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. 2013 Jul 29;8(7):e70143.
doi: 10.1371/journal.pone.0070143. Print 2013.

Coordinate Based Meta-Analysis of Functional Neuroimaging Data; False Discovery Control and Diagnostics

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Free PMC article

Coordinate Based Meta-Analysis of Functional Neuroimaging Data; False Discovery Control and Diagnostics

Christopher R Tench et al. PLoS One. .
Free PMC article

Abstract

Coordinate based meta-analysis (CBMA) is widely used to find regions of consistent activation across fMRI studies that have been selected for their functional relevance to a given hypothesis. Only reported coordinates (foci), and a model of their spatial uncertainty, are used in the analysis. Results are clusters of foci where multiple studies have reported in the same spatial region, indicating functional relevance. There are several published methods that perform the analysis in a voxel-wise manner, resulting in around 10(5) statistical tests, and considerable emphasis placed on controlling the risk of type 1 statistical error. Here we address this issue by dramatically reducing the number of tests, and by introducing a new false discovery rate control: the false cluster discovery rate (FCDR). FCDR is particularly interpretable and relevant to the results of CBMA, controlling the type 1 error by limiting the proportion of clusters that are expected under the null hypothesis. We also introduce a data diagnostic scheme to help ensure quality of the analysis, and demonstrate its use in the example studies. We show that we control the false clusters better than the widely used ALE method by performing numerical experiments, and that our clustering scheme results in more complete reporting of structures relevant to the functional task.

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. The MA values (red overlay) show the clustering of foci reported within a single experiment; 15 clusters and 71 foci.
A scatter plot showing the distribution of (non-zero only) MA values for this experiment depicts: the original MA distribution (circle marker), the MA distribution on randomisation of the clusters (- marker) with error bars (standard deviation), and the distribution after independent randomisation of the foci (triangle marker). The randomisation of the clusters preserves, on average, the observed distribution as required. The distribution of the MA values on randomising the foci independently has a higher frequency of low MA values as expected.
Figure 2
Figure 2. The random foci experiment, involving randomisations of the face perception experiment.
Shown are the numbers of clusters found for each random experiment. This experiment examines the frequency of false cluster discovery in the absence significant clustering.
Figure 3
Figure 3. Overlap measures for face perception experiment (a), and the pain stimulus experiment (b).
Figure 4
Figure 4. ALE images and statistically significant clusters found on meta-analysis of the face perception data using LocalALE (red) and GingerALE (blue).
In column 1 the left images are ALE values computed using LocalALE, and the right ALE values from GingerALE. Column 2 shows results using FDR control, and column 3 FCDR control. In columns 2–4, the left images show the ALE computed using only significant foci, while the results of the respective clustering algorithms are shown on the right.
Figure 5
Figure 5. Number of false clusters arising from randomisation of the non-significant foci only in the pain perception data; foci involved in statistically significant clusters found by LocalALE (FCDR) are not randomised.
This experiment examines the frequency of false cluster discovery in the presence of known significant clustering.
Figure 6
Figure 6. ALE images and statistically significant clusters found on meta-analysis of the thermal pain stimulus data using LocalALE (red) and GingerALE (blue).
In column 1 the left images are the ALE values computed using LocalALE, and the right ALE values from GingerALE. Column 2 shows results using FDR control, and column 3 FCDR control. In columns 2–4, the left images show the ALE computed using only significant foci, while the results of the respective clustering algorithms are shown on the right.
Figure 7
Figure 7. Clusters obtained with the pain data using the clustering algorithm described in appendix S1 (left), and also using a simplified algorithm that ignores the ALE (right).
While the clustering is different, the significant regions are very similar.

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Grant support

Funding came from the University of Nottingham. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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