Neuroscience is the scientific field dedicated to the study of the brain and the nervous system. Neuroscience is inherently an interdisciplinary field. Throughout history, advances in physics have made possible many advances in neuroscience. Early optical investigations led to the development of the microscope, the theory of electricity and magnetism led to the discovery and application of electroencephalogram (EEG), quantum mechanics led to nuclear magnetic resonance, which is the foundation of magnetic resonance imaging, and more recently, graph theory has provided models that describe brain networks and their functions. Despite the impressive progress made so far, much remains to be explored. The goal of this Research Topic is to identify and address outstanding issues in the study of structural and functional networks in the brain.
The detailed understanding of the brain is one of the main frontiers in modern science. Neuroscience offers an exceptional opportunity for interdisciplinary research where biology, physics, mathematics, statistics, engineering, and other fields of science come together to advance our knowledge on the structure and function of the brain and its many connected networks. Investigations of the brain take place on multiple scales, including macroscale at the level of brain regions, mesoescale at the level of neuronal populations, and microscale at the level of single neurons and neuron-neuron interactions. Integration over these scales requires novel techniques. New and developing technique such as optogenetics, structural and functional magnetic resonance connectivity frameworks, high field MRI (7T and above), in-vivo optical imaging, and causal models of functional data are already having a big impact.
This Frontiers Research Topic attempts to advance this area across a broad front by soliciting submissions on novel experimental and computational techniques devoted to the study of brain networks. Specific topics of interest include but not limited to: novel imaging techniques, innovative statistical techniques, multiple imaging modality integration, new hardware developments, and unique graph theoretical frameworks (i.e. complex network analysis).
Neuroscience is the scientific field dedicated to the study of the brain and the nervous system. Neuroscience is inherently an interdisciplinary field. Throughout history, advances in physics have made possible many advances in neuroscience. Early optical investigations led to the development of the microscope, the theory of electricity and magnetism led to the discovery and application of electroencephalogram (EEG), quantum mechanics led to nuclear magnetic resonance, which is the foundation of magnetic resonance imaging, and more recently, graph theory has provided models that describe brain networks and their functions. Despite the impressive progress made so far, much remains to be explored. The goal of this Research Topic is to identify and address outstanding issues in the study of structural and functional networks in the brain.
The detailed understanding of the brain is one of the main frontiers in modern science. Neuroscience offers an exceptional opportunity for interdisciplinary research where biology, physics, mathematics, statistics, engineering, and other fields of science come together to advance our knowledge on the structure and function of the brain and its many connected networks. Investigations of the brain take place on multiple scales, including macroscale at the level of brain regions, mesoescale at the level of neuronal populations, and microscale at the level of single neurons and neuron-neuron interactions. Integration over these scales requires novel techniques. New and developing technique such as optogenetics, structural and functional magnetic resonance connectivity frameworks, high field MRI (7T and above), in-vivo optical imaging, and causal models of functional data are already having a big impact.
This Frontiers Research Topic attempts to advance this area across a broad front by soliciting submissions on novel experimental and computational techniques devoted to the study of brain networks. Specific topics of interest include but not limited to: novel imaging techniques, innovative statistical techniques, multiple imaging modality integration, new hardware developments, and unique graph theoretical frameworks (i.e. complex network analysis).