title: Time-frequency phase-synchrony approaches with ERPs.
abstract: Time-frequency signal processing approaches are well-developed, and have been widely employed for the study of the energy distribution of event-related potential (ERP) data across time and frequency. Wavelet time-frequency transform (TFT) and Cohen's class of time-frequency distributions (TFD) are the most widely used in the field. While ERP TFT approaches have been most extensively developed for amplitude measures, reflecting the magnitude of regional neuronal activity, time-frequency phase-synchrony measures have gained increased utility in recent years for the assessment of functional connectivity. Phase synchrony measures can be used to index the functional integration between regions (interregional), in addition to the consistency of activity within region (intertrial). In this paper, we focus on a particular class of time-frequency distributions belonging to Cohen's class, known as the Reduced Interference Distribution (RID) for quantifying functional connectivity, which we recently introduced (Aviyente et al., 2011). The present report first summarizes common time-frequency approaches to computing phase-synchrony with ERP data in order to highlight the similarities and differences relative to the RID. In previous work, we demonstrated differences between the RID and wavelet approaches to indexing phase-synchrony, and have applied the RID to demonstrate that RID-based time-frequency phase-synchrony measures can index increased functional connectivity between medial and lateral prefrontal regions during control processing, observed in the theta band during the error-related negativity (ERN). Because ERN amplitude measures have been associated with two other widely studied medial-frontal theta components (no-go N2; feedback negativity, FN), the application of the RID phase synchrony measure in the present report extends our previous work with ERN to include theta activity during the no-go N2 (inhibitory processing) and the feedback negativity (FN; loss feedback processing). Findings support the idea that similar medial-lateral prefrontal functional connectivity underlies the ERN, no-go N2, and FN components, and provide initial validation that the proposed RID-based time-frequency phase-synchrony measure can index this activity.
authors.affiliation: Michigan State University, United States. Electronic address: aviyente@egr.msu.edu.
authors.forename: Selin
authors.initials: S
authors.lastname: Aviyente
authors.affiliation: University of Maryland, United States.
authors.forename: Anne
authors.initials: A
authors.lastname: Tootell
authors.affiliation: University of Maryland, United States. Electronic address: ebernat@umd.edu.
authors.forename: Edward M
authors.initials: EM
authors.lastname: Bernat
mesh_terms: D000328:Adult; D058407:Electroencephalography Phase Synchronization / Q000502:physiology*; D005071:Evoked Potentials / Q000502:physiology*; D056344:Executive Function / Q000502:physiology*; D006801:Humans; D017397:Prefrontal Cortex / Q000502:physiology*; D013826:Theta Rhythm / Q000502:physiology*; D055815:Young Adult
keywords: Functional connectivity; Medial frontal theta; Phase synchrony; Time-frequency