I would advise caution. Your model:

m1 = lmer(value ~ group + region + (1|subj))

will estimate a fixed effect for group and a fixed effect for region, while controlling for the non-independence within subjects, which does not answer the question of "regions showing different values between groups" as mentioned in a comment on the OP. As also mentioned in that comment, an interaction term could help. However, in your case, the region variable has 78 levels so this would not seem wise.

An alternative approach is to model region as a random effect (random intercept) and specify group as a random coefficient:

lmer(value ~ group + (group|region) + (1|subj))

Such a model will estimate a variance for the random effect of region (and subject), and the random coefficient for group will tell you if the effect of group differs among different regions

I would advise caution. Your model:

m1 = lmer(value ~ group + region + (1|subj))

will estimate a fixed effect for group and a fixed effect for region, while controlling for the non-independence within subjects, which does not answer the question of "regions showing different values between groups" as mentioned in a comment on the OP. As also mentioned in that comment, an interaction term could help. However, in your case, the region variable has 78 levels so this would not seem wise.

An alternative approach is to model region as a random effect (random intercept) and specify group as a random coefficient:

m2 <- lmer(value ~ group + (1+group|region) + (1|subj))

Such a model will estimate a variance for the random effect of region (and subject), and the random coefficient for group will tell you if the effect of group differs among different regions

I would advise caution. Your model:

m1 = lmer(value ~ group + region + (1|subj))

will estimate a fixed effect for group and a fixed effect for region which does not answer the question of "anatomical regions showing different values between groups" as mentioned in a comment on the OP. As also mentioned in that comment, an interaction term could help. However, in your case, the region variable has 78 levels so this would not seem wise.

An alternative approach is to model region as a random effect (random intercept) and specify group as a random coefficient:

lmer(value ~ group + (group|region) + (1|subj))

Such a model will estimate a variance for the random effect of region (and subject), and the random coefficient for group will tell you if the effect of group differs among different regions

I would advise caution. Your model:

m1 = lmer(value ~ group + region + (1|subj))

will estimate a fixed effect for group and a fixed effect for region, while controlling for the non-independence within subjects, which does not answer the question of "regions showing different values between groups" as mentioned in a comment on the OP. As also mentioned in that comment, an interaction term could help. However, in your case, the region variable has 78 levels so this would not seem wise.

An alternative approach is to model region as a random effect (random intercept) and specify group as a random coefficient:

lmer(value ~ group + (group|region) + (1|subj))

Such a model will estimate a variance for the random effect of region (and subject), and the random coefficient for group will tell you if the effect of group differs among different regions