Anisotropic kernels for coordinate-based meta-analyses of neuroimaging studies
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Peak-based meta-analyses of neuroimaging studies create, for each study, a brain map of effect size or peak likelihood by convolving a kernel with each reported peak. A kernel is a small matrix applied in order that voxels surrounding the peak have a value similar to, but slightly lower than that of the peak. Current kernels are isotropic, i.e., the value of a voxel close to a peak only depends on the Euclidean distance between the voxel and the peak. However, such perfect spheres of effect size or likelihood around the peak are rather implausible: a voxel that correlates with the peak across individuals is more likely to be part of the cluster of significant activation or difference than voxels uncorrelated with the peak. This paper introduces anisotropic kernels, which assign different values to the different neighboring voxels based on the spatial correlation between them. They are specifically developed for effect-size signed differential mapping (ES-SDM), though might be easily implemented in other meta-analysis packages such as activation likelihood estimation (ALE). The paper also describes the creation of the required correlation templates for gray matter/BOLD response, white matter, cerebrospinal fluid, and fractional anisotropy. Finally, the new method is validated by quantifying the accuracy of the recreation of effect size maps from peak information. This empirical validation showed that the optimal degree of anisotropy and full-width at half-maximum (FWHM) might vary largely depending on the specific data meta-analyzed. However, it also showed that the recreation substantially improved and did not depend on the FWHM when full anisotropy was used. Based on these results, we recommend the use of fully anisotropic kernels in ES-SDM and ALE, unless optimal meta-analysis-specific parameters can be estimated based on the recreation of available statistical maps. The new method and templates are freely available at http://www.sdmproject.com/.
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The analysis method used is improved and has various strengths compared to other neuroimaging meta-analytical techniques.
“we used an improved analysis method which has various strengths compared to other neuroimaging meta-analytical techniques (Radua et al., 2014)”
From Meta-analyses of the neural mechanisms and predictors of response to psychotherapy in depression and anxiety (2018) · cites this paper as Radua et al., 2014
AES-SDM has been validated and used in several structural and functional fMRI studies.
“This method, which has been validated and used in several structural and functional fMRI studies creates a brain map of the effect size of the difference between the two conditions (CS+ and CS -)for each study (either from SPMs or from peak information) and afterwards conducts a voxel-wise random-effects meta-analysis (weighting the studies for sample size and variance) 13,[20][21][22]”
From Neural signatures of human fear conditioning: an updated and extended meta-analysis of fMRI studies (2016) · cites this paper as [21]
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- Authors
- Radua, J., Rubia, K., Canales-Rodríguez, E. J., Pomarol-Clotet, E., Fusar-Poli, P., & Mataix-Cols, D.
- Affiliations
- King's College London (GB)
- Centro de Investigación Biomédica en Red de Salud Mental (ES)
- Venue
- Frontiers in Psychiatry
- Publisher
- Frontiers Media SA
- Published
- 2014
- Volume
- 5
- Pages
- 13-13
- ISSN
- 1664-0640 (ISSN-L)
- PMC
- PMC3919071
- OpenAlex
- W2017727635
- Cited by
- 406
- References
- 35
- Citations by year
- 2026: 35 · 2025: 41 · 2024: 33 · 2023: 40 · 2022: 47 · 2021: 44 · 2020: 33 · 2019: 29 · 2018: 36 · 2017: 27 · 2016: 27 · 2015: 11 · 2014: 3
- FWCI
- 12.65
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Frontiers in Psychiatry (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Research Portal (King's College London) (publishedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Keywords
- anisotropic kernels, coordinate-based meta-analysis, activation likelihood estimation, BOLD signal, white matter, cerebrospinal fluid, fractional anisotropy, full width at half maximum, neuroimaging meta-analysis
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