SDM   Seed-based d Mapping
formerly "Signed Differential Mapping"
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SDM-PSI reference manual

Before reading the manual, we suggest watching the video at the right and following the step by step Tutorial.


This manual focuses on the software developed for the Seed-based d Mapping (formerly Signed Differential Mapping; SDM) method, an improved meta-analytic approach for voxel-based neuroimaging studies.


Help Introduction Summary of the method and its main features.
Help Changes in version 6.21 Changes in the behaviour of SDM software in version 6.21 as compared to previous versions.
Help Preparation Retrieved neuroimaging data must be included in a folder (see Preparing the folder), and information on samples, groups or variables must be introduced in the SDM table (see Creation of the SDM table). The last step of the preparation is the preprocessing of the studies and their Monte Carlo randomizations (see Preprocessing), preceded by a set of special steps in case of TBSS meta-analyses (see TBSS preprocessing).
Help Globals analysis You can conduct a (rather simple) meta-analysis of the global variables, e.g., the global gray matter in a meta-analysis of VBM studies, optionally adjusting for groups or covariates.
Help Calculations It is possible to calculate meta-analytic means (see Mean analyses), comparisons between groups including covariates (see Linear model analyses: comparing groups) and meta-regressions (see Linear model analyses: meta-regression).
Help Results Results from previous calculations can be thresholded obtaining meta-analytic peak coordinates, clusters breakdowns and NIfTI (Analyze-compatible) images (see Thresholding the results) which will be authomatically open in an MRIcron template (see Settings). Results can also be extracted extracted from a Talairach label or coordinate (see Extracting masked values) using a mask (see Creation of a mask).
Help Batch processing You can easily save the commands you have used for later batch processing. Alternatively, you could create a script with your favourite language and call sdm.
Help Settings At the moment only one parameter must be specified to run SDM, the brain viewer, useful for authomatically seeing the results with MRIcron after thresholding.
Help Running SDM as
SPM extension
SDM can also be run as an SPM (Statistical Parametric Mapping) extension.
Help How to cite We have invested a lot of time and effort in creating SDM, please cite it when using it for a meta-analysis.
Help Forum,
Reported problems and solutions in SDM software.

Further reading

Please find more information on the methods in the following articles:

Introduction to voxel-based meta-analyses:

  1. Radua J and Mataix-Cols D. Meta-analytic methods for neuroimaging data explained. Biol Mood Anxiety Disord 2012; 2:6. link
  2. Müller VI et al. Ten simple rules for neuroimaging meta-analysis. Neurosci Biobehav Rev 2018; 84:151-161. link

SDM methods:

  1. First method (SDM): Radua J and Mataix-Cols D. Voxel-wise meta-analysis of grey matter changes in obsessive-compulsive disorder. Br J Psychiatry 2009; 195:393-402. link
  2. Meta-comparisons: Radua J, van den Heuvel OA, Surguladze S and Mataix-Cols D. Meta-analytical comparison of voxel-based morphometry studies in obsessive compulsive disorder vs other anxiety disorders. Arch Gen Psychiatry 2010; 67:701-711. link
  3. Effect sizes (ES-SDM): Radua J, Mataix-Cols D, Phillips ML, El-Hage W, Kronhaus DM, Cardoner N and Surguladze S. A new meta-analytic method for neuroimaging studies that combines reported peak coordinates and statistical parametric maps. Eur Psychiatry 2012; 27:605–611. link
  4. Anisotropic kernels (AES-SDM): Radua J, Rubia K, Canales-Rodriguez EJ, Pomarol-Clotet E, Fusar-Poli P and Mataix-Cols D. Anisotropic kernels for coordinate-based meta-analyses of neuroimaging studies. Front Psychiatry 2014; 5:13. link
  5. [issues of previous methods] Test for spatial convergence: Albajes-Eizagirre A and Radua J. What do results from coordinate-based meta-analyses tell us? Neuroimage 2018; 176:550-553. link
  6. [main paper of the current method] Permutation of subject images (SDM-PSI): Albajes-Eizagirre A, Solanes A, Vieta E and Radua J. Voxel-based meta-analysis via permutation of subject images (PSI): Theory and implementation for SDM. Neuroimage 2019; 186:174-184. link

SDM specific white matter, TBSS and FreeSurfer templates:

  1. White matter: Radua J, Via E, Catani M and Mataix-Cols D. Voxel-based meta-analysis of regional white-matter volume differences in autism spectrum disorder versus healthy controls. Psychol Med 2011; 41:1539-1550. link
  2. Fractional anistropy (TBSS): Peters BD, Szeszko PR, Radua J, Ikuta T, Gruner P, DeRosse P, Zhang JP, Giorgio A, Qiu D, Tapert SF, Brauer J, Asato MR, Khong PL, James AC, Gallego JA and Malhotra AK. White matter development in adolescence: diffusion tensor imaging and meta-analytic results. Schizophrenia Bull 2012; 38:1308-1317. link
  3. Cortical thickness (FreeSurfer): Li Q, Zhao Y, Chen Z, Long J, Dai J, Huang X, Lui S, Radua J, Vieta E, Kemp GJ, Sweeney JA, Li F and Gong Q. Meta-analysis of cortical thickness abnormalities in medication-free patients with major depressive disorder. Neuropsychopharmacology 2019; in Press. link

Improved atlas:

  1. Radua J, Grau M, van den Heuvel OA, de Schotten MT, Stein DJ, Canales-Rodriguez EJ, Catani M and Mataix-Cols D. Multimodal voxel-based meta-analysis of white matter abnormalities in obsessive-compulsive disorder. Neuropsychopharmacology 2014; 39:1547-57. link
  2. Thiebaut de Schotten M, Dell'Acqua F, Forkel SJ, Simmons A, Vergani F, Murphy DG and Catani M. A lateralized brain network for visuospatial attention. Nat Neurosci 2011; 14:1245-1246. link
  3. Thiebaut de Schotten M, Ffytche DH, Bizzi A, Dell'Acqua F, Allin M, Walshe M, Murray R, Williams SC, Murphy DG and Catani M. Atlasing location, asymmetry and inter-subject variability of white matter tracts in the human brain with MR diffusion tractography. Neuroimage 2011; 54:49-59. link