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INVITED REVIEW
Year : 2016  |  Volume : 11  |  Issue : 12  |  Page : 1900-1903

Multiple sclerosis: integration of modeling with biology, clinical and imaging measures to provide better monitoring of disease progression and prediction of outcome


Department of Neurology, University of Minnesota Medical School, Minneapolis, MN; Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN; Brain Sciences Center, VA Medical Center, Minneapolis, MN, USA

Correspondence Address:
Shikha Jain Goodwin
Department of Neurology, University of Minnesota Medical School, Minneapolis, MN; Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN; Brain Sciences Center, VA Medical Center, Minneapolis, MN
USA
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/1673-5374.195274

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Multiple Sclerosis (MS) is a major cause of neurological disability in adults and has an annual cost of approximately $28 billion in the United States. MS is a very complex disorder as demyelination can happen in a variety of locations throughout the brain; therefore, this disease is never the same in two patients making it very hard to predict disease progression. A modeling approach which combines clinical, biological and imaging measures to help treat and fight this disorder is needed. In this paper, I will outline MS as a very heterogeneous disorder, review some potential solutions from the literature, demonstrate the need for a biomarker and will discuss how computational modeling combined with biological, clinical and imaging data can help link disparate observations and decipher complex mechanisms whose solutions are not amenable to simple reductionism.


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