Measuring interconnection of the residual cortical functional islands in persistent vegetative state and minimal conscious state with EEG nonlinear analysis

Wu, Dawei; Cai, Gui; Zorowitz, Richard D.; Yuan, Ying; Wang, Jie; Song, Wei

doi:10.1016/j.clinph.2011.03.018

Cited by 26 publications

(23 citation statements)

References 39 publications

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“…However, MSE at high TSF could possibly show the difference of interconnection of the cortical functional islands. 18 Previous studies in patients with AD found variable results in increased or decreased complexity. [19][20][21] This diversity of results across frequencies and TSFs, emphasizes the importance of examining a range of time scales when exploring EEG signal dynamics.…”

Section: Discussionmentioning

confidence: 99%

Multiscale Entropy Analysis of Electroencephalography During Sleep in Patients With Parkinson Disease

et al. 2013

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Sleep disorders are frequently seen in patients with Parkinson disease (PD), including rapid eye movement (REM) behavior disorder and periodic limb movement disorder. However, knowledge about changes in non-REM sleep in patients with PD is limited. This study explored the characteristics of electroencephalography (EEG) during sleep in patients with PD and non-PD controls. We further conducted multiscale entropy (MSE) analysis to evaluate and compare the complexity of sleep EEG for the 2 groups. There were 9 patients with PD (Hoehn-Yahr stage 1 or 2) and 11 non-PD controls. All participants underwent standard whole-night polysomnography (PSG), which included 23 channels, 6 of which were for EEG. The raw data of the EEG were extracted and subjected to MSE analysis. Patients with PD had a longer sleep onset time and a higher spontaneous EEG arousal index. Sleep stage-specific increased MSE was observed in patients with PD during non-REM sleep. The difference was more marked and significant at higher time scale factors (TSFs). In conclusion, increased biosignal complexity, as revealed by MSE analysis, was found in patients with PD during non-REM sleep at high TSFs. This finding might reflect a compensatory mechanism for early defects in neuronal network control machinery in PD.

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Section: Discussionmentioning

confidence: 99%

Multiscale Entropy Analysis of Electroencephalography During Sleep in Patients With Parkinson Disease

et al. 2013

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“…Although both patients in VS and in MCS showed significantly lower nonlinear indices compared to healthy controls, these indices did not allow differentiating VS and MCS. In a follow-up study on a larger sample of subjects (VS = 20, MCS = 30), the same group (Wu et al, 2011b) reported that the interconnection of local and distant cortical networks, measured with the C-ApEn index, was higher in the MCS group compared to the VS group.…”

Section: Stimulus-independent Physiological and Neurophysiological Asmentioning

confidence: 94%

“…Wu et al (2011b) measured non-linear indices (LZC, ApEn, and C-ApEn, described previously in paragraph Stimulus-Independent Physiological and Neurophysiological Assessments) in the EEG of patients in VS ( n = 21) and in MCS ( n = 16) who underwent nociceptive acupuncture electrical stimulation. None of the indices differed significantly between the two patient groups.…”

Section: Stimulus-dependent Neurophysiological Assessments: Processinmentioning

confidence: 99%

Questioning the dichotomy between vegetative state and minimally conscious state: a review of the statistical evidence

Liberati

Hünefeldt

Belardinelli

2014

Front. Hum. Neurosci.

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Given the enormous consequences that the diagnosis of vegetative state (VS) vs. minimally conscious state (MCS) may have for the treatment of patients with disorders of consciousness, it is particularly important to empirically legitimate the distinction between these two discrete levels of consciousness. Therefore, the aim of this contribution is to review all the articles reporting statistical evidence concerning the performance of patients in VS vs. patients in MCS, on behavioral or neurophysiological measures. Twenty-three articles matched these inclusion criteria, and comprised behavioral, electroencephalographic (EEG), positron emission tomography (PET) and magnetic resonance imaging (MRI) measures. The analysis of these articles yielded 47 different statistical findings. More than half of these findings (n = 24) did not reveal any statistically significant difference between VS and MCS. Overall, there was no combination of variables that allowed reliably discriminating between VS and MCS. This pattern of results casts doubt on the empirical validity of the distinction between VS and MCS.

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“…Could C-ApEn be used as a feedback index of such resuscitation therapies as repetitive transcranial magnetic stimulation (rTMS) and transcranial direct current stimulation (tDCS) that could increase the excitability of cortical regions of interest? Further research is needed to answer these questions (Wu, Cai, 2011b).…”

Section: Application Of Nda In Assessing Unconsciousnessmentioning

confidence: 99%

“…Therefore, C-ApEn may be interpreted as a measure of the number of states independently accessible by the two cortical areas. Thus, an increase in C-ApEn during painful or auditory stimuli may indicate not only an increase in the number of independent microstates available for the two cortical areas, but also increased inter-cortical communication or information flow (Wu et al , 2011b).…”

Section: Cross Aproximate Entropy (C-apen)mentioning

confidence: 99%

Electroencephalography (EEG) and Unconsciousness

Wu¹,

Yuan²

2012

Advances in Clinical Neurophysiology

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Measuring interconnection of the residual cortical functional islands in persistent vegetative state and minimal conscious state with EEG nonlinear analysis

Cited by 26 publications

References 39 publications

Multiscale Entropy Analysis of Electroencephalography During Sleep in Patients With Parkinson Disease

Multiscale Entropy Analysis of Electroencephalography During Sleep in Patients With Parkinson Disease

Questioning the dichotomy between vegetative state and minimally conscious state: a review of the statistical evidence

Electroencephalography (EEG) and Unconsciousness

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