New (old) Australopithecus anamensis cranium

August 30, 2019August 30, 2019 / zcofran / Leave a comment

The Fall semester here at Vassar kicks off next week, and so of course a new fossil discovery is published this week that threatens to upend my course plans and throw my syllabi into disarray. Haile-Selassie and colleagues report a very well-preserved hominin cranium, from the Woranso-Mille region of Ethiopia and dating to 3.8 million years ago. The new cranium shares features with Australopithecus anamensis, a species previously mainly known through jaws and teeth. The fossil is therefore really important since it puts a face to the species’ name, and it is the oldest relatively complete Australopithecus cranium known. When I showed a picture of the fossil to my wife, who is not a paleoanthropologist, all she said was that it looked like the face of a dog who got stung by a bee.

The new A. anamensis fossil MRD-VP-1 (left), and a dog that lost a fight with a bee. Fossil photo from the Smithsonian‘s coverage.

The big buzz in many news stories about the fossil (for example, Nature, ScienceNews, etc.) is that it rewrites an evolutionary relationship early in human history, with Australopithecus anamensis no longer the ancestor of A. afarensis, but rather the two being contemporaries. That idea is based on a 3.9 million year old frontal bone attributed to A. afarensis from a site called Belohdelie, also in Ethiopia (Asfaw, 1987): basically, the new A. anamensis cranium reveals a hominin with a narrow frontal region of the brain, which lived 100,000 later than A. afarensis with a relatively expanded frontal region:

Top views of the reconstructed A. anamensis cranium (left), and the Belohdelie frontal (center), and my crappy photoshopped overlay of Belohdelie on A. anamensis (right). Images not to scale.

The lede, “human evolutionary tree messier than thought,” is not terribly interesting or compelling since it seems to characterize most fossil discoveries over the past several years. And in this case I don’t know how well supported the argument is, since the trait in question (narrow frontal region of the braincase or “post-orbital constriction”) can vary dramatically within a single species. The image below is from the paper itself—compare the difference in “postorbital constriction index” (left graph) between the new A. anamensis cranium (MRD) and A. afarensis (in blue). Both sets of fossils fall within the range of chimpanzees (P. troglodytes), and note the great range of variation within gorillas (G. gorilla).

Part of Figure 3 from the paper by Haile-Selassie and colleagues. On the top is a view from above of fossil humans: Sahelanthropus tchadensis, Ardipithecus ramidus, the new A. anamensis, A. afarensis, and A. africanus. Below the graphs show how species differ in narrowing of the frontal (left) and length of the skull (right).

What I find most interesting about the new find is the great front-to-back length of the cranium—check out how long and narrow the brain-case is of the fossil compared with the later hominins to the right. This is an interesting similarity with the much earlier (6 million years ago) Sahelanthropus tchadensis, which is the left-most fossil in the figure. It makes me really curious to see the brain endocast of A. anamensis and the Sahelanthropus cranium—what was brain shape like for these ancient animals, and what does that mean for the earliest stages of human brain evolution? The Sahelanthropus endocast was presented at a conference six years ago but remains unpublished. Haile-Selassie and colleagues report that they made a virtual reconstruction of the A. anamensis endocast, so hopefully we’ll get to pick its brain soon.

Scientific Racism

February 27, 2017 / zcofran / Leave a comment

The site’s been quiet in 2017, with little time to blog on top of my regular professional responsibilities, and of course watching the fascist smoke rising from the garbage fire of our 45th presidential administration with horrified disbelief. At work, my two new classes are keeping me plenty busy, and their content is quite distinct – one is on the archaeological record of Central Asia, the other centers around Homo naledi to teach about fossils. But by complete accident, examples of scientific racism came up in the readings for each course last week.

scientific-racism

Scientific racism refers to using data or evidence from the biological and social sciences to support racist arguments, that one racial group is better or worse than another group; the groups of course, are culturally determined rather than empirically discrete biological entities. This evidence is often cherry-picked, misinterpreted, and/or outright weak. Nicolas’ Wade’s 2014 A Troublesome Inheritance is a recent example of such a work. The book’s racial claims amount to nothing more than handwaving, and so egregious is the misrepresentation of genetic evidence that nearly 150 of the world’s top geneticists signed a letter to the editor rebuking Wade for “misappropriation of research from our field to support arguments about differences among human societies.” Wade’s book has no place in scientific discourse, but then almost anyone can write a book as long as a publisher thinks it will sell.

In addition to the outright misrepresentation of scientific evidence to support racist arguments, another manifestation of scientific racism is the influence of cultural biases in the interpretation of empirical observations. This may be less malicious than the first example, but is equally dangerous as it more tacitly supports systemic and pervasive racism. And this brings us to my classes’ recent readings.

First was a reference to the “Movius Line” in a review of the Paleolithic record of Central Asia (Vishnyatsky 1999) for my prehistory class. Back in the 1940s Hallum Movius, archaeologist and amazing-name-haver, noticed a distinct geographic pattern in the distribution of early stone tool technology across the Old World: “hand-axes” could be found at sites across Africa and western Eurasia, while they were largely absent from East Asian sites, which were dominated by more basic stone tools.

Movius’ illustration of the distribution of Early Paleolithic technologies. From Fig. 1 in Dennell (2015).

Robin Dennell (2016) provides a nice review of how Movius’ personal, culturally influenced perception of China colored his interpretation of this pattern. Movius read this archaeological evidence to mean that early East Asian humans were unable to create the more advanced technology of the west, a biological and cognitive deficiency resulting from cultural separation: “East Asia gives the impression of having acted (just as historical China and in sharp contrast with the Mediterranean world) as an isolated and self-sufficient area, closed to any major human migratory wave” (Movius 1941: 86, cited in Dennell 2015). Racial and cultural stereotypes about East Asia directly translated to his interpretation of an archaeological pattern.

This type of old school scientific racism also arose in a review of endocasts (Falk, 2014) for my Homo naledi class. Endocasts are negative impressions or casts of a space or cavity, and comprise the only direct evidence of what extinct animals’ brains looked like. So to see how the structure of the brain has changed over the course of human evolution, scientists can search for the impressions of important brain structures in fossil human endocasts. Falk (2014) reviews one of the most famous of these structures – the “lunate sulcus” – which was used as evidence for reorganization of the hominin brain for nearly 100 years. In the early 20th century, anatomist and anthropologist GE Smith (not GE Smith from the Saturday Night Live Band) thought he’d identified the human homologue of a groove that in apes separates the parietal lobe from the visual cortex. In humans, however, this groove was positioned more toward the back of the brain, which Smith interpreted as an expansion of an area relating to advanced cognition.

Screen Shot 2017-02-25 at 9.07.54 AM.png

The back of the brain, viewed from the left, of a chimpanzee (left) and two humans, the red line illustrating the Affenspalte or lunate sulcus (Fig. 1 from Falk 2014, which was modified from Smith 1903). The middle one also might be a grumpy fish.

It turns out that the lunate sulcus does not actually exist in humans, as the grooves identified as such are not structurally or functionally the same as the lunate sulcus in apes (Allen et al., 2006). Nevertheless, given what Smith thought the lunate sulcus was, it’s tragic to read his interpretations of human variation: “resemblance to the Simian [ape] pattern… is not quite so obvious…. in European types of brain….” (Smith 1904: 437, quoted in Falk 2014). The human condition for this trait was for it to be located in the back, reflecting an expansion of the cognitive area in front of it, and this pattern was less pronounced, according to Smith, in non-European people’s brains. This interpretation reflects two traditions at the time: 1) to refer to racial ‘types,’ ignoring variation within and overlap between groups, as well as 2) the prevailing wisdom that Europeans were more intelligent or advanced than other geographical groups.

Anecdotes such as these may seem like mere scientific and historical curios, but they should serve as important reminders both that science can be accidentally guided by cultural values, or intentionally used for malevolent ends. Misconceptions and errors of the past shouldn’t be erased, but rather touted so that we don’t repeat mistakes that can have major consequences in our not-so-post-racial society.

References

Allen JS, Bruss J, & Damasio H (2006). Looking for the lunate sulcus: a magnetic resonance imaging study in modern humans. The anatomical record. Part A, Discoveries in molecular, cellular, and evolutionary biology, 288 (8), 867-76 PMID: 16835937

Dennell, R. (2016). Life without the Movius Line: The structure of the East and Southeast Asian Early Palaeolithic Quaternary International, 400, 14-22 DOI: 10.1016/j.quaint.2015.09.001

Falk D (2014). Interpreting sulci on hominin endocasts: old hypotheses and new findings. Frontiers in human neuroscience, 8 PMID: 24822043

Vishnyatsky L (1999). The Paleolithic of Central Asia. Journal of World Prehistory, 13, 69-122.

Did Neandertal brains grow like humans’ or not?

July 26, 2016July 26, 2016 / zcofran / 1 Comment

According to Marcia Ponce de Leon and colleagues, “Brain development is similar in Neandertals and modern humans.” They reached this conclusion after comparing how the shape of the brain case changes across the growth period of humans and Neandertals. This finding differs from earlier studies of Neandertal brain shape growth (Gunz et al. 2010, 2012).

Although Neandertals had similar adult brain sizes as humans do today, the brains are nevertheless slightly different in shape:

Endocranial surfaces of a human (left, blue) and Neandertal (right, red), from Gunz et al. (2012). These surfaces reflect the size and shape of the brain, blood vessels, cerebrospinal fluid, and meninges.

Gunz et al. (2010, 2012) previously showed that endocranial development in humans, but not in Neandertals or chimpanzees, has a “globularization phase” shortly after birth: the endocranial surface becomes overall rounder, largely as a result of the expansion of the cerebellum:

Endocranial (e.g., brain) shape change in humans (blue), Neandertals (red) and chimpanzees (green), Fig. 7 from Gunz et al. (2012). Age groups are indicated by numbers. The human “globularization phase” is represented by the great difference in the y-axis values of groups 1-2 (infants). The Neandertals match the chimpanzee pattern of shape change; Neandertal neonates (LeM2 and M) do not plot as predicted by a human pattern of growth.

Ponce de Leon and colleagues now challenge this result with their own similar analysis, suggesting similar patterns of shape change with Neandertals experiencing this globularization phase as well (note that endocranial shapes are always different, nevertheless):

Endocranial shape change in humans (green) and Neandertals (red), from Ponce de Leon et al. (2016). Note that the human polygons and letters represent age groups, whereas the Neandertal polygons and labels are reconstructions of individual specimens.

The biggest reason for the difference between studies is in the fossil sample. Ponce de Leon et al. have a larger fossil sample, with more non-adults including Dederiyeh 1-2, young infants in the age group where human brains become more globular.

Comparison of fossil samples between the two studies.

But I don’t think this alone accounts for the different findings of the two studies. Overall shape development is depicted in PC 1: in general, older individuals have higher PC1 scores. The globularization detected by Gunz et al. (2010; 2012) is manifest in PC2; the youngest groups overlap entirely on PC1. The biggest difference I see between these studies is where Mezmaiskaya, a neonate, falls on PC2. In the top plot (Gunz et al., 2012), both Mezmaiskaya and the Le Moustier 2 newborn have similar PC2 values as older Neandertals. In the bottom plot (Ponce de Leon et al., 2012), the Mezmaiskaya neonate has lower PC2 scores than the other Neandertals. Note also the great variability in Mezmaiskaya reconstructions of Ponce de Leon et al. compared with Gunz et al.; some of the reconstructions have high PC2 values which would greatly diminish the similarity between samples. It’s also a bit odd that Engis and Roc de Marsal appear “younger” (i.e., lower PC1 score) than the Dederiyeh infants that are actually a little bit older.

Ponce de Leon et al. acknowledge the probable influence of fossil reconstruction methods, and consider other reasons for their novel findings, in the supplementary material. Nevertheless, a great follow-up to this, to settle the issue of Neandertal brain development once and for all, would be for these two research teams to join forces, combining their samples and comparing their reconstructions.

REFERENCES

Gunz P, Neubauer S, Maureille B, & Hublin JJ (2010). Brain development after birth differs between Neanderthals and modern humans. Current Biology : 20 (21) PMID: 21056830

Gunz P, Neubauer S, Golovanova L, Doronichev V, Maureille B, & Hublin JJ (2012). A uniquely modern human pattern of endocranial development. Insights from a new cranial reconstruction of the Neandertal newborn from Mezmaiskaya. Journal of Human Evolution, 62 (2), 300-13 PMID: 22221766

Ponce de León, M., Bienvenu, T., Akazawa, T., & Zollikofer, C. (2016). Brain development is similar in Neanderthals and modern humans Current Biology, 26 (14) DOI: 10.1016/j.cub.2016.06.022

Bioanthro lab activity: Hominin brain size

November 15, 2015November 24, 2015 / zcofran / 2 Comments

Last week in my Human Evolution class we looked at whether we could estimate hominin brain sizes, or endocranial volumes (ECV), based on just the length and width of the bony brain case. Students took these measurements on 3D surface scans…

Maximum cranial length in Australopithecus boisei specimen KNM-ER 406.

… and then plugged their data into equations relating these measurements to brain size in chimpanzees (Neubauer et al., 2012) and humans (Coqueugniot and Hublin, 2012).

The relationship between cranial length (x axis) and ECV (y axis). Left shows the chimpanzee regression (modified from Fig. 2 in Neubauer et al., 2012), while the right plot is humans (from the Supplementary Materials of Coqueugniot and Hublin, 2012).

So in addition to spending time with fossils, students also learned about osteometric landmarks with fun names like “glabella” and “opisthocranion.” More importantly, students compared their estimates with published endocranial volumes for these specimens, based on endocast measurements:

Human and chimpanzee regression equations don't do great at estimating hominin brain sizes.

Human and chimpanzee regression equations don’t do great at predicting hominin brain sizes. Each point is a hominin fossil, the x value depicting its directly-measured endocranial volume and the y value its estimated volume based on different regression equations. Black and red points are estimates based on chimpanzee cranial width and length, respectively, while green and blue points are based on human width and length, respectively. The dashed line shows y=x, or a correct estimate.

This comparison highlights the point that regression equations might not be appropriate outside of the samples on which they are developed. Here, estimates based on the relationship between cranial dimensions and brain size in chimpanzees tend to underestimate fossils’ actual values (black and red in the plot above), while the human regressions tend to overestimate hominins’ brain sizes. Students must think about why these equations perform poorly on fossil hominins.

Most of the fossil scans come from AfricanFossils.org, but a few are from Artec’s sample gallery. One of the cool, fairly recent humans at African Fossils (KNM ER 5306) will give students something else to think about:

“Why doesn’t this look like the rest of the human crania we’ve seen this semester?”

Here are the lab materials so you can use and adapt this for your own class:

Lab 4-Brain size (Instructions & questions)

Lab 4 data table (with equations)

References
Coqueugniot, H., & Hublin, J. (2012). Age-related changes of digital endocranial volume during human ontogeny: Results from an osteological reference collection American Journal of Physical Anthropology, 147 (2), 312-318 DOI: 10.1002/ajpa.21655

Neubauer, S., Gunz, P., Schwarz, U., Hublin, J., & Boesch, C. (2012). Brief communication: Endocranial volumes in an ontogenetic sample of chimpanzees from the taï forest national park, ivory coast American Journal of Physical Anthropology, 147 (2), 319-325 DOI: 10.1002/ajpa.21641

A new year of bioanthro lab activities

September 6, 2015September 6, 2015 / zcofran / 3 Comments

One of my goals in teaching is to introduce students to how we come to know things in biological anthropology, and lab activities give students hands-on experience in using scientific approaches to address research questions. Biological anthropology (really, all biology) is about understanding variation, and I’ve created some labs for students to scrutinize biological variation within the classroom.

In my Introduction class, the first aspect of human uniqueness we will focus on is the brain. To complement readings and lectures, we’ll also investigate variation in brain size among students in class. Of course, measuring their actual brain sizes is impossible without either murdering them (unethical and messy) or subjecting them to CT or MRI scanning (costly and time-consuming). Instead, it’s fast and easy to measure head circumference, so we’ll estimate just how brainy they are in a way that will also introduce them to data collection, measurement error, and the regression analysis.

The lab activity is based on a paper by Bartholomeusz and colleagues (2002), who used CT scanning to measure the external head circumferences and brain volumes of males ranging from 1-40 years. Focusing on the adults of this sample, there are several possible regression equations that students could use to estimate their brain size from their head circumference:

The relationship between head circumference and brain volume in adult humans. Note each regression line is based on different age groups. Data from Bartholomeusz et al. (2002).

Bartholomeusz et al. divided their sample into age groups, and students will learn that the relationship between the two variables differs subtly depending on the age group. Students will therefore have to decide (and justify) which equation they will use – should they pick the one based on their own age group, or the one with the lowest prediction error?

Once students have estimated their brain sizes, I’ll enter the data into R and we’ll look at how (estimated) brain size varies within the classroom, looking also at possible covariates including sex and region of birth. After discussing our data in class, students have to write up a brief report describing our research question and proposing additional hypotheses about brain size variation.

So that’s this week’s lab in Introduction to Biological Anthropology. There will be four more this semester, in three of which students will collect data on themselves, as well as four other labs for my Human Evolution course. In case you’re interested in using this activity for your class, I’m including the lab handout here. I’ll also try to post lab assignments to the blog (as I’ve done here) as the semester progresses.

Activity handout: Lab 1 Instructions and report

Reference

Bartholomeusz, H., Courchesne, E., & Karns, C. (2002). Relationship Between Head Circumference and Brain Volume in Healthy Normal Toddlers, Children, and Adults Neuropediatrics, 33 (5), 239-241 DOI: 10.1055/s-2002-36735

Brain size growth in wild and captive chimpanzees

April 3, 2015April 3, 2015 / zcofran / Leave a comment

I’m back in Astana, overcoming jet lag, after the annual conference of the American Association of Physical Anthropologists, which was held in my home state of Missouri. I’d forgotten how popular ranch dressing and shredded cheese is out there; but hey, at least you can drink the tap water! It was also nice to be immersed in a culture of evolution, primates and fossils, something so far lacking at the nascent NU.

Although I usually present in evolution and fossil-focused sessions, my recent interest in brain growth landed me in a session devoted to Primate Life History this year. The publication of endocranial volumes (ECVs) from wild chimpanzees of known age from Taï Forest (Neubauer et al., 2012) led me to ask whether this cross-sectional sample displays the same pattern of size change as seen in captive chimpanzee brain masses (Herndon et al., 1999). These are unique datasets because precise ages are known for each individual, and this information is generally lacking for most skeletal populations. We therefore have a unique opportunity to estimate patterns and rates of growth, and to compare different populations. Here are the data up to age 25 (the oldest known age of the wild chimps):

Brain size plotted against age in chimpanzees. Blue Ys are the Yerkes (captive) apes and green Ts are the Taï (wild) chimps. Note that Yerkes data are brain masses while the Taï data are endocranial volumes (ECVs). Mass and volume – as different as apples and oranges, or as oranges and tangerines? Note the relatively high “Y” at 1.25 years, who was omitted from the subsequent analysis.

This is an interesting comparison for a few reasons. First, to the best of my knowledge brain size growth hasn’t been compared between chimp populations (although it has been compared between chimps and bonobos: Durrleman et al., 2012). Second, many studies have found differences in tooth eruption, maturation and skeletal growth and development between wild and captive animals, but again I don’t think this has been examined for brain growth. Finally, and most fundamentally, it’s not clear whether ECV and brain mass follow the same basic pattern of change (brain mass but not ECV is known to decrease at older ages in humans and chimps, but at younger ages…?.

So to first make the datasets comparable, I used published data to examine the relationship between brain mass and ECV in primates, to estimate the likely ECV of the Yerkes brain masses. Two datasets examine adult brain size across different primate species (red and blue in the plot below), and one looks at brain mass and ECV of individuals for a combined sample of gorillas (McFarlin et al., 2013) and seals (Eisert et al., 2013). In short, ECV and brain mass in these datasets give regression slopes not significantly different from 1. One dataset has a negative y-intercept significantly different from 0, meaning that ECV should actually be slightly less than brain mass, but I think this pattern is driven by the really small-brained animals like New World Monkeys).

The relationship between endocranial volume and brain mass in primates (and Weddell seals). Solid lines and shaded confidence intervals are given for each regression, and the dashed line represents isometry, or a 1:1 relationship (ECV=brain mass). The rug at the bottom shows the range of the Yerkes masses. Note that the red and black regressions are not significantly different from isometry, while the blue regression is shifted slightly below isometry.

So let’s assume for now that the ECVs of the Yerkes apes are the same as their masses, meaning the two datasets are directly comparable. There are lots of ways to mathematically model growth, and as George Box famously quipped, “All models are wrong, but some are useful.” Here, I wanted to use something that explained the greatest amount of ontogenetic variation in ECV while also levelling off once adult brain size was reached (by 5 years based on visual inspection of the first plot above). This led me to the B-spline. With some tinkering I found that having two knots, one between each 0.1-2.5 and 2.6-5, provided models that fit the data pretty well, and I resampled knot combinations to find the best fit for each dataset. The result:

B-splines describing the relationship between ECV (or brain mass) and age in the TaÏ (green) and Yerkes (blue) data. Although resampling identified different knots for each sample, the regression coefficients are not significantly different.

B-splines describing the relationship between ECV (or brain mass) and age in the TaÏ (green) and Yerkes (blue) data. Note that although the Yerkes line is elevated above the Taï line after 4 years, the confidence intervals (shaded regions) overlap at all ages.

These models fit the data pretty well (r-squared >0.90), and nicely capture the major changes in growth rates. Resampling knot positions reveals best-fit models with different knots for each sample, but otherwise the two models cannot be statistically distinguished from one another: the 95% confidence intervals of both the model coefficients and brain size estimates overlap. So statistical modelling of brain growth in these samples suggests they’re the same, but there are some hints of difference.

Growth rates at each age calculated from the B-spline regressions. Note these are arithmetic velocities and not first derivatives of the growth curves. The dashed horizontal line at 0 indicates the end of brain size growth.

Converting the growth curves to arithmetic velocities we see what accounts for the subtle differences between samples. The velocity plot hints that, in these cross-sectional data, brain size increases rapidly after birth but growth slows down and ends sooner in Taï than among the Yerkes apes. I’m cautious about over-interpreting this difference, since there is great overlap between growth curves, and there is only one Taï newborn compared to about 20 in Yerkes: even just a few more newborns from Taï might reveal greater similarity with Yerkes.

So there you have it, it looks like the wild Taï and captive Yerkes chimps follow basically the same pattern of brain growth, despite living in different environments. Whereas the generally greater stressors in the wild often lead to different patterns of skeletal and dental development in wild vs. captive settings, brain growth appears pretty robust to these environmental differences. That brain growth should be canalized is not too surprising, given the importance of having a well-developed brain for survival and reproduction. But it’s cool to see this theoretical expectation borne out with empirical observations.

#AAPA2015

March 23, 2015March 23, 2015 / zcofran / Leave a comment

Tomorrow I’m heading to St. Louis, MO for the annual meeting of the American Association of Physical Anthropologists. I’ll be giving a talk on Saturday presenting results of a comparison of brain size growth between captive and wild chimpanzees. Some recent work has highlighted differences between captive and wild animals in terms of bodily growth and maturation, but so far as I know brain development has not been part of this. Here’s a teaser plot, showing how the captive (blue) and wild (green) datasets deviate from a piecewise linear regression of brain size against age (for the combined wild+captive sample):

The dashed black line is zero, or no deviation from the model. This plot shows that each dataset deviates little from the model at younger ages (when the brain is growing rapidly), but at older ages the captive animals have larger brains, and the wild animals have smaller brains, than predicted by the model. What’s the meaning of this? Find out Saturday afternoon at 3 pm…

2015 AAPA conference: More brain growth

December 13, 2014January 17, 2015 / zcofran / Leave a comment

The American Association of Physical Anthropologists is holding its annual meeting next year in St. Louis, in my home state of Missouri (I’m from Kansas City, which is by far the best city in the state, if not the entirety of the Midwest). I’ll be giving a talk comparing brain size growth in captive and wild chimpanzees, on Saturday 28 March in the Primate Life History session. Here’s a sneak peak:

Velocity curve for brain size from birth to 5 years in wild (green) and caprive (blue) chimpanzees. For the captive models, the dashed line is fit to the raw brain masses, and the solid line is fit to the estimated endocranial volumes.

Velocity curves for brain size growth from birth to 5 years in wild (green) and captive (blue) chimpanzees. The wild data are endocranial volumes, but the captive specimens are represented by brain masses. So the captive data are modeled for both the original masses (dashed) and estimated volumes (solid). Wild data are from Neubauer et al. 2011, captive data from Herndon et al., 1999.

Abstract: This study compares postnatal brain size change in two important chimpanzee samples: brain masses of captive apes at the Yerkes National Primate Research Center, and endocranial volumes (ECVs) of wild-collected individuals from the Taï Forest. Importantly, age at death is known for every individual, so these cross-sectional samples allow inferences of patterns and rates of brain growth in these populations. Previous studies have revealed differences in growth and health between wild and captive animals, but such habitat effects have yet to be investigated for brain growth. It has also been hypothesized that brain mass and endocranial volume follow different growth curves. To address these issues, I compare the Yerkes brain mass data (n=70) with the Taï ECVs (n=30), modeling both size and velocity change over time with polynomial regression. Yerkes masses overlap with Taï volumes at all ages, though values for the former tend to be slightly elevated over the latter. Velocity curves indicate that growth decelerates more rapidly for mass than ECV. Both velocity curves come to encompass zero between three and four years of age, with Yerkes mass slightly preceding Taï ECV. Thus, Yerkes brain masses and Taï ECVs show a very similar pattern of size change, but there are minor differences indicating at least a small effect of differences in habitat, unit of measurement, or a combination of both. The overall similarity between datasets, however, points to the canalization of brain growth in Pan troglodytes.

Update: Brain growth in Homo erectus, and the age of the Mojokerto fossil

July 11, 2013August 4, 2013 / zcofran / 5 Comments

The Mojokerto calvaria. You’re looking at the left side of the
skull: the face would be to the left. Check it out in 3D here.

A few months ago I posted an abridged version of the presentation I gave at this year’s meetings of the American Association of Physical Anthropologists, about brain growth in Homo erectus. This study, co-authored with Jeremy DeSilva, adopts a novel approach (see “Methods” in that earlier post) to analyze the Mojokerto fossil (right). The specimen is the only H. erectus non-adult complete enough to get a decent estimate of brain size (or rather, the overall volume of the brain case) – probably 630 to 660 cubic centimeters (Coqueugniot et al. 2004; Balzeau et al., 2004). So to study brain growth in the extinct species, we just have to connect a range of estimated brain sizes at birth (around 290 cubic centimeters, based on predictive equations by DeSilva and Lesnik, 2008) to that of Mojokerto. But, the speed of brain growth implied by this comparison depends on how old poor Mojokerto was when s/he died.

Most recently, Hélen Coqueugniot and colleagues (2004) used CT scans of the fossil to examine the fusion of its various bones, to suggest the poor kid died between six months to 1.5 years, if not even younger. Antoine Balzeau and team (2005) also studied scans of the fossil, and their analysis of its virtual endocast presented conflicting age estimates, but they argued the poor kid was probably no older than 4 years. Earlier studies had suggested the kid was up to 8 years. Now, for my previous post/conference presentation, we assumed the Coqueugniot estimate was correct – but what if we consider a full range of ages for Mojokerto, from 0.03-6.00 years?

Brain size, relative to newborns’ values, at different ages in humans (black circles) and chimpanzees (red triangles). Homo erectus median and mean are the thick solid and dashed blue lines, respectively, and the 90% and 95% confidence intervals are indicated by the thinner, dotted blue lines. Data are the same as in the previous post.

The plot above depicts brain size relative to newborns: each circle (humans) and triangle (chimpanzees) represents the proportional size difference between a newborn (less than 1 week) and an older individual, up to 6 years. Obviously, relative brain size gets bigger in humans and chimpanzees over time. Interestingly, even though humans and chimps have very different brain sizes, the proportional brain size changes overlap a lot between species, especially at younger ages. Ah, the joys of cross-sectional samples.

But what’s especially interesting here are the blue lines on the graph, indicating estimates of proportional size change in Homo erectus, assuming Mojokerto’s skull could hold 630 cc of delicious brain matter, and that the species’ skulls at birth could hold about 290 cc, give or take several cc. The thick solid and dashed lines just above 2 on the y-axis are the mean and median of our estimates – Mojokerto’s brain averages around 2.2 times larger than predicted newborns. Such a proportion is most likely to be found in humans between 6 months to a year of age, and in chimpanzees between around 6 months and 2 years. The confidence intervals, the highest and lowest bounds of our estimates for Homo erectus proportional size change, are the thinner dashed lines on the graph. They help us constrain our estimates, and further suggest that the proportional difference found for H. erectus is most likely to be found in either chimpanzees or humans around 1 year of age – just like Coqueugniot and colleagues predicted!!!

Thus, independent evidence – brain size of Mojokerto and estimated brain size at birth in Homo erectus – corroborates a previously estimated age at death for the Mojokerto fossil, the poor little Homo erectus baby. This further supports our estimates of brain growth rates in this species, as described in the previous post.

So to summarize, fairly scant fossil evidence compared with larger extant species samples using randomization statistics, argue for high, human-like infant brain growth rates in Homo erectus by around 1 million years ago. Our ancestors were badasses.

Remember, if you want the R code I wrote to do this study, just lemme know!

Those references
Balzeau A, Grimaud-Hervé D, & Jacob T (2005). Internal cranial features of the Mojokerto child fossil (East Java, Indonesia). Journal of human evolution, 48 (6), 535-53 PMID: 15927659

Coqueugniot H, Hublin JJ, Veillon F, Houët F, & Jacob T (2004). Early brain growth in Homo erectus and implications for cognitive ability. Nature, 431 (7006), 299-302 PMID: 15372030

DeSilva JM, & Lesnik JJ (2008). Brain size at birth throughout human evolution: a new method for estimating neonatal brain size in hominins. Journal of human evolution, 55 (6), 1064-74 PMID: 18789811

Pre-publication: Brain growth in Homo erectus (plus free code!)

April 16, 2013August 4, 2013 / zcofran / 1 Comment

The annual meetings of the American Association of Physical Anthropologists were going on all last week, and I gave my first talk before the Association (co-authored with Jeremy DeSilva). The talk focused on using resampling methods and the abysmal human fossil record to assess whether human-like brain size growth rates were present in our >1 mya ancestor Homo erectus. This is something I’ve actually been sitting on for a while, and wanted to wait til after the talk to post for all to see. I haven’t written this up yet for publication, but before then I’d like to briefly share the results here.

Background: Humans’ large brains are critical for giving us our unique capabilities such as language and culture. We achieve these large (both absolutely, and relative to our body size) brains by having really high brain growth rates across several years; most notable are exceptionally high, “fetal-like” rates during the first 1-2 years of life. Thus, rapid brain growth shortly after birth is a key aspect of human uniqueness – but how ancient is this strategy?

Materials: We can plot brain size at birth in humans and chimpanzees (our closest living relatives) to visualize what makes humans stand out (Figure 1).

Figure 1. Brain size (volume) at given ages. Humans=black, chimpanzees=red. Ranges of brain size at birth, and the chronological age of the Mojokerto fossil, in blue.

Human data come from Cogueugniot and Hublin (2012), and chimpanzees from Herndon et al. (1999) and Neubauer et al. (2012). The earliest fossil evidence able to address this question comes from Homo erectus. Because of the tight relationship between newborn and adult brain size (DeSilva and Lesnik 2008), we can use adult Homo erectus brain volumes (n=10, mean = 916.5 cm^3) to predict that of the species’ newborns: mean = 288.9 cm^3, sd = 17.1). An almost-recent analysis of the Mojokerto Homo erectus infant calvaria suggests a size of 663 cm^3 and an age of 0.5-1.25 years (Coqueugniot et al. 2004; this study actually suggests an oldest age of 1.5 years, but the chimpanzee sample here requires us to limit the study to no more than 1.25 years). Because we have a H. erectus fossil less than 2 years of age, and we can estimate brain size at birth, we can indirectly assess early brain growth in this species.

Methods: Resampling statistics allow inferences about brain growth rates in this extinct species, incorporating the uncertainty in both brain size at birth, and in the chronological age of the Mojokerto fossil. We thus ask of each species, what growth rates are necessary to grow one of the newborn brain sizes to any infant between 0.5-1.25 years? And from there, we compare these resampled growth rates (or rather, ‘pseudo-velocities’) between species – is H. erectus more similar to modern humans or chimpanzees? There are 294 unique newborn-infant comparisons for humans and 240 for the chimpanzee sample. We therefore compare these empirical newborn-infant pairs from extant species to 7500 resampled H. erectus pairs, randomly selecting a newborn H. erectus size based on the parameters above, and randomly selecting an age from 0.5-1.25 years for the Mojokerto specimen. This procedure is used to compare both absolute size change (the difference between an infant and a newborn size, in cm^3/year), and and proportional size change (infant/newborn size).

Results: Humans’ high early brain growth rates after birth are reflected in the ‘pseudovelocity curve’ (Figure 2). Chimps have a similar pattern of faster rates earlier on, but these are ultimately lower than humans’. Using the Mojokerto infant’s brain size (and it’s probable ages) and the likely range of H. erectus neonatal brain sizes (mean = 288, sd = 17), it is fairly clear that H. erectus achieved its infant brain size with high, human-like rates in brain volume increase.

Figure 2. Brain size growth rates (‘pseudo-velocity’) at given ages. Humans=black, chimpanzees=red, and Homo erectus,=blue.

However, if we look at proportional size change, the factor by which brain size increases from birth to a given age, we see a great deal of overlap both between age groups within a species, and between different species. Cross-sectional data create a great deal of overlap in implied proportional size change between ages within a species; it is easier to consider proportional size change between taxa, conflating ages, then (Figure 3). Humans show a massive amount of variation in potential growth rates from birth to 0.5-1.25 years, and chimpanzees also show a great deal of variation, albeit generally lower than in the human sample. Relative growth rates in Homo erectus are intermediate between the two extant species.

Figure 3. Proportional brain size increase (infant/newborn size).

Significance: Brain size growth shortly after birth is critical for humans’ adaptative strategy: growing a large brain requires a lot of energy and parental (especially maternal) investment (Leigh 2004). Plus, in humans this rapid increase may correspond with the creation of innumerable white-matter connections between regions of the brain (Sakai et al. 2012), important for cognition or intelligence. The H. erectus fossil record (1 infant and 10 adults) provides a limited view into this developmental period. However, comparative data on extant animals (e.g. brain sizes from birth to adulthood), coupled with resampling statistics, allow inferences to be made about brain growth rates in H. erectus over 1 million years ago.

Assuming the Mojokerto H. erectus infant is accurately aged (Coqueugniot et al. 2004), and that Homo erectus followed the same neonatal-adult scaling relationship as other apes and monkeys (DeSilva and Lesnik 2008), it is likely that H. erectus had human-like rates of absolute brain size growth. Thus, the energetic and parental requirements to raise such brainy babies, seen in modern humans, may have been present in Homo erectus some 1.5 million years ago or so. This may also imply rapid white-matter proliferation (i.e. neural connections) in this species, suggesting an intellectually (i.e. socially or linguistically) stimulating infancy and childhood in this species. At the same time, relative brain size growth appears to scale with overall brain size: larger brains require proportionally higher growth rates. This is in line with studies suggesting that in many ways, the human brain is a scaled-up version of other primates’ (e.g. Herculano-Houzel 2012).

This study was made possible with published data, and the free statistical programming language R.

Contact me if you want the R code used for this analysis, I’m glad to share it!!!

References
Coqueugniot H, Hublin JJ, Veillon F, Houët F, & Jacob T (2004). Early brain growth in Homo erectus and implications for cognitive ability. Nature, 431 (7006), 299-302 PMID: 15372030

Coqueugniot H, & Hublin JJ (2012). Age-related changes of digital endocranial volume during human ontogeny: results from an osteological reference collection. American journal of physical anthropology, 147 (2), 312-8 PMID: 22190338

DeSilva JM, & Lesnik JJ (2008). Brain size at birth throughout human evolution: a new method for estimating neonatal brain size in hominins. Journal of human evolution, 55 (6), 1064-74 PMID: 18789811

Herculano-Houzel S (2012). The remarkable, yet not extraordinary, human brain as a scaled-up primate brain and its associated cost. Proceedings of the National Academy of Sciences of the United States of America, 109 Suppl 1, 10661-8 PMID: 22723358

Herndon JG, Tigges J, Anderson DC, Klumpp SA, & McClure HM (1999). Brain weight throughout the life span of the chimpanzee. The Journal of comparative neurology, 409 (4), 567-72 PMID: 10376740

Leigh SR (2004). Brain growth, life history, and cognition in primate and human evolution. American journal of primatology, 62 (3), 139-64 PMID: 15027089

Sakai T, Matsui M, Mikami A, Malkova L, Hamada Y, Tomonaga M, Suzuki J, Tanaka M, Miyabe-Nishiwaki T, Makishima H, Nakatsukasa M, & Matsuzawa T (2012). Developmental patterns of chimpanzee cerebral tissues provide important clues for understanding the remarkable enlargement of the human brain. Proceedings. Biological sciences / The Royal Society, 280 (1753) PMID: 23256194

Lawn Chair Anthropology

Biological anthropology, paleontology, evolution, and development, by Dr. Zachary Cofran

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