Recognizing mild cognitive impairment based on network connectivity analysis of resting EEG with zero reference

Xu, Peng; Xiong, Xiu Chun; Qin, Xue; Tian, Yin; Peng, Yueheng; Zhang, Rui; Li, Pei Yang; Wang, Yu Ping; Yao, De Zhong

doi:10.1088/0967-3334/35/7/1279

Cited by 36 publications

(38 citation statements)

References 49 publications

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“…REST provides a true electrically neutral reference based on the physical essence of EEG generation (Yao, 2001 ). It is recommended and widely used in neural cognitive and clinical applications (Yao et al, 2005 ; Kayser and Tenke, 2010 ; Qin et al, 2010 ; Tian and Yao, 2013 ; Xu et al, 2014 ; Liu et al, 2015 ). It was shown in the simulation that the error is greatly reduced with REST compared with other commonly used references.…”

Section: Discussionmentioning

confidence: 99%

“…Studies on spectra imaging, EEG coherence, and connectivity using spontaneous EEG showed that REST tends to obtain more accurate and objective results (Yao et al, 2005 ; Marzetti et al, 2007 ; Qin et al, 2010 ). ERP components, cognitive psychology (Yao et al, 2007 ; Tian and Yao, 2013 ; Liu et al, 2015 ), and clinical EEG analysis (Xu et al, 2014 ) have shown that REST is also valuable in cognition and disease recognition.…”

Section: Introductionmentioning

confidence: 99%

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A Comparative Study on the Dynamic EEG Center of Mass with Different References

et al. 2017

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One of the most fundamental issues during an EEG study is choosing an available neutral reference. The infinity zero reference obtained by the reference electrode standardization technique (REST) has been recommended and used for its higher accuracy. This paper examined three traditional references, the average reference (AR), the linked mastoids reference (LM), and REST, in the study of the EEG center of mass (CM) using simulated and real ERPs. In the simulation, the relative error of REST was the smallest among the references. As for the ERP data with the visual oddball paradigm, the dynamic CM trajectory and its traveling velocity obtained by REST characterized three typical stages in spatial domain and temporal speed metrics, which provided useful information in addition to the distinct ERP waveform in the temporal domain. The results showed that the CM traveling from the frontal to parietal areas corresponding to the earlier positive components (i.e., P200 and P250), stays temporarily at the parietal area corresponding to P300 and then returns to the frontal area during the recovery stage. Compared with REST, AR, and LM not only changed the amplitude of P300 significantly but distorted the CM trajectory and its instantaneous velocity. As REST continues to provide objective results, we recommend that REST be used in future EEG/ERP CM studies.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Comparative Study on the Dynamic EEG Center of Mass with Different References

et al. 2017

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“…Furthermore, findings in the MCI patients are quite contradictory among studies, since some of them report no significant changes of brain network in MCI whereas others show decreased or increased “small-worldness.” Specifically, Seo et al reported that local clustering of networks was lower in MCI compared to normal cognitive subjects (Seo et al, 2013 ), whereas Vecchio et al found a significant increment of the clustering coefficient for MCI group (Vecchio et al, 2014 ). Besides, both the above two studies did not observe obvious difference in path length between two groups, whereas Xu et al found that the MCI group had increased path length; using this network feature allows to distinguish the two groups with 90% accuracy (Xu et al, 2014 ). Hence, it is still uncertain that whether MCI individuals would exhibit a disrupted small-world property similar to those of dementia patients, and more work are needed to make clear this problem, especially for the MCI patient with diabetes.…”

Section: Introductionmentioning

confidence: 98%

“…A very serious problem unique to EEG is that the active reference electrode will contribute similar components to EEG signals recorded at different electrodes and yield spurious synchronization. Xu et al have shown how different reference electrode methods, including vertex reference, average reference, and zero reference, affect the diagnosis of MCI (Xu et al, 2014 ). Moreover, nearby EEG electrodes are likely to pick up activity of common sources, which give rise to strong synchronization between recorded signals that reflect simple volume conduction rather than true functional connectivity (Srinivasan et al, 2007 ).…”

Section: Introductionmentioning

confidence: 99%

Complex network analysis of resting state EEG in amnestic mild cognitive impairment patients with type 2 diabetes

Zeng

Wang

Ouyang

et al. 2015

Front. Comput. Neurosci.

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Purpose: Diabetes is a great risk factor for dementia and mild cognitive impairment (MCI). This study investigates whether complex network-derived features in resting state EEG (rsEEG) could be applied as a biomarker to distinguish amnestic mild cognitive impairment (aMCI) from normal cognitive function in subjects with type 2 diabetes (T2D).Method: In this study, EEG was recorded in 28 patients with T2D (16 aMCI patients and 12 controls) during a no-task eyes-closed resting state. Pair-wise synchronization of rsEEG signals were assessed in six frequency bands (delta, theta, lower alpha, upper alpha, beta, and gamma) using phase lag index (PLI) and grouped into long distance (intra- and inter-hemispheric) and short distance interactions. PLI-weighted connectivity networks were also constructed, and characterized by mean clustering coefficient and path length. The correlation of these features and Montreal Cognitive Assessment (MoCA) scores was assessed.Results: Main findings of this study were as follows: (1) In comparison with controls, patients with aMCI had a significant decrease of global mean PLI in lower alpha, upper alpha, and beta bands. Lower functional connection at short and long intra-hemispheric distance mainly appeared on the left hemisphere. (2) In the lower alpha band, clustering coefficient was significantly lower in aMCI group, and the path length significantly increased. (3) Cognitive status measured by MoCA had a significant positive correlation with cluster coefficient and negative correlation with path length in lower alpha band.Conclusions: The brain network of aMCI patients displayed a disconnection syndrome and a loss of small-world architecture. The correlation between cognitive states and network characteristics suggested that the more in deterioration of the diabetes patients' cognitive state, the less optimal the network organization become. Hence, the complex network-derived biomarkers based on EEG could be employed to track cognitive function of diabetic patients and provide a new diagnosis tool for aMCI.

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“…However, scalp EEG recordings are prone to be affected by the non-zero reference choice [7][8][9], which will further the influence on EEG waveform, power spectrum, and EEG brain network [9][10][11][12]. Therefore, the choice of an ideal reference such as infinity reference (IR) with zero potential is an essential step for EEG recordings.…”

Section: Introductionmentioning

confidence: 99%

Combining canonical correlation analysis and infinite reference for frequency recognition of steady-state visual evoked potential recordings: A comparison with periodogram method

Tian

et al. 2014

Bio-Medical Materials and Engineering

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Steady-state visual evoked potentials (SSVEP) are the visual system responses to a repetitive visual stimulus flickering with the constant frequency and of great importance in the study of brain activity using scalp electroencephalography (EEG) recordings. However, the reference influence for the investigation of SSVEP is generally not considered in previous work. In this study a new approach that combined the canonical correlation analysis with infinite reference (ICCA) was proposed to enhance the accuracy of frequency recognition of SSVEP recordings. Compared with the widely used periodogram method (PM), ICCA is able to achieve higher recognition accuracy when extracts frequency within a short span. Further, the recognition results suggested that ICCA is a very robust tool to study the brain computer interface (BCI) based on SSVEP.

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Recognizing mild cognitive impairment based on network connectivity analysis of resting EEG with zero reference

Cited by 36 publications

References 49 publications

A Comparative Study on the Dynamic EEG Center of Mass with Different References

A Comparative Study on the Dynamic EEG Center of Mass with Different References

Complex network analysis of resting state EEG in amnestic mild cognitive impairment patients with type 2 diabetes

Combining canonical correlation analysis and infinite reference for frequency recognition of steady-state visual evoked potential recordings: A comparison with periodogram method

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