Brain Connectivity: A New Journal Emerges

We are excited about the launch of this new journal Brain Connectivity, which focuses on a field that has been rapidly evolving over the last several years. This journal will bring together all aspects of the functional and structural connections of the human and animal brain regardless of experimental technique. Improvements to existing neuroimaging modalities have provided unprecedented spatial and temporal resolution, and new computational and neurophysiological models are further propelling connectivity research forward. Additionally, there has been a recent trend toward the use of multimodal experiments to obtain complementary information about neural connectivity and to promote a better understanding of the underlying neurophysiological mechanisms of the phenomenon. We believe this unprecedented level of growth in a focused area of neuroscience presents a unique opportunity to begin this endeavor and to shape the future of brain connectivity research. The history of brain connectivity research starts with the founder of modern neuroscience, Santiago Ramón y Cajal. His detailed illustrations of cellular connections in the brain and staunch defense of neuron doctrine form the basis of the field. Work by his contemporary, Korbinian Brodmann, and others segmented the brain into distinct cytoarchitectonic regions, many of which were further defined functionally by Wilder Penfield, using electrophysiological mapping. However, the study of connectivity is distinct from static brain mapping. Connectivity research is concerned with anatomical pathways, interactions, and communication between distinct units of the central nervous system. These units can be categorized into levels of micro(individual neurons), meso(columns), or macro(regions) scales. At this point in history, we are constrained by the limits of temporal and spatial resolution in modern in vivo imaging techniques (magnetic resonance imaging [MRI], electroencephalography [EEG], positron emission tomography [PET], etc.) to study the brain. Consequently, the main focus of this journal will be at the network or systems level of connections. However, we intend to bring together researchers working at all scales. Beyond scale, connectivity can also be broken into structural and functional domains, with each subdivided further into static and dynamic components. Static components are defined by the regions and wiring in which communication and processing occurs. Dynamic components can be described by the functional relationship between static components. For example, functional connectivity, described as temporal coherence between physically distant activity, and effective connectivity, described as networks of directional influences of one neural element over another. Static connectivity can be measured by anatomical properties using a number of imaging methods, including high-resolution magnetic resonance (MR; cortical thickness), diffusion tensor imaging (DTI; white matter tractography), and histology (myelination). Dynamic connectivity can be measured by a wide variety of techniques, including methods with a fast timescale capable of measuring causality, such as EEG, or methods that can provide information about the spatial distribution and strength of dynamic connections, such as resting-state functional connectivity MRI (rs-fcMRI). The evolution and refinement of imaging modalities will drive brain connectivity research forward. Although we are both trained MR biophysicists, we are committed to keeping this journal independent of a specific technique. In the past few years, there has been an almost exponential increase in the number of publications in neuroimaging and, in particular, on topics related to brain connectivity (see Fig. 1; data from PubMed, search term: brain connectivity). Figure 1 shows the number of publications indexed by PubMed since 1969 when the first article using the term brain connectivity appeared. The bar chart shows the number of publications for each 5-year period. The last bar in the graph is obtained from only 2 years. The exponential trend seen in