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

• Published in: Journal of visualized experiments no. 153
• Main Authors: 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
• Format: Journal Article
• Language: English
• Published: United States 27.11.2019
• Subjects: image analysis; magnetic resonance imaging; meta-analysis; morphometry; obsessive-compulsive disorder; tomography
• DOI: 10.3791/59841
• ISSN: 1940-087X

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.