Neuron density in cortical regions of the mammalian brain follows a consistent distribution pattern. This discovery has profound implications for brain modeling and the development of brain-inspired technologies. Credit: Morales-Gregorio
Researchers from the Jurich Concentration Camp and the Human Brain Project at the University of Cologne (Germany) have shown how neuron density is distributed across and within cortical regions of the mammalian brain. They uncovered a fundamental organizational principle of cortical cellular architecture: the ubiquitous lognormal distribution of neuronal density.
The number of neurons and their spatial arrangement play an important role in shaping brain structure and function. However, despite the abundance of available cytoarchitectural data, the statistical distribution of neuronal density remains largely unexplained.New Human Brain Project (HBP) study published in journal cerebral cortexadvances our understanding of the organization of the mammalian brain.
Analysis of Datasets and Lognormal Distributions
9 public datasets of 7 datasets seed (mouse, marmoset, macaque, galago, owl monkey, baboon, and human) formed the basis of the research team’s investigation. Analysis of each cortical region showed that neuron density within these regions followed a consistent pattern, a lognormal distribution. This suggests a fundamental organizational principle underlying neuron density in the mammalian brain.
The lognormal distribution is a statistical distribution characterized by a skewed bell-shaped curve. This happens, for example, when computing the exponential function of a normally distributed variable. It differs from the normal distribution in several ways. Most importantly, the curve of the normal distribution is symmetrical, whereas the lognormal distribution is heavily tailed and asymmetric.
Meaning and relevance of findings
These insights are crucial for accurate brain modeling. “Particularly because the distribution of neuron density affects network connectivity,” says senior author Sascha Van Albada, leader of the Theoretical Neuroanatomy Group at the University of Jurich. “For example, for a given density of synapses, areas with lower neuron density receive more synapses per neuron,” she explains. Such aspects also apply to the design of brain-inspired technologies such as Neuromorphic Her hardware.
“Furthermore, since cortical regions are often differentiated based on cellular structure, knowing the distribution of neuron density can be relevant to statistically assessing differences between regions and the location of boundaries between regions. There is a risk,” added van Albada.
Understanding the lognormal distribution in brain properties
This result is consistent with previous observations that surprisingly many properties of the brain follow a lognormal distribution. “One reason it’s so common in nature is that it appears when you take the product of many independent variables,” says Alexander van Megen, co-first author of the study. In other words, the lognormal distribution arises naturally as a result of the multiplication process, just as the normal distribution appears when summing many independent variables.
“Using a simple model, we were able to show how the proliferation of neurons during development leads to the observed neuron density distribution,” explains van Megen.
According to this study, in principle, the tissue architecture of the entire cortex could be a byproduct of development or evolution without computational function. However, the fact that the same tissue architecture can be observed across several species and most cortical regions suggests that the lognormal distribution serves some purpose.
“Although we do not know what effect the lognormal distribution of neuron density has on brain function, it is likely related to the high heterogeneity of the network, which could be computationally beneficial.” There is,” said the study’s lead author, Eiter Morales-Gregorio, citing previous research. These suggest that heterogeneity in brain connectivity may facilitate efficient information transfer. Moreover, heterogeneous networks support robust learning and enhance the memory capacity of neural circuits.
Reference: “The ubiquitous lognormal distribution of neuron density in the mammalian cortex,” Aitor Morales-Gregorio, Alexander van Meegen, Sacha J van Albada, 6 July 2023, Available here. cerebral cortex.
DOI: 10.1093/cercor/bhad160