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Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
Published on: August 7, 2017
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Semi-Supervised Pattern Classification: Application to Structural MRI of Alzheimer's Disease
Dong Hye Ye1, Kilian M Pohl1, Christos Davatzikos1
1Section of Biomedical Image Analysis, University of Pennsylvania, Philadelphia, PA, United States 19104.
... International Workshop on Pattern Recognition in Neuroimaging. International Workshop on Pattern Recognition in Neuroimaging
|June 13, 2017
Summary
This study introduces an advanced image-based classification method for brain MRI scans to predict Alzheimer's Disease progression in Mild Cognitive Impairment (MCI) patients. The novel approach enhances prediction accuracy using manifold learning and semi-supervised classification.
Area of Science:
- Neuroimaging
- Machine Learning
- Medical Diagnostics
Background:
- Mild Cognitive Impairment (MCI) is a precursor to Alzheimer's Disease.
- Accurate prediction of MCI to Alzheimer's Disease conversion is crucial for timely intervention.
- Current classification methods for brain MRI data face challenges with high dimensionality.
Purpose of the Study:
- To develop and evaluate an image-based classification method for predicting Alzheimer's Disease progression in MCI patients.
- To leverage nonlinear manifold learning and semi-supervised classification for improved diagnostic accuracy.
- To assess the method's performance against existing state-of-the-art techniques.
Main Methods:
- Dimensionality reduction of brain MRI data using nonlinear manifold learning techniques.
- Feature extraction from the low-dimensional embedding.
- Application of a semi-supervised classifier utilizing both labeled and unlabeled data.
- Testing on a dataset of 237 MCI patient scans.
Main Results:
- The proposed method effectively reduces the dimensionality of complex MRI data.
- Semi-supervised classification significantly boosted performance by incorporating unlabeled data.
- The image-based classification achieved higher prediction accuracy for MCI to Alzheimer's Disease conversion compared to a state-of-the-art method.
Conclusions:
- The developed image-based classification method shows significant promise for predicting Alzheimer's Disease progression in MCI.
- Manifold learning and semi-supervised classification are effective strategies for analyzing high-dimensional neuroimaging data.
- This approach offers a potential advancement in early diagnosis and patient management for Alzheimer's Disease.
