Cognitive processes in humans require the connection of diverse local neural assemblies among spatially distant brain areas. This establishes an integrated functional network that allows the efficient transfer of information between regions. One of the bases of this functional connectivity (FC) is the synchronization of neural oscillations between brain sites. During the past 30 years, an increasing number of research has focused on FC, based on the development of diverse mathematical tools used to calculate the statistical relationships between brain signals. Thus, there has been a growing number of articles differing in methodological approach (both mathematical and experimental), as well as in characteristics of the population under study and tasks employed. This diversity, enriching as it is, makes it difficult to track progress within the field.
The aim of the present Research Topic is to review current advances in FC studies on the bases of 1) data acquisition method (e.g. electrophysiological or haemodynamic); mathematical tool implementation (e.g. coherence, phase analysis, Graph theory, etc); 3) research area (e.g. learning; memory; attention; perception, language, etc); 4) experimental paradigm, and 5) population characteristics (adults, children, neurotypical; neurodegenerative or neurdevelopmental disorders, etc). A systematic review of FC studies, based on these parameters would be an invaluable tool for researchers already working within the area of Functional Connectivity, or with and interest to do so.
The scope of the present Research Topic would cover (although is not restricted to) the following themes: 1) functional vs. effective connectivity (model-based and data driven); 2) data acquisition method (i.e. electroencephalography; magnetoencephalography; iEEG; functional magnetic resonance imaging, PET, SPECT, fNIRS, etc); 3) mathematical tool implementation (wavelet coherence; mutual information, Nonlinear interdependence, cross recurrence, phase locking index; phase coherence analysis, Graph theory, General synchronization, etc); 4) research area (e.g. learning; memory; attention; perception, language, motor control; etc); 5) experimental paradigm (e.g. inter vs. intraindividual), and 6) population characteristics (adults, children, neurotypical; brain injuries; neurodegenerative or neurodevelopmental disorders, etc).
Cognitive processes in humans require the connection of diverse local neural assemblies among spatially distant brain areas. This establishes an integrated functional network that allows the efficient transfer of information between regions. One of the bases of this functional connectivity (FC) is the synchronization of neural oscillations between brain sites. During the past 30 years, an increasing number of research has focused on FC, based on the development of diverse mathematical tools used to calculate the statistical relationships between brain signals. Thus, there has been a growing number of articles differing in methodological approach (both mathematical and experimental), as well as in characteristics of the population under study and tasks employed. This diversity, enriching as it is, makes it difficult to track progress within the field.
The aim of the present Research Topic is to review current advances in FC studies on the bases of 1) data acquisition method (e.g. electrophysiological or haemodynamic); mathematical tool implementation (e.g. coherence, phase analysis, Graph theory, etc); 3) research area (e.g. learning; memory; attention; perception, language, etc); 4) experimental paradigm, and 5) population characteristics (adults, children, neurotypical; neurodegenerative or neurdevelopmental disorders, etc). A systematic review of FC studies, based on these parameters would be an invaluable tool for researchers already working within the area of Functional Connectivity, or with and interest to do so.
The scope of the present Research Topic would cover (although is not restricted to) the following themes: 1) functional vs. effective connectivity (model-based and data driven); 2) data acquisition method (i.e. electroencephalography; magnetoencephalography; iEEG; functional magnetic resonance imaging, PET, SPECT, fNIRS, etc); 3) mathematical tool implementation (wavelet coherence; mutual information, Nonlinear interdependence, cross recurrence, phase locking index; phase coherence analysis, Graph theory, General synchronization, etc); 4) research area (e.g. learning; memory; attention; perception, language, motor control; etc); 5) experimental paradigm (e.g. inter vs. intraindividual), and 6) population characteristics (adults, children, neurotypical; brain injuries; neurodegenerative or neurodevelopmental disorders, etc).