Cacioppo S, Weiss RM, Runesha HB, Cacioppo JT. Dynamic spatiotemporal brain analyses using high performance electrical neuroimaging: theoretical framework and validation.
J Neurosci Methods 2014;
238:11-34. [PMID:
25244954 DOI:
10.1016/j.jneumeth.2014.09.009]
[Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2014] [Revised: 09/07/2014] [Accepted: 09/11/2014] [Indexed: 10/24/2022]
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.
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