Statistical model of evolution of brain parcellation

We study the distribution of brain and cortical area sizes [parcellation units (PUs)] obtained for three species: mouse, macaque, and human. We find that the distribution of PU sizes is close to lognormal. We analyze the mathematical model of evolution of brain parcellation based on iterative fragmentation and specialization. In this model, each existing PU has a probability to be split that depends on PU size only. This model shows that the same evolutionary process may have led to brain parcellation in these three species. Our model suggests that region-to-region (macro) connectivity is given by the outer product form. We show that most experimental data on non-vanishing macaque cortex macroconnectivity (62% for area V1) can be explained by the outer product power-law form suggested by our model. We propose a multiplicative Hebbian learning rule for the macroconnectome that could yield the correct scaling of connection strengths between areas. We thus propose a universal evolutionary model that may have contributed to both brain parcellation and mesoscopic level connectivity in mammals.

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