Rat intersubjective decisions are encoded by frequency-specific oscillatory contexts.
Abstract
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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https://hdl.handle.net/10161/15593Published Version (Please cite this version)
10.1002/brb3.710Publication Info
Schaich Borg, Jana; Srivastava, Sanvesh; Lin, Lizhen; Heffner, Joseph; Dunson, David;
Dzirasa, Kafui; & de Lecea, Luis (2017). Rat intersubjective decisions are encoded by frequency-specific oscillatory contexts.
Brain Behav, 7(6). pp. e00710. 10.1002/brb3.710. Retrieved from https://hdl.handle.net/10161/15593.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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Show full item recordScholars@Duke
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
Kafui Dzirasa
K. Ranga Rama Krishnan Associate Professor
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 3-D videos to gain insight into how humans and rodents make
social decisions. As a data scientist, she works on interdisciplinary teams to develop
new statistical approaches to analyze the
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