In humans, interactions between neuronal circuits, systems and signals among micro-, meso- and macro-scales of brain dynamics underpin the functional organisation of the brain that supports our daily life activity. How efficiently neuronal circuits such as cortico-cortical, cortico-thalamic and thalamo-cortical networks interact among these multiple spatial scales remains to be fully elucidated. Mathematical, computational and experimental neuroscientists apply a variety of methods, techniques and algorithms, both in animals and humans, ranging from single cell recordings to whole brain imaging, in order to identify the core mechanisms that govern the interactions among these scales. Although our knowledge of neural mechanisms, circuits and networks underlying brain dynamics and functions constantly grows, the integration of this knowledge to provide a conceptual framework of emergent behaviour and pattern formation occurring on different levels of spatial organization remains challenging. Authors are invited to submit original or review articles addressing mechanisms of emergent brain dynamics inferred from the analysis of ECoG/EEG/MEG, on all levels of complexity. Specifically, articles integrating (i) computational neuroscience, and (ii) mathematical models of biologically realistic neuronal networks and machine learning methods are welcome.
In this special issue, Guest Editor Steve Mehrkanoon, PhD, welcomes papers that address many of the challenges of mathematical and computational models of the brain networks and dynamics given the measurement data ECoG/EEG/MEG. The scope of this special issue is interdisciplinary. The list of possible topics includes, but is not limited to:
- New developments in computational and mathematical models of mean field theory integrated with ECoG/EEG/MEG modalities
- Multi-modal and combinatorial fusion of ECoG/EEG/MEG data and brain connectivity machine learning
- Development of data-driven machine learning techniques to identify the mechanisms of emergent spatiotemporal dynamics within the framework of network topology
- Development of computational and mathematical models for bridging the scales of brain dynamics
- Cortical network modelling and formal conceptual models of cortical oscillations
- Application of deep learning and machine learning algorithms in functional, structural and effective network analysis and predictive models of brain dynamics.
- New cutting-edge numerical analysis and techniques to simulate/implement
- Adaptive mathematical and computational models of network dynamics (e.g., brain development and aging studies)
- Statistical methods for assessing individual and population brain connectivity data
Contributions will receive prompt and thorough peer review and will be published online ahead of print after acceptance.
Deadline for Submission: May 31, 2018
- Dr. Steve Mehrkanoon
The University of Queensland Centre for Clinical Research, Faculty of Medicine, The University of Queensland, Brisbane, Australia
- Professor David Liley
Centre for Human Psychopharmacology, School of Health Sciences, Swinburne University of Technology, Melbourne, Australia
- Dr. Siamak Mehrkanoon
Department of Electrical Engineering (ESAT), University of Leuven, KU Leuven, Belgium