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Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
Published on: July 28, 2013
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Advanced brain diffusion MRI and image texture measures have the potential to predict multi-domain functional
Olayinka Oladosu1, Yunyan Zhang2
1Department of Radiology, University of Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, AB, Canada.
Journal of Neuroscience Methods
|August 31, 2025
Summary
Advanced MRI techniques can predict multiple sclerosis (MS) functional impairments. Phase congruency imaging of normal-appearing white matter shows promise for predicting physical functions in MS patients.
Area of Science:
- Neuroimaging
- Neurology
- Medical Image Analysis
Background:
- Multiple sclerosis (MS) presents with diverse functional impairments necessitating early and accurate characterization.
- Current methods for characterizing MS-related functional impairments are limited.
- Developing novel imaging-driven approaches is crucial for predicting MS functions.
Purpose of the Study:
- To develop and validate advanced imaging-based models for predicting functional impairments in multiple sclerosis (MS).
- To assess the utility of diffusion MRI and phase congruency texture analysis in predicting physical, neurocognitive, and affective functions in MS.
- To compare the predictive performance of models utilizing normal-appearing white matter (NAWM) versus NAWM with lesions.
Main Methods:
- 19 women with MS (RRMS and SPMS subtypes) and 19 controls underwent 3T MRI.
- Advanced diffusion MRI and phase congruency texture analysis were used to assess nerve tract integrity in the corpus callosum, corticospinal tracts, and optic radiations.
- Ridge regression was employed to predict physical, neurocognitive, and affective functions using top-ranked imaging measures.
Main Results:
- Diffusion measures (apparent fiber density, fractional anisotropy) and phase congruency measures were top predictors of MS severity.
- Models demonstrated strong prediction for physical functions, strong-to-moderate for neurocognitive functions, and weaker prediction for affective functions.
- Phase congruency models, particularly those using NAWM, outperformed diffusion-based models and showed superiority or similarity to NAWM+Lesion models.
Conclusions:
- Advanced imaging models, especially phase congruency analysis of NAWM, can effectively predict MS functional outcomes.
- These imaging-driven approaches hold potential for early intervention strategies in multiple sclerosis.
- Phase congruency NAWM models are particularly promising for predicting physical functions in MS.
