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

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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.