On the nature and use of models in network neuroscience
Network theory provides an intuitively appealing framework for studying relationships among interconnected brain mechanisms and their relevance to behaviour. As the space of its applications grows, so does the diversity of meanings of the term network model. This diversity can cause confusion, complicate efforts to assess model validity and efficacy, and hamper interdisciplinary collaboration. In this Review, we examine the field of network neuroscience, focusing on organizing principles that can help overcome these challenges. First, we describe the fundamental goals in constructing network models. Second, we review the most common forms of network models, which can be described parsimoniously along the following three primary dimensions: from data representations to first principles theory; from biophysical realism to functional phenomenology; and from elementary descriptions to coarse grained approximations. Third, we draw on biology, philosophy and other disciplines to establish validation principles for these models. We close with a discussion of opportunities to bridge model types and point to exciting frontiers for future pursuits.Modern network neuroscience involves the use of various types of models to understand the brain. In this Review, Bassett, Zurn and Gold discuss the aims of this approach before examining how network models may be categorized and validated.
Nature Reviews Neuroscience