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Updated: Mar 6, 2026
Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
Published on: July 28, 2013
Individual classification of Alzheimer's disease with diffusion magnetic resonance imaging
Tijn M Schouten1, Marisa Koini2, Frank de Vos1
1Institute of Psychology, Leiden University, The Netherlands; Department of Radiology, Leiden University, The Netherlands; Leiden Institute for Brain and Cognition, The Netherlands.
Abstract:
Diffusion magnetic resonance imaging (MRI) is a powerful non-invasive method to study white matter integrity, and is sensitive to detect differences in Alzheimer's disease (AD) patients. Diffusion MRI may be able to contribute towards reliable diagnosis of AD. We used diffusion MRI to classify AD patients (N=77), and controls (N=173). We use different methods to extract information from the diffusion MRI data. First, we use the voxel-wise diffusion tensor measures that have been skeletonised using tract based spatial statistics. Second, we clustered the voxel-wise diffusion measures with independent component analysis (ICA), and extracted the mixing weights. Third, we determined structural connectivity between Harvard Oxford atlas regions with probabilistic tractography, as well as graph measures based on these structural connectivity graphs. Classification performance for voxel-wise measures ranged between an AUC of 0.888, and 0.902. The ICA-clustered measures ranged between an AUC of 0.893, and 0.920. The AUC for the structural connectivity graph was 0.900, while graph measures based upon this graph ranged between an AUC of 0.531, and 0.840. All measures combined with a sparse group lasso resulted in an AUC of 0.896. Overall, fractional anisotropy clustered into ICA components was the best performing measure. These findings may be useful for future incorporation of diffusion MRI into protocols for AD classification, or as a starting point for early detection of AD using diffusion MRI.
Insights
Diffusion magnetic resonance imaging (MRI) effectively differentiates Alzheimer's disease (AD) patients from controls. Fractional anisotropy, clustered using independent component analysis (ICA), demonstrated the highest diagnostic performance in this study.
Area of Science:
- Neuroimaging
- Medical Physics
- Neurology
Background:
- Diffusion magnetic resonance imaging (MRI) is a non-invasive technique crucial for assessing white matter integrity.
- Alzheimer's disease (AD) diagnosis can be challenging, highlighting the need for sensitive biomarkers.
- Diffusion MRI shows promise in detecting subtle changes associated with AD.
Purpose of the Study:
- To evaluate the efficacy of various diffusion MRI analysis methods for classifying Alzheimer's disease (AD) patients.
- To compare the diagnostic performance of voxel-wise measures, independent component analysis (ICA) clustering, and structural connectivity graphs.
- To identify the optimal diffusion MRI approach for reliable AD classification.
Main Methods:
- Utilized diffusion MRI data from 77 AD patients and 173 controls.
- Applied skeletonized voxel-wise diffusion tensor measures using tract-based spatial statistics.
- Employed independent component analysis (ICA) for clustering diffusion measures and extracted mixing weights.
- Determined structural connectivity using probabilistic tractography and analyzed graph theory measures.
Main Results:
- Voxel-wise diffusion tensor measures achieved an Area Under the Curve (AUC) between 0.888 and 0.902.
- ICA-clustered diffusion measures yielded AUCs ranging from 0.893 to 0.920.
- Structural connectivity graphs showed an AUC of 0.900, while associated graph measures ranged from 0.531 to 0.840.
- Fractional anisotropy clustered into ICA components emerged as the top-performing measure with the highest AUC.
Conclusions:
- Diffusion MRI analysis, particularly fractional anisotropy via ICA, offers high accuracy for Alzheimer's disease classification.
- These findings support the integration of diffusion MRI into AD diagnostic protocols.
- This approach could serve as a foundation for the early detection of Alzheimer's disease.
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