Prediction of Amyloid β-Positivity with both MRI Parameters and Cognitive Function Using Machine Learning
Purpose:
To investigate the MRI markers for the prediction of amyloid β (Aβ)-positivity in mild cognitive impairment (MCI) and Alzheimer's disease (AD), and to evaluate the differences in MRI markers between Aβ-positive (Aβ [+]) and -negative groups using the machine learning (ML) method.
Materials And Methods:
This study included 139 patients with MCI and AD who underwent amyloid PET-CT and brain MRI. Patients were divided into Aβ (+) (n = 84) and Aβ-negative (n = 55) groups. Visual analysis was performed with the Fazekas scale of white matter hyperintensity (WMH) and cerebral microbleeds (CMB) scores. The WMH volume and regional brain volume were quantitatively measured. The multivariable logistic regression and ML using support vector machine, and logistic regression were used to identify the best MRI predictors of Aβ-positivity.
Results:
The Fazekas scale of WMH (p = 0.02) and CMB scores (p = 0.04) were higher in Aβ (+). The volumes of hippocampus, entorhinal cortex, and precuneus were smaller in Aβ (+) (p < 0.05). The third ventricle volume was larger in Aβ (+) (p = 0.002). The logistic regression of ML showed a good accuracy (81.1%) with mini-mental state examination (MMSE) and regional brain volumes.
Conclusion:
The application of ML using the MMSE, third ventricle, and hippocampal volume is helpful in predicting Aβ-positivity with a good accuracy.
Insights
Machine learning accurately predicts amyloid positivity in mild cognitive impairment and Alzheimer's disease using MRI markers. Key predictors include the mini-mental state examination score and specific brain volumes, aiding early diagnosis.
Area of Science:
- Neurology
- Radiology
- Biomedical Engineering
Background:
- Amyloid-beta (Aβ) positivity is a hallmark of Alzheimer's disease (AD) and mild cognitive impairment (MCI).
- Predicting Aβ status non-invasively is crucial for early diagnosis and intervention.
- Magnetic Resonance Imaging (MRI) offers potential biomarkers for Aβ detection.
Purpose of the Study:
- To identify MRI markers for predicting Aβ-positivity in MCI and AD patients.
- To compare MRI marker differences between Aβ-positive (Aβ [+]) and Aβ-negative groups.
- To evaluate the efficacy of machine learning (ML) in predicting Aβ-positivity.
Main Methods:
- 139 MCI and AD patients underwent amyloid PET-CT and brain MRI.
- Visual assessment included Fazekas scale for white matter hyperintensity (WMH) and cerebral microbleeds (CMB).
- Quantitative MRI analysis measured WMH and regional brain volumes; ML models (SVM, logistic regression) were employed.
Main Results:
- Aβ (+) patients showed higher WMH and CMB scores (p=0.02, p=0.04).
- Reduced volumes in hippocampus, entorhinal cortex, and precuneus were observed in Aβ (+) individuals (p<0.05).
- Increased third ventricle volume was noted in Aβ (+) patients (p=0.002); ML achieved 81.1% accuracy.
Conclusions:
- ML models integrating mini-mental state examination (MMSE) and regional brain volumes effectively predict Aβ-positivity.
- Third ventricle and hippocampal volumes are significant MRI predictors.
- This approach shows promise for non-invasive Aβ status prediction in clinical settings.
