Category Archives: Neurology & Psychiatry

I’ve done what I can

Here is the current state of play on my idea that chronic fatigue syndrome might be due to an excess of senescent cells releasing inflammatory mediators. The idea is explained in a copy of the post below, which should be read before proceeding.

The best way to test the idea would be to look for p16^INK4a, a transcription elevated in senescent cells.  This is relatively specific for senescence, far more so than the 74 or so inflammatory mediators which are part of the Senescence Associated Secretory Phenotype (SASP).

The best place to do this would be the Mayo clinic which is currently vigorously investigating the role of senescent cells in aging, cancer and what have you.  When I was in practice, the Mayo clinic linkage system for medical records was legendary.  When Elsewhere General had 10 patients in a series of disease X, Mayo would have 100.  I’m sure they have seen tons of chronic fatigue patients and could easily measure p16^INK4a levels on them.  I’ve corresponded with one worker there (Bennett Childs) and urged him to try the idea — e.g. measure white blood cell levels of p16^INK4a in CFS patients and compare it to controls.  This is cleaner than measuring SASP, because inflammatory mediators can be released by almost any type of infection or pathologic condition.  I noted today, that one researcher is working on developing senolytics, which would be the therapy of choice should my idea pan out.

I”ve written the authors of the PNAS editorial (Komaroff) and the paper (Davis) in the past week but have heard nothing back.

I’ve also contacted the Open Medicine Foundation® (OMF) which appears to be a patient organization founded by a woman whose daughter came down with it.  They apparently even fund research.  They said that they’d share it with their research team.

I wrote the research director of another patient organization but have heard nothing back.

The  post below has been read 250 times.

I am a 79 year old retired neurologist with no academic affiliation, and no way to test the idea.  Hopefully some of those I’ve been in contact with will do so.   The patients are waiting.

Is the era of precision medicine for chronic fatigue syndrome at hand?

If an idea of mine is correct, it is possible that some patients with chronic fatigue syndrome (CFS) can be treated with specific medications based on the results of a few blood tests. This is precision medicine at its finest.  The data to test this idea has already been acquired, and nothing further needs to be done except to analyze it.

Athough the initial impetus for the idea happened only 3 months ago, there have been enough twists and turns that the best way explanation is by a timeline.

First some background:

As a neurologist I saw a lot of people who were chronically tired and fatigued, because neurologists deal with muscle weakness and diseases like myasthenia gravis which are associated with fatigue.  Once I ruled out neuromuscular disease as a cause, I had nothing to offer then (nor did medicine).  Some of these patients were undoubtedly neurotic, but there was little question in my mind that many others had something wrong that medicine just hadn’t figured out yet — not that it hasn’t been trying.

Infections of almost any sort are associated with fatigue, most probably caused by components of the inflammatory response.  Anyone who’s gone through mononucleosis knows this.    The long search for an infectious cause of chronic fatigue syndrome (CFS) has had its ups and downs — particularly downs — see https://luysii.wordpress.com/2011/03/25/evil-scientists-create-virus-causing-chronic-fatigue-syndrome-in-lab/

At worst many people with these symptoms are written off as crazy; at best, diagnosed as depressed  and given antidepressants.  The fact that many of those given antidepressants feel better is far from conclusive, since most patients with chronic illnesses are somewhat depressed.

The 1 June 2017 Cell had a long and interesting review of cellular senescence by Norman Sharpless [ vol. 169 pp. 1000 – 1011 ].  Here is some background about the entity.  If you are familiar with senescent cell biology skip to the paragraph marked **** below

Cells die in a variety of ways.  Some are killed (by infections, heat, toxins).  This is called necrosis. Others voluntarily commit suicide (this is called apoptosis).   Sometimes a cell under stress undergoes cellular senescence, a state in which it doesn’t die, but doesn’t reproduce either.  Such cells have a variety of biochemical characteristics — they are resistant to apoptosis, they express molecules which prevent them from proliferating and — most importantly — they secrete a variety of proinflammatory molecules collectively called the Senescence Associated Secretory Phenotype — SASP).

At first the very existence of the senescent state was questioned, but exist it does.  What is it good for?  Theories abound, one being that mutation is one cause of stress, and stopping mutated cells from proliferating prevents cancer. However, senescent cells are found during fetal life; and they are almost certainly important in wound healing.  They are known to accumulate the older you get and some think they cause aging.

Many stresses induce cellular senescence of which mutation is but one.  The one of interest to us is chemotherapy for cancer, something obviously good as a cancer cell turned senescent has stopped proliferating.   If you know anyone who has undergone chemotherapy, you know that fatigue is almost invariable.

****

One biochemical characteristic of the senescent cell is increased levels of a protein called p16^INK4a, which helps stop cellular proliferation.  While p16^INK4a can easily be measured in tissue biopsies, tissue biopsies are inherently invasive. Fortunately, p16^INK4a can also be measured in circulating blood cells.

What caught my eye in the Cell paper was a reference to a paper about cancer [ Cancer Discov. vol. 7 pp. 165 – 176 ’17 ] by M. Demaria, in which the levels of p16^INK4a correlated with the degree of fatigue after chemotherapy.  The more p16^INK4a in the blood cells the greater the fatigue.

I may have been the only reader of both papers with clinical experience wth chronic fatigue syndrome.  It is extremely difficult to objectively measure a subjective complaint such as fatigue.

As an example of the difficulty in correlating subjective complaints with objective findings, consider the nearly uniform complaint of difficulty thinking in depression, with how such patients actually perform on cognitive tests — e. g. there is  little if any correlation between complaints and actual performance — here’s a current reference — Scientific Reports 7, Article number: 3901(2017) —  doi:10.1038/s41598-017-04353.

If the results of the Cancer paper could be replicated, p16^INK4 would be the first objective measure of a patient’s individual sense of fatigue.

So I wrote both authors, suggesting that the p16^INK4a test be run on a collection of chronic fatigue syndrome (CFS) patients. Both authors replied quickly, but thought the problem would be acquiring patients.  Demaria said that Sharpless had a lab all set up to do the test.

Then fate (in the form of Donald Trump) supervened.  A mere 9 days after the Cell issue appeared, Sharpless was nominated to be the head of the National Cancer Institute by President Trump.  This meant Dr. Sharpless had far bigger fish to fry, and he would have to sever all connection with his lab because of conflict of interest considerations.

I also contacted a patient organization for chronic fatigue syndrome without much success.  Their science advisor never responded.

There matters stood until 22 August when a paper and an editorial about it came out [ Proc. Natl. Acad. Sci. vol. 114 pp. 8914 – 8916, E7150 – E7158 ’17 ].  The paper represented a tremendous amount of data (and work).  The blood levels of 51 cytokines (measures of inflammation) and adipokines (hormones released by fat) were measured in both 192 patients with CFS (which can only be defined by symptoms) and 293 healthy controls matched for age and gender.

In this paper, levels of 17 of the 51 cytokines correlated with severity of CFS. This is a striking similarity with the way the p16^INK4 levels correlated with the degree of fatigue after chemotherapy).  So I looked up the individual elements of the SASP (which can be found in Annu Rev Pathol. 21010; 5: 99–118.)  There are 74 of them. I wondered how many of the 51 cytokines measured in the PNAS paper were in the SASP.  This is trickier than it sounds as many cytokines have far more than one name.  The bottom line is that 20 SASPs are in the 51 cytokines measured in the paper.

If the fatigue of CFS is due to senescent cells and the SASPs  they release, then they should be over-represented in the 17 of the 51 cytokines correlating with symptom severity.  Well they are; 9 out of the 17 are SASP.  However although suggestive, this increase is not statistically significant (according to my consultants on Math Stack Exchange).

After wrote I him about the new work, Dr. Sharpless noted that CFS is almost certainly a heterogeneous condition. As a clinician with decades of experience, I’ve certainly did see some of the more larcenous members of our society who used any subjective diagnosis to be compensated, as well as a variety of individuals who just wanted to withdraw from society, for whatever reason. They are undoubtedly contaminating the sample in the paper. Dr. Sharpless thought the idea, while interesting, would be very difficult to test.

But it wouldn’t at all.  Not with the immense amount of data in the PNAS paper.

Here’s how. Take each of the 9 SASPs and see how their levels correlate with the other 16 (in each of the 192 CSF patients). If they correlate better with SASPs than with nonSASPs, than this would be evidence for senescent cells being the cause some cases of CFS. In particular, patients with a high level of any of the 9 SASPs should be studied for such correlations.  Doing so should weed out some of the heterogeneity of the 192 patients in the sample.

This is why the idea is testable and, even better, falsifiable, making it a scientific hypothesis (a la Karl Popper).  The data to refute it is in the possession of the authors of the paper.

Suppose the idea turns out to be correct and that some patients with CFS are in fact that way because, for whatever reason, they have a lot of senescent cells releasing SASPs.

This would mean that it would be time to start trials of senolyic drugs which destroy senescent cells on the group with elevated SASPs. Fortunately, a few senolytics are currently inc linical use.  This would be precision medicine at its finest.

Being able to alleviate the symptoms of CFS would be worthwhile in itself, but SASP levels could also be run on all sorts of conditions associated with fatigue, most notably infection. This might lead to symptomatic treatment at least.  Having gone through mono in med school, I would have loved to have been able to take something to keep me from falling asleep all the time.

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Is the era of precision medicine for chronic fatigue syndrome at hand?

If an idea of mine is correct, it is possible that some patients with chronic fatigue syndrome (CFS) can be treated with specific medications based on the results of a few blood tests. This is precision medicine at its finest.  The data to test this idea has already been acquired, and nothing further needs to be done except to analyze it.

Athough the initial impetus for the idea happened only 3 months ago, there have been enough twists and turns that the best way explanation is by a timeline.

First some background:

As a neurologist I saw a lot of people who were chronically tired and fatigued, because neurologists deal with muscle weakness and diseases like myasthenia gravis which are associated with fatigue.  Once I ruled out neuromuscular disease as a cause, I had nothing to offer then (nor did medicine).  Some of these patients were undoubtedly neurotic, but there was little question in my mind that many others had something wrong that medicine just hadn’t figured out yet — not that it hasn’t been trying.

Infections of almost any sort are associated with fatigue, most probably caused by components of the inflammatory response.  Anyone who’s gone through mononucleosis knows this.    The long search for an infectious cause of chronic fatigue syndrome (CFS) has had its ups and downs — particularly downs — see https://luysii.wordpress.com/2011/03/25/evil-scientists-create-virus-causing-chronic-fatigue-syndrome-in-lab/

At worst many people with these symptoms are written off as crazy; at best, diagnosed as depressed  and given antidepressants.  The fact that many of those given antidepressants feel better is far from conclusive, since most patients with chronic illnesses are somewhat depressed.

The 1 June 2017 Cell had a long and interesting review of cellular senescence by Norman Sharpless [ vol. 169 pp. 1000 – 1011 ].  Here is some background about the entity.  If you are familiar with senescent cell biology skip to the paragraph marked **** below

Cells die in a variety of ways.  Some are killed (by infections, heat, toxins).  This is called necrosis. Others voluntarily commit suicide (this is called apoptosis).   Sometimes a cell under stress undergoes cellular senescence, a state in which it doesn’t die, but doesn’t reproduce either.  Such cells have a variety of biochemical characteristics — they are resistant to apoptosis, they express molecules which prevent them from proliferating and — most importantly — they secrete a variety of proinflammatory molecules collectively called the Senescence Associated Secretory Phenotype — SASP).

At first the very existence of the senescent state was questioned, but exist it does.  What is it good for?  Theories abound, one being that mutation is one cause of stress, and stopping mutated cells from proliferating prevents cancer. However, senescent cells are found during fetal life; and they are almost certainly important in wound healing.  They are known to accumulate the older you get and some think they cause aging.

Many stresses induce cellular senescence of which mutation is but one.  The one of interest to us is chemotherapy for cancer, something obviously good as a cancer cell turned senescent has stopped proliferating.   If you know anyone who has undergone chemotherapy, you know that fatigue is almost invariable.

****

One biochemical characteristic of the senescent cell is increased levels of a protein called p16^INK4a, which helps stop cellular proliferation.  While p16^INK4a can easily be measured in tissue biopsies, tissue biopsies are inherently invasive. Fortunately, p16^INK4a can also be measured in circulating blood cells.

What caught my eye in the Cell paper was a reference to a paper about cancer [ Cancer Discov. vol. 7 pp. 165 – 176 ’17 ] by M. Demaria, in which the levels of p16^INK4a correlated with the degree of fatigue after chemotherapy.  The more p16^INK4a in the blood cells the greater the fatigue.

I may have been the only reader of both papers with clinical experience wth chronic fatigue syndrome.  It is extremely difficult to objectively measure a subjective complaint such as fatigue.

As an example of the difficulty in correlating subjective complaints with objective findings, consider the nearly uniform complaint of difficulty thinking in depression, with how such patients actually perform on cognitive tests — e. g. there is  little if any correlation between complaints and actual performance — here’s a current reference — Scientific Reports 7, Article number: 3901(2017) —  doi:10.1038/s41598-017-04353.

If the results of the Cancer paper could be replicated, p16^INK4 would be the first objective measure of a patient’s individual sense of fatigue.

So I wrote both authors, suggesting that the p16^INK4a test be run on a collection of chronic fatigue syndrome (CFS) patients. Both authors replied quickly, but thought the problem would be acquiring patients.  Demaria said that Sharpless had a lab all set up to do the test.

Then fate (in the form of Donald Trump) supervened.  A mere 9 days after the Cell issue appeared, Sharpless was nominated to be the head of the National Cancer Institute by President Trump.  This meant Dr. Sharpless had far bigger fish to fry, and he would have to sever all connection with his lab because of conflict of interest considerations.

I also contacted a patient organization for chronic fatigue syndrome without much success.  Their science advisor never responded.

There matters stood until 22 August when a paper and an editorial about it came out [ Proc. Natl. Acad. Sci. vol. 114 pp. 8914 – 8916, E7150 – E7158 ’17 ].  The paper represented a tremendous amount of data (and work).  The blood levels of 51 cytokines (measures of inflammation) and adipokines (hormones released by fat) were measured in both 192 patients with CFS (which can only be defined by symptoms) and 293 healthy controls matched for age and gender.

In this paper, levels of 17 of the 51 cytokines correlated with severity of CFS. This is a striking similarity with the way the p16^INK4 levels correlated with the degree of fatigue after chemotherapy).  So I looked up the individual elements of the SASP (which can be found in Annu Rev Pathol. 21010; 5: 99–118.)  There are 74 of them. I wondered how many of the 51 cytokines measured in the PNAS paper were in the SASP.  This is trickier than it sounds as many cytokines have far more than one name.  The bottom line is that 20 SASPs are in the 51 cytokines measured in the paper.

If the fatigue of CFS is due to senescent cells and the SASPs  they release, then they should be over-represented in the 17 of the 51 cytokines correlating with symptom severity.  Well they are; 9 out of the 17 are SASP.  However although suggestive, this increase is not statistically significant (according to my consultants on Math Stack Exchange).

After wrote I him about the new work, Dr. Sharpless noted that CFS is almost certainly a heterogeneous condition. As a clinician with decades of experience, I’ve certainly did see some of the more larcenous members of our society who used any subjective diagnosis to be compensated, as well as a variety of individuals who just wanted to withdraw from society, for whatever reason. They are undoubtedly contaminating the sample in the paper. Dr. Sharpless thought the idea, while interesting, would be very difficult to test.

But it wouldn’t at all.  Not with the immense amount of data in the PNAS paper.

Here’s how. Take each of the 9 SASPs and see how their levels correlate with the other 16 (in each of the 192 CSF patients). If they correlate better with SASPs than with nonSASPs, than this would be evidence for senescent cells being the cause some cases of CFS. In particular, patients with a high level of any of the 9 SASPs should be studied for such correlations.  Doing so should weed out some of the heterogeneity of the 192 patients in the sample.

This is why the idea is testable and, even better, falsifiable, making it a scientific hypothesis (a la Karl Popper).  The data to refute it is in the possession of the authors of the paper.

Suppose the idea turns out to be correct and that some patients with CFS are in fact that way because, for whatever reason, they have a lot of senescent cells releasing SASPs.

This would mean that it would be time to start trials of senolyic drugs which destroy senescent cells on the group with elevated SASPs. Fortunately, a few senolytics are currently inc linical use.  This would be precision medicine at its finest.

Being able to alleviate the symptoms of CFS would be worthwhile in itself, but SASP levels could also be run on all sorts of conditions associated with fatigue, most notably infection. This might lead to symptomatic treatment at least.  Having gone through mono in med school, I would have loved to have been able to take something to keep me from falling asleep all the time.

 

How do neural nets do what they do?

Isn’t it enough that neural nets beat humans at chess, go and checkers, drive cars, recognize faces, find out what plays Shakespeare did and didn’t write?  Not at all.  Figuring out how they do what they do may allow us to figure out how the brain does what it does.

Science recently had a great bunch of articles on neural nets, deep learning [ Science vol. 356 pp. 16 – 30 ’17 ].  Chemists will be interested in p. 27 “Neural networks learn the art of chemical synthesis”.  The articles are quite accessible to the scientific layman.

To this retired neurologist, the most interesting of the bunch was the article (pp. 22 – 270 describing attempts to figure out how neural nets do what they do. Welcome to the world of the neuroscientist where a similar problem has engaged us for centuries.  DARPA is spending 70 million on exactly this according to the article.

If you are a little shaky on all this — I’ve copied a previous post on the subject (along with a few comments it inspired) below the ****

Here are four techniques currently in use:

  1. Counterfactual probes — the classic black box technique — vary the input (text, images, sound, ..  )and watch how it affects output.  It goes by the fancy name of Local Interpretable Model agnostic Explanations (LIME).  This allows the parts of the input most important in the net’s original judgement.
  2. Start with a black image or a zeroed out array of text and transition step by step toward the example being tested.  Then you watch the jumps in certainty the net makes, and you can figure out what it thinks is important.
  3. General Additive Model (GAM) is a statistical technique based on linear regression.  It operates on data to massage it.  The net is then presented with a variety of operations of GAM and studied to see which are the best at data massage so the machine can make a correct decision.
  4. Glass Box wires monotonic relationships (e.g the price of a house goes up with the number of square feet) INTO the neural net — allowing better control of what it does

The articles don’t appear to be behind a paywall, so have at it.

***

NonAlgorithmic Intelligence

Penrose was right. Human intelligence is nonAlgorithmic. But that doesn’t mean that our physical brains produce consciousness and intelligence using quantum mechanics (although all matter is what it is because of quantum mechanics). The parts (even small ones like neurotubules) contain so much mass that their associated wavefunction is too small to exhibit quantum mechanical effects. Here Penrose got roped in by Kauffman thinking that neurotubules were the carriers of the quantum mechanical indeterminacy. They aren’t, they are just too big. The dimer of alpha and beta tubulin contains 900 amino acids — a mass of around 90,000 Daltons (or 90,000 hydrogen atoms — which are small enough to show quantum mechanical effects).

So why was Penrose right? Because neural nets which are inherently nonAlgorithmic are showing intelligent behavior. AlphaGo which beat the world champion is the most recent example, but others include facial recognition and image classification [ Nature vol. 529 pp. 484 – 489 ’16 ].

Nets are trained on real world images and told whether they are right or wrong. I suppose this is programming of a sort, but it is certainly nonAlgorithmic. As the net learns from experience it adjusts the strength of the connections between its neurons (synapses if you will).

So it should be a simple matter to find out just how AlphaGo did it — just get a list of the neurons it contains, and the number and strengths of the synapses between them. I can’t find out just how many neurons and connections there are, but I do know that thousands of CPUs and graphics processors were used. I doubt that there were 80 billion neurons or a trillion connections between them (which is what our brains are currently thought to have).

Just print out the above list (assuming you have enough paper) and look at it. Will you understand how AlphaGo won? I seriously doubt it. You will understand it less well than looking at a list of the positions and momenta of 80 billion gas molecules will tell you its pressure and temperature. Why? Because in statistical mechanics you assume that the particles making up an ideal gas are featureless, identical and do not interact with each other. This isn’t true for neural nets.

It also isn’t true for the brain. Efforts are underway to find a wiring diagram of a small area of the cerebral cortex. The following will get you started — https://www.quantamagazine.org/20160406-brain-maps-micron-program-iarpa/

Here’s a quote from the article to whet your appetite.

“By the end of the five-year IARPA project, dubbed Machine Intelligence from Cortical Networks (Microns), researchers aim to map a cubic millimeter of cortex. That tiny portion houses about 100,000 neurons, 3 to 15 million neuronal connections, or synapses, and enough neural wiring to span the width of Manhattan, were it all untangled and laid end-to-end.”

I don’t think this will help us understand how the brain works any more than the above list of neurons and connections from AlphaGo. There are even more problems with such a list. Connections (synapses) between neurons come and go (and they increase and decrease in strength as in the neural net). Some connections turn on the receiving neuron, some turn it off. I don’t think there is a good way to tell what a given connection is doing just by looking a a slice of it under the electron microscope. Lastly, some of our most human attributes (emotion) are due not to connections between neurons but due to release of neurotransmitters generally into the brain, not at the very localized synapse, so it won’t show up on a wiring diagram. This is called volume neurotransmission, and the transmitters are serotonin, norepinephrine and dopamine. Not convinced? Among agents modifying volume neurotransmission are cocaine, amphetamine, antidepressants, antipsychotics. Fairly important.

So I don’t think we’ll ever truly understand how the neural net inside our head does what it does.

 Here are a few of the comments

“So why was Penrose right? Because neural nets which are inherently nonAlgorithmic are showing intelligent behavior. ”

I picked up a re-post of this comment on Quanta and thought it best to reply to you directly. Though this appears to be your private blog I can’t seem to find a biography, otherwise I’d address you by name.

My background is computer science generally and neural networks (with the requisite exposure to statistical mechanics) in particular and I must correct the assertion you’ve made here; neural nets are in fact both algorithmic and even repeatable in their performance.

I think what you’re trying to say is the structure of a trained network isn’t known by the programmer in advance; rather than build a trained intelligence, the programmer builds an intelligence that may be trained. The method is entirely mathematical though, mostly based on the early work of Boltzmann and explorations of the Monte-Carlo algorithms used to model non-linear thermodynamic systems.

For a good overview of the foundations, I suggest J.J. Hopfield’s “Neural networks and physical systems with emergent collective computational abilities”, http://www.pnas.org/content/79/8/2554.abstract

Regards,
Scott.

Scott — thanks for your reply. I blog anonymously because of the craziness on the internet. I’m a retired neurologist, long interested in brain function both professionally and esthetically. I’ve been following AI for 50 years.

I even played around with LISP, which was the language of AI in the early days, read Minsky on Perceptrons, worried about the big Japanese push in AI in the 80’s when they were going to eat our lunch etc. etc.

I think you’d agree that compared to LISP and other AI of that era, neural nets are nonAlgorithmic. Of course setting up virtual neurons DOES involve programming.

The analogy with the brain is near perfect. You can regard our brains as the output of the embryologic programming with DNA as the tape.

But that’s hardly a place to stop. How the brain and neural nets do what they do remains to be determined. A wiring diagram of the net is available but really doesn’t tell us much.

Again thanks for responding.

Scott

Honestly it would be hard for me to accept that the nets I worked on weren’t algorithmic since they were literally based on formal algorithms derived directly from statistical mechanics, most of which was based on Boltzmann’s work back in the 19th century. Hopfield, who I truly consider the “father” of modern neural computing, is a physicist (now at Princeton I believe). Most think he was a computer scientists back on the 70’s when he did his basic work at Cal Tech, but that’s not really the case.

I understand what you’re trying to say, that the actual training portion of NN development isn’t algorithmic, but the NN software itself is and it’s extremely precise in its structure, much more so than say, for instance, a bubble sort. It’s pretty edgy stuff even now.

I began working on NN’s in ’82 after reading Hopfield’s seminal paper, I was developing an AI aimed at self-diagnosing computer systems for a large computer manufacturer now known as Hewlett-Packard (at the time we were a much smaller R&D company who were later aquired). We also explored expert systems and ultimately deployed a solution based on KRL, which is a LISP development environment built by a small Stanford AI spinoff. It ended up being a dead end; that was an argument I lost (I advocated the NN direction as much more promising but lost mostly for political reasons). Now I take great pleasure in gloating 🙂 even though I’m no longer commercially involved with either the technology or that particular company.

Luysii

I thought we were probably in agreement about what I said. Any idea on how to find out just how many ‘neurons’ (any how many levels) there are in AlphaGo? It would be interesting to compare the numbers with our current thinking about the numbers of cortical neurons and synapses (which grows ever larger year by year).

Who is the other Scott — Aronson? 100 years ago he would have been a Talmudic scholar (as he implies by the title of his blog).

Yes neural nets are still edgy, and my son is currently biting his fingernails in a startup hoping to be acquired which is heavily into an application of neural nets (multiple patents etc. etc.)

A possible new player

Drug development is very hard because we don’t know all the players inside the cell. A recent paper describes an entirely new class of player — circular DNA derived from an ancient virus.  The authoress is Laura Manuelidis, who would have been a med school classmate had I chosen to go to Yale med instead of Penn.   She is the last scientist standing who doesn’t believe Prusiner’s prion hypothesis.  She didn’t marry the boss’s daughter being female, so she married the boss instead;  Elias Manuelidis a Yale neuropathologist who would be 99 today had he not passed away at 72 in 1992.

The circular DNAs go by the name of SPHINX  an acronym  for  Slow Progressive Hidden INfections of X origin.  They have no sequences in common with bacterial or eukaryotic DNA, but there some homology to a virus infecting Acinebacter, a wound pathogen common in soil and water.

How did she find them?  By doggedly pursuing the idea the neurodegenerative diseases such as Cruetzfeldt Jakob Disease (CJD) and scrapie were due to an infectious agent triggering aggregation of the prion protein.

As she says:  “The cytoplasm of CJD and scrapie-infected cells, but not control cells, also contains virus-like particle arrays and because we were able to isolate these nuclease-protected particles with quantitative recovery of infectivity, but with little or no detectable PrP (Prion Protein), we began to analyze protected nucleic acids. Using Φ29 rolling circle amplification, several circular DNA sequences of <5 kb (kilobases) with ORFs (Open Reading Frames) were thereby discovered in brain and cultured neuronal cell lines. These circular DNA sequences were named SPHINX elements for their initial association with slow progressive hidden infections of X origin."

SPHINX itself codes for a 324 amino acid protein, which is found in human brain, concentrated in synaptic boutons.  Strangely, even though the DNAs are presumably viral derived, they contain intervening sequences which don't code for protein.

The use of rolling circle amplification is quite clever, as it will copy only circular DNA.

Stanley Prusiner is sure to weigh in.  Remarkably, Prusiner was at Penn Med when I was and was even in my med school fraternity (Nu Sigma Nu)  primarily a place to eat lunch and dinner.  I probably ate with him, but have no recollection of him whatsoever.

Circular DNAs outside chromosomes are called plasmids. Bacteria are full of them. The best known eukaryote containing plasmids is yeast. Perhaps we have them as well. Manuelidis may be the first person to look.

The best laid plans of mice and men

I sent a copy of the previous post (reprinted below) about an idea to diagnose and treat chronic fatigue syndrome to Dr. Norman Sharpless, the author of the Cell review on cellular senescence.  He thought the idea was “great”; and, even better, he ran the lab which did the test I wanted to try.  I also sent a copy to a patient group.  “Solve ME/CFS Initiative”, and they want to use the post on their website.

Sharpless noted that the problem with ideas like this is accumulating patients, something the patient group could probably provide.  So all went well until 8 days ago when Dr. Sharpless was named to be the head of the National Cancer Institute, with its 4.5 billion dollar  budget by President Trump.  Being a full prof at the University of North Carolina Medical School, he would have been the ideal individual to run the study (or find someone to do it), but he now has far bigger fish to fry.

After I wrote to congratulate him, he wrote back reiterating that the idea was good, but he said he had to sever all connections with the lab he founded due to conflict of interest considerations.  He did give me the name of someone to contact there, which is where the matter stands presently.

Since the idea is based on the correlation between the amount of fatigue after chemotherapy with the level of a white cell protein (p16^INK4a), he would have had no problem accumulating chemotherapy patients as head of NCI, but again the spectre of conflict of interest rears its ugly head.  Repeating the chemotherapy study to make sure the results are in fact real is the first order of business.

So there you have a research idea, endorsed by the new head of the NCI.  I am a retired neurologist, who no longer has a license to practice medicine (but who doesn’t need a license to think).

If you’re an academic out there, looking for something to do, write up a grant proposal.  The current treatments do help people live with chronic fatigue syndrome, but they are in no sense treatments of the underlying problem.

Here is the original post

How to (possibly) diagnose and treat chronic fatigue syndrome (myalgic encephalomyelitis)

As a neurologist I saw a lot of people who were chronically tired and fatigued, because neurologists deal with muscle weakness and diseases like myasthenia gravis which are associated with fatigue.  Once I ruled out neuromuscular disease as a cause, I had nothing to offer then (nor did medicine).  Some were undoubtedly neurotic, but there was little question in my mind that some of them had something wrong that medicine just hadn’t figured out.  Not it hasn’t been trying.

Infections of almost any sort are associated with fatigue, probably because components of the inflammatory response cause it.  Anyone who’s gone through mononucleosis knows this.    The long search for an infectious cause of chronic fatigue syndrome (CFS) has had its ups and downs — particularly downs — see https://luysii.wordpress.com/2011/03/25/evil-scientists-create-virus-causing-chronic-fatigue-syndrome-in-lab/

At worst many people with these symptoms are written off as crazy; at best, depressed  and given antidepressants.  The fact that many of those given antidepressants feel better is far from conclusive, since most patients with chronic illnesses are somewhat depressed.

Even if we didn’t have a treatment, just having a test which separated sufferers from normal people would at least be of some psychological help, by telling them that they weren’t nuts.

Two recent papers may actually have the answer. Although neither paper dealt with chronic fatigue syndrome directly, and I can find no studies in the literature linking what I’m about to describe to CFS they at least imply that there could be a diagnostic test for CFS, and a possible treatment as well.

Because I expect that many people with minimal biological background will be reading this, I’ll start by describing the basic biology of cellular senescence and death

Background:  Most cells in our bodies are destined to die long before we do. Neurons are the longest lasting (essentially as long as we do).  The lining of the intestines is renewed weekly.  No circulating blood cell lasts more than half a year.

Cells die in a variety of ways.  Some are killed (by infections, heat, toxins).  This is called necrosis. Others voluntarily commit suicide (this is called apoptosis).   Sometimes a cell under stress undergoes cellular senescence, a state in which it doesn’t die, but doesn’t reproduce either.  Such cells have a variety of biochemical characteristics — they are resistant to apoptosis, they express molecules which prevent them from proliferating and most importantly, they secrete proinflammatory molecules (this is called the Senescence Associated Secretory Phenotype — SASP).

At first the very existence of the senescent state was questioned, but exist it does.  What is it good for?  Theories abound, one being that mutation is one cause of stress, and stopping mutated cells from proliferating prevents cancer. However, senescent cells are found during fetal life; and they are almost certainly important in wound healing.  They are known to accumulate the older you get and some think they cause aging.

Many stresses induce cellular senescence.  The one of interest to us is chemotherapy for cancer, something obviously good as a cancer cell turned senescent has stopped proliferating.   If you know anyone who has undergone chemotherapy, you know that fatigue is almost invariable.

One biochemical characteristic of the senescent cell is increased levels of a protein called p16^INK4a, which helps stop cellular proliferation.  While p16^INK4a can easily be measured in tissue biopsies, tissue biopsies are inherently not easy. Fortunately it can also be measured in circulating blood cells.

The following study — Cancer Discov. vol. 7 pp. 165 – 176 ’17 looked at 89 women with breast cancer undergoing chemotherapy. They correlated the amount of fatigue experienced with the levels of p16^INK4a in a type of circulating white blood cell (T lymphocyte).  There was a 44% incidence of fatigue in the highest quartile of  p16^INK4a levels, vs. a 5% incidence of fatigue in the lowest. The cited paper didn’t mention CFS nor did the highly technical but excellent review on which much of the above is based [ Cell vol. 169 pp. 1000 -1011 ’17 ]

But it is definitely time to measure p16^INK4a levels in patients with chronic fatigue and compare them to people without it.  This may be the definitive diagnostic test, if people with CFS show higher levels of p16^INK4a.

If this turns out to be the case, then there is a logical therapy for chronic fatigue syndrome.  As mentioned above, senescent cells are resistant to apoptosis (voluntary suicide).  What stops these cells from suicide? Naturally occurring cellular suicide inhibitors (with names like BCL2, BCL-XL, BCL-W) do so .  Drugs called sensolytics already exist to target the inhibitors causing senescent cells to commit suicide.

So if excessive senescent cells are the cause of CFS, then killing them should make things better. Sensolytics do exist but there are problems; one couldn’t be used because of side effects.  Others do exist (one such is Venetoclax) and have been approved by the FDA for leukemia — but it isn’t as potent .

So there is a potentially both a diagnostic test and a treatment for CFS.

The initial experiment should be fairly easy for research to do — just corral some CSF patients and controls and run a test for p16^INK4a levels in their blood cells. Also easy on the patients as only a blood draw is involved.

This, in itself, would be great, but there is far more to think about.

If CFS patients have too many senescent cells, getting rid of them — although (hopefully) symptomatically beneficial — will not get rid of what caused the senescent cells to accumulate in the first place. In addition, getting rid of all of them at once would probably cause huge problems causing something similar to the tumor lysis syndrome – https://en.wikipedia.org/wiki/Tumor_lysis_syndrome.

But these are problems CFS patients and

Progress has been slow but not for want of trying

Progress in the sense of therapy for Alzheimer’s disease and Glioblastoma multiforme is essentially nonexistent, and we could use better therapy for Parkinsonism. This doesn’t mean that researchers have given up. Far from it. Three papers all in last week’s issue of PNAS came up with new understanding and possibly new therapeutic approaches for all three.

You’ll need some serious molecular biological and cell physiological chops to get through the following.

l. Glioblastoma multiforme — they aren’t living much longer than they were when I started pracice 45 years ago (about 2 years — although of course there are exceptions).

The human ZBTB family of genes consists of 49 members coding for transcription factors. BCL6 is also known as ZBTB27 and is a master regulator of lymph node germinal responses. To execute its transcriptional activity, BCL6 requires homodimerization and formation of a complex with a variety of cofactors including BCL6 corerpressor (BCoR), nuclear receptor corepressor 1 (NCoR) and Silencing Mediator of Retinoic acid and Thyroid hormone receptor (SMRT). BCL6 inhibitors block the interaction between BCL6 and its friends, selectively killing BCL6 addicted cancer cells.

The present paper [ Proc. Natl. Acad. Sci. vol. 114 pp. 3981 – 3986 ’17 ] shows that BCL6 is required for glioblastoma cell viability. One transcriptional target of BCL6 is AXL, a tyrosine kinase. Depletion of AXL also decreases proliferation of glioblastoma cells in vitro and in vivo (in a mouse model of course).

So here are two new lines of attack on a very bad disease.

2. Alzheimer’s disease — the best we can do is slow it down, certainly not improve mental function and not keep mental function from getting worse. ErbB2 is a member of the Epidermal Growth Factor Receptor (EGFR) family. It is tightly associated with neuritic plaques in Alzheimer’s. Ras GTPase activation mediates EGF induced stimulation of gamma secretase to increase the nuclear function of the amyloid precursor protein (APP) intracellular domain (AICD). ErbB2 suppresses the autophagic destruction of AICD, physically dissociating Beclin1 vrom the VPS34/VPS15 complex independently of its kinase activity.

So the following paper [ Proc. Natl. Acad. Sci. vol. 114 pp. E3129 – E3138 ’17 ] Used a compound downregulating ErbB2 function (CL-387,785) in mouse models of Alzheimer’s (which have notoriously NOT led to useful therapy). Levels of AICD declined along with beta amyloid, and the animals appeared smarter (but how smart can a mouse be?).

3.Parkinson’s disease — here we really thought we had a cure back in 1972 when L-DOPA was first released for use in the USA. Some patients looked so good that it was impossible to tell if they had the disease. Unfortunately, the basic problem (death of dopaminergic neurons) continued despite L-DOPA pills supplying what they no longer could.

Nurr1 is a protein which causes the development of dopamine neurons in the embryo. Expression of Nurr1 continues throughout life. Nurr1 appears to be a constitutively active nuclear hormone receptor. Why? Because the place where ligands (such as thyroid hormone, steroid hormones) bind to the protein is closed. A few mutations in the Nurr1 gene have been associated with familial parkinsonism.

Nurr1 functions by forming a heterodimer with the Retinoid X Receptor alpha (RXRalpha), another nuclear hormone receptor, but one which does have an open binding pocket. A compound called BRF110 was shown by the following paper [ Proc. Natl. Acad. Sci. vol. 114 pp. 3795 – 3797, 3999 – 4004 ’17 ] to bind to the ligand pocked of RXRalpha increasing its activity. The net effect is to enhance expression of dopamine neuron specific genes.

More to the point MPP+ is a toxin pretty selective for dopamine neurons (it kills them). BRF110 helps survival against MPP+ (but only if given before toxin administration). This wouldn’t be so bad because something is causing dopamine neurons to die (perhaps its a toxin), so BRF110 may fight the decline in dopamine neuron numbers, rather than treating the symptoms of dopamine deficiency.

So there you have it 3 possible new approaches to therapy for 3 bad disease all in one weeks issue of PNAS. Not easy reading, perhaps, but this is where therapy is going to come from (hopefully soon).

How the brain really works (maybe) – 2

I sent the previous post to a very intelligent friend — a PhD Electrical Engineer who responded as follows

“Correct me if I’m wrong, but it sounds like you are proposing that in addition to direct communication in the nervous system via electrical and chemical synapses, you are proposing that there could also be communication coupling in nerve fibers via local electric fields. But isn’t this a known phenomenon, ephaptic coupling? See
https://en.wikipedia.org/wiki/Ephaptic_coupling&#8221;

I didn’t think EE’s knew about such things (but I told you he’s very smart). Here are a few extra points of mine concerning his response and the article in general.

Excellent point. Thanks. What I propose could certainly be called ephaptic transmission. It has been well described between two axons in peripheral nerves (this was the initial description). Ephaptic transmission is fairly well established in muscle (which also has action potentials spreading along the muscle fiber allowing it to contract). Investigation in the brain has primarily been between adjacent neurons or adjacent axons. Questions have arisen as to whether it could be a mechanism of seizure generation.

As far as I can tell, the following ideas are actually original.
(1) Ephaptic transmission could normally occur between dendrites in the cerebral cortex.
(2) The brain and cerebral cortex is built the way it is to allow dendritic ephaptic transmission to occur.
(3) This is the way serious computations are carried out by the cerebral cortex.

Why now? Probably because there was no way of measuring dendritic electric potential changes directly before this paper (prior to this calcium levels in dendrites were used as a surrogate). Another example of new technology driving the science.

I didn’t put it in the original post, but actual paper notes that the potential flucutations across the dendritic membrane were much larger than the fluctuations recorded at the cell body.

People have wondered for years how various electrical activities in the brain could be synchronized over large areas (every electrical wave seen in the electroencephalogram is the activity of hundreds of thousands to millions of neurons). This may be an explanation — previously people had figured that it was coming from neurons lower in the brain (particularly the thalamus) sending axons all over the place stimulating neurons simultaneously. Even this doesn’t really work, because various areas of the brain are separted from each other, axonal speed is thought to be constant, and the impulses have different distances to travel.

One disturbing aspect to the picture in the previous post — If you regard that neuron as embedded in a cube 50 x 50 x 50 microns on a side, you’d get about 8,000 neurons per cubic millimeter (1,000 x 1,000 x 1,000 cubic microns). The literature says over twice that at 20,000 neurons/cubic millimeter.

I doubt that the above constitutes all the implications of these ideas. Any comments? I am quite interested to hear them.

How the brain really works (maybe)

Stare at the picture just below long and hard. It’s where the brain probably does its calculation — no, not the neuron in the center. No, not the astrocyte just above. Enlarge the picture many times. It’s all those tiny little circles and ellipses you see around the apical dendrite. They all represent nerve and glial processes. A few ellipses have very dark borders — this is myelin (which insulates them allowing them to conduct nerve impulses faster, and which also insulates them from being affected by the goings on of nerve processes next to them). Note that most of the nerve processes do NOT have myelin around them.

Now look at the bar at the lower right in the picture which tells you the magnification. 5 um is 5 microns or 50,000 Angstroms or roughly 10 times the wavelength of visible light (4,000 – 8,000 Angstroms). Look at the picture again and notice just how closely the little circles and ellipses are applied to each other (certainly closer than 1/10 of the bar). This is exactly why there was significant debate between two of the founders of neuroHistology — Camillo Golgi and Ramon Santiago y Cajal.

Unlike every other tissue in the body the brain is so tightly packed that it is impossible to see the cells that make it up with the usual stains used by light microscopists. People saw nuclei all right but they thought the brain was a mass of tissue with nuclei embedded in it (like a slime mold). It wasn’t until the late 1800′s that Camillo Golgi developed a stain which would now and then outline a neuron with all its processes. Another anatomist (Ramon Santiago y Cajal) used Golgi’s technique and argued with Golgi that yes the brain was made of cells. Fascinating that Golgi, the man responsible for showing nerve cells, didn’t buy it. This was a very hot issue at the time, and the two received a joint Nobel prize in 1906 (only 5 years after the prizes began).

The paper discussed below gives a possible reason why the brain is built like this — e.g. it’s how it works !!

Pictures are impressive, but could it be all artifact? To see something with an electron microscope (which this picture is) you really have to process the tissue to a fare-thee-well. One example from way back in the day when I started medical school (1962). Electron microscopy was just coming in, and the first thing we were supposed to see was something called the unit membrane surrounding each cell –two dark lines surrounding a light line, the whole mess being about 60 – 80 Angstroms thick. The dark lines were held to be proteins and the light line was supposed to be lipid. Fresh off 2 years of grad school in chemistry, I tried to figure out just what the chemical treatments used to put tissue in a form suitable for electron microscopy would do to proteins and lipids. It was impossible, but I came away impressed with just how vigorous and traumatic what the microscopists were doing actually was.

To make a long story short — the unit membrane was an artifact of fixation. We now know that the cell membrane has a thickness half that of the unit membrane, with all sorts of proteins going through the lipid.

This is something to keep in mind, for you to avoid being snowed by such pictures. Clinical neurologists and neurosurgeons know quite well that a brain lacking oxygen and glucose swells (a huge clinical problem), and dead brain is exactly that.

Even with all these caveats about electron microscopy of the brain, I think the picture above is pretty close to reality. In favor of tight packing is the following work (along with the staining work of over a century ago). [ Proc. Natl. Acad. Sci. vol. 103 pp. 5567 – 5572 ’06 ] injected spheres of different sizes (quantum dots actually) into the rat cerebral cortex, and watched how far they got from the site of injection. Objects ‘as large as’ 350 Angstroms were able to diffuse freely. This was larger than the width seen on electron microscopy (180 Angstroms) but still quite small and too small to be ‘seen’ with visible light.

What’s the point of all this? Simply that the neuropil of the cerebral cortex (all the stuff in the picture which isn’t the cell body of the neuron or the astrocyte) could be where the real computations of the cerebral cortex actually take place. In my opinion, ‘could be’ should be ‘is’ in the previous sentence.

Why? Because of the work described in a previous post — which is repeated in toto below the line of ****

Briefly, the authors of that paper claim to be able to look at the electrical activity of these small processes in the neuropil. How small? A diameter of 5 microns or less. This had never been done before. It was a tremendous technical achievement to do this in a living animal. What they found was that the frequency of spikes in these processes (likely dendrites) during sleep was 7 times greater than the frequency of spikes recorded next to the cell body (soma) which had been done many times before. During wakefulness, the frequency of spikes in the neuropil was 10 fold greater.

I’ve always found it remarkable that most neurons in the cerebral cortex aren’t firing all that rapidly (a few spikes per second — Science vol. 304 pp. 523 – 524, 559 – 563 ’04 ). Neurons (particularly sensory neurons) can fire a lot faster than that — ‘up to’ 500/second.

Perhaps this work explains why — the real calculations are being done in the neuropil by the dendrites.

Even more remarkably, it is possible that the processes of the neuropil are influencing each other without synapses between them because they are so closely packed. The membrane potential shifts the authors measured were much larger than the spikes in the dendrites. So the real computations being performed by the brain might not involve synapses at all ! This would be an explanation of why brain cells and their processes are so squeezed together. So they can talk to each other. No other organ in the body is like this throughout.

This post is already long enough, but the implications are worth exploring further. I’ve written about wiring diagrams of the brain, and how it is at least possible that they wouldn’t tell you how the brain worked — https://luysii.wordpress.com/2011/04/10/would-a-wiring-diagram-of-the-brain-help-you-understand-it/.

There is another possible reason that the wiring diagram wouldn’t be enough to give you an understanding. Here is an imperfect analogy. Suppose you had a complete map of every road and street in the USA, along with the address of every house, building and structure in it. In addition you could also measure the paths of all the vehicles on the roads for one day. Would this tell you how the USA worked? It would tell you nothing about what was going on inside the structures, or how it influenced traffic on the roads.

The paper below is seminal, because for the first time, it allows us to see what brain neurons are doing in all their parts — not just the cell body or the axon (which is all we’ve been able to look at before).

If these speculations are true, the brain is a much more powerful parallel processor than anything we are able to build presently (and possibly in the future). Each pyramidal neuron in the cortex would then be a microprocessor locally influencing all those in its vicinity — and in a cubic millimeter of the cerebral cortex (1,000 x 1,000 x 1,000 microns) there are 20,000 – 100,000 neurons (Science vol. 304 pp. 523 – 524, 559 – 563 ’04).

Fascinating stuff — stay tuned
*****

A staggeringly important paper (if true)

Our conception of how our brain does what it does has just been turned upside down, inside out and from the middle to each end — if the following paper holds up [ Science vol. 355 pp. 1281 eaaj1497 1 –> 10 ’17 ] The authors claim to be able to measure electrical activity in dendrites in a living, behaving animal for days at a time. Dendrites are about the size of the smallest electrodes we have, so impaling them destroys them. The technical details of what they did are crucial, as much of what they report may be artifact due to injury produced by the way they acquired their data.

First a picture of a pyramidal neuron of the cerebral cortex — https://en.wikipedia.org/wiki/Pyramidal_cell — the cell body is only 20 microns in diameter (the giant pyramidal neurons giving rise to the corticospinal tract are much larger with diameters of 100 microns). Look at the picture in the article. If the cell body is (soma) 20 microns the dendrites arising from it (particularly the apical dendrite) are at most 5 microns thick.

Here’s what they did. A tetrode is a bundle of 4 very fine electrodes. Bundle diameter is only 30 – 40 microns with a 5 micron gap between the tips. This allows an intact dendrite to be caught in the gap. The authors note that chronically implanted tetrodes produce an immune response, in which glial cells proliferate and wall off the tetrode, shielding it from the extracellular medium by forming a high impedance sheath. This allows the tetrode to measure the electrical activity of a dendrite caught between the 4 tips (and hopefully little else).

How physiologic is this activity? Remember that epilepsy developing after head trauma is thought to be due to abnormal electrical activity due to glial scars, and a glial scar is exactly what is found around the tetrode. So a lot more work needs to be done replicating this, and studying similar events in neuronal culture (without glia present).

Well those are the caveats. What did they find? The work involved 9 rats and 22 individually adjustable tetrodes. They found that spikes in the dendrites were quite different than the spikes found by a tetrode next to the pyramidal cell body. The dendritic spikes were larger (570 -2,100 microVolts) vs. 80 microVolts recorded extracellularly for spikes arising at the soma. Of course when the soma is impaled by an electrode you get a much larger spike.

More importantly, the dendritic spike rates were 5 times greater than the somatic spike rates during slow wave sleep and 10 times greater during exploration when awake. The authors call these dendritic action potentials (DAPs). Their amplitude was always positive.

They were also able to measure how the membrane potential of the dendrite fluctuated. The membrane potential fluctuations were always larger than the dendritic spikes themselves (by 7 fold). The size of the flucuations correlated with DAP magnitude and rate.

So all the neuronal spikes and axonal action potentials we’ve been measuring over the years (because it was all we could measure), may be irrelevant to what the brain is really doing. Maybe the real computation is occuring within dendrites.

Now we know we can put an electrode in the brain outside of any neuron and record something called a local field potential — which is held to be a weighted sum of transmembrane currents due to synaptic and dendritic activity and arises within 250 microns of the electrode (and probably closer than that).

So fluctuating potentials are out there in the substance of the brain, outside any neuronal structure. Is it possible that the changes in membrane potential in dendrites are felt by other dendrites and if so is this where the brain’s computations are really taking place? Could synapses be irrelevant to this picture, and each pyramidal neuron not be a transistor but a complex analog CPU? Heady stuff. It certainly means goodbye to the McCullouch Pitts model — https://en.wikipedia.org/wiki/Artificial_neuron.

A staggeringly important paper (if true)

Our conception of how our brain does what it does has just been turned upside down, inside out and from the middle to each end — if the following paper holds up [ Science vol. 355 pp. 1281 eaaj1497 1 –> 10 ’17 ] The authors claim to be able to measure electrical activity in dendrites in a living, behaving animal for days at a time. Dendrites are about the size of the smallest electrodes we have, so impaling them destroys them. The technical details of what they did are crucial, as much of what they report may be artifact due to injury produced by the way they acquired their data.

First a picture of a pyramidal neuron of the cerebral cortex — https://en.wikipedia.org/wiki/Pyramidal_cell — the cell body is only 20 microns in diameter (the giant pyramidal neurons giving rise to the corticospinal tract are much larger with diameters of 100 microns). Look at the picture in the article. If the cell body is (soma) 20 microns the dendrites arising from it (particularly the apical dendrite) are at most 5 microns thick.

Here’s what they did. A tetrode is a bundle of 4 very fine electrodes. Bundle diameter is only 30 – 40 microns with a 5 micron gap between the tips. This allows an intact dendrite to be caught in the gap. The authors note that chronically implanted tetrodes produce an immune response, in which glial cells proliferate and wall off the tetrode, shielding it from the extracellular medium by forming a high impedance sheath. This allows the tetrode to measure the electrical activity of a dendrite caught between the 4 tips (and hopefully little else).

How physiologic is this activity? Remember that epilepsy developing after head trauma is thought to due to abnormal electrical activity due to glial scars, and a glial scar is exactly what is found around the tetrode. So a lot more work needs to be done replicating this, and studying similar events in neuronal culture (without glia present).

Well those are the caveats. What did they find? The work involved 9 rats and 22 individually adjustable tetrodes. They found that spikes in the dendrites were quite different than the spikes found by a tetrode next to the pyramidal cell body. The dendritic spikes were larger (570 -2,100 microVolts) vs. 80 microVolts recorded extracellularly for spikes arising at the soma. Of course when the soma is impaled by an electrode you get a much larger spike.

More importantly, the dendritic spike rates were 5 times greater than the somatic spike rates during slow wave sleep and 10 times greater during exploration when awake. The authors call these dendritic action potentials (DAPs). Their amplitude was always positive.

They were also able to measure how the membrane potential of the dendrite fluctuated. The membrane potential fluctuations were always larger than the dendritic spikes themselves (by 7 fold). The size of the flucuations correlated with DAP magnitude and rate.

So all the neuronal spikes and axonal action potentials we’ve been measuring over the years (because it was all we could measure), may irrelevant to what the brain is really doing. Maybe the real computation is occuring within dendrites.

Now we know we can put an electrode in the brain out side of any neuron and record something called a local field potential — which is held to be a weighted sum of transmembrane currents due to synaptic and dendritic activity and arises within 250 microns of the electrode (and probably closer than that).

So fluctuating potentials are out there in the substance of the brain, outside any neuronal structure. Is it possible that the changes in membrane potential in dendrites are felt by other dendrites and if so is this where the brain’s computations are really taking place? Could synapses be irrelevant to this picture, and each pyramidal neuron not be a transistor but a complex analog CPU? Heady stuff. It certainly means goodbye to the McCullouch Pitts model — https://en.wikipedia.org/wiki/Artificial_neuron.

20,000 NanoSensors under the Cell (apologies to Jules Verne)

Too bad Jules Verne isn’t around to read PNAS vol. 114 pp. 1789 – 1794 ’17 where 20,000 fluorescent nanoSensors were placed under a single PC12 cell. PC12 cells are derived from a pheochromocytoma, a tumor which secretes catecholamines like dopamine and norepinephrine. So they’re almost neurons, and they contain vesicles containing dopamine, just like neurons, but they don’t form synapses.

The pictures they show of the cells shows the cell bodies sitting on a slide to be about 100 microns in diameter, with multiple protrusions so how are you going to get 20,000 sensors underneath them. Assuming them to be circular that’s about 3 per square micron. A micron is 10,000 Angstroms. The authors used Single Walled Carbon NanoTubes (SWCNTs) — e.g. rolled up graphene. They have a diameter of from 5 – 20 Angstroms, so there’s plenty of room for many in a square micron.

Here’s what they did. “Previously we found that the corona phase around SWCNTs can be engineered to recognize certain small analytes––a phenomenon we termed Corona Phase Molecular Recognition (CoPhMoRe) (7, 25, 26). Specifically, DNA-wrapped SWCNTs were found to increase their near InfraRed fluorescence in the presence of catecholamines . Here, we synthesized and characterized different DNA/SWCNT com- plexes and identified the best candidates for dopamine detection.

What they found is less remarkable than having the guts to try something like this. They could stimulate the cells to release dopamine using potassium (maddeningly I couldn’t find the concentration anywhere). Then with the density of sensors they could find out where it was released (the edges of the cell) with a time resolution of .1 second. It wasn’t generally released, but in hotspots — what you’d expectd if it were being released due to vesicles containing dopamine fusing with the cell membrane.

Remarkable — hard to see how they’re going to get this sort an array into a living organism, but their use in the study of brain slices can’t be far away.