During brain data acquisition, from the neural to the behavioral scale, the targeted brain function is typically secluded from the influence of other brain functions. Such an approach results in data acquired at a single spatial and temporal scale of the brain and limits the analysis and understanding of the intrinsically multiscale and parallel brain functions. This calls for a paradigm shift from examining the brain as a monolithic structure to examining –and interacting with– the brain holistically.
The proposed Research Topic aims to attract papers that will form a new computational infrastructure resource by combining novel integrative methods in Computational Neuroscience. Specifically, the research focus will be on: 1) The development of new methods and algorithms that can deal with multiscale dynamics and integrate data from synchronized behavioral and neural activity; and 2) The improved understanding of the neural dynamics in behavioral states. The understanding of brain functionality across its scales will have an unprecedented health, social and financial impact.
The proposed Research Topic will host advanced mathematical modeling efforts and computational approaches that seek to understand how the interplay among cellular, anatomical and neuronal connectome empowers complex brain functions such as vision, attention, memory, learning and emotion. Tools include, but are not limited to, linear and nonlinear time series analysis of single unit recordings, EEG, MEG, fMRI and PET as well as multiscale modeling and analysis of neuronal systems dynamics, causal connectivity analysis (Granger-based, Phase Synchronization, Mutual Information, Transfer Entropy etc), manifold and machine learning methodologies for handling data complexity (PCA, ICA, ISOMAP, Diffusion Maps) etc.
We seek to present research efforts that bridge the bottom-up and top-down approaches. A bottom-up research includes modelling the dynamics of neurons and/or networks of neurons to approximate the complex emergent behavior whereas a top-down research would try to reveal how macroscale phenomena observed in integrative EEG, MEG or fMRI signals can be used to infer about the brain organization and ultimately explain the underlying functional mechanisms. Specific areas of interest include but are not limited to: the reconstruction of functional networks that govern the information flow of the brain signals, the identification of distinct spatio-temporal patterns that can suggest the mechanisms of specific higher cognitive functions, and the modeling and analysis of resting-state activity. We will try to cover studies targeting the healthy brain as well as studies targeting neurologic and psychiatric disorders in adults (such as epilepsy, Parkinson’s disease Schizophrenia and other Psychotic disorders) and/or in children and adolescents (such as child epilepsy and autistic spectrum disorders).
During brain data acquisition, from the neural to the behavioral scale, the targeted brain function is typically secluded from the influence of other brain functions. Such an approach results in data acquired at a single spatial and temporal scale of the brain and limits the analysis and understanding of the intrinsically multiscale and parallel brain functions. This calls for a paradigm shift from examining the brain as a monolithic structure to examining –and interacting with– the brain holistically.
The proposed Research Topic aims to attract papers that will form a new computational infrastructure resource by combining novel integrative methods in Computational Neuroscience. Specifically, the research focus will be on: 1) The development of new methods and algorithms that can deal with multiscale dynamics and integrate data from synchronized behavioral and neural activity; and 2) The improved understanding of the neural dynamics in behavioral states. The understanding of brain functionality across its scales will have an unprecedented health, social and financial impact.
The proposed Research Topic will host advanced mathematical modeling efforts and computational approaches that seek to understand how the interplay among cellular, anatomical and neuronal connectome empowers complex brain functions such as vision, attention, memory, learning and emotion. Tools include, but are not limited to, linear and nonlinear time series analysis of single unit recordings, EEG, MEG, fMRI and PET as well as multiscale modeling and analysis of neuronal systems dynamics, causal connectivity analysis (Granger-based, Phase Synchronization, Mutual Information, Transfer Entropy etc), manifold and machine learning methodologies for handling data complexity (PCA, ICA, ISOMAP, Diffusion Maps) etc.
We seek to present research efforts that bridge the bottom-up and top-down approaches. A bottom-up research includes modelling the dynamics of neurons and/or networks of neurons to approximate the complex emergent behavior whereas a top-down research would try to reveal how macroscale phenomena observed in integrative EEG, MEG or fMRI signals can be used to infer about the brain organization and ultimately explain the underlying functional mechanisms. Specific areas of interest include but are not limited to: the reconstruction of functional networks that govern the information flow of the brain signals, the identification of distinct spatio-temporal patterns that can suggest the mechanisms of specific higher cognitive functions, and the modeling and analysis of resting-state activity. We will try to cover studies targeting the healthy brain as well as studies targeting neurologic and psychiatric disorders in adults (such as epilepsy, Parkinson’s disease Schizophrenia and other Psychotic disorders) and/or in children and adolescents (such as child epilepsy and autistic spectrum disorders).
