Computational Methodologies in Brain Imaging and Connectivity: EEG and MEG Applications

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Robert Coben

Integrated Neuroscience Services (INS)

Sheraz Khan

Massachusetts General Hospital, Harvard Medical School

Iman Zadeh

Oracle Corporation

Urs Ribary

Simon Fraser University

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Over the last several years, sophisticated mathematical and computational approaches (including independent component analysis and neural source localization) have been utilized to study neurological disorders using different neuroimaging modalities such as electroencephalography (EEG) and magnetoencephalography (MEG). These approaches have recently been applied to analyze functional and effective brain connectivity to models underlying neurobiological processes in subjects with neurological disorders.

In this Research Topic we welcome papers with novel approaches to these applications including those that include (but not limited to) the use of both multi-channel data and their source estimations in brain connectivity approaches such as: Granger causality, multivariate estimators of directedness, estimators of dynamical propagation (such as Dual Kalman Filter), and Bayesian hierarchical modeling. In this way, our aim is to promote and move forward conceptualization and computational methodologies that will help to more accurately depict neural connectivity and how it changes over time.

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Editors

Robert Coben

Integrated Neuroscience Services (INS)

Sheraz Khan

Massachusetts General Hospital, Harvard Medical School

Iman Zadeh

Oracle Corporation

Urs Ribary

Simon Fraser University

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Frontiers in Neural Circuits

Frontiers in Computational Neuroscience

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