Xiao-Jing Wang - US grants

9409202 Wang Several different types of rhythmic oscillations can be recorded from the surface of the brain during different behaviors, particularly during different stages of sleep. Oscillations of about 7-14 Hz occur during early stages of sleep and slower oscillations of 0.5-5 Hz occur during later stages of sleep. It is known that the 7-14 Hz oscillations arises from the thalamus and there is some evidence that the slower oscillation may also be a thalamic oscillation. However, it is not known exactly how the neurons of the thalamus produce these oscillations. In this study from a young physicist, mathematical and computational modelling studies of these rhythmic oscillations will be carried out. The role of connections between the thalamus and cortex and the role of properties of individual thalamic nerve cells in the production of these oscillations will be examined. These theoretical studies should provide insights into the original of these oscillations as well as those that occur in the brain during sensory perception and those associated with other behaviors.

This three-year award will support U.S.-France cooperative research in biophysical modeling between Xiao-Jing Wang of the University of Pittsburgh and David Hansel of the Ecole Polytechnique, Paliseau, France. The objective of their research is to undertake biophysical modeling of 40 Hz oscillations in neurons and neural systems. They will address: rhythmogenesis; network mechanisms underlying synchronization and large variability; and the intermittent nature of 40 Hz oscillations. Rhythmic activity in mammalian brains at the 30 to 50 Hz oscillation rate has been shown to coincide with increased focal attention and may have important functional roles in perception and information processing. The proposed collaboration focuses on these physiological and cognitive issues by combining unique abilities of the investigators. The U.S. investigator brings to this collaboration expertise in biophysical modeling. This is complemented by the French investigator's expertise in the dynamics of large neural networks. Their joint effort will advance our knowledge of the physiological and biophysical mechanisms underlying neuronal cooperative rhythmically and its role in the organization of mammalian brain activity.

We propose to undertake computational studies of rhythmic oscillations during quiet sleep in the thalamus and the CA1 hippocampus. Although the thalamic 7-14 Hz spindles and 0.5-5 Hz delta waves, and the hippocampal sharp wave (SPWs) with 200 Hz "ripples," occur as rhythmic and stereotyped events, they are also highly complex and display strongly intermittent characteristics (sparse firing of single cells, sparse populations firing patterns). We hypothesize that in both cases, the recurrent synaptic inhibition is central to the genesis and synchronization of the oscillatory phenomena, as well as to the spatio-temporal intermittency. Specific projects include the following. 1. Study in computer simulations the reciprocal connection between inhibitory thalamic reticular (RE) cells and excitatory thalamocortical (TC) relay cells, as a possible mechanism to synchronize the thalamic network oscillations. 2. Formulate and test by modeling the hypothesis that transitions from wakefulness to the spindle sleep state, then to the delta wave sleep state, can be induced by a gradual hyperpolarization of the TC cells via neuromodulatory systems. 3. During spindles the TC cells remain subthreshold for most of the population rhythmic cycles, and fire rebound bursts only intermittently. Our working hypothesis is that this sparse firing phenomenon is produced by an interplay between the synaptic inhibition of the RE origin and an intrinsic property (the hyperpolarization-activated cation (h-type) current) of the TC cells. The modeling is closely related to analysis of intracellular recording data from spindling thalamic slices, provided by Dr. D. A. McCormick. 4. We shall perform statistical analyses of the experimental data provided by Dr. G. Buzsaki's laboratory on hippocampal SPWs, in order to quantitatively characterize the intermittent aspects as well as the oscillatory aspects of this cooperative phenomenon, in CA1 hippocampus. 5. We shall undertake a biophysical modeling of the CA1 network, in order to assess the following ideas. (a) The fast (200 Hz) SPW ripple in CA1 hippocampus are generated by rhythmic firing of interneurons in the strata pyramidale and oriens, and the population synchrony is brought about by synaptic connections between these interneurons. (b) The pyramidal cell population can be synchronized very rapidly during 200 Hz ripples by the divergent projection from interneurons, and the recruiting process as well as the extremely sparse firing patterns in the CA1 network are regulated by the recurrent synaptic inhibitory mechanisms. The proposed research program is designed to elucidate mechanisms underlying normal population oscillations in the thalamus and hippocampus, as well as the pathological petit mal (absence) seizure in the thalamus and focal epilepsy in the hippocampus. St the theoretical level, this work could lead to a general framework to describe ryhthmic, yet strongly intermittent, neuronal cooperative firing patterns.

9419247 Buzsaki The behavior of organisms is controlled by electrical signaling in large populations of brain cells. One of the great technical challenges for brain scientists is to describe the electrical activity of neuronal populations in a way that is related to behavior. With this award, a team comprised of a neuroscientist, a mathematician and an engineer, and headed by Dr. Gyorgy Buzsaki, will construct a system for monitoring the signals produced by large numbers of brain cells recorded from animals while they are engaged in bouts of behavior. The team will test their methods by recording, analyzing and displaying the signals of cells within the hippocampus, a structure believed to be essential for certain types of learning. This work should have broad impact on the methods used in future research by both experimental and theoretical neuroscientists in the attempt to understand the neural control of behavior.

IBN 97-33006 WANG. Working memory is an active form of short-term memory. It is indispensable for any cognitive process where information has to be maintained with attentive effort for a few seconds, and manipulated "on-line", before it is erased and new information is loaded. Examples are dialing a telephone number, planning and executing a sequence of motor movements, reading, and thinking. Experiments on behaving animals have started to uncover the neuronal basis of working memory in the prefrontal cortex. Dr. Wang will use theoretical approaches, in complement with laboratory experiments, to investigate how the working memory function can be understood in terms of the physiology of single neurons/synapses, the cortical circuit architecture, and emergent collective network behavior. In particular, possible neuronal mechanisms will be studied by computer model simulations for sustained neuronal activity (in seconds) and its selectivity to memory items, including (a) reverberating activity across the cortical network, (b) slow cellular and synaptic dynamics, and (c) the role of recurrent synaptic inhibition. Results from this research will help to understand the functioning of normal working memory, its neuromodulation, as well as its malfunctions.

The goal of this research program is to help solve the long-standing problem of the neural mechanisms of working memory, at the cellular level. To this end, we will undertake a biophysical model of prefrontal cortical circuits that is based on cortical physiology and that reproduces neural data from the behaving monkey. Modeling projects will be combined with analysis of experimental data, and will be carried out in parallel with ongoing experiments in the laboratories of PI's collaborators. Ultimately, progress in this area will contribute to our understanding of the cellular substrate of cognitive deficits in schizophrenia. The proposal is focused on three major issues: (a) neural mechanisms of persistent activity and its stimulus selectivity; (b) robustness of spatial working memory storage in a recurrent network; (c) the role of working memory in selective attention. Our research projects are designed to test the following hypotheses. (1) Generation of persistent activity. The generation of a stable persistent activity by reverberation, as a network phenomenon, requires not only a high synaptic efficacy of recurrent connections, but also specific temporal dynamics of synaptic transmission. NMDA receptors at the recurrent synapses play a critical role. Synaptically-generated persistent activity depends sensitively on the intrinsic ionic mechanisms of single cells, and can be controlled by their neuromodulations. (2) Robustness of spatial working memory. A continuum of `bump attractors' (spatially localized activity patterns) may not be robust in the presence of heterogeneity. This problem can be solved by adaptive mechanisms, such as homeostatic synaptic scaling, which homogenize a working memory network and thereby endow the network with robust memory of spatial location as an analog quantity. (3) Working memory and selective attention. The prefrontal cortex is capable of a `biased competition' mechanism for selective attention: persistent activity provides a sustained, location- or feature-specific, top- down bias signal to the sensory cortical areas; whereas its intrinsic inhibitory circuit endows the prefrontal cortex with a `winner-take-all' competition for filtering out behaviorally irrelevant stimuli (distractors).

This IGERT award establishes a multidisciplinary graduate training program of education and research in the Physics and Chemistry of Biological Systems at Brandeis University. The program will ensure that a) biologists can work effectively with rigorous quantitative methods, new technologies and models, b) physicists and chemists obtain hands-on experience with biological systems and methods, and c) students with a variety of backgrounds learn multiple scientific languages so that they can communicate and work with investigators with skill sets and training different from their own. Graduate students will be carrying out state-of-the-art research in a wide variety of topics including protein complexes, signal transduction and transcription, neuronal networks, biological oscillators, and cognitive processes and behavior. Trainees thesis research will involve quantitative approaches to a biological problem. The educational plan includes laboratory rotations and courses that include modeling and quantitative methods; several new courses will be developed specifically for this program. Trainees will also participate in a semester-long course on the responsible conduct of research, invite and host outside seminar speakers, participate in journal clubs and serve as teaching assistants. To enhance the broader impacts of the grant, trainees will receive formal training in presenting science to lay audiences at two area science museums, and/or through several campus-based educational outreach programs. The IGERT program will provide a free Saturday morning lecture series for local high school teachers, students, and the interested public. Undergraduate minority students will be acquainted with research opportunities at Brandeis. IGERT is an NSF-wide program intended to meet the challenges of educating U.S. Ph.D. scientists and engineers with the interdisciplinary background, deep knowledge in a chosen discipline, and the technical, professional, and personal skills needed for the career demands of the future. The program is intended to catalyze a cultural change in graduate education by establishing innovative new models for graduate education and training in a fertile environment for collaborative research that transcends traditional disciplinary boundaries.

DESCRIPTION (provided by applicant): The long-term goal of our research is to elucidate the cellular and circuit mechanisms of decision making and its executive control. Flexible behavior in humans and animals relies on the brain's ability to accumulate information over time, deliberate about choice options, inhibit prepotent responses, and select purposeful actions. The frontal and parietal cortices are known to be critical to decision making, but the operation of this complex cognitive network is still poorly understood at the mechanistic level. We propose that accumulation of sensory information or planned action in decision making is instantiated by neural activity of strongly recurrent circuits that can be conceptualized as attractor networks. Moreover, the time integration process is not fixed, but can be readily adjusted to optimize behavior. We will test this hypothesis using neurophysiologically-based spiking network models, in close collaboration with experimentalists. Our models will be quantitatively tested against behavioral and physiological data (single-cell and local field potential) collected from behaving monkeys in oculomotor decision tasks. Model predictions will be checked experimentally. The structure of the oculomotor system is similar in humans and monkeys, therefore the knowledge gained in our work will be likely to contribute to our understanding of human decision making. This application has four Specific Aims. In Aim 1 we will analyze stochastic, yet correlated, reverberatory neural dynamics in a cortical circuit that underlies the slow time integration of sensory evidence and the variability of reaction times in perceptual decisions. Aim 2 will investigate the interplay between sensory and motor processes, and inhibitory control of action, in a parieto-frontal circuit. In Aim 3, we will examine how decision making depends on the number of choice alternatives and their similarity, and how analog decision computation leads to the readout of a categorical choice, in a large-scale circuit model encompassing cortex, basal ganglia, and superior colliculus. Aim 4 will be focused on optimality and flexibility of decision making instantiated by reward-dependent synaptic plasticity and the concerted action of several executive control mechanisms. Taken together, the proposed research will advance, for the first time, a detailed circuit model of sensory-motor decisions in the parieto-fronto-basal ganglia network.

DESCRIPTION (provided by applicant): The proposed research will investigate the cortical circuit mechanisms of visual categorization, the process of learning to classify visual stimuli into groups of objects that are equivalent in terms of their behavioral significance. Previous work revealed that individual neurons in the prefrontal cortex (PFC) and the lateral interparietal (LIP) area encode the category membership of stimuli during visual categorization tasks. Built on these findings, we will combine biophysically-realistic neural modeling and single-unit recording from behaving monkeys, to elucidate the mechanistic questions concerning category learning and category-based behavior. First, we will develop a spiking network model of the reciprocally interacting sensory circuit and parieto-prefrontal circuit, to elucidate the cortical basis of key neural computations underlying a delayed match-to-category (DMC) task (do the attributes of a sample and a test stimulus belong to the same category?) versus delayed match-to-sample (DMS) task (are the attributes of the sample and test identical?). Second, we will examine how categories are learnt through discrete training stages, from identity-based match-to-sample to fine category discrimination with stimuli near an arbitrary category boundary. This will be done using models endowed with reward-dependent synaptic learning, monkey behavioral assessment and single-unit recordings from monkeys at different stages of training. Third, we will examine task switching, on a trial-by-trial basis, between the identity-based DMS versus category-based DMC, to clarify the differential neural coding of stimulus identity and category, as well as task-rule representation in visual categorization, in the LIP and PFC. Together, these studies will shed important insights and yield a computational framework for understanding how the brain encodes the learned significance, or category membership, of visual stimuli. Intellectual Merits: Without the ability to classify or categorize stimuli, it would be difficult to perceive and comprehend the world; concepts and language would seem impossible. Therefore, elucidating the neural mechanisms of categorization is a crucial step in our quest for a neurobiological understanding of higher cognition. While much is known about how the brain processes sensory attributes (such as orientation and direction of motion), much less is known about how the brain achieves more abstract knowledge acquisition such as how attributes are grouped into categories through learning, and what are the computational advantages of category-based behavior. A mechanistic understanding of these issues, at the neural circuit level, necessitates a concerted computational and experimental effort. Thus, the results of our proposed research program are likely to represent a significant advance in this area, with broad implications. Our highly promising preliminary computational, behavioral and neuronal studies have validated our approach, and have ensured that all aspects of this project have a high likelihood of success. Broader Impacts and Integration of Education and Research Activities: Both PIs are actively involved with teaching. Dr. Wang teaches for the Interdepartmental Neuroscience graduate program and for the new Physics/Engineering/Biology (PEB) integrated graduate program at Yale. Dr Freedman is preparing new workshop course called Methods in neuronal data analysis to both graduate and undergraduate students. Lessons and exercises will revolve around computational and statistical analysis of real data collected in his laboratory during the experiments proposed here. Dr Wang is a member of the Oversight Committee for Description Standards in Neural Network Modeling, International Neuroinformatics Coordinating Facility (INCF). Models developed in his lab will be made available to the computational community. Broaden Participation of under-represented groups-Both PI have a strong track record of recruiting and mentoring students from under-represented groups. At this time, Dr. Wang has a female graduate student and a female postdoctoral fellow (Dr Tatiana Engel who will spearhead the proposed research in his laboratory). Over the past two years four graduate students in Dr. Freedman's laboratory are from underrepresented groups (one is African American and the others are women). Outreach to general public- Both PIs have been active in outreach. Dr Wang has given lectures on the brain at the Hopkins School in New Haven; Dr Freedman has been involved in the Science and Technology Outreach and Mentoring Program, The Young Scientist Training Program, and the student science fair at Kenwood Academy public school, in Chicago. Our work focuses on the brain mechanisms of learning and memory, a topic which is both accessible and of great interest to the general public. For our outreach and mentorship efforts, we will use data generated during the proposed work to produce educational demonstrations of how the brain learns and processes visual information that will be accessible to a lay audience. These demonstrations will be used in K-12 classroom presentations and also available online.

DESCRIPTION (provided by applicant): This proposal requests partial support for a new Gordon Research Conference on Neurobiology of Cognition, to be held in Waterville, NH. The meeting series is planned to take place biennially;this proposal requests funds for partial support of the first meeting in 2010. The impetus for this meeting is the recent developments in Neuroscience that have begun to uncover the brain circuit basis of higher cognitive functions at the level of systems analysis, computational principles, and neurophysiological mechanisms. The broad and long-term goal of the conference is to provide an informal forum with the highest quality for investigators from three fields (behavioral and circuit neurophysiology, cognitive neuroscience, and computational/theoretical neurobiology) to exchange ideas and data, and to foster collaboration and communication. The specific aims of this meeting will be to convene about 40 speakers and discussion leaders with a total of up to 200 participants for a four day/five night conference in a relatively isolated setting, with a format specially designed to foster informal discussions. The tentative program has eight sessions that address current issues on the brain mechanisms of working memory, decision making, selective attention, task rule and task switching, memory networks, large-scale brain circuit dynamics, the neural basis of fMRI signals, and circuit basis of cognitive deficits associated with mental illness. In addition, short talks and two poster sessions will permit all participants to contribute to these topics. The significance of this application is that our new and highly cross-disciplinary field urgently needs such a meeting, which will define and propel research in this important area of modern science. This meeting is highly relevant to healthcare, as progress in our understanding of the brain mechanisms of cognitive functions will directly affect our ability to treat cognitive deficits associated with mental disorders such as schizophrenia and ADHD. PUBLIC HEALTH RELEVANCE: This proposal is highly relevant to healthcare, as progress in our understanding of the brain mechanisms of cognitive functions will directly translate into novel and better treatments of cognitive deficits associated with mental disorders such as dysfunctions of working memory and decision making in schizophrenia, attention abnormalities in ADHD, and impairments of social cognition with autism.

DESCRIPTION (provided by applicant): Given the same sensory information, we generate different responses in different situations, depending on our goal, task rule, etc. This flexibilit is a hallmark of cognition and involves gated routing of information flow in the brain circuitry, the underlying mechanism remains largely mysterious. The overarching objective of the proposed research is to elucidate computational principles and circuit basis of gated computation and flexible sensori-motor mapping in decision-making. I propose that gating is accomplished by four complementary mechanisms: (a) tunable dendritic inhibition by a subclass of GABAergic neurons for input gating, (b) pathway-specific excitation-inhibition balance for processing in discrete stages, (c) top-down signaling from rule representation for network selection, (d) a STOP process for inhibitory control of automatic response. I will rigorously investigate these mechanisms in biologically based circuit models, in collaboration with experimentalists who are carrying out single-neuron physiological experiments in which monkeys perform flexible visuo-motor tasks. In a paradigmatic task of controlled action, the appropriate response to a sensory target is to either shift the gaze toward it (pro-saccade) or away from it (anti-saccade) depending on task context. In colored-target tasks, a perceptual decision about an ambiguous stimulus or a probabilistic inference based on sensory cues must be made, before the subject knows when and how the decision will be used to guide action selection. Our model will be tested by quantitatively reproducing single-neuron activity data as well as behavioral performance, and uncovering the underlying circuit mechanisms that will be testable in new experiments. In Aim 1, I will examine circuit mechanisms for gating, including input-specific dendritic inhibition and the idea of pathway-specific excitation-inhibition balance. Aim 2 will be devoted to rule-based action selection, by developing a detailed circuit model of the pro-/anti-saccade task. In Aim 3, I will examine gated neural dynamics when perceptual decision and action selection are temporally separated. We will build spiking network models for versions of the random dot motion direction discrimination task and weather prediction task. The pro-/anti-saccade and colored-target visuo-motor tasks all depend on flexible routing of information from a sensory decision circuit to an action selection circuit, which confers a strong cohesiveness to this application. This research program will shed insights into complex dynamics of neurons endowed with mixed selectivity that underlie adaptive coding in flexible behavior.

SUMMARY Our Lung, Head and Neck (LHN) Cancer Training Program (LHNTP), funded since 2013, is a multi-disciplinary translational program with a mission to train the next generation of researchers and physician scientists in LHN cancers. Although our T32 program is less than five years old and the majority of our trainees are still in training, the LHNTP value-added to our trainee's development has become obvious. This competitive renewal will continue our mission and further improve and strengthen our program based on the advances in research and clinical practice in the past few years. Our changes include training faculty, curriculum and activities that better incorporate expertise, innovative research and clinical advances. These changes will accelerate scientific discoveries, further enrich training environment and enhance career development. Based on the NCI's postdoctoral/predoctoral ratio requirement, we will continue to appoint 3 new postdoctoral fellows and 1 new predoctoral student per award year. Training faculty are selected from members of UCCC and graduate faculty based on their scientific/clinical expertise, track records of mentorship and active funding to achieve training of translational LHN cancer research. Our faculty members are from both basic science and clinical departments. In addition to laboratory training, we have designed mandatory didactic coursework for all trainees and clinical coursework for non-MD trainees to enhance the trainee's scientific background and the translational aspect of our program. Our specific goal is that program trainees will acquire the professional skills for productive academic careers in basic and translational LHN cancer research. We will vigorously select trainees from external and internal trainee pools based on their academic records and commitment to LHN cancer research.

Categorization is the brain's ability to recognize the meaning of objects and events in our environment, and is an essential cognitive process underlying decision making. Categorical decisions are often flexible, and depend on the demands on the task at hand. The current project aims to understand the brain mechanisms which underlie flexible categorical decision making, as well as computational algorithms for making such decisions my artificially intelligent systems. Experiments will record from ensembles of cortical neurons during flexible categorization tasks. Computational modeling work will train recurrent neural networks to perform the same flexible categorization tasks used in the experiments, with parameters of the model inspired by the experimental data. This will result in a greater understanding of the neural mechanisms underlying categorization and decision making, as well as improvements in computational algorithms for flexible categorization by artificially intelligent systems. The broader impacts of the project include substantial training opportunities for undergraduates, Ph.D. students, and postdoctoral researchers in both experimental and computational approaches to flexible decision making. The project will also generate new experimental data and computational tools that will be shared with the broader scientific community.

This project combines multi-channel neurophysiological recordings and neural circuit modeling to investigate the neural circuit mechanisms of flexibility and generalization in visual categorization. The project leverages a collaboration by the researchers that has proven fruitful in our previous joint research on category learning. The focus of the present project is on flexible task switching between discrimination and categorization, and between categorization rules, in the behavioral, experimental, and computational work. The task paradigms will also directly test the 'exemplar model' of categorization from cognitive psychology, linking behavioral models to neural circuit processes. The project will develop a novel modeling framework, based on training recurrent neural networks to learn to perform multiple tasks. This approach offers a potentially powerful data analysis tool and conceptualization of neural circuit computation in terms of neural population trajectories in a high-dimensional state space, and this perspective is urgently needed to analyze simultaneous recording from many single neurons during performance of complex cognitive tasks, a major thread of modern Data-Intensive Neuroscience and Cognitive Science.

PROGRAM SUMMARY The Training Program in Computational Neuroscience (TPCN) will support integrated undergraduate and graduate training in computational neuroscience at New York University. The program will be hosted by the Center for Neural Science (CNS), with participation of faculty in the Departments of Psychology, Mathematics, and Computer Science, and the Institute of Neuroscience at the School of Medicine. The TPCN will fit well with NYU's unique strengths and recent developments: (1) NYU is one of a few universities with a critical mass of computational neuroscientists. NYU has had a Sloan-Swartz Center for Theoretical Neuroscience since 1994. In the past three years alone, NYU has hired three computational neuroscientists. (2) CNS established an undergraduate major in neuroscience as early as 1992, and thus has a long track record in undergraduate education, it now has 136 students in the current academic year. (3) Recent faculty hiring in CNS, Psychology, and the School of Medicine has greatly expanded our teaching and research capabilities in the neuroscience of cognitive functions and their impairments associated with mental disorders. (3) As NYU is undertaking a merge of two historically separated neuroscience graduate programs (at CNS and the School of Medicine), this training grant will ensure that computational modeling, which has become indispensible in neuroscience, will be front-and-center in the integrated graduate program. (4) NYU is a major center of Artificial Intelligence and Data Science, with close links to Facebook's AI Center and the Simons Center for Data Analysis. Our training faculty together with these connections will give our students ample opportunities to acquire machine learning techniques for data analysis and learn about brain-like AI algorithms. The proposed training program will support coherent undergraduate and graduate training in computational neuroscience at NYU. It will have several unique features: (1) Innovative mentorship methods: For example, (a) graduate trainees will mentor undergraduate trainees, (b) faculty will explicitly discuss human factors in academic practice; (c) there will be post-mortems after seminars by outside speakers. (2) Computational psychiatry: We propose new courses and research opportunities that are designed specifically to link cognitive function and the neurobiology of neural circuits. We propose innovative education in the nascent field of Computational Psychiatry, to bring theory and circuit modeling to clinical research in mental health. (3) Broad preparation: We aim to prepare trainees for jobs not only in academia, but also in medical and industry research. To achieve this, we will utilize our strength in machine learning and data science to broaden computational neuroscience training. The Program Directors have complementary strengths and will have complementary roles in the program. Wang will supervise graduate trainees and focus on training in mechanistic/circuit-level side of computational neuroscience as well as computational psychiatry. Ma will supervise undergraduate trainees and focus on the computational/behavioral side.

The Training Program in Computational Neuroscience (TPCN) will support integrated undergraduate and graduate training in computational neuroscience at New York University. The program will be hosted by the Center for Neural Science (CNS), with participation of faculty in the Departments of Psychology, Mathematics, and Computer Science, and the Institute of Neuroscience at the School of Medicine. The TPCN will fit well with NYU?s unique strengths and recent developments: (1) NYU is one of a few universities with a critical mass of computational neuroscientists. NYU has had a Sloan-Swartz Center for Theoretical Neuroscience since 1994. In the past three years alone, NYU has hired three computational neuroscientists. (2) CNS established an undergraduate major in neuroscience as early as 1992, and thus has a long track record in undergraduate education, it now has 136 students in the current academic year. (3) Recent faculty hiring in CNS, Psychology, and the School of Medicine has greatly expanded our teaching and research capabilities in the neuroscience of cognitive functions and their impairments associated with mental disorders. (3) As NYU is undertaking a merge of two historically separated neuroscience graduate programs (at CNS and the School of Medicine), this training grant will ensure that computational modeling, which has become indispensible in neuroscience, will be front-and-center in the integrated graduate program. (4) NYU is a major center of Artificial Intelligence and Data Science, with close links to Facebook?s AI Center and the Simons Center for Data Analysis. Our training faculty together with these connections will give our students ample opportunities to acquire machine learning techniques for data analysis and learn about brain-like AI algorithms. The proposed training program will support coherent undergraduate and graduate training in computational neuroscience at NYU. It will have several unique features: (1) Innovative mentorship methods: For example, (a) graduate trainees will mentor undergraduate trainees, (b) faculty will explicitly discuss human factors in academic practice; (c) there will be post-mortems after seminars by outside speakers. (2) Computational psychiatry: We propose new courses and research opportunities that are designed specifically to link cognitive function and the neurobiology of neural circuits. We propose innovative education in the nascent field of Computational Psychiatry, to bring theory and circuit modeling to clinical research in mental health. (3) Broad preparation: We aim to prepare trainees for jobs not only in academia, but also in medical and industry research. To achieve this, we will utilize our strength in machine learning and data science to broaden computational neuroscience training. The Program Directors have complementary strengths and will have complementary roles in the program. Wang will supervise graduate trainees and focus on training in mechanistic/circuit-level side of computational neuroscience as well as computational psychiatry. Ma will supervise undergraduate trainees and focus on the computational/behavioral side.

Project Summary/Abstract It is crucial to better understand immune evasion mechanisms in head and neck cancers in order to enhance their susceptibility to immunotherapy. About 90% of head and neck cancers are squamous cell carcinomas (HNSCC). Recurrent or metastatic HNSCCs are being treated with checkpoint blockade immunotherapy targeting programmed death 1 (PD-1), a co-inhibitory receptor on T cells. However, only a subset of HNSCC patients responded to such anti-PD-1 therapy (10-20%). Thus, there is an urgent need to elucidate mechanisms underlying therapy unresponsiveness to single blockade of PD-1. Apart from PD-1, T cells express other co-inhibitory receptors that can also induce immunosuppressive phenotypes, such as lymphocyte activation gene-3 (LAG-3). However, the role of such receptors remains poorly defined in immune evasion of HNSCCs (e.g., LAG-3). Our preliminary data show that HNSCC patients exhibit a highly heterogeneous pattern of tumor infiltrating lymphocytes (TILs); however, the molecular drivers underlying such differential immune phenotypes remain largely unknown. Completion of our proposed studies may generate novel insight into the mechanisms that determine the success or failure of checkpoint blockade immunotherapy. We expect our studies to delineate the comprehensive immune landscape of HNSCCs in human patients. The knowledge gained would provide critical steps toward improving immunotherapy by targeting additional co-inhibitory receptors with a more rational design and overcoming the dysfunctional progression of TILs. Our long-term goal is to elucidate immune evasion mechanism and improve therapeutic strategies of HNSCCs. HNSCC development often associates with oncogenic mutations, such as heterozygous loss of Smad4, gain-of-function mutations of PIK3CA or loss-of-function mutations of Notch1. It remains largely unknown how HNSCCs evade immune recognition. To address this question, we performed studies with a transplanted SCC model caused by combining KrasG12D mutation and Smad4 loss (termed KRS-SCC). We found that KRS-SCC tumors completely escaped T cell-mediated anti-tumor responses, manifested with exhausted CD8 and CD4 TILs co-expressing PD-1 and LAG-3. Consistently, dual inhibition of PD-1 and LAG-3 suppressed the growth of KRS-SCCs. We propose to employ our unique mouse models and human patient samples to further elucidate immune evasion mechanisms of HNSCCs. Our proposed studies may substantially advance our understanding in mechanisms that underlie therapy failure of single PD-1 blockade. Relevance to public health. We envision that our studies will provide substantial advances in understanding the mechanisms that underlie therapy failure of PD-1 blockade in HNSCCs. We anticipate that our proposed studies will reveal the connection between intrinsic characteristics of tumor cells and immune signature of TILs. These studies not only address fundamental questions in cancer immunology but also lay a scientific foundation for developing novel therapy of HNSCCs in the targeted patient populations with a more rationalized design.

Humans have remarkable ability to acquire a rich repertoire of concepts stored in semantic memory, which can be deplored in ?learning to lean? that facilitates rapid new learning or even one-shot learning. Nonhuman animals are also endowed with ?learning to learn?; on the other hand, there is evidence that primates but not rodents possess this mental capability. The underlying brain mechanisms are completely unknown and represent a widely open question at the frontier of Neuroscience today. The present computational project, in conjecture with the experimental projects of this application, has the primary goal of elucidating the neural circuit basis of rapid learning. Progress is this research direction will represent a major step forward in bridging nonhuman primate and human neuroscientific understanding of higher cognition. Our modeling approach integrates large-scale circuit modeling of primate brain based on measured mesoscopic connectivity and training recurrent neural networks to perform cognitive tasks. Together with the proposed experiments in this application, we will develop tools to describe and elucidate neural population dynamics in single trials, which is crucial for neurophysiological analysis of rapid learning (even one-shot learning) without averaging over many repetitive trials in a steady state situation. The main hypothesis is that learning to learn depends on the formation of an abstraction of sensori-motor representations, such as that of task structure or ?schema?, which is manifested in a shift of neural representation from the hippocampus to the prefrontal cortex; this conceptual representation enables rapid future learning by efficient changes of connection weights within a low dimensional subspace. This hypothesis will be tested using the state space analysis and dimensionality reduction of the recurrent neural network dynamics. Aim 1 will to be to advance a mesoscopic connectivity-based multi-regional neural network model for rapid learning in categorization, flexible sensori-motor mapping and object-location association. The model will be systematically tested and validated by comparison with behavioral data from category learning and associative learning tasks. Aim 2 will be to uncover neural population dynamics and circuit mechanism of rapid learning in single trials, using state-space analysis and identifying a subspace of neural population dynamics as well as a subspace of connection weights that may correspond to the formation of semantic memory. Aim 3 will be to dissect the differential roles of HPC, PFC, PPC and their dynamical interactions underlying rapid learning, by simulating ?area lesion? at different time points of a learning process. A spiking network version of our model will enable us to uncover inter-areal dynamical interactions and their role in rapid learning. Advances in this area would not only be important for the Neuroscience of learning and memory, but also have potentially major implications for the future development of AI, and for shedding insights into the brain mechanism of deficits in semantic memory, which is at the core of fronto-temporal dementia.

Project Summary The goal of this grant renewal is to develop theory and large-scale circuit modeling of the primate cortex with many interconnected areas, for distributed working memory and flexible decision processes as well as its dopamine modulation. This research program is designed to meet the challenge of understanding cognition beyond local circuits towards the complex global brain. The proposed work is now feasible thanks to recently available mesoscopic directed- and weighted data for inter-areal connections of macaque cortex. We have already published several papers on the development of a large-scale, multi-regional dynamical cortical circuit model of macaque monkey, including a population rate model endowed with a laminar cortical structure and a spiking network model. Our computational work will be undertaken in collaborations with five experimental labs working on macaque monkeys (Earl Miller (MIT), John Duncan (Oxford University, UK), Stefan Everling (Western University, Ontario, Canada), Henry Kennedy (ISERM, Lyon, France) and Karl Zilles (Jülich University, Germany)). Specific Aim 1 will be to build a large-scale cortical circuit model of macaque monkey for distributed working memory. Hypothesis: distributed self-sustained activity patterns underlying working memory depend on a combination of the mesoscopic inter-areal connection properties and gradients of circuit properties across a brain?s hierarchy. Specific Aim 2 will be to investigate high-dimensional dynamics of persistent activity underlying a high degree of temporal variations. Hypothesis: NMDA/AMPA receptor ratio is higher at top-down projections than bottom-up ones, and there is a macroscopic gradient of short-term plasticity. The two combined contribute to complex spatiotemporal mnemonic neural population activity that is better described by trajectories in a high-dimensional state space. Specific Aim 3 will be to expand the model to simulate decision- making and rule-based flexible sensorimotor behavior. Hypothesis: the same model endowed with different representations (spatial location, object features such as color and motion direction, rule encoding, respectively) in different cortical areas can be used to simulate rule-based flexible decision tasks as well as working memory tasks. Specific Aim 4 will be to study of neuromodulation and NMDA deficits of this large-scale primate cortical model. Hypothesis: dopamine modulation displays a macroscopic gradient along the cortical hierarchy, and impairment of NMDA receptors preferentially affects top-down rather than bottom-up signaling in the global brain. Taken together, the proposed research will shed fundamental insights into complex dynamics and cognitive functions in the global brain. It will also yield a pioneering and powerful computational platform for basic research on large-scale brain systems of the primates, as well as cross-level circuit mechanistic studies of psychiatric disorders like Schizophrenia.

A key goal of the NIMH is to define the mechanisms of complex behaviors. This necessitates an approach that spans biological scales, but we have been lacking a single theoretical framework that links neurotransmitter receptor function, large-scale patterns of brain activity and cognition. Working memory is a key building block of cognition, but our understanding of why working memory emerges in sorne parts of cortex, but not others, is limited. The macaque is an important model for studying higher cognitive function with relevance to psychiatric disease, due to their intelligent behavior and relatively similar brains. We propose the creation of a high-resolution 3D atlas of the macaque brain, and use it to create data-driven neural circuit models that will give us key insights into the relationship between regional variations in receptor densities and the emergence of distributed working memory. The atlas will enable quantification of the regional and laminar distribution of 14 types of receptors (receptor fingerprints) and cell densities across the entire macaque cortex. This will allow for interrogation of receptor fingerprints and cell densities with submillimeler precision. It will be accompanied by a novel parcellation scheme based on the gradients of cyto- and receptor-architecture across cortex. This atlas will be registered lo the NIMH Macaque Template for easy integration with macaque /MRI data. We will identify the principal gradients that underlie the distribution of receptors across cortex and compare these to hierarchies of sensory systems and cognitive networks using open-access laminar tract-tracing and /MRI data. This approach will uncover the receptor signature underlying distinct functional hierarchies. We will use the idea of bifurcations from mathematics to interrogate how changes lo the density of receptors across cortex may lead to the emergence of working-memory like persistent activity in particular regions of cortex. Crucially, we will allow the experimentally measured receptor densities lo scale the effects of each receptor in each cortical area and layer. We hypothesize that the pattern of working memory activity observed across cortical areas and lamina depends critically on the regional distribution of the receptors. Further, we will expand our model to include inter-areal connectivity data and investigate how release of neuromodulators can shift cortical activity between cognitive networks. The results of our proposed research are likely to significantly advance this area of research, with broad implications. The highly promising preliminary results have confirmed the validity of the approach, ensuring that all aspects of the program have a high likelihood of success. RELEVANCE (See instructions): Cognitive symptoms of mental illnesses are difficult to treat, but can be the biggest impediment to patients living independently. Cognitive functions rely on many brain areas communicating through chemicals and receptors, which are affected by mental illness and medication. We will map the pattern of these receptors in the brain, so we can build computer models to understand why cognitive functions differ across brain regions. This could lead to the development of new treatments for cognitive symptoms in mental illness.

SUMMARY, Project 2 Radiation therapy (RT) is commonly used for locally recurrent head and neck squamous cell carcinoma (HNSCCs), yet no standard concomitant systemic therapy exists, and RT resistance rates are high. Antibodies against programmed death-1 (PD-1) are FDA approved for treating relapsed/recurrent HNSCCs, but the response rate is low. RT induces anti-tumor immunity by causing DNA damage and tumor cell killing that release neoantigens to trigger an ?in situ tumor vaccine? and activation of STING (stimulator of IFN genes) for local and systemic immune activation. Conversely, RT also induces transforming growth factor-?1 (TGF?1), an immune suppressor, and PD-L1, a ligand of PD-1. These RT effects make dual TGF?/PD-L1 inhibition a rational combination being tested in this project. We have reported that TGF?1 is elevated in >60% of tobacco-associated HNSCCs. TGF?1-mediated DNA repair contributes to RT resistance. TGF?1 also contributes to RT-induced toxicity, e.g., oral mucositis and fibrosis. Using our mouse HNSCC models, we found that TGF?/PD-L1 dual inhibition eradicated SCCs better than anti-PD-L1 alone in tumors with high TGF?1 levels and high numbers of PD-L1+/CD11b+ cells. We also found that TGF? inhibition reduced metastases in athymic mice correlated with reduced CD11b+ myeloid cells. We hypothesize that in advanced HNSCCs, TGF?/PD-L1 dual inhibition enhances RT-induced in situ vaccination, reverses immune suppression, and overcome RT resistance via T cell-dependent and -independent mechanisms. We will test this hypothesis with experimental therapeutics, mechanistic studies and analyses of HNSCC patient specimens. Aim 1 will determine if TGF?/PD- L1 dual inhibition enhances RT-induced in situ vaccination and systemic immune activation in oral SCC mouse models. Experimental therapeutics of RT plus TGF?/PD-L1 dual inhibition will be performed using mouse SCC lines transplanted orthotopically to syngeneic mice, and T-cell dependent anti-tumor mechanisms will be analyzed at the cellular and molecular levels. Aim 2 will determine how RT regimens in combination with TGF?/PD-L1 inhibition target tumor epithelial death and myeloid cells in mouse and human HNSCC models. T cell-independent therapeutic benefit of RT in combination with TGF?/PD-L1 inhibition will be analyzed. Aim 3 will conduct a Phase Ib trial for RT with M7824 (TGF?/PD-L1 bidirectional inhibitor) in locally recurrent and oligometastatic HNSCC patients and identify cellular and molecular markers as therapeutic targets. By performing the proposed studies, we aim to bring a therapeutic intervention in real time to simultaneously enhance immunotherapy and reduce RT resistance in HNSCC patients with poor prognosis. Additionally, identifying predictive markers to the proposed treatment will lead to a true biomarker-driven Phase II trial with pre-selected patients.

PROJECT SUMMARY, Overall The main goal of the Colorado (CO) Head and Neck Cancer (HNC) SPORE is to advance translational research to improve survival and quality of life for HNC patients. Optimal treatment for HNC patients is critically important because the head and neck organs support critical functions such as breathing, nourishing and communicating, and thus HNC can lead to significant morbidity and mortality. The CO HNC SPORE takes advantage of our expertise in basic and clinical sciences, and uses unique model systems to identify novel molecular and cellular mechanisms of HNC pathogenesis targetable by therapeutic interventions to treat all cancer types arising from head and neck anatomic sites. Three projects cover the treatment spectrum of head and neck squamous cell carcinoma (HNSCC) and include both tobacco-related and human papillomavirus (HPV)-related HNSCC. Project 1 studies novel immunotherapy mechanisms by inhibiting EphB4-EFNB2 interactions between immune cells and the endothelium. It will test if blockade of EphB4-EFNB2 signaling at the tumor endothelial barrier hinders Tregs' and TAMs' ability to infiltrate and promote cancer survival or suppress Teff function. The applicability of pre-clinical data to clinic will be assessed in samples from HNSCC patients treated with an EphB4- EFNB2 inhibitor during a window trial. Project 2 investigates if dual inhibition of TGF?/PD-L1 combined with radiation therapy (RT) induces in situ vaccination, reverses immune suppression, and overcomes RT resistance. It will translate its findings with a trial of the TGF?/PD-L1 dual inhibitor M7824 combined with RT in locally recurrent and oligometastatic HNSCC patients. Project 3 will study mechanisms of protein elongation inhibition in HNSCC, identifying key proteins targeted by the novel inhibitor SVC112 (a drug discovered in Colorado that is nearing clinical testing), and translating our findings by testing the distribution and prognostic significance of its target (eEF2) in patient samples. It will use immune relevant models including syngeneic and humanized mice to study immune-dependent and ?independent mechanisms of SVC112 and study if protein elongation inhibition impacts the tumor microenvironment and enhances RT in HNSCC. The developmental research program (DRP) is designed to attract current HNC researchers and researchers from other fields to conduct innovative research in all types of cancers arising from head and neck tissues. The career enhancement program (CEP) is designed to solicit junior researchers to develop research projects to transition into independent HNC researchers. We encourage underrepresented minority (URM) and people with disabilities to apply for DRP and CEP projects. The CO HNC SPORE also includes Biospecimen/Pathology, Data Science, and Administrative Cores. In sum, the CO HNC SPORE will solidify in-depth HNC translational research and expand our team of dedicated HNC researchers. These activities will improve the care spanning the entire spectrum of HNC treatment from improving cures to developing innovative palliation strategies for HNC patients.

Summary Radiodermatitis is skin tissue damage that occurs in 95% of cancer patients receiving radiotherapy (RT). Although modern RT, e.g., intensity-modulated RT (IMRT) or stereotactic body RT (SBRT), spares more skin areas from RT damage than regular RT, radiodermatitis is still the major toxicity in skin cancer patients treated with SBRT as well as in other cancer patients treated with regular RT where IMRT/SBRT are either unavailable or not suitable, e.g., breast cancer patients. Severe radiodermatitis causes skin erosion, opiate-resistant pain and long-term fibrosis. Current treatments largely aim to ameliorate ?skin burn? rather than promoting healing. Allander Biotechnologies, LLC. has developed a proprietary drug to treat radiodermatitis via anti-inflammation, anti-fibrosis, promoting DNA damage repair, and promoting re-epithelialization. To date, we have established a drug production platform, feasibility for efficacy, safety, and pharmacodynamics (PD) markers to develop our drug into a topically applied drug product. In this Phase II application, we will develop Good Manufacturing Practice (GMP)-scalable production and establish reference standards for our drug substance and drug product. We will perform IND (investigational new drug)-enabling PD and pharmacokinetics (PK) studies. We will evaluate potential toxicity of our drug during topical treatment of radiodermatitis in mice and dogs. We will also perform Good Laboratory Practice (GLP) compliant systemic toxicology studies in rodents via intentional systemic delivery of our drug. By the end of Phase II funding, we will have IND data for CMC (Chemistry, Manufacturing and Controls), preclinical PD/PK, potential cutaneous and systemic toxicities in mice and dogs resulted from topical treatment doses, and GLP systemic toxicology in rodents. These data will allow us to design GLP toxicology studies in a larger species, which will be completed with additional funding for IND filing before initiating a Phase I clinical trial.