2019: a lightly bamboozled review of the year in neuroscience
By: Mark Humphries
Tags: Neuroscience, Science, Psychology, Artificial Intelligence

1. “What’s so special about being human?”

Yes, it’s Jose Mourinho. If I had to explain, it wouldn’t be funny.

What’s so special about the human brain? A question broad and deep, one that has obsessed thinkers since Antiquity. Our once god-like status, apart and above from all animals, now steadily eroded by science, from natural selection placing us as but one species evolved from and in parallel to many others, to genetics putting the boot in by showing that we share 90 percent of our DNA with cats. Including my cat Bob, and he constantly falls off the back of the sofa.

You’d have thought neuroscience would have been all over the question of what’s special about the human brain, what with it being the study of the brain and all. But what we could do has been very limited. Largely we have only been able to observe behaviour, stuff we can do that other species cannot: talking endlessly in complex grammars, voting for bell-ends, that sort of thing. And supplement these observations with crude measures of the brain’s structure — how many neurons it has, which bits are thicker or larger than others, which bits are folded — compare them to other species, and going “ooo look it’s different”.

But to understand why the human brain is “special” we need some kind of theory as to why any of those crude brain differences would make any contribution to that specialness. For a start, to know what’s different about the neurons themselves: what’s different about the types of neurons that exist, or the signals they send, or both. Which is the preserve of us “systems” neuroscientists. Yet systems neuroscience hasn’t had much to say about this question, because of the deep problems of measuring neurons in humans. Until, that is, this year.

A. Special codes

In a brave paper, Pryluk and colleagues attempted a direct comparison of how the code used by neurons differed between humans and monkeys. They took long recordings of single neurons from the amygdala and cingulate cortex of monkeys. And compared them to similar recordings from the same regions in humans. These human recordings are ultra-rare: they came from patients with epilepsy that was both so serious and so unresponsive to drugs that they were being prepped for surgery to remove the part of the brain causing the seizures — and to find that part, they had electrodes implanted for a week or more. And while these electrodes were in there, and while laying in their hospital bed, the patients graciously agreed to do a series of tasks for the experimenters.

With these precious data to hand, Pryluk and friends asked a straightforward question: how much information are these neurons sending? In practice, this was a tough question to ask, as there are all sorts of things to compensate for, like correcting for differing firing rates across neurons and between species. But if we believe their measurement of how much information a neuron sends, their end result is clear. Human neurons in both the amygdala and cingulate cortex send more information — in that they are closer to the maximum possible rate of information sending — and do so more efficiently : they send fewer spikes for the same amount of information. Which means? Who knows. But their results point to human cortex having an increased capacity, so that much more information can be represented across a population of neurons, but at the cost of less robust coding — if fewer spikes are used, so the message being transmitted is more sensitive to failure and noise. And as know, the human brain is very sensitive to failure.

B. Special neurons?

While the Pryluk paper hinted at something special about how the human cortex encodes information, it didn’t tell us anything about whether this is because the types of neurons are special to humans. We get much of our deep understanding of types of neurons from mice, thanks to their being the workhorse of genetics (a good thing that the actual workhorse is not the workhorse of genetics, otherwise Janelia Farm would be, literally, a farm. And about 10000 square miles in size). Hence “what’s so special about the human brain?” translates in genetics to: how do we differ from mice?

A Nature paper from the Allen Brain Institute tackled this question head-on by directly comparing the gene expression between the cortex of the human and mouse. To do that, they first had to solve the small problem of accurately sequencing the RNA-expression of single neurons in the human cortex. Having cracked that, they then grouped all their neurons into types according to the similarity of their expressed genes. The result? 69 different types of neurons in the human cortex, of which 24 are excitatory (as in, they expressed glutamate) and 45 are inhibitory.

So which of these 69 types of neuron are unique, are responsible for endowing we humans with our special brain thinking stuff? None. All 69 are also closely matched in the mouse cortex. The major difference is not the type of neuron, but where they are found. In mice, all 24 types of excitatory cell stick to one particular layer of the cortex. In humans, many of those same excitatory types appear in more than one layer of the cortex. And why would this make the human cortex special? Who knows.

Yet their results are strong confirmation of what we all know: evolution is a tinkerer. We may have diverged from rodents about 90 million years ago, but in something as complex as the mammalian brain, in which genes define merely an outline of the details of the adult brain, big changes will almost always be catastrophic. So the main difference between the neurons in the mouse and human cortex is not in the proliferation of brand new types of neurons, but in the repurposing of what is already to hand. Oh, and the fact that mice have about 10 millions neurons in their cortex, and we have about 17 billion.

C. Dendrites predict intelligence?

We like to think humans are the most intelligent species on the planet, in the face of overwhelming evidence to the contrary. A provocative paper at the very end of 2018 asked what it is about our neurons that makes us intelligent. And the answer is: the more complex the dendrites of pyramidal neurons in our cortex, the higher our IQ. Wow!

Well, maybe. Such unusual claims deserve close scrutiny. After all, extraordinary claims demand extraordinary evidence. As we have no theory which predicts that more complex dendrites have anything to do with intelligence, so we need some pretty compelling evidence to believe this is not just happenstance correlation. The researchers obtained another rare sample of human cortical neurons: in this case, from bits of the temporal lobe removed during brain surgery, placed on ice, then popped into the experimental set-up as soon as practically possible while the neurons still lived. They took a range of measurements from the neurons. Each patient took an IQ test. And the researchers correlated some of the measurements with the IQ scores. Why these measurements? No reasons given — so already alarm bells are sounding about what other measurements were correlated with IQ, found to be lacking, and omitted from the paper.

Is the evidence compelling? No. The key evidence is the correlation between the total length of the dendrites and the IQ of the patient. Namely, this figure:

From Goriounova et al (2018). Each symbol is the average over the neurons in one subject; error bars are one standard deviation. The correlation (r value), regression (black line), and confidence interval of regression (blue shading) all appear to be taken from the symbols — i.e. the average scores.

Leaving aside the fact that this is the best correlation they have, and it is still weak (explaining 26% of the variance), take a closer look. Each data-point is a patient, so the value for the length of the dendrites is an average over the measured neurons in that patient. Now you don’t have to be a neuroanatomy geek to know that pyramidal neurons come in a bewildering variety of shapes and sizes, so averaging over them is a bit…. Well, charitably we’d call it weird. More bluntly, meaningless. And I’ve just told you that human cortex contains about 24 types of excitatory neuron, and most of those are some kind of pyramidal neuron. This correlation contains just 72 pyramidal neurons in total. So it hideously undersamples the diversity of pyramidal neuron dendrites in human cortex.

Worse, the above figure and others in the paper are textbook examples of how not to compute a correlation. The correlations are computed using averages — without taking into account how wrong those averages might be. And they could be so wrong that the correlation disappears completely. Indeed, looking at the range of data variation (the error bars) in the above figure, I’d wager the correlation would indeed disappear if tested properly (more on this in the Appendix below).

Finally, a simple thought experiment. These neurons happen to come from the temporal lobe of the cortex, a region plausibly involved in some kind of “thinking” that might contribute to an IQ score. But that was just because these patients had epilepsy, and the temporal lobe usually contains the region that starts epileptic brain activity. But what if these samples had been from primary visual cortex (V1)? They’d find the same diversity of sizes of pyramidal neuron dendrites, because types of pyramidal neuron are largely consistent across the cortex. But if they’d reported a correlation between the size of dendrites in V1 and a person’s IQ score, who would have taken them seriously?