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Identifying Parkinson’s disease with mild cognitive impairment by using combined MR imaging and electroencephalogram

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A Correction to this article was published on 23 July 2021

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Abstract

Objectives

To analyse the changes of quantitative electroencephalogram (qEEG) and cortex structural magnetic resonance (MR) imaging in Parkinson’s disease with mild cognitive impairment (PD-MCI) and to explore the “composite marker”–based machine learning model in identifying PD-MCI.

Methods

Retrospective analysis of patients with PD identified 36 PD-MCI and 35 PD with normal cognition (PD-NC). QEEG features of power spectrum and structural MR features of cortex based on surface-based morphometry (SBM) were extracted. Support vector machine (SVM) was established using combined features of structural MR and qEEG to identify PD-MCI. Feature importance evaluation algorithm of mean impact value (MIV) was established to sort the vital characteristics of qEEG and structural MR.

Results

Compared with PD-NC, PD-MCI showed a statistically significant difference in 5 leads and waves of qEEG and 7 cortical region features of structural MR. The SVM model based on these qEEG and structural MR features yielded an accuracy of 0.80 in the training set and had a high prediction accuracy of 0.80 in the test set (sensitivity was 0.78, specificity was 0.83, area under the receiver operating characteristic curve was 0.77), which was higher than the model built by the feature separately. QEEG features of theta wave in C3 had a marked impact on the model for classification according to the MIV algorithm.

Conclusions

PD-MCI is characterized by widespread structural and EEG abnormality. “Composite markers” could be valuable for the individualized diagnosis of PD-MCI by machine learning.

Key Points

• Explore the brain abnormalities in Parkinson’s disease with mild cognitive impairment by using the quantitative electroencephalogram and cortex structural MR simultaneously.

• Multimodal features based support vector machine for identifying Parkinson’s disease with mild cognitive impairment has an acceptable performance.

• Theta wave in C3 is the most influential feature of qEEG and cortex structure MR imaging in identifying Parkinson’s disease with mild cognitive impairment using support vector machine.

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Abbreviations

MIV:

Mean impact value

MR:

Magnetic resonance

PD:

Parkinson’s disease

PD-MCI:

Parkinson’s disease with mild cognitive impairment

PD-NC:

Parkinson’s disease with normal cognition

QEEG:

Quantitative electroencephalogram

ROC:

Receiver operating characteristic

SBM:

Surface-based morphometry

SVM:

Support vector machine

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Acknowledgements

We thank the support of the patients, their families, and control subjects for the study.

Funding

This study has received funding from the National Natural Science Foundation of China (No. 81501112), Guangdong Natural Science Foundation (No. 2016A030310327), Guangdong Natural Science Foundation (No. 2019A1515110061), National Key R&D Program of China (No. 2017YFC1310200), The Fundamental Research Funds for the Central Universities (No. 2018MS27), Key Program of Natural Science Foundation of Guangdong Province, China (No. 2017B030311015), Guangzhou Municipal People’s Livelihood Science and Technology Project (No. 201803010085), and Medical Science and Technology Foundation of Guangdong Province (No. A2018137).

Author information

Authors and Affiliations

  1. The Second School of Clinical Medicine, Southern Medical University, No.1023, South Shatai Road, Baiyun District, Guangzhou, 510515, China

    Jiahui Zhang & Lijuan Wang

  2. Department of Neurology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangdong Neuroscience Institute, No.106, Zhongshan 2nd Road, Yuexiu District, Guangzhou, 510080, China

    Jiahui Zhang, Yuyuan Gao, Shujun Feng, Qingxi Zhang, Jiehao Zhao, Zhiheng Huang, Limin Wang, Guixian Ma, Yuhu Zhang, Kun Nie & Lijuan Wang

  3. Department of Neuroelectrophysiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China

    Xuetao He

  4. Communication and Computer Network Lab of Guangdong, School of Computer Science and Engineering, South China University of Technology, Guangzhou, 510641, China

    Jinlong Hu

Authors
  1. Jiahui Zhang
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  2. Yuyuan Gao
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Corresponding authors

Correspondence to Kun Nie or Lijuan Wang.

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Guarantor

The guarantor name of this publication is Lijuan Wang.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

One of the authors has significant statistical expertise.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

The study had been approved by Guangdong General Hospital for Research with Human Subjects (Ethical Approval No. GDREC2015195H).

Study subjects or cohorts overlap

Some study subjects or cohorts have been previously reported in the article (Gao, Y., K. Nie, M. Mei, et al, Changes in Cortical Thickness in Patients With Early Parkinson’s Disease at Different Hoehn and Yahr Stages. Front Hum Neurosci, 2018. 12: p. 469.)

Methodology

• retrospective

• observational

• performed at one institution

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The original online version of this article was revised: The information was missing that Jiahui Zhang and Yuyuan Gao are co-first authors and that Kun Nie and Lijuan Wang are co-corresponding authors, and the presentation of Figure 2 was incorrect.

Jiahui Zhang and Yuyuan Gao are co-first authors.

Kun Nie and Lijuan Wang are co-corresponding authors

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Zhang, J., Gao, Y., He, X. et al. Identifying Parkinson’s disease with mild cognitive impairment by using combined MR imaging and electroencephalogram. Eur Radiol 31, 7386–7394 (2021). https://doi.org/10.1007/s00330-020-07575-1

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  • DOI: https://doi.org/10.1007/s00330-020-07575-1

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