Abstract: Background: Although intelligence is thought to be heritable,
there is evidence for environmental effects on cognitive
performance as evidenced by a strong increase in average
intelligence in the second half of the previous century (FlynnEffect).
It is highly suggestive that environmental factors
interact with genotype by regulation of gene expression and
thus contribute to individual malleability. This might be re-
flected in recent observations of an association between the
dopamine-dependent encoding of reward prediction errors
and intelligence, which was modulated by environmental
Methods: In a sample of 1475 young healthy adolescents
from the IMAGEN cohort, general IQ was assessed. As predictors,
we used polygenic scores for intelligence, methylation
count in CpG-islands relevant for dopaminergic
neurotransmission, gray matter in the striatum and brain
activation elicited by temporarily surprising reward-predicting
Results: We could show, that IQ is positively associated with
1) polygenic scores for intelligence (3.2% of variance
explained, p¼7.3x10-8), 2) epigenetic modification of DRD2
gene (2.7%, p¼3.2x10-4), 3) gray matter density in the striatum
(0.71%, p¼1.7x10-3), and 4) functional activation during
reward anticipation (1.4%, p¼4.1x10-6). Comparing the relative
importance in an overlapping subsample, our results point
to the equal importance of genetic variance, epigenetic
modification, as well as functional activation.
Conclusions: Our findings suggest that functional activation
of the reward system, epigenetic control of dopaminergic
neurotransmission and genetic markers contribute equally to
IQ. Peripheral epigenetic markers are in need of confirmation in
the central nervous system and should be tested specifically
assessing individual and social stress factors that can modify
the epigenetic markers.
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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).