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Updated: Nov 23, 2025
Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
Published on: July 24, 2019
Identifying Parkinson's disease with mild cognitive impairment by using combined MR imaging and electroencephalogram
Jiahui Zhang1,2, Yuyuan Gao2, Xuetao He3
1The Second School of Clinical Medicine, Southern Medical University, No.1023, South Shatai Road, Baiyun District, Guangzhou, 510515, China.
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.
Insights
Parkinson's disease with mild cognitive impairment (PD-MCI) shows widespread brain abnormalities on quantitative electroencephalogram (qEEG) and MRI. A machine learning model combining these markers effectively identifies PD-MCI.
Area of Science:
- Neuroscience
- Medical Imaging
- Machine Learning
Background:
- Parkinson's disease (PD) can present with mild cognitive impairment (PD-MCI).
- Identifying PD-MCI early is crucial for management.
- Current diagnostic methods may not fully capture the complexity of PD-MCI.
Purpose of the Study:
- To analyze quantitative electroencephalogram (qEEG) and cortex structural magnetic resonance (MR) imaging changes in PD-MCI.
- To develop and evaluate a machine learning model using combined qEEG and MR features for PD-MCI identification.
Main Methods:
- Retrospective analysis of 36 PD-MCI and 35 PD-Normal Cognition (PD-NC) patients.
- Extraction of qEEG power spectrum and surface-based morphometry (SBM) MR features.
- Development of a Support Vector Machine (SVM) model using multimodal features.
Main Results:
- Statistically significant differences in qEEG (5 leads/waves) and structural MR (7 cortical regions) between PD-MCI and PD-NC groups.
- The multimodal SVM model achieved 80% accuracy in training and testing sets.
- Theta wave at C3 was identified as a key discriminating feature by the Mean Impact Value (MIV) algorithm.
Conclusions:
- PD-MCI is associated with widespread structural and electrophysiological abnormalities.
- "Composite markers" from multimodal data show promise for individualized PD-MCI diagnosis via machine learning.
