Christine M. Constantinople - US grants

Project Summary The ability to estimate the probabilities of different outcomes is a cognitive function critical for decision- making in uncertain environments. A pervasive feature of human decision-making is probability distortion: humans tend to overweight small probabilities and underweight large probabilities. For example, when individuals decide to purchase insurance or play the lottery, these decisions are influenced by how likely they perceive low probability outcomes to be. Decision-making is disrupted in psychiatric disorders including schizophrenia and bipolar disorder. Therefore, a circuit-level understanding of how the brain represents probabilistic outcomes during decision-making has enormous consequences for human health. I will use high-throughput behavioral training to develop behavioral paradigms for studying probability distortion in rats, enabling application of powerful tools to monitor and manipulate neural circuits (Aim 1, K99 phase). I have recently developed a system that enables cellular resolution imaging of large populations of neurons in rats performing cognitive behaviors during voluntary head-restraint. I will use this system, combined with newly developed transgenic rats expressing the calcium indicator GCaMP6f, to record from 100s-1000s of cortical neurons as behaving rats estimate probabilities (Aim 2, K99 phase). I will develop and apply decoding methods to explicitly test hypotheses about how neural populations represent probabilities. Combining the imaging data and decoding methods, I will determine the stage of cortical processing at which probability distortion emerges (Aim 2, K99 phase). Finally, I will perform optogenetic and pharmacological perturbation experiments to delineate the functional causal circuits underlying probability distortion (Aim 3, R00 phase). I will then combine optogenetics and two-photon imaging of interconnected brain regions, to evaluate how representations of probability propagate across and are represented by multiple brain regions. Together, these experiments will establish the rat as a cost-effective, tractable mammalian model for studying probability distortion, and will produce well-informed working models of the relevant circuits and mechanisms by which animals compute, represent, and distort estimates of probabilities. The rat voluntary head-restraint imaging system has been exclusively developed as part of a collaboration between the Tank and Brody laboratories, making Princeton the only place for me to learn these techniques. In addition, the strong, collaborative environment at the Princeton Neuroscience Institute makes it an ideal place for me to pursue these research goals. My training plan provides a detailed strategy for acquiring the necessary skills in the K99 phase from a team of co-mentors with extensive, proven expertise in the relevant techniques. Technical training, as well as frequent data presentations, attendance of professional courses, seminars, and conferences, and development of my writing and leadership skills, will allow me to transition to an independent position. In the independent R00 phase, I will use these acquired skills to complete the proposed aims and build a laboratory focused on the study of probability distortion and decision- making using innovative behavioral, imaging, computational, and optogenetic approaches.

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).

Project Summary A central challenge in neuroscience is to understand how the connectivity patterns and dynamics of local and long-range synaptic inputs enable behaviorally-relevant computations in individual neurons. A fundamental computation that all mammals perform is determining the value of different outcomes, including their valence, or whether they are perceived as a gain or a loss. Behavioral economics provides a useful quantitative framework for describing how people and animals subjectively assign value to outcomes, and use those value estimates to make decisions. Here, we aim to understand the multi-regional neural circuit mechanisms by which economic variables driving decision-making are computed and represented by neurons in the brain. A hallmark of economic choice behavior is that people exhibit ?reference dependence,? wherein they evaluate outcomes as gains or losses relative to an internal reference point. A related phenomenon, called ?loss aversion,? refers to the observation that most people are more sensitive to losses than to equivalent gains. This proposal will combine state-of-the-art viral and transgenic approaches for circuit dissection, in vivo paired recordings of long-range synaptically connected neurons whose responses have been characterized during behavior, novel techniques for neurochemical sensing, high-throughput behavioral training of rats, and quantitative behavioral modeling to identify how neural representations of quantifiable cognitive variables -the reference point and loss aversion- derive from dynamics and patterns of local and long range synapses. Specifically, the proposed work will delineate the thalamocortical circuitry supporting reference-dependent computations, determine the circuit mechanisms of arithmetic subtraction of the reference point from value signals, and identify neuromodulatory systems driving individual variability in loss aversion. The results will bridge cellular, circuit, and systems-level descriptions of neural mechanisms underlying consequential economic judgments, while revealing general neural circuit motifs supporting arithmetic computations including summation, subtraction, and multiplication.