Abstract: Participation in contact sports places athletes at elevated risk for repeated head injuries and is associated with negative mental health outcomes later in life. The current study identified changes observable on neuroimaging that persisted beyond the apparent resolution of acute symptoms of concussion. Sixteen young adult ice hockey players with a remote history of concussion but no subjective complaints were compared against 13 of their teammates with no history of concussion. Participants completed a detailed phenotypic assessment and a neuroimaging battery including diffusion kurtosis imaging and resting-state functional magnetic resonance imaging. Athletes with a history of concussion performed no differently from those without on phenotypic assessment, but showed significantly elevated fractional anisotropy (FA) in the left genu and anterior corona radiata relative to those without. Post hoc analyses revealed that elevated FA was associated with increased microstructural complexity perpendicular to the primary axon (radial kurtosis). Athletes with concussion history also showed significant differences in the organization of the default mode network (DMN) characterized by stronger temporal coherence in posterior DMN, decreased temporal coherence in anterior DMN, and increased functional connectivity outside the DMN. In the absence of deficits on detailed phenotypic assessment, athletes with a history of concussion displayed changes to the microstructural architecture of the cerebral white matter and to the functional connectivity of the brain at rest. Some of these changes are consistent with those previously associated with persisting deficits and complaints, but we also report novel, complementary changes that possibly represent compensatory mechanisms.
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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.
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.
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.
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.
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).