Oscillatory dynamics of cortical functional connections in semantic prediction

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Abstract

An event related potential, known as the N400, has been particularly useful in investigating language processing as it serves as a neural index for semantic prediction. There are numerous studies on the functional segregation of N400 neural sources; however, the oscillatory dynamics of functional connections among the relevant sources has remained elusive. In this study we acquired magnetoencephalography data during a classic N400 paradigm, where the semantic predictability of a fixed target noun was manipulated in simple German sentences. We conducted inter‐regional functional connectivity (FC) and time–frequency analysis on known regions of the semantic network, encompassing bilateral temporal, and prefrontal cortices. Increased FC was found in less predicted (LP) nouns compared with highly predicted (HP) nouns in three connections: (a) right inferior frontal gyrus (IFG) and right middle temporal gyrus (MTG) from 0 to 300 ms mainly within the alpha band, (b) left lateral orbitofrontal (LOF) and right IFG around 400 ms within the beta band, and (c) left superior temporal gyrus (STG) and left LOF from 300 to 700 ms in the beta and low gamma bands. Furthermore, gamma spectral power (31–70 Hz) was stronger in HP nouns than in LP nouns in left anterior temporal cortices in earlier time windows (0–200 ms). Our findings support recent theories in language comprehension, suggesting fronto‐temporal top–down connections are mainly mediated through beta oscillations while gamma band frequencies are involved in matching between prediction and input.

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Dynamic predictions: oscillations and synchrony in top-down processing.

A K Engel, P Fries, W. Singer (2001)

Classical theories of sensory processing view the brain as a passive, stimulus-driven device. By contrast, more recent approaches emphasize the constructive nature of perception, viewing it as an active and highly selective process. Indeed, there is ample evidence that the processing of stimuli is controlled by top-down influences that strongly shape the intrinsic dynamics of thalamocortical networks and constantly create predictions about forthcoming sensory events. We discuss recent experiments indicating that such predictions might be embodied in the temporal structure of both stimulus-evoked and ongoing activity, and that synchronous oscillations are particularly important in this process. Coherence among subthreshold membrane potential fluctuations could be exploited to express selective functional relationships during states of expectancy or attention, and these dynamic patterns could allow the grouping and selection of distributed neuronal responses for further processing.

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Spatiotemporal signal space separation method for rejecting nearby interference in MEG measurements.

S Taulu, J Simola (2006)

Limitations of traditional magnetoencephalography (MEG) exclude some important patient groups from MEG examinations, such as epilepsy patients with a vagus nerve stimulator, patients with magnetic particles on the head or having magnetic dental materials that cause severe movement-related artefact signals. Conventional interference rejection methods are not able to remove the artefacts originating this close to the MEG sensor array. For example, the reference array method is unable to suppress interference generated by sources closer to the sensors than the reference array, about 20-40 cm. The spatiotemporal signal space separation method proposed in this paper recognizes and removes both external interference and the artefacts produced by these nearby sources, even on the scalp. First, the basic separation into brain-related and external interference signals is accomplished with signal space separation based on sensor geometry and Maxwell's equations only. After this, the artefacts from nearby sources are extracted by a simple statistical analysis in the time domain, and projected out. Practical examples with artificial current dipoles and interference sources as well as data from real patients demonstrate that the method removes the artefacts without altering the field patterns of the brain signals.

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Measuring phase synchrony in brain signals

Francisco J. Varela, Eugenio Rodriguez, Jean-Philippe Lachaux … (1999)

This article presents, for the first time, a practical method for the direct quantification of frequency‐specific synchronization (i.e., transient phase‐locking) between two neuroelectric signals. The motivation for its development is to be able to examine the role of neural synchronies as a putative mechanism for long‐range neural integration during cognitive tasks. The method, called phase‐locking statistics (PLS), measures the significance of the phase covariance between two signals with a reasonable time‐resolution (<100 ms). Unlike the more traditional method of spectral coherence, PLS separates the phase and amplitude components and can be directly interpreted in the framework of neural integration. To validate synchrony values against background fluctuations, PLS uses surrogate data and thus makes no a priori assumptions on the nature of the experimental data. We also apply PLS to investigate intracortical recordings from an epileptic patient performing a visual discrimination task. We find large‐scale synchronies in the gamma band (45 Hz), e.g., between hippocampus and frontal gyrus, and local synchronies, within a limbic region, a few cm apart. We argue that whereas long‐scale effects do reflect cognitive processing, short‐scale synchronies are likely to be due to volume conduction. We discuss ways to separate such conduction effects from true signal synchrony. Hum Brain Mapping 8:194–208, 1999. © 1999 Wiley‐Liss, Inc.

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Author and article information

Journal

Title: Human Brain Mapping

Abbreviated Title: Hum Brain Mapp

Publisher: Wiley

ISSN (Print): 1065-9471

ISSN (Electronic): 1097-0193

Publication date Created: November 26 2018

Publication date Created: April 15 2019

Publication date (Electronic): December 07 2018

Publication date (Print): April 15 2019

Volume: 40

Issue: 6

Pages: 1856-1866

Affiliations

[1 ]Department of RadiologyMassachusetts General Hospital, Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School Boston Massachusetts

[2 ]Department of PsychologyUniversity of Lübeck Lübeck Germany

[3 ]Department of NeuropsychologyMax Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany

[4 ]MEG and Cortical Networks Group, Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany