Study Identifies New Subtypes of Multiple Sclerosis Based on Hidden Patterns of Damage Seen by MRI
Background: Currently, types of multiple sclerosis (MS) are determined by a combination of symptoms and somewhat subjective observations of disease changes, rather than by specific markers of disease. These observations usually guide the timing and choice of treatment.
Research: To better understand different MS subtypes and identify markers of disease, researchers have leveraged billions of dollars invested in MS clinical trials by accessing MRI brain scans from thousands of people followed in past clinical trials. They used self-training computers and artificial intelligence (machine learning) to examine MRI scans from over 6,000 people with MS. The computers identified patterns of shared characteristics of evolving disease damage. They then validated the initial findings in another set of MRI images from over 3,000 people with MS.
Findings: From this study, the researchers found they could classify people with MS into three specific subgroups based on shared patterns of tissue damage detected on MRI brain scans. These subgroups are different than the standard clinical types of MS that people are generally diagnosed with. The team defined three MS subtypes based on where the underlying disease activity initially appeared on scans, and the evolution of damage and brain tissue shrinkage (atrophy) in specific brain regions over time. The three MS subtypes identified are:
- The “cortex-led” subtype showed early signs of tissue shrinkage (atrophy) in the outer layer of the brain;
- The “normal-appearing white matter-led” subtype began with diffuse tissue abnormalities in the middle of the brain;
- The “lesion-led” subtype started with widespread accumulation of damaged areas (lesions), followed by early and severe atrophy in several brain areas. This subtype had the highest relapse rates and risk of disability progression, and in some clinical trials showed more benefits of treatment.
Impact: Additional research will be needed to translate these findings into practical use for guiding clinical care, making treatment choices, and identifying those who would best respond to a particular therapy. In the future, these subgroups might help predict those who are more likely to have disease progression, help target treatments for individuals, and enable researchers to do clinical trials of compounds that target a person’s specific underlying pathology.
NOTE: This work was supported in large part by the International Progressive MS Alliance, an unprecedented global collaboration to end progressive MS. The work was conducted by an international collaborative research network led by Dr. Douglas Arnold (Montreal Neurological Institute, McGill University, Canada).
For more information on the research study, refer to publication in Nature Communications:
“Identifying multiple sclerosis subtypes using unsupervised machine learning and MRI data,” by Arman Eshaghi, Alexandra Young, Peter Wijeratne, Ferran Prados, Douglas Arnold, Sridar Narayanan, Charles Guttmann, Frederik Barkhof, Daniel C Alexander, Alan Thompson, Declan Chard, Olga Ciccarelli, was published in Nature Communications on April 6, 2021. This is an open-access paper that can be read in full by anyone.
About the International Progressive MS Alliance
The Alliance exists to accelerate the development of effective treatments for people with progressive forms of multiple sclerosis to improve quality of life worldwide. It is an unprecedented global collaboration of MS organisations, researchers, health professionals, the pharmaceutical industry, companies, trusts, foundations, donors and people affected by progressive MS, working together to address the unmet needs of people with progressive MS ─ rallying the global community to find solutions. Our promise is more than hope, it is progress.
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