opts.fit.coMM3 and opts.fit.FWHMcoMM3 for MEGAPRESS

Dear Osprey experts,

I have been using Osprey to analyse MEGA-PRESS data acquired in the motor cortex and (recently) occipital cortex. Until recently, I’ve been including a MM basis set (as suggested) in my job file, which has included the following parameters:

% Determine the baseline knot spacing (in ppm) for the metabolite spectra
opts.fit.bLineKnotSpace     = 0.55;              % [ppm] Default: 0.4.

% Add macromolecule and lipid basis functions to the fit? 
opts.fit.fitMM              = 1;                % OPTIONS:    - 0 (no)
                                                %             - 1 (yes, default)
opts.fit.coMM3              = '3to2MM';
opts.fit.FWHMcoMM3              = 14;

However, for my two most recently acquired datasets, using these parameters has led to all the GABA signal being attributed to the MM3 signal (i.e., GABA:Raw Water ratio = 0 for these participants). If I comment out the opts.fit.coMM3 and opts.fit.FWHMcoMM3,

opts.fit.fitMM              = 1;                % OPTIONS:    - 0 (no)
                                                %             - 1 (yes, default)
% opts.fit.coMM3              = '3to2MM';
% opts.fit.FWHMcoMM3              = 14;

I get a higher GABA:Raw Water ratio (e.g., 3.4e-5) and quantified values after segmentation are e.g., GABA:tCr = .113782. But… I get no peak at all for MM09 at 3 ppm - I didn’t know if this was an issue or not.

Based on your NMR paper, I think I have been using the MM09Hard model, which was recommended to me by @admin a while back, so I wanted to ask your opinion as to whether using the default settings for MMfit are appropriate (I could not see what this model was from the scripts in the Osprey fit directory sorry) or whether I have a bigger problem on my hands (i.e., no GABA signal despite pretty good quality data).

Thanks and I look forward to hearing your thoughts.
Carolyn McNabb

In case anyone else has this issue, I’ve solved it by using

opts.fit.coMM3 = '1to1GABAsoft' 

Hi @CarolynMcNabb,

sorry for the late reply.

As you have seen in your case, it is possible that the 3to2MM model is not the best option for all datasets. I think it is quite possible that it fails if the ratio of GABA/MM at 3 ppm is relatively low which could possibly happen for some brain regions.

Switiching to another MM model can solve this problem as you have observed. You can either pick, the 1to1GABAsoft option or the 1to1GABA. If you want to omit any MM contributions at 3 ppm, you could also use ‘none’, but I would not recommend to use this at all.

The possible options are listed below:

% How do you want to model the co-edited macromolecules at 3 ppm for GABA-edited MRS?                                                
opts.fit.coMM3              = '3to2MM';         % OPTIONS:    - {'3to2MM'} (default) 
                                                %             - {'3to2MMsoft'}
                                                %             - {'1to1GABA'} 
                                                %             - {'1to1GABAsoft'} 
                                                %             - {'freeGauss'} 
                                                %             - {'fixedGauss'}
                                                %             - {'none'} 


Thanks @Helge, this is really helpful!