Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images (SDM-PSI)

Most methods for conducting meta-analysis of voxel-based neuroimaging studies do not assess whether effects are not null, but whether there is a convergence of peaks of statistical significance, and reduce the assessment of the evidence to a binary classification exclusively based on p-values (i.e.,...

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Vydáno v:Journal of visualized experiments číslo 153
Hlavní autoři: Albajes-Eizagirre, Anton, Solanes, Aleix, Fullana, Miquel Angel, Ioannidis, John P. A., Fusar-Poli, Paolo, Torrent, Carla, Solé, Brisa, Bonnín, Caterina Mar, Vieta, Eduard, Mataix-Cols, David, Radua, Joaquim
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States 27.11.2019
Témata:
image analysis
magnetic resonance imaging
meta-analysis
morphometry
obsessive-compulsive disorder
tomography
ISSN:1940-087X, 1940-087X
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Shrnutí:Most methods for conducting meta-analysis of voxel-based neuroimaging studies do not assess whether effects are not null, but whether there is a convergence of peaks of statistical significance, and reduce the assessment of the evidence to a binary classification exclusively based on p-values (i.e., voxels can only be "statistically significant" or "non-statistically significant"). Here, we detail how to conduct a meta-analysis using Seed-based d Mapping with Permutation of Subject Images (SDM-PSI), a novel method that uses a standard permutation test to assess whether effects are not null. We also show how to grade the strength of the evidence according to a set of criteria that considers a range of statistical significance levels (from more liberal to more conservative), the amount of data or the detection of potential biases (e.g., small-study effect and excess of significance). To exemplify the procedure, we detail the conduction of a meta-analysis of voxel-based morphometry studies in obsessive-compulsive disorder, and we provide all the data already extracted from the manuscripts to allow the reader to replicate the meta-analysis easily. SDM-PSI can also be used for meta-analyses of functional magnetic resonance imaging, diffusion tensor imaging, position emission tomography and surface-based morphometry studies.
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ISSN:1940-087X
1940-087X
DOI:10.3791/59841