Advanced Signal Processing in Brain Networks

  in Special Issue   Posted on August 28, 2015

Information for the Special Issue

Special Issue Call for Papers:

Network models of the brain have become an important tool of modern neurosciences to study fundamental organizational principles of brain structure & function. Their connectivity is captured by the so-called connectome, the complete set of structural and functional links of the network. There is still an important need for advancing current methodology; e.g., going towards increasing large-scale models; incorporating multimodal information in multiplex graph models; dealing with dynamical aspects of network models; and matching data-driven and theoretical models.

These challenges form multiple opportunities to develop and adapt emerging signal processing theories and methods at the interface of graph theory, machine learning, applied statistics, simulation, and so on, to play a key role in the analysis and modeling and to bring our understanding of brain networks to the next level for key applications in cognitive and clinical neurosciences, including brain-computer interfaces.

s in cognitive and clinical neurosciences, including brain-computer interfaces.
Topics of Interest include (but are not limited to):
Multi-layer/multiplex networks
Various types of brain data including (f)MRI, M/EEG, NIRS, ECoG/multi-electrode arrays, genomics, …
Novel subspace decompositions (e.g., tensor models, sparsity-driven regularization, low-rank
Multiscale decompositions (e.g., graph wavelets)
Advanced statistical inference (e.g., two-step procedures, Riemannian statistics)
Machine learning (e.g., graph kernels, structured penalties, deep neural networks)
Dynamical systems and simulation approaches
Time delay techniques for brain networks
Big data methods for brain networks (e.g., approximate inference, distributed computing on graphs)
Dynamical graphical models (e.g., Bayesian non-parametrics, structure learning)
Clustering (e.g., overlapping/fuzzy communities)

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