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Comprehensive Autopsy Program for Individuals with Multiple Sclerosis
Published on: July 19, 2019
Clinical, structural, and functional MRI features predicting PIRMA at 5-year follow-up in multiple sclerosis
Elena Barbuti1, Claudia Piervincenzi1, Viola Baione1
1Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy.
Multiple Sclerosis (Houndmills, Basingstoke, England)
|May 23, 2026
Summary
Progression independent of relapse and MRI activity (PIRMA) in multiple sclerosis (MS) is associated with aging, gray matter atrophy, and altered functional connectivity. These factors help predict silent disease progression using MRI.
Area of Science:
- Neuroimaging
- Neurology
- Radiology
Background:
- Multiple Sclerosis (MS) is a chronic demyelinating disease of the central nervous system.
- Progression independent of relapse and MRI activity (PIRMA) represents a form of silent neurodegeneration in MS.
- Identifying predictors of PIRMA is crucial for understanding MS pathophysiology and managing disease progression.
Purpose of the Study:
- To identify baseline clinical, structural, and functional magnetic resonance imaging (MRI) features that predict PIRMA in MS patients.
- To evaluate the utility of integrating various MRI metrics for detecting silent progression.
Main Methods:
- Analysis of patients from the Italian Neuroimaging Network Initiative with no clinical or MRI activity at 5-year follow-up.
- Comparison of baseline clinical data, white matter (WM) lesion volume, gray matter (GM) volumes, spinal cord area, and functional connectivity (FC) in resting-state networks (RSNs) between PIRMA progressors and stable patients.
- Step-wise logistic regression and kernel principal component analysis (k-PCA) were used to identify PIRMA predictors.
Main Results:
- Of 109 eligible patients, 33% experienced PIRMA.
- PIRMA progressors were older, had higher Expanded Disability Status Scale (EDSS) scores, greater WM lesion volume, GM atrophy, and altered FC.
- Higher EDSS, age, cortical atrophy, and FC alterations were significant predictors of PIRMA. Four k-PCA components, linked to GM atrophy, FC decrements, age, and EDSS, predicted PIRMA with substantial explanatory power (pseudo-R²=0.61).
Conclusions:
- PIRMA in MS is significantly associated with aging, gray matter atrophy, and disrupted functional connectivity.
- Integrating structural and functional MRI markers provides valuable insights into detecting silent progression in MS.
- These findings underscore the importance of advanced neuroimaging techniques for comprehensive MS assessment.
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