With the increasing availability of advanced imaging technologies, we are/nentering a new era of neuroscience. Detailed descriptions of the complex brain/nnetwork enable us to map out a structural connectome, characterize it with/ngraph theoretical methods, and compare it to the functional networks with/nincreasing detail. To link these two aspects and understand how dynamics/nand structure interact to form functional brain networks in task and in the/nresting state, we use theoretical models. The ...
With the increasing availability of advanced imaging technologies, we are/nentering a new era of neuroscience. Detailed descriptions of the complex brain/nnetwork enable us to map out a structural connectome, characterize it with/ngraph theoretical methods, and compare it to the functional networks with/nincreasing detail. To link these two aspects and understand how dynamics/nand structure interact to form functional brain networks in task and in the/nresting state, we use theoretical models. The advantage of using theoretical/nmodels is that by recreating functional connectivity and time series explicitly/nfrom structure and pre-defined dynamics, we can extract critical mechanisms by/nlinking structure and function in ways not directly accessible in the real brain./nRecently, resting state models with varying local dynamics have reproduced/nempirical functional connectivity patterns, and given support to the view that/nthe brain works at a critical point at the edge of a bifurcation of the system./nHere, we present an overview of a modeling approach of the resting brain network/nand give an application of a neural mass model in the study of complexity/nchanges in aging.
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