Voxel-based meta-analysis via permutation of subject images (PSI): Theory and implementation for SDM

• Published in: NeuroImage (Orlando, Fla.) Vol. 186; pp. 174 - 184
• Main Authors: Albajes-Eizagirre, Anton, Solanes, Aleix, Vieta, Eduard, Radua, Joaquim
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.02.2019; Elsevier Limited
• Subjects: Activation likelihood estimation; Algorithms; Bias; Brain - diagnostic imaging; Brain mapping; Brain research; Coordinate-based meta-analysis; Familywise error rate; Humans; Image Processing, Computer-Assisted - methods; Medical imaging; Mental disorders; Meta-analysis; Meta-Analysis as Topic; Methods; Models, Statistical; Neuroimaging; Neuroimaging - methods; Seed-based d mapping; Signed differential mapping; Software; Statistical analysis; Studies; Systematic review; Tests for spatial convergence; Seed-based d mapping; Activation likelihood estimation; Signed differential mapping; Familywise error rate; Coordinate-based meta-analysis; Tests for spatial convergence
• DOI: 10.1016/j.neuroimage.2018.10.077
• ISSN: 1053-8119; 1095-9572

Coordinate-based meta-analyses (CBMA) are very useful for summarizing the large number of voxel-based neuroimaging studies of normal brain functions and brain abnormalities in neuropsychiatric disorders. However, current CBMA methods do not conduct common voxelwise tests, but rather a test of convergence, which relies on some spatial assumptions that data may seldom meet, and has lower statistical power when there are multiple effects. Here we present a new algorithm that can use standard voxelwise tests and, importantly, conducts a standard permutation of subject images (PSI). Its main steps are: a) multiple imputation of study images; b) imputation of subject images; and c) subject-based permutation test to control the familywise error rate (FWER). The PSI algorithm is general and we believe that developers might implement it for several CBMA methods. We present here an implementation of PSI for seed-based d mapping (SDM) method, which additionally benefits from the use of effect sizes, random-effects models, Freedman-Lane-based permutations and threshold-free cluster enhancement (TFCE) statistics, among others. Finally, we also provide an empirical validation of the control of the FWER in SDM-PSI, which showed that it might be too conservative. We hope that the neuroimaging meta-analytic community will welcome this new algorithm and method. •We present a new algorithm for coordinate-based meta-analyses (CBMA) methods.•Opposed to current methods, it conducts common permutation tests.•It may be implemented in several CBMA methods.•We detail and validate its implementation for seed-based d mapping (SDM).