2015 AAPA conference: More brain growth

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.

Avoid the Noid… I mean Noise

As alluded to yesterday, my dissertation compares growth in an extinct animal with growth in living humans; this study is necessarily cross-sectional, meaning that it examines individuals at a single point in time. Alternatively, longitudinal data sample individuals from several points in time. So for instance if I constructed a growth curve by measuring the stature of a bunch of people of different ages in just a day, that would be cross-sectional. But if I had the time and wherewithal to measure some people’s heights once a year from birth to adulthood, well that’d be longitudinal. Cross-sectional data lack the resolution of longitudinal data, whereas longitudinal data can be prohibitively difficult to collect (such as in long-lived, slow-maturing animals like humans, or in extinct animals like Australopithecus robustus).

Some researchers abhor cross-sectional data, pointing out that the intricacies of individuals’ longitudinal growth will not be adequately captured in with cross-sectionally. American anthropology founder Franz Boas himself discussed this in a paper nearly 82 years ago. Anyway, I was reminded of this dichotomy today when perusing a paper that examined longitudinal brain activity in a cohort of adolescent kids (right, from Campbell et al. in press). The mess of jagged lines are individuals’ measurements from age 9-18, and the smoothed blue and red curves are the cross-sectionalized curves calculated from these kids. Oy, look at all that variation and caprice that gets left out in the cross-sectionalized curves!

Of course, this doesn’t mean that we should never use cross-sectional data to study growth – like I’d mentioned above, the fossil record necessitates a cross-sectional approach to the study of growth. As always, you have to understand and acknowledge the limits of your data.

ResearchBlogging.orgRead on
Boas, F. (1930). OBSERVATIONS ON THE GROWTH OF CHILDREN Science, 72 (1854), 44-48 DOI: 10.1126/science.72.1854.44

Campbell, I., Grimm, K., de Bie, E., & Feinberg, I. (2012). Sex, puberty, and the timing of sleep EEG measured adolescent brain maturation Proceedings of the National Academy of Sciences DOI: 10.1073/pnas.1120860109