SDM reference manual

Introduction

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

Index

	Introduction	Summary of the method and its main features.
	Changes in v3.11	Changes in the behaviour of SDM software in v3.11 as compared to previous versions.
	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).
	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.
	Calculations	It is possible to calculate meta-analytic means (see Mean analyses), quartiles (see Descriptive analysis of quartiles), jackknife sensitivity analysis (see Jackknife sensitivity analysis), comparisons between groups including covariates (see Linear model analyses: comparing groups) and meta-regressions (see Linear model analyses: meta-regression).
	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 of two meta-analyses of studies conducted in different modalities, e.g. VBM and fMRI, may be combined in a single map (see Multimodal meta-analyses). Results can also be extracted extracted from a Talairach label or coordinate (see Extracting masked values) using a mask (see Creation of a mask).
	Batch processing	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.
	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.
	Running SDM as SPM extension	SDM can also be run as an SPM (Statistical Parametric Mapping) extension.
	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.
	Troubleshooting	Reported problems and solutions in SDM software.

Further reading

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

1. Introduction to voxel-based meta-analyses: Radua J and Mataix-Cols D. Meta-analytic methods for neuroimaging data explained. Biol Mood Anxiety Disord 2012; 2:6.
2. SDM method 1/3 (original algorithms): 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.
3. SDM method 2/3 (comparisons and linear models): 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.
4. SDM method 3/3 (effect-sizes and variances): 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.
5. SDM specific white matter template: 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.
6. SDM specific TBSS preprocessing and template: 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.
7. Multimodal meta-analyses (theory): Radua J, Romeo M, Mataix-Cols D and Fusar-Poli P. A general approach for combining voxel-based meta-analyses conducted in different neuroimaging modalities. Curr Med Chem 2013; 20:462-466.
8. Multimodal meta-analyses (example): Radua J, Borgwardt S, Crescini A, Mataix-Cols D, Meyer-Lindenberg A, McGuire PK and Fusar-Poli P. Multimodal meta-analysis of structural and functional brain changes in first episode psychosis and the effects of antipsychotic medication. Neurosci Biobehav Rev 2012; 36:2325–2333.