Cerebral blood flow rates in recent great apes are greater than in Australopithecus species that had equal or larger brains

Cerebral blood flow rates in recent great apes are greater than in Australopithecus species that had equal or larger brains. Roger S. Seymour, Vanya Bosiocic, Edward P. Snelling, Prince C. Chikezie, Qiaohui Hu, Thomas J. Nelson, Bernhard Zipfel and Case V. Miller. Volume 286, Issue 1915, November 13 2019. https://doi.org/10.1098/rspb.2019.2208

Abstract: Brain metabolic rate (MR) is linked mainly to the cost of synaptic activity, so may be a better correlate of cognitive ability than brain size alone. Among primates, the sizes of arterial foramina in recent and fossil skulls can be used to evaluate brain blood flow rate, which is proportional to brain MR. We use this approach to calculate flow rate in the internal carotid arteries (Q˙ICA), which supply most of the primate cerebrum. Q˙ICA is up to two times higher in recent gorillas, chimpanzees and orangutans compared with 3-million-year-old australopithecine human relatives, which had equal or larger brains. The scaling relationships between Q˙ICA and brain volume (Vbr) show exponents of 1.03 across 44 species of living haplorhine primates and 1.41 across 12 species of fossil hominins. Thus, the evolutionary trajectory for brain perfusion is much steeper among ancestral hominins than would be predicted from living primates. Between 4.4-million-year-old Ardipithecus and Homo sapiens, Vbr increased 4.7-fold, but Q˙ICA increased 9.3-fold, indicating an approximate doubling of metabolic intensity of brain tissue. By contrast, Q˙ICA is proportional to Vbr among haplorhine primates, suggesting a constant volume-specific brain MR.

[Q with a dot is first derivative of Q (rate of change with time, in this case)]

1. Introduction

Brain size is the usual measure in discussions of the evolution of cognitive ability among primates, despite recognized shortcomings [1]. Although absolute brain size appears to correlate better with cognitive ability than encephalization quotient, progression index or neocortex ratio [2,3], an even better correlate might be brain metabolic rate (MR), because it represents the energy cost of neurological function. However, brain MR is difficult to measure directly in living primates and impossible in extinct ones.

One solution to the problem has been to measure oxygen consumption rates and glucose uptake rates on living mammals in relation to brain size and then apply the results to brain sizes of living and extinct primates. Because physiological rates rarely relate linearly to volumes or masses of tissues, any comparison requires allometric analysis. For example, brain MR can be analysed in relation to endocranial volume (≈ brain volume, Vbr) with an allometric equation of the form, MR = aVbrb, where a is the elevation (or scaling factor, indicating the height of the curve) and b is the scaling exponent (indicating the shape of the curve on arithmetic axes). If b = 1.0, then MR is directly proportional to brain size. If b is less than 1, then MR increases with brain size, but the metabolic intensity per unit volume of neural tissue decreases. If b is greater than 1, the metabolic intensity of neural tissue increases. The exponent for brain MR measured as oxygen consumption and glucose use across several mammalian species is approximately 0.86, and the exponent for cortical brain blood flow rate in mammals is between 0.81 and 0.87 [4,5]. The similarity of the exponents indicates that blood flow rate is a good proxy for brain MR in mammals in general. The exponents are less than 1.0, which shows that brain MR and blood flow rate increase with brain size but with decreasing metabolic and perfusion intensities of the neural tissue.

Recent studies show that blood flow rate in the internal carotid artery (Q˙ICA)$({\dot{Q}}_{\mathrm{ICA}})$ can be calculated from the size of the carotid foramen through which it passes to the brain [6]. The artery occupies the foramen lumen almost entirely [7–9], therefore defining the outer radius of the artery (ro), from which inner lumen radius (ri) can be estimated, assuming that arterial wall thickness (ro – ri) is a constant ratio (w) with lumen radius (w = (ro – ri)/ri), according to the law of Laplace. The haemodynamic equation used to calculate Q˙ICA${\dot{Q}}_{\mathrm{ICA}}$ is referred to as the ‘shear stress equation’, and attributed to Poiseuille: Q˙=(τπr3i)/(4η)$\dot{Q}=(\tau \pi r_i^3)/(4\eta )$, where Q˙$\dot{Q}$ is the blood flow rate (cm3 s−1), τ is the wall shear stress (dyn cm−2), ri is the arterial lumen radius (cm) and η is the blood viscosity (dyn s cm−2) [10]. The technique was validated in mice, rats and humans, but was initially criticized [11], defended [12] and subsequently accepted [13]. However, the calculations involved three questionable assumptions: flow in the cephalic arteries conforms to Poiseuille flow theory, arterial wall shear stress can be calculated accurately from body mass (although there is no clear functional relationship between them) and the arterial wall thickness-to-lumen radius ratio (w) was a certain constant derived from only two values in the literature.

We have now made significant advancements to the initial methodology by replacing the shear stress equation, and its assumptions, with a new equation derived empirically from a meta-analysis of Q˙$\dot{Q}$ versus ri in 30 studies of seven cephalic arteries of six mammalian genera, arriving at an allometric, so-called ‘empirical equation’, Q˙$\dot{Q}$ = 155 ri2.49 (R2 = 0.94) [14]. The equation is based on stable cephalic flow rates, which vary little between rest, intense physical activity, mental exercise or sleep [14]. The equation also eliminates reliance on the somewhat tenuous estimation of arterial wall shear stress from body mass. We have also improved the calculation with a more extensive re-evaluation of carotid arterial wall thickness ratio (w = 0.30) from 14 imaging studies on humans (electronic supplementary material, text and table S1 for data and references). The present investigation implements these recent methodological advancements and re-evaluates the scaling of Q˙ICA${\dot{Q}}_{\mathrm{ICA}}$ as a function of Vbr in extant haplorhine primates and in fossil hominins. The point of our study is to clarify these relationships between Homo sapiens, Australopithecus and modern great apes (Pongo, Pan, Gorilla) to resolve an apparent allometric conundrum within our previous studies: one analysis based on 34 species of extant Haplorhini, including H. sapiens, resulted in the equation Q˙ICA =8.82×10−3${\dot{Q}}_{\mathrm{ICA}}\hspace{0.17em}=8.82\times {10}^{-3}$Vbr0.95 [6], while another analysis of 11 species of fossil hominin, also including H. sapiens, produced the equation Q˙ICA=1.70×10−4${\dot{Q}}_{\mathrm{ICA}}=1.70\times {10}^{-4}$Vbr1.45 [15]. Humans are on both analyses with the largest brains, but the exponents of these equations are markedly different, and the lines converge. The present study confirms that hominin ancestors had lower Q˙ICA${\dot{Q}}_{\mathrm{ICA}}$ than predicted from Vbr with the haplorhine equation. Q˙ICA${\dot{Q}}_{\mathrm{ICA}}$ in modern great apes is about twice that in Australopithecus species, despite similar or smaller Vbr.