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Meso-scale structure of neural systems

Divisions of brain networks into communities give a coarse-grained view of a network and highlight topological regularities. My work in this area includes both methods development as well as applications to human lifespan and task reconfiguration.

Betzel, R. F., et al. (2018). Diversity of meso-scale architecture in human and non-human connectomesNature Communications.

Sporns, O., & Betzel, R. F. (2016). Modular brain networksAnnual review of psychology67, 613-640. (link)

Betzel, R. F., et al. (2013). Multi-scale community organization of the human structural connectome and its relationship with resting-state functional connectivityNetwork Science1(03), 353-373.

Betzel, R. F., et al. (2017). The modular organization of human anatomical brain networks: Accounting for the cost of wiringNetwork Neuroscience.


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Models of inter-areal communication

Brain areas communicate and exchange signals with one another along fiber tracts and axonal projections. We can build network models to better understand the communication capacities of particular brain areas or connections.

Betzel, R.F. & Bassett, D.S. (2018). The specificity and robustness of long-distance connections in weighted, interareal connectomesProceedings of the National Academy of Sciences, USAdoi.org/10.1073/pnas.1720186115.

Mišić, B. et al (2015). Cooperative and competitive spreading dynamics on the human connectomeNeuron, 86 (6), 1518-1529.

Goñi, J., et al (2014). Resting-brain functional connectivity predicted by analytic measures of network communication. Proceedings of the National Academy of Sciences111(2), 833-838.


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Time-varying functional brain networks

Functional network organization fluctuates over timescales ranging from seconds to minutes. We use multi-layer network analysis to characterize these changes and understand how the temporal flexibility (or inflexibility) of brain regions is related to human behavior.

Betzel, R. F., et al. (2017). Positive affect, surprise, and fatigue are correlates of network flexibility. Scientific Reports7.

Betzel, R. F., et al. (2016). Dynamic fluctuations coincide with periods of high and low modularity in resting-state functional brain networksNeuroImage127, 287-297.

Betzel, R. F., et al. (2012). Synchronization dynamics and evidence for a repertoire of network states in resting EEGFrontiers in computational neuroscience6, 74.


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Structure-function relationships

Many factors contribute to shape the brain's functional organization, including genetics, geometry, and structure. We can build models (both simplified and biophysically realistic) that integrate multiple data modalities to glean insight in these relationships.

Betzel, R.F., et al (2017). Inter-regional ECoG correlations predicted by communication dynamics, geometry, and correlated gene expression. arXiv:1706.06088.

Mišić, B., et al. (2016). Network-level structure-function relationships in human neocortexCerebral Cortex26(7), 3285-3296.

Goñi, J., et al (2014). Resting-brain functional connectivity predicted by analytic measures of network communication. Proceedings of the National Academy of Sciences111(2), 833-838.

Betzel, R. F., et al. (2014). Changes in structural and functional connectivity among resting-state networks across the human lifespan. Neuroimage102, 345-357.


Network generative models

Over time anatomical brain networks grow and develop as connections weaken and strengthen. We can identify putative mechanisms that govern these changes by studying so-called generative models of the brain - simple wiring rules that yield synthetic networks similar to those we observe in vivo.

Betzel, R. F., et al. (2016). Generative models of the human connectome. Neuroimage124, 1054-1064.

Betzel, R.F., et al (2017). Generative Models for Network Neuroscience: Prospects and PromiseJournal of the Royal Society: Interface.


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Brain network control

Control theory offers a set of tools for steering dynamical systems (like the brain) to and from specific states along particular trajectories. Recently, we and others have begun to investigate the control properties of brain networks to understand which of their features support transitions between specific states or certain classes of transitions.

Betzel, R. F., et al. (2016). Optimally controlling the human connectome: the role of network topologyScientific Reports, 6.

Gu, S., Betzel R.F., et al (2017). Optimal trajectories of brain state transitions. Neuroimage, 148.

Wu-Yan, E., Betzel R.F., et al (2018). Benchmarking measures of network controllability on canonical graph models. Journal of Nonlinear Science.

Kennett Y., et al (2018). Driving the brain towards creativity and intelligence: A network control theory analysisNeuropsychologia.