Network Science and Cognitive Neuroscience: Explaining How the Brain Works?

May 23-25, 2016
Enchantment Resort
Sedona, Arizona, USA

Breakout 1: Comparing and integrating causal & dynamical models 

Group Chairs: David Danks and Randy McIntosh

Group Members:  Jonathan Power, Luis Amaral, Jean Carlson, Doug Rothman, Conor Houghton, Susan Fitzpatrick

  • What are the advantages and limitations of causal and dynamical network modeling frameworks for understanding aspects of brain connectivity and brain function?
  • How do causal and dynamical models address the multi-scale organization of the brain, both in terms of spatial scales (neurons to systems) as well as temporal scales?
  • What are major future directions for causal and dynamical network models in neuroscience?
  • In the context of neuroscientific questions, can we (and/or should we) integrate dynamical models (from network science) and causal models (from machine learning)?


Breakout 2: Explanatory power of network models

Group Chairs: Luciano Floridi and Olaf Sporns

Group Members:  Scott Grafton, Maurizio Corbetta, Carl Craver, Tim Verstynen, Niko Schiff, Trevor Robbins, and Stephen Hanson

  • Are “classic” brain activation studies (e.g. task dependent BOLD fMRI) and more recent connectivity and network based approaches mutually informative?  Contradictory?  Unrelated?
  • Are there common principles or explanatory schema that unify (some of) the new insights that have resulted from using network science in neuroscience? For example, are there specific measures of network structure that are particularly useful and explanatory in developmental or rehabilitation contexts?
  • Are there “privileged” timescales for neural functioning at different levels of brain organization? Can network models help us understand how processes at these different levels relate to each other?
  • How can we use network models and ideas to better explicate, model, and test claims of localization, modularity, development/aging, and related notions? What are important steps to move network neuroscience from being mainly descriptive to becoming capable of prediction and control?


Breakout 3: Networks & cognition / behavior

Group Chairs: Steve Petersen and Kia Nobre

Group Members:  UnCheol Lee, Katie Gates, Tom Carr, Bharat Biswal, Paul Garcia, Catherine Hanson, Thomas Hund

  • Which network science methods or frameworks are best suited for studying structural and functional brain networks, and which are best positioned to add to our understanding of brain function and cognition in the future?
  • What is the relationship between "classic" brain activation studies and more recent connectivity and network based approaches? How do network-based studies of task-related functional connectivity go beyond classic activation?
  • Can network approaches add to our understanding of individual differences in brain structure/function relationships? Or help explain altered states of consciousness, mental disorders, or developmental disorders?
  • Do network approaches offer new interpretations/insights about task performance assessed in different cohorts (age, experiences, culture) and across lifespan?