Statistical model of evolution of brain parcellation

TL;DR

This paper presents a universal evolutionary model explaining brain parcellation and macroconnectivity across species, based on iterative fragmentation and specialization processes that produce lognormal distributions and power-law connectivity patterns.

Contribution

The study introduces a novel mathematical model of brain evolution that accounts for parcellation and connectivity patterns in mice, macaques, and humans.

Findings

PU size distribution is close to lognormal.

Most macaque macroconnectivity data fits the outer product power-law form.

A multiplicative Hebbian learning rule may explain connection strength scaling.

Abstract

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.