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.

GD was supported by the ERC Advanced Grant: DYSTRUCTURE (no. 295129), by the Spanish Research ProjectSAF2010-16085 and by the CONSOLIDER-INGENIO 2010 ProgrammeCSD2007-00012, and the FP7-ICT BrainScales. The research reported herein was supported by the Brain Network Recovery Group through the James S. McDonnell Foundation. TTN was supported by the SUR of the DEC of the Catalan Government and by the FSE.