Evidence of White Matter Neuroinflammation in [ME/CFS]: A Diffusion-Based Neuroinflammation Imaging Study 2026 Yu et al

[forestglip]

So if the association with NII-RF is largely explained by say age, you’d get no significance for the ME/CFS variable because the model without that variable performed just as well as the model with it.

Yes, but this is in the case of age being correlated to both ME/CFS status and NII-RF. Age can be correlated to only NII-RF, in which case adding this variable to a model that only had ME/CFS does lead to better predictive ability for NII-RF.

 

[jnmaciuch]

Yes, but this is in the case of age being correlated to both ME/CFS status and NII-RF. Age can be correlated to only NII-RF, in which case adding this variable to a model that only had ME/CFS does lead to better predictive ability for NII-RF.

The association they should be reporting is only the one between the ME/CFS variable and NII-RF in a model that also includes the covariates, which you derive by doing a test between a model containing all covariates + ME/CFS vs. a model containing covariates only. The p-value of the initial model with ME/CFS and covariates is not the relevant one

 

[Hutan]

For some reason the chart I attached upthread isn't showing (fixed now) - here it is again, from the Keri paper:


These are all for isotropic signals. there are other measures for anisotropic movement (movement confined to particular planes). The one on the left is the restricted fraction - around the 0.3 mark. that's the one where water can only move a very short distance e.g. in cells. In the middle is the hindered fraction. and there's a free fraction on the right.

 

[forestglip]

The p-value of the initial model with ME/CFS and covariates is not the relevant one

I'm not sure what exactly you mean by the p-value of the initial model. The p-value of the ME/CFS status coefficient in a model including all covariates? I think this is the same p-value you would get from an F-test comparing the model with and without ME/CFS status.

 

[Jonathan Edwards]

So a lower hindered index and restricted fraction would go with a brain that is just wetter, without more cells? That might be consistent with postural effects.

 

[Hutan]

Sort of, I think. More free water (maybe more edema, more CSF), relative to the water in the cells (RF) and the water in the hindered fraction is (extracellular spaces, in tissues). Maybe less well hydrated cells and tissues, and/or more water hanging around outside of the cells and tissues

There are no absolutes i.e. definitively 'wetter' than the control brains, because these measures are relative fractions.

 

[jnmaciuch]

I'm not sure what exactly you mean by the p-value of the initial model. The p-value of the ME/CFS status coefficient in a model including all covariates? I think this is the same p-value you would get from an F-test comparing the model with and without ME/CFS status.

that's what the link you shared is referring to by precision. It's saying that including the covariate improves the precision of the overall model NII-RF ~ ME/CFS label + age + BMI ....
Which gives it's own p-value if you do an F-test comparing to an intercept only model. That's what I thought you meant, but there might be some confusion using the same terms to mean different things.

If you are just assessing the association between ME/CFS and NII-RF within the model with covariates, the precision of the model NII-RF ~ ME/CFS label + age + BMI .... doesn't actually matter. I'll try to write it out to make it more clear

 

[Hutan]

I expect @SNT Gatchaman is busy on other things right now, but I hope he will comment more on the paper at some point.

 

[jnmaciuch]

(Int = fitted model intercept)
Testing the association of NII-RF and ME/CFS without covariates:

p-value of ME/CFS derived by comparing difference in model performance between:
model1: NII-RF ~ Int
model2: NII-RF ~ Int + Beta1 * ME/CFS (0/1 binary)

model1 and model2 each have their own precision. If a model containing ME/CFS is better at predicting NII-RF scores than a model containing just the intercept, we say there is a significant association.

Testing the association of NII-RF and ME/CFS with covariates:
p-value of ME/CFS derived by comparing difference in model performance between:
model3: NII-RF ~ Int + Beta2 * age
model4: NII-RF ~ Int + Beta1 * ME/CFS (0/1 binary) + Beta2 * age

If the model performance improves when the ME/CFS variable is added, we say that ME/CFS is significantly associated with NII-RF accounting for age.

If ME/CFS has any predictive power for NII-RF, you will get p < 0.05 comparing models 1 and 2. If you want to ask whether that association is confounded by age, you compare models 3 and 4. The improvement in precision gained by adding age would be present in both models, so it doesn't matter.

[Edit: and for ME/CFS to be significant in the model3 vs. model4 comparison, it by definition has to have good predictive power for NII-RF. So you see why it would be weird to get significance comparing models 3 and 4, but no significance comparing models 1 and 2 (for all the measurements except NII-RF, which was significant in both comparisons)]

 

[forestglip]

The improvement in precision gained by adding age would be present in both models, so it doesn't matter.

In the case of models 3 and 4, we can be more confident about the amount that ME/CFS status improves the model, since the age variable is included and can explain some of the variance.

I'm not able to right now, but I might code it with simulated data later. It should be relatively straightforward to test what happens in this scenario when NII-RF is correlated to both age and ME/CFS, but where age and ME/CFS are not correlated to each other.

 

[jnmaciuch]

It should be relatively straightforward to test what happens in this scenario when NII-RF is correlated to both age and ME/CFS, but where age and ME/CFS are not correlated to each other.

That’s what I’ve been trying to explain this whole time. It simply does not matter. The way the comparison is structured, it’s only looking at the difference between two models where the only change is whether ME/CFS is added to the model.

It does not matter at all if age has a separate association with NII-RF because the p-value theyre reporting should be from testing whether a model with age and ME/CFS performs significantly better than a model with age alone. Any performance boost from age is kept constant.

 

[Jonathan Edwards]

The pictures are interesting in that the colour signals are mostly in sensory association areas and seem biased to the right. The pattern does not look like an inflammatory one to me unless there is an immune reaction to a local antigen associated with particular functions. Even then, the bias to right looks much more like a neurocognitive pattern than an immunological one.

I wonder if we are just looking at effects of (shifts in water associated with) blood flow associated with neural function - rather as in BOLD studies.

It is a pity that the authors keep referring to neuroinflammation as if it was a homogeneous entity rather than sticking to shifts in physiology.