MS Speaks
Multiple Sclerosis => MS - RESEARCH AND NEWS => Topic started by: agate on April 28, 2017, 02:53:29 pm
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A neurologist has told me twice now that my MRI shows my brain to be that of someone 10-12 years older than my actual age--and that it is the MS that has done this. The researchers apparently are taking a look at this way of determining the impact of MS.
Presented at the annual AAN conference in Boston, April 22-28, 2017:
Brain Age: A novel approach to quantify the impact of multiple sclerosis on the brain
Joel Raffel
, James Cole
, Chris Record
, Sujata Sridharan
, David Sharp
, Richard Nicholas
Imperial College London
Objective:
To evaluate a novel machine learning MRI model, and its ability to quantify ‘how much older’ a multiple sclerosis (MS) brain is than the patient’s chronological age.
Background:
A predictive model of aging was previously built using machine learning in 1,537 healthy individuals, and can accurately predict chronological age based solely upon T1-weighted brain MRI (r=0.92). This brain age model can be applied to disease cohorts to quantify accelerated aging in diseased brains.
Design/Methods:
Brain age was estimated using T1-weighted brain MRIs from 10 healthy controls (HCs) and 17 people with MS, both before and after 1 year of natalizumab treatment. Chronological age was subtracted from brain age, to give
the ‘predicted age difference’ (BrainPAD) in each individual. This was also repeated after ‘lesion filling’ the MRIs.
Results:
The MS brains were ‘10 years older’ than their chronological age (HC BrainPAD: -1.38±2.73; MS BrainPAD:+10.00±1.97; p=0.003). ‘Older brains’ correlated strongly with existing markers of disease severity in individuals
(BrainPAD vs total lesion load; r=0.78, p<0.001).
MS brains ‘got younger’ longitudinally after natalizumab in those
who responded to treatment, versus those with ongoing disease activity (Change in BrainPAD: -0.67±0.41 vs+1.09±0.58; p<0.05). T1 lesions in MS contributed towards only 16% of the BrainPAD score.
Conclusions:
MS brains were estimated to be far older than true chronological age. BrainPAD correlated with markers of disease severity and the ‘aging’ process was slowed by treatment. These findings are driven by MRI features other than focal MS lesions.
The brain age model requires only a standard T1-weighted brain MRI, and can be
applied to large cohorts with minimal user input. The machine learning taps into data beyond that possible with standard MRI analysis, and can produce results which are highly intuitive for researchers, clinicians and patients.