Structured thalamocortical connectivity revealed by random walks on complex networks

TL;DR

This paper uses complex network analysis and random walks to uncover highly structured features of thalamocortical connectivity in the cat brain, revealing organization principles that influence cortical dynamics.

Contribution

It introduces a novel application of random walks on large-scale anatomical data to identify non-random, structured features of thalamocortical networks.

Findings

Thalamocortical connections show strong correlation between outdegree and occupancy rate.

Organizational principles underlying connectivity patterns are identified and discussed.

Random walk analysis reveals structured features influencing cortical activation.

Abstract

The segregated regions of the mammalian cerebral cortex and thalamus form an extensive and complex network, whose structure and function are still only incompletely understood. The present article describes an application of the concepts of complex networks and random walks that allows the identification of non-random, highly structured features of thalamocortical connections, and their potential effects on dynamic interactions between cortical areas in the cat brain. Utilizing large-scale anatomical data sets of this thalamocortical system, we investigate uniform random walks in such a network by considering the steady state eigenvector of the respective stochastic matrix. It is shown that thalamocortical connections are organized in such a way as to guarantee strong correlation between the outdegree and occupancy rate (a stochastic measure potentially related to activation) of each cortical area. Possible organizational principles underlying this effect are identified and discussed.