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Lexical influences on speech perception: a Granger causality analysis of MEG and EEG source estimates.

ABSTRACT: Behavioral and functional imaging studies have demonstrated that lexical knowledge influences the categorization of perceptually ambiguous speech sounds. However, methodological and inferential constraints have so far been unable to resolve the question of whether this interaction takes the form of direct top-down influences on perceptual processing, or feedforward convergence during a decision process. We examined top-down lexical influences on the categorization of segments in a /s/-/integral/ continuum presented in different lexical contexts to produce a robust Ganong effect. Using integrated MEG/EEG and MRI data we found that, within a network identified by 40 Hz gamma phase locking, activation in the supramarginal gyrus associated with wordform representation influences phonetic processing in the posterior superior temporal gyrus during a period of time associated with lexical processing. This result provides direct evidence that lexical processes influence lower level phonetic perception, and demonstrates the potential value of combining Granger causality analyses and high spatiotemporal resolution multimodal imaging data to explore the functional architecture of cognition.

SUBMITTER: Gow DW

PROVIDER: S-EPMC2585985 | biostudies-literature | 2008 Nov

REPOSITORIES: biostudies-literature

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Lexical influences on speech perception: a Granger causality analysis of MEG and EEG source estimates.

Gow David W DW Segawa Jennifer A JA Ahlfors Seppo P SP Lin Fa-Hsuan FH

NeuroImage 20080725 3

Behavioral and functional imaging studies have demonstrated that lexical knowledge influences the categorization of perceptually ambiguous speech sounds. However, methodological and inferential constraints have so far been unable to resolve the question of whether this interaction takes the form of direct top-down influences on perceptual processing, or feedforward convergence during a decision process. We examined top-down lexical influences on the categorization of segments in a /s/-/integral/ ...[more]

PMID: 18703146

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Project description:ObjectiveCombined source-imaging techniques and directional connectivity analysis can provide useful information about the underlying brain networks in a noninvasive fashion. Source-imaging techniques have been used successfully to either determine the source of activity or to extract source time-courses for Granger causality analysis, previously. In this work, we utilize source-imaging algorithms to both find the network nodes [regions of interest (ROI)] and then extract the activation time series for further Granger causality analysis. The aim of this work is to find network nodes objectively from noninvasive electromagnetic signals, extract activation time-courses, and apply Granger analysis on the extracted series to study brain networks under realistic conditions.MethodsSource-imaging methods are used to identify network nodes and extract time-courses and then Granger causality analysis is applied to delineate the directional functional connectivity of underlying brain networks. Computer simulations studies where the underlying network (nodes and connectivity pattern) is known were performed; additionally, this approach has been evaluated in partial epilepsy patients to study epilepsy networks from interictal and ictal signals recorded by EEG and/or Magnetoencephalography (MEG).ResultsLocalization errors of network nodes are less than 5 mm and normalized connectivity errors of ∼20% in estimating underlying brain networks in simulation studies. Additionally, two focal epilepsy patients were studied and the identified nodes driving the epileptic network were concordant with clinical findings from intracranial recordings or surgical resection.ConclusionOur study indicates that combined source-imaging algorithms with Granger causality analysis can identify underlying networks precisely (both in terms of network nodes location and internodal connectivity).SignificanceThe combined source imaging and Granger analysis technique is an effective tool for studying normal or pathological brain conditions.

Dataset Information

Lexical influences on speech perception: a Granger causality analysis of MEG and EEG source estimates.

Publications

Lexical influences on speech perception: a Granger causality analysis of MEG and EEG source estimates.

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