Rat intersubjective decisions are encoded by frequency-specific oscillatory contexts.

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INTRODUCTION: It is unknown how the brain coordinates decisions to withstand personal costs in order to prevent other individuals' distress. Here we test whether local field potential (LFP) oscillations between brain regions create "neural contexts" that select specific brain functions and encode the outcomes of these types of intersubjective decisions. METHODS: Rats participated in an "Intersubjective Avoidance Test" (IAT) that tested rats' willingness to enter an innately aversive chamber to prevent another rat from getting shocked. c-Fos immunoreactivity was used to screen for brain regions involved in IAT performance. Multi-site local field potential (LFP) recordings were collected simultaneously and bilaterally from five brain regions implicated in the c-Fos studies while rats made decisions in the IAT. Local field potential recordings were analyzed using an elastic net penalized regression framework. RESULTS: Rats voluntarily entered an innately aversive chamber to prevent another rat from getting shocked, and c-Fos immunoreactivity in brain regions known to be involved in human empathy-including the anterior cingulate, insula, orbital frontal cortex, and amygdala-correlated with the magnitude of "intersubjective avoidance" each rat displayed. Local field potential recordings revealed that optimal accounts of rats' performance in the task require specific frequencies of LFP oscillations between brain regions in addition to specific frequencies of LFP oscillations within brain regions. Alpha and low gamma coherence between spatially distributed brain regions predicts more intersubjective avoidance, while theta and high gamma coherence between a separate subset of brain regions predicts less intersubjective avoidance. Phase relationship analyses indicated that choice-relevant coherence in the alpha range reflects information passed from the amygdala to cortical structures, while coherence in the theta range reflects information passed in the reverse direction. CONCLUSION: These results indicate that the frequency-specific "neural context" surrounding brain regions involved in social cognition encodes outcomes of decisions that affect others, above and beyond signals from any set of brain regions in isolation.





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Schaich Borg, Jana, Sanvesh Srivastava, Lizhen Lin, Joseph Heffner, David Dunson, Kafui Dzirasa and Luis de Lecea (2017). Rat intersubjective decisions are encoded by frequency-specific oscillatory contexts. Brain Behav, 7(6). p. e00710. 10.1002/brb3.710 Retrieved from https://hdl.handle.net/10161/15593.

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Schaich Borg

Jana Schaich Borg

Associate Research Professor in the Social Science Research Institute

Dr. Jana Schaich Borg uses neuroscience, computational modeling, and emerging technologies to study how we make social decisions that influence, or that are influenced by, other people.  As a neuroscientist, she employs neuroimaging, ECOG, simultaneous electrophysiological recordings in rats, and computational analysis of video interactions to gain insight into how we make social decisions.  As a data scientist, she works on interdisciplinary teams to develop new statistical approaches to analyze these high-dimensional multi-modal data to uncover principles of how the brain integrates complex social information with internal representations of value to motivate social actions.

Dr. Schaich Borg’s most current research projects focus on developing Moral Artificial Intelligences and understanding the role of “social synchrony” in empathy, social connection, psychiatric disease, and mental health.  Issues related to these research projects have led her become involved in efforts to develop practical strategies for ethical AI development, and passionate about initiatives to use storytelling and data visualization to communicate the impact of complex analytical problems to diverse audiences.


David B. Dunson

Arts and Sciences Distinguished Professor of Statistical Science

My research focuses on developing new tools for probabilistic learning from complex data - methods development is directly motivated by challenging applications in ecology/biodiversity, neuroscience, environmental health, criminal justice/fairness, and more.  We seek to develop new modeling frameworks, algorithms and corresponding code that can be used routinely by scientists and decision makers.  We are also interested in new inference framework and in studying theoretical properties of methods we develop.  

Some highlight application areas: 
(1) Modeling of biological communities and biodiversity - we are considering global data on fungi, insects, birds and animals including DNA sequences, images, audio, etc.  Data contain large numbers of species unknown to science and we would like to learn about these new species, community network structure, and the impact of environmental change and climate.

(2) Brain connectomics - based on high resolution imaging data of the human brain, we are seeking to developing new statistical and machine learning models for relating brain networks to human traits and diseases.

(3) Environmental health & mixtures - we are building tools for relating chemical and other exposures (air pollution etc) to human health outcomes, accounting for spatial dependence in both exposures and disease.  This includes an emphasis on infectious disease modeling, such as COVID-19.

Some statistical areas that play a prominent role in our methods development include models for low-dimensional structure in data (latent factors, clustering, geometric and manifold learning), flexible/nonparametric models (neural networks, Gaussian/spatial processes, other stochastic processes), Bayesian inference frameworks, efficient sampling and analytic approximation algorithms, and models for "object data" (trees, networks, images, spatial processes, etc).


Kafui Dzirasa

A. Eugene and Marie Washington Presidential Distinguished Professor

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