In the last decade, the study of functional and effective brain connectivity from multivariate neuroimage data has become a very active and fruitful field of research, which has provided novel and crucial insight into normal brain function in resting state as well as during cognitive tasks and their disruption in brain pathologies. This approach somehow challenges the classical locationist approach that assigned one cognitive function to one brain area. Whether from LFP, EEG/MEG or fMRI data, connectivity analysis exploits the relationship between (or among) the corresponding signals to describe brain activity in terms of cooperative networks in which neural assemblies at different time and special scales interact to produce a wide spectrum of brain functions. Very often, however, the potentially rich information inherent to the different neurophysiological signals is reduced to a static network representation, i.e. network whose connectivity pattern and coupling strengths are static.
Yet, in the real world, almost every network is dynamic, its connectivity topology does change over time, following a rule whether stochastic or deterministic. Therefore, any static understanding would not be able to capture nor represent the truly dynamic nature of network connectivity patterns and its temporal evolution. Recent evidence suggests that brain networks are not static, but neuronal connectivity is instead a dynamic phenomenon, both during task related activity and during resting state, which is not surprising if we take into account the omnipresent dynamical variations across brain's structural and functional levels, Further, the temporal evolution of the network structure might reveal new details about the pathological brain.
Therefore, we believe it is time to inject 'time' into the immensely influential research field of complex brain network. Submissions are therefore sought that explore the dynamical aspects of functional and effective brain connectivity at different time scales (from sub-seconds in MEG/EEG or LFP to seconds, minutes or beyond with these same techniques in fMRI) by applying the paradigm of temporal and adaptive networks, and the role of this dynamics in normal and pathological brain function. Likewise, contributions proposing new methods for the analysis of such networks with a clear applicability to multivariate neuroimage data are also welcome. It is fervently hoped that the contributions will help an enhanced understanding of the interaction between the temporal evolution of the network structure and the overall network dynamics, but also facilitate designing more optimized intervention paradigms including brain stimulation.
In the last decade, the study of functional and effective brain connectivity from multivariate neuroimage data has become a very active and fruitful field of research, which has provided novel and crucial insight into normal brain function in resting state as well as during cognitive tasks and their disruption in brain pathologies. This approach somehow challenges the classical locationist approach that assigned one cognitive function to one brain area. Whether from LFP, EEG/MEG or fMRI data, connectivity analysis exploits the relationship between (or among) the corresponding signals to describe brain activity in terms of cooperative networks in which neural assemblies at different time and special scales interact to produce a wide spectrum of brain functions. Very often, however, the potentially rich information inherent to the different neurophysiological signals is reduced to a static network representation, i.e. network whose connectivity pattern and coupling strengths are static.
Yet, in the real world, almost every network is dynamic, its connectivity topology does change over time, following a rule whether stochastic or deterministic. Therefore, any static understanding would not be able to capture nor represent the truly dynamic nature of network connectivity patterns and its temporal evolution. Recent evidence suggests that brain networks are not static, but neuronal connectivity is instead a dynamic phenomenon, both during task related activity and during resting state, which is not surprising if we take into account the omnipresent dynamical variations across brain's structural and functional levels, Further, the temporal evolution of the network structure might reveal new details about the pathological brain.
Therefore, we believe it is time to inject 'time' into the immensely influential research field of complex brain network. Submissions are therefore sought that explore the dynamical aspects of functional and effective brain connectivity at different time scales (from sub-seconds in MEG/EEG or LFP to seconds, minutes or beyond with these same techniques in fMRI) by applying the paradigm of temporal and adaptive networks, and the role of this dynamics in normal and pathological brain function. Likewise, contributions proposing new methods for the analysis of such networks with a clear applicability to multivariate neuroimage data are also welcome. It is fervently hoped that the contributions will help an enhanced understanding of the interaction between the temporal evolution of the network structure and the overall network dynamics, but also facilitate designing more optimized intervention paradigms including brain stimulation.
