Dynamic spatiotemporal brain analyses using high performance electrical neuroimaging: theoretical framework and validation
Stephanie Cacioppo, Robin M Weiss, Hakizumwami Birali Runesha, John T Cacioppo, Stephanie Cacioppo, Robin M Weiss, Hakizumwami Birali Runesha, John T Cacioppo
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.
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.
Copyright © 2014 The Authors. Published by Elsevier B.V. All rights reserved.
Source: PubMed