Christian Castro Ruiz

Expensive brains:“brainy”rodents have higher metabolic rate

Background: 

Certainly, previous studies have demosntrated that the brain, in terms of mass-specific metabolic rate, is nine times higher than comparing the body as a whole. Interestingly, given the fact that brain tissue is energetically costly, the organisms need to develop strategies to segregate their energy storage for other important body funtions. Here in this study, researchers compared (using rodents as a model) the basal-metabolic rate (BMR) and the brain mass (BrM) to figure out if there is a correlation.

Hypothesis

BMR and BrM are correlated within rodents (after taking into consideration both body mass and phylogeny).

Methods

Cytochrome b for 132 rodent species and six rabbits as outgroups were downloaded from GenBank, and one sequence donated. Cytochrome b was chosen as that marker has proven to be of high utility for species level phylogenetics. Sequences were aligned using ClustalX 1.83  The pre- ferred model for the Bayesian analyses was selected with Modeltest using the AIC criterion (Posada and Buckley,2004).The best-fitting model was GTR + γ + I (Yang, 1994). Bayesian analyses were carried out using MrBayes V3.12 (Huelsenbeck and Ronchist, 2001) with the settings as specified in Agnarsson and May-Collado (2008). The Markov chain Monte Carlo search was ran with 10,000,000 generations sampling the Markov chain every 1,000 generations, and the sample points of the first 7,000,000 generations were removed (“burnin”), after which the chain had reached stationarity.

Data on the log of BrM and BM (g), and BMR were used in this study. To describe the evolutionary relationship between BMR and BrM they performed various phylogenetic analyses.

(i) The PDAP module in Mesquite was used to estimate IC . They used BL as estimated by MrBayes testing them for statistic appropriateness using PDAP. To correct for BM we regressed BMR and BrM against BM and subsequently regressed the residuals from these regressions. If the residuals are correlated then that is consistent with a relationship among these variables (BMR and BrM), that is independent of the BM of, and phylogenetic relationship among, species. Regression of residuals was performed using SPSS 2007 (SPSS Inc.). They also regressed BMR and BrM directly.

(ii) To evaluate the correlated evolution among BMR, BrM, and BM, they assessed the phylogenetic effect on the trends in character relationships between taxa (i.e., the observed pattern) using the best model of evolution that was found for each character. To do this they evaluated the significance of the relationships between the pair of characters using a measure of correlated evolution (CORR) in a Bayesian framework implemented in BayesTrait 1.0, assessing the probability of positively correlated (CORR > 0) and negatively correlated evolution (CORR < 0). As the null hypothesis they used a model in which the covariance between characters was set to zero (i.e., complete character independence, CORR = 0), and the alternative hypothesis was, then, the observed covariance between characters. If the null hypothesis was rejected (i.e., a significant historical relationship between characters exists), then they concluded that the phylogenetic relationship and the models of evolution of the characters influence the observed patterns, and they corroborate the hypothesis of correlated evolution between BMR, BrM, and BM.

Conclusions

As we may know, brains are an important part of the central nervous system of vertebrates. It contols the organ systems of the body and coordinate responses to changes in the ecological and social environment. However,  brain mass per se does not capture the complexity of brain function. There is general evidence that relative brain size roughly correlates with cognitive ability. Hence the evolution of brain size is of broad interest, including what factors may favor and constrain the evolution of relatively large, modular and complex brains.

In this study, researchers corroborate the hypothesis of Isler and van Schaik (2006) that an increase in brain mass is accompanied by an increase in basic metabolic rate, and is suggested that this pattern could be general across mammals. Their findings corroborate the hypothesis that large brains evolve when the payoff for increased brain mass is greater than the energetic cost they incur.

Opinion

I agree with the findings. In addition, their conclusions were pretty obvious and expectable. However, something that I would like to see in this research could be the integration of human data to study the same parameters that they studied with rodents. It will make more interesting and groundbraking this study. They argue that their findings can be generalized accross mammals, but still, it will be better if they included humans.