Evidence of White Matter Neuroinflammation in [ME/CFS]: A Diffusion-Based Neuroinflammation Imaging Study 2026 Yu et al
jnmaciuch
Senior Member (Voting Rights)
I think its a reasonable assumption if you have reasonable likelihood of infiltration actually happening. What I'm puzzled by (and it seems I'm not alone in this) is how to interpret lower RF compared to controls when controls probably have no infiltration.
Yes that's my main thought--from the earlier glymphatics discussion, it seems like astrocytes serve as a buffer zone for water to help the parenchymal space maintain a fairly constant concentration. They do not change size fast enough to be particularly important to fluid flow but they have been observed to swell and contract dramatically. So my thought is that any restriction fraction seen in healthy brains is likely to represent what is stored in astrocytes.
I guess the question is what else? My instinct says it would have to be a small space enclosed in hydrophobic boundaries.
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ME/CFS Science Blog
Senior Member (Voting Rights)
There are two lines of dicussions.
One is about the statistics. We find it weird that there were more significant findings when confounders like age, sex, depression, anxiety were added to the model. It's possible that this happens but usually adding confounders like this will result in less significant results, not more. There were also some concerns that they excluded 9 ME/CFS patients that could not be matched to healthy controls and how these were selected.
The other discussion is about the interpretation of the data. The authors argue that their findings suggest neuro-inflammation but if we look those up they suggest the opposite.
- ME/CFS patients for example had relatively fewer water molecules that can move in any direction (isotropic) and are confined to tight spaces (restricted). Previous studies assumed that this might reflect water molecules trapped inside cells. So this measure could be seen as an indicator of immune cell infiltration, there being more cells. But in inflammation this measure is increased while in ME/CFS it was decreased.
- The same with water molecules that can move in any direction (isotropic) but aren't restricted. This has been used as an indicator of edema, water flowing into tissue. Some studies reported this to be increased in inflammation but in ME/CFS it was decreased.
- What was increased in ME/CFS was water molecules flowing in a particular direction (anisotropic). These are assumed to be water molecules in axons: they are trapped in a tube and can only go in one direction. In inflammation this is sometimes decreased but in ME/CFS it was increased.
Jonathan Edwards
Senior Member (Voting Rights)
That would fit with more extracellular water )assuming everything is proportions) but agree it doesn't fit with more cells.
Jonathan Edwards
Senior Member (Voting Rights)
There is lots of water in extracellular matrix that doesn't move much - bound to sulphate residues etc.. But if it exchanges with other compartments very quickly then the separate phase may not be apparent. I spent about ten years working on water compartments before MRI became available. The most important thing I learnt was how complicated and counterintuitive it all was, even if the biological solutions had a simple elegance. The next thing I learnt was that almost everyone working in the field of water phases in articular tissue had all their basic concepts wrong. But five years later they admitted their mistakes. My recent foray into brain water with various eminent Norwegian groups suggests that none of them understand diffusion!!
jnmaciuch
Senior Member (Voting Rights)
Ah okay I see the previous discussion about hindered fraction now. So the mystery is how you get a bigger proportion of extracellular water without an increased in hindered fraction
so it would have to be water in an extracellular matrix that doesn't exchange much with other compartments to end up in the restricted fraction
Jonathan Edwards
Senior Member (Voting Rights)
Yes. And if the recent literature on CSF flux that I have been reading is anything to go by it is quite plausible that the whole idea of these fractions and compartments is bad physical chemistry. The data will mean something but maybe not what these names imply. Mathematical modellers, in my experience, only too often fail to understand the dynamic geometry at fine grain.
jnmaciuch
Senior Member (Voting Rights)
And it seems like it would take a massive amount of ground work to be able to interpret correctly, if the findings aren't a proxy for neuroinflammation. Which seems more and more likely. If there is something here, my sense is that we'll make sense of it by working backwards once the mechanism of ME/CFS is already known, rather than being lead to the mechanism by these findings.
ScoutB
Senior Member (Voting Rights)
Technically I'm supposed to be good at math, they gave me a PhD. This is the first time in years I've been unfoggy enough to think about it though, so apologies for ramblingI'm not that good at math but I think the very free water (with diffusion > 2.5 μm2/ms) doesn't even enter their equation.
The second part represents the isotropic components, an integral from a to b where "a and b are the low and high diffusivity limits for the isotropic diffusion spectrum f(D)." Given the further explanation in the text, one is tempted to assume that a = 0.3 and b = 2.5 μm2/ms. That would explain why the free fraction is never mention in the paper.
Anyway, I think the bounds on this integral have to to be big enough to cover any water diffusion that could contribute to the signal the MRI machine sees for the math to work out**. So, I'd assume b at least goes up to 3 μm2/ms, unless they are confident a priori there's no free diffusion, I guess. But either way, something at least equivalent to what you're saying is true, because after they use this equation to figure out what the function f is, there is an additional step (which is kind of glossed over in the paper) where they use that function f to actually compute NII-RF (and the other values). And in that additional step they are ignoring the free fraction.
**Rough explanation of how I think the math works:
Suppose we knew there was no anisotropic diffusion, so the sum on the left is zero and we can ignore it. Then we can think of the integral on the right as (loosely speaking) adding up the amount of signal coming from each rate of diffusion D, and the sum of all that should equal the signal the MRI machine sees, S_k. For example, f(2.5) sort of represents how much water diffusing at D=2.5 μm2/ms is contributing to the signal. I'm also going to think of the integral as a sum so we can reason about it. Then that equation in the screenshot (really, the system of equations they are considering for k=1,...,K) becomes a system of equations of the form
MRI signal = sum of f(D) x exp(D), added up over all the possible diffusion rates D.
(where the MRI signal and exponential function differ for each k). Then they are doing some kind of fourier transform to figure out what f is from that system of equations. If the sum (really the integral) excluded some diffusion rate D that actually did contribute to the MRI signal, the f they get as a result would be artificially large in other places to compensate.
jnmaciuch
Senior Member (Voting Rights)
Thanks for the explanation, that's really helpful. It's been quite a long time since I've done diff eq, I'm sure a lot has leaked out of my brain by now.
So is the idea that once they get f from fourier transformation, they're computing values for everything in the 0 < D < 0.3 range and calling that NII-RF? Or rather, if it's a fraction, they're computing it for every "bucket" of diffusion coefficients within a voxel and then expressing each bucket as a fraction of the whole? I don't know how the latter would work if they're not taking the free fraction into account but also I'm likely to be misunderstanding something here
Ok, I did a bit of visualization to confirm and demonstrate why even if the groups are matched by age, controlling for age can still make the effect of ME/CFS status more significant.
We can imagine that we have 20 controls and 20 cases, where each control has a matching case in terms of age. Lets say that we are looking at the effect of ME/CFS status on a "Brain Metric", and lets say the actual effect of having ME/CFS is to increase the brain metric by 1 unit. And for every 5 years of age, the brain metric increases by 1 unit.
I also added a bit of randomness.
We plot and do linear regression on our matched cohort, just based on ME/CFS status:
This is essentially a t-test with a lot of variance. So even though the predicted slope (1.0019) is fairly close to the true effect of ME/CFS (1.0), the result is not very significant because the model is noisy (p=0.293). We can't be very sure about the true slope from the model because of how much variance there is in the brain metric in each group.
Now we add age as a covariate in the regression:
The predicted slope is again accurate (1.000), but now that we control for age and have a model with very little variance unexplained by the included variables, the result is very significant (p<0.001). In the plot, one can see that the brain metric increases by 1 when ME/CFS status increases by 1, and there's very little noise in the data for the slope to change much.
What happens if we don't match for age? Here I made the ME/CFS group about 10 years older than the control group on average, meaning that the brain metric for the ME/CFS group will be about 1 unit higher from having ME/CFS, plus about 2 units higher from being older (1 unit per 5 years).
With simple linear regression, now the predicted slope is 3.0072, close to the expected 3. The slope is biased by age, so we can't be sure what part of the effect is caused by age and what part by ME/CFS status.
And with a regression that controls for age in the unmatched cohort:
Now the effect of ME/CFS status on the brain metric is again fairly accurate (0.9799). So controlling for age takes care of the age bias without having to match for age. There may still be reasons to match for age, even when also controlling for age in the regression, but I'm not sure exactly what they are.
Python:
#!/usr/bin/env python
# coding: utf-8
# In[118]:
import numpy as np
import pandas as pd
import statsmodels.api as sm
import plotly.express as px
import plotly.graph_objects as go
# In[180]:
base_age = np.random.normal(loc = 50, scale = 15, size = 20)
age = np.concatenate([base_age, base_age]) + np.random.normal(0, 0.5, 40)
mecfs_status = np.concatenate([np.full(20, 0), np.full(20, 1)])
brain_metric = (0.2 * age) + mecfs_status + np.random.normal(loc=0, scale=0.1, size=40)
df = pd.DataFrame({
"Age": age,
"ME/CFS Status": mecfs_status,
"Brain Metric": brain_metric,
})
df.head()
# In[181]:
X = sm.add_constant(df['ME/CFS Status'])
y = df['Brain Metric']
model = sm.OLS(y, X)
results = model.fit()
fig = px.scatter(df, x='ME/CFS Status', y='Brain Metric', color="Age", opacity=0.65)
fig.update_traces(marker=dict(size=10))
fig.add_traces(go.Scatter(x=X['ME/CFS Status'], y=results.predict(X), showlegend=False))
fig.update_layout(
width=500,
height=500,
title={
'text': "Regression with only ME/CFS Status (Matched for age)",
'y':0.95,
'x':0.5,
'xanchor': 'center',
'yanchor': 'top'
}
)
fig.show()
print(results.summary())
# In[182]:
X = sm.add_constant(df[['ME/CFS Status', 'Age']])
y = df['Brain Metric']
results = sm.OLS(y, X).fit()
xrange = [X['ME/CFS Status'].min(), X['ME/CFS Status'].max()]
yrange = [X['Age'].min(), X['Age'].max()]
xx, yy = np.meshgrid(xrange, yrange)
pred = results.predict(sm.add_constant(np.c_[xx.ravel(), yy.ravel()]))
pred = pred.reshape(xx.shape)
# Generate the plot
fig = px.scatter_3d(df, x='ME/CFS Status', y='Age', z='Brain Metric', color="Age")
fig.update_traces(marker=dict(size=3))
fig.add_traces(go.Surface(
x=xrange,
y=yrange,
z=pred,
opacity=0.5,
colorscale=[[0, 'lightblue'], [1, 'lightblue']],
showscale=False,
))
fig.update_layout(
width=800,
height=600,
title={
'text': "Regression with ME/CFS Status and Age (Matched for age)",
'y':0.95,
'x':0.5,
'xanchor': 'center',
'yanchor': 'top'
})
fig.show()
print(results.summary())
# In[ ]:
# In[184]:
base_age = np.random.normal(loc=50, scale=15, size=20)
age = np.concatenate([base_age, base_age + 10]) + np.random.normal(0, 0.5, 40)
mecfs_status = np.concatenate([np.full(20, 0), np.full(20, 1)])
brain_metric = (0.2 * age) + mecfs_status + np.random.normal(loc=0, scale=0.1, size=40)
df = pd.DataFrame({
"Age": age,
"ME/CFS Status": mecfs_status,
"Brain Metric": brain_metric,
})
df.head()
# In[185]:
X = sm.add_constant(df['ME/CFS Status'])
y = df['Brain Metric']
model = sm.OLS(y, X)
results = model.fit()
fig = px.scatter(df, x='ME/CFS Status', y='Brain Metric', color="Age", opacity=0.65)
fig.update_traces(marker=dict(size=10))
fig.add_traces(go.Scatter(x=X['ME/CFS Status'], y=results.predict(X), showlegend=False))
fig.update_layout(
width=500,
height=500,
title={
'text': "Regression with only ME/CFS Status (Unmatched ages)",
'y':0.95,
'x':0.5,
'xanchor': 'center',
'yanchor': 'top'
}
)
fig.show()
print(results.summary())
# In[186]:
X = sm.add_constant(df[['ME/CFS Status', 'Age']])
y = df['Brain Metric']
results = sm.OLS(y, X).fit()
xrange = [X['ME/CFS Status'].min(), X['ME/CFS Status'].max()]
yrange = [X['Age'].min(), X['Age'].max()]
xx, yy = np.meshgrid(xrange, yrange)
pred = results.predict(sm.add_constant(np.c_[xx.ravel(), yy.ravel()]))
pred = pred.reshape(xx.shape)
# Generate the plot
fig = px.scatter_3d(df, x='ME/CFS Status', y='Age', z='Brain Metric', color="Age")
fig.update_traces(marker=dict(size=3))
fig.add_traces(go.Surface(
x=xrange,
y=yrange,
z=pred,
opacity=0.5,
colorscale=[[0, 'lightblue'], [1, 'lightblue']],
showscale=False,
))
fig.update_layout(
width=600,
height=600,
title={
'text': "Regression with ME/CFS Status and Age (Unmatched ages)",
'y':0.95,
'x':0.5,
'xanchor': 'center',
'yanchor': 'top'
})
fig.show()
print(results.summary())
Lies, damned lies and statistics. Very nice FG.
I see that the samples in this study have quite a spread in age (19 to 65 for the controls; 24 to 65 for the ME/CFS group).
I guess the issue with controlling for age (or anything) with a statistical model is that usually we don't know exactly what the effect of the variable truly is. It's fine when we know for sure that age increases the variable by 1 each decade. But, when we are using our sample to calculate the effect that we will control for, it all becomes very circular, and possibly adjustments are spurious.
It makes me think that
1. ideally we want to control as much as possible in the sample, so, in this case, maybe try to get all females in the age range 35 to 45 - or get a big enough sample that some stratification can be done to see if the effect shows up in different subsets
2. be even more suspicious of studies that make extensive use of models that control for 'confounders' than I was already
Is that reasonable?
I see that the samples in this study have quite a spread in age (19 to 65 for the controls; 24 to 65 for the ME/CFS group).
I guess the issue with controlling for age (or anything) with a statistical model is that usually we don't know exactly what the effect of the variable truly is. It's fine when we know for sure that age increases the variable by 1 each decade. But, when we are using our sample to calculate the effect that we will control for, it all becomes very circular, and possibly adjustments are spurious.
It makes me think that
1. ideally we want to control as much as possible in the sample, so, in this case, maybe try to get all females in the age range 35 to 45 - or get a big enough sample that some stratification can be done to see if the effect shows up in different subsets
2. be even more suspicious of studies that make extensive use of models that control for 'confounders' than I was already
Is that reasonable?