Hi @Linda_S ,
For many metabolites, we expect different concentrations in gray matter vs white matter, which may confound findings. Alpha correction aims to account for this difference, by adjusting measurements to some nominated standard tissue composition. This can be quite effective, particularly in cohorts with age- or disease-related atrophy; see for example: Impact of tissue correction strategy on GABA-edited MRS findings
The “original” alpha correction aims to normalise GABA estimates to an equivalent pure-GM voxel (ie, 100% GM, 0% WM, 0% CSF). This is straightforward and convenient, especially if comparing across groups/locations with different compositions. However, the efficacy can be limited by the accuracy of the underlying segmentation, and moreover knowledge/assumptions on the relative concentration of the metabolites in different tissue. Often the assumption is simply that GABA concentration in WM is half that of GM; this works fairly well in most cases, but might not be accurate in every scenario (and almost certainly won’t translate to other metabolites). Especially if your actual voxel has a relatively large WM fraction, minor inaccuracies in the segmentation or the underlying assumptions can amplify to give larger variability in the alpha-corrected (pure GM) estimate. Figure 1 of that paper nicely illustrates one aspect of that risk.
Alpha correction to a group norm aims to mitigate these issues, by normalising to the typical voxel composition in your data, rather than a pure GM voxel – meaning the adjustments are generally on a more moderate range, hence there’s less risk of amplifying errors.
In summary:
- If you have a high proportion of GM in your voxels and/or want to maximise comparability across regions etc, ordinary alpha correction to pure GM is a good choice.
- If your GM fraction is a bit lower, group-normalised alpha correction may be a safer option.