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Principal Investigator: Christine M Constantinople
Organization: NEW YORK UNIVERSITY
Fiscal Year: 2024
Award: $337,222
Funding agency: National Institute of Mental Health
A key computation that all mammals perform is determining the value of different outcomes. People and
animal models evaluate outcomes as gains or losses relative to an internal reference point, likely
reflecting their experience-based expectations. For example, if someone is told they will receive a
particular salary at a new job, but when they start, they find that the salary is substantially less, they will
view that salary (which is a net increase in wealth) as a loss relative to their reference point. Reference
dependence is a consequential, ubiquitous phenomenon, driving decisions about insurance, financial
products, labor, and retirement savings. The proposed work seeks to uncover how large populations of
neurons represent a cognitive variable –the reference point- during value-based decision-making. This
work involves complementary, synergistic interactions between experimentalists and theorists in the labs
of Dr. Christine Constantinople and Dr. Cristina Savin, respectively.
This proposal will develop a novel behavioral paradigm for studying reference dependence in rats,
enabling application of powerful tools to monitor large-scale neural dynamics. High-throughput behavioral
training will generate dozens of trained subjects for experiments in parallel. We will also develop a
behavioral model to quantify key aspects of rats' behavior, including individual differences in behavior
across animals (Aim 1). We will use new silicon probes with high channel counts (“Neuropixels” probes) to
record from populations of neurons in dozens of rats during behavior. Recordings will be obtained from
the orbitofrontal cortex (OFC), a key brain structure implicated in value-based decision-making. We will
develop novel latent dynamics models that will infer the reference point directly from populations of
simultaneously recorded neurons in OFC, without any knowledge of the task or rats' behavior. This model
will also be able to identify aspects of neural dynamics that are common across dozens of rats, and
aspects that are variable across animals, reflecting individual differences in behavior (Aim 2). Finally, we
will use complementary, state-of-the-art machine-learning techniques to train recurrent neural networks
(RNNs) on our behavioral and neural data. This approach will generate concrete hypotheses about the
neural circuit architectures performing reference-dependent subjective valuation in our task (Aim 3).
Terms: <Affect><Animal Model><Animal Models and Related Studies><Animals><Architecture><Automobile Driving><Aves><Avian><Behavior><Behavioral><Behavioral Model><Behavioral Paradigm><Biologic Models><Biological Models><Bipolar Affective Psychosis><Bipolar Disorder><Birds><Brain><Brain Nervous System><Capuchin Monkey><Cebus><Choice Behavior><Chronic><Cognitive><Collaborations><Common Rat Strains><Complex><Data><Decision Making><Decision Theory><Dependence><Dimensions><Disease><Disorder><Encephalon><Engineering / Architecture><Future><Health><History><Human><Hydrogen Oxide><Impairment><Implant><Individual><Individual Differences><Insurance><Jobs><Knowledge><Machine Learning><Mammalia><Mammals><Manic-Depressive Psychosis><Mental disorders><Mental health disorders><Model System><Modeling><Modern Man><Modernization><Monitor><Nerve Cells><Nerve Unit><Neural Cell><Neurocyte><Neurons><Occupations><Outcome><Persons><Population><Population Dynamics><Process><Professional Positions><Psychiatric Disease><Psychiatric Disorder><Rat><Rats Mammals><Rattus><Recording of previous events><Retirement><Rewards><Ring-Tail Monkey><Salaries><Savings><Schizophrenia><Schizophrenic Disorders><Si element><Silicon><Structure><Techniques><Testing><Training><Wages><Water><Work><behavior measurement><behavioral economics><behavioral measure><behavioral measurement><bipolar affective disorder><bipolar disease><bipolar illness><bipolar mood disorder><capuchin><dementia praecox><driving><dynamic system><dynamical system><expectation><experience><experiment><experimental research><experimental study><experiments><histories><improved><machine based learning><manic depressive disorder><manic depressive illness><mental illness><model of animal><neural><neural circuit><neural circuitry><neurocircuitry><neuronal><neuropsychiatric disease><neuropsychiatric disorder><novel><preservation><psychiatric illness><psychological disorder><recurrent neural network><retirements><reward processing><schizophrenic><synaptic circuit><synaptic circuitry><tool>