multiple sclerosis ( ms ) , received its name in 1955 , is an inflammatory disorder of brain and spinal cord where in body 's immune system incorrectly attacks its own central nervous system ( cns ) , causing variable and unpredictable symptoms ( some examples including slurred speech , blurred vision , loss of balance , poor coordination , tremors , numbness , extreme fatigue , problems with memory and concentration , paralysis , and blindness ) . even though a lot of advances have been made in past few decades , exact causes of this disorder remains unknown(environmental factor combined with genetic predisposition ) . there is a high cost associated with this disorder and it reduces life expectancy as well as quality of life . unfortunately , there is no cure for ms , and most of the current drugs available in the market help treat the symptoms . ms is not the same in any two people who have it , since the underlying cause is demyelination ( along with axon degeneration ) , which can target any brain area , resulting in a broad range of clinical symptoms ( compston and coles , 2002 ; alastair compston et al . in addition , brain lesions can sometimes outnumber the clinical symptoms by as much as 10:1 , and lesions in noncritical areas may not result in obvious functional deficits , even if brain function is in fact altered . there are four subtypes of the disease - relapsing - remitting ms , primary progressive ms , secondary progressive ms and progressive relapsing ms , which differ in relapse rate , clinical symptoms , trajectory , underlying causes and approaches to disease management . combined all these aspects of the disease ( various locations for demyelination , clinically silent timeline , and a highly variable disease course ) make the approach to care and treatment complex ( compston and coles , 2008 ; trapp and nave , 2008 ) . current behavioral and imaging methods used to assess disease severity are insufficient by themselves to allow confident prediction of disease progression . the addition of functional modeling ( modeling that correlates the functional loss to disease progression ) and objective functional measurements ( imaging as well as other assessments combined with disease status ) may greatly improve such prediction . currently , magnetic resonance imaging ( mri ) along with clinical manifestation of the disease are used as clinical tools for diagnosing ms ( filippi and grossman , 2002 ) . the 2010 revision to the mcdonald diagnostic criteria ( polman et al . , 2011 ) uses a combination of lesion and clinical attacks for the most confident diagnosis of ms . lesions must be disseminated in time or space or have positive identification from cerebral spinal fluid ( csf ) measure ( lublin et al . , 2014 ) . unfortunately , discrepancy still exists between clinical symptoms and structural measurements , because of the poor correlation between the presence of lesions and symptoms . the two common measures used for diagnosis and monitoring with mri are hyperintense lesions on dual - echo mr , which is a nonspecific measure of macroscopic tissue injury , and enhanced abnormalities on t1 lesion weighted images , which is a measure of lesions . however , neither of these measures do provide extent and severity of inflammation , cellular component , or resultant tissue damage information ( filippi and grossman , 2002 ) . thus , there is a need for supplemental information from alternative techniques to have a more holistic approach . there are emerging techniques that hold promise to quantify the brain imaging data , and relate that with clinical outcomes . some promising examples include magnetization transfer mri ( quantitative and continuous measure of loss of myelin and reduction in axonal density ) , diffusion weighted mri ( quantitative measure of size , shape , geometry , and orientation of tissues ) , proton magnetic resonance spectroscopy ( mrs , provides measure of two major pathologic aspects of ms - the active inflammatory / demyelinating process and axonal injury ) , functional mri ( provides functional information about brain activation during motor , sensitive , and cognitive tasks ) , high field strength imaging ( ex 7 t mri , leads to improved signal - to - noise ratio , speed , and resolution in both mri and mrs ) and transcranial magnetic stimulation ( tms , highly sensitive technique to evaluate cortico - spinal conduction abnormalities in ms ) . since t1 scan is transient , it should be only used in patients that have frequent mri , and should not be used for comparison between subjects . instead , given the availability of multiple brain imaging modalities , the need still exists for creating a combined assessment tool . quite a few studies have attempted to combine various measures ( petzold et al . , 2006 ; daumer et al . , 2007 , 2009 ; castro - borrero et al . , 2012 ; sormani , 2013 ; giffroy et al . , multiple sclerosis severity scale ( msss ) was developed to relate expanded disability status scale ( edss ) to the disease duration . it has been shown that msss correlates with axonal biomarkers but not with glial biomarkers ( petzold et al . , 2006 ) . this scale was tested on 195 ms patients to predict accrual of disability over time to see if current therapies have impact on disease severity over time . pachner and steiner ( 2009 ) concluded that the current disease modifying drug therapies lack emphasis on disease severity . an online analytical processing tool was developed that can be used for prognosis of near term future course of an individual patient ( daumer et al . , 2007 ) . this tool is based on matching algorithm ( using statistical analysis ) and contains data of 1,059 patients . outcome was to show the probable progression over time which can be useful for subjects , physicians , researchers and other professionals who counsel the patients . in an attempt to test the developing treatment methods , sormani ( 2013 ) explored the use of various biomarkers that can be used to predict the clinical response to interferon beta ( ifn- ) treatment . he concluded with the need for precise , meaningful measures of disease progression , integrated with clinical measures to help with personalized treatment for ms . a computational classifier was modeled that can be used for predicting short term course of ms ( bejarano et al . , 2011 ) . it combined clinical data , with mri and motor evoked potential ( mep ) , and it was found that the model did good job on predicting short time scale disability . a description of the use of various potential biomarkers with their pros and cons was created . it ranges from markers for immunological activation , as well as markers for demyelination , axonal damage , oxidative stress , remyelination , glycoses , and details of specimen such as blood , urine , tears and csf ( bielecki et al . , 2010 ) . ( 2010 ) concluded with a need for unification and standardization of results of various measurements and techniques . there have been many advances in modeling and designing tools and predictive capabilities for ms . ( 2015 ) studied 1,959 patients from 19952013 with clinically isolated syndrome and found that demographic and topographic characteristics had a low effect on prognostic factors for ms , while the presence of oligoclonal band ( oligoclonal bands are proteins called immunoglobulins and their presence indicates inflammation of the central nervous system ) had medium effect , and the number of lesions had the highest impact . in an another study , 2014 ) recruited 652 patients and studied them for prediction factors that can be classified as disseminated in time ( dit ) , disseminated in space ( dis ) , or both , and concluded that there is a need of more predictive factor combinations to help risk for ms . an analysis of 598 ms patients was performed in norway to determine factors responsible for life expectancy , and the conclusion was that high age at the onset was correlated with unfavorable prognosis ( riise et al . , 1988 ) . in another study on early prognostic features on the late course for ms in world war veterans , it was found that pyramidal and cerebellar scores are the best predictors ( kurtzke et al . , 1972 ) . wang et al . ( 2015 ) performed a study to combine in vivo biomarker using electrical vestibular stimulation and eye movement recording to measure evoked vestibular - ocular reflexed ( evor ) in 18 subjects with ms ( wang et al . , 2015 ) . the goal was to measure the axonal conduction velocity to understand the myelin process and provide a way for assessing efficacy of novel reparative therapies in ms . there still exists a need to for quantification , combination of various results from studies , and longer longitudinal studies . lastly , a computational approach will be necessary for various biological , clinical , imaging findings to form an integrative modeling system that encompasses grey matter pathology , myelin sheath aqueous layers , energy metabolism , and perhaps most importantly , multi - scale or integrated modeling . my recent paper ( chaubey and goodwin , 2016 ) is a computational modeling study which makes it possible to make quantitative predictions about the degree to which electrical signals will move more slowly through axonal pathways as a function of how much of their myelin sheath they have lost as a result of ms ( using data from diffusion tensor imaging ( dti ) , and other modalities ) . the paper identifies a new biomarker for network failure in ms that should improve our ability to predict and track loss of sensory , motor and cognitive function in the disease and a better way to measure the efficacy of new treatments . the potential also exists to relate this  slow down  to disease progression , and eventually , to disease prediction , in which we could use the existing model to predict the disease symptoms in subsequent years . a study in my research center ( brain sciences center , va medical center ) used magnetoencephalography ( meg ) resting state recordings as a functional biomarker to classify ms and various other patient populations to their respective groups , by assessing synchronous neural interactions at high temporal resolution as a measurement of the dynamic synchronous neural interactions , an essential aspect of brain function ( georgopoulos et al . , 2007 ) . another study used similar analysis in 50 ms patients ( 31 rrms , 15 spms , 4 ppms ) and 214 healthy controls ( carpenter et al . , 2011 ) . ancova was performed at each of the 30,628 sensor pairs ( pccs ) using group ( ms , control ) as a fixed factor and age , gender and handedness as covariates . similar analyses were done using each ms phenotype ( rrms , spms , etc . ) as a factor and 300 ( out of 30,628 ) sensor pairs were found to have a significant group differences after correction for multiple comparisons . there is a need to combine such objective measures of brain function with modeling techniques to improve assessment and prediction of the disease over what is possible using established methods . to list few direct advantages of these predictions : 1 ) these predictions can identify high - risk patients who require early and more aggressive therapies , 2 ) they can help patients with clinically isolated syndrome ( cis , one of the ms disease courses , refers to a first episode of neurologic symptoms , but it is not alone enough to have diagnosis of ms , as there needs to be two episodes disseminated in time or space ) to predict how likely and at what time frame the ms diagnosis is likely , 3 ) they can provide an individual plan for various subjects depending on their current status and symptoms ( bergamaschi and montomoli , 2016 ) . there is a need for modeling to help direct research using testable predictions and fixing the current gap in knowledge . there is enormous amount of data available , a lot of which is open access , and it is important to make sense of it all . computational modeling can be an essential tool to help make use and sense of all this data and lead to better understanding , diagnosis , prediction of various diseases , especially ms . as there is no cure for ms , most treatments typically focus on slowing the progression of the disease and managing symptoms . since tissue destruction and disease onset begins much earlier than the actual clinical symptoms appear , it will be important to test future therapeutics to detect early axonal loss , axonal dysfunction and efficiency of these treatment methods . advances in imaging technology and use of new techniques need to be established and validated to determine the success of remyelination and cessation of old damage . finally , a combination of modeling will allow for prediction and better quality of life for people with ms . there is a huge need for a reliable and personalized disease prediction model of ms . it would benefit patients with ms , as they can plan and select the best individualized treatment option . it would be helpful to the clinician working with patients , especially early stage patients , to help with selection of the best therapeutic treatment options . integration of modeling , biology , clinical and imaging data can help provide more personalized and reliable monitoring of disease progression and in turn lead to better disease prediction .
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 .