In the last few years, network neuroscience has emerged as an interdisciplinary field of extensive research, aiming to understand the role of human brain networks during affective and cognitive processing, as in the explanation of more subtle phenotypic characteristics, like personality. Network neuroscience shifted our mind-set from unimodal (activation/deactivation of certain regions) to multimodal models (collaboration between different brain regions), which seem to be closer to the brain’s complexity. In the last years, this became even more meaningful, considering the increase of brain regions that can be simultaneously recorded either by different neuroimaging modalities like electroencephalography (EEG), magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI).
Despite the major advances in the field of network neuroscience, research on its application to increase our understanding of emotions and cognitive functioning, as well as in individual differences is still scarce. This is mainly due to the limitations of current computational approaches, as to their application in enough neuroimaging data (Big Data) to safely answer biologically inspired neuroscientific questions.
The current Special Issue will cover state-of-the-art research in network neuroscience focused on computational approaches, as well as on their applications in the fields of affective, cognitive, and personality neuroscience, using both neurophysiological (EEG, MEG, etc.) and neuroimaging (fMRI, DWI, etc.) modalities, with the main aim to expand our current knowledge and produce new theoretical frameworks.
Topics may include, but are not limited to, the following:
Computational approaches in estimating the functional and/or structural connectivity
Graph theoretical models in network neuroscience
Multi-scale and multi-graph models of the human brain
Brain Networks in the field of the affective/cognitive/personality neuroscience
Brain networks in brain–computer interface-based applications