Tag Archives: voxel

Does getting COVID19 shrink your brain?

Does getting COVID19 shrink your brain?  A paper from last Thursday’s Nature says yes.  Not only that, but it slows you mentally. Here’s a link: https://www.nature.com/articles/s41586-022-04569-5.pdf.   and a reference: Nature vol. 604 pp. 697 – 707 ’22.

Here’s what they did.  Take 785 people over 50 from England. Have 401 get infected with the pandemic virus, after obtaining MRI scans, all sorts of data including mental function about them.   Then repeat the MRI and mental tests  4 – 5 months after the infection.  Compare the two groups and there’s your answer.

The moral among you must be wondering how they ever got this past an Institutional review board.  It didn’t.  This was an experiment of nature on participants in the UK Biobank — https://en.wikipedia.org/wiki/UK_Biobank.  Starting in 2006 and ending in 2010 some 100,000 people (ages 40 – 69 on entry) from the United Kingdom (UK) were intensively studied (they donated urine, saliva and blood, filled out questionnaires, and consented to access to their electronic health records).   Planned follow up is 30 years.  All this before we had any idea about the pandemic to hit us in 2020.

Obviously the control group without infection, must be as similar as possible to the infected group and I think the authors tried their hardest.  Even so the control group was a bit older, and the infected group had slightly lower cognitive abilities.

The average time between the two scans was 3 years.  The average time from COVID19 to the second scan was 141 days.  The scans were done before Omicron hit.  Even so only 15/401 had to be hospitalized.  This is consistent with the mildness of the pandemic presently.  On 9 April 22 Shanghai reported some 23,000 positive PCR tests (for Omicron), but only one thousand or so were symptomatic.   Excluding the 15 from analysis didn’t change the result.  I’ve heard from clinicians, that the severely ill are usually obese.  This is partly true for the 15 hospitalized (average Body Mass Index 29.3) vs. the 386 not hospitalized (BMI – 26.6).

So the clickbait is that being infected with the virus shrinks your brain. But does it? It is stated that there was a decrease in thickness of the cerebral cortex (the gray matter on the surface of the brain) concerned with smell and taste.

The decreases were minimal.  Have a look at figure 1a p. 701.  The changes between scans are plotted vs. age, and separately for cases and controls. As we get older the brain shrinks.  This was true for both patients and the controls, but the patients showed more shrinkage (measured by the change between successive MRIs).

What sort of shrinkage in the thickness of he cerebral cortex are we talking about here?  At most 3% and usually under 2%.  But 3% of what?  Most estimates of the thickness of the human cerebral cortex place it around 2 – 3 millimeters (range 1 to 5 millimeters).  So I got out a clear plastic ruler and found that 1 milliMeter is about the thickness of a penny?  Are they really saying that the MRI can measure thickness differences of 2 – 3% of something only 2 – 3 millimeters.

It gets worse.  Most of us have seen MRI pictures by now.  If you look closely, you’ll see that they are slices made of pixels.  These are computed slices of 3 dimensional cubes (voxels).  And what dear reader is the size of an MRI voxel — around 1 x 1 x 1 milliMeters.  So they are measuring cortical thickness with a rather blunt instrument which is 30 – 50% the size of cortical thickness.  Do you think, even with averaging of hundreds of people, that they can pick up a change in cortical thickness of several percent in something so small.

I don’t, and am amazed that the reviewers let them get away with this.

The cognitive changes are on much better ground.  But that’s for the next post.  This post is long enough.

Functional MRI research is a scientific sewer — take 2

You’ve heard of P-hacking, slicing and dicing your data until you get a statistically significant result.  I wrote a post about null-hacking –https://luysii.wordpress.com/2019/12/22/null-hacking-reproducibility-and-its-discontents-take-ii/.  Welcome to the world of pipeline hacking.  Here is a brief explanation of the highly technical field of functional magnetic resonance imaging (fMRI).   Skip to the **** if you know this already.

Chemists use MRI all the time, but they call it Nuclear Magnetic Resonance. Docs and researchers quickly changed the name to MRI because no one would put their head in something with Nuclear in the name.

There are now noninvasive methods to study brain activity in man. The most prominent one is called BOLD (Blood Oxygen Level Dependent), and is based on the fact that blood flow increases way past what is needed with increased brain activity. This was actually noted by Wilder Penfield operating on the brain for epilepsy in the 1930s. When a patient had a seizure on the operating table (they could keep things under control by partially paralyzing the patient with curare) the veins in the area producing the seizure turned red. Recall that oxygenated blood is red while the deoxygenated blood in veins is darker and somewhat blue. This implied that more blood was getting to the convulsing area than it could use.

BOLD depends on slight differences in the way oxygenated hemoglobin and deoxygenated hemoglobin interact with the magnetic field used in magnetic resonance imaging (MRI). The technique has had a rather checkered history, because very small differences must be measured, and there is lots of manipulation of the raw data (never seen in papers) to be done. 10 years ago functional magnetic imaging (fMRI) was called pseudocolor phrenology.

Some sort of task or sensory stimulus is given and the parts of the brain showing increased hemoglobin + oxygen are mapped out. As a neurologist as far back as the 90s, I was naturally interested in this work. Very quickly, I smelled a rat. The authors of all the papers always seemed to confirm their initial hunch about which areas of the brain were involved in whatever they were studying.

****

Well now we know why.  The data produced by and MRI is so extensive and complex that computer programs (pipelines) must be used to make those pretty pictures.  The brain has a volume of 1,200 cubic centimeters (or 1,200,000 cubic millimeters).  Each voxel of an MRI (like the pixels on your screen is about 1 cubic millimeter) and basically gives you a number of how much energy is absorbed by the voxel.  Computer programs (called pipelines) must be used to process it and make those pretty pictures you see.

Enter Nature vol. 582 pp. 36 – 37, 84 – 88 ’20 and the Neuroimaging Analysis Replication and Prediction Study (NARPS).  70 different teams were given the raw data from 108 people, each of whom was performing one or the other of two versions of a task through to study decision making under risk.  The groups were asked to analyze the data to test 9 different hypotheses about what part of the brain should light up in relation to  specific feature of the task.

Now when a doc orders a hemoglobin from the lab he’s pretty should that they’ll all give the same result because they determine hemoglobin by the same method.  Not so for functional MRI.  All 70 teams analyzed the data using different pipelines and workflows.

Was there agreement.  20% of the teams reported a result different from most teams.  Random is 50%.  Remember they all got the same raw data.

From the News and Views commentary  on the the paper.

“It is unfortunately common for researchers to explore various pipelines to find the ver­sion that yields the ‘best’ results, ultimately reporting only that pipeline and ignoring the others.”

This explains why I smelled a rat 30 years ago.  I call this pipeline hacking.

Further infelicities in the field can be found in the following posts

l. it was shown in 2014 that 70% of people having functional MRIs (fMRIs) were asleep during the test, and that until then fMRI researchers hadn’t checked for it. For details please see
https://luysii.wordpress.com/2014/05/18/how-badly-are-thy-researchers-o-default-mode-network/. You don’t have to go to med school, to know that the brain functions quite differently in wake and sleep.

2. A devastating report in [ Proc. Natl. Acad. Sci. vol. 113 pp. 7699 – 7600, 7900 – 7905 ’16 ] showed that certain common settings in 3 software pacakages (SPM, FSL, AFNI) used to analyze fMRI data gave false positive results ‘up to’ 70% of the time. Some 3,500 of the 40,000 fMRI studies in the literature over the past 20 years used these settings. The paper also notes that a bug (now corrected after being used for 15 years) in one of them also led to false positive results.  For details see — https://luysii.wordpress.com/2016/07/17/functional-mri-research-is-a-scientific-sewer/

In fairness to the field, the new work and #1 and #2 represent attempts by workers in fMRI to clean it up.   They’ve got a lot of work to do.

Functional MRI research is a scientific sewer

First a primer about the science underlying functional Magnetic Resonance Imaging (fMRI). Chemists use MRI all the time, but they call it Nuclear Magnetic Resonance. Docs and researchers quickly changed the name to MRI because no one would put their head in something with Nuclear in the name.

There are now noninvasive methods to study brain activity in man. The most prominent one is called BOLD (Blood Oxygen Level Dependent), and is based on the fact that blood flow increases way past what is needed with increased brain activity. This was actually noted by Wilder Penfield operating on the brain for epilepsy in the 1930s. When a patient had a seizure on the operating table (they could keep things under control by partially paralyzing the patient with curare) the veins in the area producing the seizure turned red. Recall that oxygenated blood is red while the deoxygenated blood in veins is darker and somewhat blue. This implied that more blood was getting to the convulsing area than it could use.

BOLD depends on slight differences in the way oxygenated hemoglobin and deoxygenated hemoglobin interact with the magnetic field used in magnetic resonance imaging (MRI). The technique has had a rather checkered history, because very small differences must be measured, and there is lots of manipulation of the raw data (never seen in papers) to be done. 10 years ago functional magnetic imaging (fMRI) was called pseudocolor phrenology.

Some sort of task or sensory stimulus is given and the parts of the brain showing increased hemoglobin + oxygen are mapped out. As a neurologist as far back as the 90s, I was naturally interested in this work. Very quickly, I smelled a rat. The authors of all the papers always seemed to confirm their initial hunch about which areas of the brain were involved in whatever they were studying. Science just isn’t like that. Look at any issue of Nature or Science and see how many results were unexpected. Results were largely unreproducible. It got so bad that an article in Science 2 August ’02 p. 749 stated that neuroimaging (e.g. functional MRI) has a reputation for producing “pretty pictures” but not replicable data. It has been characterized as pseudocolor phrenology (or words to that effect). Keep reading you’re about to find out just why this was.

What was going on? The data was never actually shown, just the authors’ manipulation of it. Acquiring the data is quite tricky — the slightest head movement alters the MRI pattern. Also the difference in NMR signal between hemoglobin without oxygen and hemoglobin with oxygen is small (only 1 – 2%). Since the technique involves subtracting two data sets for the same brain region, this doubles the error.

Under two years ago, it was shown that 70% of people having functional MRIs (fMRIs) were asleep during the test, and that until then fMRI researchers hadn’t checked for it. For details please see
https://luysii.wordpress.com/2014/05/18/how-badly-are-thy-researchers-o-default-mode-network/. You don’t have to go to med school, to know that the brain functions quite differently in wake and sleep.

Recent work shows that functional MRI work is even worse. A devastating report in [ Proc. Natl. Acad. Sci. vol. 113 pp. 7699 – 7600, 7900 – 7905 ’16 ] showed that certain common settings in 3 software pacakages (SPM, FSL, AFNI) used to analyze fMRI data gave false positive results ‘up to’ 70% of the time. Some 3,500 of the 40,000 fMRI studies in the literature over the past 20 years used these settings. The paper also notes that a bug (now corrected after being used for 15 years) in one of them also led to false positive results.

Here’s a bit more detail on what they did. It turns out that analyzing one voxel (essentially a single MRI pixel) at a time produces valid results. The problem comes when multiple voxels (clusters) are analyzed together. Clusterwise inference considers both the strength of activity at spots throughout the brain as well as the size of the spots. When a parameter called the cluster defining threshold (CDT) is set too low, the analysis is more likely to be false positive. This was true for all 3 packages tested. Parametric statistical methods produce the problem (not for voxels but for clusters). It relies on Gaussian Random Field Theory (RFT) for clusters , which depends on two other assumptions (1) the spatial autocorrelation function has a squared exponential shape — e.g. Gaussian (2) the spatial smoothness of the fMRI signal is constant over the brain. Neither of these assumptions is correct. Those of you who’ve read Nassim Nicholas Taleb about the stock market know about ‘fat tails’. It turns out that the spatial correlation function has them. Here’s what a fat tail is all about. Human height goes fall quite nicely into a Gaussian distribution. There are 7 and 8 footers about but they are rare. If the human height distribution wasn’t Gaussian but had a fat tail, we’d see 12 and 15 footers.

If that wasn’t bad enough,the following is even worse (in my opinion). 40% of 241 recent fMRI studies didn’t report using well known methods for correcting for multiple testing. They may have done so, but every biomedical paper, and drug study says so explicitly. Not only that but drug studies are required to explicitly state the hypothesis (or hypotheses) they are testing.

This is probably why in the early days, fMRI researchers always confirmed their original hypothesis. They could test the massive fMRI for statistical rarity, and since the data was so large, find it and post hoc propter hoc publish it. Possibly they did so out of ignorance, but even so this is inexcusable