SDM   Seed-based d Mapping
formerly "Signed Differential Mapping"
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Seed-based d Mapping
Neuroimaging software library including meta-analytic methods
for fMRI, VBM, DTI and PET and other tools

Download software | Meta-analysis Tutorial | Meta-analysis Manual | SDM tools manual
New SDM-PSI version 6.12 (Aug 2019) available +

Please replace older versions of SDM software by SDM-PSI link version 6.12, which includes the following new features:

  • (nearly) unbiased estimation of effect sizes based on MetaNSUE algorithmslink.
  • Familywise correction for multiple comparisons using common permutation tests, i.e., permuting subject images (PSI). Note that previous methods used instead tests for spatial convergence, which relay on spatial assumptions that may not be met and are have a lower power in the presnce of multiple effects link.
  • Freedman-Lane-based permutation, for its optimal statistical properties link.
  • Threshold-free cluster enhancement (TFCE) statistics, which was neither too conservative nor too liberal in the simulation work (whereas voxel-based statistics were too strict and cluster-based statistics too liberal) link.
  • ... and some other improvements ;-)

Please feel free to download the new software

Introduction

Seed-based d Mapping (formerly "Signed Differential Mapping") is a statistical technique for meta-analyzing studies on differences in brain activity or structure which used neuroimaging techniques such as fMRI, VBM, DTI or PET. The methods have been fully validated in several studies (see references below), and meta-analyses using this method have been already published at the highest quality journals, such as Archives of General Psychiatry / JAMA Psychiatry link link link link, the American Journal of Psychiatry link link link, Biological Psychiatry link link link link, Molecular Psychiatry link link, or Neuroscience & Biobehavioral Reviews link link link link link link link link link link, to cite a few.

The method

An introduction of the method can be found in the SDM-PSI Reference Manual.

Briefly, some of the features are:

The software

Please feel free to download the software created by the SDM Project to carry out such meta-analyses.

References

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 and TBSS 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

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
Other known studies using SDM (probably not all of them): [THIS LIST WAS LAST UPDATED IN 2014] +