Brooks Casas
· Assistant Professor of Biomedical EngineeringVerifiedVirginia Tech · Biomedical Engineering and Sciences
Active 1997–2024
About
Brooks Casas, Ph.D., is a professor at the Fralin Biomedical Research Institute at VTC, Virginia Tech, with appointments across multiple departments including the College of Science, Department of Psychology, Department of Biomedical Engineering and Mechanics, and the School of Medicine's Department of Psychiatry and Behavioral Medicine. His research focuses on understanding the neural foundations of human thought and action, particularly how humans make decisions about, among, and for one another. Casas seeks insights into the neural computations underlying normative social behavior, exploring how learning generated by neural interactions influences social decision-making. His lab investigates broad areas such as the neural underpinnings of valuation and learning in social contexts, and how social and economic preferences impact these processes. The research employs a combination of decision neuroscience, behavioral economics, and social psychology to address complex social phenomena, including trust, balancing self-interest with others' interests, risk preferences across the lifespan, and social influence. Additionally, Casas pursues understanding of neural computations related to social decision-making abnormalities in psychopathology, studying how impairments in decision-making manifest in psychiatric illnesses like substance abuse and borderline personality disorder. His work aims to leverage normative research to identify neural substrates that give rise to aberrant behaviors, contributing to the broader understanding of social cognition and mental health.
Research topics
- Psychology
- Computer Science
- Artificial Intelligence
- Developmental psychology
- Chemistry
- Neuroscience
- Clinical psychology
- Medicine
Selected publications
Biological Psychiatry Cognitive Neuroscience and Neuroimaging · 2023 · 8 citations
Senior authorCorresponding- Artificial Intelligence
- Computer Science
- Psychology
BACKGROUND: Socioecological factors such as family environment and parenting behaviors contribute to the development of substance use. While biobehavioral synchrony has been suggested as the foundation for resilience that can modulate environmental effects on development, the role of brain similarity that attenuates deleterious effects of environmental contexts has not been clearly understood. We tested whether parent-adolescent neural similarity-the level of pattern similarity between parent-adolescent functional brain connectivity representing the level of attunement within each dyad-moderates the longitudinal pathways in which household chaos (a stressor) predicts adolescent substance use directly and indirectly via parental monitoring. METHODS: In a sample of 70 parent-adolescent dyads, similarity in resting-state brain activity was identified using multipattern connectivity similarity estimation. Adolescents and parents reported on household chaos and parental monitoring, and adolescent substance use was assessed at a 1-year follow-up. RESULTS: The moderated mediation model indicated that for adolescents with low neural similarity, but not high neural similarity, greater household chaos predicted higher substance use over time directly and indirectly via lower parental monitoring. Our data also indicated differential susceptibility in the overall association between household chaos and substance use: Adolescents with low neural similarity exhibited high substance use under high household chaos but low substance use under low household chaos. CONCLUSIONS: Neural similarity acts as a protective factor such that the detrimental effects of suboptimal family environment and parenting behaviors on the development of adolescent health risk behaviors may be attenuated by neural similarity within parent-adolescent bonds.
Noradrenaline tracks emotional modulation of attention in human amygdala
Current Biology · 2023 · 38 citations
- Neuroscience
- Psychology
- Chemistry
with the pupil and NA estimates being positively correlated for oddball stimuli in a high-arousal but not a low-arousal state. Our study provides proof of concept that neuromodulator monitoring is now possible using depth electrodes in standard clinical use.
JAMA Psychiatry · 2021 · 111 citations
- Psychology
- Clinical psychology
- Psychiatry
Importance: Major depressive disorder is prevalent and impairing. Parsing neurocomputational substrates of reinforcement learning in individuals with depression may facilitate a mechanistic understanding of the disorder and suggest new cognitive therapeutic targets. Objective: To determine associations among computational model-derived reinforcement learning parameters, depression symptoms, and symptom changes after treatment. Design, Setting, and Participants: In this mixed cross-sectional-cohort study, individuals performed reward and loss variants of a probabilistic learning task during functional magnetic resonance imaging at baseline and follow-up. A volunteer sample with and without a depression diagnosis was recruited from the community. Participants were assessed from July 2011 to February 2017, and data were analyzed from May 2017 to May 2021. Main Outcomes and Measures: Computational model-based analyses of participants' choices assessed a priori hypotheses about associations between components of reward-based and loss-based learning with depression symptoms. Changes in both learning parameters and symptoms were then assessed in a subset of participants who received cognitive behavioral therapy (CBT). Results: Of 101 included adults, 69 (68.3%) were female, and the mean (SD) age was 34.4 (11.2) years. A total of 69 participants with a depression diagnosis and 32 participants without a depression diagnosis were included at baseline; 48 participants (28 with depression who received CBT and 20 without depression) were included at follow-up (mean [SD] of 115.1 [15.6] days). Computational model-based analyses of behavioral choices and neural data identified associations of learning with symptoms during reward learning and loss learning, respectively. During reward learning only, anhedonia (and not negative affect or arousal) was associated with model-derived learning parameters (learning rate: posterior mean regression β = -0.14; 95% credible interval [CrI], -0.12 to -0.03; outcome sensitivity: posterior mean regression β = 0.18; 95% CrI, 0.02 to 0.37) and neural learning signals (moderation of association between striatal prediction error and expected value signals: t97 = -2.10; P = .04). During loss learning only, negative affect (and not anhedonia or arousal) was associated with learning parameters (outcome shift: posterior mean regression β = -0.11; 95% CrI, -0.20 to -0.01) and disrupted neural encoding of learning signals (association with subgenual anterior cingulate prediction error signals: r = -0.28; P = .005). Symptom improvement following CBT was associated with normalization of learning parameters that were disrupted at baseline (reward learning rate: posterior mean regression β = 0.15; 90% CrI, 0.001 to 0.41; loss outcome shift: posterior mean regression β = 0.42; 90% CrI, 0.09 to 0.77). Conclusions and Relevance: In this study, the mapping of reinforcement learning components to symptoms of major depression revealed mechanistic features associated with these symptoms and points to possible learning-based therapeutic processes and targets.
Valuation of peers’ safe choices is associated with substance-naïveté in adolescents
Proceedings of the National Academy of Sciences · 2020 · 34 citations
- Computer Security
- Psychology
- Developmental psychology
Social influences on decision-making are particularly pronounced during adolescence and have both protective and detrimental effects. To evaluate how responsiveness to social signals may be linked to substance use in adolescents, we used functional neuroimaging and a gambling task in which adolescents who have and have not used substances (substance-exposed and substance-naïve, respectively) made choices alone and after observing peers' decisions. Using quantitative model-based analyses, we identify behavioral and neural evidence that observing others' safe choices increases the subjective value and selection of safe options for substance-naïve relative to substance-exposed adolescents. Moreover, the effects of observing others' risky choices do not vary by substance exposure. These results provide neurobehavioral evidence for a role of positive peers (here, those who make safer choices) in guiding adolescent real-world risky decision-making.
Frequent coauthors
- 42 shared
Pearl H. Chiu
Biomedical Research Institute
- 17 shared
Patricia J. Deldin
- 13 shared
Kirby Deater‐Deckard
University of Massachusetts Amherst
- 13 shared
Dan Bang
University of Oxford
- 12 shared
Mark A. Orloff
Roanoke College
- 12 shared
Jungmeen Kim‐Spoon
Virginia Tech
- 12 shared
Dongil Chung
- 9 shared
Mary R. Newsome
Ben Taub Hospital
Awards & honors
- Brooks Casas named College of Science Faculty Fellow (2023)
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