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Five Minutes with Olaf Sporns

“The ubiquity and utility of networks has given rise to a new interdisciplinary field called network science, devoted to new methods, tools, and theoretical ideas that aim to understand complex systems from a network perspective.”

We’re excited to introduce Network Neuroscience, the latest journal to join our open access program. We speak with Dr. Olaf Sporns, MIT Press author and Distinguished Professor, Provost Professor, and Robert H. Shaffer Chair, Department of Psychological and Brain Sciences at Indiana University, Bloomington. Dr. Sporns is editor of Network Neuroscience, which will launch in 2017.

What most excites you about the intersection of brain and network sciences? Why did you decide to position Network Neuroscience there?

As neuroscientists, we have seen our discipline grow and expand at an incredible rate over the past couple of decades. Part of this expansion is driven by the availability of sophisticated techniques that generate ever more detailed, comprehensive, and challenging data sets. More and more, these data are about neurobiological systems composed of many elements (molecules, cells, areas) that are interconnected into complex networks. Networks are defining a common data format that cuts across many systems—basically, any data set that reports on relationships among neural elements (co-expression, connection, correlation, causality) can be represented, modeled and analyzed as a network. And networks are pervasive not only in neuroscience, but in virtually all scientific disciplines and even beyond, in social and technological systems (think Facebook and Twitter).

The ubiquity and utility of networks has given rise to a new interdisciplinary field called network science, devoted to new methods, tools, and theoretical ideas that aim to understand complex systems from a network perspective. Network Neuroscience is the first journal to bring together these emerging trends—bigger and more complex network data from neuroscience, and a growing arsenal of models and theories from network science. I believe that the intersection of these two trends has enormous potential for moving neuroscience forward.

What do you hope Network Neuroscience will add to the neuroscience field?

More and more neuroscience data record networks of interactions. How can we make sense of these data and use them to understand the brain? This is where Network Neuroscience can make a difference. By providing a single forum for empirical studies, computational analyses, data and methods on networks at all scales in the brain, Network Neuroscience will facilitate scientific exchange among all researchers whose aim is to understand the brain from an integrated network perspective. I see the journal as a way to build a new research community that is not defined by a specific methodology or model system, but instead is devoted to the open sharing and exchange of findings, ideas, and tools that advance fundamental understanding of the brain as a complex networked system.

Who is the audience for Network Neuroscience?

Network Neuroscience will be fully open-access—all articles will be available world-wide free of charge. Thus, I hope that the journal’s audience will be truly global. I also hope that Network Neuroscience will appeal to both empirical and computational researchers, as well as neuroscientists and network scientists, and cover the brain across all scales and systems. The common focus is on networks—maintaining this focus, the journal will aim to be broad and inclusive, as well as forward-looking and open to new ideas.

A really important goal right from the beginning is to cover network approaches to brain function across levels (molecules to cells to systems), systems (model organisms to humans), and methodologies (including, among others, molecular, anatomical, electrophysiological, and imaging techniques). Networks are pervasive across all levels and systems, and Network Neuroscience wants to be a forum for building bridges of collaboration and fruitful exchange. Anyone interested in an integrative network approach to the brain should find Network Neuroscience appealing.

What kind of content/research is Network Neuroscience looking for?

In terms of topics and research areas, Network Neuroscience will cast a wide net, with one key requirement: all submissions must address aspects of network organization and function in a neurobiological system. The journal aims to cover all aspects of brain networks. Examples include biochemical and genetic networks; connectomics at all scales, from EM reconstructions to whole-brain studies; network studies of circuits and brain activity maps, human brain networks constructed from structural, functional, and effective connectivity; network studies of development; clinical and translational applications of networks; computational network models; empirical and computational studies of brain dynamics; and new tools and methods for capturing and modeling network neuroscience data.

In terms of types of articles, first and foremost Network Neuroscience aims to publish high-quality and significant new research in the field. Beyond Research Articles, the journal will also publish Reviews to provide scholarly and integrative overviews in exciting areas, as well as Perspectives to transmit novel ideas and facilitate discussion and exchange. Additionally, the journal will publish Methods articles that introduce new tools for recording and analyzing brain networks, as well as Data articles that are primarily devoted to introducing, documenting and sharing new network data sets.

You’ve long been at the forefront of the latest research in computational neuroscience, brain networks, and complex systems. What advice would you give to researchers who are new to the field?

First of all, I think the area of brain networks is an exciting area to be in right now. New technologies continually create new challenges, which can be addressed by increasingly powerful network and data science tools, and there is no shortage of exciting research questions! I think that the integration between data (from neuroscience) and tools (from network science) will significantly advance our understanding of the brain in coming years. Network Neuroscience hopes to be able to document and shape the future of the field.


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The MIT PressLog is the official blog of MIT Press. Founded in 2005, the Log chronicles news about MIT Press authors and books. The MIT PressLog also serves as forum for our authors to discuss issues related to their books and scholarship. Views expressed by guest contributors to the blog do not necessarily represent those of MIT Press.