Datta G, Colasanti A, Rabiner EA, Gunn RN, Malik O, Ciccarelli O, Nicholas R, Van Vlierberghe E, Van Hecke W, Searle G, Santos-Ribeiro A, Matthews PM. Neuroinflammation and its relationship to changes in brain volume and white matter lesions in multiple sclerosis. Brain. 2017. doi: 10.1093/brain/awx228. [Epub]
As I have said repeatedly, we have had paper after paper claiming this or that parameter is associated with this or that disability years down the line. I have said many times that the correlations are usually so weak that it is meaningless as a predictor of any individuals course of disease, yet still we get imaging after imaging paper doing just that.
Don’t believe me? Here are a few examples:
Among OCT metrics, only GCIPL was associated with cognitive impairment (rp =.448, p=.036) and predictive of objective cognitive impairment (Wald =4.40, p=.036). Controlling for demographics, normalized brain volume (NBV) and thalamic volume were correlated with GCIPL (respectively rp =.427, p=.047 and rp =.674, p=.001) and cognitive scores (respectively rp =.593, p=.004 and rp =.501, p=.017), with thalamic volume nearly mediating the association between GCIPL and cognition (Sobel z=1.86, p=.063).
If you lose nerves in the eye this is associated with cognitive impairment. Of course the two are not linked, but if you have lost nerves in the eye (in the ganglion cell inner plexiform layer) due to disease activity, you are more likely to have lost nerves elsewhere. The correlations are so weak and those in the thalamus are not significant.
Rocca MA, Sormani MP, Rovaris M, Caputo D, Ghezzi A, Montanari E, Bertolotto A, Laroni A, Bergamaschi R, Martinelli V, Comi G, Filippi M. Long-term disability progression in primary progressive multiple sclerosis: a 15-year study. Brain. 2017. doi: 10.1093/brain/awx250. [Epub].
At 15 years, 90% of the patients had disability progression. Integrating clinical and imaging variables at 15 months predicted disability changes at 15 years better than clinical factors at 5 years (R2 = 61% versus R2 = 57%). The model predicted long-term disability change with a precision within one point in 38 of 49 patients (77.6%). Integration of clinical and imaging measures allows identification of primary progressive multiple sclerosis patients at risk of long-term disease progression 4 years earlier than when using clinical assessment alone.
If you look at disease in 15 months it predicts disability at 15 years, but there is 40% of the problem is not explained, so imaging may give you a risk (which aid in decisions) but at the individual level it can’t say what will happen.
Anyway rant over, but it is important that you realize this if you are reading these papers. Interpretation of MRI data is not always “black and white” . Anyway MD3 may want to tell you what the paper said (Datta et al. 2017).
Brain magnetic resonance imaging is an important tool in the diagnosis and monitoring of multiple sclerosis patients. However, magnetic resonance imaging alone provides limited information for predicting an individual patient’s disability progression. In part, this is because magnetic resonance imaging lacks sensitivity and specificity for detecting chronic diffuse and multi-focal inflammation mediated by activated microglia/macrophages. The aim of this study was to test for an association between 18 kDa translocator protein brain positron emission tomography signal, which arises largely from microglial activation, and measures of subsequent disease progression in multiple sclerosis patients. Twenty-one patients with multiple sclerosis (seven with secondary progressive disease and 14 with a relapsing remitting disease course) underwent T1- and T2-weighted and magnetization transfer magnetic resonance imaging at baseline and after 1 year. Positron emission tomography scanning with the translocator protein radioligand 11C-PBR28 was performed at baseline. Brain tissue and lesion volumes were segmented from the T1- and T2-weighted magnetic resonance imaging and relative 11C-PBR28 uptake in the normal-appearing white matter was estimated as a distribution volume ratio with respect to a caudate pseudo-reference region. Normal-appearing white matter distribution volume ratio at baseline was correlated with enlarging T2-hyperintense lesion volumes over the subsequent year (ρ = 0.59, P = 0.01). A post hoc analysis showed that this association reflected behaviour in the subgroup of relapsing remitting patients (ρ = 0.74, P = 0.008). By contrast, in the subgroup of secondary progressive patients, microglial activation at baseline was correlated with later progression of brain atrophy (ρ = 0.86, P = 0.04). A regression model including the baseline normal-appearing white matter distribution volume ratio, T2 lesion volume and normal-appearing white matter magnetization transfer ratio for all of the patients combined explained over 90% of the variance in enlarging lesion volume over the subsequent 1 year. Glial activation in white matter assessed by translocator protein PET significantly improves predictions of white matter lesion enlargement in relapsing remitting patients and is associated with greater brain atrophy in secondary progressive disease over a period of short term follow-up.