Modelling influence of structural brain connectivity on functional brain connectivity and its application for early diagnosis of cognitive impairment

大脑的神经功能以激活的大脑区域和它们之间的连通性为特征。大脑区域之间有三种类型的连接——结构连接、功能连接和有效连接。解剖（结构）、功能和有效连接之间的关系仍然是一个灰色地带，因此，努力探索和理解这一领域是理论神经科学的主要兴趣。功能连接在时间上是动态的，任务依赖的，在毫秒的时间尺度上变化迅速。然而，大脑的结构连通性是相对静态的，不会随着时间和月份的变化而变化。静态结构连接网络如何影响任务依赖的动态功能连接的发生，以及为什么两个结构连接的大脑区域没有功能连接，反之亦然，这仍然是未知的。研究表明，许多神经退行性疾病的根本原因是神经连接的中断。所以理解结构和功能连接之间的关系对于理解大脑网络的损伤特征是很重要的。本研究的目的是利用基于电路理论的建模方法来表征大脑的结构连通性及其对功能连通性的影响。使用电路理论建模结构连接将允许在时域和频域分析信号传播。到目前为止，对结构与功能连接关系的研究都是从信号处理的时域角度出发的。但功能连接的相位相关测量表明，功能连接的底层物理连接具有类似滤波器的特性，并保持频率相位特性。在这项工作中，我们将(1)从结构MRI数据中通过非解剖等面积分割过程定义脑区域（roi），(2)从扩散MRI数据中提取白质束，(3)提取白质束的几何特性，(4)设计单个轴突的电路模型(5)从电路模型中定义单个轴突的传递函数并分析其频率响应。在未来的工作中，我们将在电路设计中模拟信号传播过程中两个髓鞘轴突之间的耦合效应。目标是使用这个耦合方程来连接单个轴突的传递函数，以模拟白质束的动态系统。分析该系统的信号传播特性，得到相邻roi之间的频率相关相位关系，即功能连通性。我们将用fMRI/EEG实验数据验证相同的结果。

The neural function of the brain is characterized by activated brain regions and the connectivity among them. There are three types of connectivity between brain regions - structural, functional and effective connectivity. The relationship between anatomical (structural), functional and effective connectivity is still a grey area and hence, working towards exploring and understanding this area is a major interest in theoretical neuroscience. Functional connectivity is temporally dynamic, task dependent and changes rapidly in temporal scale of a millisecond. Whereas, structural connectivity of brain is relatively static and does not change over days and month. It is still unknown, how a static structural connectivity network affects the occurrence of task-dependent dynamic functional connectivity or why two structurally connected brain regions, are not functionally connected and vice-versa. Studies have shown, the underlying cause for many neuro-degenerative diseases is the disruptions in neural connections. So understanding the relationship between structural and functional connectivity is important for understanding the impairments characteristics in the brain networks. The purpose of this work is to characterize the structural connectivity and its influence on functional connectivity of brain by applying circuit theory based modelling approach. Modelling structural connection using circuit theory will allow the analysis of signal propagation in both time and frequency domains. So far the studies on correlation between structural and functional connectivity were done from time domain perspective of signal processing. But measurement of phase correlation for functional connectivity signifies that underlying physical connection of functional connectivity has filter like properties and holds the frequency-phase characteristics. In this work, we will (1) define the brain areas (ROIs) by a non-anatomical equal area parcellation process from structural MRI data, (2) extract white matter tracts from diffusion MRI data, (3) extracted geometrical properties of white matter tracts, (4) design the circuit model for single axon (5) define the transfer function for single axon from the circuit model and analyse its frequency response. In future work, we will model the coupling effects between two myelinated axons during signal propagation in circuit design. The goal is to use this coupling equation to join the transfer functions of a single axon, to model a dynamic system for white matter tracts. Analysing signal propagation characteristics of this system will give the frequency-dependent phase relationship between the adjacent ROIs from which is basically the functional connectivity. We will validate the same with the fMRI/EEG experimental data.