On the nature and use of models in network neuroscience

By D. Bassett, Perry Zurn, Joshua I. Gold. Nature Reviews Neuroscience, 2018

365 citations recorded by the source corpus.

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.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/178654caaea5af54ddf1d3dcdd1135197793dcb9

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Citations in the corpus

Integrative Benchmarking to Advance Neurally Mechanistic Models of Human Intelligence. (2020, background): “By now, there are dozens of such models (e.g., Krizhevsky et al., 2012; He et al., 2015; Huang et al., 2017; Howard et al., 2017; Zoph and Le, 2017; Bassett et al., 2018; Richards et al., 2019; Cichy and Kaiser, 2019).”
Learning in brain-computer interface control evidenced by joint decomposition of brain and behavior (2019, methodology): “In this formal modeling approach [9], network nodes represent brain regions or sensors and network edges represent statistical relations or socalled functional connections between regional time series [30].”