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Swartz Center for Computational Neuroscience
a laboratory of the Institute for Neural Computation,
University of California San Diego

The goal of the Swartz Center for Computational Neuroscience is to observe and model how functional activities in multiple brain areas interact dynamically to support human awareness, interaction and creativity.

Center research involves development computational methods and software, experimental methods and equipment, collection and analysis of human cognitive experiments, and collaborations to analyze data collected by other groups in such experiments. The core facilities of the Center include a network of Linux workstations and, soon, a cluster of 64-bit compute servers, and a unique state-of-the-art 256-channel electroencephalographic (EEG) acquisition system that can be reconfigured as a two-person-each-136 channel system for simultaneous data collection from two subjects performing separate or interacting tasks. We are now building a 64-bit dual Opteron computer cluster to allow us to analyze the data from this unique EEG system, as well as even larger data sets expected from the new UCSD EEG/MEG system, and to connect us to the larger computing and data storage services available at the San Diego Supercomputer Center Current research underway or begun in the laboratory may be grouped into five areas (A-E):

A. Ongoing research projects aimed at widening the scope of EEG research to include the brain dynamics of affective, emotional and social cognition.

1. Modeling long-range brain interactions during affective processing. We are using independent component analysis (ICA) and time/frequency analysis techniques to study a phenomenon we have detected in high-dimensional EEG data, theta band synchronization events that appear to be produced in the EEG only in response to significant having immediate implications for reshaping behavioral planning. We are studying these events using a set of continuous performance tasks involving frequent choice behavior and immediate performance feedback. We believe the TSEs index coordinated activity of a corticolimbic system for assessing the emotional and behavioral significance of events and automatically adjusting the focus of attention (and distraction) accordingly. They appear to provide an opportunity to observe and monitor the cortical dynamics associated with subjects' "gut reaction" responses to stimuli and events.

2. EEG dynamics of emotional processing. We are studying EEG dynamics during 3-5 minute periods when the subject actively imagines a situation provoking a suggested emotion, attempting to recreate the bodily feelings associated with the emotion. First results on data from 15 such emotion imagination conditions shows that EEG dynamic changes in different emotional states are complex, requiring multidimensional modeling. Potentially, these results could be extended to emotion monitoring for clinical and other purposes.

3. The neurodynamics of social interaction. As social interactions are of high importance to our survival and propagation, both as individuals and as societies, it is natural to suppose that the brains our highly evolved for interacting with other humans. It is possible that the brain dynamics supporting these interactions cannot be (and have not been) recorded in the absence of experiments involving actual (or, to an unknown extent, simulated) interactions. Currently, use of hemodynamic brain imaging during social interactions is being explored in several university research centers. However, hemodynamic imaging only indexes the activity of the blood resupply system subsequent to brain activity. EEG and MEG imaging, on the other hand, observe the full time course and frequency range of macroscopic neural dynamics. A first experiment using our unique two-person high-density EEG and video recording system involves a two-person computer-mediated guessing game with financial (bonus) rewards.

B. Other projects concern the brain dynamics of learning and memory

7. EEG dynamics of learning. We are looking for EEG signs of growing expertise in a difficult visual task. We propose to give subjects EEG-based feedback in addition to standard performance-based feedback, to determine whether and how this might accelerate learning. Under a proposed DARPA project, we would extend this approach to subjects whose job is to review static and video imagery for security purposes. Under pilot Kavli Foundation funding, we will explore the application of these ideas to motor learning with Howard Poizner (UCSD), and under a proposed UCSD NSF Science of Learning Center, we would extend this approach to other types of learning.

6. EEG dynamics of memory processing. During short term working memory, 4-8 Hz theta band activity in the EEG recorded over the frontal midline increases in mean amplitude. Our research shows that the mean increase only captures one aspect of the dynamics of the EEG changes associated with memory processing. We have isolated several dynamic modes involving at least three frequency bands in which dynamic changes occur in frontal midline EEG related to current memory load. Other brain locations show a variety of other dynamic changes that this project seeks to model and to relate to behavior, task context, etc.

C. Ongoing basic research on the origin, functions, and dynamics of human EEG activityincludes:

4. Multiscale analysis of scalp and intracranial data. In this project, we are analyzing EEG and intracranial EEG (iEEG or ECOG) data collected by our collaborator Dr. Greg Worrell of the Mayo Clinic, Rochester MN, and soon, by our UCSD collaborator, neurosurgeon Dr. Robert Buchanan. We are using ICA to determine the relationship between electrical field activity recorded in the brain and on the scalp. This project will investigate the degrees to which synchronization of cortical field activity at smaller spatial scales produces activity recorded at a larger spatial scale on the scalp, and how much activity reaching the scalp can contribute to analysis of local phenomena collected from within the brain itself.

9. Advanced applications of independent component analysis to brain imaging data. Several efforts are underway in the Center to apply current developments in blind source separation to EEG and fMRI data. These include EMSICA of Arthur Tsai, working with Center associate director Tzyy-Ping Jung. EMSICA (ElectroMagnetic Spectrotemporal ICA) simultaneously maximizes the probability of a linear multi-source model of EEG specified by a time course of activation and a map of relative activity strength on a model of the cortex itself. If successful, this may be an advance in the decomposition of high-dimensional scalp EEG data into anatomically localized sources.

Other projects, in conjunction with students of UCSD engineering Profs. Rao and Kreutz-Delgado, are studying the application of sparse decomposition methods to EEG time series data. With Lars Kai Hansen of the Danish Technica University and his student Mads Dyrholm, we have been studying the application of convolutive ICA modeling to EEG analysis.

D. With collaborators from the UCSD medical school and elsewhere, we are beginning to explore medical research applications of our new analysis methods:

8. Independent factor analysis of structural and chemical brain imaging data. With collaborators from Taiwan and the National Institutes of Health USA, we are applying ICA methods to factor analysis of co-registered MRI, PET and SPECT brain images from clinical subject groups and controls, to determine the brain imaging correlates of clinical disease classification.

E. Finally, in addition to publishing our methods and results in international science journals, we are creating and freely distributing via the world wide web two open source environments for applying our analysis advances in brain data analysis. These open source platforms are also providing environments for other groups to test and distribute their new analysis approaches:

9. Open source software development and distribution - EEGLAB and FMRLAB. In 1997, we began putting Matlab functions implementing the new ICA and related analysis methods we first developed at Salk Institute for free download. The Swartz Center (SCCN) web site (sccn.ucsd.edu) is now the download home for two software suites, EEGLAB and FMRLAB, that implement a wide range of analyses on EEG and functional magnetic resonance imaging (fMRI) data, respectively. Development and maintenance of EEGLAB is proceeding under a grant from the National Institutes of Health. We recently hosted the first international EEGLAB workshop on the UCSD campus. We are building these two Matlab software suites as open source environments for nearly any advanced processing of dynamic brain imaging data, and plan to add a third, bridging suite for analysis of EEG data collected during fMRI scanning.