Decoding Brain Networks Using Functional Magnetic Resonance Imaging

Recent studies have shown that disparate systems can be described as some complex networks,that is,assemblies of nodes and links with nontrivial topological properties,examples of which include social systems,technological systems and biological systems.The human brain is also a dynamic system inherently during task or even at rest,in which the communication between regions creates and reshapes continuously complex functional networks of correlated dynamics.An important goal in neuro-science is to decode these spatio-temporal patterns of brain activity.This paper proposes two methods to construct functional networks based on two different brain activities,as revealed by functional magnetic resonance imaging(fMRI)in humans,and analyze them in the context of the current understanding of complex networks.Put forward network eigen entropy that was used to decode scale-free of human brain.Information entropy is a measurement of randomness of a system,which is introduced by the concept of thermodynamics.The more uniform of the energy distribution of a system,the small of information entropy.We put forward to use entropy to analyze human brain network.We defined network eigen entropy(NEE)by eigenvector centrality for an individual subject,and we used it characterize the randomness of human brain network throughout the life span.Our finding revealed the U-shaped development and aging trajectory of functional organization in resting-state human brain.The results demonstrated that NEE decreased from child to early middle aged but remained stable from middle aged to early old.Further,the slightly increased NEE was observed in the older people.Construct a specific brain network model related to human brain scene recognition and classification.Decoding the human brain's scene recognition system,which is beneficial to solve the obstacle between machine vision and biological visual,develop an efficient and robust scene perception algorithm.In this paper,we constructed a specific brain network model related to human brain scene recognition and classification,and then utilized the pattern recognition methods to predict the category of the natural scene.It was observed that not only the neural activity patterns of PPA,RSC and TOS,but also the intensity of functional connectivity among five ROIs can be used to decode the natural scene category.