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Open AccessJournal ArticleDOI

Neural Network Reorganization Analysis During an Auditory Oddball Task in Schizophrenia Using Wavelet Entropy

TLDR
The findings suggest that the activation response is weakly phase-locked to stimulus onset in SCH and related to the default mode and salience networks.
Abstract
The aim of the present study was to characterize the neural network reorganization during a cognitive task in schizophrenia (SCH) by means of wavelet entropy (WE). Previous studies suggest that the cognitive impairment in patients with SCH could be related to the disrupted integrative functions of neural circuits. Nevertheless, further characterization of this effect is needed, especially in the time-frequency domain. This characterization is sensitive to fast neuronal dynamics and their synchronization that may be an important component of distributed neuronal interactions; especially in light of the disconnection hypothesis for SCH and its electrophysiological correlates. In this work, the irregularity dynamics elicited by an auditory oddball paradigm were analyzed through synchronized-averaging (SA) and single-trial (ST) analyses. They provide complementary information on the spatial patterns involved in the neural network reorganization. Our results from 20 healthy controls and 20 SCH patients showed a WE decrease from baseline to response both in controls and SCH subjects. These changes were significantly more pronounced for healthy controls after ST analysis, mainly in central and frontopolar areas. On the other hand, SA analysis showed more widespread spatial differences than ST results. These findings suggest that the activation response is weakly phase-locked to stimulus onset in SCH and related to the default mode and salience networks. Furthermore, the less pronounced changes in WE from baseline to response for SCH patients suggest an impaired ability to reorganize neural dynamics during an oddball task.

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Phase entrainment of human delta oscillations can mediate the effects of expectation on reaction speed (Journal of Neuroscience (2010) (13578-13585))

TL;DR: The results suggest that low-frequency oscillations play a functional role in human anticipatory mechanisms, presumably by modulating synchronized rhythmic fluctuations in the excitability of large neuronal populations and by facilitating efficient task-related neuronal communication among brain areas responsible for sensory processing and response execution.
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Application of stationary wavelet entropy in pathological brain detection

TL;DR: The classification performance improved after replacing wavelet entropy (THE AUTHORS), wavelet energy (WN), and discrete wavelet transform (DWT) with the proposed SWE, which is superior to THEY, WN, and DWT.
Journal ArticleDOI

Quantification of Graph Complexity Based on the Edge Weight Distribution Balance: Application to Brain Networks

TL;DR: The findings revealed that SGC does not need a comparison with null-hypothesis networks constructed by a surrogate process, and results on the real dataset suggest that schizophrenia is associated with a deficit in the brain dynamic reorganization related to secondary pathways of the brain network.
Journal ArticleDOI

Functional EEG network analysis in schizophrenia: Evidence of larger segregation and deficit of modulation

TL;DR: A deficit in fast modulation of functional network properties during an odd‐ball task was found in schizophrenia patients and this deficit was also found in first‐episode patients and a significant association between network modulation deficits and cognition in the patients.
Journal ArticleDOI

Exploring non-stationarity patterns in schizophrenia: neural reorganization abnormalities in the alpha band.

TL;DR: The findings suggest anomalous reorganization of neural dynamics in SCH during an oddball task, and the abnormal frequency-dependent modulation found in SCH patients during relevant tones is in agreement with the hypothesis of aberrant salience detection in SCH.
References
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A mathematical theory of communication

TL;DR: This final installment of the paper considers the case where the signals or the messages or both are continuously variable, in contrast with the discrete nature assumed until now.
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A wavelet tour of signal processing

TL;DR: An introduction to a Transient World and an Approximation Tour of Wavelet Packet and Local Cosine Bases.
Journal ArticleDOI

A Practical Guide to Wavelet Analysis.

TL;DR: In this article, a step-by-step guide to wavelet analysis is given, with examples taken from time series of the El Nino-Southern Oscillation (ENSO).
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