Modeling Of The Relation Between Human Brain Structural Connectivity And Functional Connectivity Based On BOLD Time Series

The relationship between the relative fixed structural architecture and rich functionality of the human brain has always been a hotspot in neuroscience,many studies indicate that there is a correlation between the structural connectivity(SC)and functional connectivity(FC).With the advent of non-invasive brain imaging technologies such as diffusion MRI(d MRI)and functional MRI(f MRI),it is possible to explore the relationship between SC and FC of the human brain.At present,SC is usually measured by d MRI,and the widely used way to measure FC is based on the blood oxygenation level dependent(BOLD)signals.In terms of the relationship between SC and FC of the human brain,existing methods mainly include the network attribute analysis of brain structural network and establishing the relationship between SC and synaptic signals based on neural-mass models(NMM).The network attribute analysis cannot reflect the real neural activity due to ignoring the information contained in the original BOLD time series,and NMM based methods can only simulate the nerve activity approximately.Therefore,this paper aims to model SC and FC of human brain based on the BOLD time series,and further analyze the relationship between structure and function of the human brain.The main work of this paper is as follows.1.The conceptions of SC and FC along with their relationship are studied based on the complex network theory.In addition,the commonly used computational modelling methods for analyzing the relationship between SC and FC are summarized in detail.2.A new method based on Kuramoto model is proposed to predict FC from SC.The proposed method fits the generation of neural activity according to the phase synchronization in Kuramoto model.Then the predicted FC can be obtained from the predicted neural activity signals.The experimental results on low and high resolution(66-ROIs and 998-ROIs)datasets show that this method has better performance than the traditional DMF model.3.A model based on network structure estimation is proposed to infer SC from FC.The model combines network structure estimation in complex network analysis with coupling function of dynamic model,which utilizes BOLD time series as input directly.This model is capable of inferring SC from BOLD signals.Experimental results on three simulation datasets(66-ROIs,90-ROIs,and 68-ROIs)show that about 60% of the structural links can be correctly inferred for datasets.