Joshua Gold - US grants

DESCRIPTION (provided by applicant): Our long-term goal is to understand how experience shapes neural mechanisms of visual perception. We previously showed that months of training can improve sensitivity on a visual discrimination task via an increasingly selective readout of the most sensitive neurons in a sensory population. Performance on this and other visual tasks can also depend strongly on the temporal dynamics of visual processing. We propose to study the computational principles and neural mechanisms that govern how these temporal dynamics are shaped by recent experience to optimize perceptual sensitivity in a dynamic environment. We build on a theoretical framework we developed recently that describes optimal information accumulation in dynamic environments. Using the framework as a starting point, we will test two primary hypotheses. First, the temporal dynamics of visual processing reflect learned expectations about the temporal dynamics of the relevant inputs, consistent with the optimal model. Second, this process involves two complementary mechanisms that have long been known to contribute to change detection but whose specific, computational roles in shaping the learned temporal dynamics of visual processing are not yet known: sensory adaptation in visual cortex and the processing of uncertainty in the anterior cingulate cortex (ACC) and related arousal systems. We test these hypotheses using three Specific Aims. First, we will establish roles for sensory adaptation and arousal in optimizing the temporal dynamics of visual motion processing using human psychophysics and computational modeling. Second, we will determine how history-dependent modulation of adaptation dynamics of motion- sensitive neurons in the middle temporal area (MT) of extrastriate visual cortex can contribute to the temporal dynamics of visual motion processing in monkeys. Three, we will determine how the representation of expected and unexpected changes in stimulus dynamics represented in the ACC can contribute to the temporal dynamics of visual processing in monkeys, particularly in terms of recognizing change-points that reset evidence accumulation. Each Aim alone will provide new insights into how sensory adaptation and ACC- mediated computations can affect the temporal dynamics of visual processing. Together, these studies will provide a novel, unified view of how these mechanisms can interact to help optimize how the brain processes dynamic visual input over time.

DESCRIPTION (provided by applicant): Decision-making is an important cognitive ability that often requires uncertain evidence to be weighed and accumulated over time before committing to a course of action. Much of our understanding of the neural mechanisms responsible for this process of evidence accumulation comes from neurophysiological studies in monkeys performing demanding perceptual decision- making tasks. In one particularly well-studied model, monkeys are trained to decide the direction of motion of a noisy visual stimulus and indicate their decision with an eye movement to a target located in the perceived direction of motion. These studies suggest that neurons in the lateral intraparietal area (LIP) of parietal cortex play a role in accumulating noisy motion evidence used to instruct the oculomotor response. However, in this task the close relationship between formation of the direction decision and selection of the appropriate oculomotor response makes it difficult to identify the exact role of LIP. In particular, does LIP encode the decision about motion direction independent of the motor act, or does it reflect an oculomotor selection process that can access relevant perceptual information? Our central hypothesis is that LIP encodes an oculomotor selection process. To test this hypothesis we will train monkeys using an innovative task design in which their decision about the direction of motion is associated with the color, instead of the location, of the choice target (red for rightward motion, green for leftward). For a given session the locations of the two targets are fixed, but we randomly assign to them the two colors. Critically, we manipulate the time when the colors are assigned and measure the effects on the representation of evidence accumulation in LIP. When the color assignment occurs before motion viewing, the direction decision can be used directly to select the appropriate oculomotor response and LIP is likely to be engaged. When the color assignment occurs after motion viewing, the specific oculomotor response is not known while the decision is formed and LIP might not be engaged. When the color assignment occurs during motion viewing, LIP neurons might be able to gain dynamic access to a process of evidence accumulation that is being carried out elsewhere in the brain. The results will clarify the role of LIP in perceptual decision-making and provide a more general understanding of the principles that govern how behavioral context can influence how and where in the brain high-order abilities like decision-making are generated. PUBLIC HEALTH RELEVANCE: The proposed work is basic research, designed to provide new insights into how a healthy nervous system evaluates evidence to form a decision. Thus, direct benefits to public health are expected to come in the longer term, as these new insights can be used to design new ways to diagnose and treat disorders that affect perception and judgment.

DESCRIPTION (provided by applicant): The locus coerulus (LC) is a small brainstem nucleus that is the primary source of norepinephrine (NE) to the cortex, thalamus, midbrain, cerebellum, and spinal cord. The LC-NE system is thought to play important roles in many normal brain functions, including arousal and sensory-motor processing. In addition, the LC-NE system has been implicated in a number of clinical disorders, including attention-deficit/hyperactivity disorder (ADHD), anxiety, depression, and schizophrenia. Thus, a better understanding of the properties of the LC-NE system is likely to provide important insights into both normal and abnormal brain function. A major obstacle to such research is the size and location of the LC, which make it difficult to target for recording or imaging. One possible solution to this problem is to measure pupil diameter, which is relatively straightforward to measure and under certain low-light conditions is thought to reflect LC activation. However, despite some strong claims to the contrary, the relationship between LC activation and pupil diameter has never been established with sufficient rigor to allow pupillometry measurements to be interpreted in terms of LC function. The goal of the proposed studies is to characterize this relationship in detail. We will use three complementary approaches: 1) simultaneous recording of individual LC neurons and pupil diameter in awake, behaving monkeys under a variety of conditions; 2) simultaneous application of electrical microstimulation in the LC and measurements of pupil diameter in awake, behaving monkeys; and 3) measurements of pupil diameter in human subjects under comparable conditions to those tested with monkeys. Together, these studies will provide by far the most rigorous assessment to date of the relationship between LC activation and pupil diameter. Given the growing interest in the role of the LC-NE system in behavior, cognition, and disease, and the relative ease with which pupil diameter can be measured, these results are highly likely to make a strong impact on the design and interpretation of many future studies.

DESCRIPTION (provided by applicant): We learn from experience to make more effective decisions, often adjusting our expectations to match outcomes that we have experienced in the past. In a dynamic world, this adjustment process must itself be adaptive, because sometimes changes occur that render past outcomes irrelevant to future expectations. For example, a forager must recognize that a dying tree will no longer yield as much fruit as when it was healthy. The goal of the proposed research is to test the novel hypothesis that certain individual differences in decision- making, including some that depend systematically on age, result from differences in how observed changes in the world affect the use of past experiences to inform future decisions. We propose that individuals differ substantially in how they recognize and respond to two different forms of uncertainty. One form of uncertainty, called noise, represents random fluctuations in an otherwise stable process. The other form of uncertainty, called volatility, represents fundamental changes in the process itself. Individual, adaptive decision-making ranges from a tendency to always adjust expectations in the face of either noisy or volatile new data, to a tendency to form stable expectations that are relatively unaffected by new data. We have identified computational principles that govern this kind of adaptive belief updating, which include the importance of prior expectations about the rate of occurrence of volatile changes in the current environment. We also propose that the important underlying computations are encoded by the activity of neurons in the anterior cingulate cortex (ACC). To test these hypotheses, we use two complementary Aims that provide multiple measures of ACC activity in individual subjects of different ages performing tasks designed to measure directly the amount of influence each new piece of information has on existing beliefs about a dynamic environment. The first Aim uses combined behavior and neurophysiological recordings in younger and older monkeys, providing measurements of the relevant neural computations with high spatial and temporal resolution. The second Aim uses combined behavior and fMRI- and EEG-based measurements of brain activity in human subjects, which can be compared directly to the higher-resolution monkey data. The human studies also include several versions of the task, including one designed to manipulate and measure prior expectations about the rate of environmental changes directly, that provide a broader view of the individual and age-related differences that occur with these kinds of decisions. The proposed research represents a novel field of study that is likely to provide far-reaching insights into how individuals make effective decisions in a dynamic world. PUBLIC HEALTH RELEVANCE: A critical task for decision makers is to determine the relevance of past experiences to the current environment. The proposed work tests a novel hypothesis about specific brain mechanisms responsible for this process, which we propose are responsible for certain individual, age-related differences in how we make decisions. This work will establish foundational, basic knowledge that, in the long term, will help to guide the development of new tools to diagnose and counteract conditions associated with abnormal decision making, including ADHD and schizophrenia, along with cognitive deficits that occur with aging.

Project Summary The basal ganglia (BG) pathway has been hypothesized to contribute in several important ways to decision- making, including accumulating evidence, controlling decision termination and commitment, transforming abstract decisions to appropriate motor responses, and providing the machinery for evaluating and adjusting the decision performance. However, many of these functions have been proposed in computational modeling studies but have yet to be examined in detail in the brain, leaving important knowledge gaps that seriously impede our ability to understand normal BG function in healthy brains and to diagnose and treat clinical disorders that affect BG function. Our long-term goal is to conduct experiments that allow us to understand the exact nature of the BG pathway's causal contributions to decision-making. Our original project examined the causal roles of the caudate nucleus, an input structure in the BG, in incorporating reward and visual evidence to make saccade decisions. Here we propose to examine the causal roles of two other BG structures, the substantia nigra pars reticulata (SNr, the output structure in the oculomotor BG) and subthalamic nucleus (STN, a BG nucleus with high clinical importance), in decision-making. Guided by predictions of several prominent theoretical models, we combine computational, behavioral, and neurophysiological techniques to examine how SNr (Aim 1) and STN (Aim 2) neurons contribute to decision deliberation and commitment given uncertain visual input alone, and in the context of flexible decisions that must also take into account changes in reward expectation (Aim 3). Results from the proposed project will provide the first direct experimental evidence for how these specific, clinically relevant nuclei contribute to decision-making. These findings will be particularly useful for constraining and informing theories about the neural implementation of decision-making in the primate brain. These findings will also serve as a foundation for investigating the cognitive impairments associated with BG dysfunction.

DESCRIPTION (provided by applicant): We propose to continue a flexible interdisciplinary Graduate Training Program, currently in its 33rd year, designed to prepare exceptional students for productive research careers in Systems & Integrative Biology (SIB). Our Predoctoral Training Program trains graduate students to work towards understanding the operation of the nervous system, including education and research opportunities to identify and ameliorate many dysfunctional and disease conditions such as stroke, epilepsy, neurodegenerative disorders and addiction. This program is based in the Neuroscience Graduate Group (NGG), an interdepartmental group of 102 faculty members from 24 departments in 4 Schools of the University of Pennsylvania. Graduate education in the Life Sciences at Penn is based on such Graduate Groups. The Office of Biomedical Graduate Studies (BGS) ensures curricular development, quality control and uniform admission standards across all of these Graduate Groups. Direct management of the SIB Training Program is done by a five-person Executive Committee that sets and reviews policy and selects trainees. SIB Faculty membership is governed by three criteria: (1) expertise in a relevant field of study, (2) significant contributio to training, and (3) extramural funding to support trainees. Admission of students to Graduate Programs is decided by a BGS-wide admissions committee. Subsequent admission to the SIB Program is decided by its Executive Committee. Support for each trainee will encompass their first 21 months in graduate school. The Training Program will consist of two years of coursework plus at least two lab rotations. All students will take a yearly course on the responsible conduct of scientific research. Students will also receive training through seminars, journal clubs, annual retreats, scientific meetings, paper and poster presentations, and social events that encourage interactions. Successful completion of a comprehensive Candidacy examination marks the start of independent research toward the dissertation. Thesis research is conducted under the supervision of a faculty advisor and is monitored by a thesis committee and the NGG Academic Review Committee. The dissertation defense takes place when the thesis advisor and committee agree that the work is complete. Most graduates move on for advanced (postdoctoral) training and pursue an academic career. Based on the number of potential trainees, we request support for 12 predoctoral trainees per year for the next 5 years.

What makes people behave so differently from one another? Consider how we make decisions. Some people are quick and decisive but overly rigid, unable to adapt effectively to new opportunities or threats. In contrast, others may be more deliberative and less confident, making their decisions less predictable but more adaptable to changing circumstances. With funding from the National Science Foundation, Drs. Joshua Gold and Joseph Kable of the University of Pennsylvania are investigating a new theory that in the real world, there is a fundamental tradeoff between these two extremes. The theory includes a novel proposal that what has previously been dismissed by researchers as random variability in human behavior might instead reflect uncertain, adaptable decision-making linked with norepinephrine, a neurochemical implicated in learning and arousal. Does this characteristic explain other aspects of human personality and behavior? Can norepinephrine levels in the brain be manipulated to affect complex learning and decision-making behaviors? In answering these questions, this work will establish foundational, basic knowledge that, in the long term, will help to guide the development of new tools to diagnose and counteract conditions associated with abnormal learning and decision-making, including attention deficit hyperactivity disorder (ADHD), anxiety, depression, and schizophrenia. This knowledge about individual differences in learning will also inform how to best tailor educational and learning practices, as well as how to design computer programs that learn adaptively from experience. Other benefits of this work are resources that will assist research and education in cognitive and neural systems, including publically available datasets, computer code and machine learning algorithms; increased participation of underrepresented groups in this kind of integrative research, via summer research experiences for high school and undergraduate students; and an increased public awareness of neuroscience via public lectures, Brain Awareness Week activities, and contributions to a website that explains brain research in laymen's terms.

The work is based on a novel hypothesis about brain mechanisms that are responsible for certain idiosyncratic learning and decision processes. Specifically, in our unpredictable world, decision-makers face an inherent trade-off: higher certainty leads to more precise and accurate choices when the world is stable but an inability to adjust to change, whereas less certainty can lead to greater adaptability but also more variable and imprecise decisions. The investigators propose that this trade-off is regulated by interactions between arousal and cortical systems. To test this hypothesis, they use an interdisciplinary and integrative set of approaches with three primary objectives: 1) develop a theoretical framework describing inherent trade-offs between output stability and learning in hierarchical, probabilistic inference processes in unpredictable environments; 2) identify behavioral, physiological, and neural correlates of variability in how individuals navigate these trade-offs while making choices in unpredictable environments; and 3) identify causal influences of the brainstem nucleus locus coeruleus, a key component of the arousal system, on the variability in adaptive inference. The work forges meaningful connections across theory and experiment, spanning multiple spatial and temporal scales and levels of abstraction, to identify computational and physiological underpinnings of individual differences in learning.

? DESCRIPTION (provided by applicant): Interactions between arousal and brain function are well established, particularly as they relate to extremes of arousal state such as fight/flight versus relaxed, awake versus asleep, and attentive versus inattentive. However, recent studies suggest far more prevalent and nuanced effects of arousal on moment-by-moment, attentive information processing in the normal and clinically affected brain. Less is known about the mechanisms responsible for these more nuanced effects. Based in part on recent studies linking arousal- mediated modulations of information processing to release of norepinephrine (NE) from neurons in the brainstem nucleus locus coeruleus (LC) to much of the rest of the brain and our own preliminary data, here we test the hypothesis that moment-by-moment changes in arousal state during attentive processing modulate coordinated neuronal dynamics within and across brain regions. Three key features of this study will substantially broaden its impact. First, we wil study the effects of fluctuations of arousal on brain function under conditions that are in widespread use in human and animal studies: an attentive individual performing a task requiring information processing and controlled behaviors over many hundreds of trials. Second, we will study coordinated neural dynamics, which are particularly susceptible to changes in brain state and can only be interpreted correctly when such influences are taken into account. Third, and perhaps most critically, we will help to identify which of several readily available, non-invasive physiological measures of arousal, including pupillometry, skin conductance responses (SCR), heart rate variability (HRV), and electroencephalography (EEG), can be used to effectively characterize ongoing effects of arousal on simultaneously measured brain activity and behavior. Specifically, we will: 1) Determine relationships between arousal measures (pupil, SCR, HRV, and EEG) and coordinated neuronal dynamics within and across several brain regions in attentive monkeys; and 2) Develop mathematical tools to relate arousal measures to patterns of coordinated neural activity within and across brain regions. Together, these Aims will improve scientific knowledge about the neural basis of these kinds of arousal effects, and provide new, practical approaches for taking these effects into account when interpreting coordinated brain activity.

Perceptual, reward-based, and other decisions are deliberative processes that depend on the ability to accumulate uncertain information over time. In dynamic environments, this process must be adaptive to account effectively for changes in the relevance and reliability of new inputs. For example, environmental changes can occur mid-decision. Such changes can render previous inputs obsolete and thus require adjustment of the accumulation process. Recent work has begun to examine decision-making under these kinds of dynamic conditions, resulting in a growing understanding of computational, behavioral, and physiological properties of adaptive evidence accumulation. However, a critical gap remains in our understanding of the underlying neural mechanisms: no study to date has identified representations of this kind of adaptive decision variable that flexibly accumulate information to drive behavior. Our goal is to fill this gap using highly interacting theoretical and experimental approaches to understand how and where in the brain such decision variables are encoded. Specifically, we will test the hypothesis that brain circuits that encode near-perfect integration of evidence under static conditions are highly flexible and implement more adaptive processes that approximate key features of ideal-observer models under dynamic conditions. We propose three Specific Aims, as follows. Aim 1 is to develop computational models of neural circuits that can approximate normative evidence accumulation in dynamic environments. Aim 2 is to determine principles of adaptive evidence accumulation used by human subjects performing dynamic decision tasks. Aim 3 is to identify-representations-of-adaptive-evidence-accumulation-in-parietaJ-and-prefrontaJ- ne1.Jra1-acti11ity-of monkeys performing dynamic decision tasks. Together, these integrated computational, behavioral, and neurophysiological approaches will provide novel insights into the many aspects of higher brain function and complex behaviors that depend on processing information in a manner that is not tied reflexively to immediate sensory inputs or motor outputs. We also have a strong data-sharing strategy that will help ensure that this unique data set will be made available for research and teaching purposes. 1 --- RELEVANCE (See instructions): The proposed work is basic research designed to identify mechanisms of flexible decision-making. In the long run, this work will help inform new diagnoses and treatments of disorders that include deficits in cognition and decision-making, including schizophrenia, autism, and attention-deficit/hyperactivity disorder (ADHD).

Project Summary/Abstract Deep Brain Stimulation (DBS) is a surgical procedure that is used to treat the debilitating symptoms of Parkinson's Disease (PD). In the process of surgically implanting the stimulating electrodes, surgeons and researchers have a unique opportunity to measure and manipulate the activity of individual neurons while the awake PD patient performs a perceptual, cognitive, or other kind of relatively simple task. These studies are important because they far surpass the spatial and temporal resolution of state-of-the-art human imaging techniques and can yield insights into the basic building blocks of higher brain function, and how those building blocks may be disrupted in PD. Our proposed studies take advantage of this opportunity to establish a novel and sustainable research program to identify mechanisms of decision-making at the single-neuron level. We target the Substantia Nigra, Pars Reticulata (SNr), an output nucleus of the basal ganglia (BG) that acts as a gating mechanism that suppresses unwanted eye movements but allows wanted ones. Because goal-directed eye movements are used to select and attend to features of the visual scene for further processing, their underlying mechanisms must incorporate rapid and sophisticated decision-making. Ours will be the first research program to systematically test the SNr's role in these decision processes. These studies will have a major impact because of our use of: 1) our established and high-volume infrastructure and clinical program to obtain reliable SNr recordings and apply microstimulation in awake, behaving patients undergoing DBS surgery; 2) a visual motion-saccadic decision (?dots?) task that has been used with PD patients and is amenable to the kinds of quantitative modeling approaches that we use regularly; 3) task manipulations that are differentially sensitive to PD-related deficits, allowing us to gain insights into normal and abnormal BG function; 4) complementary studies in non-human primates that act as critical, healthy controls; and 5) electrical microstimulation to test if and how the SNr can play a causal role in the decision process, even with the BG in a pathological state. The proposed project has three Specific Aims. Aim 1 is to identify single-unit correlates of evidence accumulation and commitment in SNr of PD patients and monkeys. Aim 2 is to identify single-unit correlates of speed-accuracy and choice-bias instructions in SNr of PD patients. Aim 3 is to use electrical microstimulation to test for a causal role of the SNr in oculomotor decisions. Together, these Aims will form a solid foundation for a long-term program to understand how the dynamic response properties of individual neurons in the SNr and BG contribute to flexible decision-making. The use of complementary monkey studies is particularly noteworthy, allowing us to firmly establish the quantitative rigor and reproducibility of the human work. We will then build on this solid foundation to better understand the neuronal basis of normal decision-making, decision-making deficits associated with BG malfunction, and potential causes of and remedies to the cognitive side effects associated with DBS.

PROJECT SUMMARY/ABSTRACT We propose to create a flexible, interdisciplinary, Jointly Sponsored NIH Predoctoral Training Program in the Neurosciences (JSPTPN) to prepare exceptional predoctoral students in their first two years of graduate school for productive careers in basic neuroscience research and related fields. Our proposed program trains students to work towards understanding the operation of the nervous system, including education and research opportunities to identify and ameliorate many dysfunctional and disease conditions such as stroke, epilepsy, traumatic brain injury, neurodegenerative disorders, and addiction. This program is based in the Neuroscience Graduate Group (NGG), an interdisciplinary PhD program that includes faculty from 22 Departments in 6 Schools of the University of Pennsylvania plus the affiliated Children's Hospital of Philadelphia. Graduate education in biomedical sciences at Penn is based on this kind of interdepartmental Graduate Group and is overseen by the Office of Biomedical Graduate Studies (BGS). BGS ensures effective curricular development, quality control, and uniform admission standards across all relevant Graduate Groups, including the NGG. Direct management of the proposed training program is done by a five-person Executive Committee that sets and reviews policy and selects trainees. Faculty membership is governed by: 1) expertise in a relevant field of study, 2) significant contribution to training, 3) commitment to the goals of the program, and 4) extramural funding to support trainees. Junior faculty receive extensive guidance on mentoring. Admission of students to the NGG is vetted by a BGS-wide admissions committee. Subsequent admission to the proposed JSPTPN will be decided by its Executive Committee. Support for each trainee will encompass their first 21 months in graduate school. The Training Program will consist of two years of coursework plus at least two lab rotations. All students will take a yearly course on the responsible conduct of scientific research and will participate in several newly developed training components that focus on instruction in the scientific method and statistical methodology. Students will also receive training through seminars, journal clubs, annual retreats, scientific meetings, paper and poster presentations, and social events that encourage interactions. Successful completion of a comprehensive Candidacy Examination marks the start of independent research toward the dissertation. Thesis research is conducted under the supervision of a faculty advisor and is monitored by a Thesis Committee and the NGG Academic Review Committee. The dissertation defense takes place when the thesis advisor and committee agree that the work is complete. Most graduates move on for advanced (postdoctoral) training and pursue an academic career. Based on the number of potential trainees, we request support for 12 predoctoral trainees/year for the next 5 years.

PROJECT SUMMARY Complex decisions require appropriate interactions within and across regions of a large brain network. It remains unclear how decision-related computations are implemented by such a network, even for the most extensively examined oculomotor decisions. We propose to explore the contributions of zona incerta (ZI) to the oculomotor decision process. The ZI has diverse connections to most areas of the cortex, thalamus, substantia nigra pars compacta and pars reticulata, and superior colliculus. Because these latter areas have all been shown or implicated to be involved in oculomotor decision process, the ZI is anatomically well-positioned to exert control over the decision process. There is, however, a knowledge gap in the computational roles of ZI for decision making and cognition in general, largely due to lack of neurophysiological data from awake, behaving animals. We propose to explore the roles of ZI in non-human primates performing oculomotor decision tasks, using a combination of behavioral, neurophysiological and computational techniques. Specifically, in Aim 1, we will perform single-unit recordings of ZI neurons in monkeys performing on a demanding visual perceptual decision task with reward manipulations and characterize the task-related modulation patterns of ZI activity using descriptive statistics. In Aim 2, we will relate decision-related ZI activity to the drift-diffusion framework to infer ZI's specific computational roles. These results are expected to advance our understanding of neuronal mechanisms underlying decision-making, particularly ZI's contributions to cognition, and, in the longer term, facilitate the development and refinement of clinical interventions that target the ZI.

PROJECT SUMMARY Neuroscience is producing increasingly complex data sets, including measures and manipulations of sub- cellular, cellular, and multi-cellular mechanisms operating over multiple timescales and in the context of different behaviors and task conditions. These data sets pose several fundamental challenges. First, for a given data set, what are the relevant spatial, temporal, and computational scales in which the underlying information-processing dynamics are best understood? Second, what are the best ways to design and select models to account for these dynamics, given the inevitably limited, noisy, and uneven spatial and temporal sampling used to collect the data? Third, what can increasingly complex data sets, collected under increasingly complex conditions, tells us about how the brain itself processes complex information? The goal of this project is to develop and disseminate new, theoretically grounded methods to help researchers to overcome these challenges. Our primary hypothesis is that resolving, modeling, and interpreting relevant information- processing dynamics from complex data sets depends critically on approaches that are built upon understanding the notion of complexity itself. A key insight driving this proposal is that definitions of complexity that come from different fields, and often with different interpretations, in fact have a common mathematical foundation. This common foundation implies that different approaches, from direct analyses of empirical data to model fitting, can extract statistical features related to computational complexity that can be compared directly to each other and interpreted in the context of ideal-observer benchmarks. Starting with this idea, we will pursue three specific aims: 1) establish a common theoretical foundation for analyzing both data and model complexity; 2) develop practical, complexity-based tools for data analysis and model selection; and 3) establish the usefulness of complexity-based metrics for understanding how the brain processes complex information. Together, these Aims provide new theoretical and practical tools for understanding how the brain integrates information across large temporal and spatial scales, using formal, universal definitions of complexity to facilitate the analysis and interpretation of complex neural and behavioral data sets.

PROJECT SUMMARY/ABSTRACT Certain memory deficits are a natural part of healthy aging but can be severe in numerous age-related brain disorders, including Alzheimer's Disease (AD). Given the devastating effects that these deficits can have on patients and their families, plus the fact that our population is aging, there is an urgent need to identify new approaches to combat age- and disease-related memory deficits. One promising potential approach uses vagus nerve stimulation (VNS), which is thought to enhance memory abilities via activation of the locus coeruleus (LC)-norepinephrine (NE) system. However, exactly how VNS affects the LC-NE system, and how those effects relate to memory enhancements, is not well understood, particularly in the primate brain. This knowledge gap is problematic because the LC is one of the first brain regions to degrade in AD and more generally tends to decline in size and neuron number with aging. Thus, establishing VNS as a viable treatment for age- and AD-related memory deficits requires first understanding its dependence on the intact and potentially degraded LC. The goal of this study is to establish a new understanding of how VNS affects memory via LC-NE activation in younger and older primate brains, using rhesus monkeys as a model system for probing the underlying mechanisms in detail. To achieve this goal, we pair VNS with neural recordings in LC and first focus on identifying patterns of VNS (e.g., with respect to timing, frequency, and duration of stimulation) that most effectively activate LC. We then use VNS combined with LC recordings in monkeys while they perform the Mnemonic Similarity task, a visual memory task demonstrated in humans to differentiate individuals with mild cognitive impairment and AD from other forms of decline. We pursue two specific Aims. For Aim one, we relate VNS-induced changes in LC neural activation patterns to memory performance in adolescent (age 5?10 yr) rhesus monkeys. For Aim two, we relate VNS-induced changes in neural activation patterns to memory performance in older (age 18?23 yr) rhesus monkeys. Together these Aims will provide mechanistic insights into how VNS can enhance memory via LC and cortical activation patterns in both young and old primate brains. The results will support future development of VNS as a treatment for memory deficits in age-related diseases including AD.

When decisions are made, which can be as simple as where to have lunch or as complicated as what career to pursue, time is often spent deliberating over uncertain evidence and possible outcomes. This deliberation process is central to behavioral and cognitive flexibility but requires overcoming a major challenge: determining when to stop deliberating and commit to a course of action. Many models that have been used to study human decision-making and to implement machine decision-making assume that decision commitment occurs when the accumulated evidence reaches a fixed value or bound. These “accumulate-to-bound” models, whose development last century helped formalize and improve decisions related to codebreaking, manufacturing, and other real-world applications, have also provided a useful starting point for understanding decision commitment in the brain. However, fixed bounds are most appropriate under fixed conditions, which are often used in laboratory experiments but rarely encountered in the real world. The goal of this study is to move beyond the “fixed bound” form of decision commitment and instead consider more flexible ways the brain uses to end deliberating and arrive at a decision. The research starts with a new, mathematically grounded theory that describes the advantages of using flexible forms of commitment under changing conditions. The goal of the project is to use this theory to design and interpret experiments that will provide a comprehensive new view of how human brains commit to decisions even when they are based on deliberations that occur during uncertain and changing conditions. This work will provide interdisciplinary training at the interface of mathematics, cognitive science, psychology, and neuroscience for undergraduate and graduate students from diverse backgrounds. The team will use research-related activities to encourage the participation of underrepresented groups in science. Also, the team will develop and disseminate resources, including novel datasets and analytic tools, that will benefit research and education in how the brain makes decisions. Finally, the team will increase public awareness of computational neuroscience and address the urgent need to increase scientific literacy and understanding of how science can benefit society. This will be accomplished via public lectures, contributions to the program “Engines of Our Ingenuity” broadcast by National Public Radio stations nationwide, Brain Awareness Week activities, and contributions to a website that explains brain research to non-specialists.<br/> <br/>Deliberative decisions free the brain from the immediacy of reflexive processing but pose a critical challenge: how does the brain decide when to stop deliberating and commit to a course of action? Our understanding of this commitment process has been dominated by a computational framework that assumes decisions are terminated once accumulated evidence reaches a predefined level or bound. These “accumulate-to-bound” models have close ties to normative theory and can explain a range of behavioral and neural findings. However, they are optimal only under the highly restrictive conditions used in many decision studies, in which the informativeness, rate of acquisition, and other features of the evidence are stable and known in advance. It is not known how the brain balances decision deliberation and commitment more generally, when temporally extended decisions must contend with our dynamic and uncertain world. The goal is to advance our understanding of the decision rules used by the brain under these conditions. The PIs start with a novel theoretical foundation that includes flexible decision bounds that are not predefined but instead can be updated while the decision is being formed to optimize performance even under changing conditions. They use this framework to guide the design and analysis of behavioral studies in humans and combine behavioral and neurophysiological studies in non-human primates, which they use to test their primary hypothesis that the primate brain uses dynamic, adaptive rules to support rational decision-making in changing and uncertain environments.<br/><br/>This project is co-funded by the Division of Mathematical Sciences (DMS) within the Mathematical and Physical Sciences Directorate (MPS), Division of Information and Intelligent Systems (IIS) in the Directorate of Computer and Information Science and Engineering (CISE), and Division of Behavioral and Cognitive Sciences (BCS) within the Directorate for Social, Behavioral, and Economic Sciences (SBE).<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.