Stephanie Cacioppo1, Robin M Weiss2, Hakizumwami Birali Runesha3, John T Cacioppo4. 1. Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL 60637, USA; CCSN High Performance Electrical Neuroimaging Laboratory, University of Chicago, Chicago, IL 60637, USA. Electronic address: scacioppo@bsd.uchicago.edu. 2. CCSN High Performance Electrical Neuroimaging Laboratory, University of Chicago, Chicago, IL 60637, USA; Research Computing Center, University of Chicago, Chicago, IL 60637, USA. Electronic address: robinweiss@uchicago.edu. 3. Research Computing Center, University of Chicago, Chicago, IL 60637, USA. Electronic address: runesha@uchicago.edu. 4. Center for Cognitive and Social Neuroscience, University of Chicago, Chicago, IL 60637, USA; Department of Psychology, University of Chicago, Chicago, IL 60637, USA. Electronic address: cacioppo@uchicago.edu.
Abstract
BACKGROUND: Since Berger's first EEG recordings in 1929, several techniques, initially developed for investigating periodic processes, have been applied to study non-periodic event-related brain state dynamics. NEW METHOD: We provide a theoretical comparison of the two approaches and present a new suite of data-driven analytic tools for the specific identification of the brain microstates in high-density event-related brain potentials (ERPs). This suite includes four different analytic methods. We validated this approach through a series of theoretical simulations and an empirical investigation of a basic visual paradigm, the reversal checkerboard task. RESULTS: Results indicate that the present suite of data-intensive analytic techniques, improves the spatiotemporal information one can garner about non-periodic brain microstates from high-density electrical neuroimaging data. COMPARISON WITH EXISTING METHOD(S): Compared to the existing methods (such as those based on k-clustering methods), the current micro-segmentation approach offers several advantages, including the data-driven (automatic) detection of non-periodic quasi-stable brain states. CONCLUSION: This suite of quantitative methods allows the automatic detection of event-related changes in the global pattern of brain activity, putatively reflecting changes in the underlying neural locus for information processing in the brain, and event-related changes in overall brain activation. In addition, within-subject and between-subject bootstrapping procedures provide a quantitative means of investigating how robust are the results of the micro-segmentation.
BACKGROUND: Since Berger's first EEG recordings in 1929, several techniques, initially developed for investigating periodic processes, have been applied to study non-periodic event-related brain state dynamics. NEW METHOD: We provide a theoretical comparison of the two approaches and present a new suite of data-driven analytic tools for the specific identification of the brain microstates in high-density event-related brain potentials (ERPs). This suite includes four different analytic methods. We validated this approach through a series of theoretical simulations and an empirical investigation of a basic visual paradigm, the reversal checkerboard task. RESULTS: Results indicate that the present suite of data-intensive analytic techniques, improves the spatiotemporal information one can garner about non-periodic brain microstates from high-density electrical neuroimaging data. COMPARISON WITH EXISTING METHOD(S): Compared to the existing methods (such as those based on k-clustering methods), the current micro-segmentation approach offers several advantages, including the data-driven (automatic) detection of non-periodic quasi-stable brain states. CONCLUSION: This suite of quantitative methods allows the automatic detection of event-related changes in the global pattern of brain activity, putatively reflecting changes in the underlying neural locus for information processing in the brain, and event-related changes in overall brain activation. In addition, within-subject and between-subject bootstrapping procedures provide a quantitative means of investigating how robust are the results of the micro-segmentation.
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