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Updated: Oct 2, 2026
Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
Published on: July 28, 2013
Analysis of sub-anatomic diffusion tensor imaging indices in white matter regions of Alzheimer with MMSE score
Ravindra B Patil1, S Ramakrishnan1
1Non Invasive Imaging and Diagnostics Laboratory, Biomedical Engineering Group, Department of Applied Mechanics, Indian Institute of Technology Madras, Chennai 600036, India.
Abstract:
In this study, an attempt has been made to find the correlation between diffusion tensor imaging (DTI) indices of white matter (WM) regions and mini mental state examination (MMSE) score of Alzheimer patients. Diffusion weighted images are obtained from the ADNI database. These are preprocessed for eddy current correction and removal of non-brain tissue. Fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD) and axial diffusivity (DA) indices are computed over significant regions (Fornix left, Splenium of corpus callosum left, Splenium of corpus callosum right, bilateral genu of the corpus callosum) affected by Alzheimer disease (AD) pathology. The correlation is computed between diffusion indices of the significant regions and MMSE score using linear fit technique so as to find the relation between clinical parameters and the image features. Binary classification has been employed using support vector machine, decision stumps and simple logistic classifiers on the extracted DTI indices along with MMSE score to classify Alzheimer patients from healthy controls. It is observed that distinct values of DTI indices exist for the range of MMSE score. However, there is no strong correlation (Pearson's correlation coefficient 'r' varies from 0.0383 to -0.1924) between the MMSE score and the diffusion indices over the significant regions. Further, the performance evaluation of classifiers shows 94% accuracy using SVM in differentiating AD and control. In isolation clinical and image features can be used for prescreening and diagnosis of AD but no sub anatomic region correlation exist between these features set. The discussion on the correlation of diffusion indices of WM with MMSE score is presented in this study.
Insights
This study found no strong correlation between diffusion tensor imaging (DTI) indices in specific white matter regions and the Mini-Mental State Examination (MMSE) score in Alzheimer
Area of Science:
- Neuroimaging
- Biomedical Engineering
- Neurology
Background:
- Alzheimer's disease (AD) is a progressive neurodegenerative disorder.
- Diffusion Tensor Imaging (DTI) measures white matter integrity.
- Mini-Mental State Examination (MMSE) assesses cognitive function.
Purpose of the Study:
- To investigate the correlation between DTI indices and MMSE scores in Alzheimer's patients.
- To explore the relationship between white matter integrity and cognitive decline in AD.
- To evaluate the utility of DTI and MMSE for AD classification.
Main Methods:
- Acquired diffusion-weighted images from the ADNI database.
- Computed DTI indices (FA, MD, RD, DA) in specific white matter regions.
- Applied linear fit for correlation analysis and machine learning classifiers (SVM) for classification.
Main Results:
- No significant correlation was found between DTI indices and MMSE scores (r values from 0.0383 to -0.1924).
- Distinct DTI index values were observed across the range of MMSE scores.
- Support Vector Machine (SVM) achieved 94% accuracy in differentiating AD patients from controls.
Conclusions:
- While DTI and MMSE are useful individually for AD prescreening, no direct correlation exists between DTI indices in specific WM regions and MMSE scores.
- DTI metrics and cognitive scores may reflect different aspects of AD pathology.
- Machine learning models show promise in classifying AD using combined DTI and MMSE data.
