CSF- and serum-borne autoantibodies against brain proteins are known to cause a wide range of cognitive sequelae due autoimmune attack. For example, when antibodies are raised against the protein LGI1, which is thought to act as a voltage-gated K+ channel, a common result is encephalopathy.

As a result, LGI1 is often included in autoimmune panels, along with several other proteins including CASPR2, NMDA and AMPA subunits, GABA-B receptors, GAD65, CRMp-5, ANNA-1, and ANNA-2.

Recently, Ariño et al presented a summary of 76 patients with LGI1-associated cognitive deterioration, 13% of which had forms of cognitive deterioration distinct from limbic encephalitis. At 2 years their major outcomes were:

  • 35% fully recovered
  • 35% regained independence but to baseline levels
  • 23% required assistance due to cognitive defects
  • 6% died

In mice, LGI1 is primary expressed at the RNA level in neurons at the RNA level, while in humans its expressed in both mature astrocytes and neurons (data from here and here), eg in the Darmanis et al 2015 human data set its actually expressed higher in astrocytes:


It might be interesting to see whether encephalopathies are generally only caused by autoantibodies against proteins expressed in neurons, or whether or cell type-expressed proteins can also lead to a similar clinical outcome.


It has been well-established for over a decade that synaptic vesicle release further away from a particular receptor cluster is associated with a decreased probability of receptor open state and therefore a decreased postsynaptic current (at least at glutamatergic synapses).


Franks et al 2003; PMC2944019

A few months ago Tang et al published an article in which they reported live imaging of cultured rat hippocampal neurons to investigate this.

They showed that critical vesicle priming and fusion proteins are preferentially found near to one another within presynaptic active zones. Moreover, these regions were associated with higher levels of postsynaptic receptors and scaffolding proteins.

On this basis, the authors suggest that there are transynaptic columns, which they call “nanocolumns” (I employ scare quotes here quite intentionally because I don’t prefix any word with nano- until I am absolutely forced to).

They have a nice YouTube video visualizing this arrangement at a synapse:

They propose that this arrangement allows the densest presynaptic active zones to match the densest postsynaptic receptor densities, maximizing the efficiency, and therefore strength, of the synapse.

In their most elegant validation experiment of this model, they inhibited synapses by activating postsynaptic NMDA receptors and found that this led to a decreased correspondence between synaptic active zones and postsynaptic densities (PSDs).


Tang et al 2016; doi:10.1038/nature19058

As you can see, the time-scale of the effect of NMDA receptor activation was pretty fast, at only 5 mins. My guess is that this effect is so fast because active positive regulation maintains the column organization, and without it, proteins rapidly diffuse away.

It is almost certain that synaptic cleft adhesion systems or retrograde signaling mechanisms regulate synaptic column organization, and the race is on to identify them and precisely how they work.

In the meantime, Tang et al’s work is a great example of synaptic strength variability that is dependent on protein localization, and should inform our models of how the brain works.

Interesting article from Gizowski et al last week on the role of the suprachiasmatic nucleus in regulating pre-sleep anticipatory thirst via the OVLT in mice.

In med school we had to memorize the major role of both of these regions, and my mnemonics were that the suprachiasmatic nucleus makes you charismatic (because you are well-rested), while the OVLT controls how much ovaltine you should drink.

Rodents are known to increase their fluid intake 1-2 hours before sleeping, which is called anticipatory thirst because the rodents want to make sure that they will have enough water in them to make it through the night (presumably without a dry mouth!).

Gizowski et al’s definitive experiment to show the interplay between these brain regions involved expressing two types of channelrhodopsins in vasopressin-expressing neurons in the suprachiasmatic nucleus in two groups of mice.

This manipulation allowed them to shine blue light in some mice to cause activation of the SCN -> OVLT pathway (G below), and yellow light in others to inhibit this pathway (H below). Here’s what they found:



As you can see, blue light in the blue-responsive mice caused increased water intake PRIOR to the normal anticipatory thirst at levels ~ 3x above baseline.

On the other hand, yellow light in the yellow-responsive mice caused decreased water intake at the expected anticipatory thirst to an insane degree. Basically, responsive mice didn’t drink at all when they shined yellow light and inactivated the SCN -> OVLT pathway.

This effect of yellow light seems too strong to me. Shouldn’t it just be stopping the increase that is typically seen pre-sleep? Instead, it seems to be completely eliminating drinking behavior entirely.

One way to explain this finding is that the SCN -> OVLT pathway might be active at other times other than pre-sleep, and it is just MORE active in the hour or two before sleep.

Despite a pretty extensive search, I can’t figure out whether humans have also been shown to have increased thirst prior to sleep.

On one hand, there are plenty of anecdotal reports of this, while on the other hand, rodents and humans have pretty different life histories. Plus, the authors probably would’ve mentioned it if there was good evidence of pre-sleep anticipatory thirst in humans.

Even if humans don’t have pre-sleep anticipatory thirst, this is still quite an interesting study, as this system is likely a good model of how suprachiasmatic nucleus axons project (with vasopressin-producing neurons?) to several other brain regions to control activities that are regulated by the perceived time of day.

There are three types of experiments one can perform in neuroscience: lesions, stimulations, and recording. Obviously, a particular study can use more than one of them.

Screen Shot 2016-07-05 at 1.46.12 PM

The most basic natural experiment that one can harness in neuroscience is to study lesions, due to problems in development, disease, and/or trauma.

Of these, perhaps the most striking lesions come from patients with severe hydrocephalus. Hydrocephalus is the accumulation of cerebrospinal fluid in the brain that causes ventricles to enlarge and compress the surrounding brain tissue.

A 2007 case study by Feuillet et al. of a 44-year old man with an IQ of 75 and a civil-servant career is probably the most famous, since they provide a nice brain set of brain scans of the person:

Screen Shot 2016-07-05 at 1.08.10 PM

LV = lateral ventricle; III = third ventricle; IV = fourth ventricle; image from Feuillet et al. 2007

A 1980 paper is also famous for its report of a person with an IQ of 126 and an impressive educational record who also had extensive hydrocephalus. But no image, so not quite as famous.

The 2007 case has been cited as evidence to a) question dogma about the role of the brain in consciousness, b) speculate on how two minds might coalesce following mind uploading, and c) — of course — postulate the existence of extracorporeal information storage. There are also some great comments about this topic at Neuroskeptic.

As far as I can tell, volume loss in moderate hydrocephalus is initially and primarily due to compression of white matter just adjacent to ventricles. On the other hand, in severe hydrocephalus such as the above, the grey matter and associated neuropil also must be compressed.

Most of the cases with normal cognition appear to be due to congenital or developmental hydrocephalus, causing a slow change in brain structure. On the other hand, rapid changes in brain structure due to acute hydrocephalus, such as following trauma, are more likely to lead to more dramatic changes in cognition.

What can we take away from this? A couple of things:

  1. This is yet another example of the remarkable long-term plasticity of both the white matter and the grey matter of the brain. Note that this plasticity is not always a good thing, but yes, it exists and can be profound.
  2. It is evidence for hypotheses that the relative positions and locations of neurons and other brain cell types in the brain is the critical component of maintaining cognition and continuity of consciousness, as opposed to their absolute positions in space within the brain. An example of a theory in the supported class is Seung’s “you are your connectome” theory.
  3. Might it not make the extracellular space theories of memory a little less plausible?

In the 1940s two Danish researchers, Erik Jacobsen and Jens Hald, tested a series of substances in an attempt to identify drugs that might rid the body of intestinal worms. After one of them worked in rabbits, Jacobsen tried it on himself, as he had a fun little habit of trying all of his invented drugs on himself.

“During the course of self-experimentation” as Larimer reports, both Jacobsen and Hald noted this substance — called disulfiram — led to a substantial increase in their sensitivity to the toxic effects of alcohol.

In late 1947 and 1948, Oluf Martensen-Larsen, an expert on the treatment of alcoholism, was able to convince his colleagues to allow him to try disulfiram in the treatment of alcohol abuse. In this classic paper, he reported on 83 patients that he had treated with disulfiram (also called Antabuse) for the treatment of their alcohol addiction.

At the time the mechanism was not known, but it was known that giving the drug prophylactically led people to become violently ill with hangover-like effects of alcohol. It is now almost certain that its effects are due to the inhibition of aldehyde dehydrogenase, which causes acetaldehyde to build up in the blood stream following alcohol consumption and cause all sorts of unpleasant toxicity.

In cultured cortical neurons, acetaldehyde causes a substantial loss of MAP2-positive neuronal processes, indicative of the fact that the toxicity of acetaldehyde does not spare the CNS:

Screen Shot 2016-05-23 at 3.50.17 PM

PMID: 11132090

One of the patients he described as having a successful reaction to the treatment was a middle-aged woman. After starting on disulfiram, she began to blush after taking only a mouthful of liquor. As Martensen-Larsen reports, “Her abstinence might be explained by her desire to avoid the humiliation associated with the blushing, but she insists that this is not the deciding factor, and that she has lost the taste for wine and spirits.”

Overall, he classifies 32/83 (39%) of patients as successes, 29/83 (35%) as partial successes as long as their blood and urine checks indicated that they will still on the drug, 13/83 (16%) as successes only as long as the physician can successfully encourage them to stay on the drug, and 9/83 (11%) as not responding to the drug, at least in because they refused to continue on it.

Disulfiram is still used today as a part of a comprehensive treatment for alcohol addiction that includes psychosocial factors as well.


Larimer R 1952 JAMA Treatment Of Alcoholism with Antabuse. doi:10.1001/jama.1952.03680020013004

Arghya Pal, Raman Deep Pattanayak, Rajesh Sagar. (2015) “Tracing the journey of disulfiram: From an unintended discovery to a treatment option for alcoholism.” Journal of Mental Health and Human Behavior. DOI: 10.4103/0971-8990.164826

Martensen-Larsen O. Treatment of alcoholism with a sensitizing drug. Lancet 1948;2:1004. Back to cited text no.

Wan JY, Wang JY, Wang Y, Wang JY. A comparison between acute exposures to ethanol and acetaldehyde on neurotoxicity, nitric oxide production and NMDA-induced excitotoxicity in primary cultures of cortical neurons. Chin J Physiol. 2000;43(3):131-8.

This classic 2009 review paper by Fletcher and Frith, currently cited 456 times, attempts to explain the two major positive symptoms of schizophrenia, hallucinations and delusions, as due to a common high-level cognitive mechanism.

But first, they consider one of the simplest hypotheses: might people with schizophrenia have disordered reasoning in general? The authors reject this hypothesis because patients with schizophrenia do not have problems with logic in general; at least in the n = 32 study they cited, control as opposed to deluded people were actually slightly more likely to fall for fallacies in logical questions.

Instead,Fletcher and Frith’s hypothesis relates to a failure to correctly perform conditional reasoning.

As they point out, stimuli that do not challenge a belief are usually ignored, which is often necessary in order to deal with the large number of stimuli in one’s environment.

Intriguingly, in animals, this prediction error-dependent learning is highly dependent on the dopaminergic system. And there is a wealth of evidence showing that the dopamine system is implicated in schizophrenia, including the ventral striatum.

Screen Shot 2016-05-19 at 10.59.00 AM

Berns et al., in a 2001 fMRI study, showed that the bilateral nucleus accumbens (a part of the ventral striatum) is involved in responses to predictable stimuli 

Their hypothesis, then, is that there is a quantitative divergence in the prediction error-dependent learning for every day stimuli in schizophrenia.

This leads normal stimuli to feel unduly important and thus makes properly attending to one’s environment challenging. This can explain delusions, because people must attempt to explain why those stimuli feel so surprising.

This could also make internal thoughts (which are perceived stimuli just like any other) appear more likely to be under external rather than internal control, because they are imbued with a particular sense of surprisingness. This, of course, can explain hallucinations.

Of course, this is just a model and is probably flawed in various ways, but it’s a pretty thorough one and worthy of consideration.


Fletcher PC, Frith CD. Perceiving is believing: a Bayesian approach to explaining the positive symptoms of schizophrenia. Nat Rev Neurosci. 2009;10(1):48-58.

A few weeks an interesting preprint from Antilla et al. was published. They set out to measure the genetic correlation between a variety of brain disorders — both “psychiatric” and “neurologic” — by comparing risk markers from a set of 23 different GWAS’s. They called themselves the “Brainstorm consortium” (for which they win creativity points). A major finding in their paper is that there is a substantial correlation between psychiatric disorders (e.g., OCD, schizophrenia, MDD, bipolar disorder), while there is less or no correlation among neurologic disorders (e.g., Alzheimer’s, Parkinson’s, MS). This data set is based on comparing polygenic risk variants from individual studies, and it’s certainly possible to draw too strong of conclusions from this type of data, as it is confounded by the societal structure of the people who participated in the studies, among other factors. That said, this should stimulate a number of interesting follow-up studies. One of their most interesting sections is on the genetic correlations between these disorders and other traits:

Two correlations especially jump out to me here:

  1.  The positive correlation between autism spectrum disorder risk and variants associated with measures of cognitive performance. This fits with at least one finding that there is a positive association between ASD prevalence and socioeconomic status, which is sometimes attributed to increased paternal age, but as this study shows, that is potentially not the whole story. I’m certainly not an expert in ASD epidemiology and this is just my initial impression, and I could totally be off.
  2. The inverse correlation between variants associated with measures of cognitive performance and risk of stroke and intracerebral hemorrage. This fits with my priors that good blood flow is critical for proper brain function. In my experience is not as widely known by people without a medical background (such as myself prior to my preclinical med school training).
Antilla et al. 2016 Analysis of shared heritability in common disorders of the brain. doi:http://dx.doi.org/10.1101/048991