The scaling of brain metabolism has important implications for brain function and evolution.
For each brain structure volume we calculated the volume of the remaining brain as a log-transformed difference from total brain volume, and used these volumes to account for the scaling of brain components with total brain volume [40].
The scaling of brain metabolism, therefore, is best described as a function of the total number of neurons in the brain, regardless of how that relates to brain mass or neuronal density across species.
These features may represent an additional piece of the multifarious presence of noise, a pervasive trait throughout the bottom-up (or top-down) scaling of brain analyses [21], that affects the immediate detection of accessible regular and repetitive patterns.
Given that the availability of energy could limit brain size expansion in evolution, particularly in primates [11], the scaling of brain metabolism could influence brain circuitry and activity patterns by exerting selective pressure toward metabolically efficient wiring patterns [12] [15], neuronal morphology [16] and neural codes [17] [19].
Thus, the linear scaling of brain metabolism with its number of neurons also accounts for the larger specific metabolic rates in tissues with larger neuronal densities: the metabolic cost per gram of tissue, as shown here, increases directly with the number of neurons per gram of tissue.
The study of behavioural processes and their environmental correlates may thus approach the time-scale of brain processes and we may get closer to directly measure brain-behavioural interactions.
Also, a highly significant negative correlation was found between body mass and the brain/body mass ratio (p < 10-6), possibly due to allometric scaling of brain size.
However, the metabolic exponent 0.86 undermines Martin's [ 26, 27] well-known argument that the 0.75 scaling of brain size reflects allometric isometry between brain metabolic needs and its size (exponent 1).
At a macroscopic scale, knowledge of brain circuitry can be utilized to build computational models of large-scale networks that can generate brain activity.
Large scale measures of brain structure and changes in brain structure were modestly correlated with cognitive function but only partially explained diabetes-related deficits.
