title: Dynamic spatiotemporal brain analyses using high performance electrical neuroimaging: theoretical framework and validation.
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.
  
  
  METHODS
  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.
  
  
  CONCLUSIONS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: 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.
authors.forename: Stephanie
authors.initials: S
authors.lastname: Cacioppo
authors.affiliation: 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.
authors.forename: Robin M
authors.initials: RM
authors.lastname: Weiss
authors.affiliation: Research Computing Center, University of Chicago, Chicago, IL 60637, USA. Electronic address: runesha@uchicago.edu.
authors.forename: Hakizumwami Birali
authors.initials: HB
authors.lastname: Runesha
authors.affiliation: 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.
authors.forename: John T
authors.initials: JT
authors.lastname: Cacioppo
mesh_terms: D000465:Algorithms; D001921:Brain / Q000502:physiology*; D016000:Cluster Analysis; D003198:Computer Simulation; D004569:Electroencephalography / Q000379:methods*; D005071:Evoked Potentials; D005260:Female; D006801:Humans; D008297:Male; D008959:Models, Neurological; D009010:Monte Carlo Method; D012815:Signal Processing, Computer-Assisted; D014796:Visual Perception / Q000502:physiology; D055815:Young Adult
keywords: Bootstrapping; Brain modeling; Cosine distance metric; Data-driven; Electrical neuroimaging; Electrodynamics; Electroencephalography; Event-related potentials; Image segmentation; Mean square error methods; Open source; Root mean square; Topographic analysis