In the last decade, the rising field of network neuroscience has opened new perspectives on the study of the brain and its function. This has been accompanied by the exponential growth of connectivity algorithms and methods designed to capture the intrinsic dynamics of the human brain at rest and during active tasks. A new research avenue foresees the final understanding of the brain as a time-varying network that flexibly evolves in support of a vast repertoire of functions.
But how far are we from this understanding? Brain function is extremely fast and distributed: in a few hundreds of milliseconds, we are capable of recognizing a complex image, recalling its content from our memories, and performing the proper action. Human perception, cognition, and behavior emerge from coordinated activity in large-scale brain networks that evolve at the sub-second time scale. Despite marked advances in the field, modeling whole-brain dynamics with the necessary resolution remains a major challenge.
In the present Research Topic, we invite work promoting theoretical, methodological, and empirical advances in the study of dynamic (time-varying) functional and causal connectivity.
In particular, we welcome:
1) theoretical work on the interpretative meaning of the available connectivity metrics and the general notions of causality, emergence, and functional relations in the study of brain networks;
2) studies reviewing and comparing existing methods, with critical emphasis on the advantages and limitations;
3) empirical work exploiting high-temporal resolution electrophysiology and magneto/electroencephalography recordings (M/EEG) with cutting-edge approaches to characterize neural network dynamics at rest and during perceptual and cognitive tasks;
4) work using techniques other than electrophysiology (e.g., functional magnetic resonance imaging, computational modeling) to characterize time-varying networks in relation to specific brain function.
We encourage authors of empirical or methodological works to share their processed data and code to guarantee the reproducibility of their findings and the availability of new methods.
We expect that the works collected in this Research Topic will provide new fundamental insights and rigorous tools for the emerging field of time-varying brain network analysis and its application in the study of human perception, cognition, and behavior.
In the last decade, the rising field of network neuroscience has opened new perspectives on the study of the brain and its function. This has been accompanied by the exponential growth of connectivity algorithms and methods designed to capture the intrinsic dynamics of the human brain at rest and during active tasks. A new research avenue foresees the final understanding of the brain as a time-varying network that flexibly evolves in support of a vast repertoire of functions.
But how far are we from this understanding? Brain function is extremely fast and distributed: in a few hundreds of milliseconds, we are capable of recognizing a complex image, recalling its content from our memories, and performing the proper action. Human perception, cognition, and behavior emerge from coordinated activity in large-scale brain networks that evolve at the sub-second time scale. Despite marked advances in the field, modeling whole-brain dynamics with the necessary resolution remains a major challenge.
In the present Research Topic, we invite work promoting theoretical, methodological, and empirical advances in the study of dynamic (time-varying) functional and causal connectivity.
In particular, we welcome:
1) theoretical work on the interpretative meaning of the available connectivity metrics and the general notions of causality, emergence, and functional relations in the study of brain networks;
2) studies reviewing and comparing existing methods, with critical emphasis on the advantages and limitations;
3) empirical work exploiting high-temporal resolution electrophysiology and magneto/electroencephalography recordings (M/EEG) with cutting-edge approaches to characterize neural network dynamics at rest and during perceptual and cognitive tasks;
4) work using techniques other than electrophysiology (e.g., functional magnetic resonance imaging, computational modeling) to characterize time-varying networks in relation to specific brain function.
We encourage authors of empirical or methodological works to share their processed data and code to guarantee the reproducibility of their findings and the availability of new methods.
We expect that the works collected in this Research Topic will provide new fundamental insights and rigorous tools for the emerging field of time-varying brain network analysis and its application in the study of human perception, cognition, and behavior.