ClusterTDP-SPM: An SPM extension for estimating true discovery proportion (TDP) in cluster inference
Poster No:
1050
Submission Type:
Abstract Submission
Authors:
Xu Chen1, Wouter Weeda2, Jelle Goeman3
Institutions:
1University of Essex, Colchester, Essex, 2Leiden University, Leiden, South Holland, 3Leiden University Medical Center, Leiden, South Holland
First Author:
Co-Author(s):
Introduction:
The cluster extent inference based on random field theory (RFT) has become standard in neuroimaging (Penny et al., 2007). While powerful for finding regions of brain activations, this approach does not offer any further quantification or localisation of signals (Woo et al., 2014; Eklund et al., 2016). Several methods have been proposed to assess the amount of signal by estimating the proportion of truly activated voxels, i.e., true discovery proportion (TDP), within clusters (Rosenblatt et al., 2018; Blain et al., 2022; Andreella et al., 2023; Vesely et al., 2023). However, applying such methods could lead to inconsistent results with the standard cluster inference, which makes it difficult to interpret the findings. Goeman et al. (2023) proposed an improvement of the current RFT-based cluster extent inference by additionally returning the TDP measures for every detected cluster, where the consistency of results is retained.
The free and open-source Statistical Parametric Mapping (SPM) software (Penny et al., 2007), which was designed for the analyses of brain imaging data, is one of the most widely used software packages in this area. The toolboxes that are developed based on SPM are recognised as SPM extensions, which enable the SPM users to carry out specific inferences customised by the developers.
Here we introduce a new SPM extension "clusterTDP-SPM" that presents an add-on analysis of the standard cluster inference by providing TDP measures for detected clusters.
The free and open-source Statistical Parametric Mapping (SPM) software (Penny et al., 2007), which was designed for the analyses of brain imaging data, is one of the most widely used software packages in this area. The toolboxes that are developed based on SPM are recognised as SPM extensions, which enable the SPM users to carry out specific inferences customised by the developers.
Here we introduce a new SPM extension "clusterTDP-SPM" that presents an add-on analysis of the standard cluster inference by providing TDP measures for detected clusters.
Methods:
Goeman et al. (2023) introduced a novel approach to calculate lower and upper bounds for the TDP to improve upon the standard cluster inference, where the lower bound retains the error control guarantee but is conservative; the upper bound is more accurate, but at the cost of losing error control if the method does not fully converge. The corresponding results are consistent with those by the standard cluster inference, i.e., non-zero TDP can only be found for significant clusters.
As demonstration, we randomly selected 20 subjects from an fMRI dataset consisting of 218 healthy subjects, where the participants were asked to distinguish between vocal and non-vocal sounds during the experiment (Pernet et al., 2015). The Vocal>Non-vocal contrasts were derived using SPM12 (the latest version of SPM; see more details at https://www.fil.ion.ucl.ac.uk/spm/software/spm12/).
As demonstration, we randomly selected 20 subjects from an fMRI dataset consisting of 218 healthy subjects, where the participants were asked to distinguish between vocal and non-vocal sounds during the experiment (Pernet et al., 2015). The Vocal>Non-vocal contrasts were derived using SPM12 (the latest version of SPM; see more details at https://www.fil.ion.ucl.ac.uk/spm/software/spm12/).
Results:
The results of clusterTDP-SPM can be visualised with the SPM graphical user interface, where the summary table is very similar to the result table of SPM12. Here, a newly added column "TDPlb" at the cluster level shows the estimated lower bounds of the TDP bounds. Figure 1 shows the summary table on analysing the subsample of the auditory dataset by running clusterTDP-SPM.
Conclusions:
We have proposed a new SPM extension "clusterTDP-SPM", which is publicly available at https://github.com/xuchen312/clusterTDP-SPM. clusterTDP-SPM can be employed to strengthen the current cluster inference by providing an additional quantification of activations using the TDP.
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI) 1
Methods Development
Univariate Modeling
Other Methods 2
Keywords:
FUNCTIONAL MRI
Open-Source Code
Statistical Methods
Other - true discovery proportion (TDP), cluster inference, SPM extension, random field theory (RFT), localise activation, quantify activation
1|2Indicates the priority used for review
Abstract Information
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[2] Blain, A., Thirion, B. and Neuvial, P. (2022). Notip: Non-parametric true discovery proportion control for brain imaging. NeuroImage, 260, 119492.
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[4] Goeman, J.J., Górecki, P., Monajemi, R., Chen, X., Nichols, T.E. and Weeda, W. (2023). Cluster extent inference revisited: quantification and localisation of brain activity. Journal of the Royal Statistical Society Series B: Statistical Methodology, 85(4), 1128-1153.
[5] Penny, W.D., Friston, K.J., Ashburner, J.T., Kiebel, S.J. and Nichols, T.E. (2007). Statistical Parametric Mapping: The Analysis of Functional Brain Images. Elsevier.
[6] Pernet, C.R., McAleer, P., Latinus, M., Gorgolewski, K.J., Charest, I., Bestelmeyer, P.E.G., Watson, R.H., Fleming, D., Crabbe, F., Valdes-Sosa, M. and Belin, P. (2015). The human voice areas: spatial organization and inter-individual variability in temporal and extra-temporal cortices. NeuroImage, 119, 164-174.
[7] Rosenblatt, J.D., Finos, L., Weeda, W.D., Solari, A. and Goeman, J.J. (2018). All-Resolutions Inference for brain imaging. NeuroImage, 181, 786-796.
[8] Vesely, A., Finos, L. and Goeman, J.J. (2023), Permutation-based true discovery guarantee by sum tests. Journal of the Royal Statistical Society Series B: Statistical Methodology, 85(3), 664-683.
[9] Woo, C.W., Krishnan, A. and Wager, T.D. (2014). Cluster-extent based thresholding in fMRI analyses: pitfalls and recommendations. NeuroImage, 91, 412-419.