Dynamic Changes of Brain Networks during Working Memory Tasks in Schizophrenia

Ye, Rong; Xue, Jiayue; Yang, Pengfei; Wang, Qianshan; Gao, Peng; Yang, Xiaofeng; Deng, Hongxia; Tan, Shuping; Li, Haifang

doi:10.1016/j.neuroscience.2020.11.007

Cited by 9 publications

(5 citation statements)

References 85 publications

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“…Phase-locked synchronization of neural signals in the brain may be the key mechanism for brain information integration ( Michel and Koenig, 2018 ). Therefore, in this study, functional connectivity in the alpha band was constructed by the phase-locking value (PLV) method ( Lachaux et al, 1999 ; Duc and Lee, 2019 ; Yao et al, 2021 ). Microstate-specific connectivity was computed by connected instantaneous phase signals belonging to a particular microstate class.…”

Section: Introductionmentioning

confidence: 99%

EEG Microstate-Specific Functional Connectivity and Stroke-Related Alterations in Brain Dynamics

et al. 2022

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The brain, as a complex dynamically distributed information processing system, involves the coordination of large-scale brain networks such as neural synchronization and fast brain state transitions, even at rest. However, the neural mechanisms underlying brain states and the impact of dysfunction following brain injury on brain dynamics remain poorly understood. To this end, we proposed a microstate-based method to explore the functional connectivity pattern associated with each microstate class. We capitalized on microstate features from eyes-closed resting-state EEG data to investigate whether microstate dynamics differ between subacute stroke patients (N = 31) and healthy populations (N = 23) and further examined the correlations between microstate features and behaviors. An important finding in this study was that each microstate class was associated with a distinct functional connectivity pattern, and it was highly consistent across different groups (including an independent dataset). Although the connectivity patterns were diminished in stroke patients, the skeleton of the patterns was retained to some extent. Nevertheless, stroke patients showed significant differences in most parameters of microstates A, B, and C compared to healthy controls. Notably, microstate C exhibited an opposite pattern of differences to microstates A and B. On the other hand, there were no significant differences in all microstate parameters for patients with left-sided vs. right-sided stroke, as well as patients before vs. after lower limb training. Moreover, support vector machine (SVM) models were developed using only microstate features and achieved moderate discrimination between patients and controls. Furthermore, significant negative correlations were observed between the microstate-wise functional connectivity and lower limb motor scores. Overall, these results suggest that the changes in microstate dynamics for stroke patients appear to be state-selective, compensatory, and related to brain dysfunction after stroke and subsequent functional reconfiguration. These findings offer new insights into understanding the neural mechanisms of microstates, uncovering stroke-related alterations in brain dynamics, and exploring new treatments for stroke patients.

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Section: Introductionmentioning

confidence: 99%

EEG Microstate-Specific Functional Connectivity and Stroke-Related Alterations in Brain Dynamics

et al. 2022

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“…These stable segments were proposed to be “atoms of thought” 53 , which may correspond to different information processing steps. Shrinking microstate durations have been associated with various pathologies, including schizophrenia 54 – 59 , AD 60 – 62 , PD 63 – 65 , depression 32 , 33 , mood alterations 66 , or panic disorder 67 . Shortening of specific microstate classes, in combination with altered microstate syntax, have been interpreted to reflect disturbances in the information processing stream 49 , 50 .…”

Section: Discussionmentioning

confidence: 99%

Telling functional networks apart using ranked network features stability

Zanin

Güntekin

Aktürk

et al. 2022

Sci Rep

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Over the past few years, it has become standard to describe brain anatomical and functional organisation in terms of complex networks, wherein single brain regions or modules and their connections are respectively identified with network nodes and the links connecting them. Often, the goal of a given study is not that of modelling brain activity but, more basically, to discriminate between experimental conditions or populations, thus to find a way to compute differences between them. This in turn involves two important aspects: defining discriminative features and quantifying differences between them. Here we show that the ranked dynamical stability of network features, from links or nodes to higher-level network properties, discriminates well between healthy brain activity and various pathological conditions. These easily computable properties, which constitute local but topographically aspecific aspects of brain activity, greatly simplify inter-network comparisons and spare the need for network pruning. Our results are discussed in terms of microstate stability. Some implications for functional brain activity are discussed.

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“…The EEG activity is nonlinear, nonstationary time‐series data and more often the time‐domain based feature extraction methods limit the amount of information from on‐going EEG activity 45 . In previous studies, various nonlinear feature extraction methods have been proposed to transfer nonlinear dynamical information of EEG signals 46 . In present study, with intentions to carry forward the concept of nonlinearity and dynamical information estimation, time‐domain, frequency‐domain and TF domain‐based EEG features have been extracted.…”

Section: Methodsmentioning

confidence: 99%

“…45 In previous studies, various nonlinear feature extraction methods have been proposed to transfer nonlinear dynamical information of EEG signals. 46 In present study, with intentions to carry forward the concept of nonlinearity and dynamical information estimation, time-domain, frequency-domain and TF domain-based EEG features have been extracted. Also, much commonly used feature estimators viz.…”

Section: Feature Extractionmentioning

confidence: 99%

A novel EEG channel selection and classification methodology for multi‐class motor imagery‐based BCI system design

Jindal

Upadhyay

Singh

2022

Int J Imaging Syst Tech

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Multi-class MI EEG analysis is an extensively used paradigm in BCI. However, multiple EEG channels lead to redundant information extraction and would reduce the distinction among various MI tasks. Therefore, optimal channel selection from multi-channel EEG activity still remains a challenging task. In this study, to enhance the multi-class BCI system's performance, a novel channel selection, and features optimization methodology have been proposed.First, multi-channel EEG dataset is reduced to an optimum no. of channels subset using developed MDA-SOGWO based EEG channel selection criterion.After that, the discriminable feature-set is generated from time, frequency, and FAWT based time-frequency domain of EEG dataset. Then, an informative feature-set is constructed from extracted features through CCA-RFE based feature selection criterion. Finally, training and validation of selected feature-set are carried out using ELM, LDA, RF, and MLP classifiers. The proposed methodology is evaluated on multi-class MI datasets of BCI Competition IV 2a and BCI Competition III 3a, yielding classification accuracy of 85.65% and 95.15%, respectively. The classification results pose the proposed methodology as an efficient method for future BCI system design.

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Dynamic Changes of Brain Networks during Working Memory Tasks in Schizophrenia

Cited by 9 publications

References 85 publications

EEG Microstate-Specific Functional Connectivity and Stroke-Related Alterations in Brain Dynamics

EEG Microstate-Specific Functional Connectivity and Stroke-Related Alterations in Brain Dynamics

Telling functional networks apart using ranked network features stability

A novel EEG channel selection and classification methodology for multi‐class motor imagery‐based BCI system design

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