Signed differential mapping or SDM 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. It may also refer to a specific piece of software created by the SDM Project to carry out such meta-analyses.
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SDM adopted and combined various positive features from previous methods, such as ALE or MKDA, and introduced a series of improvements and novel features.[1] One of the new features, introduced to avoid positive and negative findings in the same voxel as seen in previous methods, was the representation of both positive differences and negative differences in the same map, thus obtaining a signed differential map (SDM).
The method has three steps. First, studies and coordinates of cluster peaks (e.g. the voxels were the differences between patients and healthy controls were highest) are selected according to SDM inclusion criteria. Second, these coordinates are used to create an SDM map for each study. Finally, study maps are meta-analyzed using several different tests to complement the main outcome with sensitivity and heterogeneity analyses.
It is not uncommon in neuroimaging studies that some regions (e.g. a priori regions of interest) are more liberally thresholded than the rest of the brain. However, a meta-analysis of studies with such intra-study regional differences in thresholds would be biased towards these regions, as they are more likely to be reported just because authors apply more liberal thresholds in them. In order to overcome this issue SDM introduced a criterion in the selection of the coordinates: while different studies may employ different thresholds, you should ensure that the same threshold throughout the whole brain was used within each included study.[1]
After conversion of coordinates to Talairach space, an SDM map is created for each study within a specific gray or white matter template.[2] This consists in recreating the clusters of difference by means of an un-normalized Gaussian Kernel, so that voxels closer to the peak coordinate have higher values. A rather large full-width at half-maximum (FWHM) of 25mm is used to account for different sources of spatial error, e.g. coregistration mismatch in the studies, the size of the cluster or the location of the peak within the cluster. Within a study, values obtained by close Gaussian kernels are summed, though values are limited to [-1,1] to avoid a bias towards studies reporting various coordinates in close proximity, as voxels can achieve rather large values.[1]
SDM provides several different statistical analyses in order to complement the main outcome with sensitivity and heterogeneity analyses.
The statistical significance of the analyses is checked by standard randomization tests. It is recommended to use uncorrected p-values = 0.001, as this significance has been found in this method to be approximately equivalent to a corrected p-value = 0.05.[1] A false discovery rate (FDR) = 0.05 has been found in this method to be too conservative. Values in a Talairach label or coordinate can also be extracted for further processing or graphical presentation.
SDM is software written by the SDM project to aid the meta-analysis of voxel-based neuroimaging data. It is distributed as freeware including a graphical interface and a menu/command-line console. It can also be integrated as an SPM extension.