Introduction

Over the past two decades, interest in network modeling and analysis of nervous systems has grown [Bullmore & Sporns, 2009]. In the case of human network neuroscience [Bassett & Sporns, 2017], this interest has been spurred on as a result of data-sharing initiatives (like the Human Connectome Projects [van Essen et al., 2013]), access to high-performance computing resources [Esteban et al, 2019], and openly available software [Rubinov & Sporns, 2010]. As a consequence, it is now relatively easy to construct network maps of the brain’s white-matter pathways (structural connectivity; SC) and the inter-regional correlation structure of brain activity (functional connectivity; FC) [Park & Friston, 2013]. While SC and FC represent different imaging modalities, they can both be modeled as networks of nodes and edges [Bullmore & Sporns, 2009; Bassett & Sporns, 2017; Rubinov & Sporns, 2010]. Network analysis of SC and FC organization is common and has provided new perspectives on brain development and aging [Gu et al, 2015; Betzel et al 2014], has revealed network correlates of neuropsychiatric disorders and brain function [Fornito et al. 2015; Deco & Kringelbach, 2014], and (more generally) offered deeper insight into the role of brain networks for basic cognitive function [Cole et al., 2014]. Despite this, there remain many unanswered questions related to SC and FC. Are SC and FC related to one another? If so, what does that mapping look like? What are the appropriate methods for studying the relationship of SC to FC? What additional information are we missing if we want to answer these questions?

Target audience and objectives

In this talk I will discuss recent advances that help address these and other related questions. The talk will be suitable for newcomers to network neuroscience, as we will define basic terminology and cover the canonical findings, but also of interest to advanced users, as we will cover recent methodological advances. Upon leaving this talk, attendees will have a clearer understanding of the many ways in which SC and FC can be related to one another the current methodology used for doing so (with an emphasis on methods originating in network science).

Methods

Spontaneous brain activity is the product of an evolving dynamical system whose interactions are constrained by the underlying SC; FC is simply the correlation structure that emerges as a result. Though there are many correlational methods for relating SC to FC, [Hagmann et al. 2008; Becker et al. 2018], arguably the more insightful are those that build dynamic, generative models and explicitly constrain those models according to empirical estimates of SC [Honey et al. 2007, Honey et al., 2009; Adachi et al. 2011]. These generative models yield synthetic activity time series, whose correlation structure can be compared against empirical FC. The aim, then, is to select model parameters so that the correspondence of empirical and synthetic FC is maximized. This general approach, though fruitful, prompts some important questions. What is the “correct” model dynamics? The ones that can produce the maximum correspondence? The ones with the greatest biophysical plausibility? In this talk, we will cover some of these models, including computationally intensive and biophysically-realistic neural mass models [Honey et al. 2007, Honey et al., 2009; Adachi et al. 2011], and contrast those models with simpler models based on idealized “communication” dynamics [Goñi et al, 2014; Misic et al 2015; Betzel et al 2019]. Though these classes of models are dissimilar, they each provide unique insight into the relationship of SC with FC.

Conclusion

In conclusion, understanding the relationship of SC with FC is of importance to the field of network neuroscience, and by deepening this understanding we can better understand the underpinnings of neuropsychiatric disorders, development and aging, and cognitive and psychological processes. There exist many strategies for linking these network modalities to one another, each of which possesses clear advantages and disadvantages and capable of generating unique insight into brain function.

Acknowledgements

No acknowledgement found.

References

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