Related Experiment Video
Updated: Nov 2, 2025
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
Published on: June 26, 2013
Brain connectivity markers in advanced Parkinson's disease for predicting mild cognitive impairment
Hai Lin1,2,3, Zesi Liu1,4, Wei Yan5
1Department of Neurosurgery, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, 3002# Sungang West Road, Futian District, Shenzhen, 518035, China.
Objectives:
Mild cognitive impairment (MCI) is a well-defined non-motor manifestation and a harbinger of dementia in Parkinson's disease. This study is to investigate brain connectivity markers of MCI using diffusion tensor imaging and resting-state functional MRI, and help MCI diagnosis in PD patients.
Methods:
We evaluated 131 advanced PD patients (disease duration > 5 years; 59 patients with MCI) and 48 healthy control subjects who underwent a diffusion-weighted and resting-state functional MRI scanning. The patients were randomly assigned to training (n = 100) and testing (n = 31) groups. According to the Brainnetome Atlas, ROI-based structural and functional connectivity analysis was employed to extract connectivity features. To identify features with significant discriminative power for patient classification, all features were put into an all-relevant feature selection procedure within cross-validation loops.
Results:
Nine features were identified to be significantly relevant to patient classification. They showed significant differences between PD patients with and without MCI and positively correlated with the MoCA score. Five of them did not differ between general MCI subjects and healthy controls from the ADNI database, which suggested that they could uniquely play a part in the MCI diagnosis of PD. On basis of these relevant features, the random forest model constructed from the training group achieved an accuracy of 83.9% in the testing group, to discriminate patients with and without MCI.
Conclusions:
The results of our study provide preliminary evidence that structural and functional connectivity abnormalities may contribute to cognitive impairment and allow to predict the outcome of MCI diagnosis in PD.
Key Points:
• Nine MCI markers were identified using an all-relevant feature selection procedure. • Five of nine markers differed between MCI and NC in PD, but not in general persons. • A random forest model achieved an accuracy of 83.9% for MCI diagnosis in PD.
Insights
Brain connectivity markers can help diagnose mild cognitive impairment (MCI) in Parkinson's disease (PD) patients. This study identified unique brain network differences, achieving 83.9% accuracy in predicting MCI in PD.
Area of Science:
- Neuroimaging
- Neurology
- Cognitive Science
Background:
- Mild cognitive impairment (MCI) is a common non-motor symptom and predictor of dementia in Parkinson's disease (PD).
- Accurate diagnosis of MCI in PD is crucial for timely intervention and management.
- Current diagnostic methods may not fully capture the underlying neurobiological changes associated with MCI in PD.
Purpose of the Study:
- To investigate brain connectivity markers for diagnosing MCI in Parkinson's disease (PD) patients.
- To utilize diffusion tensor imaging (DTI) and resting-state functional MRI (rs-fMRI) to identify structural and functional brain network alterations.
- To develop a predictive model for MCI diagnosis in PD.
Main Methods:
- Evaluated 131 advanced PD patients (59 with MCI) and 48 healthy controls using DTI and rs-fMRI.
- Employed ROI-based structural and functional connectivity analysis based on the Brainnetome Atlas.
- Utilized an all-relevant feature selection procedure within cross-validation for identifying discriminative connectivity features.
Main Results:
- Identified nine brain connectivity features significantly relevant for classifying PD patients with and without MCI.
- Five of these markers showed differences specific to MCI in PD patients compared to healthy controls.
- A random forest model achieved 83.9% accuracy in discriminating MCI in the PD testing group.
Conclusions:
- Structural and functional brain connectivity abnormalities are associated with cognitive impairment in PD.
- The identified connectivity markers show potential for predicting MCI diagnosis in Parkinson's disease.
- This study provides preliminary evidence for using neuroimaging-based connectivity analysis in MCI diagnosis for PD.
More Related Videos
07:26Characterizing the Relationship Between Eye Movement Parameters and Cognitive Functions in Non-demented Parkinson's Disease Patients with Eye Tracking
Published on: September 26, 2019
10:28Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
Published on: July 24, 2019
Related Concept Videos
01:26Alzheimer's Disease: Overview
The clinical diagnosis of AD hinges on the presence of memory and other cognitive impairments. Biomarkers, such as changes in Aβ...
01:37Neural Regulation
01:15Parkinson's Disease: Overview