WORKSHOP ON ELECTRICAL NEUROIMAGING OF THE SOCIAL BRAIN: PRELIMINARY PROGRAM

Pre-SfN Workshop in San Diego, CA

November 10, 2016

INTRO TO BEST PRACTICES IN

COGNITIVE AND SOCIAL NEUROSCIENCE 

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Program:

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This meeting will take place in San Diego, CA.

When: November, 10, 2016.

Where: Marriot Marquis San Diego Marina 333 West Harbor Drive  San Diego  California  92101  USA 

Workshop Organized by:

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FEATURED SPEAKERS:

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Dr. Aina Puce is the Eleanor Cox Riggs Professor of the Department of Psychological and Brain Sciences at Indiana University.

Talk Title: Issues related to data acquisition and analysis in neurophysiological activation studies

Abstract: Scalp EEG studies are increasingly using more sensors in high-density arrays to acquire data to complex brain activation tasks. Additionally, new data analysis methods in neuroimaging studies are becoming more removed from the raw data and are relying more on automated artifact rejection to eliminate non-brain signals. It has been known for many decades that scalp recorded EEG activity is prone to a number of artifacts, and these need to be effectively identified in the data. Before the data can be analyzed, preprocessing must identify experimental trials with artifacts. Subsequent courses of action require: (1) the exclusion of these artifactual trials from subsequent analyses; (2) the removal of artifacts from artifactual trials, so that these artifact-free trials can be incorporated in data analyses. I will discuss various approaches for removing artifacts, including physiological artifacts related to eyeblinks, cardiac and muscle activity, and non-physiological artifacts created by man-made sources. Issues such as artifact/EEG signal interactions such as microsaccades and their relationship to gamma EEG activity will also be examined. Recommendations for data acquisition and analysis are that: (i) adequately sampling of both desired EEG signals and associated artifacts is required for the successful rejection of artifacts in EEG data; (ii) vigilance during both data acquisition and analysis, and multiple reality checks are increasingly required as data analysis methods increase in their complexity. I will also discuss some approaches for improving the reproducibility of scalp EEG data.

More about Dr. Puce: Her current research program is devoted to understanding the neural basis of social cognition – the brain’s ability to interpret the actions, intentions and emotions of others. A main focus of her research is non-verbal communication, particularly from cues sent by the face, as direction of gaze, and emotional expressions. Her current work has implications for disorders of social cognition, which can occur in autism spectrum disorders and also schizophrenia. While her experiments use combinations of different techniques including behavior, functional MRI, electrical activity of brain, and eye tracking , her keynote lecture will focus on the art of electrical neuroimaging. Dr. Puce received her Master of Applied Science in Physics from Swinburne Institute of Technology in Melbourne, Australia and her PhD in Medicine from the University of Melbourne. She then went on to do a postdoctoral fellowship in Neurosurgery at Yale University and has held faculty positions at Swinburne University of Technology, West Virginia University, and currently Indiana University. 

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Dr. Scott Makeig directs the Swartz Center for Computational Neuroscience of the Institute for Neural Computation, UCSD, now housed in a suite of new offices and laboratories in the Supercomputer Center building on the UCSD campus.

Talk Title: High-Resolution EEG Brain and Brain/Body Imaging: New Methods for Social Neuroscience Research

Abstract: Non-invasive imaging of human brain function at the speed of thought, feeling, and action using high-density scalp EEG is now becoming a true functional brain imaging modality thanks to applications of mathematical approaches to separating scalp-recorded data into its effective cortical (brain) and non-brain ('artifact')  sources. A parallel can be drawn the development of magnetic resonance (MR) and fMRI brain imaging, for which inverse method that transforms the raw recorded radio-frequency (sensor) data into brain source space (voxel) data is critical. In both modalities (EEG imaging and MRI/fMRI) the sensor data per se are best regarded as epiphenomena and the derived brain source activity data the biological phenomena of scientific interest. In MR/fMR imaging research this is now taken for granted, where as in the EEG field this point of view is still novel to many researchers. I will detail an approach to source-resolved EEG analysis based on application of independent component analysis (ICA) using tools freely available in the EEGLAB software environment (sccn.ucsd.edu/eeglab). As an example, I will discuss studies of cortical dynamics supporting imagined emotion and gestural communication of musical feeling.

Experiments in social neuroscience experiments using source-resolved EEG imaging have strong potential advantages. In particular,  participants in social experiments may face each other and interact naturally. Simultaneously measuring  their brain activity and their behavior using EEG, body motion capture, wearable eye tracking, audiovisual recording, etc., allows observation, for the first time, of brain dynamics that accompany and support social interactions. I have termed the simultaneous high-bandwidth recording of brain dynamics and behavior 'Mobile Brain/Body Imaging' (MoBI). At the Swartz Center, UCSD, we have developed a first MoBI laboratory and are making available software tools to record, store, review, and analyze multimodal multi-subject data. I will show a first application of the MoBI approach to the study of expressive movements and will discuss possibilities for a rich new range of new social neuroscience experiment designs made possible by the mobile brain/body recording and analysis approach.

More about Dr. Makeig: His primary research interest is in analysis and modeling of human cognitive event-related brain dynamics as captured by high-dimensional EEG, MEG and other imaging modalities including simultaneous eye tracking and body motion capture. Dr. Scott Makeig and colleagues have pioneered several brain imaging analysis methods, including EEGLAB,  time-frequency analysisindependent component analysis (ICA), and neural network and machine learning. With Mark Inlow, Tzyy-Ping Jung and others, he has studied the dynamics of performance and electrophysiology accompanying alertness lapses during sustained monitoring tasks, and have used the results of this research to design real-time alertness monitoring systems, an example of the rapidly emerging field of neural human-system interface technology. He was the originator and remain the PI and co-developer with Arnaud Delorme of the widely used EEGLAB signal processing environment for MATLAB. Currently, Dr. Makeig is working to apply Independent Component Analysis to EEG and related data to open new windows for noninvasive observation of the relationship between brain dynamics, cognition, and behavior -- in effect working to develop a new brain imaging modality that he calls Mobile Brain/Body Imaging (MoBI).

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Dr. Dimitrios Pantazis directs the recently established Magnetoencephalography (MEG) Laboratory within the Martinos Imaging Center at MIT.  He is also a key developer of Brainstorm,  an open-source environment dedicated to the analysis of brain recordings (MEG, EEG, NIRS, ECoG, depth electrodes, animal electrophysiology) with 13,000+ registered users and 400+ related publications.

Talk TitleAnalysis of EEG/MEG signals at the sensor and source level using the Brainstorm software.

Abstract: The aim of the workshop is to introduce methods and offer hands-on experience for the analysis of electroencephalography (EEG) and magnetoencephalography (MEG) signals.  It will cover the basic principles of EEG/MEG signal processing at the sensor level (preprocessing and filtering, event-related responses, spectral and time-frequency decompositions, cortical rhythms) and the cortical source level (forward and inverse models, dipole fitting and minimum norm solutions, connectivity analysis, statistics). The workshop will also provide fundamentals in EEG/MEG, including the electrophysiological basis of signals, instrumentation, and experimental design. It will present tools needed to perform data analysis, and illustrate a step-by-step processing of example data using the popular open-source software Brainstorm (http://neuroimage.usc.edu/brainstorm/). No prior knowledge of EEG or MEG is required.

More about Dr. Pantazis: Before moving to MIT, he was a research assistant professor at the University of Southern California. His research focuses on the study of brain cognitive networks, and he has over a decade of experience in developing methods for the analysis of MEG data. Dr. Pantazis has over 100 publications in international journals, book chapters, and conference proceedings, with prominent articles in Nature Neuroscience, Proceedings of the National Academy of Sciences, and NeuroImage. His work has been featured in the Scientific American Mind Magazine, Boston Magazine, the front-page of the MIT website , and several Greek media outlets. He is involved in numerous professional and outreach activities, including contributions to MIT and Harvard educational courses, and organization of tours to introduce imaging technology to local communities, high school science teachers, local biotechnology companies, and legislative and judicial representatives.

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Dr. Don Tucker  is an internationally renowned expert in brain electrophysiology and is Professor of psychology, Associate Director, Neuro-informatics Centre at the University of Oregon. He is the author of over 150 publications in the fields of psychology and neuroscience. He is also a co-founder and the inventor of the Geodesic Sensor Net, and serves  as Chairman and Chief Executive Officer of Electrical Geodesics, Incorporated (EGI)

Talk Title: Individual Head Models for Electrical Neuroimaging and Neuromodulation

Abstract:  Dense array technology now allows the electroencephalogram (EEG) to be recorded with up to 256 channels in routine research and clinical settings. Electrical source imaging requires an accurate model of the physical relation between the EEG electrodes and the cerebral cortex, the primary generator of the EEG. The position of the electrodes can be measured with photogrammatic and other methods, the geometry of the head tissues can be imaged with structural MRI, and the conductivity of head tissues can be measured with electrical impedance tomography. The skull is the major resistive barrier to electrical fields, and its bone density can be estimated with precision through x-ray attenuation in computed tomography. By careful attention to each of the electrophysical components of the human head, highly accurate electrical neuroimaging is now achievable for individual subjects. With this level of accuracy now understood for individuals, it is now possible to evaluate the approximation with atlas models without MRI scans for achieving highly affordable workflows for routine human electrical neuroimaging.

More about Dr. Tucker: His research uses methods of cognitive psychology to assess the influence of specific forms of emotional arousal, such as anxiety and depression. To assess the neural activity associated with emotional states and cognitive operations, this research includes computerized analysis of the electrical activity of the brain with dense array EEG measures, developed at EGI. A particular interest now is mechanisms of the limbic system that seem to regulate learning and memory according to strategic motivational controls. For example, anxiety may engage the amygdala and ventral limbic networks that not only focus immediate attention, but facilitate continuing consolidation of threat-related information.

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Mr. Wilkey Wong is the Director of Knowledge Services at Tobii Pro North America. In addition to designing and delivering customer training activities including workshops, courses, webinars, and e-learning resources, he consults in client research for eye tracking study design and analysis.

Talk Title: Co-registration of Eye Movements and Event-Related Potentials

Abstract: Event-related potentials (ERPs) is a common methodology used in neuroscience and psychology research to measure the brain's response to specific stimuli.  This technique, however, is quite sensitive to eye movements as they produce artefacts in the EEG signal. Recently there has been strong interest in co-registering eye tracking and ERPs from simultaneous recording to take advantage of the strengths of both techniques. One example is the eye-fixation related potentials (EFRP): a recent ERP methodology that uses eye tracking data and allows to create more ecological paradigms. Another example is the use of gaze-contingent techniques in infancy research to maximize the EEG data quality. This talk will present a review of the current methodologies used to simultaneously record and analyze eye tracking and EEG data as well as some of the most important application examples where eye tracking can help improve ERP research.

More about Mr. Wong: Since 2009, he has conducted field research in user experience and market research fields such as pharmaceuticals and in-store retail. Wilkey has delivered talks at the BigDesign, UXPA, HCI, ACS International conferences. He has advanced degrees in engineering and education and has conducted graduate research with children in developmental psychology.

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Mike Chi, Cognionics, Inc

Talk Title: Dry and Mobile EEG Solutions for Real-world Neuroimaging

Cognionics will demonstrate their latest advancements in mobile EEG systems for conducting EEG/ERP research in real-world environments. The interactive session will start with a brief overview of our enabling technologies including dry electrodes, wearable form-factors and precision wireless triggering. This will be followed up a hands-on demonstration where participants will have an opportunity to learn how to apply our dry electrode devices and perform an ERP experiment involving multiple subjects, all synchronized via our wireless trigger. Time permitting, we will also demonstrate the ability of our system to interface with external sensors (e.g., ECG/EMG/EOG, GSR, respiration, etc.) and other peripherals.