Aaron M. Bornstein, Ph.D. - US grants

DESCRIPTION (provided by applicant): How humans and animals make decisions and decisions for rewards have been a subject of intense focus recently. These questions are compelling both for their critical relevance to real-world concerns, from daily purchases to economic policy, and their relationship to neurotransmitter systems underlying diseases from Parkinson's to schizophrenia. Much contemporary study of decisions has been spurred by the development of computational models that make specific predictions about how decisions arise. These models, based on the theories of reinforcement learning, have provided an invaluable tool for teasing apart the cognitive and physiological mechanisms of decision-making. However these models have to date only been applied to habitual decisions - those decisions that result from learning to expect a particular outcome from a particular action. Real-world decision- making also encompasses another class of decisions, which involve planning in spite of any experience with the outcome of your potential actions. When making non-habitual decisions, individuals may use information that they originally learned without any reward. For instance, we choose to sample new dishes or eat at entirely new restaurants even though we may have never before entered them. Modeling this sort of behavior has proven extremely difficult, due in part to the wide variety of information that may be brought to bear on such decisions. Recently, we have developed a reduced, constrained experimental learning task that allows us to separately measure both learned habits and non-habitual learning, simultaneously, in humans. We have modeled this second form of learning, and, using functional magnetic resonance imaging (fMRI), identified neural structures that represent the learned information. These include the hippocampus, a structure critical for normal memory, and whose dysfunction is implicated in several major mental health disorders, such as major depression and schizophrenia. The place of the hippocampus in decisions for reward is, however, unclear. This proposal builds on our previous results to identify how this information is used to make decisions, by asking participants to apply this information to making money. Specifically, we examine brain systems known to participate in decision-making, and ask what methods they use to parse through the information now available to them. We have reason to believe that these systems employ strategies to reduce the amount of information they need to work with, and that hippocampus is uniquely capable of implementing these strategies. Understanding these strategies is essential to understanding how decisions are made in the real world, and will provide valuable and novel insight into the fundamental mechanisms of hippocampal function. PUBLIC HEALTH RELEVANCE: I aim to elucidate the mechanisms by which hippocampus interacts with striatal and cortical decision structures to effect goal-directed planning behavior. This work is relevant to public health as functional and structural hippocampal deficits are strongly associated with numerous severe mental health disorders. In particular, several of these disorders - for example, schizophrenia and major depression - exhibit core symptoms which reflect dysfunction of exactly the sorts of associative learning mechanisms proposed to underlie goal-directed decisions. An understanding of these mechanisms will thus provide crucial insights into the nature and extent of such disruptions and inform increasingly sophisticated and targeted development of behavioral and physiological therapies.

Project Summary/Abstract The objective of this R21 proposal is to investigate the behavioral and neural signatures of age-related decline in memory function in service of multi-step planning. We anticipate that the results obtained here will serve as preliminary findings in support of a research program using neuroimaging to evaluate how these circuits are altered in diseases of aging, such as Alzheimer's Disease and Mild Cognitive Impairment (MCI). Previous research into decision-making in aging has focused primarily on tasks that involve repeated reinforcement of specific actions or stimuli (e.g. sequential reinforcement learning tasks), or on comparisons between items whose values are directly instructed (e.g. gambles). A separate form of decisions, arguably more germane to everyday experience, are those in which choices depend on planning: searching through disparate events from our past experience, and reassembling them to achieve new goals introduced in the moment (e.g. seeking an ice cream store you may have passed by but never entered). These types of decisions, in addition to being relatively under-studied in older adults, are also distinct in that they depend on long-term, episodic memory. Episodic memory is known to decline in age. A specific aspect of episodic memory that is known to decline with age is the computation called pattern separation: the ability to create divergent neural patterns that reflect inputs with similar or overlapping sensory features (e.g. two flavors of ice cream). Pattern separation allows us to rapidly retrieve and re-use information even in the face of interference. We can measure pattern separation in behavior using the Mnemonic Similarity Task (MST), a short, widely used assay that predicts cognitive and neural deficits across the lifespan. While we know that pattern separation and planning both decline with age, and we know that both functions are supported by the same neural structures in healthy adults, we do not know if they co-exist in the same neural circuits, nor do we know if a decline in pattern separation yields a decline in planning ability. Here, we aim to fill this gap in knowledge, examining (Aim 1) the ability of older adults to construct multi-step plans, and how it corresponds to pattern separation; (Aim 2) to further ask whether taking account of an individual?s decline in pattern separation can allow us to structure their experiences in a way that improves their ability to later construct plans on the basis of those experiences; (Aim 3) whether neural circuits for pattern separation and planning overlap, and how they interact in normal cognitive aging. In sum, the proposed research will determine whether we can improve individual decision-making by taking account of individual differences in memory function; the findings will inform research into early detection and treatment of a wide range of diseases of aging.