Biomed Res Int. 2015;2015:569123. doi: 10.1155/2015/569123.

Multicontrast MRI Quantification of Focal Inflammation and Degeneration in Multiple Sclerosis.

 

Bonnier G1, Roche A2, Romascano D3, Simioni S4, Meskaldji DE5, Rotzinger D6, Lin YC7, Menegaz G7, Schluep M4, Du Pasquier R4, Sumpf TJ8, Frahm J8, Thiran JP9, Krueger G10, Granziera C11.
  • 1Advanced Clinical Imaging Technology Group, Siemens, Innovation Park, EPFL, 1015 Lausanne, Switzerland ; LTS5, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland ; Department of Neurology, Centre Hospitalier Universitaire Vaudois and University of Lausanne, 1011 Lausanne, Switzerland.
  • 2Advanced Clinical Imaging Technology Group, Siemens, Innovation Park, EPFL, 1015 Lausanne, Switzerland ; LTS5, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland ; Department of Radiology, Centre Hospitalier Universitaire Vaudois and University of Lausanne, 1011 Lausanne, Switzerland.
  • 3Advanced Clinical Imaging Technology Group, Siemens, Innovation Park, EPFL, 1015 Lausanne, Switzerland ; LTS5, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland.
  • 4Department of Neurology, Centre Hospitalier Universitaire Vaudois and University of Lausanne, 1011 Lausanne, Switzerland.
  • 5Department of Radiology and Medical Informatics, University of Geneva, 1211 Geneva, Switzerland ; Medical Image Processing Laboratory (MIPLAB), Institute of Bioengineering, EPFL, 1015 Lausanne, Switzerland.
  • 6Department of Radiology, Centre Hospitalier Universitaire Vaudois and University of Lausanne, 1011 Lausanne, Switzerland.
  • 7Department of Computer Science, University of Verona, 37134 Verona, Italy.
  • 8Biomedizinische NMR Forschungs GmbH, Max Planck Institute for Biophysical Chemistry, 37077 Goettingen, Germany.
  • 9LTS5, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland.
  • 10Advanced Clinical Imaging Technology Group, Siemens, Innovation Park, EPFL, 1015 Lausanne, Switzerland ; LTS5, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland ; Healthcare Sector IM&WS S, Siemens Schweiz AG, 1020 Renens, Switzerland.
  • 11Advanced Clinical Imaging Technology Group, Siemens, Innovation Park, EPFL, 1015 Lausanne, Switzerland ; LTS5, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland ; Department of Neurology, Centre Hospitalier Universitaire Vaudois and University of Lausanne, 1011 Lausanne, Switzerland ; Laboratoire de Recherche en Neuroimagerie, Department of Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois and University of Lausanne, 1011 Lausanne, Switzerland.

 

Abstract

INTRODUCTION: Local microstructural pathology in multiple sclerosis patients might influence their clinical performance. This study applied multicontrast MRI to quantify inflammation and neurodegeneration in MS lesions. We explored the impact of MRI-based lesion pathology in cognition and disability.

METHODS: 36 relapsing-remitting MS subjects and 18 healthy controls underwent neurological, cognitive, behavioural examinations and 3 T MRI including (i) fluid attenuated inversion recovery, double inversion recovery, and magnetization-prepared gradient echo for lesion count; (ii) T1, T2, and T2(*) relaxometry and magnetisation transfer imaging for lesion tissue characterization. Lesions were classified according to the extent of inflammation/neurodegeneration. A generalized linear model assessed the contribution of lesion groups to clinical performances.

RESULTS: Four lesion groups were identified and characterized by (1) absence of significant alterations, (2) prevalent inflammation, (3) concomitant inflammation and microdegeneration, and (4) prevalent tissue loss. Groups 1, 3, 4 correlated with general disability (Adj-R (2) = 0.6; P = 0.0005), executive function (Adj-R (2) = 0.5; P = 0.004), verbal memory (Adj-R (2) = 0.4; P = 0.02), and attention (Adj-R (2) = 0.5; P = 0.002).

CONCLUSION: Multicontrast MRI provides a new approach to infer in vivo histopathology of plaques. Our results support evidence that neurodegeneration is the major determinant of patients’ disability and cognitive dysfunction.

PMID: 26295042

 

Supplement        

Multiple sclerosis (MS) is the most common disabling neurological disease of young adults around the world, affecting about 2 million of people of any age and gender. Typical symptoms include visual, motor, sensory and cognitive deficits as well as fatigue leading to physical and cognitive disability. Therefore, MS has a significant personal, social and economic impact for patients and healthcare services. MS is a chronic autoimmune disease of the central nervous system (CNS). It affects the brain and the spinal cord through alteration of brain tissue manifested by the presence of lesions; In the last decades, Magnetic Resonance Imaging (MRI) studies revealed the hallmark of MS pathology, which is characterized by local and diffuse damage in cortical and subcortical regions. More recently, the development of advanced MR techniques such as T1, T2, T2* relaxometry and Magnetization Transfer Ratio (MTR) imaging provided new and sensitive ways to characterize tissue biology and pathology in vivo in MS patients, opening new perspectives to improve diagnostic, prognostic and follow-up methods.

 

Figure1

Figure 1. Biophysical basis of MRI contrasts. ++: increase; – : decrease. Big red arrow: large increase; small red arrow: small increase. Big blue arrow: large decrease; small blue arrow: small decrease.

 

These techniques are quantitative or semi-quantitative MRI (qMRI) as they provide information on the tissue properties that is theoretically independent of hardware and sequence parameters. Quantification allows comparisons across different sites, between different patients and different time-points. In addition, qMRI provide biophysical parameters maps, which are more specific for the microscopic structure of tissue than the mixed contrast images of conventional MRI. Relaxometry and Magnetization Transfer Imaging are complementary and have a different sensitivity to changes in the different pools of molecules present in the brain and affecting MRI such as water, macromolecules (i.e. myelin) and iron (Figure 1). Recently, we have proposed to combine these MRI techniques to to study pathophysiology of neuroinflammatory and neurodegenerative disease. The combination of different MR modalities or contrasts provides MR parameters sensitive to complementary tissue characteristics (microstructure damage, demyelization, edema, iron deposition…), which have a great potential to detect and identify pathological changes in the brain. In addition, multi-contrast approaches also show strong potential to improve disability prediction.

In this context, we have applied this combined approach to assess brain tissue microstructure properties of MS patients in vivo. In a previous study, we performed our analysis on diffuse changes in normal appearing tissue and detected subtle inflammation of the brain by MS patients [1]. Here, we have extended our analysis to the focal pathology (i.e. MS lesions) and used the information obtained from multiple MRI contrast to classify the lesion type, according to the extent of inflammatory and neurodegenerative phenomena. Subsequently, we have correlation of MRI metrics of lesion pathology with clinical outcome in patients.

 

Figure2

Figure 2. Example of lesions in T2, T1 and MTR contrast.

 

We performed our analyses on a population of patients affected by relapsing remitting multiple sclerosis and a group of healthy controls. Lesions were segmented by hand in all patients (Figure 2). For each contrast (T1, T2, T2* and MTR) and for each MS lesion, we computed a “z-score”, which measures the difference between the lesion intensity and a “normal” distribution intensity. We separated the z-score into 3 pools: pool 1 the intensity is significantly lower (z-score < -2), pool 2 intensity shows no significant change and pool 3 the intensity is significantly higher (z-score > 2). Finally, we classified lesion from the least to the more severe stage according to the pool they belong in each contrast (T1, T2, T2* and MTR) (Figure 3). From this classification we extracted 4 groups of lesions. Group 1 was constituted by lesions that did not show any significant contrast change, possibly due to pathophysiological causes (i.e. presence of more efficient reparative processes in early stages of disease) and/or technical aspects (lack of sensitivity/spatial resolution). The other three groups were constituted by lesions exhibiting prevalent inflammation (Class 2), micro-degeneration with/without inflammation (Class 3) or predominant tissue loss (Class 4).

Based on this classification, our results showed that 48% of lesions showed high T1 z-score only, 32% exhibited high T1 z-score combined with high T2 or T2* (Class 3) and 18% were characterized by high T1 z-score combined with low MTR (Class 4). Lesions with high T2 and/or T2* only counted less than 3% of the total number of lesions (Class 2) (Figure 4). Interestingly, we did not observe any T1/T2/T2* decrease in local plaques, suggesting that no significant iron accumulation occurs in our cohort of patients. However, since we performed an average lesion analysis, this observation does not exclude the presence of local iron increase.

 

Figure3

Figure 3. List of the 12 combinations of MS lesions z-scores for T1, T2, T2* and MTR q/sq MRI from the least (combination 1) to the most (combination 12) severe stage. The presence of irreversible tissue loss was considered a sign of higher severity than inflammation.

 

We also wanted to study which lesion type had most significant impact on patients clinical outcome. We performed linear regression applied to the mean lesion volume in each lesion class, together with age and sex, to predict patients clinical scores. Ad we considered clinical scores reflecting physical and cognitive disability but also general fatigue, anxiety and depression. Our results showed significant correlation with general disability (Adj-R = 0.6, p = 0.0005), execution (Adj-R = 0.5, p = 0.002) and verbal memory (Adj-R = 0.4, p = 0.002).

In summary, our study proposes a new approach to infer histo-pathological information from MS plaques, based on multi-contrast approach to characterized the heterogeneity of tissue damage in MS lesions through a classification framework. Our results support important evidence that MRI metrics of lesion pathology are strong determinants of patients’ clinical performance in our cohort of patients.

 

Figure4

Figure 4. Distribution of lesions among combinations and distribution of combination among patients. Combinations 1 (non significant z-score in all contrasts) and 4 (isolated high T1 z-score) count more than 50% of all lesions and are the combinations mostly found in all patients.

 

References:

  1. G. Bonnier, A. Roche, D. Romascano, et al., “Advanced MRI unravels the nature of tissue alterations in early multiple sclerosis,” Annals of Clinical and Translational Neurology, vol. 1, no. 6, pp. 423–432, 2014.

 

 

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