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Hello,
We are trying to harmonize DTI maps of subjects scanned in 6 different sites. Since the participants are in majority patients, to avoid any bias due to the disease we would like to consider only the healthy controls and then, for each site, apply the estimated harmonization to all the other subjects.
Looking at the code, we saw that the function “adjust_data_final” employs the parameters returned in the “estimates” dictionary however in input it does not take the original data but “s_data”. So, to solve our problem we thought to do the following:
Run the function neuroCombat passing in input dat=data_HC (which contains only the health controls’ data) and saving the dictionary estimates
Run the code of neuroCombat up to standardize_across_features() (line 127) using dat=data_PT (which contains only the patients’ data)
Call adjust_data_final(s_data, design, gamma_star, delta_star, s_mean, mod_mean, v_pool, info_dict, dat) using the gamma_star and delta_star obtained at step 1.
Is this approach correct or is there a more straightforward way to do it?
Thank you for the help.
Best,
Ilaria
The text was updated successfully, but these errors were encountered:
Hello,
We are trying to harmonize DTI maps of subjects scanned in 6 different sites. Since the participants are in majority patients, to avoid any bias due to the disease we would like to consider only the healthy controls and then, for each site, apply the estimated harmonization to all the other subjects.
Looking at the code, we saw that the function “adjust_data_final” employs the parameters returned in the “estimates” dictionary however in input it does not take the original data but “s_data”. So, to solve our problem we thought to do the following:
Is this approach correct or is there a more straightforward way to do it?
Thank you for the help.
Best,
Ilaria
The text was updated successfully, but these errors were encountered: