The brain is fascinating. It is the entity that exerts central control over our functions. But our understanding of it is still limited, and the area is in need of much research.
A team led by Professor Kwang-Hyun Cho from the Department of Bio and Brain Engineering has recently made a step towards understanding the brain. By studying high-resolution brain networks from different organisms — including the fruit fly, nematode worm, mouse, cat, macaque, and even human — it published a paper titled “The Hidden Control Architecture of Complex Brain Networks” in iScience last month.
The brain is a very powerful tool that is both efficient and robust when performing complex cognitive functions. Even though efficiency and robustness are considered to have a trade-off, the human brain exhibits both qualities simultaneously.
This characteristic originates from the specific coordinated control of interconnected brain regions. Professor Cho and his team studied the intrinsic control architectures of structural brain networks. After conducting studies on the various species, they carried out analyses by comparing the results with 26 real complex networks, both biological and artificial. The artificial networks ranged from social to technological and infrastructural. The study also included a reconstruction of 100 human brain networks.
Recently, MDSets are being used as a suitable way to analyze different biological networks. MDSets are the minimal subset of nodes (MD-nodes) in a network that control the remaining nodes with a one-step interaction. Based on this technology, the team developed a structure to describe the control architecture of a complex network. From the results of the study, it was seen that MDSets don’t just occupy strategic locations suitable for network control, but also perform various biological tasks. After examining MD-nodes in different networks, the team was able to precisely describe the control architecture of each network and determine the characteristics of each type of computer architecture. After comparing all the data, the team was able to reveal the control architecture of brain networks that makes the brain so efficient and robust in its functioning.