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Updated: May 5, 2026
Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
Published on: July 28, 2013
Unraveling Microstructural and Macrostructural Brain Age Dynamics in Multiple Sclerosis
Xinjie Chen1,2,3, Po-Jui Lu1,2,3, Mario Ocampo-Pineda1,2,3
1Department of Biomedical Engineering, Translational Imaging in Neurology (ThINk) Basel, Faculty of Medicine, University Hospital Basel and University of Basel, Switzerland.
Background And Objectives:
In multiple sclerosis (MS), neurodegeneration results from the interplay between disease-specific pathology and normal aging. Conventional MRI captures morphologic changes in neurodegeneration, while quantitative MRI (qMRI) provides biophysical measures of microstructural alterations. Combining these modalities may reveal how aging and pathology interact and contribute to disability progression in people with MS.
Methods:
We analyzed cross-sectional and longitudinal morphometry data from 1,353 patients with MS and 3,462 healthy controls (HCs). In addition, cross-sectional qMRI data, available for 378 HCs and 169 patients with MS, were analyzed separately. Morphometric measures and quantitative metrics were used to estimate brain-predicted age differences (brain-PADs) with machine learning. We assessed the added value of quantitative metrics over a model based exclusively on morphometric measures in brain age prediction. We also investigated the associations of brain-PADs derived from conventional and qMRI-based predictive models with clinical disability, serum inflammatory biomarkers of neuroaxonal and astrocytic injury, and lesion burden.
Results:
Models combining morphometry and qMRI data achieved the best performance (mean absolute error: 5.73), outperforming those based on qMRI (6.62) or morphometry alone (8.00). Cross-sectional and longitudinal morphometry-based brain-PAD correlated with clinical disability, serum neurofilament light chain, and serum glial fibrillary acidic protein levels (all p < 0.01), with significant longitudinal interactions with time (all p < 0.05). Cross-sectional qMRI-based brain-PAD correlated with white matter lesion count (p = 0.042, R2 = 0.028) and paramagnetic rim lesion volume (p = 0.028, R2 = 0.020).
Discussion:
Integrating qMRI improves brain age predictions. Brain-PAD serves as an imaging biomarker to quantify MS-associated aging and track disability and neuroinflammation progression.
Insights
Quantitative MRI (qMRI) combined with conventional MRI improves brain age prediction in multiple sclerosis (MS). This brain-predicted age difference (brain-PAD) can track MS progression, disability, and neuroinflammation.
Area of Science:
- Neuroimaging
- Biomarkers
- Neurodegeneration
Background:
- Multiple sclerosis (MS) involves neurodegeneration influenced by disease pathology and aging.
- Conventional MRI shows structural changes, while quantitative MRI (qMRI) measures microstructural alterations.
- Combining MRI techniques may clarify aging-pathology interactions in MS disability.
Purpose of the Study:
- To assess the added value of qMRI in predicting brain age differences (brain-PAD) in MS.
- To investigate associations between brain-PAD, clinical disability, and neuroinflammation biomarkers.
- To determine if integrated MRI models improve brain age prediction over morphometry or qMRI alone.
Main Methods:
- Analysis of cross-sectional and longitudinal morphometry data from 1,353 MS patients and 3,462 healthy controls (HCs).
- Analysis of cross-sectional qMRI data from 169 MS patients and 378 HCs.
- Machine learning models used to estimate brain-PAD from morphometric and qMRI data; associations with clinical and biomarker data were assessed.
Main Results:
- Integrated morphometry and qMRI models yielded the best brain age prediction performance (mean absolute error: 5.73).
- Morphometry-based brain-PAD correlated with disability, neurofilament light chain, and glial fibrillary acidic protein.
- qMRI-based brain-PAD correlated with white matter lesion count and paramagnetic rim lesion volume.
Conclusions:
- Integrating qMRI enhances the accuracy of brain age prediction in MS.
- Brain-PAD derived from integrated MRI serves as a valuable imaging biomarker.
- This approach can quantify MS-associated aging and monitor disease progression, disability, and neuroinflammation.
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