Unbiased clustering of true neural components to reveal task specific brain activations

Abstract

Event Abstract Back to Event Unbiased clustering of true neural components to reveal task specific brain activations Nikhilesh Natraj1*, J. C. Mizelle1 and Lewis Wheaton1 1 Georgia Institute of Technology, School of Applied Physiology, United States We sought to evaluate whether clustering independent components (ICs) of EEG neural activation can be a measure of quantifying the networks involved in planning skilled hand movements. Fifteen normal right-handed subjects were instructed to perform simple movements and complex tool use pantomime movements while recording 64-channel electroencephalography (EEG) and electromyography (EMG) from forearm and hand muscles. Both movements were similar in kinematics but differed in context (naively performing a twist gesture vs. pantomiming a twist screwdriver; naively performing a push gesture vs. pantomiming a novel "yankee" style push screwdriver). Subjects were first instructed to perform the naive movements, and then perform the pantomimes after practicing with the two types of screwdrivers. Trials were marked for EMG onset and epoched 3.5s - EMG -0.5s. We then decomposed the EEG signal into its statistically most independent components, using the popular ICA algorithm. We identified the strongest component that contributed to power of EEG signal just before movement [500ms to EMG onset] in a pantomime twist condition, and sought significant correlations (r>0.9) to other components in all subjects and conditions. The expectation is that we can find unique, condition specific activations in single subjects based on this unbiased, automated technique. Preliminary results revealed a cluster of highly correlated (r>0.9) left hemispheric parietal components for the pantomime twist condition, across all subjects. This is in line with prior research, indicating that the parietal areas store representations of previously learned tool movements. This component was not well correlated in other conditions, across subjects. Neuroanatomical correlates of the identified components were calculated by source localization and estimating dipoles of neural activity. This enabled us to characterize True Neural Components (TNC) in the data. Correlating ICs appears to be a robust method of blindly clustering TNC in multivariable datasets. We will further test this method by correlating template ICs for all conditions to find neural evidence of other exclusive motor related components. Conference: 2010 South East Nerve Net (SENN) and Georgia/South Carolina Neuroscience Consortium (GASCNC) conferences, Atlanta , United States, 5 Mar - 7 Mar, 2010. Presentation Type: Poster Presentation Topic: Posters Citation: Natraj N, Mizelle JC and Wheaton L (2010). Unbiased clustering of true neural components to reveal task specific brain activations. Front. Neurosci. Conference Abstract: 2010 South East Nerve Net (SENN) and Georgia/South Carolina Neuroscience Consortium (GASCNC) conferences. doi: 10.3389/conf.fnins.2010.04.00067 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 17 Mar 2010; Published Online: 17 Mar 2010. * Correspondence: Nikhilesh Natraj, Georgia Institute of Technology, School of Applied Physiology, Atlanta, United States, niki@gatech.edu Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Nikhilesh Natraj J. C Mizelle Lewis Wheaton Google Nikhilesh Natraj J. C Mizelle Lewis Wheaton Google Scholar Nikhilesh Natraj J. C Mizelle Lewis Wheaton PubMed Nikhilesh Natraj J. C Mizelle Lewis Wheaton Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.