {1},\n'
' number = {1},\n'
' pages = {7},\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 = {NeuroLibre}\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_)
Out:
0%| | 0/10 [00:00<?, ?it/s]/home/docs/checkouts/readthedocs.org/user_builds/nimare/envs/0.1.1/lib/python3.8/site-packages/nilearn/masking.py:975: UserWarning: Data array used to create a new image contains 64-bit ints. This is likely due to creating the array with numpy and passing `int` as the `dtype`. Many tools such as FSL and SPM cannot deal with int64 in Nifti images, so for compatibility the data has been converted to int32.
return new_img_like(mask_img, unmasked, affine)
10%|# | 1/10 [00:00<00:03, 2.97it/s]/home/docs/checkouts/readthedocs.org/user_builds/nimare/envs/0.1.1/lib/python3.8/site-packages/nilearn/masking.py:975: UserWarning: Data array used to create a new image contains 64-bit ints. This is likely due to creating the array with numpy and passing `int` as the `dtype`. Many tools such as FSL and SPM cannot deal with int64 in Nifti images, so for compatibility the data has been converted to int32.
return new_img_like(mask_img, unmasked, affine)
20%|## | 2/10 [00:00<00:02, 3.04it/s]/home/docs/checkouts/readthedocs.org/user_builds/nimare/envs/0.1.1/lib/python3.8/site-packages/nilearn/masking.py:975: UserWarning: Data array used to create a new image contains 64-bit ints. This is likely due to creating the array with numpy and passing `int` as the `dtype`. Many tools such as FSL and SPM cannot deal with int64 in Nifti images, so for compatibility the data has been converted to int32.
return new_img_like(mask_img, unmasked, affine)
30%|### | 3/10 [00:00<00:02, 3.06it/s]/home/docs/checkouts/readthedocs.org/user_builds/nimare/envs/0.1.1/lib/python3.8/site-packages/nilearn/masking.py:975: UserWarning: Data array used to create a new image contains 64-bit ints. This is likely due to creating the array with numpy and passing `int` as the `dtype`. Many tools such as FSL and SPM cannot deal with int64 in Nifti images, so for compatibility the data has been converted to int32.
return new_img_like(mask_img, unmasked, affine)
40%|#### | 4/10 [00:01<00:01, 3.09it/s]/home/docs/checkouts/readthedocs.org/user_builds/nimare/envs/0.1.1/lib/python3.8/site-packages/nilearn/masking.py:975: UserWarning: Data array used to create a new image contains 64-bit ints. This is likely due to creating the array with numpy and passing `int` as the `dtype`. Many tools such as FSL and SPM cannot deal with int64 in Nifti images, so for compatibility the data has been converted to int32.
return new_img_like(mask_img, unmasked, affine)
50%|##### | 5/10 [00:01<00:01, 3.10it/s]/home/docs/checkouts/readthedocs.org/user_builds/nimare/envs/0.1.1/lib/python3.8/site-packages/nilearn/masking.py:975: UserWarning: Data array used to create a new image contains 64-bit ints. This is likely due to creating the array with numpy and passing `int` as the `dtype`. Many tools such as FSL and SPM cannot deal with int64 in Nifti images, so for compatibility the data has been converted to int32.
return new_img_like(mask_img, unmasked, affine)
60%|###### | 6/10 [00:01<00:01, 3.09it/s]/home/docs/checkouts/readthedocs.org/user_builds/nimare/envs/0.1.1/lib/python3.8/site-packages/nilearn/masking.py:975: UserWarning: Data array used to create a new image contains 64-bit ints. This is likely due to creating the array with numpy and passing `int` as the `dtype`. Many tools such as FSL and SPM cannot deal with int64 in Nifti images, so for compatibility the data has been converted to int32.
return new_img_like(mask_img, unmasked, affine)
70%|####### | 7/10 [00:02<00:00, 3.08it/s]/home/docs/checkouts/readthedocs.org/user_builds/nimare/envs/0.1.1/lib/python3.8/site-packages/nilearn/masking.py:975: UserWarning: Data array used to create a new image contains 64-bit ints. This is likely due to creating the array with numpy and passing `int` as the `dtype`. Many tools such as FSL and SPM cannot deal with int64 in Nifti images, so for compatibility the data has been converted to int32.
return new_img_like(mask_img, unmasked, affine)
80%|######## | 8/10 [00:02<00:00, 3.07it/s]/home/docs/checkouts/readthedocs.org/user_builds/nimare/envs/0.1.1/lib/python3.8/site-packages/nilearn/masking.py:975: UserWarning: Data array used to create a new image contains 64-bit ints. This is likely due to creating the array with numpy and passing `int` as the `dtype`. Many tools such as FSL and SPM cannot deal with int64 in Nifti images, so for compatibility the data has been converted to int32.
return new_img_like(mask_img, unmasked, affine)
90%|######### | 9/10 [00:02<00:00, 3.04it/s]/home/docs/checkouts/readthedocs.org/user_builds/nimare/envs/0.1.1/lib/python3.8/site-packages/nilearn/masking.py:975: UserWarning: Data array used to create a new image contains 64-bit ints. This is likely due to creating the array with numpy and passing `int` as the `dtype`. Many tools such as FSL and SPM cannot deal with int64 in Nifti images, so for compatibility the data has been converted to int32.
return new_img_like(mask_img, unmasked, affine)
100%|##########| 10/10 [00:03<00:00, 3.05it/s]
100%|##########| 10/10 [00:03<00:00, 3.06it/s]
Description:
('A kernel density (KDA) meta-analysis \\citep{wager2007meta} was performed '
'was performed with NiMARE 0.1.1+0.g2b73f3b.dirty (RRID:SCR_017398; '
'\\citealt{Salo2022}), 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{Salo2022,\n'
' doi = {10.55458/neurolibre.00007},\n'
' url = {https://doi.org/10.55458/neurolibre.00007},\n'
' year = {2022},\n'
' publisher = {The Open Journal},\n'
' volume = {1},\n'
' number = {1},\n'
' pages = {7},\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 = {NeuroLibre}\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_)
Out:
0%| | 0/10 [00:00<?, ?it/s]
10%|# | 1/10 [00:00<00:06, 1.39it/s]
20%|## | 2/10 [00:01<00:05, 1.41it/s]
30%|### | 3/10 [00:02<00:04, 1.43it/s]
40%|#### | 4/10 [00:02<00:04, 1.44it/s]
50%|##### | 5/10 [00:03<00:03, 1.42it/s]
60%|###### | 6/10 [00:04<00:02, 1.41it/s]
70%|####### | 7/10 [00:04<00:02, 1.42it/s]
80%|######## | 8/10 [00:05<00:01, 1.44it/s]
90%|######### | 9/10 [00:06<00:00, 1.45it/s]
100%|##########| 10/10 [00:06<00:00, 1.44it/s]
100%|##########| 10/10 [00:06<00:00, 1.43it/s]
Description:
('An activation likelihood estimation (ALE) meta-analysis '
'\\citep{turkeltaub2002meta,turkeltaub2012minimizing,eickhoff2012activation} '
'was performed with NiMARE 0.1.1+0.g2b73f3b.dirty (RRID:SCR_017398; '
'\\citealt{Salo2022}), 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{Salo2022,\n'
' doi = {10.55458/neurolibre.00007},\n'
' url = {https://doi.org/10.55458/neurolibre.00007},\n'
' year = {2022},\n'
' publisher = {The Open Journal},\n'
' volume = {1},\n'
' number = {1},\n'
' pages = {7},\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 = {NeuroLibre}\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.
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_)
Out:
0%| | 0/10 [00:00<?, ?it/s]
10%|# | 1/10 [00:03<00:32, 3.62s/it]
20%|## | 2/10 [00:03<00:13, 1.67s/it]
30%|### | 3/10 [00:04<00:07, 1.05s/it]
40%|#### | 4/10 [00:04<00:04, 1.31it/s]
50%|##### | 5/10 [00:04<00:02, 1.67it/s]
60%|###### | 6/10 [00:05<00:02, 2.00it/s]
70%|####### | 7/10 [00:05<00:01, 2.28it/s]
80%|######## | 8/10 [00:05<00:00, 2.52it/s]
90%|######### | 9/10 [00:06<00:00, 2.70it/s]
100%|##########| 10/10 [00:06<00:00, 2.83it/s]
100%|##########| 10/10 [00:06<00:00, 1.56it/s]
0%| | 0/228483 [00:00<?, ?it/s]
0%| | 1000/228483 [00:00<00:22, 9992.50it/s]
1%| | 2004/228483 [00:00<00:22, 10016.55it/s]
1%|1 | 3018/228483 [00:00<00:22, 10071.47it/s]
2%|1 | 4026/228483 [00:00<00:22, 10028.60it/s]
2%|2 | 5029/228483 [00:00<00:22, 10027.36it/s]
3%|2 | 6032/228483 [00:00<00:22, 10025.41it/s]
3%|3 | 7039/228483 [00:00<00:22, 10038.77it/s]
4%|3 | 8044/228483 [00:00<00:21, 10040.18it/s]
4%|3 | 9052/228483 [00:00<00:21, 10051.04it/s]
4%|4 | 10058/228483 [00:01<00:21, 9980.45it/s]
5%|4 | 11067/228483 [00:01<00:21, 10013.16it/s]
5%|5 | 12076/228483 [00:01<00:21, 10034.21it/s]
6%|5 | 13084/228483 [00:01<00:21, 10046.01it/s]
6%|6 | 14093/228483 [00:01<00:21, 10056.37it/s]
7%|6 | 15099/228483 [00:01<00:21, 10041.46it/s]
7%|7 | 16104/228483 [00:01<00:21, 9963.28it/s]
7%|7 | 17101/228483 [00:01<00:21, 9955.59it/s]
8%|7 | 18104/228483 [00:01<00:21, 9975.52it/s]
8%|8 | 19102/228483 [00:01<00:21, 9950.30it/s]
9%|8 | 20114/228483 [00:02<00:20, 9999.74it/s]
9%|9 | 21123/228483 [00:02<00:20, 10024.88it/s]
10%|9 | 22135/228483 [00:02<00:20, 10052.60it/s]
10%|# | 23146/228483 [00:02<00:20, 10069.26it/s]
11%|# | 24153/228483 [00:02<00:20, 10032.02it/s]
11%|#1 | 25157/228483 [00:02<00:20, 10024.65it/s]
11%|#1 | 26160/228483 [00:02<00:20, 10013.66it/s]
12%|#1 | 27162/228483 [00:02<00:20, 10005.81it/s]
12%|#2 | 28172/228483 [00:02<00:19, 10033.53it/s]
13%|#2 | 29176/228483 [00:02<00:19, 10012.43it/s]
13%|#3 | 30178/228483 [00:03<00:19, 9969.61it/s]
14%|#3 | 31177/228483 [00:03<00:19, 9972.79it/s]
14%|#4 | 32187/228483 [00:03<00:19, 10009.21it/s]
15%|#4 | 33188/228483 [00:03<00:19, 10008.91it/s]
15%|#4 | 34196/228483 [00:03<00:19, 10029.71it/s]
15%|#5 | 35199/228483 [00:03<00:19, 9999.58it/s]
16%|#5 | 36199/228483 [00:03<00:19, 9929.47it/s]
16%|#6 | 37193/228483 [00:03<00:19, 9839.69it/s]
17%|#6 | 38187/228483 [00:03<00:19, 9869.07it/s]
17%|#7 | 39176/228483 [00:04<00:29, 6390.48it/s]
18%|#7 | 40186/228483 [00:04<00:26, 7189.97it/s]
18%|#8 | 41201/228483 [00:04<00:23, 7886.41it/s]
18%|#8 | 42199/228483 [00:04<00:22, 8412.77it/s]
19%|#8 | 43203/228483 [00:04<00:20, 8842.85it/s]
19%|#9 | 44212/228483 [00:04<00:20, 9184.30it/s]
20%|#9 | 45216/228483 [00:04<00:19, 9422.92it/s]
20%|## | 46226/228483 [00:04<00:18, 9614.94it/s]
21%|## | 47233/228483 [00:04<00:18, 9746.53it/s]
21%|##1 | 48228/228483 [00:05<00:18, 9790.65it/s]
22%|##1 | 49240/228483 [00:05<00:18, 9885.56it/s]
22%|##1 | 50244/228483 [00:05<00:17, 9930.39it/s]
22%|##2 | 51249/228483 [00:05<00:17, 9963.56it/s]
23%|##2 | 52255/228483 [00:05<00:17, 9990.72it/s]
23%|##3 | 53258/228483 [00:05<00:17, 9999.21it/s]
24%|##3 | 54261/228483 [00:05<00:17, 9907.12it/s]
24%|##4 | 55257/228483 [00:05<00:17, 9921.74it/s]
25%|##4 | 56254/228483 [00:05<00:17, 9934.45it/s]
25%|##5 | 57249/228483 [00:05<00:17, 9923.54it/s]
25%|##5 | 58256/228483 [00:06<00:17, 9965.47it/s]
26%|##5 | 59266/228483 [00:06<00:16, 10003.32it/s]
26%|##6 | 60271/228483 [00:06<00:16, 10014.72it/s]
27%|##6 | 61283/228483 [00:06<00:16, 10044.05it/s]
27%|##7 | 62288/228483 [00:06<00:16, 10000.09it/s]
28%|##7 | 63293/228483 [00:06<00:16, 10012.25it/s]
28%|##8 | 64295/228483 [00:06<00:16, 10010.99it/s]
29%|##8 | 65297/228483 [00:06<00:16, 10008.88it/s]
29%|##9 | 66303/228483 [00:06<00:16, 10022.93it/s]
29%|##9 | 67306/228483 [00:06<00:16, 10018.90it/s]
30%|##9 | 68308/228483 [00:07<00:16, 9968.17it/s]
30%|### | 69314/228483 [00:07<00:15, 9993.51it/s]
31%|### | 70321/228483 [00:07<00:15, 10016.10it/s]
31%|###1 | 71327/228483 [00:07<00:15, 10026.59it/s]
32%|###1 | 72338/228483 [00:07<00:15, 10049.70it/s]
32%|###2 | 73343/228483 [00:07<00:15, 10023.41it/s]
33%|###2 | 74346/228483 [00:07<00:15, 9941.32it/s]
33%|###2 | 75341/228483 [00:07<00:15, 9934.37it/s]
33%|###3 | 76337/228483 [00:07<00:15, 9940.65it/s]
34%|###3 | 77332/228483 [00:07<00:15, 9934.55it/s]
34%|###4 | 78341/228483 [00:08<00:15, 9979.65it/s]
35%|###4 | 79346/228483 [00:08<00:14, 9999.67it/s]
35%|###5 | 80356/228483 [00:08<00:14, 10029.18it/s]
36%|###5 | 81364/228483 [00:08<00:14, 10043.71it/s]
36%|###6 | 82369/228483 [00:08<00:14, 10007.89it/s]
36%|###6 | 83370/228483 [00:08<00:14, 10001.33it/s]
37%|###6 | 84371/228483 [00:08<00:14, 9995.15it/s]
37%|###7 | 85372/228483 [00:08<00:14, 9998.44it/s]
38%|###7 | 86381/228483 [00:08<00:14, 10025.33it/s]
38%|###8 | 87384/228483 [00:09<00:22, 6333.47it/s]
39%|###8 | 88389/228483 [00:09<00:19, 7123.34it/s]
39%|###9 | 89387/228483 [00:09<00:17, 7788.46it/s]
40%|###9 | 90385/228483 [00:09<00:16, 8334.48it/s]
40%|###9 | 91377/228483 [00:09<00:15, 8750.05it/s]
40%|#### | 92349/228483 [00:09<00:15, 9012.89it/s]
41%|#### | 93339/228483 [00:09<00:14, 9261.01it/s]
41%|####1 | 94340/228483 [00:09<00:14, 9473.93it/s]
42%|####1 | 95328/228483 [00:09<00:13, 9591.45it/s]
42%|####2 | 96332/228483 [00:10<00:13, 9722.23it/s]
43%|####2 | 97337/228483 [00:10<00:13, 9818.69it/s]
43%|####3 | 98342/228483 [00:10<00:13, 9886.17it/s]
43%|####3 | 99351/228483 [00:10<00:12, 9945.37it/s]
44%|####3 | 100351/228483 [00:10<00:12, 9928.12it/s]
44%|####4 | 101353/228483 [00:10<00:12, 9954.74it/s]
45%|####4 | 102353/228483 [00:10<00:12, 9966.97it/s]
45%|####5 | 103354/228483 [00:10<00:12, 9977.34it/s]
46%|####5 | 104359/228483 [00:10<00:12, 9997.32it/s]
46%|####6 | 105360/228483 [00:10<00:12, 9991.41it/s]
47%|####6 | 106360/228483 [00:11<00:12, 9941.13it/s]
47%|####6 | 107362/228483 [00:11<00:12, 9962.27it/s]
47%|####7 | 108366/228483 [00:11<00:12, 9985.38it/s]
48%|####7 | 109367/228483 [00:11<00:11, 9992.41it/s]
48%|####8 | 110369/228483 [00:11<00:11, 9999.77it/s]
49%|####8 | 111370/228483 [00:11<00:11, 9994.27it/s]
49%|####9 | 112370/228483 [00:11<00:11, 9904.95it/s]
50%|####9 | 113364/228483 [00:11<00:11, 9914.27it/s]
50%|##### | 114356/228483 [00:11<00:11, 9912.17it/s]
50%|##### | 115352/228483 [00:11<00:11, 9923.58it/s]
51%|##### | 116355/228483 [00:12<00:11, 9952.78it/s]
51%|#####1 | 117360/228483 [00:12<00:11, 9980.58it/s]
52%|#####1 | 118365/228483 [00:12<00:11, 10000.52it/s]
52%|#####2 | 119372/228483 [00:12<00:10, 10020.32it/s]
53%|#####2 | 120375/228483 [00:12<00:10, 9979.96it/s]
53%|#####3 | 121377/228483 [00:12<00:10, 9990.94it/s]
54%|#####3 | 122377/228483 [00:12<00:10, 9980.48it/s]
54%|#####3 | 123380/228483 [00:12<00:10, 9993.67it/s]
54%|#####4 | 124389/228483 [00:12<00:10, 10022.06it/s]
55%|#####4 | 125392/228483 [00:12<00:10, 10001.89it/s]
55%|#####5 | 126393/228483 [00:13<00:10, 9949.29it/s]
56%|#####5 | 127393/228483 [00:13<00:10, 9962.16it/s]
56%|#####6 | 128402/228483 [00:13<00:10, 9997.51it/s]
57%|#####6 | 129402/228483 [00:13<00:09, 9995.40it/s]
57%|#####7 | 130408/228483 [00:13<00:09, 10014.10it/s]
58%|#####7 | 131410/228483 [00:13<00:09, 9948.36it/s]
58%|#####7 | 132405/228483 [00:13<00:09, 9895.97it/s]
58%|#####8 | 133399/228483 [00:13<00:09, 9906.37it/s]
59%|#####8 | 134390/228483 [00:13<00:09, 9907.40it/s]
59%|#####9 | 135381/228483 [00:13<00:09, 9838.64it/s]
60%|#####9 | 136392/228483 [00:14<00:09, 9916.82it/s]
60%|###### | 137389/228483 [00:14<00:09, 9931.53it/s]
61%|###### | 138398/228483 [00:14<00:09, 9976.91it/s]
61%|######1 | 139403/228483 [00:14<00:08, 9998.25it/s]
61%|######1 | 140403/228483 [00:14<00:08, 9970.05it/s]
62%|######1 | 141405/228483 [00:14<00:08, 9983.86it/s]
62%|######2 | 142406/228483 [00:14<00:08, 9990.25it/s]
63%|######2 | 143406/228483 [00:14<00:08, 9991.33it/s]
63%|######3 | 144414/228483 [00:14<00:08, 10016.52it/s]
64%|######3 | 145416/228483 [00:14<00:08, 9994.76it/s]
64%|######4 | 146416/228483 [00:15<00:08, 9959.87it/s]
65%|######4 | 147416/228483 [00:15<00:08, 9970.28it/s]
65%|######4 | 148414/228483 [00:15<00:13, 6133.14it/s]
65%|######5 | 149394/228483 [00:15<00:11, 6894.95it/s]
66%|######5 | 150377/228483 [00:15<00:10, 7566.90it/s]
66%|######6 | 151372/228483 [00:15<00:09, 8154.50it/s]
67%|######6 | 152361/228483 [00:15<00:08, 8605.55it/s]
67%|######7 | 153362/228483 [00:15<00:08, 8985.63it/s]
68%|######7 | 154367/228483 [00:16<00:07, 9281.65it/s]
68%|######8 | 155376/228483 [00:16<00:07, 9510.58it/s]
68%|######8 | 156379/228483 [00:16<00:07, 9659.53it/s]
69%|######8 | 157389/228483 [00:16<00:07, 9786.41it/s]
69%|######9 | 158385/228483 [00:16<00:07, 9794.90it/s]
70%|######9 | 159396/228483 [00:16<00:06, 9885.77it/s]
70%|####### | 160393/228483 [00:16<00:06, 9899.08it/s]
71%|####### | 161403/228483 [00:16<00:06, 9957.33it/s]
71%|#######1 | 162409/228483 [00:16<00:06, 9986.54it/s]
72%|#######1 | 163411/228483 [00:16<00:06, 9978.26it/s]
72%|#######1 | 164411/228483 [00:17<00:06, 9948.18it/s]
72%|#######2 | 165415/228483 [00:17<00:06, 9974.55it/s]
73%|#######2 | 166422/228483 [00:17<00:06, 10001.91it/s]
73%|#######3 | 167425/228483 [00:17<00:06, 10008.49it/s]
74%|#######3 | 168430/228483 [00:17<00:05, 10020.28it/s]
74%|#######4 | 169433/228483 [00:17<00:05, 9974.40it/s]
75%|#######4 | 170431/228483 [00:17<00:05, 9942.51it/s]
75%|#######5 | 171428/228483 [00:17<00:05, 9948.77it/s]
75%|#######5 | 172424/228483 [00:17<00:05, 9942.31it/s]
76%|#######5 | 173430/228483 [00:17<00:05, 9976.74it/s]
76%|#######6 | 174437/228483 [00:18<00:05, 10004.00it/s]
77%|#######6 | 175448/228483 [00:18<00:05, 10034.08it/s]
77%|#######7 | 176459/228483 [00:18<00:05, 10056.15it/s]
78%|#######7 | 177466/228483 [00:18<00:05, 10058.27it/s]
78%|#######8 | 178472/228483 [00:18<00:04, 10010.21it/s]
79%|#######8 | 179478/228483 [00:18<00:04, 10023.29it/s]
79%|#######8 | 180481/228483 [00:18<00:04, 10014.26it/s]
79%|#######9 | 181488/228483 [00:18<00:04, 10028.59it/s]
80%|#######9 | 182500/228483 [00:18<00:04, 10053.71it/s]
80%|######## | 183506/228483 [00:18<00:04, 10025.82it/s]
81%|######## | 184509/228483 [00:19<00:04, 9993.30it/s]
81%|########1 | 185510/228483 [00:19<00:04, 9995.79it/s]
82%|########1 | 186518/228483 [00:19<00:04, 10017.98it/s]
82%|########2 | 187524/228483 [00:19<00:04, 10028.36it/s]
83%|########2 | 188532/228483 [00:19<00:03, 10042.37it/s]
83%|########2 | 189537/228483 [00:19<00:03, 9959.91it/s]
83%|########3 | 190534/228483 [00:19<00:03, 9942.11it/s]
84%|########3 | 191529/228483 [00:19<00:03, 9920.84it/s]
84%|########4 | 192524/228483 [00:19<00:03, 9928.12it/s]
85%|########4 | 193529/228483 [00:19<00:03, 9962.91it/s]
85%|########5 | 194539/228483 [00:20<00:03, 10003.51it/s]
86%|########5 | 195545/228483 [00:20<00:03, 10017.71it/s]
86%|########6 | 196555/228483 [00:20<00:03, 10039.54it/s]
86%|########6 | 197562/228483 [00:20<00:03, 10047.21it/s]
87%|########6 | 198567/228483 [00:20<00:02, 9994.92it/s]
87%|########7 | 199575/228483 [00:20<00:02, 10019.22it/s]
88%|########7 | 200577/228483 [00:20<00:02, 9926.50it/s]
88%|########8 | 201583/228483 [00:20<00:02, 9965.38it/s]
89%|########8 | 202593/228483 [00:20<00:02, 10003.40it/s]
89%|########9 | 203594/228483 [00:20<00:02, 9967.93it/s]
90%|########9 | 204591/228483 [00:21<00:02, 9933.01it/s]
90%|########9 | 205595/228483 [00:21<00:02, 9964.05it/s]
90%|######### | 206601/228483 [00:21<00:02, 9991.10it/s]
91%|######### | 207609/228483 [00:21<00:02, 10014.98it/s]
91%|#########1| 208614/228483 [00:21<00:01, 10024.13it/s]
92%|#########1| 209617/228483 [00:21<00:01, 9936.64it/s]
92%|#########2| 210611/228483 [00:21<00:01, 9915.38it/s]
93%|#########2| 211603/228483 [00:21<00:01, 9913.26it/s]
93%|#########3| 212595/228483 [00:21<00:01, 9903.21it/s]
93%|#########3| 213606/228483 [00:21<00:01, 9963.94it/s]
94%|#########3| 214611/228483 [00:22<00:01, 9988.86it/s]
94%|#########4| 215620/228483 [00:22<00:01, 10016.76it/s]
95%|#########4| 216626/228483 [00:22<00:01, 10027.23it/s]
95%|#########5| 217639/228483 [00:22<00:01, 10055.71it/s]
96%|#########5| 218645/228483 [00:22<00:00, 9984.94it/s]
96%|#########6| 219655/228483 [00:22<00:00, 10017.32it/s]
97%|#########6| 220657/228483 [00:22<00:00, 9852.06it/s]
97%|#########7| 221665/228483 [00:22<00:00, 9917.87it/s]
97%|#########7| 222658/228483 [00:23<00:01, 5795.50it/s]
98%|#########7| 223666/228483 [00:23<00:00, 6645.41it/s]
98%|#########8| 224664/228483 [00:23<00:00, 7381.47it/s]
99%|#########8| 225671/228483 [00:23<00:00, 8026.01it/s]
99%|#########9| 226669/228483 [00:23<00:00, 8522.78it/s]
100%|#########9| 227631/228483 [00:23<00:00, 8815.05it/s]
100%|##########| 228483/228483 [00:30<00:00, 7458.99it/s]
Description:
('An activation likelihood estimation (ALE) subtraction analysis '
'\\citep{laird2005ale,eickhoff2012activation} was performed with NiMARE '
'v0.1.1+0.g2b73f3b.dirty (RRID:SCR_017398; \\citealt{Salo2022}), 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{Salo2022} 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{Salo2022,\n'
' doi = {10.55458/neurolibre.00007},\n'
' url = {https://doi.org/10.55458/neurolibre.00007},\n'
' year = {2022},\n'
' publisher = {The Open Journal},\n'
' volume = {1},\n'
' number = {1},\n'
' pages = {7},\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 = {NeuroLibre}\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 31.461 seconds)