Publications


MixItUp Demo - Toggle Filtering AND Logic



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Abstract: The field of neuroimaging has truly become data rich, and novel analytical methods capable of gleaning meaning- ful information from large stores of imaging data are in high demand. Those methods that might also be applicable on the level of individual subjects, and thus potentially useful clinically, are of special interest. In the present study, we introduce just such a method, called nonlinear functional mapping (NFM), and demonstrate its application in the analysis of resting state fMRI (functional Magnetic Resonance Imaging) from a 242-subject subset of the IMAGEN project, a European study of adolescents that includes longitudinal phenotypic, behavioral, genetic, and neuroimaging data. NFM employs a computational technique inspired by biological evolution to discover and mathematically char- acterize interactions among ROI (regions of interest), without making linear or univariate assumptions. We show that statistics of the resulting interaction relationships comport with recent independent work, constituting a preliminary cross-validation. Furthermore, nonlinear terms are ubiquitous in the models generated by NFM, suggesting that some of the interactions characterized here are not discoverable by standard linear methods of analysis. We discuss one such nonlinear interaction in the context of a direct comparison with a procedure involving pairwise correlation, designed to be an analogous linear version of functional mapping. We find another such interaction that suggests a novel distinction in brain function between drinking and non-drinking adolescents: a tighter coupling of ROI associated with emotion, re- ward, and interoceptive processes such as thirst, among drinkers. Finally, we outline many improvements and extensions of the methodology to reduce computational expense, complement other analytical tools like graph-theoretic analysis, and allow for voxel level NFM to eliminate the necessity of ROI selection.



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Joshua Bongard - Department of Computer Science, Associate Professor

Bongard's work focuses on understanding the general nature of cognition, regardless of whether it is found in humans, animals or robots. This unique approach focuses on the role that morphology and evolution plays in cognition. Addressing these questions has taken him into the fields of biology, psychology, engineering and computer science.


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    Josh Bongard, Victor Zykov, Hod Lipson. Resilient Machines Through
    Continuous Self-Modeling.
    Science 314, 1118 (2006). [Journal Page]
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    Joey Anetsberger and Josh Bongard. Robots can ground crowd-proposed symbols by forming theories of group mind. Proceedings of the Artificial Life Conference 2016. [Link to Proceedings]
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    Sam Kriegman, Nick Cheney, and Josh Bongard. How morphological development can guide evolution. arXiv 2017. [arXiv]


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Chris Danforth -Department of Mathematics and Statistics, Flint Professor of Mathematical, Natural, and Technical Sciences

Danforth is an applied mathematician interested in modeling a variety of physical, biological, and social phenomenon. He has applied principles of chaos theory to improve weather forecasts as a member of the Mathematics and Climate Research Network, and developed a real-time remote sensor of global happiness using messages from Twitter: the Hedonometer. Danforth co-runs the Computational Story Lab with Peter Dodds, and helps run UVM's reading group on complexity.

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    Peter Sheridan Dodds , Kameron Decker Harris, Isabel M. Kloumann, Catherine A. Bliss, Christopher M. Danforth. Temporal Patterns of Happiness and Information in a Global Social Network: Hedonometrics and Twitter. PLoS ONE 2011. [Journal Page].
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    Lewis Mitchell , Morgan R. Frank, Kameron Decker Harris, Peter Sheridan Dodds, Christopher M. Danforth. The Geography of Happiness: Connecting Twitter Sentiment and Expression, Demographics, and Objective Characteristics of Place. PLoS ONE 2013. [Journal Page].
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    Andrew G Reece and Christopher M Danforth. Instagram photos reveal predictive markers of depression. EPJ Data Science 2017. [Journal Page].


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Laurent Hébert-Dufresne - Assistant Professor, Computer Science

Laurent studies the interaction of structure and dynamics. His research involves network theory, statistical physics and nonlinear dynamics along with their applications in epidemiology, ecology, biology, and sociology. Recent projects include comparing complex networks of different nature, the coevolution of human behavior and infectious diseases, understanding the role of forest shape in determining stability of tropical forests, as well as the impact of echo chambers in political discussions.

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    Laurent Hébert‐Dufresne Adam F. A. Pellegrini Uttam Bhat Sidney Redner Stephen W. Pacala Andrew M. Berdahl. Edge fires drive the shape and stability of tropical forests. Ecology Letters 2018. [Journal Page]
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    Samuel V. Scarpino, Antoine Allard, Laurent Hébert-Dufresne. The effect of a prudent adaptive behaviour on disease transmission. Nature Physics 2016. [Journal Page]
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    Laurent Hébert-Dufresne, Joshua A. Grochow, Antoine Allard. Multi-scale structure and topological anomaly detection via a new network statistic: The onion decomposition. Nature Scientific Reports 2016. [Journal Page]


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Paul Hines - School of Engineering, Associate Professor

Hines' work broadly focuses on finding ways to make electric energy more reliable, more affordable, with less environmental impact. Particular topics of interest include understanding the mechanisms by which small problems in the power grid become large blackouts, identifying and mitigating the stresses caused by large amounts of electric vehicle charging, and quantifying the impact of high penetrations of wind/solar on electricity systems.

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    Paul D. H. Hines, Ian Dobson, Pooya Rezaei. Cascading Power Outages Propagate Locally in an Influence Graph That is Not the Actual Grid Topology. IEEE Transactions on Power Systems ( Volume: 32, Issue: 2, March 2017 ). [Journal Page]
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    Mert Korkali, Jason G. Veneman, Brian F. Tivnan, James P. Bagrow & Paul D. H. Hines. Reducing Cascading Failure Risk by Increasing Infrastructure Network Interdependence. Scientific Reports volume 7, Article number: 44499 (2017. [Journal Page]
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    Pooya Rezaei, Paul D. H. Hines, Margaret J. Eppstein. Estimating Cascading Failure Risk With Random Chemistry. IEEE Transactions on Power Systems ( Volume: 30, Issue: 5, Sept. 2015 ). [Journal Page]


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James Bagrow - Assistant Professor, Department of Mathematics and Statistics

Bagrow's interests include: Complex Networks (community detection, social modeling and human dynamics, statistical phenomena, graph similarity and isomorphism), Statistical Physics (non-equilibrium methods, phase transitions, percolation, interacting particle systems, spin glasses), and Optimization(glassy techniques such as simulated/quantum annealing, (non-gradient) minimization of noisy objective functions).

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    Y.-Y. Ahn, J. P. Bagrow and S. Lehmann. Link communities reveal multiscale complexity in networks. Nature, 466: 761-764 (2010). [Journal Page].
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    M. R. Frank, J. R. Williams, L. Mitchell, J. P. Bagrow, P. S. Dodds, C. M. Danforth. Constructing a taxonomy of fine-grained human movement and activity motifs through social media. In preparation. (2015). [Journal Page].
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    J. P. Bagrow and L. Mitchell. The quoter model: a paradigmatic model of the social flow of written information. To appear, Chaos (2018). [Journal Page].