Don M. Tucker - US grants

A depressed mood seems to preferentially influence the right hemisphere's electrophysiological activity and attentional function. Given the importance of right hemisphere function in human emotion, a better understanding of this phenomenon could help integrate biological and psychological approaches to depression. In this research, we examine how depression influences the EEG and ERP recorded as the subject performs elementary attentional processes that can be related to specific neuropsychological mechanisms. Experiment 1 examines visual orienting mechanisms that can be related to parietal lobe contributions to attention. Experiment 2 examines auditory attentional processes that have been shown to be influenced by depression and that have proved useful in research on the role of the frontal lobes in attention. Both experiments are repeated in three studies: the first uses mood induction to contrast a depressed with a neutral mood in normals, the second examines students who report high levels of depression, and the third examines clinically depressed subjects. By developing sensitive measures of the right hemisphere's attentional capacity in depression, it may be possible to study a model system that clarifies how depressive affect simultaneously regulates neural and cognitive function.

Depression and anxiety are often seen as pathological emotional states, without adaptive value. Although they may become pathological, for the normal person these emotions may be essential control processes, guiding thought and behavior. Understanding the distortions of judgement caused by pathological anxiety and depression may first require understanding the adaptive process through which emotional responses to life events regulate the cognitive appraisal of those events. Emotions may influence multiple levels of neural organization, including elementary brainstem arousal mechanisms, limbic representations of threat and pleasure, and differential hemispheric contributions to cognitive representation. Our initial studies have shown that optimistic or pessimistic mood states influence normal subjects' expectancies for the outcomes of daily life events, and this influence may be indexed by the N400 brain electrical effect that is sensitive to semantic expectancy. The present research has six aims. (1) Examine mood-congruent biases in the evaluation of daily life event that may occur with normal states of depression and anxiety. Test whether the initial mood induciton findings will generalize to individual differences in depression and anxiety in normal university students. (2) Examine mood-congruent biases in the evaluation of daily life events in community samples who meet diagnostic criteria for major depressive disorder, generalized anxiety disorder, and dysthymic disorder. (3) Determine whether anxiety and depression influence the evaluation of major life events. For both the student and community samples, analyze whether the N400 mood-congruence effect in evaluating daily life events will be predictive of the subjective ratings of major life events. (4) Develop a new paradigm for examining emotional influences on the process of normal self-evaluation. The rapid (400 ms) response of the brain electrical measure may provide a unique window on the subject's self-evaluative semantic processes that are shown only indirectly by conventional self- report psychometrics. Analysis of social desirability measures may clarify whether depression operates at the primitive, automatic or the more elaborated, self-presentation level of the self-concept. (5) Extend this paradigm to amine the emotional influence on self-evaluation in community samples meeting diagnostic criteria for major depressive disorder, generalized anxiety disorder, and dysthymic disorder. (6) Clarify the nature of the N400 effect with methodological advances in dense array measurement, single-trial analysis, 3D spline-Laplalcian analysis, principal components analysis of superposition, and a new approach to anatomically-constrained source localization of the N400 and LPC effects.

Depression and anxiety are often seen as pathological emotional states, without adaptive value. Although they may become pathological, for the normal person these emotions may be essential control processes, guides for directing thought and behavior. Understanding the distortions of judgment caused by anxiety and depression may first require understanding the adaptive process through which emotional responses to life events regulate the cognitive appraisal of those events. Emotions may influence multiple levels of neural organization, including elementary brainstem arousal mechanisms, limbic representations of threat and pleasure, and differential hemispheric contributions to cognitive representation. In the initial studies of our research project, subjects who adopted an optimistic emotional mood showed a pattern of brain electrical activity indicating a priming or facilitation of the perception of favorable outcomes as they read brief stories of daily life events. Subjects adopting a pessimistic mood showed a brain electrical pattern reflecting their expectation of unfavorable outcomes to the stories. These results suggest that a person's current mood state primes mood-congruent domains of expectation. The proposed research (1) replicates this mood induction study with improved, high-density arrays (64- and 128-channels) of scalp electrodes, (2) introduces improved signal analysis methods for assessing the time course and scalp topography of the brain electrical activity, (3) applies the new methods to examine the cognitive influences of the naturalistic mood states of subjects experiencing clinically significant depression and anxiety, and (4) extends the analysis to examine emotional influences on the process of self-evaluation.

Future progress in cognitive neuroscience research will rely increasingly on the application of systems for high-performance computation and high-volume data management to address the challenges of integrated neuroimaging, multi-modality sensor fusion, and cognitive modeling.
With a Major Research Instrumentation award from the National Science Foundation, the University of Oregon will establish the Integrated COgnitive Neuroscience, Informatics, and Computation (ICONIC) Grid, composed of parallel computing clusters, large-scale data servers, workstations, and interactive visualization devices. Connected by a high-bandwidth campus network linking the Department of Psychology, the Center for Neuroimaging , the Neuroinformatics Center, the Department of Computer and Information Science, and the Computational Science Institute, the ICONIC Grid will enhance Oregon's excellence in cognitive neuroscience with needed computing power to solve neuroimaging problems of tissue/feature segmentation, dense-array EEG source localization, multi-modal MRI integration, and functional components analysis. The ICONIC Grid will be organized as a distributed computing environment to promote grid-style collaboration among cognitive neuroscience research groups. Computer science research in high-performance parallel and distributed computing, scientific databases, informatics, and interactive visualization will enhance the ICONIC Grid for highly productive use as a computational science tool.

The interchange between cognitive neuroscience and computational science is now important at both theoretical and empirical levels. For several decades, cognitive psychology has drawn from concepts of cybernetics and information processing in the development of models of human mental function. However, it is in the integration of psychological with neural evidence that the methodological demands for computational advances have become particularly intense. Many investigators in cognitive neuroscience now recognize the limitations of individual brain imaging methods, such as in the temporal or spatial resolution, or practical implementation of the technology. The result is an increasing demand for integrated imaging and analysis, in which convergent methods are brought to bear on a particular issue of brain mechanisms.

The University of Oregon began the decade with a bold Brain, Biology, and Machine Initiative (BBMI) to promote interdisciplinary research between neuroscience, cognitive science, molecular biology, genomics, and computational science. The establishment of the Center for Neuroimaging , which houses a new Siemans Allegra 3-Tesla fMRI machine, and the Neuroinformatics Center, were Oregon's first steps towards integrative cognitive neuroscience. The ICONIC Grid is the next critical piece of the puzzle providing an essential resource to further advancements in cognitive neuroscience research, collaboration, education, and outreach.

The broader impact of the ICONIC Grid will be important for the University's educational goals, for minority recruitment and retention, and for extending advances in computation to medical advances in society. With on-campus access to both advanced imaging facilities and the computational and visualization infrastructure that processes and presents the experimental data, students in Psychology will be exposed to a state-of-the-art problem-solving environment for cognitive neursocience education. New Psychology curricula are planned for providing students training in the use of such tools. Similarly, the CIS department's academic objectives in parallel and distributed computing, computational science, networking, human-computer interaction, and visualization will benefit greatly from hands-on access to parallel cluster and distributed grid technology.

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).

Building on the success of a previousMRI-funded project, an interdisciplinary group of computer scientists, psychologists, biologists, chemists, and physicists at the University of Oregon is acquiring a large-scale computational resource, the Applied Computational Instrument for Scientific Synthesis (ACISS), to support continued cutting-edge scientific research in these areas. The ACISS hardware will consist of general purpose multicore computing nodes, high performance computing nodes augmented with GPGPU acceleration, a 400TB storage system, high-bandwidth networking infrastructure and additional computing resources that will be incorporated into an existing visualization lab in the Department of Computer and Information Science. A key part of the proposed infrastructure is the unique opportunity to manage ACISS as a computational science cloud.

The ACISS infrastructure will allow an expanded the scope for the current projects in the areas of software tools for performance measurement, programming environments and languages for describing and executing complex simulations and scientific work flows, new algorithms for multiple sequence alignment and phylogenetic inference and undertake new projects in support of the domain sciences. Research projects that will benefit include: a) modeling neural networks in C. elegans to better understand the neural mechanisms responsible for chemotaxis and klinotaxis, and investigation of the evolution of genes involved in development and their role in speciation and phenotypic variation; b) development of neuroinformatic techniques used in brain imaging and analysis, integrating structural information from fMRI and other sources with EEG data; c) molecular modeling research, including the definition of new techniques for meso-scale modeling and applying computational methods to understand phase transitions and nitrogen fixation; d) astrophysical simulations of turbulent plasma flows that influence the early stages of planet formation.

The ACISS infrastructure will provide the computational resources necessary for future multidisciplinary research. ACISS will establish a novel paradigm for computational science research and practice. The experience gained in early adoption of the ACISS cloud computing technologies will allow us to more rapidly apply this knowledge to create new scientific work flows, more productive research collaborations, and enhanced multidisciplinary education programs. Farther reaching, ACISS can be seen as a model for translational computational science, in which ACISS-based services function as cyber-incubators where new work flows for scientific research are prototyped.