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Updated: Aug 4, 2025
Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
Published on: July 28, 2013
Reproducible Abnormalities and Diagnostic Generalizability of White Matter in Alzheimer's Disease
Yida Qu1,2, Pan Wang3, Hongxiang Yao3
1Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
Abstract:
Alzheimer's disease (AD) is associated with the impairment of white matter (WM) tracts. The current study aimed to verify the utility of WM as the neuroimaging marker of AD with multisite diffusion tensor imaging datasets [321 patients with AD, 265 patients with mild cognitive impairment (MCI), 279 normal controls (NC)], a unified pipeline, and independent site cross-validation. Automated fiber quantification was used to extract diffusion profiles along tracts. Random-effects meta-analyses showed a reproducible degeneration pattern in which fractional anisotropy significantly decreased in the AD and MCI groups compared with NC. Machine learning models using tract-based features showed good generalizability among independent site cross-validation. The diffusion metrics of the altered regions and the AD probability predicted by the models were highly correlated with cognitive ability in the AD and MCI groups. We highlighted the reproducibility and generalizability of the degeneration pattern of WM tracts in AD.
Insights
White matter (WM) integrity is impaired in Alzheimer's disease (AD). Diffusion tensor imaging revealed consistent WM degeneration patterns in AD and mild cognitive impairment (MCI) patients, correlating with cognitive decline.
Area of Science:
- Neuroimaging
- Neurology
- Biomedical Engineering
Background:
- Alzheimer's disease (AD) is characterized by white matter (WM) tract impairment.
- Neuroimaging markers are crucial for early AD detection and understanding disease progression.
Purpose of the Study:
- To validate white matter (WM) as a neuroimaging biomarker for Alzheimer's disease (AD).
- To assess the reproducibility and generalizability of WM changes in AD using multisite data.
Main Methods:
- Utilized multisite diffusion tensor imaging (DTI) datasets from 321 AD, 265 mild cognitive impairment (MCI), and 279 normal control (NC) participants.
- Employed automated fiber quantification and random-effects meta-analyses to identify WM degeneration patterns.
- Developed and validated machine learning models using tract-based features with independent site cross-validation.
Main Results:
- A reproducible pattern of decreased fractional anisotropy (FA) was observed in WM tracts of AD and MCI groups compared to NC.
- Machine learning models demonstrated good generalizability across independent sites.
- Diffusion metrics and predicted AD probability strongly correlated with cognitive abilities in AD and MCI groups.
Conclusions:
- White matter (WM) integrity alterations are a reproducible and generalizable neuroimaging marker in Alzheimer's disease (AD).
- These WM changes are associated with cognitive impairment in AD and MCI.
- DTI-based WM analysis holds potential for AD diagnosis and monitoring.
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