Bioanthro lab activity: Hominin brain size

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 meshes…

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

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).

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.

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)

ResearchBlogging.orgReferences
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

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.

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

ResearchBlogging.orgReference

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