Category Archives: Neurology & Psychiatry

Should your teen use marihuana?

Is marihuana bad for teen brain development?  The short answer is no one knows.  The long answer can be found here — https://www.pnas.org/content/117/1/7.  It’s probably the best thing out there on the question [ Proc. Natl. Acad. Sci. vol. 117 pp. 7 – 11 ’20 ].  The article basically says we don’t know, but lays out the cons (of which there are many) and the pros (of which there are equally many).

If you’re not a doc, reading the article with its conflicting arguments harmful vs. nonharmful, and then deciding what to tell your kid is very close to what practicing medicine is like.  Important decisions are to be made, based on very conflicting data, and yet the decisions can’t be put off.  Rote memory is of no use and it’s time to think and think hard.

Assuming you don’t have a PNAS subscription, or you can’t follow the link here are a few points the article makes.

It starts off with work on rats. “Tseng, based at the University of Illinois in Chicago, investigates how rats respond to THC (tetrahydrocannabinol), the main psychoactive ingredient in cannabis. He’s found that exposure to THC or similar molecules during a specific window of adolescence delays maturation of the prefrontal cortex (PFC), a region involved in complex behaviors and decision making”

Pretty impressive, but not if you’ve spent decades watching various treatments for stroke which worked in rodents crash and burn when applied to people (there are at least 50 such studies).  What separates us from rodents physically (if not morally) is our brains.  Animal studies, with all their defects of applicability to man is one of the two approaches we have — no one is going to randomize a bunch of 13 year olds to receive marihuana or not and watch what happens.

== Addendum 9 Jan ’20 — too good to pass up — Science vol. 367 pp. 83 – 87  ’20 shows just how different we are from rodents.  In addition to our cerebral cortex being 3 times thicker, human cortical neurons show something not found in any other mammal — These are graded action potentials in apical dendrites, important because they allow single neurons to calculate XORs (either a or b but not both and not none), something previously only thought possible for neuron ensembles.  XORs are important in Boolean algebra, hence in computation. ==

The other approach is observational studies on people which have led us down the garden path many times– see the disaster the women’s health study avoided here — https://luysii.wordpress.com/2016/08/23/the-plural-of-anecdote-is-not-data-in-medicine-at-least/.

45,000 Swedish military conscripts examined at conscription (age 19) and 15 years later.  Those who had used cannabis over 50 times before conscription were 6 times as likely to be diagnosed with schizophrenia.

Against that, is the fact that cannabis use has exploded since the 60s but schizophrenia has not (remaining at a very unfortunate 1% of the population).

In the Dunedin study, cannabis use by 15 was associated with a fourfold risk of schizophrenia at 26 (but not if they started using cannabis after 16 years of age. — https://en.wikipedia.org/wiki/Dunedin_Multidisciplinary_Health_and_Development_Study.

You can take the position that all drugs we use to alter mental state (coffee, cigarettes, booze, marihuana, cocaine, heroin) are medicating underly conditions which we don’t like.  Perhaps marihuana use is just a marker for people susceptible to schizophrenia.  Mol. Psychiat. vol. 19 pp. 1201 – 1204 ’14 — 2,000 healthy adults were studied looking a genome variants known to increase the risk of schizophrenia.  Those with high risk variants were ‘more likely’ to use marihuana — not having read the actual paper i don’t know how much more.

There is a lot more in the article about the effects of cannabis on cognition and cognitive development — the authors note that ‘they have not replicated well’.  You’ll have to read the text (which you can get by following the link) for this.

One hope for the future is the ABCD study (Adolescent Brain Cognitive Development Study) — aka the ABCD study.  By 2018 it reached its goal of  accumulating 10,000 kids between the ages of 9 and 10.  They will be followed for a decade (probably longer if the results are interesting).  It’s the hope for the future — but that doesn’t tell you what to say to your kid now.  Read the article, use your best judgement and welcome to the world of the physician.

What is sad, is how little the field has advanced, since I wrote the (rather technical) post on marihuana in 2014.

Here it is below

Why marihuana scares me

There’s an editorial in the current Science concerning how very little we know about the effects of marihuana on the developing adolescent brain [ Science vol. 344 p. 557 ’14 ]. We know all sorts of wonderful neuropharmacology and neurophysiology about delta-9 tetrahydrocannabinol (d9-THC) — http://en.wikipedia.org/wiki/Tetrahydrocannabinol The point of the authors (the current head of the Amnerican Psychiatric Association, and the first director of the National (US) Institute of Drug Abuse), is that there are no significant studies of what happens to adolescent humans (as opposed to rodents) taking the stuff.

Marihuana would the first mind-alteraing substance NOT to have serious side effects in a subpopulation of people using the drug — or just about any drug in medical use for that matter.

Any organic chemist looking at the structure of d9-THC (see the link) has to be impressed with what a lipid it is — 21 carbons, only 1 hydroxyl group, and an ether moiety. Everything else is hydrogen. Like most neuroactive drugs produced by plants, it is quite potent. A joint has only 9 milliGrams, and smoking undoubtedly destroys some of it. Consider alcohol, another lipid soluble drug. A 12 ounce beer with 3.2% alcohol content has 12 * 28.3 *.032 10.8 grams of alcohol — molecular mass 62 grams — so the dose is 11/62 moles. To get drunk you need more than one beer. Compare that to a dose of .009/300 moles of d9-THC.

As we’ve found out — d9-THC is so potent because it binds to receptors for it. Unlike ethanol which can be a product of intermediary metabolism, there aren’t enzymes specifically devoted to breaking down d9-THC. In contrast, fatty acid amide hydrolase (FAAH) is devoted to breaking down anandamide, one of the endogenous compounds d9-THC is mimicking.

What really concerns me about this class of drugs, is how long they must hang around. Teaching neuropharmacology in the 70s and 80s was great fun. Every year a new receptor for neurotransmitters seemed to be found. In some cases mind benders bound to them (e.g. LSD and a serotonin receptor). In other cases the endogenous transmitters being mimicked by a plant substance were found (the endogenous opiates and their receptors). Years passed, but the receptor for d9-thc wasn’t found. The reason it wasn’t is exactly why I’m scared of the drug.

How were the various receptors for mind benders found? You throw a radioactively labelled drug (say morphine) at a brain homogenate, and purify what it is binding to. That’s how the opiate receptors etc. etc. were found. Why did it take so long to find the cannabinoid receptors? Because they bind strongly to all the fats in the brain being so incredibly lipid soluble. So the vast majority of stuff bound wasn’t protein at all, but fat. The brain has the highest percentage of fat of any organ in the body — 60%, unless you considered dispersed fatty tissue an organ (which it actually is from an endocrine point of view).

This has to mean that the stuff hangs around for a long time, without any specific enzymes to clear it.

It’s obvious to all that cognitive capacity changes from childhood to adult life. All sorts of studies with large numbers of people have done serial MRIs children and adolescents as the develop and age. Here are a few references to get you started [ Neuron vol. 72 pp. 873 – 884, 11, Proc. Natl. Acad. Sci. vol. 107 pp. 16988 – 16993 ’10, vol. 111 pp. 6774 -= 6779 ’14 ]. If you don’t know the answer, think about the change thickness of the cerebral cortex from age 9 to 20. Surprisingly, it get thinner, not thicker. The effect happens later in the association areas thought to be important in higher cognitive function, than the primary motor or sensory areas. Paradoxical isn’t it? Based on animal work this is thought to be due pruning of synapses.

So throw a long-lasting retrograde neurotransmitter mimic like d9-THC at the dynamically changing adolescent brain and hope for the best. That’s what the cited editorialists are concerned about. We simply don’t know and we should.

Having been in Cambridge when Leary was just getting started in the early 60’s, I must say that the idea of tune in turn on and drop out never appealed to me. Most of the heavy marihuana users I’ve known (and treated for other things) were happy, but rather vague and frankly rather dull.

Unfortunately as a neurologist, I had to evaluate physician colleagues who got in trouble with drugs (mostly with alcohol). One very intelligent polydrug user MD, put it to me this way — “The problem is that you like reality, and I don’t”.

Null hacking — Reproducibility and its Discontents — take II

Most scientific types have heard about p hacking, but not null hacking.

Start with p hacking.  It’s just running statistical test after statistical test on your data until you find something unlikely to occur by chance more than 5% of the time (a p of .05) making it worthy of publication (or at least discussion).

It’s not that hard to do, and I faced it day after day as a doc and had to give worried patients a quick lesson in statistics.  The culprit was something called a chem-20, which measured 20 different things (sodium, potassium, cholesterol, kidney tests, liver tests, you name it).  Each of the 20 items had a normal range in which 95% of the values from a bunch of (presumably) normal people would fall.  This of course means that 2.5% of all results would be outside the range on the low side, and 2.5% would be outside the range on the upside.

Before I tell you, how often would you expect to get a test where all 20 tests were normal?

The chance of a single test being normal is .95, two tests .95 * .95 = .90, 4 tests .90 * .90 = .81, 8 tests .81 * .81 = .65, 16 tests .65 *.65 = .42, 20 tests .42 * .81 = .32.

Less than 1/3 of the time.

That’s p hacking.  It has been vigorously investigated in the past few years in psychology, because a lot of widely cited results in supposedly high quality journals couldn’t be reproduced.  See the post of 6/16 at the end for the initial work.

It arose because negative results don’t win you fame and fortune and don’t get published as easily.

So there has been a very welcome and salutary effort to see if results could be confirmed — only 39% were — see the copy of the old post at the end.

So all is sweetness and light with the newly found rigor.  Not so fast says Proc. Natl. Acad. Sci. vol. 116 pp. 25535 – 25545 ’19.  The same pressures that lead investigators to p hack their result to get something significant and publishable, leads the replicators to null hack their results to win fame and fortune by toppling a psychological statue.

At this point it’s time for a Feynman quote “The first principle is that you must not fool yourself and you are the easiest person to fool.”

The paper talks about degrees of freedom available to the replicator, which in normal language just means how closely do you have to match the conditions of the study you are trying to replicate.

Obviously this is impossible for one of the studies and its replication they discuss — whether the choice of language used in a mailing  to urge people to vote in an election had any effect on whether they actually voted.  Obviously you can’t arrange to have the two hard fought elections in which there was a lot of interest of the initial study run again.  But the replicators choose a bunch of primaries in which interest and turnout was low, casting doubt on their failure to replicate the original results (which was that language DID make a difference in voter turnout).

Then the authors of the PNAS paper reanalyzed the data of the replicators a different way, and found that the original study was replicated.  This is the second large degree of freedom, the choice of the way to analyze the raw data — the same as the original authors or differently — “reasonable people may differ” about these matters.

There’s a lot more in the paper including something called the Bayesian Causal Forest which is a new method of data analysis which the authors favor (which I confess I don’t understand).

Here’s the old post  of 6/16

Reproducibility and its discontents

“Since the launch of the clinicaltrials.gov registry in 2000, which forced researchers to preregister their methods and outcome measures, the percentage of large heart-disease clinical trials reporting significant positive results plummeted from 57% to a mere 8%”. I leave it to you to speculate why this happened, but my guess is that probably the data were sliced and diced until something of significance was found. I’d love to know what the comparable data is on anti-depressant trials. The above direct quote is from Proc. Natl. Acad. Sci. vol. 113 pp. 6454 – 6459 ’16. The article looked at the 100 papers published in ‘top’ psychology journals, about which much has been written — here’s the reference to the actual paper — Open Science Collaboration (2015) Psychology. Estimating the reproducibility of psychological science. Science 349(6251):aac4716.

The sad news is that only 39% of these studies were reproducible. So why beat a dead horse? The authors came up with something quite useful — they looked at how sensitive to context each of the 100 studies actually was. By context they mean the time of the study (e.g., pre- vs. post-Recession), culture (e.g., individualistic vs. collectivistic culture), the location (e.g., rural vs. urban setting), or the population (e.g., a racially diverse population vs. a predominantly White or Black or Latino population). Their conclusions were that the contextual sensitivity of the research topic was associated with replication success (e.g. the more context sensitive, the less likely it was that the study could be reproduced). This was even after statistically adjusting for several methodological characteristics (e.g., statistical power, effect size, etc. etc). The association between contextual sensitivity and replication success did not differ across psychological subdisciplines.

Addendum 15 June ’16 — Sadly, the best way to say this is — The more likely a study is to be true (replicable) the more likely it is to be not generally applicable (e.g. useful).

So this is good. Up to now the results of psychology studies have been reported in the press as of general applicability (particularly those which enforce the writer’s preferred narrative). Caveat emptor is two millenia old. Carl Sagan said it best — “Extraordinary claims require extraordinary evidence.”

For an example data slicing and dicing, please see — https://luysii.wordpress.com/2009/10/05/low-socioeconomic-status-in-the-first-5-years-of-life-doubles-your-chance-of-coronary-artery-disease-at-50-even-if-you-became-a-doc-or-why-i-hated-reading-the-medical-literature-when-i-had-to/

 

The neuropharmacological brilliance of the meningococcus

The meningococcus can kill you within 12 hours after the spots appear — https://en.wikipedia.org/wiki/Waterhouse–Friderichsen_syndrome.  Who would have thought that it would be teaching us neuropharmacology.   But it is —  showing us how to make a new class of drugs, that no one has ever thought of.

One of the most important ways that the outside of a cell tells the inside what’s going on and what to do is the GPCR (acronym for G Protein Coupled Receptor).  Our 20,000 protein coding genome contains 826 of them. 108 G-protein-coupled receptors (GPCRs) are the targets of 475 Food and Drug Administration (FDA)-approved drugs (slightly over 1/3).   GPCRs are embedded in the outer membrane of the cell, with the protein going back and forth through the membrane 7 times (transmembrane segment 1 to 7 (TM1 – TM7). As the GPCR sits there usually the 7 TMs cluster together, and signaling molecules such as norepinephrine, dopamine, serotonin etc. etc. bind to the center of the cluster.   This is where the 475 drugs try to modify things.

Not so the meningococcus. It binds to the beta2 adrenergic receptor on the surface of brain endothelial cells lining cerebral blood vessels, turning on a signaling cascade which eventually promotes opening junctions of the brain endothelial cells with each other, so the bug can get into the brain.  All sorts of drugs are used to affect beta2 adrenergic receptors, in particular drugs for asthma which activate the receptor causing lung smooth muscle to relax.  All of them are small molecules which bind within the 7 TM cluster.

According to Nature Commun. vol 10 pp. 4752 –> ’19, the little hairs (pili) on the outside of the organism bind to sugars attached to the extracellular surface of the receptor, pulling on it activating the receptor.

This a completely new mechanism to alter GPCR function (which, after all,  is what our drugs are trying to do).  This means that we potentially have a whole new class of drugs, and 826 juicy targets to explore them with.

Here is one clinical experience I had with the meningococcus.  A middle aged man presented with headache, stiff neck and fever.  Normally spinal fluid is as clear as water.  This man’s was cloudy, a very bad sign as it usually means pus (lots of white blood cells).  I started the standard antibiotic (at the time)  for bacterial meningitis — because you don’t wait for the culture to come back which back then took two days.  The lab report showed no white cells, which I thought was screwy, so I went down to the lab to look for myself — there weren’t any.  The cloudiness was due to a huge number of meningococcal bacteria.  I though he was a goner, but amazingly he survived and went home. Unfortunately his immune system was quite abnormal, and the meningitis was the initial presentation of multiple myeloma.

Is the microtubule alive ??

When does inanimate matter become animate?  How about cilia — they beat and move around.  No one would call  the alpha/beta tubulin dimer from which they are formed alive.  The tubulin proteins contain 450 amino acids or so and form a globule 40 Angstroms (4 nanoMeters) in diameter.  The dimer is then 40 x 80 Angstroms and looks like an oil drum.  Then they form protofilaments stacked end to end — e.g. alpha beta alpha beta.  Then 13 protofilaments then align side by side to form the microtubule (which is 250 Angstroms in diameter, with a central hole about half that size.  Do you think you could design a protein to do this?

Lets make it a bit more complicated, and add another 10 protofilaments forming a second incomplete ring.  This is the microtubule doublet, and each cilium has 9 of them all arranged in a circle.

Hopefully you have access to the 31 October cell where the repeating unit of the microtubule doublet is shown in exquisite detail — https://www.cell.com/action/showPdf?pii=S0092-8674%2819%2931081-5. — Cell 179, 909–922 ’19

The structure is from the primitive eukaryote Chlamydomonas, the structure repeats every 960 Angstroms (e.g every 12 alpha/beta tubulin dimers).  So just for one repeating unit which is just under 1/10 of a micron (10,000 Angstroms) there are (13 + 10) * 12 = 276 dimers.  The cilium is 12 microns long so that’s 12 * 276 * 100 = 298,080 alpha tubulin dimers/microtubule doublet. The cilium has 9 of these + another doublet in the center, so thats 2,980,800 alpha tubulin dimers/cilium.

The cell article is far better than this, because it shows how the motor proteins which climb along the outside of the doublet (such as dynein) attach.The article also describes the molecular ruler (basically a 960 Angstrom coil coil which spans the repeat. They found some 38 different proteins associated with the microtubule repeat.  They repeat as well at 80, 160, 240, 480 and 960 Angstrom periodicity.  The proteins in the hole in the center of the microtubule (e.g. the lumen) are rich in a protein module called the EF hand which binds calcium, and which likely causes movement of the microtubule, at which point the damn thing (whose structure we now know) appears alive.

Because of the attachment of the partial ring (B ring) to the complete ring of protofilaments, each of the 23 protofilaments has a unique position in the doublet, and each of the proteins in the lumen is bound to a specific mitotubule profilament. There are 6 different coiled coil proteins inside the A ring, occupying  specific furrows between the protofilaments.

Staggering complexity built from a simple subunit, but then Monticello is only made of bricks.

Barking up the wrong therapeutic tree in Alzheimer’s disease

Billions have been spent by big pharma (and lost) trying to get rid of the senile plaque of Alzheimer’s disease.  The assumption has always been that the plaque is the smoking gun killing neurons.  But it’s just an assumption which a recent paper has turned on its ear [ Proc. Natl. Acad. Sci. vol. 116 23040 – 23049 ’19 ]

It involves a protein, likely to be a new face even to Alzheimer’s cognoscenti.  The protein is called SERF1A (in man) and MOAG-4 in yeast. It enhances amyloid formation, the major component of the senile plaque.  SERF1A is clearly doing something important as it has changed little from the humble single yeast cell to man.

The major component of the senile plaque is the aBeta peptide of 40 and/or 42 amino acids.  It polymerizes to form the amyloid of the plaque.  The initial step of amyloid formation is the hardest, getting a bunch of Abeta peptides into the right conformation (e.g. the nucleus) so others can latch on to it and form the amyloid fiber.   It is likely that the monomers and oligomers of Abeta are what is killing neurons, not the plaques, otherwise why would natural selection/evolution keep SERF1A around?

So, billions of dollars later, getting rid of the senile plaque turns out to be a bad idea. What we want to do is increase SERF1A activity, to sop up the monomers and oligomers. It is a 180 degree shift in our thinking. That’s the executive summary, now for the fascinating chemistry involved.

First the structure of SERF1A — that is to say its amino acid sequence.  (For the nonChemists — proteins are linear string of amino acids, just as a word is a linear string of characters — the order is quite important — just as united and untied mean two very different things). There are only 68 amino acids in SERF1A of which 14 are lysine 9 are arginine 5 Glutamic acid and 5 Aspartic acid.  That’s interesting in itself, as we have 20 different amino acids, and if they occurred randomly you’d expect about 3 -4 of each.  The mathematicians among you should enjoy figuring out just how improbable this compared to random assortment. So just four amino acids account for 33 of the 68 in SERF1A  Even more interesting is the fact that all 4 are charged at body pH — lysine and arginine are positively charged because their nitrogen picks up protons, and glutamic and aspartic acid are negatively charged  giving up the proton.

This means that positive and negative can bind to each other (something energetically quite favorable).  How many ways are there for the 10 acids to bind to the 23 bases?  Just 23 x 22 x 21 X 20 X 19 X 18 x 17 x 16 x 15 x 14 or roughly 20^10 ways.  This means that SERF1A doesn’t have a single structure, but many of them.  It is basically a disordered protein.

The paper shows exactly this, that several conformations of SERF1 are seen in solution, and that it binds to Abeta forming a ‘fuzzy complex’, in which the number of Abetas and SERF1s are not fixed — e.g. there is no fixed stoichiometry — something chemists are going to have to learn to deal with — see — https://luysii.wordpress.com/2018/12/16/bye-bye-stoichiometry/.  Also different conformations of SERF1A are present in the fuzzy complex, explaining why it has such a peculiar amino acid composition.  Clever no?  Let’s hear it for the blind watchmaker or whatever you want to call it.

The paper shows that SERF1 increases the rate at which Abeta forms the nucleus of the amyloid fiber.  It does not help the fiber grow.  This means that the fiber is good and the monomers and oligomers are bad.  Not only that but SERF1 has exactly the same effect with alpha-synuclein, the main protein of the Lewy body of Parkinsonism.

So the paper represents a huge paradigm shift in our understanding of the cause of at least 2 bad neurological diseases.   Even more importantly, the paper suggests a completely new way to attack them.

Technology marches on — or does it?

Technology marches on — perhaps.  But it certainly did in the following Alzheimer’s research [ Neuron vol. 104 pp. 256 – 270 ’19 ] .  The work used (1) CRISPR (2) iPSCs (3) transcriptomics (4) translatomics to study Alzheimer’s.  Almost none of this would have been possible 10 years ago.

Presently over 200 mutations are known in (1) the amyloid precursor protein — APP (2) presenilin1 (3) presenilin2.  The presenilins are components of the gamma secretase complex which cleaves APP on the way to the way to the major components of the senile plaque, Abeta40 and Abeta42.

There’s a lot of nomenclature, so here’s a brief review.  The amyloid precursor protein (APP) comes in 3 isoforms containing 770, 751 and 695 amino acids.  APP is embedded in the plasma membrane with most of the amino acids extracellular.  The crucial enzyme for breaking APP down is gamma secretase, which cleaves APP inside the membrane.  Gamma secretase is made of 4 proteins, 2 of which are the presenilins.  Cleavage results in a small carboxy terminal fragment (which the paper calls beta-CTF) and a large amino terminal fragment. If beta secretase (another enzyme) cleaves the amino terminal fragment Abeta40 and Abeta42 are formed.  If alpha secretase (a third enzyme) cleaves the amino terminal fragment — Abeta42 is not formed.   Got all that?

Where do CRISPR and iPSCs come in?  iPSC stands for induced pluripotent stem cells, which can be made from cells in your skin (but not easily).  Subsequently adding the appropriate witches brew can cause them to differentiate into a variety of cells — cortical neurons in this case.

CRISPR was then used to introduce mutations characteristic of familial Alzheimer’s disease into either APP or presenilin1.  Some 16 cell lines each containing a different familial Alzheimer disease mutation were formed.

Then the iPSCs were differentiated into cortical neurons, and the mRNAs (transcriptomics) and proteins made from them (translatomics) were studied.

Certainly a technological tour de force.

What did they find?  Well for the APP and the presenilin1 mutations had effects on Abeta peptide production (but they differered).  Both however increased the accumulation of beta-CTF.  This could be ‘rescued’ by inhibition of beta-secretase — but unfortunately clinical trials have not shown beta-secretase inhibitors to be helpful.

What did increased beta-CTF actually do — there was enlargement of early endosomes in all the cell lines.   How this produces Alzheimer’s disease is anyone’s guess.

Also quite interesting, is the fact that translatomics and transcriptomics of all 16 cell lines showed ‘dysregulation’ of genes which have been associated with Alzheimer’s disease risk — these include APOE, CLU and SORL1.

Certainly a masterpiece of technological virtuosity.

So technology gives us bigger and better results

Or does it?

There was a very interesting paper on the effect of sleep on cerebrospinal fluid and blood flow in the brain [ Science vol. 366 pp. 372 – 373 ’19 ] It contained the following statement –”

During slow wave sleep, the cerebral blood flow is reduced by 25%, which lowers cerebral blood volume  by ~10%.  The reference for this statement was work done in 1991.

I thought this was a bit outre, so I wrote one of the authors.

Dr. X “Isn’t there something more current (and presumably more accurate) than reference #3 on cerebral blood flow in sleep?  If there isn’t, the work should be repeated”

I got the following back “The old studies are very precise, more precise than current studies.”

Go figure.

How does ketamine lift depression?

The incredibly rapid improvement in depression (hours) produced by ketamine is unprecedented and surely is telling us something vitally important about depression.  If only we could figure out what it is.  Clinicians were used to waiting weeks for antidepressants of all sorts to work.  As a neurologist, I’d see it work in a week or so in my MS patients depressed due a relapse.

Two recent papers show just how hard it is going to be [Neuron  vol. 104 pp. 182 – 182, 338 – 352 ’19 ]. First off you have to accept the idea that even though animals (usually mice) can’t tell us how they feel, we still have reasonable animal models of depression (tail suspension test, forced swim test).  We can at least get a handle on anhedonia using the sucrose preference test.

Throw ketamine at an animal and measure the biochemical or the neurophysiologic effect of your choice. There are zillions of them.  Throw just about anything at the brain, and all sorts of things change.  The problem is showing that the change is relevant.  Is the known blockade of NMDA receptors by ketamine how it helps depression.  Give enough and you get out of body experiences and all sorts of craziness, not an antidepressant effect.

Homer1a is a protein found at the synapse, and like all scaffold proteins, it interacts with a bunch of different proteins. It links another type of glutamic acid receptor (mGluR1 and mGluR5) to inositol 1, 4, 5 trisphosphate receptors (IP3Rs) on the endoplasmic reticulum.  It also links mGluR1 and mGluR5 to NMDARs and other ion channels.

So what?

Other work by the authors showed that knockdown of Homer1a (using small interfering RNA – siRNA) in the medial prefrontal cortex (mPFC) abolished the antidepressant effects (in animal models) to ketamine.  Well that’s good, but even better is that knockdown also abolished the antidepressant effects of a tricyclic antidepressant (imipramine).

The present work showed that increasing the expression of Homer1a (the protein comes in various isoforms) in the frontal cortex reduced depression in the various models.

Pretty good — all we have to do is increase Homer1a expression to have a treatment of depression.

Don’t get your hopes up, and this is why depression research is so — well depressing.

Increasing Homer1a expression in another brain region (the hippocampus) has exactly the opposite effects.

Why don’t serotonin neurons die like dopamine neurons do in Parkinson’s disease

Say what ?  “This proportion will likely be higher in rat dopaminergic neurons, which have even larger axonal arbors with ~500,000 presynapses, or in human serotonergic neurons, which are estimated to extend axons for 350 meters” – from [ Science vol. 366 3aaw9997 p. 4 ’19 ]

I thought I was reasonably well informed but I found these numbers astounding, so I looked up the papers.  Here is how such statement can be made with chapter and verse.

“The validity of the single-cell axon length measurements for dopaminergic and cholinergic neurons can be independently checked with calculations based on the total volume of the target territory, the density of the particular type of axon (axon length per volume of target territory), and the number of neuronal cell bodies giving rise to that type of axonThese population analyses are made possible by the availability of antibodies that localize to different types of axons: anti-ChAT for cholinergic axons (also visualized with acetylcholine esterase histochemistry), anti-tyrosine hydroxylase for striatal dopaminergic axons, and anti-serotonin for serotonergic axons.

The human data for axon density and neuron counts have been published for forebrain cholinergic neurons and for serotonergic neurons projecting from the dorsal raphe nucleus to the cortex, and cortical volume estimates for humans are available from MRI analyses; forebrain cholinergic neuron data is also available for chimpanzees. These calculations lead to axon length estimates of 107 m and 31 m, respectively, for human and chimpanzee forebrain cholinergic neurons, and an axon length estimate of 170–348 meters for human serotonergic neurons.”

H. Wu, J. Williams, J. Nathans, Complete morphologies of basal forebrain cholinergic neurons in the mouse. eLife 3, e02444 (2014). doi: 10.7554/eLife.02444; pmid: 24894464

How in the world can these neurons survive as long as they do?

Not all of them do–  At birth there are 450,000 neurons in the substantia nigra (one side or both sides?), declining to 275 by age 60.  Patients with Parkinsonism all had cell counts below 140,000 [  Ann. Neurol. vol. 24 pp. 574 – 576 ’88 ]. Catecholamines such as dopamine and norepinephrine are easily oxidized to quinones, and this may be the ‘black stuff’ in the substantia nigra (which is latin for black stuff).

Here are the numbers for serotonin neurons in the few brain nuclei (dorsal raphe nucleus) in which they are found.  Less than dopamine.  A mere 165,000 +/- 34,000 — https://www.ncbi.nlm.nih.gov › pubmed

So being too small to be seen with a total axon length of a football field, they appear to last as long as we do.  Have we missed a neurological disease due to loss of serotonin neurons?

Why should the axons of dopamine, serotonin and norepinephrine neurons be so long and branch so widely?  Because they release their transmitters diffusely in the brain, and diffusion is too slow, so the axonal apparatus must get it there and release it locally into the brain’s extracellular space, no postsynaptic specializations are present in volume neurotransmission — that’s the point.  This is one of the reasons that a wiring diagram of the brain isn’t enough — https://luysii.wordpress.com/2011/04/10/would-a-wiring-diagram-of-the-brain-help-you-understand-it/.

Just think of that dopamine neuron with 500,000 presynapses.  Synthesis and release must be general, as the neuron couldn’t possibly address an individual synapse.

The more we know the more remarkable the brain becomes.

 

Neurons synapsing with tumor cells, unbelievable but true

As a neurologist, I’ve seen more than enough breast cancer metastatic to the brain.  I never, in a million years, would have though that brain neurons would be forming synapses with them, helping them grow in the process.  But that’s exactly what two papers in the current Nature prove [ Nature vol. 573 pp. 499 – 501, 526 – 531 ’19 ]

The evidence is pretty good.  There are electron micrographs of brain metastases showing breast cancer cells acting like glia, surrounding a synapse between two neurons.  There are synaptic vesicles right next to the presynaptic membrane of the neuron which is apposed to the postsynaptic neuron (that’s what a synapse is after all). They are also present in the same neuron, whose membrane is tightly apposed  to a tumor cell, which stains positive for a type of glutamic acid receptor (the NMDAR).

Breast cancer types have been subdivided by the proteins they contain and don’t contain.  A particularly nasty one, is called triple negative — lacking the estrogen receptor the progesterone receptor and the herceptin receptor. Triple negative breast cancers account for 15 – 20% of all breast cancers, and some 40% of this group will die of brain metastases.  This paper may explain why.

The paper did some work using immunodeficient mice, transplanting human triple negative breast cancer cells into the brain.  Synapses formed between the mouse neurons and the breast cancer cells.

It is known that NMDAR signaling promotes growth tumor growth in other cancer types, and that increased NMDAR expression in breast cancer cells is associated with poor prognosis.

It is incredible to think that the brain is forming synapses with metastatic tumor cells to help them grow, but that’s what must be faced.

The excellent study confined itself to breast cancer metastatic to brain, but the study of other tumors (particularly lung) is sure to follow.

Prolegomena to reading Fall by Neal Stephenson

As a college freshman I spent hours trying to untangle Kant’s sentences in “Prolegomena to Any Future Metaphysics”  Here’s sentence #1.   “In order that metaphysics might, as science, be able to lay claim, not merely to deceitful persuasion, but to insight and conviction, a critique of reason itself must set forth the entire stock of a priori concepts, their division according to the different sources (sensibility, understanding, and reason), further, a complete table of those concepts, and the analysis of all of them along with everything that can be derived from that analysis; and then, especially, such a critique must set forth the possibility of synthetic cognition a priori through a deduction of these concepts, it must set forth the principles of their use, and finally also the boundaries of that use; and all of this in a complete system.”

This post is something to read before tackling “Fall” by Neal Stephenson, a prolegomena if you will.  Hopefully it will be more comprehensible than Kant.   I’m only up to p. 83 of a nearly 900 page book.  But so far the book’s premise seems to be that if you knew each and every connection (synapse) between every neuron, you could resurrect the consciousness of an individual (e.g. a wiring diagram).  Perhaps Stephenson will get more sophisticated as I proceed through the book.  Perhaps not.  But he’s clearly done a fair amount neuroscience homework.

So read the following old post about why a wiring diagram of the brain isn’t enough to explain how it works.   Perhaps he’ll bring in the following points later in the book.

Here’s the old post.  Some serious (and counterintuitive) scientific results to follow in tomorrow’s post.

Would a wiring diagram of the brain help you understand it?

Every budding chemist sits through a statistical mechanics course, in which the insanity and inutility of knowing the position and velocity of each and every of the 10^23 molecules of a mole or so of gas in a container is brought home.  Instead we need to know the average energy of the molecules and the volume they are confined in, to get the pressure and the temperature.

However, people are taking the first approach in an attempt to understand the brain.  They want a ‘wiring diagram’ of the brain. e. g. a list of every neuron and for each neuron a list of the other neurons connected to it, and a third list for each neuron of the neurons it is connected to.  For the non-neuroscientist — the connections are called synapses, and they essentially communicate in one direction only (true to a first approximation but no further as there is strong evidence that communication goes both ways, with one of the ‘other way’ transmitters being endogenous marihuana).  This is why you need the second and third lists.

Clearly a monumental undertaking and one which grows more monumental with the passage of time.  Starting out in the 60s, it was estimated that we had about a billion neurons (no one could possibly count each of them).  This is where the neurological urban myth of the loss of 10,000 neurons each day came from.  For details see https://luysii.wordpress.com/2011/03/13/neurological-urban-legends/.

The latest estimate [ Science vol. 331 p. 708 ’11 ] is that we have 80 billion neurons connected to each other by 150 trillion synapses.  Well, that’s not a mole of synapses but it is a nanoMole of them. People are nonetheless trying to see which areas of the brain are connected to each other to at least get a schematic diagram.

Even if you had the complete wiring diagram, nobody’s brain is strong enough to comprehend it.  I strongly recommend looking at the pictures found in Nature vol. 471 pp. 177 – 182 ’11 to get a sense of the  complexity of the interconnection between neurons and just how many there are.  Figure 2 (p. 179) is particularly revealing showing a 3 dimensional reconstruction using the high resolutions obtainable by the electron microscope.  Stare at figure 2.f. a while and try to figure out what’s going on.  It’s both amazing and humbling.

But even assuming that someone or something could, you still wouldn’t have enough information to figure out how the brain is doing what it clearly is doing.  There are at least 3 reasons.

l. Synapses, to a first approximation, are excitatory (turn on the neuron to which they are attached, making it fire an impulse) or inhibitory (preventing the neuron to which they are attached from firing in response to impulses from other synapses).  A wiring diagram alone won’t tell you this.

2. When I was starting out, the following statement would have seemed impossible.  It is now possible to watch synapses in the living brain of awake animal for extended periods of time.  But we now know that synapses come and go in the brain.  The various papers don’t all agree on just what fraction of synapses last more than a few months, but it’s early times.  Here are a few references [ Neuron vol. 69 pp. 1039 – 1041 ’11, ibid vol. 49 pp. 780 – 783, 877 – 887 ’06 ].  So the wiring diagram would have to be updated constantly.

3. Not all communication between neurons occurs at synapses.  Certain neurotransmitters are generally released into the higher brain elements (cerebral cortex) where they bathe neurons and affecting their activity without any synapses for them (it’s called volume neurotransmission)  Their importance in psychiatry and drug addiction is unparalleled.  Examples of such volume transmitters include serotonin, dopamine and norepinephrine.  Drugs of abuse affecting their action include cocaine, amphetamine.  Drugs treating psychiatric disease affecting them include the antipsychotics, the antidepressants and probably the antimanics.

Statistical mechanics works because one molecule is pretty much like another. This certainly isn’t true for neurons. Have a look at http://faculties.sbu.ac.ir/~rajabi/Histo-labo-photos_files/kora-b-p-03-l.jpg.  This is of the cerebral cortex — neurons are fairly creepy looking things, and no two shown are carbon copies.

The mere existence of 80 billion neurons and their 150 trillion connections (if the numbers are in fact correct) poses a series of puzzles.  There is simply no way that the 3.2 billion nucleotides of out genome can code for each and every neuron, each and every synapse.  The construction of the brain from the fertilized egg must be in some sense statistical.  Remarkable that it happens at all.  Embryologists are intensively working on how this happens — thousands of papers on the subject appear each year.