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

Thanks ScoutB.
I think this study may well have found something interesting in the RF result, but it would have been better if they had not rushed to put a label of inflammation on it. As Jonathan says, it is possible that the lower RF finding does actually say something about a version of inflammation, or something else. .

I've sent off a query to Qiang Yu, the first and corresponding author of the paper, about the relationship of the restricted fraction to inflammation, and also about the use of HADS. Hopefully Qiang will join us here.
 
Thanks Hutan and ScoutB, I was running into the same issues.

Here's how I understand it: hindered water ratio (NII-HR) indicates places were water is very free to diffuse while restricted fraction (NII-RF) are places where water is very restricted to diffuse. In inflammation both measures are increased. NII-HR because of water flowing to the inflamed area, and NII-RF because of cells moving into the tissue and in them water is restricted.

But in this ME/CFS study both NII-HR and NII-RF were reduced? So the key measured are the opposite of what is seen in neuroinflammation?
 
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echoing thanks for the analysis in this thread. It really puzzles me.

I'll note that this sentence in the discussion:
Reduced NII-RF, linked to cellularity changes, confirm histopathological reports of immune cell infiltration in ME/CFS (Mandarano et al. 2020).
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cites this paper

Myalgic encephalomyelitis/chronic fatigue syndrome patients exhibit altered T cell metabolism and cytokine associations - PMC

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a complex disease with no known cause or mechanism. There is an increasing appreciation for the role of immune and metabolic dysfunction in the disease. ME/CFS has historically presented ...
pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov

which is a paper on circulating T cells in ME/CFS that doesn't include any histology and doesn't show any infiltration. I have not looked through all the citations. Was there ever a study in ME/CFS showing infiltration via histology? In the brain almost certainly not.

I feel like I have the same complaint in all brain imaging studies where the methods state that a family-wise p-value correction was performed, but the text seems to imply that the correction was across brain regions/voxels, and not across all the different measures assessed. I would really like clarification on that. [Edit: if these are all meant to be various proxy measures for "neuroinflammation" I'm not sure there's a strong argument for treating each measurement as a separate hypothesis]
 
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Another thing that confused me:

3.4 Group Comparison of NII- and DTI-Derived Metrics Without Controlling for Confounding Factors​


Without controlling for confounding factors, Figure S23 (Appendix B.3 in Supporting Information) exhibits that the NII-RF in the ME/CFS patient group was significantly lower than those in the HCs in several association, commissural, and projection fibres. There were no other significant group differences in TBSS for NII-derived metrics between ME/CFS and HCs when no confounding factors were controlled for. In addition, there were no significant group differences in any DTI-derived metrics between ME/CFS and HCs that did not control for potential confounding factors.
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Per the methods, they state:
Group comparisons in each NII/DTI-derived metrics were performed using general linear models controlling for sex, age, BMI, MET, depression and anxiety scores via FSL's randomise tool (Winkler et al. 2014) with 10,000 permutations at each voxel, and multiple regression analyses were also performed using the randomise tool to examine the associations between NII/DTI-derived metrics and clinical scores.
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So I have to assume that the results in Table 2, which seem to be the results referenced in the abstract, are the p-values from the model with the 6 covariates. I have no idea how you end up with most of your associations falling out of significance when you don't correct for confounders.
Anyone see something I might be missing to explain this?
 
ME/CFS Science Blog said:
Here's how I understand it: hindered water ratio (NII-HR) indicates places were water is very free to diffuse while restricted fraction (NII-RF) are places where water is very restricted to diffuse. In inflammation both measures are increased. NII-HR because of water flowing to the inflamed area, and NII-RF because of cells moving into the tissue and in them water is restricted.
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I am not sure that is right but I have not been able to work it out. 'Hindered' and 'restricted' appear to be quite different parameters here. I think restricted may mean anisotropic - which would go with water in and around axons being able to move in ne axis but not so much in others. But I cannot see a clear explanation of this.

The examples of using this technique to identify 'neuroinflammation' are odd. Thy are MS, Alzheimer's and obesity. I doubt there is significant inflammation in obesity but there are probably changes in parameters for other reasons. Demyelinating lesions are very different from the increase in microglia of chronic degenerative changes like Alzheimer's.

I think we deserve a better exposition of what these things really measure. We also need to see better confirmation with specific histological correlation (I am not sure there was any for any of the studies in other diseases quoted).
 
jnmaciuch said:
I have no idea how you end up with most of your associations falling out of significance when you don't correct for confounders.
Anyone see something I might be missing to explain this?
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I don't think it's an issue. Often significance decreases after controlling for a variable because a covariate is correlated to both the exposure and the outcome, so it explains part of the relationship. But significance can increase if controlling for a covariate that is mainly correlated to just the outcome, as it explains some of the variance in the relationship of exposure and outcome, leading to higher precision.

For example, there might be a regression of having ME/CFS (y-axis) on NII-RF (x-axis), but with a lot of variance in the y-axis due to various other things that affect ME/CFS status, which leads to a high standard error/low significance. If age is also associated with having ME/CFS, but not so much with NII-RF, then controlling for it doesn't change the coefficient of NII-RF much, but it decreases the variance, leading to a lower p-value.
 
jnmaciuch said:
Was there ever a study in ME/CFS showing infiltration via histology? In the brain almost certainly not.
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The Dutch autopsy study found none:


forestglip said:
But significance can increase if controlling for a covariate that is mainly correlated to just the outcome, as it explains some of the variance in the relationship of exposure and outcome, leading to higher precision.
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Agree, but it does increase the risk of p-hacking because you can introduce several other variables into your model to see whether they lower the p-values.

Same with this decision.
To avoid group-size imbalance and maintain a 1:1 ratio of patients to HCs, nine ME/CFS participants (five PI-ME/CFS and four GO-ME/CFS participants, respectively) from the pooled cohort in Yu et al. (2025) were excluded to match the HCs.
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This might be reasonable, but it also creates an opportunity to present the findings as more impressive, depending on the participants that you exclude.
 
The restricted fraction is the water inside small confined structures such as cells. It is water that is isotropic - it can move in all different directions, just not very far.

The hindered fraction is also isotropic, but I think excludes the restricted fraction and the FF. The NII-HR, the hindered water ratio is the ratio of that to the rest of the signal. I think it might be to the rest of the isotropic signal.

(I'm not yet understanding the FF. If you look at that chart of signals I included up thread there are three signals. On the left with a low coefficient is the restricted fraction, in the middle is the hindered fraction. There is one signal at the far right which seems to be when water is free to diffuse a long way, isotropically. So, I thought the F stood for free, but sometimes it seems to be referred to as fibre. I haven't got to figuring that out. But, for the purposes of the RF(restricted fraction) and HR (hindered ratio) measures, it probably doesn't matter a lot.)
 
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forestglip said:
But significance can increase if controlling for a covariate that is mainly correlated to just the outcome, as it explains some of the variance in the relationship of exposure and outcome, leading to higher precision.
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What has higher precision? The model?
 
ME/CFS Science Blog said:
Same with this decision.
This might be reasonable, but it also creates an opportunity to present the findings as more impressive, depending on the participants that you exclude.
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I also wasn't sure why it would be necessary to match group size.
To avoid group-size imbalance and maintain a 1:1 ratio of patients to HCs, nine ME/CFS participants (five PI-ME/CFS and four GO-ME/CFS participants, respectively) from the pooled cohort in Yu et al. (2025) were excluded to match the HCs. This ensured that there were no significant group differences in age, sex, body mass index (BMI), metabolic equivalents (MET) rate or MRI scan time, which was essential for reducing potential confounding in the imaging analyses.
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Are they saying they excluded ME/CFS participants that didn't have similar metrics to a healthy control? Or they just randomly removed ME/CFS participants to match group size?

Either way, I don't really see the reason for doing this. They're already controlling for these variables in the regression, and this reduces the ME/CFS group size by around 11%.
 
jnmaciuch said:
What has higher precision? The model?
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Yeah, decreased standard error for the coefficient of the main exposure on the outcome since some of the noise is accounted for by the covariate.

Edit: This goes a bit into it, talking about how controlling for covariates increases precision: https://methods.egap.org/guides/analysis-procedures/covariates_en.html
The real gains come in the precision of our estimates. The standard error (the standard deviation of the sampling distribution) of our estimated ATE [average treatment effect] when we ignore covariates is 0.121. When we include covariates in the model, our estimate becomes a bit tighter: the standard error is 0.093. Because our covariates were prognostic of our outcome, including them in the regression explained some noise in our data so that we could tighten our estimate of ATE.
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Hutan said:
The restricted fraction is the water inside small confined structures such as cells. It is water that is isotropic - it can move in all different directions, just not very far.
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Then wouldn't the reduced restricted fraction in patients indicated fewer cells rather than more?
 
forestglip said:
For example, there might be a regression of having ME/CFS (y-axis) on NII-RF (x-axis), but with a lot of variance in the y-axis due to various other things that affect ME/CFS status, which leads to a high standard error/low significance. If age is also associated with having ME/CFS, but not so much with NII-RF, then controlling for it doesn't change the coefficient of NII-RF much, but it decreases the variance, leading to a lower p-value.
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The way you would want to do the regression is NII-RF ~ ME/CFS label + age + sex + BMI + …..

When you’re reporting the association of the ME/CFS variable accounting for confounders, what you should be assessing is whether a model with that variable performs significantly better than a model including all the variables except for that one (usually done by something like ANOVA between two fitted models). 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. If you were just assessing a model with only the ME/CFS variable and the significance is only due to a confounder, you should get significance of the univariate model (comparing with variable to intercept only) but not when comparing model with variable to model without variable
 
jnmaciuch said:
So I have to assume that the results in Table 2, which seem to be the results referenced in the abstract, are the p-values from the model with the 6 covariates. I have no idea how you end up with most of your associations falling out of significance when you don't correct for confounders.
Anyone see something I might be missing to explain this?
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Yes, I had the same concern about the addition of controlling for the 'confounders' producing significant results that would not have been there without. I think the age and sex probably are valid, but I thought that the groups were matched on those anyway, in which case there is less reason to do that.

I can't see any good reason to control for anxiety and depression, and so it makes me think that that was done simply because it produced some significant results.

I've talked about how the HADS is probably actually an (imperfect) measure of physical health in people with ME/CFS, rather than a good measure of anxiety and depression, with its questions about not being able to do the things you enjoyed before, and having worries about the future etc. I'm a bit concerned that by controlling for BMI, 'anxiety and depression', they have actually controlled for physical health and activity levels, although in a less than perfect way.

So, yeah, I have felt concerned about the black box of dealing with all those 'confounding factors', which is why I have concentrated on what was found without the confounding factors being taken into account. And that is the Restricted Fraction signal.
 
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Jonathan Edwards said:
Then wouldn't the reduced restricted fraction in patients indicated fewer cells rather than more?
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When there is a high RF signal, it indicates increased cellularity, hence the idea of inflammatory cell infiltration. But the signal was low in the ME/CFS group, so yes, it seems to indicate lower cell numbers in the white matter than in the controls, and so potentially less inflammatory infiltration. But that is the opposite of what the authors of the paper seem to be saying. Hence the questions.
 
I would expect water both inside and outside cells in white matter to have a degree of anisotropy to its diffusivity, certainly for outside and for nerve fibres. FF refers to fibres I think.

Is it that the anisotropy measures stratify across the 'hindered' and 'restricted' components?

Hindered seems an odd term for relatively free water outside cells but I guess it may mean slowed but not limited to a tiny domain. Maybe a reduced hindered ratio means more free unhindered water? That at least would fit with low grade cerebral vessel transudation.

I think we can be reasonably sure that there will be no signal from inflammatory cells present in the white matter. If they formed a significant proportion of the water the person would likely be comatose.
 
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