Parameters use to fit the meta-analytic estimator.
Parameters use to fit the corrector.
The following figure provides an interactive window to explore the meta-analytic map in detail.
This panel shows the the corrrected meta-analytic map.
X | Y | Z | Peak Stat | Cluster Size (mm3) | ||
---|---|---|---|---|---|---|
Tail | Cluster ID | |||||
Positive | 1 | 38.00 | 4.00 | 2.00 | 4.84 | 6912 |
1a | 38.00 | 14.00 | -6.00 | 4.14 | ||
1b | 34.00 | 20.00 | 0.00 | 3.98 | ||
1c | 38.00 | -10.00 | -6.00 | 2.87 | ||
2 | 54.00 | -28.00 | 20.00 | 4.31 | 1736 | |
3 | -34.00 | 14.00 | 0.00 | 4.14 | 1280 | |
4 | 2.00 | 4.00 | 52.00 | 3.72 | 5760 | |
4a | -6.00 | 8.00 | 42.00 | 3.43 | ||
4b | 2.00 | -4.00 | 64.00 | 2.99 | ||
4c | -8.00 | 14.00 | 34.00 | 2.95 | ||
5 | -32.00 | -60.00 | -34.00 | 3.47 | 2880 | |
5a | -26.00 | -66.00 | -38.00 | 2.69 | ||
5b | -28.00 | -58.00 | -46.00 | 2.27 | ||
6 | -62.00 | -22.00 | 20.00 | 2.97 | 1832 | |
6a | -54.00 | -32.00 | 22.00 | 2.84 | ||
6b | -64.00 | -22.00 | 28.00 | 2.30 | ||
7 | -36.00 | 4.00 | -16.00 | 2.81 | 552 | |
8 | 20.00 | -102.00 | -4.00 | 2.72 | 496 | |
9 | -6.00 | -48.00 | 54.00 | 2.46 | 576 | |
10 | -4.00 | -70.00 | 50.00 | 2.23 | 168 | |
11 | -36.00 | -22.00 | 10.00 | 2.23 | 208 | |
12 | -46.00 | -58.00 | -54.00 | 2.21 | 128 | |
13 | 36.00 | -18.00 | 12.00 | 2.21 | 112 | |
14 | -34.00 | -90.00 | -12.00 | 2.19 | 152 | |
15 | -54.00 | -66.00 | 2.00 | 2.14 | 248 | |
16 | -52.00 | -8.00 | 6.00 | 2.11 | 88 | |
17 | 10.00 | -68.00 | 36.00 | 2.09 | 176 | |
18 | 36.00 | 40.00 | 30.00 | 1.97 | 88 |
The Jackknife analysis characterizes the relative contribution of each experiment in a meta-analysis to the resulting clusters by looping through experiments, calculating the Estimator's summary statistic for all experiments except the target experiment, dividing the resulting test summary statistics by the summary statistics from the original meta-analysis, and finally averaging the resulting proportion values across all voxels in each cluster.
We kindly ask to report results preprocessed with this tool using the following boilerplate.
An activation likelihood estimation (ALE) meta-analysis \citep{turkeltaub2002meta,turkeltaub2012minimizing,eickhoff2012activation} was performed with NiMARE 0.2.2+0.g07ac3b6.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. False discovery rate correction was performed with the Benjamini-Hochberg procedure \citep{benjamini1995controlling}.
@article{Salo2023, doi = {10.52294/001c.87681}, url = {https://doi.org/10.52294/001c.87681}, year = {2023}, volume = {3}, pages = {1 - 32}, 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}, title = {NiMARE: Neuroimaging Meta-Analysis Research Environment}, journal = {Aperture Neuro} } @article{benjamini1995controlling, title={Controlling the false discovery rate: a practical and powerful approach to multiple testing}, author={Benjamini, Yoav and Hochberg, Yosef}, journal={Journal of the Royal statistical society: series B (Methodological)}, volume={57}, number={1}, pages={289--300}, year={1995}, publisher={Wiley Online Library}, url={https://doi.org/10.1111/j.2517-6161.1995.tb02031.x}, doi={10.1111/j.2517-6161.1995.tb02031.x} } @article{eickhoff2012activation, title={Activation likelihood estimation meta-analysis revisited}, author={Eickhoff, Simon B and Bzdok, Danilo and Laird, Angela R and Kurth, Florian and Fox, Peter T}, journal={Neuroimage}, volume={59}, number={3}, pages={2349--2361}, year={2012}, publisher={Elsevier}, url={https://doi.org/10.1016/j.neuroimage.2011.09.017}, doi={10.1016/j.neuroimage.2011.09.017} } @article{turkeltaub2002meta, title={Meta-analysis of the functional neuroanatomy of single-word reading: method and validation}, author={Turkeltaub, Peter E and Eden, Guinevere F and Jones, Karen M and Zeffiro, Thomas A}, journal={Neuroimage}, volume={16}, number={3}, pages={765--780}, year={2002}, publisher={Elsevier}, url={https://doi.org/10.1006/nimg.2002.1131}, doi={10.1006/nimg.2002.1131} } @article{turkeltaub2012minimizing, title={Minimizing within-experiment and within-group effects in activation likelihood estimation meta-analyses}, author={Turkeltaub, Peter E and Eickhoff, Simon B and Laird, Angela R and Fox, Mick and Wiener, Martin and Fox, Peter}, journal={Human brain mapping}, volume={33}, number={1}, pages={1--13}, year={2012}, publisher={Wiley Online Library}, url={https://doi.org/10.1002/hbm.21186}, doi={10.1002/hbm.21186} }