Association between pathological and MRI findings in multiple sclerosis

Filippi, Massimo; Rocca, Maria A.; Barkhof, Frederik; Brück, Wolfgang; Chen, Jacqueline T.; Comı, Gıancarlo; DeLuca, Gabriele C.; Stefano, Nicola De; Erickson, Bradley J.; Evangelou, Nikos; Fazekas, Franz; Geurts, Jeroen J. G.; Lucchinetti, Claudia F.; Miller, David H.; Pelletier, Daniel; Popescu, Bogdan; Lassmann, Hans

doi:10.1016/s1474-4422(12)70003-0

Cited by 395 publications

(306 citation statements)

References 145 publications

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“…we observe reductions in f and τ i and increases in α in patients' NAWM compared to healthy subjects. It has been shown previously that NAWM in MS subjects is somewhat damaged compared to the normal tissue (Filippi et al, 2012) and our results support this suggestion. These are just preliminary results because the number of both MS and healthy subjects is quite small.…”

Section: Ms Subjectssupporting

confidence: 92%

Machine Learning Based Compartment Models with Permeability for White Matter Microstructure Imaging

Nedjati-Gilani

Schneider

Hall

et al. 2014

Lecture Notes in Computer Science

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A B S T R A C TSome microstructure parameters, such as permeability, remain elusive because mathematical models that express their relationship to the MR signal accurately are intractable. Here, we propose to use computational models learned from simulations to estimate these parameters. We demonstrate the approach in an example which estimates water residence time in brain white matter. The residence time τ i of water inside axons is a potentially important biomarker for white matter pathologies of the human central nervous system, as myelin damage is hypothesised to affect axonal permeability, and thus τ i . We construct a computational model using Monte Carlo simulations and machine learning (specifically here a random forest regressor) in order to learn a mapping between features derived from diffusion weighted MR signals and ground truth microstructure parameters, including τ i . We test our numerical model using simulated and in vivo human brain data. Simulation results show that estimated parameters have strong correlations with the ground truth parameters (R = {0.88, 0.95, 0.82, 0.99} 2 ) for volume fraction, residence time, axon radius and diffusivity respectively), and provide a marked improvement over the most widely used Kärger model (R = {0.75, 0.60, 0.11, 0.99} 2 ). The trained model also estimates sensible microstructure parameters from in vivo human brain data acquired from healthy controls, matching values found in literature, and provides better reproducibility than the Kärger model on both the voxel and ROI level. Finally, we acquire data from two Multiple Sclerosis (MS) patients and compare to the values in healthy subjects. We find that in the splenium of corpus callosum (CC-S) the estimate of the residence time is 0.57 ± 0.05 s for the healthy subjects, while in the MS patient with a lesion in CC-S it is 0.33 ± 0.12 s in the normal appearing white matter (NAWM) and 0.19 ± 0.11 s in the lesion. In the corticospinal tracts (CST) the estimate of the residence time is 0.52 ± 0.09 s for the healthy subjects, while in the MS patient with a lesion in CST it is 0.56 ± 0.05 s in the NAWM and 0.13 ± 0.09 s in the lesion. These results agree with our expectations that the residence time in lesions would be lower than in NAWM because the loss of myelin should increase permeability. Overall, we find parameter estimates in the two MS patients consistent with expectations from the pathology of MS lesions demonstrating the clinical potential of this new technique.

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Section: Ms Subjectssupporting

confidence: 92%

Machine Learning Based Compartment Models with Permeability for White Matter Microstructure Imaging

Nedjati-Gilani

Schneider

Hall

et al. 2014

Lecture Notes in Computer Science

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“…The normal range of [ 11 C]PiB binding values found in the NAWM of patients with MS may appear contradictory to the changes detected outside visible lesions with several advanced MRI techniques 5. However, this finding is in line with histopathological evidence showing that the pathological abnormalities affecting the NAWM mainly consist of axonal damage and loss,24 microglial activation,25 and disorganized nodes of Ranvier,26 but do not include major demyelination.…”

Section: Discussionmentioning

confidence: 78%

Dynamic Imaging of Individual Remyelination Profiles in Multiple Sclerosis

Bodini

Veronese

García-Lorenzo

et al. 2016

Annals of Neurology

197

110

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BackgroundQuantitative in vivo imaging of myelin loss and repair in patients with multiple sclerosis (MS) is essential to understand the pathogenesis of the disease and to evaluate promyelinating therapies. Selectively binding myelin in the central nervous system white matter, Pittsburgh compound B ([11C]PiB) can be used as a positron emission tomography (PET) tracer to explore myelin dynamics in MS.MethodsPatients with active relapsing‐remitting MS (n = 20) and healthy controls (n = 8) were included in a longitudinal trial combining PET with [11C]PiB and magnetic resonance imaging. Voxel‐wise maps of [11C]PiB distribution volume ratio, reflecting myelin content, were derived. Three dynamic indices were calculated for each patient: the global index of myelin content change; the index of demyelination; and the index of remyelination.ResultsAt baseline, there was a progressive reduction in [11C]PiB binding from the normal‐appearing white matter to MS lesions, reflecting a decline in myelin content. White matter lesions were characterized by a centripetal decrease in the tracer binding at the voxel level. During follow‐up, high between‐patient variability was found for all indices of myelin content change. Dynamic remyelination was inversely correlated with clinical disability (p = 0.006 and beta‐coefficient = –0.67 with the Expanded Disability Status Scale; p = 0.003 and beta‐coefficient = –0.68 with the MS Severity Scale), whereas no significant clinical correlation was found for the demyelination index.Interpretation[11C]PiB PET allows quantification of myelin dynamics in MS and enables stratification of patients depending on their individual remyelination potential, which significantly correlates with clinical disability. This technique should be considered to assess novel promyelinating drugs. Ann Neurol 2016;79:726–738

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“…33,34 Generally speaking, brain volume changes can be an important measure of tissue damage in patients with MS. 32 Indeed, baseline atrophy and high rates of subsequent volume loss are associated with cognitive impairment, fatigue and disability progression over the long term. 32,[35][36][37][38][39][40][41] In a complex disease such as MS, brain volume loss results from the sum of and interactions between various destructive pathological processes, 42 including irrever sible demyelination, and axonal and/or neuronal loss. The neurodegenerative pathology that occurs in MS is an important target for treatment; thus, MRI brain volume measures have been used in randomized clinical trials to monitor the effects of disease modifying thera pies on these parameters.…”

Section: Focal Lesionsmentioning

confidence: 99%