Mingzhou Ding - US grants

Visual processing in the mammalian brain is not done simply with several parallel channels leading to higher areas. We now know that visual input pathways diverge into multiple processing streams, with feedback at all levels in each stream and crosstalk between streams. The visual cortex can be seen in this view at a dynamic system of interconnected areas interacting flexibly in different combinations at different stages of processing. This renewal project builds on technological advances and analytical tools for with high spatial, temporal and frequency resolution, developed from prior support. These comprehensive advances make it possible to monitor multi-area functional interdependency patterns that arise in the cortex, and to measure and analyze how one cortical area can affect others. The novel approach in the current project is to examine the mesoscopic scale of functional brain organization, offering a complementary level between the microscopic recording of single cell activity in a local area or layer, and the macroscopic derivation of images from whole brains using scanning technologies such as PET and fMRI.
Results will have an impact by providing new insights into the dynamics of functional interdependency in the visual cortex, and going beyond visual neuroscience to make available digital signal processing tools potentially useful for a handling large-scale neural systems in a range of cognitive studies, and potentially leading to designing better complex artificial neural networks. This project also provides excellent cross-disciplinary training opportunities for students.

DESCRIPTION (provided by applicant): Neural oscillation occurs in many parts of the brain across many species and is often a network phenomenon involving the participation of diverse cell ensembles and brain areas. As such, multi-electrode recordings are seen as key to provide answers regarding mechanisms and functionalities of neural oscillations. Statistically, a natural framework for analyzing oscillatory neural signals is spectral analysis. Although widely used, the application of spectral analysis to multi-channel neural recordings require certain adaptation and expansion of the traditional approach to meet unique challenges posed by the nervous system. First, the brain changes its functional states on rapid time scales (40 to 50 ms, possibly shorter) during cognitive performance. Second, functional couplings among multiple signals are currently assessed by symmetric measures (no directionality) like coherence spectra, but increasingly more elaborate theories of neural oscillation demands that directionality be added to neural interactions. We meet these challenges by (a) developing a MultiVariate AutoRegressive (MVAR) time series modeling approach to spectral analysis which is capable of examining neural signals over analysis windows as brief as 50 to 60 ms and by (b) incorporating Granger Causality spectra into the MVAR approach to evaluate causal influences and directions of driving among multiple neural signals. We propose to analyze two existing datasets from behaving monkeys by the new methodology to explore its applicability and effectiveness. The first dataset consists of local field potentials simultaneously recorded from up to 16 bipolar intracortical electrodes chronically implanted in one hemisphere while the monkey performed a visuomotor GO/NOGO task. The goal here is to study the dynamical organization of a large-scale oscillatory sensorimotor network supporting the maintenance of pressure on a depressed mechanical lever. The second dataset consists of local field potentials recorded from two linear electrodes, each with multiple contacts spanning all the cortical layers, placed simultaneously in V1 and LGN while the monkey performed an intermodal selective attention task (visual versus auditory). The goal here is to study synchronized oscillations between V1 and LGN and the question of top-down influence during visual attention deployment by examining laminar patterns of neural interactions.

DESCRIPTION (provided by applicant): Neural signals are extremely variable. Understanding the variability and harnessing its rich content lie at the very heart of contemporary neuroscience. In this application we propose to tackle 1 aspect of this vast field: estimation of event related field potential signals on a trial-by-trial basis and applying the estimated single trial event related parameters to address questions related to functions of neural systems. There are 2 Specific Aims. In Aim 1 we propose to further develop and thoroughly validate a single-trial analysis methodology termed differentially variable component analysis (dVCA). In Aim 2 we propose to apply the methodology to analyze local field potentials from 2 existing datasets: 1 from macaque monkeys performing a visuomotor pattern discrimination task and the other from macaque monkeys performing an intermodal (visual versus auditory) selective attention task. The first dataset is unique in that it consists of local field potentials simultaneously recorded from up to 16 bipolar intracortical electrodes chronically implanted in one hemisphere, and is therefore ideally suited for addressing issues related to timing and large-scale networking of neural activations and their task relevance. The second dataset is unique in consisting of local field potentials recorded simultaneously from multiple contacts along a linear electrode spanning all the cortical layers in the primary visual cortex. Our goal is to study how visual information processing is modulated by attention.

DESCRIPTION (provided by applicant): Psychophysical and physiological experiments have demonstrated that attending to an anticipated stimulus before its actual onset can enhance its sensory processing. Disruption of the neural mechanism that controls anticipatory attention is symptomatic of a number of psychiatric and neurological disorders including schizophrenia and epilepsy. Neuroimaging studies have identified the brain areas that contribute to anticipatory attention and suggested a top-down control mechanism. What remains not well understood is the neurophysiological signal that implements such control. The present project has three objectives. First, we wish to establish that alpha range (10 Hz) activities in local field potentials and in surface EEGs are the signals that mediate different aspects of top-down attentional control in the visual cortex. Second, we wish to establish that the inferotemporal cortex may serve as a way station that directs the top-down control signal from the prefrontal cortex to lower visual areas. Third, by comparing the attentional effect before and after stimulus onset, we wish to examine the interplay between top-down control and bottom-up processing. These objectives will be addressed by utilizing a unique resource - 120 GB of high quality multielectrode local field potential, surface EEG and multiunit activity data recorded from monkeys performing an intermodal selective attention task. A key enabling component for accomplishing these objectives is a set of signal processing methods for analyzing multivariate neural recordings developed and validated over the past decade by the PI and his colleagues. Initial applications of the analysis methods to the data have already yielded a number of insights in support of our research aims which lay the foundation for the successful execution of the proposed project.

DESCRIPTION (provided by applicant): Cognitive operations like selective attention are thought to involve coordinated activity of neuronal ensembles in multiple brain areas. It is abundantly clear that attention enhances visual responses within local ensembles of neurons throughout the visual system, the corollary idea that attention also facilitates the transmission of visual inputs between neuron ensembles in different cortical layers and in different cortical regions has not been thoroughly investigated. Similarly, while it is generally agreed that attentional modulation of low level visual processing is controlled by a higher order network, the specific circuits and physiological processes by which top-down control is imposed are not well understood. With these two problems in mind the overall goal of this project is to define the magnitude and physiological mechanisms of attention's influence on feedforward communication in low level visual processing. Using a combination of spike correlation, standard coherence and Granger causality analyses, we will analyze data from multielectrode recordings in V1 and V2 in monkeys performing a single, well-studied (intermodal) attention task. We have shown that because of the predictability of stimulus rhythms in this paradigm, attention can use low frequency oscillations as instruments to enhance neuronal responses to task relevant stimuli. This finding has wide ramifications because rhythm and predictability are prominent in many aspects of natural behavior. To follow it up, we will test the hypothesis that attention can use low frequency oscillatory phase synchrony to facilitate feedforward communication between neuronal ensembles in the visual pathways. Our specific aims are: 1) to characterize attention's influence on feedforward transmission between cortical layers, 2) to characterize attention's influence on transmission between V1 and V2, and 3) to define the brain mechanisms underlying attentional modulation of interlaminar and interareal interactions. Concurrent sampling of laminar current source density (CSD) and multiunit activity (MUA) profiles in V1 and V2 will index synaptic activity and firing patterns in neuronal ensembles at key locations in the supragranular, granular and infragranular layers. Laminar profiles of attention effects in V1 and V2, along with Granger causality analyses will help to differentiate between several of the alternative control circuits. Single trial analysis of both pre- stimulus and poststimulus oscillatory dynamics and of related variations in neuronal firing patterns in these locations will help to relate dynamics to underlying physiology. PUBLIC HEALTH RELEVANCE: Our methods allow use of the monkey as a model for understanding the neuronal mechanisms of ERP and EEG generation in humans. This study will also provide a data set that can be used to evaluate new functional connectivity analyses developed for use in humans, particularly those targeting the study of epilepsy.

DESCRIPTION (provided by applicant): Clinical neuroscience studies have suggested that dysfunctional reactivity of the brain circuits mediating emotion may be a key factor in the etiology and maintenance of many psychiatric disorders. It is also well established that the sensory cortical response to emotional stimuli is an important part of a cascade of events - physiological, cognitive, and behavioral - that define emotional reactivity in humans. For instance, anxiety patients show visual responses that are both biased towards threat cues and lacking discriminative accuracy. Mechanistic knowledge is needed that addresses the question of how such perceptual biases towards threat features are acquired (and unlearned) in the human visual system. This has been difficult because reliable methods to quantify single trials of neural activity are not available at this time. In this multidisciplinary research project, we propose to use novel computational and experimental approaches to fill this gap. We aim to objectively characterize and quantify - on a trial by trial basis - the temporal evolution of neura changes in the human visual system that accompany the acquisition and extinction of conditioned fear. An objective and reliable description of the time course of visual changes during fear learning will assist in ongoing efforts aiming to develop objective diagnostic categories of fear disorders, to understand and quantify effects of treatment, and to develop new forms of attention/perception trainings in the fear and anxiety disorders.

This INSPIRE award is partially funded by the Perception, Action, and Cognition Program, the Cognitive Neuroscience Program, and the Social Psychology Program in the Division of Behavioral and Cognitive Sciences in the Directorate for Social, Behavioral, and Economic Sciences, the Research and Evaluation on Education in Science and Engineering Program in the Division of Research on Learning in Formal and Informal Settings in the Directorate for Education and Human Resources, and the Control Systems Program in the Division of Civil, Mechanical, and Manufacturing Innovation in the Directorate for Engineering.

The goal of the project is to understand naturalistic human social interaction, specifically in group contexts. While neuroscientists are increasingly recording from two participants concurrently, the neural basis of group dynamics remains uninvestigated. Capitalizing on the growing body of knowledge about the role of brain rhythms, the project builds on the hypothesis that one can characterize coupled neural oscillations between individuals as one candidate mechanism that tracks successful social communication in a dynamic context. This aim is pursued by using novel portable EEG technology to record brain activity from a large number of participants concurrently (between 10-20) in ecological situations, specifically a classroom. This will address the significant hardware and software challenges associated with recording data sets from groups. Moreover, the new type and amount of data will also require a novel analytic toolbox, which will form the basis of modeling multiple brains engaged in socially relevant situations.

The research will impact education and technology, and provide significant outreach opportunities. First, the key experiments will be performed in a high school classroom, in collaboration with the science teachers. As such, the project provides a new type of platform to provide hands-on STEM training. Second, the successful implementation of the wearable mobile brain EEG recording system will have significant impact on future neuroscience research, providing a valuable tool for research outside of the lab (e.g., in a crowd: theatres, schools), with populations that are otherwise difficult to reach (e.g., children, patients, the elderly). Finally, by comparing communication between people in the same room to people at a distance (e.g., MOOCs), this project contributes to issues surrounding the relevance of real-life behavioral cues to successful communication and teaching.

The ability to detect and rapidly respond to changes in the environment is evolutionarily conserved across the animal kingdom. Yet, the neural computation underlying novelty detection have not been precisely defined or quantified. Drs. Wu (Georgetown University), Ding (University of Florida) and Chou (University of California, Los Angeles) will test the hypothesis that dynamics in neuronal activity at the tissue level play a two-step role in novelty-detection. First, a constant or repetitive stimulus establishes a spatiotemporal pattern of neuronal activity that stores an expectation of regularity within the system. Second, stimuli that violate the expected regularity are registered as changes in the patterns of neuronal activity, triggering a response. The research team will use voltage-sensitive dye imaging, optogenetics, high-density EEG, and fMRI, to measure brain activity maps resulting from a common stimulus sequence. The expected neuronal activity and the novelty response to changing the stimulus will be measured in turtles, mice, and humans. The investigators will then quantify these activity maps using mathematical/statistical analysis and develop physically-motivated theoretical models.

This project is expected to provide the initial identification of shared computational principles as well as species- and system-specific implementation of such mechanisms. Results from this research may shed light on a wider range of cognitive functions that rely on novelty detection and novelty-controlled neuromodulation, including attention, learning and memory, and decision-making. Given the importance of such cognitive functions, the proposed research may potentially have long-term, broad societal impact. For example, the opportunity to relate novelty detection capacity of college students to their classroom performance may lead to the development of novelty-stimuli based tools for more effective classroom education. Moreover, the comparative investigation of different species may provide insight into the evolution of learning capacity.

DESCRIPTION (provided by applicant): Clinical neuroscience studies have suggested that dysfunctional reactivity of the brain circuits mediating emotion may be a key factor in the etiology and maintenance of many psychiatric disorders. It is also well established that the sensory cortical response to emotional stimuli is an important part of a cascade of events - physiological, cognitive, and behavioral - that define emotional reactivity in humans. For instance, anxiety patients show visual responses that are both biased towards threat cues and lacking discriminative accuracy. Mechanistic knowledge is needed that addresses the question of how such perceptual biases towards threat features are acquired (and unlearned) in the human visual system. This has been difficult because reliable methods to quantify single trials of neural activity are not available at this time. In this multidisciplinary research project, we propose to use novel computational and experimental approaches to fill this gap. We aim to objectively characterize and quantify - on a trial by trial basis - the temporal evolution of neura changes in the human visual system that accompany the acquisition and extinction of conditioned fear. An objective and reliable description of the time course of visual changes during fear learning will assist in ongoing efforts aiming to develop objective diagnostic categories of fear disorders, to understand and quantify effects of treatment, and to develop new forms of attention/perception trainings in the fear and anxiety disorders.

DESCRIPTION (provided by applicant): Fatigue is associated with increased mortality in older adults and is the leading cause of activity restrictions in this population. Unfortunately, our understanding of the causes of fatigue in older adults is quite limited and we have no proven treatments. While it is known that aging has profound effects on the central nervous system (CNS) and cognitive function there have been no studies to date attempting to understand how CNS and cognitive factors contribute to fatigue complaints and activity restrictions in older adults. This is a striking gap as CNS and cognitive factors may play a central role in many processes suspected to drive fatigue in older adults and would thus be a potent target for therapeutic interventions. Fatigue is a complex construct which includes subjective perceptions (perceived fatigue) and objective changes in performance (fatigability). The research objectives of this proposal are to: 1) Determine the association between an objective measure of cognitive performance fatigability and activity levels in older adults; and 2) Identify potential neuronal mechanisms of this fatigability. The long-term goal is to determine whether interventions directed at cognition and/or CNS targets restore activity levels in older adults afflicted by fatige. The central hypothesis is that cognitive performance fatigability in older adults contributes to activity restrictions and is due to aging related neurocognitive changes. This hypothesis has been formulated on the basis of our preliminary data showing: 1) Significant correlations between cognitive fatigability and both age and fatigue complaints in older adults; and 2) Associations between physiologic markers of cognitive reserve and cognitive fatigability. The rationale for the proposed research is that better objective measures of cognitive and CNS factors are needed to advance our understanding of fatigability in older adults and to identify targets for therapeutic interventions. The hypothesis will be tested through two Specific Aims: 1) Determine the correlation between cognitive performance fatigability and activity levels in older adults; and 2) Determine the relationship between cognitive fatigability and neurophysiological markers of cognitive reserve using electroencephalography (EEG). The approach is innovative because it represents the first study to examine objective cognitive fatigability as a cause of restricted activity in older adults and is the first study to determine whether physiologic measures of cerebral reserve impact cognitive fatigability. The proposed research is significant because it is expected to advance out understanding of the mechanisms of fatigability in older adults and to contribute new tools, mechanistic insights and therapeutic targets essential for advancing this field. The knowledge and techniques developed in this R21 proposal will serve as a foundation for future R01 proposals to investigate the potential for cognitive interventions t improve activity restrictions in older adults and mechanistic studies to better understand the relationship and interaction of potential causes of restricted activity in older adults including cognitive, CNS, muscular, cardiovascular and metabolic factors.

Attention is about enhancing what is important to us and suppressing what is not. Nearly everything we do, ranging from reading a book to looking for a friend in the mall, requires attention. One of the fundamental questions in cognitive neuroscience is how networks of neurons in the brain flexibly control the allocation of attentional resources to the task at hand. Dr. Mingzhou Ding of University of Florida will investigate this question using innovative computational tools. The novel set of theoretical constructs and effective physiological measures proposed in this project will help to better understand attention as well as its impairment. As such, the results can inform dysfunctions of attention control that are characteristic of many devastating neurological and psychiatric disorders such as Parkinson's disease and ADHD by providing objective and quantitative markers for the affected individuals. In addition, the planned free distribution of the software codes implementing the novel methods used in the project and the large amount of well-characterized human and monkey electrophysiological data that will be made available will benefit a much broader community of researchers in science and engineering, many of whom have no access to such data and methodology. The project will also enhance the educational mission of the NSF by providing materials for course development and resources for mentoring the next generation of interdisciplinary cognitive neuroscience researchers at both graduate and undergraduate levels.


Combining theoretical/computational approaches with experimental approaches, Dr. Ding will test a novel computational model that links ongoing neural activity prior to the appearance of a sensory stimulus with selective enhancement and suppression of stimulus processing by analyzing brain electrical signals (electroencephalography, EEG) in both humans and monkeys. Furthermore, Dr. Ding will apply novel measures to multiple types of neural data to go beyond the conventional EEG measures observed from outside of the human brain. This project aims to provide not only new insight into our current understanding of attention processes, but more importantly, new tools and approaches that will enable neuroscientists to characterize the interaction between neural signals from different regions inside of the brain (source analysis), particularly how electrical activity in one brain region influences the other (Granger causality). These new tools and approaches are expected to be applicable to the investigation of a wide range of cognitive functions, and is thus potentially transformative in making EEG, a relatively inexpensive and widely available brain imaging tool, useful for understanding cognitive functions in natural context.

Project Summary    Emotional dysfunction is at the core of many psychiatric disorders, in particular fear, anxiety, post-­traumatic,  and mood disorders. Describing the neural mechanisms associated with emotional processing is therefore a  critical issue in mental health care. Previous attempts to define the neurophysiology of human emotions in the  cognitive  neuroscience  laboratory  have  been  hampered  by  the  unavailability  of  conceptual  and  methodological  frameworks  for  studying  complex  emotional  responses  in  context  and  with  conflicting  information present. The proposed research establishes a novel technique for combining electrophysiological  recordings,  high  in  temporal  precision,  with  functional  brain  imaging,  which  is  high  in  spatial  precision.  This  approach, called steady-­state potential frequency-­tagging, achieves stimulus specificity, temporal, and spatial  resolution across the whole brain. It is unique in that it allows researchers to identify distinct brain networks  selectively activated by different elements of a complex visual scene?even when the elements are spatially  overlapping  and  accompanied  by  stimulation  in  other  sensory  modalities.  We  combine  this  innovative  approach with a novel conceptual framework that considers changes in visual perception an active part of an  observer?s  emotional  response,  to  address  the  following  Aims:  (1)  We  characterize  the  large-­scale  brain  dynamics  mediating  the  emotional  response  to  an  element  that  is  embedded  in  a  complex  visual  array.  (2)  We  determine  how  conflicting  appetitive  and  aversive  information,  visual  and  auditory,  affects  these  brain  dynamics.  (3)  Finally,  we  translate  this  novel  method  to  socially  anxious  observers,  testing  mechanistic  hypotheses  regarding  the  interactive  effects  of  trait  anxiety  and  chronic  stress  on  short-­term  reactivity  to  emotional  challenge.  The  long-­term  clinical  implications  of  the  proposed  research  are  manifold:  For  diagnostic assessment and for monitoring treatment efficacy, a quantitative brain-­based marker of emotional  engagement  opens  avenues  for  objectively  evaluating  pre-­  to  post-­treatment  changes  in  appetitive/aversive  neural  reactivity.  It  also  enables  measuring  neural  circuit  function  to  enable  quantitative  measurements  of  specific psychopathology and for identifying treatment targets in a personalized medicine framework. 

PROJECT SUMMARY/ABSTRACT Selective attention is an essential cognitive ability that permits us to effectively process and act upon relevant information while ignoring distracting events. A network involving frontal and parietal cortex for top-down attentional control, referred to as the Dorsal Attention Network (DAN), is active during both spatial and non- spatial (feature-based) attention. However, we know very little about the fine structure of attentional control activity in the DAN, how this structure changes to represent different to-be-attended stimulus features, how the connectivity within the DAN, and between the DAN and sensory cortex shifts when attending different features, or how these top-down processes and their influence in sensory cortex unfold over time. This gap in our knowledge is a critical problem for our models and theories of attention, and because attentional deficits are involved in a wide variety of neuropsychiatric disorders including autism, attention deficit disorder, dementia, and schizophrenia. The working model guiding this research is that top-down attentional control, based on different to-be-attended stimulus attributes, is guided by a smaller-scale neural fine structure within the DAN and prefrontal cortex that makes specific connections with specialized areas of visual cortex coding the attended attributes. Moreover, the time course of activity within the DAN in relation to that in sensory cortex follows a top-down cascading model, being earliest in frontal, then parietal cortex, and finally sensory cortex for preparatory, voluntary, attentional control. To identify the functional networks for attentional control for different forms of attention, and to define their time courses, this project uses innovative simultaneous recording of electroencephalographic (EEG) and functional magnetic resonance imaging (fMRI) data. Advanced signal processing and modeling, including multivariate pattern analysis (MVPA), graph theoretic connectivity analysis, and Granger causality analysis will be used to reveal the fine functional anatomy and time course of attentional control and selection. The project includes three experiments that vary the to-be-attended stimulus attributes from spatial location to stimulus features (color and motion), and pursues three aims. Aim 1 is to reveal the fine structure of top-down preparatory attentional control for different to-be-attended stimulus features. Aim 2 is to elucidate the specific connectivity between fine structures for preparatory attentional control in the DAN and their target sensory structures in sensory cortex. Aim 3 is to reveal the time course of top-down attentional control for different to-be-attended stimulus attributes.

Project Summary/Abstract: Working memory is an essential cognitive faculty. Individual differences in working memory functioning can be quantified by working memory capacity (WMC). Higher WMC enables better performance in a diverse set of cognitive operations, including attention, reading comprehension, planning, and problem solving. There is evidence suggesting that higher WMC even confers the individual with the ability to better resist cognitive impairments in brain disorders. Despite its importance as a psychological construct, the neurophysiological underpinnings of WMC, however, remain not well understood. We will address this issue by pursuing Aim 1 in which we will investigate the individual differences in the task-related modulation of frontoparietal theta oscillations during working memory encoding and retention. Specifically, we will test the hypotheses that frontal theta power and frontoparietal theta coherence decrease with increasing working memory load during encoding and increase with increasing working memory load during retention and that theta modulation by working memory load during encoding and retention is positively correlated with working memory capacity. Research to date on the relation between neuronal oscillations and cognition tends to be correlative. Noninvasive neuromodulation provides a means to uncover the causal role of neuronal oscillations in cognition. In Aim 2 we will test the efficacy of tACS stimulation at theta frequency in enhancing task-related theta modulation and working memory capacity. Specifically, we will test the hypotheses that in-phase theta tACS stimulation of the frontoparietal network upregulates task-related modulation of theta oscillations in working memory and enhances working memory capacity and that individuals with low working memory capacity will benefit more from in-phase theta tACS stimulation than individuals with high working memory capacity.

The temporal dynamics of blood flows through the network of cerebral arteries and veins provides a window into the health of the human brain. Since the brain is vulnerable to disrupted blood supply, brain dynamics serves as a crucial indicator for many kinds of neurological diseases such as stroke, brain cancer, and Alzheimer's disease. Existing efforts at characterizing brain dynamics have predominantly centered on 'isolated' models in which data from single-voxel, single-modality, and single-subject are characterized. However, the brain is a vast network, naturally connected on structural and functional levels, and multimodal imaging provides complementary information on this natural connectivity. Thus, the current isolated models are deemed not capable of offering the platform necessary to enable many of the potential advancements in understanding, diagnosing, and treating neurological and cognitive diseases, leaving a critical gap between the current computational modeling capabilities and the needs in brain dynamics analysis. This project aims to bridge this gap by exploiting multi-scale structural (voxel, vasculature, tissue) connectivity and multi-modal (anatomical, angiography, perfusion) connectivity to develop an integrated connective computational paradigm for characterizing and understanding brain dynamics.

The approach consists of three thrusts: (1) multi-scale structural connectivity modeling to quantify brain dynamics beyond a single voxel; (2) multimodal dynamic dictionary learning for mining hidden complementary information; and (3) multicenter evaluation to assess the efficacy of the proposed models at three nationally renowned healthcare systems. Successful project completion would potentially transform the rapidly evolving field of brain dynamics modeling, facilitate basic neuroscience discovery and enable comprehensive identification of neurovascular diseases. Aiming to broaden its impact this project will also implement educational initiatives to expose students, middle school teachers, and medical professionals to 'CS for All,' to foster interests in STEM and cross-disciplinary careers, and to promote research on the convergence of computer science and computational thinking for brain health and neuromedicine.

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.

Project Summary Classical aversive conditioning is a well-established laboratory model for studying acquisition and extinction of defensive responses. In experimental animals, as well as in humans, research to date has been mainly focused on the role of limbic structures (e.g., the amygdala) in these responses. Recent evidence has begun to stress the important contribution by the brain?s sensory and attention control systems in maintaining the neural representations of conditioned responses and in facilitating their extinction. The proposed research breaks new ground by combining novel neuroimaging techniques with advanced computational methods to examine the brain?s visual and attention processes underlying fear acquisition and extinction in humans. Major advances will be made along three specific aims. In Aim 1, we characterize the brain network dynamics of visuocortical threat bias formation, extinction, and recall in a two-day learning paradigm. In Aim 2, we establish and test a computational model of threat bias generalization. In Aim 3, we examine the relation between individual differences in generalization and recall of conditioned visuocortical threat biases and individual differences in heightened autonomic reactivity to conditioned threat, a potential biomarker for assessing the predisposition to developing the disorders of fear and anxiety. It is expected that accomplishing these research aims will address two NIMH strategic priorities: defining the circuitry and brain networks underlying complex behaviors (Objective 1) and identifying and validating new targets for treatment that are derived from the understanding of disease mechanisms (Objective 3). It is further expected that this project will enable a paradigm shift in research on dysfunctional attention to threat from one that focuses primarily on limbic-prefrontal circuits to one that emphasizes the interactions among sensory, attention, executive control and limbic systems.