Developing a Strategy to Investigate Structural and Functional Connectivity as a Whole Brain System
Allocated in Academic year 2015-16
Neuroimaging allows the investigation of both functional and structural connectivity between different brain regions. Damage to brain areas can affect both functional and structural domains and can have serious consequences on patient prognosis, management and quality of life. Although neuroimaging studies have tended to adopt a functional or structural focus for different neurological diseases, it is evident that there is an intrinsic relationship between the two and that their mutual influence in terms of propagation of damage needs to be modelled and quantified throughout disease evolution. Mathematical, statistical and experimental models of these interactions have received great attention in the past few years, especially with projects such as the Human Connectome and the Human Brain Projects.
Our proposal intends to investigate and optimize strategies that assess the interplay of functional and structural connectivity using information from magnetic resonance imaging, and to use these strategies to model the effects that different mechanisms of disease may have on the whole brain, seen as a functionally and structurally connected system. The project will begin with healthy subject data, including resting state functional MRI (RS-fMRI), high resolution T3D-T1-weighted structural MRI and high angular resolution diffusion weighted data. The student will start by defining the connectome subtending the RS-fMRI networks, investigating key functional and structural properties and develop a model to derive metrics that can assess the overall structural/functional integrity of the whole brain system.
Once the strategy for this whole brain assessment has been established and validated, models of disease mechanisms and hypotheses of their actions on the overall system will be defined. Finally, a pilot study in selected patients with specific characteristics (e.g. presenting lesions in certain areas of the brain) will assess the efficiency of the model in predicting disease outcome.