F67. Increased Amygdalar Activation to Angry Faces is Linked to Reduced Prefrontal Cortical Thickness and Hyperactive/Inattentive Symptomatology in Adolescents
Biological Psychiatry, 83, , 2018
Prior studies have reported increased amygdalar activation in response to emotional stimuli among individuals with attention-deficit/hyperactivity disorder (ADHD). Herein, we investigate the extent to which amygdalar activation to angry faces is associated with ADHD symptomology and cortical morphology in a population-based sample of adolescents.
Data were obtained from the IMAGEN study, which includes 2,223 adolescents. While undergoing functional imaging, participants passively viewed video clips of a face that started from a neutral expression and progressively turned angry, or, instead, turned to a second neutral expression. Left and right amygdala ROIs were used to extract mean BOLD signal from the angry face minus neutral face contrast for all subjects. T1-weighted images were processed through the CIVET pipeline (version 2.1.0). ADHD symptomatology was assessed using the Development and Well-Being Assessment, and Strengths and Difficulties Questionnaire.
Youths exhibiting increased left amygdalar activation (+1.5 SDs) (92 participants; 40 females) possessed significantly greater parent- and self-reported ADHD symptomatology relative to all other subjects (p = .012-.038). Compared to the rest of the sample, youths exhibiting increased left amygdalar activation did not differ with regard to demographic variables, or other forms of psychopathology, including mood/anxiety symptoms. Increased amygdalar activation was associated with reduced cortical thickness in orbital/ventromedial prefrontal regions. Further analysis revealed significant negative associations between parent-reported ADHD symptoms and thickness in orbital/ventromedial prefrontal cortices—cortical thickness in these regions was negatively correlated with left amygdalar activation.
These findings suggest that cortico-amygdalar circuitry may underpin aspects of core ADHD symptomatology, not simply co-occurring mood and anxiety problems.
[edit database entry]
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).