Suzana Herculano-Houzel on cognitive ability and brain size

Luke Muehlhauser: Much of your work concerns the question “Why are humans smarter than other animals?” In a series of papers (e.g. 2009, 2012), you’ve argued that recent results show that some popular hypotheses are probably wrong. For example, the so-called “overdeveloped” human cerebral cortex contains roughly the percentage of total brain neurons (19%) as do the cerebral cortices of other mammals. Rather, you argue, the human brain may simply be a “linearly scaled-up primate brain”: primate brains seem to have more economical scaling rules than do other mammals, and humans have the largest brain of any primate, and hence the most total neurons.

Your findings were enabled by a new method for neuron quantification developed at your lab, called “isotropic fractionator” (Herculano-Houzel & Lent 2005). Could you describe how that method works?

Suzana Herculano-Houzel: The isotropic fractionator consists pretty much of turning fixed brain tissue into soup – a soup of a known volume containing free cell nuclei, which can be easily colored (by staining the DNA that all nuclei contain) and thus visualized and counted under a microscope. Since every cell in the brain contains one and only one nucleus, counting nuclei is equivalent to counting cells. The beauty of the soup is that it is fast (total numbers of cells can be known in a few hours for a small brain, and in about one month for a human-sized brain), inexpensive, and very reliable – as much or more than the usual alternative, which is stereology.

Stereology, in comparison, consists of cutting entire brains into a series of very thin slices; processing the slices to allow visualization of the cells (which are otherwise transparent); delineating structures of interest; creating a sampling strategy to account for the heterogeneity in the distribution of cells across brain regions (a problem that is literally dissolved away in the detergent that we use in the isotropic fractionator); acquiring images of these small brain regions to be sampled; and actually counting cells in each of these samples. It is a process that can take a week or more for a single mouse brain. It is more powerful in the sense that spatial information is preserved (while the tissue is necessarily destroyed when turned into soup for our purposes), but on the other hand, it is much more labor-intensive and not appropriate for working on entire brains, because of the heterogeneity across brain parts.

Luke: Your own work emphasizes importance of the brain’s sheer number of neurons for cognitive ability. What do you think of other recent results (e.g. Smaers & Soligo 2013), which emphasize the apparent importance of mosaic brain reorganization?

Suzana: Mosaic brain organization is a fact. It describes the independent scaling of different parts of the brain across species in evolution, as opposed to every brain part scaling in line with every other part (what Barbara Finlay describes as “linked regularities”). Mosaic scaling in evolution is seen for example in the enormous size that some structures exhibit in some species but not others, relative to the rest of the brain: the common squirrel, for instance, has an enormous superior colliculus, involved in visual processing, that other rodents of a similar brain size do not have; moles and shrews, who rely heavily on olfaction, have even more neurons in the olfactory bulb than in the cerebral cortex – something that is quite different from rodents of a similar brain size (this is work under review).

In the context of our work, mosaic brain evolution means that the numbers of neurons allocated to different brain structures can vary independently across said structures: while, say, the superior colliculus and the visual thalamus tend to gain neurons hand in hand, a particular species can gain neurons much faster in the superior colliculus than in the visual thalamus, for instance. Mosaic brain evolution also refers to the possibility of one system (for instance, vision) expanding faster than another system (say, audition). There is the occasional surprise, however. For instance, we have found that, while primates are highly visual and have a large proportion of the cortex devoted to vision (indeed, much larger than the cortical areas devoted to audition), this proportion (as well as the relative number of cortical neurons devoted to vision) does NOT increase together with increasing brain size. Many more cortical neurons are involved in visual than in auditory processing, yes – but that proportion is stable across primate species. Still, species that rely more heavily on other sensory modalities should have a different distribution of neurons. Indeed, the mouse, contrary to primates, has a far larger percentage of cortical neurons involved in somatosensory processing than primates; and, as I mentioned above, moles and shrews have more neurons in the olfactory bulb than in the whole cortex – a pattern that is not seen in other brains of a similar size.

Even more remarkably, we have found that the apparent expansion of the cerebral cortex in mammalian evolution, varying from less than 40% of brain size in the smallest mammals to over 80% in humans and other even larger brains, is NOT the result of an expansion in numbers of neurons in the cortex: regardless of the relative size of the cortex across different species, it has about 20% of all brain neurons – even in the human brain. That’s another example of how apparent mosaic evolution (of one structure taking over the others) can actually not be mosaic evolution. It all depends on the precise variable examined.

Luke: To be more specific: Do you think your view that the human brain is essentially a “linearly scaled-up primate brain” is in significant tension with Smaers & Soligo (2013)‘s principal component analysis (PCA) of neural structure variation in primate species?

Smaers and Soligo claim their PCA shows that while (1) the principal component which accounts for 25.8% of the variance is closely correlated with brain size, it’s also the case that (2) the remaining principal components — which account for a large majority of the variance — are not closely correlated with brain size. In particular, they claim that their phylogenetic analysis shows that “a clade-specific investment in particular brain formations (prefrontal white matter, prefronto-striatal and higher motor control) in combination with increased absolute brain size differentiates great apes (and humans) from other primates” (emphasis added).

Suzana: No, there is no tension. What we see is that the human cerebral cortex as a whole, like the human cerebellum as a whole, and the remaining areas of the brain as a whole, are linearly scaled-up in their numbers of neurons compared to the same structures in other primate brains. This means that the relationship between the particular size of a brain structure and its number of neurons is constant and shared across primate species. This does not at all imply or require that all brain areas have the same ratios of numbers of neurons relative to one another, which is what mosaic evolution states: given brain regions can become relatively enlarged or reduced compared to others, and still maintaing the same relationship between their number of neurons and mass as seen across species.

Having said that: yes, the human brain as a whole does fit the relationship between brain mass and total number of neurons that we found in other primates. As far as I understand, the relative differences that Jeroen Smaers concentrates on are very small – he is looking at the residuals of the relationships, and as many still do, using normalization to external parameters. I believe it is time that we stop assuming that things such as brain mass, or worse, body mass, are true independent parameters (which they very likely aren’t; brain mass, in particular, is the result of the cellular composition of the brain and its parts, and as such cannot determine much), and start looking at the absolute values of the different parameters – which is what we have been doing in my lab, trying to keep the number of assumptions to a minimum.

Luke: What are the current estimates of neuron quantities for the largest brains, in elephants and whales? Has you isotropic fractionator process been used on those brains yet, or are their current plans to do so?

Suzana: We have a paper under review on the number of neurons in the brain of the African elephant. The elephant is a great test of our hypothesis that numbers of neurons are a strong limiting factor to cognitive abilities exactly because of its large brain, at 4-5 kg, which is about 3x the mass of the human brain: we predicted that it should have fewer neurons than the human brain, despite being larger than ours.

As it turns out, the answer was even more interesting: the elephant brain as a whole has 3 times the number of neurons of the human brain, 257 billion neurons against an average 86 billion in ours, BUT 98% of those neurons are located in the elephant cerebellum, which turns out to be a major outlier in the numeric relationship between numbers of neurons in the cerebral cortex and cerebellum. While other mammals (humans included) have about 4 neurons in the cerebellum to every neuron in the cerebral cortex, the elephant has 45 neurons in the cerebellum to every neuron in the cerebral cortex. All we can do for now is to speculate on the reason for this extraordinary number of neurons in the elephant cerebellum, and the most likely candidates right now is to me the fine sensorimotor control of the trunk, a 200-pound appendage that has amazingly fine sensory and motor capabilities, which are known to involve the cerebellum.

Despite the enormous number of neurons in the elephant cerebellum, its cerebral cortex, which is twice the size of ours, has only one third of the neurons in an average human cerebral cortex. Taken together, these results suggest that the limiting factor to cognitive abilities is not the number of neurons in the whole brain, but in the cerebral cortex (to which I would add, “provided that the cerebellum has enough neurons to shape activity in the cerebral cortex”).

We don’t have data on whales yet, but that research is underway in our lab – along with research on carnivores, who we predict to have more neurons than the large artiodactyls that they prey upon.

Luke: What other results in this line of research to you hope to have from your lab or other labs in the next 5 years?

Suzana: We’re extending our analysis to the other mammalian branches — xenarthrans, marsupials, carnivores, chiropterans and perissodactyls — and to non-mammalian vertebrates (birds, reptiles, fish, amphibians) and even some invertebrates. The goal is to achieve a full appreciation and understanding of brain evolution, which will give us, amongst other things, a view into the mechanisms that have led to the generation of brain diversity in evolution. Such a comparative analysis also gives us insights onto the most basic features of the brain: those that are shared by all mammals. As it turns out, there are some, and they are very revealing. One of them, for instance, is the addition of glial cells to the brain, in numbers which seem to be regulated by a self-organized process that is shared across all species examined so far.

We are also focusing our analysis on the prefrontal cortex, that is, the associative areas of the cerebral cortex. While it has been very informative to compare total numbers of neurons in the cerebral cortex across species, it is supposedly those neurons in the associative areas that should really limit the cognitive abilities of the species. This more specific analysis should allow us a new glimpse into the brains of different species and how they compare to the human brain. In this regard, we have a paper in the works comparing the distribution of neurons along the human cerebral cortex with that in other, non-human primate species.

We are also moving into the spacial properties of the tissue: how neurons are distributed, and how this is related to the distribution of astrocytes and vasculature, for instance. But one large question that remains is how numbers of synapses compare across humans and other species. That is also something that we are working on.

Luke: Thanks, Suzana!