Author Archives: luysii

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The four hour cure for depression: what is Ketamine doing?

It is a sad state of affairs when you look forward to writing a post on depression.

From Nature 2 July — “G4 a type of swine flu virus from China can proliferate in human airway cells.  34/338 pig farm workers in China have antibodies to it.  In ferrets G4 causes lung inflammation and coughing.”

Well that’s enough reason to flee to the solace of the basic neuroscience of depression.



The drugs we use for depression aren’t great.  They don’t help at least a third of the patients, and they usually take several weeks to work for endogenous depression.  They seemed to work faster in my MS patients who had a relapse and were quite naturally depressed by an exogenous event completely out of their control.

Enter Ketamine which, when given IV, can transiently lift depression within a few hours.  You can find more details and references in an article in  Neuron ( vol. 101 pp. 774 – 778 ’19)  written by the guys at Yale who did some of the original work. However, here’s the gist of the article.  A single dose of ketamine produced antidepressant effects that began within hours peaked in 24 – 72 hours and dissipated within 2 weeks (if ketamine wasn’t repeated).  This occurred in 50 – 75% of people with treatment resistant depression.  Remarkably one third of treated patients went into remission.

This simply has to be telling us something very important about the neurochemistry of depression.

Naturally there has been a lot of work on the neurochemical changes produced by ketamine, none of which I’ve found convincing ( see ) until the following paper [ Neuron  vol. 106 pp. 715 – 726 ’20 ].

In what follows you have to have some basic knowledge of synaptic structure, but here’s a probably inadequate elevator pitch.  Synapses have two sides, pre- and post-.  On the presynaptic side neurotransmitters are enclosed in synaptic vesicles.  Their contents are released into the synaptic cleft when an action potential arrives from elsewhere in the neuron.  The neurotransmitters flow across the very narrow synapse to bind to receptors on the postsynaptic side, triggering (or not) a response of the postsynaptic neuron.  Presynaptic terminals vary in the number vesicles they contain.

Synapses are able to change their strength (how likely an action potential is to produce a postsynaptic response).  Otherwise our brains wouldn’t be able to change and learn anything.  This is called synaptic plasticity.

One way to change the strength of a synapse is to adjust the number of synaptic vesicles found on the presynaptic side.   Presynaptic neurons form synapses with many different neurons.  The average neuron in the cerebral cortex is post-synaptic to thousands of neurons.

We think that synaptic plasticity involves changes at particular synapses but not at all of them.

Not so with ketamine according to the paper.  It changes the number of presynaptic vesicles at all synapses of a given neuron by the same percentage — this is called synaptic scaling.  Given 3 synapses containing 60  50 and 40 vesicles, upward synaptic scaling by 20% would add 12 vesicles to the first 10 to the second and 8 to the third.   The paper states that this is exactly what ketamine does to neurons using glutamic acid (the major excitatory neurotransmitter found in brain).  Even more interesting, is the fact that lithium which treats mania has the opposite effects decreasing the number of vesicles in each synapse by the same percentage.

I found this rather depressing when I first read it, as I realized that there was no chemical process intrinsic to a neuron which could possibly work quickly enough to change all the synapses at once.  To do this you need a drug which goes everywhere at once.

But you don’t. There are certain brain nuclei which send their processes everywhere in the brain.  Not only that but their processes contain varicosities which release their neurotransmitter even where there is no post-synaptic apparatus.  One such nucleus (the pars compacta of the substantia nigra) has extensively ramified processes so much so that “Individual neurons of the pars compact are calculated to give rise to 4.5 meters of axons once all the branches are summed”  — [ Neuron vol. 96 p. 651 ’17 ].  So when that single neuron fires, dopamine is likely to bathe every neuron in the brain.  We think that something similar occurs in the locus coeruleus of the lower brain which has only 15,000 neurons and releases norepinephrine, and also in the raphe nuclei of the brainstem which release serotonin.

It should be less than a surprise that drugs which alter neurotransmission by these neurotransmitters are used to treat various psychiatric diseases.  Some drugs of abuse alter them as well (Cocaine and speed release norepinephrine, LSD binds one of the serotonin receptors etc, etc.)

The substantia nigra contains only 450,000 neurons at birth, so you don’t need a big nucleus to affect our 80 billion neuron brains.

So the question before the house, is have we missed other nuclei in the brain which control volume neurotransmission by glutamic acid?   If they exist, could their malfunction be a cause of mania and/or depression?  There is plenty of room for 10,000 to 100,000 neurons to hide in an 80 billion neuron brain.

Time to think outside the box people. Here is an example:  Since ketamine blocks activation of one receptor for glutamic acid, could there be a system using volume neurotransmission which releases a receptor inhibitor?

Addendum 7 July — I sent a copy of the post to the authors and received this back from one of them. “Thank you very much for your kind words and interest in our work. Your explanation is quite accurate (my only suggestion would be to replace “vesicles” with “receptors”, as the changes we propose are postsynaptic). Reading your blog reassures us that our review article accomplished its main goal of reaching beyond the immediate neuroscience community to a wider audience like yourself.”


Where are the deaths?

Our current model of the pandemic is that if the number of people testing positive for the viral genome increases, deaths will increase.   Could the model be wrong?  We’re about to find out.  The number of cases diagnosed daily has markedly increased recently in Georgia and Florida.

The number of hospitalizations for illness due to the virus (e.g. the old meaning of Covid19)  in Miami Dade county rose from 607 on 15 June to 1,062 on 28 June. Certainly deaths are sure to show a similar increase.  Aren’t they?

Well so far deaths are falling as diagnosed cases are rising.  has data through 28 June (a Sunday, where reporting is likely to be slow).

If anyone knows how to get these graphics into a WordPress post, please let me know (just write a comment).  Every time I try my post collapses and nothing shows up.  The WordPress Gods must be angry with me.  The links will get you there however, but even then you’ll have to root around to find what I’m talking about.  Apologies.

There is a new WordPress editor out, and I’ll try it to see if it helps.

Florida is particularly good to study because every Friday they tally the number of cases with a positive antibody test for the virus for the past week.  These are people who have recovered, and who likely have never been very sick.  There would be little reason to test someone hospitalized with COVID19 for antibodies.  Here’s a link —

It boils down to the fact that about 35% of 51,982 newly diagnosed cases of infection in the two weeks ending 26 June are really positive antibody tests.

Unfortunately Florida doesn’t have available a statewide number for the total hospitalized cases of COVID19 — like Massachusetts–  — but with a population more (6.9 million) than  Miami Dade metropolitan area (5.5 million), there were only 760 cases statewide.

Now on to Georgia, which I’ve been following because they were one of the first states to lift restrictions.  As of 3PM today 29 June,  the 7 day moving average of daily deaths was 15 (this number is for 16 June, because Georgia doesn’t regard its numbers as solid until two weeks have passed).  On 25 April, the day the lockdown was partially lifted, the 7 day moving average of daily deaths was 41.

The number of cases in Georgia diagnosed (using both antibodies to the virus and the genome) has risen markedly in the past 2 weeks. Unfortunately I’ve been unable to find what percentage of the positive tests in Georgia are due to antibodies to the virus.

Both Florida and Georgia are so typical for what docs face all the time.  The data you have is never quite the data you’d like to have.

Now the time from hospitalization with COVID19 to death is unknown, but it’s unlikely to be greater than a month. However, for both states, given the rise in diagnosed cases, we had better see a rise in deaths, or something is seriously wrong with our model.

Has this ever happened before?  You bet.  The nationwide rise in obesity over the past several decades was predicted to have awful effects on mortality.  Yet life expectancy continued increasing.  For details see a copy of an old post after the ****

So I’ll revisit these states in two weeks or so to see if deaths have risen.  This post is long enough, but it’s worthwhile inserting two pieces of data from family and friends.  Family spies tell me that yuppies in Brooklyn are partying in the street without any protection.  Similarly, a friend from Baltimore notes  “Not many people are wearing masks in Baltimore or Washington, particularly individuals at high risk.”

Although Trump’s medical pronouncements are rightly ridiculed, I find it improbable that the bunch described above take what he says as holy writ and that he’s responsible for their behavior.

We are currently witnessing a massive social and medical experiment which would never get past an institutional review board.


Something is still wrong with the model

We’re getting fatter and fatter as a nation and with fatness comes diabetes, hypertension, elevated lipids, strokes, heart attacks and death.  That’s the model.  There’s something wrong with it however, as people in the USA are living longer and longer, and deaths are dropping. The following is one of the first posts I wrote on the blog and it got a lot of play. (I’ll reproduce it here at the end of this post)

What’s happened since?  The following year the Center for Disease Control (CDC) reported a one month dip in expectancy to 77 years and 11 months.  Last week the CDC announced that because of a computer programming error the dip didn’t happen.   They also announced new data for the most ‘recent’ year available (2009 not 2010) and life expectancy continues to increase (now 78 years and two months for a child born today).  This is probably not a statistical fluke.  The data is based on death certificates. Why in the world we don’t have data for 2010 yet and why it took 14+ months for the CDC to collate the data for 2009 I leave to your imagination.

The absolute number of deaths  dropped by 36,000.  Now docs misdiagnose a lot of things but death isn’t one of them.  So my guess is that life expectancy is even higher, because the CDC is probably using the numbers the census counts rather than the numbers of people who are actually here (e.g. undocumented immigrants etc. etc.).

As noted earlier, one self serving explanation is that medical care is just getting better and better, and certainly it is, but it is very unevenly distributed, which was one of the points in passing ObamaCare.  More likely, in my opinion, is that obesity just isn’t as bad as its cracked up to be.  This goes against years and years of experience as a practicing physician.  Next time you visit a friend in the hospital, look at what’s lying in the beds — you will find the percentage of really heavy people much higher than the people walking the streets.  How many times have I seen an obese diabetic hypertensive, hyperlipidemic patient improve all 3 (and presumably their risk of premature death) by losing weight.   Yet facts must be faced — we’re not dropping like flies even though we’re getting fatter as a nation.  Any thoughts?


HERE’s the old post

Back in grad school when a theory came up with a wrong prediction, we all clapped hands because it showed us exactly where a new theory was needed, and just how it failed. No casting about for something to work on. A program that crashes intermittently is very hard to fix. Once you’ve found input that consistently makes it crash the job becomes much easier.

The Center for Disease Control released new data for 2007 (based on 90% of all USA death certificiates) showing that mortality rates dropped again (by over 2%) to 760/100,000 population. It’s been dropping for the past 8 years, and viewed longer term is half of what it was 60 years ago. Interestingly death rates from heart disease dropped a staggering 5% and even cancer dropped 2%.

But the populace is fat and getting fatter. This has been going on for 30 years. You can Google NHANES for the gory details, but the following should be enough. [ Science vol. 299 pp. 853 – 855, 856 – 858 ’03 ] The data from a recent NHANES (’99 – ’00) shows that the percentage of obese (as opposed just overweight) increased from 23% in the surveys from ’88 to ’94 to 31%. This is based on the body mass index (BMI). Someone 6′ 1″ would have to weigh 225 pounds to be obese.

We are told to be prepared for an epidemic of diabetes, high blood pressure, elevated blood lipids because of this. Every doc has seen blood sugar drop, blood pressure lowered, lipids come down in people with any/all of the above when they are able to lose a significant amount of weight. These diseases are significant only if they kill people, which they certainly seem to do in my experience. The next time you’re visiting a friend in the hospital, look at what’s lying in the beds. Very likely, many more than 31% of them are obese.

So why are death rates dropping and people living longer? Something must be wrong with the model — it’s pretty hard to quarrel with the data as being inadequate. Certainly the increased incidence of obesity should have produced something by this time (it started 30 years ago).

Well, the self serving answer for the drug developers is that their drugs are better. MDs would like to think it’s due to better care. Possibly. Here’s some detail.

#1: More people are exercising than they used to. How many joggers and walkers did you see on the streets 20, 30 years ago?

#2: Fewer people are smoking. Forget lung cancer (if you can). The big risk for smokers is premature vascular disease. Normally we all have carbon monoxide in our blood (it comes from the breakdown of hemoglobin). [ Brit. Med. J. vol. 296 pp. 78 – 79 ’88 ] Natural carbon monoxide production would lead to a carboxyhemoglobin level of .4 – .7%, but normal levels in nonsmokers in urban areas are 1 – 2%. Cigarette smoke contains 4% carbon monoxide, so smokers have levels of 5 – 6%. This can’t be good for their blood vessels.

#3: Doctors know more than they did. My brother is a very competent internist. He took over the practice of a similarly competent internist after his very untimely many death years ago. Naturally he got all the medical records on the patients. He found letters (now over 25 years old) from the late MD to his patients informing them of their lab results, and assuring them that their cholesterol was just fine at 250 mg%.

#4: The drugs are better. In addition they may be working in ways that we have yet to fathom. Consider the statins — their effect on vascular disease is far greater than their effect on blood lipids (cholesterol, triglyerides) — particularly when compared to other agents that lower blood lipids to the same extent.

Any further thoughts?

Death rates from coronavirus drop in half 2 months after Georgia loosens lockdown restrictions

There were apocalyptic predictions of doom when Georgia loosened its lockdown restrictions against the pandemic coronavirus SARS-CoV-2 on 25 April.  Here they are

From The Atlantic — “Georgia’s Experiment in Human Sacrifice — The state is about to find out how many people need to lose their lives to shore up the economy.” —

A month later (25 May) not much had happened —

7 day moving average of new cases of COVID19 ending 25 April — 740

7 day moving average of new cases of COVID19 ending 13 May — 525 (the state allows 14 days for all the data to roll in, so the last date they regard as having secure numbers is the 7th of May and here the number is 539)

7 day moving averages of deaths from COVID19 ending 25 April — 35

7 day moving average of deaths from COVID19 ending 13 May — 24 (the state allows 14 days for all the data to roll in, so the last date they regard as having secure numbers is the 7th of May and here the number is 27).

Back on 25 May I wrote “People who assumed (on purely correlative evidence) that lockdowns prevented new cases, and that lifting them would cause a marked increase in new cases and deaths, are clearly wrong.  It’s possible that cases will spike in the future proving them right, but pretty unlikely.  It’s only fair to give the doomsayers a sporting chance and followup is planned in a month.”

So here’s the followup.   The 7 day moving average of daily deaths had dropped to 17 as of 11 June.  Remember Georgia waits 14 days as data filters in to regard the numbers as definitive.  Here’s the link —

So the death rate from COVID-19 dropped in half 2 months after Georgia loosened some of the lockdown restrictions.

There are only two useful statistics in all of this.  The moving average of the daily death rate and the number of COVID19 cases in the hospital.  I no longer follow the number of new cases, because they include people with a positive antibody test (all of whom have recovered).  We know that most cases are asymptomatic.  It’s very hard to get the second number of people sick in the hospital with COVID19 (I’ve tried with no luck).  COVID19 used to mean that you were sick — no longer, it now counts positive antibody tests, rendering the number relatively useless.  By choosing who to test, numbers can be easily inflated —

Daily death rates are great for cherry picking scare headlines — it’s worth looking at this article from Tampa —

It contains a great figure with the number of deaths each day from March onward on which is superimposed the moving average — the range is from 10 to 100.  Even more impressive is the fall on weekends and the rise during the week.

Fortunately, every Friday  Florida releases the weekly results for antibody testing, so we’ll be able to see how many of these new cases of COVID19 are people who have recovered from it.

Here’s another link — well worth looking at — with the number of new cases in Florida in one graph (with the marked increase in the past week) and the number of death from the disease just below.  The deaths in the past week are the lowest they’ve been in a month —

New York City Covid-19 cases spiked today. Stock market futures tank

22 June:  A private testing firm (Legkoverny Testing) today reported 10,000 newly found Covid-19 cases in New York City today. Stock market futures immediately tanked.  They had been going around the Bronx for several weeks offering free antibody testing to anyone wanting it.  Some 30,000 residents took up the offer.

Legkoverny knew where to go to get these results. — communities of color in the Bronx where positive tests for the antibody occur in up to  50% of the population.

Fortunately, officials noted that a positive antibody test means that the individual had been infected and recovered, as all 30,000 or so weren’t on respirator in ICUs.  In fact they were walking around the streets of the city, and most had never been sick.

This sort of thing has not been noted by the mainstream press reporting an upsurge in Covid-19 cases.

Increasing the number of tests done daily will increase the number of  positive tests for cases.  Every state in the country has been increasing the number of tests done daily.

Now Covid-19 used to mean, clinical illness with the SARS-CoV-19, the pandemic virus.  It isn’t being reported that way now, just a positive antibody test for the virus appears to be enough

Here’s Florida’s weekly report of positive antibody tests for the week ending 19 June —  200,000+ people were tested, and 8,627 were found positive which is nearly 1/3 of the total new cases for that week.

So if you want to know if the number of serious cases is increasing (which is  what we all would like to know), forget these these numbers, which are partly due to increased testing.  Concentrate on two statistics (assuming you can get them)

l. The daily death rate from the virus — even better a 7 day moving average as Georgia does

2. The number of cases currently in the hospital.

Not every state gives out this information, but Massachusetts does.

There will be a lot of egg on a lot of faces if easing the lockdown restrictions doesn’t cause an increase in illness and death, which is probably why the pandemic is being reported this way.

Also ignore daily spikes in the number of cases — this can be an artifact of the way cases filter in to state health departments.  For an early example of this please see  —

Good luck




The Pleasures of Reading Feynman on Physics – IV

Chemists don’t really need to know much about electromagnetism.  Understand Coulombic forces between charges and you’re pretty much done.   You can use NMR easily without knowing much about magnetism aside from the shielding of the nucleus from a magnetic field by  charge distributions and ring currents. That’s  about it.  Of course, to really understand NMR you need the whole 9 yards.

I wonder how many chemists actually have gone this far.  I certainly haven’t.  Which brings me to volume II of the Feynman Lectures on Physics which contains over 500 pages and is all about electromagnetism.

Trying to learn about relativity told me that the way Einstein got into it was figuring out how to transform Maxwell’s equations correctly (James J. Callahan “The Geometry of Spacetime” pp. 22 – 27).  Using the Galilean transformation (which just adds velocities) an observer moving at constant velocity gets a different set of Maxwell equations, which according to the Galilean principle of relativity (yes Galileo got there first) shouldn’t happen.

Lorentz figured out a mathematical kludge so Maxwell’s equations transformed correctly, but it was just that,  a kludge.  Einstein derived the Lorentz transformation from first principles.

Feynman back in the 60s realized that the entering 18 yearolds had heard of relativity and quantum mechanics.  He didn’t like watching them being turned off to physics by studying how blocks travel down inclined planes for 2 or more years before getting to the good stuff (e. g. relativity, quantum mechanics).  So there is special relativity (no gravity) starting in volume I lecture 15 (p. 138) including all the paradoxes, time dilation length contraction, a very clear explanation fo the Michelson Morley experiment etc. etc.

Which brings me to volume II, which is also crystal clear and contains all the vector calculus (in 3 dimensions anyway) you need to know.  As you probably know, moving charge produces a magnetic field, and a changing magnetic field produces a force on a moving charge.

Well and good but on 144 Feynman asks you to consider 2 situations

  1. A stationary wire carrying a current and a moving charge outside the wire — because the charge is moving, a magnetic force is exerted on it causing the charge to move toward the wire (circle it actually)

2. A stationary charge and a  moving wire carrying a current

Paradox — since the charge isn’t moving there should be no magnetic force on it, so it shouldn’t move.

Then Feynman uses relativity to produce an electric force on the stationary charge so it moves.  (The world does not come equipped with coordinates) and any reference frame you choose should give you the same physics.

He has to use the length (Fitzgerald) contraction of a moving object (relativistic effect #1) and the time dilation of a moving object (relativistic effect #2) to produce  an electric force on the stationary charge.

It’s a tour de force and explains how electricity and magnetism are parts of a larger whole (electromagnetism).  Keep the charge from moving and you see only electric forces, let it move and you see only magnetic forces.  Of course there are reference frames where you see both.


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 –  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 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 —

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.

The brain is far more wired up than we thought

The hardest thing in experimental psychology is being smart enough to think of something truly clever.  Here is a beauty, showing that the brain is far more wired together than we ever thought.

First some background.  You’ve probably heard of the blind spot (although you’ve never seen it).  It’s the part in your eye were all the never fibers from the sensory part of the eye (the retina) are collected together forming the optic nerve.  Through an ophthalmoscope it appears like a white oval (largest diameter under 2 milliMeters)  It’s white because it’s all nerve fibers (1,000,000 of them) with no sensory retina overlying it.  So if you shine a very narrow light on it, you’ll see nothing.   That’s the blind spot.

Have a look at Both eyes project to both halves of he brain.  Because the blind spot is off to one side in your visual field, the other eye maps a different part of the retina to the same area of the brain.  But if you patch that eye, on one side of the brain the blind spot gets no input.


 In the healthy visual system, the cortical representation of the blind spot (BS) of the right eye receives information from the left eye only (and vice versa). Therefore, if the left eye is patched, the cortex corresponding to the BS of the right eye is deprived of its normal bottom-up input.

Proc. Natl. Acad. Sci. vol. 117 pp. 11059 – 11067 ’20

Hopefully you’ll be able to follow the link and look at figure 1 p. 11060 which will explain things.

Patching the left eye deprives that area of visual cortex of any input at all.

Here comes the brunt of the paper — within minutes of patching the left eye, the cortical representation of that spot begins to widen.  It starts responding to stimuli from areas outside its usual receptive field.

Nerves just don’t grow that fast, so the connections have to have been there to begin with.   So the brain is more wired together than we thought.  Perhaps this is just true of the visual system.

If not, the work has profound implications for neurologic rehabilitation.

I do apologize for not being able to explain this better, but the work is sufficiently important that you should know about it.

Addendum 4 June — here’s another shot at explaining things.

    • As you look straight ahead, light falls on the part of your retina with the highest spatial resolution (the macula). The blind spot due to the optic nerve is found closer to your nose, which means that in the right eye, the retina surrounding the blind spot ‘sees’ light coming from toward your ear. Light from the same direction ( your right ear) will NOT fall on the optic nerve of your left eye (which is toward your nose) so information from that area gets back to the brain (which is why you don’t see your blind spot).

      Now visual space (say looking toward the right) is sent back to the brain coherently, so that areas of visual space transmitted by either eye go to the same place in the brain.

      So if you now cover your left eye, there is an area of the brain (corresponding to the blind spot of the right eye) which is getting no information from the retina at all. So it is effectively blind. Technology permits us to actively stimulate the retina anywhere we want.. We also have ways to measure activity of the brain in any small area (functional MRI). Activity increases with visual input.

      Now with the left eye patched, stimulate with light directed at the right eye’s blind spot. Nothing happens (no increase in activity) in the cortical area representing that part of the visual field. It isn’t getting any input. So it is possible to accurately map the representation of the right eye’s blind spot in the brain in terms of the brain areas responding to it.

      Next visually stimulate the right eye with light hitting the retina adjacent to the right eye’s blind spot. Initially the blind spot area of the brain shows no activity, After just a few minutes, the area of the brain for the right eye’s blind spot begins to respond to stimuli it never responded to initially. This implies that those two areas of the brain have connections between them, that were always there, as new nerve processes just don’t grow that fast.

      To be clever enough to think of a way to show this is truly brilliant. Bravo to the authors.


I don’t trust models and Governor Cuomo doesn’t either

“I’m out of that business because we all failed at that business. Right? All the early national experts. Here’s my projection model. Here’s my projection model. They were all wrong. They were all wrong.”

That’s New York State Governor Cuomo speaking on Memorial Day.  Welcome to the club.  I’ve been watching models fail for 50 years.

Cuomo has a right to be bitter.  The models told him he’d need 30,000 respirators, but Trump only gave him 4,000 prompting him to ask Trump to pick the 26,000 people he wanted to die.   Later he shipped his excess ventilators to other states. The faith he showed in the models he was fed would put a medieval theologian to shame.

Hopefully, in the future,  those in power will be more cynical about the models presented to them.

So here’s a ‘greatest hits list’  of a few models which failed.  Let’s start with

l. The population bomb (Paul Ehrlich) — ”

The battle to feed all of humanity is over. In the 1970s hundreds of millions of people will starve to death in spite of any crash programs embarked upon now. At this late date nothing can prevent a substantial increase in the world death rate.

2. The Club of Rome released the following broadside in 1974, “The Limits to Growth”Here is a direct quote from the jacket flap.

“Will this be the world that your grandchildren with thank you for? A world where industrial production has sunk to zero. Where population has suffered a catastrophic decline. Where the air, sea and land are polluted beyond redemption. Where civilization is a distant memory. This is the world that the computer forecasts. What is even more alarming, the collapse will not come gradually, but with awesome suddenness, with no way of stopping it”

You can read more about these two in the following post —

3.  My cousin runs an advisory service for institutional investors (hedge funds, retirement funds, stock market funds etc. etc.)  Here is the beginning of his latest post 16 June ’17

There were 3 great reads yesterday. First was Neil Irwin’s article in the NY Times “Janet Yellen, the Fed and the Case of the Missing Inflation.”  He points out that Yellen is a labor market scholar who anticipated the sharp decline in the unemployment rate. However the models on which the Fed has relied anticipate higher levels of inflation. Yet every inflation measure that the Fed uses has fallen well short of the Fed’s 2% stability rate. If they continue raising short-term rates in the face of low inflation, then “real” rates could restrain future economic growth.Second was Greg Ip’s article “Lousy Raise? It Might Not Get Better.” Greg makes the point that tight labor markets are a global phenomenon in many industrialized countries, yet wage inflation remains muted. Writes Greg “If a labor market this tight can’t generate better pay, quite possibly it never will in Germany & Japan.”

Third was an article by Glenn Hubbard (Dean of Columbia Business School & former chairman of the Council of Economic Advisors under George W. Bush). His Wall Street Journal op-ed was titled “How to Keep the Fed from Following its Models off a Cliff.”  Hubbard suggests that Fed officials should interact more with market participants and business people. And Fed governors should be selected because of their varied life experiences, and they should encourage a healthy skepticism of prevailing economic models.

Serious money was spent developing these models.  Do you think that climate is in some way simpler than the US economy, so that they are more likely to be accurate?  I do not.

4.  Americans are getting fatter yet living longer, contradicting the model that being mildly overweight is bad for you.  It is far too long to go into so here’s the link —

The first part is particularly fascinating, in that data showed that overweight (not obese) people tended to live longer.  The article describes how people who had spent their research careers telling the public that being overweight was bad, tried to discount the data. The best quote in the article is the following ““We’re scientists. We pay attention to data, we don’t try to un-explain them.”,

4. The economic predictions of the Congressional Budget Office on just about anything –inflation, gross national product, economic growth, the deficit — are consistently wrong —

Addendum 28 June “White house economists overestimated annual economic growth by about 80 percent on average for a six year stretch during Barack Obama’s presidency, according to Freedom Works economic consultant Stephen Moore.

Economists predicted growth between 3.2 to 4.6 percent for the years 2010 through 2015. Actual economic growth never hit above 2.6 percent.”

5.  Animal models of stroke:  There were at least 60, in which some therapy or other was of benefit.  None of them worked in people. It got so bad I stopped reading the literature about it.  We still have no useful treatment for garden variety strokes

6: Live by the model, die by the model. A fascinating book “Shattered” about the Hillary Clinton campaign, explains why the campaign did no polling in the final 3 weeks of the campaign. The man running the ‘data analytics’ (translation: model) Robby Mook, thought the analytics were better and more accurate (p. 367).

I might add that I have no special mistrust of climate models, I just mistrust all models of complex systems.   For some thoughts on climate models please see —

Data Cherry Picking 101

A friend sent me the following link —

It starts off like this — dates in parentheses added by me.

“Health officials in the midwestern U.S. state of Wisconsin reported a record number of new COVID-19 cases Thursday, (28 May) two weeks after the state Supreme Court struck down a state-wide stay-at-home order issued by the governor and enacted by the state health department.

The Wisconsin Department of Health Services reported 599 new known COVID-19 cases Wednesday, (27 May) with 22 known deaths, the highest recorded daily rise since the pandemic began. The department reports the state had more than 16,460 known cases and 539 known deaths as of Wednesday.”

Well that proves it, doesn’t it?   Removing restrictions has clearly  been a disaster.

No it doesn’t.  This is data cherry picking par excellence — one day’s cases — after a long holiday (Memorial Day)  weekend means nothing.  The ‘spike’ is an artifact of how cases are reported.

Here are the daily new COVID-19 cases from Massachusetts (which has relaxed nothing so far)
24 May 382
25 May 281
26 May 197
27 May 688 

Do not forget that there are huge agendas at stake in how data is reported after loosening of the restrictions.  It shouldn’t be that way but it is.

Here are a few apocalyptic predictions about what would happen after Georgia lifted its restrictions 25 April.  Future predictions and definitive statements from these sources should be taken with a grain or more of salt.

From The Atlantic — “Georgia’s Experiment in Human Sacrifice — The state is about to find out how many people need to lose their lives to shore up the economy.” —

A way to end the pandemic — an update

Back on 5 April I wrote a post suggesting that we might be able to end the current coronavirus pandemic by infecting people with a cocktail of the 4 coronaviruses known to cause the common cold.  That post appears verbatim after the *** , along with a comment from a follower and my reply.

In the most recent Science ( — Science  22 May 2020: Vol. 368, Issue 6493, pp. 809-810 the following appeared

” The La Jolla team studied stored blood samples collected between 2015 and 2018, well before the current pandemic began, and detected these cross-reactive helper T cells in about half of them. The researchers think these cells were likely triggered by past infection with one of the four human coronaviruses that cause colds; proteins in these viruses resemble those of SARS-CoV-2.”

This is exactly what I was hoping for by giving the cocktail.  So there is cross reactivity. The quoted material  was tantalizingly brief, so I’ve written the people in La Jolla for more information, but it’s Memorial Day tomorrow.

A degree of immunity, however small, may explain some of the confounding aspects of the epidemic. First off, based on the presence of antibodies to the pandemic coronavirus (SARS-CoV-19), well over 90% of people with them simply aren’t sick. Second, given that 33% of the people in the Bronx have these antibodies, why doesn’t everyone?  Surely the 67% of the Bronx population lacking the antibodies have come in contact with someone who was infected, yet in some way they were immune.  Third, given enough exposure for a long enough time just about everyone gets infected — see some of the horrible examples in the excellent website —

Susceptibility to clinical illness due to SARS-CoV-19 might be analogous to susceptibility to epilepsy.  We know that given enough electrical stimulation, every brain will convulse (see electroconvulsive therapy — ECT). 2% of children and 1% of adults do have spontaneous convulsions (epilepsy). Differential susceptibility to electrically induced convuslions is exactly how Dilantin (phenytoin), one of the first anticonvulsants was discovered in 1938.  All sorts of compounds were thrown at hapless experimental animals, and the amount of electricity needed to convulse them was measured.  An animal given Dilantin required more.

It’s important to note that the Science article wasn’t talking about antibodies, but something else called cellular immunity.  Hopefully the folks in La Jolla will write back and I’ll have more for you on these points in the near future.

Shane Crotty and Alessandro Sette, immunologists at the La Jolla Institute for Immunology




A way to end the pandemic

Could infecting people with the four or so coronaviruses that cause the common cold protect them against the new coronavirus causing the pandemic?   The official name for the new virus is SARS-CoV-2, the name for the disease is COVID-19.

“According to Marie-Louise Landry, MD, an infectious disease expert at Yale Medical School and the Director of the Yale Clinical Virology Laboratory, four common human coronaviruses cause 15-30% of common colds”

Now ask yourself how she could make a statement like this.  I’m going to try to get in touch with her tomorrow, but it is very likely that these cold causing coronaviruses are detected by measuring antibodies to them, carried in the blood of people who have been infected by them in the past.

Could one coronavirus (even a benign one) give partial immunity to others?  It’s possible and it’s time to find out.  We could know  in a few weeks.

Assume the test to measure the antibodies to cold coronaviruses exists.  Then measure them in our real, honest to God, modern day heroes on the front lines  — the nurses, docs, EMTs, orderlies, housekeeping, cops, etc. etc.  who are exposed every day to COVID-19.

Every hospital in the country could at least draw blood on them, look to see if antibodies are present and wait.   I doubt that many would refuse the test.

Sadly, it wouldn’t be long before some of them became infected with SARS-CoV-2.  Then investigators couldlook to see if those with the antibodies to the cold causing coronaviruses were protected.

If so, then make a cocktail of the 4 or so coronaviruses and give it to everyone.   It would be Edward Jenner and the cowpox all over again —

Even if the protection was only partial, decreasing the number of susceptible individuals would be enough to slow the pandemic and possibly even stop it.

  • loupgarous On April 18, 2020 at 12:32 am

    Not afraid of a dengue fever-type antibody-dependent enhancement problem?

Loupgarous: I’m not worried about this with the coronaviruses causing colds. People are worried about immune enhancement with vaccine development for dengue, SARS and RSV and now SARS-CoV-2 [ Proc. Natl. Acad. Sci. vol. 1176 pp. 8218 – 8221 ’20 ]. Immune enhancement definitely happens with clinical dengue (

Why no worries? Because we’ve all had colds, lots of them, probably multiple ones with coronaviruses and no one has seen immune enhancement with colds (of any type). Naturally occurring cold causing coronaviruses are what I’d use if the experiment described in the post showed protection.

However, it is possible that such has happening with coronavirus caused colds, and we’ve been misdiagnosing it as influenza (which does kill a lot of people every year). This seems pretty remote.

Thanks for commenting. I really hadn’t considered this.