teaching course 11 magnims quantitative mr imaging in the management of multiple sclerosis ·...

Teaching Course 11 MAGNIMS Quantitative MR imaging in the management of multiple sclerosis

Chairs: A. Rovira (Barcelona, ES)

H. Vrenken (Amsterdam, NL)

23 Focal lesion load quantitative MR measures


24 Brain and spinal cord volume measurements

A. Rovira (Barcelona, ES)

25 Advanced quantitative brain MR measures

M. Filippi (Milano, IT)

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Wednesday, 7 October, 10.30 – 12.00

Teaching Course 11 MAGNIMS

Quantitative MR imaging in the management of multiple sclerosis

Chairs:



Focal lesion load quantitative MR measures


Brain and spinal cord volume measurements


Advanced quantitative brain MR measures

M. Filippi (Milan, IT)

2

Teaching course 11: “Quantitative MR imaging in the management of multiple sclerosis”

Focal lesion load quantitative measures

H. Vrenken, VU University Medical Center, Amsterdam, the Netherlands.

Focal lesions in the white matter (WM) were long considered the pathological hallmark of

MS, and play an important role in the diagnostic criteria for MS (Polman, 2011). And

although grey matter lesions and atrophy of both white and grey matter have gained

prominence (e.g., Geurts, 2012; Sastre-Garriga, 2004; Shiee, 2012), focal WM lesions still

remain important to this day. Most work on automated measurement has been done on WM

lesions in the brain, while far less has been done towards contrast-enhancing, grey matter, or

spinal cord lesions. In view of the usability in the clinical management of MS, WM lesions

are also most easily accessible through standard clinical MS imaging protocols. We therefore

focus this lecture on the quantitative measurement of brain WM lesion volumes and their

change over time.

Since many of the disease modifying treatments currently available for MS act primarily on

the inflammatory component of the disease, the focal WM lesions provide an important

indicator of the success of treatment. As we will see in this lecture, the exploitation of

quantitative measures to assess focal lesion loads is lagging.

Current use of MRI of lesions

Diagnosis

As prescribed in the diagnostic criteria of MS (Polman, 2011), the emphasis in the diagnostic

evaluation of focal lesions is on the number of lesions and their locations. Although the size

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of the lesions plays a role in discriminating MS from other disorders, lesion sizes or total

lesion volumes are not generally quantitatively assessed in routine clinical diagnosis.

Prognosis

While some studies have related (early) lesion load to later clinical worsening (e.g., Popescu,

2013), there are no guidelines on how such information might be used in individual patient

cases to predict the evolution of disease. Leaving aside the problem of how to account for

variable treatment regimens in assessing this relation, the absence of reliable methods to

measure and gauge quantitative focal lesion loads certainly also contributes to this problem.

Treatment response

The latest treatments for MS are successful in reducing the numbers of new relapses and new

lesions. One might therefore argue that in a patient with a substantial amount or volume of

new lesions under treatment, the treatment may be partially or wholly ineffective, and a

change of treatment may be considered. However, it is also possible that the same patient may

have had a much less favorable course without the treatment, and therefore a change of

treatment may not result in any improvement. Here too, there are no guidelines to help the

clinician make their decision based on an observed increase in focal lesion load. And again,

the absence of reliable methods for the assessment of lesion volume change hampers its

clinical use.

Questions

Three key questions may be identified, that are inter-connected, and together summarize the

challenges.

1. Will it be useful to include quantitative measures of focal lesion load?

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2. Is it possible to measure them in a clinical setting?

3. How will the clinician use the focal lesion load measures?

We will discuss these questions and their answers now.

Will it be useful to include quantitative measures of focal lesion load?

First we have to assess the clinical need for quantitative measures of focal lesion load.

In diagnosing MS, precise volumetric information of the focal lesion volumes does not appear

to add information over the existing information.

By contrast, to assess treatment efficacy, reliable quantitative information on lesion volume

change over time may yield important insights into individual patient pathology worsening –

information that is currently only available in a qualitative sense. Since there are now several

treatments that can reduce the incidence of new lesions, precise quantitative measurement of

lesion change may help to identify the best treatment for an individual patient.

Possibly, quantitative lesion volume and lesion volume change assessment may also help to

improve prognostic accuracy, if the influence of other factors, most notably past and current

medication, can be adequately modeled.

Clearly, on both issues, appropriate steps will also have to be taken in order to allow the

neurologists to use this information in their practice.

Is it possible to quantitatively measure focal lesion load (change) in a clinical setting?

Requirements

If the focal lesion load and its change over time are to be measured in a clinical setting, two

things are required. First, image acquisition should include suitable images for assessing small

lesion changes over time. Preferably these should be of the 3D Flair or 3D T2-weighted type,

with sufficiently small voxel size to allow reliable quantification of small changes between

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imaging time-points (see, e.g., Vrenken, 2013). The 2015 revision of the recommended

standardized MS imaging protocol specifically emphasizes the use of 3D acquisition for this

purpose (http://www.mscare.org/?page=MRI_protocol; Simon, 2006).

Second, quantification of lesion loads and their change over time should be reliably possible.

An important challenge for both cross-sectional and change measurement, is that the

automated methods should perform well independent of the scanner and pulse sequence used

to acquire the data.

Lesion volume measurement

A very nice review of automated methods for MS lesion segmentation is provided by

Mortazavi and colleagues (Mortazavi, 2012). A fairly large number of methods have been

developed, based on various principles which may, with Mortazavi, be characterized as data

driven, statistical, “intelligent”, or deformable contours methods. Some methods require

additional reference images with lesion labels in order to work, others are fully independent.

Many methods require optimization of parameters to improve performance for the data at

hand.

Despite serious advances over the past years, including recently (e.g., Guizard, 2015; Roura,

2015; Wang, 2015), the performance of automated methods still leaves substantial room for

improvement. The comparison of segmentations by automated methods with those by expert

manual raters shows that at the voxel level, there is substantial disagreement between them.

Dice similarity indices, which can range between 0 and 1 where 1 indicates perfect agreement

and 0 no overlap, seldom reach values above 0.7 (Mortazavi, 2012), implying a volume of

disagreement that is on the order of 30% of the lesion volume. Rates of false positive voxels,

i.e., image voxels labelled as lesion voxels by the automated method but not by the expert, are

generally on the order of 10-20%. The same holds for the rates of false negative voxels, i.e.,

http://www.mscare.org/?page=MRI_protocol

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those lesion voxels missed by the automated methods; these too are generally around 10-20%

for optimized methods.

It should be noted here that the manual expert labels, often referred to as the “gold standard”,

generally exhibit substantial variability both within and between raters. For example, in Flair

images with 1x1x1 mm resolution, the Dice similarity index between two expert manual

raters was 0.84 (Steenwijk, 2013). This highlights one of the difficulties in further optimizing

automated methods: if the manual reference is not perfect, an automated segmentation that is

superior to the manual one will be interpreted as being inferior because of its disagreement

with the imperfect reference. By combining multiple labels from different raters e.g. using

STAPLE software (Warfield, 2004), such problems may be somewhat alleviated, but the

fundamental problem that one cannot outperform the reference remains.

For individual patient treatment, these errors have to be reduced, or the uncertainty in the

automated methods has to be taken into consideration when interpreting the lesion volumes in

a clinical situation.

Lesion volume change measurement

For a very nice overview of MS lesion change detection and quantification methods, the paper

by Lladó and colleagues is recommended (Lladó, 2012). Methods may be separated into

detection of lesion change, and quantification of the volumetric effects involved, including

both the change of the actual lesions, and the change of the surrounding tissue due to any

mass effects that may be present, e.g., due to edema. In case of two time points, subtraction of

the images obtained on the two time points may be of help, especially for visual change

analysis. In case of multiple time points, modeling of the signal intensities as a function of

time may be more appropriate.

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Most methods have been tested in relatively small datasets, but have yielded reasonable

reproducibility, with inter-scan coefficients of variation down to just under 1%, and

reasonable agreement with manual assessment yielding Dice similarity coefficients around 0.6

to 0.8. Lladó and colleagues conclude that improvement is needed before clinical application

is feasible. Recently, there have been efforts directed at obtaining more reliable measures of

lesion volume change in MS in a coordinated way (http://iacl.ece.jhu.edu/MSChallenge).

How will the clinician use the focal lesion load measures?

So far, we have addressed requirements and performance of current methods from a technical

perspective. An equally important perspective perhaps, is how clinicians would use the

information on lesion volume and change of lesion volume if reliable measurements were to

become available. We leave aside here, apart from this brief mention, the necessity to have

analysis methods developed according to certified protocols, and regulatory approval for their

use in a clinical setting, according to standardized guidelines. What is of concern to us now, is

that such guidelines for clinical use of lesion volumes and lesion volume changes, have to be

developed in order to give the treating physician sufficient guidance to make well-informed

decisions incorporating the lesion volume information. It will likely not be possible to provide

precise information on how to interpret an observed lesion volume increase in a patient for

any disease duration, and any possible combination of past treatments, relapses, and

progression of disability. Nevertheless, it is important that at least basic clinical guidelines

become available. Even if imaging-related technical challenges are all overcome, use of lesion

volume measures in the clinic can only take place if supported by such guidelines.

Conclusion

http://iacl.ece.jhu.edu/MSChallenge

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While much work has been done over the past decades, there are still no completely

satisfactory automated methods for MS WM lesion volume measurement or for lesion volume

change measurement. Remaining disagreements with an, albeit imperfect, manual reference,

generally remain at least 10-20% of the lesion volume or lesion volume change under

investigation, warranting some caution for individual patient application. Robustness across

scanners and pulse sequences remains an important requirement to provide a feasible clinical

tool.

References

Geurts JJ, Calabrese M, Fisher E, Rudick RA. Measurement and clinical effect of grey

matter pathology in multiple sclerosis. Lancet Neurol. 2012;11(12):1082-92.

Guizard N, Coupé P, Fonov VS, Manjón JV, Arnold DL, Collins DL. Rotation-

invariant multi-contrast non-local means for MS lesion segmentation. Neuroimage

Clin. 2015 May 13;8:376-89. doi: 10.1016/j.nicl.2015.05.001. eCollection 2015.

Jain S, Sima DM, Ribbens A, Cambron M, Maertens A, Van Hecke W, De Mey J,

Barkhof F, Steenwijk MD, Daams M, Maes F, Van Huffel S, Vrenken H, Smeets D.

Automatic segmentation and volumetry of multiple sclerosis brain lesions from MR

images. Neuroimage Clin. 2015 May 16;8:367-75. doi: 10.1016/j.nicl.2015.05.003.

eCollection 2015.

Lladó X, Ganiler O, Oliver A, Martí R, Freixenet J, Valls L, Vilanova JC, Ramió-

Torrentà L, Rovira A. Automated detection of multiple sclerosis lesions in serial brain

MRI. Neuroradiology. 2012 Aug;54(8):787-807. doi: 10.1007/s00234-011-0992-6.

Epub 2011 Dec 20.

Polman CH, Reingold SC, Banwell B, Clanet M, Cohen JA, Filippi M, Fujihara K,

Havrdova E, Hutchinson M, Kappos L, Lublin FD, Montalban X, O'Connor P,

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Sandberg-Wollheim M, Thompson AJ, Waubant E, Weinshenker B, Wolinsky JS.

Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria.

Ann Neurol. 2011 Feb;69(2):292-302. doi: 10.1002/ana.22366.

Popescu V, Agosta F, Hulst HE, Sluimer IC, Knol DL, Sormani MP, Enzinger C,

Ropele S, Alonso J, Sastre-Garriga J, Rovira A, Montalban X, Bodini B, Ciccarelli O,

Khaleeli Z, Chard DT, Matthews L, Palace J, Giorgio A, De Stefano N, Eisele P, Gass

A, Polman CH, Uitdehaag BM, Messina MJ, Comi G, Filippi M, Barkhof F, Vrenken

H; MAGNIMS Study Group. Brain atrophy and lesion load predict long term

disability in multiple sclerosis. J Neurol Neurosurg Psychiatry. 2013 Oct;84(10):1082-

91. doi: 10.1136/jnnp-2012-304094. Epub 2013 Mar 23.

Roura E, Oliver A, Cabezas M, Valverde S, Pareto D, Vilanova JC, Ramió-Torrentà

L, Rovira À, Lladó X. A toolbox for multiple sclerosis lesion segmentation.

Neuroradiology. 2015 Jul 31. [Epub ahead of print]

Sastre-Garriga J, Ingle GT, Chard DT, Ramió-Torrentà L, Miller DH, Thompson AJ.

Grey and white matter atrophy in early clinical stages of primary progressive multiple

sclerosis. Neuroimage 2004;22(1):353-9.

Shiee N, Bazin PL, Zackowski KM, Farrell SK, Harrison DM, Newsome SD,

Ratchford JN, Caffo BS, Calabresi PA, Pham DL, Reich DS. Revisiting brain atrophy

and its relationship to disability in multiple sclerosis. PLoS One. 2012;7(5):e37049.

Simon JH, Li D, Traboulsee A, Coyle PK, Arnold DL, Barkhof F, Frank JA,

Grossman R, Paty DW, Radue EW, Wolinsky JS. Standardized MR imaging protocol

for multiple sclerosis: Consortium of MS Centers consensus guidelines. AJNR Am J

Neuroradiol. 2006 Feb;27(2):455-61.

Steenwijk MD, Pouwels PJ, Daams M, van Dalen JW, Caan MW, Richard E, Barkhof

F, Vrenken H. Accurate white matter lesion segmentation by k nearest neighbor

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classification with tissue type priors (kNN-TTPs). Neuroimage Clin. 2013 Oct

14;3:462-9. doi: 10.1016/j.nicl.2013.10.003. eCollection 2013.

Vrenken H, Jenkinson M, Horsfield MA, Battaglini M, van Schijndel RA, Rostrup E,

Geurts JJ, Fisher E, Zijdenbos A, Ashburner J, Miller DH, Filippi M, Fazekas F,

Rovaris M, Rovira A, Barkhof F, de Stefano N; MAGNIMS Study Group.

Recommendations to improve imaging and analysis of brain lesion load and atrophy in

longitudinal studies of multiple sclerosis. J Neurol. 2013 Oct;260(10):2458-71. doi:

10.1007/s00415-012-6762-5. Epub 2012 Dec 21. Review.

Wang R, Li C, Wang J, Wei X, Li Y, Zhu Y, Zhang S. Automatic segmentation and

volumetric quantification of white matter hyperintensities on fluid-attenuated

inversion recovery images using the extreme value distribution. Neuroradiology. 2015

Mar;57(3):307-20. doi: 10.1007/s00234-014-1466-4. Epub 2014 Nov 19.

Warfield SK, Zou KH, Wells WM. Simultaneous truth and performance level

estimation (STAPLE): an algorithm for the validation of image segmentation. IEEE

Trans Med Imaging. 2004 Jul;23(7):903-21.

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Teaching Course 11

MAGNIMS Quantitative MR imaging in the management of MS

“Brain and spinal cord volume measurements in MS”

Àlex Rovira Unitat de RM. Servei de Radiologia

Hospital Vall d’Hebron Barcelona

[email protected]

www.magnims.eu

New Therapies Allow Re-evaluation of Treatment Goals and Greater Consideration of the Patient Perspective

1. IFNB MS Study Group. Neurology 1993;43:655-61; 2. PRISMS Study Group. Lancet 1998;352:1498-504; 3. Kappos L et al. N Engl J Med 2010;362:387-

401; 4. Cohen JA et al. Lancet 2012;380:1819-28; 5. Coles AJ et al. Lancet 2012;380:1829-39; 6. O'Connor P et al. N Engl J Med 2011;365:1293-303; 7. Twyman C et al. AAN 2013. Poster. P07.098

Current Goals3-7 Traditional Goals1,2

Delay disability progression

Reduce relapse frequency

Freedom from clinical

disease activity

Freedom from MRI disease

activity

MRI disease activity

Prevent new /enlarging T2 lesion and new Gd+ T1 lesions

Clinical disease activity

New Therapies Allow Re-evaluation of Treatment Goals and Greater Consideration of the Patient Perspective

1. IFNB MS Study Group. Neurology 1993;43:655-61; 2. PRISMS Study Group. Lancet 1998;352:1498-504; 3. Kappos L et al. N Engl J Med 2010;362:387-

401; 4. Cohen JA et al. Lancet 2012;380:1819-28; 5. Coles AJ et al. Lancet 2012;380:1829-39; 6. O'Connor P et al. N Engl J Med 2011;365:1293-303; 7. Twyman C et al. AAN 2013. Poster. P07.098

Current Goals3-7 Traditional Goals1,2

Delay disability progression

Reduce relapse frequency

Freedom from clinical

disease activity

Freedom from MRI disease

activity

MRI disease activity

Prevent new /enlarging T2 lesion and new Gd+ T1 lesions

Clinical disease activity

Improve disability

Improve QOL and Patient reported

outcomes

Emerging Goals1,2

Reduction in brain atrophy

MR imaging Biomarkers in Multiple Sclerosis

Purpose Measure

Disease activity New/enlarging T2 lesions Gadolinium enhancing lesions CUAL (combined unique activity lesions)

Disease burden or load T1/T2 lesion load, MTR

Demyelination / Remyelination MTR DTI (FA, MD) T2 mapping

Neuroaxonal loss Atrohy: BPF, NBV (global, regional), PBVC, UCCA T1 lesion load MRS (NAA) DTI (FA, MD)

Brain plasticity Functional (BOLD) MRI

Iron content R2 maps, QSM

Two overlapping pathogenetic components of MS

Preclinical Relapsing–remitting Secondary progressive

Lesion load T2

Brain volume atrophy measures

Disability

Time (years)

Inflammation Neurodegeneration

Anti-inflammatory/ immunomodulatory

therapies Myelin/neural repair/ neuroprotection

Active lesions

Weight of evidence suggests that strongest surrogate marker correlation with disability in MS is brain (spinal cord) atrophy

MS, multiple sclerosis. Modified from Roland Martin, personal communication.

• Global marker of neuroaxonal loss in MS1,2

• Occurs in all clinical stages of MS at a rate of 0.5–1.3%/year vs. 0.1–0.3%/year in healthy subjects1,2

• Measures of global brain atrophy are robust, sensitive and relatively easy to standardise2

• Correlates with and predicts disability progression on group-wise level

• Generally measured on 2D/3D T1-weighted images

BPF, brain parenchymal fraction; SIENA, Structural Image Evaluation, using Normalisation, of Atrophy; VBM, voxel-based morphometry 1. Filippi M & Agosta F. J Magn Reson Imaging 2010;31:770-88; 2. Giorgio A, et al. Neuroimaging Clin N Am 2008;18:675-86

1997 2002 2003 1995 2005

Road map for validating brain atrophy as a proxy for (long-term) disability progression

2

• BVL measurement is prone to variability due to factors such as the pseudoatrophy effect, steroid use, dehydration, comorbidities (smoking, alcohol, BMI) BMI, body mass index; BVL, brain volume loss. 1. De Stefano N et al. Oral presentation S13.006 at AAN 2014; 2. Filippi M, Agosta F. J Magn Reson Imaging 2010; 3. Giorgio A et al. Neuroimaging Clin N Am 2008; 4. Barkhof F et al. Nat Rev Neurol 2009; 5. Bermel RA, Bakshi R. Lancet Neurol 2006; 6. Kappos L et al. Oral FC1.5 presented at ECTRIMS-ACTRIMS 2014

Threshold of -0.4% / year proposed as it falls between the ranges of mean annual BVL in healthy adults and patients with MS6

Healthy adults (-0.1 to -0.3% per year)1-3

An

nu

al B

VL

(%)

Untreated patients with MS (-0.5 to -1.35% per year)4,5

-0.4% per year

Brain volume loss Healthy adults vs. multiple sclerosis

-1.0

-0.5

0.0

0.5

1.0

All patientsN=176

2nd attackN=76

MRI-only MSN=32

Pure CISN=68

**

p=0.003 Brain volume change rates

(mean with 95% confidence interval)

Mult Scler J 2013

Clinical impact of early brain atrophy in CIS

BPF, brain parenchymal fraction; EDSS, Expanded Disability Status Scale; IFN β, beta-interferon; MSFC, Multiple Sclerosis Functional Composite; RRMS, relapsing–remitting MS.

1. Fisher E, et al. Mult Scler 2000; 2. Fisher E, et al. Neurology 2002.

Change in BPF from baseline to year 2 (%) (N = 138)

1.3 to –0.26 –0.27 to –0.89 –0.90 to –1.7 –1.7 to –6.5

Patients with EDSS score ≥ 6 at year 8 (%)2

14.3 29.4 28.6 55.9

Measures of overall brain atrophy predict disability and disability progression

• Follow-up study ≈ 8 years of 160 RRMS patients included in a 2-year Phase III trial of IFNβ-1a

– BPF was significantly correlated with EDSS score and MSFC score at baseline, and after 1, 2 and 8 years1

– The change in BPF during the 2-year study significantly correlated with EDSS1 and MSFC1,2 at year 8

• Multi-centre study in 977 patients with MS

• GM, but not WM, volumes decreased with advancing disease stage

CIS, clinically isolated syndrome; EDSS, Expanded Disability Status Scale; GM, grey matter; nGMV, normalized grey matter volume; nWMV, normalized white matter volume; PASAT, Paced Auditory Serial Addition Test;

PP, primary progressive; RR, relapsing–remitting; SP, secondary progressive; WM, white matter. Roosendaal SD, et al. Mult Scler 2011

* p < 0.01

** p < 0.001

• GM volume predicted physical disability (EDSS) and cognitive impairment (PASAT) better than WM volume or T2 lesion volume

• Disability and cognitive impairment are better predicted by GM than WM volume

nW

MV

CIS RRMS SPMS PPMS

0.85

0.80

0.75

0.70

0.65

** *

nG

MV

CIS RRMS SPMS PPMS

0.90

0.85

0.80

0.75

0.70

** **

** *

Clinical relevance of WM and GM atrophy

• Regions of GM loss (A) participants with RRMS + CIS

• (B) RRMS only subgroup

Deep grey matter bilaterally / pre- and post-central bilaterally / cingulate

bilaterally

CIS, clinically isolated syndrome; GM, grey matter; RRMS, relapsing–remitting MS. Lansley J, et al. Neurosci Biobehav Rev 2013.

A. RRMS including CIS*

B. RRMS without CIS

*Ceccarelli et al. 2008; Henry et al. 2008; Raz et al. 2010; Audoin et al. 2010

Localized grey matter atrophy in multiple sclerosis: A meta-analysis of

voxel-based morphometry studies and associations with functional disability

Lansley J, Mataix-Cols D, Grau M, Radua J, Sastre-Garriga J

GM atrophy in MS

Among all GM structures, the thalamus is the most consistently implied in MS

Thalamic atrophy • Occurs within the first five years of MS onset, when most patients are still minimally

disabled (Davies et al., 2005; Henry et al., 2009).

• Inversely correlated with lesion load in MS (Shiee et al., 2012)

• Associated with a wide range of clinical manifestations: cognitive decline, motor deficits, disability, fatigue, painful syndromes, and ocular motility disturbances (Minagar et al., 2013; Shiee et al., 2012, Batista et al., 2012; Hulst et Geurts, 2012).

• Associated with conversion from CIS to definite MS over 2 years (Zivadinov et al., 2013;

Vaneckova et al., 2013).

Subcortical GM atrophy in MS

NeuroQuant FIRST-FSL Freesurfer

3

• EDSS score:

– In early RRMS patients (N = 425): most significant correlations between cortical thickness and EDSS score for the anterior pole of the left inferior temporal gyrus, the middle and superior frontal gyri bilaterally, and the bilateral anterior cingulate cortex and left post-central cortical area

• Cognitive impairment:

– Hippocampal atrophy associated with impaired memory encoding and retrieval

– Atrophy of the putamen predicts poor performance in tests assessing cognitive performance

• Fatigue:

– Atrophy in the frontal lobes and progression of corpus callosum atrophy over 5 years associated with severity of fatigue

EDSS, Expanded Disability Status Scale; RRMS, relapsing–remitting MS. Charil A, et al. NeuroImage 2007; Rocca MA, et al. J Neurol 2012.

Clinical correlates of regional brain atrophy

Oral drugs Fingolimod FREEDOMS Ph. III, n=1033, 2y

FTY 0.5mg vs FTY 1.25mg vs placebo PBVC (SIENA)

Overall, 30% reduction in pooled FTY-treated arms

TRANSFORMS Ph. III, n=1153, 1y FTY 0.5mg vs FTY 1.25mg vs im IFN-β-1a

PBVC (SIENA)

FTY 0.5mg: 31% reduction FTY 1.25mg: 33% reduction

Ph. III, n=799, 1y extension phase FTY 0.5mg and FTY 1.25mg open-label

PBVC (SIENA)

FTY 0.5mg: 51% reduction FTY 1.25mg: 62% reduction

FREEDOMS II Ph. III, n=1033, 2y FTY 0.5mg vs FTY 1.25mg vs placebo

PBVC (SIENA)

Overall, 45% reduction in FTY-treated arms

Dimethyl-fumarate DEFINE Ph. III, n=540, 2y Dimethyl-fumarate BID vs TID vs placebo

PBVC (SIENA)

6mo. to 2y: 30% reduction in the BID dose, negative results for the TID dose

Teriflunomide TEMSO Ph. III, n=1074, 2y Teriflunomide 7mg vs 14mg vs placebo

BPF, GMF, WMF changes

BPF and GMF change: No significant differences WMF: 83% (7mg) and 164% (14mg) relative change in teriflunomide-treated arms

Monoclonal antibodies

Natalizumab AFFIRM Ph. III, n=942, 2y Natalizumab vs placebo

BPF change

BL to 2y: no significant differences BL to 1y: 40% greater atrophy in NAT-treated arm 1 to 2y: 44% reduction in NAT-treated arm

SENTINEL Ph. III, n=1003, 2y IFN-β-1a im+natalizumab vs IFN-β-1a im+placebo

BPF change

BL to 2y: no significant differences BL to 1y: 19% greater atrophy in NAT-treated armf 1 to 2y: 23% reduction in NAT-treated arm

Alemtuzumab CAMSS223 Ph. II, n=334, 3y Alemtuzumab 12mg vs 24 mg vs sc IFN-β-1a 44 g

BPF change

72% reduction in alemtuzumab-treated arm g

CARE-MS-I Ph. III, n=581, 2y Alemtuzumab 12mg vs sc IFN-β-1a 44 g

BPF change

42% reduction in alemtuzumab-treated arm

CARE-MS-II Ph. III, n=840, 2y Alemtuzumab 12mg vs 24 mg vs sc IFN-β-1a 44 g

BPF change

24% reduction in pooled alemtuzumab-treated arms

Drug Clinical Trial Characteristics Measure a Results b, c

Reported data from CTs

Effects of DMTs on Brain Atrophy (RRMS)

Vidal-Jordana et al. J Neurol 2015

Capturing temporal patterns of structural brain changes requires: • adequate MRI protocols • accurate and robust image analysis tools

It might be challenging the use of MRI-based assessments on individual basis to reliably classify subjects into patient versus normal, as opposed to assessing significant group differences

Atrophy measurements

Registration-based

Global or regional automated segmentation

BPF=brain parenchymal fraction BPF: brain volume/ intracranial volume

NBV= Normalized brain volume

single time-point

PBVC=percentage of brain volume loss

two time-points

IC volume

Brain volume

MRI Time 1

MRI Time 2

Co-registered image

Segmentation-based

• Heterogeneous results • Influenced by the quality of T1-

weigthed images • Not recommended for longitudinal

studies • Designed to analyse regional volumes

• Sensitive to changes over time • Robust and less sensitive to the quality

of MR imaging acquisitions • Recommended for longitudinal studies • Not usually designed to analyse regional

volume changes over time

Brain atrophy in MS: MRI-based methods

Measurement errors

0.62% 0.82% 0.13% 0.17% 0.77% 1.06% 0.23% median

Courtesy of Wim Van Hecke. Icometrix

0.5–1.3%/year MS

0.1–0.3%/year in HC

Multiple regression model used to calculate the normalised brain volume (NBV) according to baseline characteristics (age, gender, disease duration, EDSS, T2LL)

NBV distance from expected = NBV observed-NBV expected

Small NBV: • Higher risk of disability progression • Higher treatment effect

Sormani MP, et al. ECTRIMS 2014 PS 12.3

Brain atrophy in MS: segmentation-based

Placebo group Fingolimod group

EDSS, Expanded Disability Status Scale; LL, lesion load

4

3D T1

Brain regional atrophy measurement (regional)

GM

WM

CSF

CSF, cerebrospinal fluid; GM, grey matter; SIENA, Structural Image Evaluation, using Normalisation, of Atrophy; WM, white matter.

GM and WM volumes can also be calculated separately, using SIENAx, SPM

Lesion filling and GM atrophy measure

SPM 52% of lesion voxels misclassified as GM (Chard, et al. Brain 2002) Effect: Lesion volume GM volume

0

2

4

6

8

0 50 100 150

lesion volume (ml)

err

or

in g

ray m

att

er

vo

lum

e (

%)

test-retest

CIS

RR

PP

Error in GM volume estimation as a function of lesion volume for all patients included.

D. Pareto. Hospital Vall d’Hebron

Lesion volume errors: 0.3-2.5%

Chard, et al. 2010; Battaglini et al. 2011; Popescu et al. 2014

Methods

Expectation maximization segmentation (Van Leemput et al., 2001)

Lesion-TOADS (Shiee et al., 2010)

Lesion segmentation toolbox (LST) (Schmidt et al., 2012)

CASCADE (Damangir et al., 2012)

kNN-Tissue Type Prior (Steenwijk et al., 2013)

HAMMER-White matter lesion (Yu et al., 2002)

Lesion segmentation tool for 3D slicer (Scully et al., 2010)

Msmetrix (Jain et al., 2015)

Lesion segmentation

Lesion segmentation toolbox (LST)

Lesion filling and GM atrophy measure

SPM 52% of lesion voxels misclassified as GM (Chard, et al. Brain 2002) Effect: Lesion volume GM volume

FLAIR+LST mask

Segmentation (without and with LST)

Baseline 1 year 2 years

SIENA [Structural Image Evaluation using Normalization of Atrophy]

Registration-based measures

Baseline 1 year 2 years

SIENA (Structural Image Evaluation, using

Normalisation, of Atrophy)

SIENA (Structural Image Evaluation, using

Normalisation, of Atrophy)

aPBVC first year: -1,6% aPBVC second year: -1,8%

SIENA [Structural Image Evaluation using Normalization of Atrophy]

Registration-based measures

5

Segmentation-based measures

MSmetrix

baseline 1 year 5 years

RRMS. EDSS 1.5 aPBVC: -1.51%

aPBVC (grey matter): -1.60% T2LL: 60cc to 85cc

EDSS 3.0

Segmentation/registration-based measures

De Stefano N, et al. J Neurol Neurosurg Psychiatry 2015

Cut-off values that identify a PBVC distinguishing healthy controls from MS patients

Brain atrophy in MS: registration-based

In 0-7 years: 46%-8% (Rotstein et al. JAMA Neurol 2014)

NEDA at 2 years had a positive predictive value (78%) for no progression at 7 years

Associated with lower brain volume loss

Concept of no evidence of disease activity (NEDA)

Free of gad

and new T2

lesions

Free of

disability

progression

Free of

disease

Free of

relapses

Free of clinical activity/progression

Free of MRI activity

NEDA 3

Increase in EDSS score at 6 months of: • 1.5 points if baseline score is 0 • 1.0 point if baseline score is ≥1.0 • 0.5 points from a baseline score of >5.0

Free of clinically confirmed

relapses

Brain volume loss rates might be helpful in expanding the concept of NEDA

Concept of no evidence of disease activity (NEDA)

Free of

disability

progression

Free of

disease

Free of

relapses

Free of clinical activity/progression

Brain atrophy

NEDA 4 No volume loss >0.4% per year

Free of gad

and new T2

lesions Free of MRI activity

Free of MRI progression

Increase in EDSS score at 6 months of: • 1.5 points if baseline score is 0 • 1.0 point if baseline score is ≥1.0 • 0.5 points from a baseline score of >5.0

Free of clinically confirmed

relapses*

*New neurological symptoms present for at least 24 hours, in the absence of fever or infection, manifesting ≥30 days from onset of a preceding demyelinating event and confirmed by an independent evaluating physician within 7 days of onset.

Pipelines • Non-standardised • Variability • Off-line • Resources

MRI-related factors • Variation in imaging protocols • Artefacts: signal heterogeneity, spatial distortions, motion, etc. • Lack of normative data acquired with the same MR protocol

MS-related factors • Pseudoatrophy effect (long-term follow-up required)

Non-MS related factors (accelerate brain volume decrease) • High body mass index • Genetic factors: glycated haemoglobin, APOE Σ4

• High alcohol consumption • Smoking • Dehydration • Cardiovascular risk factors

Brain atrophy in MS: translation to clinical practice

Challenges

6

PBVC, percentage brain volume change Modified from Claudia Wheeler-Kingshoot, London, personal communication

PB

VC

/ y

ear

–0.8 (IQR –1.3; –0.3)

–1.3 (IQR –1.7; –0.5)

No disability progression

Disability progression

–3

–2

–1

0

1

shortcut

Pre-processing of data

Segmentation and registration of data

Strategies for extracting atrophy

Intra- and inter-subject comparison

Errors Technically demanding

Time consuming Cost (reimbursement?)

Atrophy measurements: off-line

• Fully automated pipelines: o Cross-sectional/longitudinal volumetric analysis o Automated lesion filling o Quality control o Normative data

• Commercially available (NeuroQuant®; IcoMetrix®)

• Integrated in all major MR vendors post-processing software • Information transferred into PACS • Reimbursement (public health systems, private insurance)

shortcut

Atrophy measurements: actions

IcoMetrix /MSMetrix

NeuroQuant

ANTs /Atropos-DIReCT

Freesurfer

Software Library/ Tool

FSL /SIENAX – SIENA – FLIRT – FIRST

CIVET

TOADS, CRUISE, Lesion

SPM /LST, Segment

IcoMetrix, Leuven, Belgium

Advanced Radiology, Baltimore, Maryland, USA

Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, USA

Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School/Massachusetts General Hospital, Charlestown, USA;

FMRIB Centre, John Radcliffe Hospital, Oxford, UK

McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada

Johns Hopkins University, Baltimore, USA

Wellcome Trust Centre for Neuroimaging,, London, UK

Commercial

Commercial

Open source

Open source

Open source

Free

Freeware

Open source

web-based interface or installed on Mac

OS

C++ / depends on the Insight Toolkit

C

C++

web-based interface - Perl scripts/ C/C++

JAVA, plug-ins for the MIPAV

software package

matlab

Institution License Language

Pipelines for atrophy measurements (in MS)

Mechanisms producing brain volume loss

• Tissue loss (i.e., loss of myelin, glial cells, neurons and axons due to the inflammatory demyelination and neurodegeneration),

Brain atrophy in MS

Atrophy

Brain atrophy in MS Brain atrophy in MS

Mechanisms producing brain volume loss

• Tissue loss (i.e., loss of myelin, glial cells, neurons and axons due to the inflammatory demyelination and neurodegeneration),

• Change in non-tissue components (i.e., fluids shifts)

The respective contribution of each component to brain volume loss may depend on many factors, such as disease stage, brain region affected, type of pharmacological

treatment, presence of comorbidities and other factors unrelated to the disease

Brain atrophy in MS

Pseudoatrophy Atrophy

Brain atrophy in MS

Ventricular volume change, obtained using FreeSurfer segmentation results, in percent in comparison to normal hydration (set to 100%)

Streitbürger DP, et al. Plos One 2012.

Ventricular volume change

Measurement timepoints

Pe

rce

nta

ge c

han

ge o

f ve

ntr

icu

lar

volu

me

re

lati

ve t

o n

orm

al h

ydra

tio

n

Hyperhydr. 1st dehydr. 2nd dehydr. 3rd dehydr. –5

–4

–3

–2

–1

0

1

2

3

4

5

Brain volume loss: hydration

7



Drug Global effect on

Brain Volume 1

Immediate effect

on Brain Volume 2

Delayed effect

on Brain Volume 3

Able to cross Blood-

Brain Barrier

Placebo-controlled studies Interferon beta 1a No No Yes No Glatiramer Acetate No Noa NAb No Fingolimod Yes Yes Yes Yes Dimethyl-fumarate Yesc Nod Yesd No Teriflunomide No No No No Laquinimod Yes NA NA Yes Natalizumab No No Yes No

Active comparator4 Interferon vs glatiramer acetate

Yes (GA)e Yes (GA)e Yes (GA)e No

Fingolimod vs im IFN-β-1a

Yes (FTY) Yes (FTY) NAf Yes (FTY)

Alemtuzumab vs sc IFN-β-1a 44 g

Yes (AL) NAg NAg No


Fingolimod treatment has a consequence on brain volume loss

* p < 0.05 for fingolimod vs. placebo; ** p < 0.01 for fingolimod vs. placebo; *** p ≤ 0.001 for fingolimod vs. placebo; Rank analysis of covariance (ANCOVA) adjusted for treatment group,

country and baseline normalized brain volume; n = number of patients with non-missing value Presented by Radue et al. ENS 2012. 219.

24.7% reduction

27.6% reduction

n=395 384 383

n=383 371 358

n=357 332 331

n=290 252 254

n=115 77 93

Point of switch from placebo–fingolimod

-0.44–0.45% / year treated -0.69% / year placebo -0.1-0.3% / year HC (expected) M

ean

pe

rce

nt

chan

ge in

b

rain

vo

lum

e f

rom

bas

elin

e

Brain atrophy data from FREEDOMS extension study

Brain atrophy data from CARE MS I study

*Alemtuzumab vs SC IFNB-1a, p<0.0001 BPF= brain parenchymal fraction; CI=confidence interval; SC IFNB-1a=subcutaneous interferon beta-1a

Arnold D, Cohen J, Barkhof F et al. Neurology 2014;83;e35.

• Alemtuzumab slowed the reduction in BPF over 3 years, despite 82% of patients only receiving the initial 2 courses of alemtuzumab at Month 0 and Month 12 in the core study

• The median yearly rate of brain volume loss with alemtuzumab decreased progressively over time

CARE MS I: Percentage Change in Brain Parenchymal Fraction

Alemtuzumab 12 mg SC IFNB-1a 44 µg

-1.8

-1.6

-1.4

-1.2

-1.0

-0.8

-0.6

-0.4

-0.2 0.0

Me

dia

n C

han

ge F

rom

B

ase

line,

% (

95%

CI)

*

*

42% slowing of brain volume loss vs SC IFNB-1a at Year 2

0 1 2 3 Year

171 – 185 176

351 323 371 367

No. of patients

Median yearly BPF change

‒0.59% ‒0.25% ‒0.19% ‒

‒0.94% ‒0.50% – –

Alemtuzumab treatment has a consequence on brain volume loss


Drug Global effect on

Brain Volume 1

Immediate effect

on Brain Volume 2

Delayed effect

on Brain Volume 3

Able to cross Blood-

Brain Barrier

Placebo-controlled studies Interferon beta 1a No No Yes No Glatiramer Acetate No Noa NAb No Fingolimod Yes Yes Yes Yes Dimethyl-fumarate Yesc Nod Yesd No Teriflunomide No No No No Laquinimod Yes NA NA Yes Natalizumab No No Yes No

Active comparator4 Interferon vs glatiramer acetate

Yes (GA)e Yes (GA)e Yes (GA)e No

Fingolimod vs im IFN-β-1a

Yes (FTY) Yes (FTY) NAf Yes (FTY)

Alemtuzumab vs sc IFN-β-1a 44 g

Yes (AL) NAg NAg No



• Particularly relevant in the first months of treatment • More evident with highly anti-inflammatory DMDs • Evident in patients with inflammatory activity (Gad) • Mainly affect white matter (grey matter is insensitive) • Not demonstraed in the spinal cord

Ne

t b

rain

par

en

chym

al

volu

me

loss

Treatment duration (months)

PREVENTION

PSEUDOATROPHY

IRREVERSIBLE ATROPHY

>–2.0%

–1.5%

–1.0%

–0.5%

0 12 36 6 24

Untreated

Low-dose IFN

Natalizumab

GA

High-dose IFN / Chemotherapeutics

Zivadinov R, et al. J Neurol 2008;Suppl 1:61-74

Vidal-Jordana A, et al. Mult Scler 2013; 19:1175-81

DMD-induced pseudoatrophy effect

Linear regression analyses were performed to predict PBVC, WMF and GMF evolution using the following independent variables: age, disease duration, presence of Gd-enhancing lesions at baseline MRI, corticosteroid treatment one month prior to baseline MRI and corticosteroid treatment between baseline and one-year MRI.

Pseudatrophy effect in the first year of treatment with Natalizumab


8

n=39

Presence of Gd+ at baseline

Partial correlation= -0.37

p=0.012

Linear regression analyses were performed to predict PBVC, WMF and GMF evolution using the following independent variables: age, disease duration, presence of Gd-enhancing lesions at baseline MRI, corticosteroid treatment one month prior to baseline MRI and corticosteroid treatment between baseline and one-year MRI.

Month 0 to 12


Pseudatrophy effect in the first year of treatment with Natalizumab

• Exclude brain atrophy changes during the first months after treatment onset

• Grey matter assessment (insensitive to fluctuations in water content) • Normalisation

o Degree of inflammatory activity (Gad lesions) o White matter T2 relaxometry

Pre-treatment (DX, FU) Baseline 6 months Follow-up every 12 months

-1 0 1 Treatment onset

Minimise pseudoatrophy effect

Alternatives to brain volume assessment (as a measure of atrophy)

oCorpus callosum index

o Spinal cord area

Figueira et al, Arq Neuropsiq 2007

a

b a'

b' c'

c

CCI: aa’ + bb’ + cc’ /ab

UCCA, Upper cervical cord cross-sectional area; Lukas, et al. Radiology 2013

UC

CA

mm

2

Corpus callosum atrophy

Corpus callosum index

a

b

a'

b'

c'

c

CCI: aa’ + bb’ + cc’ /ab

Modyfied from Figueira et al. Arq Neuropsiquiatr 2007

Corpus callosum area

CIS CDMS

Decrease in corpus callosum area at 6 months ≥ 1%, and baseline T2 lesion volume ≥ 5 cm3

CDMS, clinically definite MS; CIS, clinically isolated syndrome; GM, grey matter; MRI, magnetic resonance imaging; WM, white matter.

Kalincik T, et al. PLoS One 2012.

Time (months)

Pat

ien

ts w

ith

CD

MS

(%)

HR = 1.2 (0.7–2.2)

HR = 6.5 (3.4–12)

Cumulative risk of CDMS

100

90

70

60

80

10

0

20

40

30

50

0 6 12 18 24

Number of MRI predictors:

2 1 0

0 12 6 18 24

p = 10–4

–5

–4

–3

–2

–1

0

1

Corpus callosum area change

Time (months)

%

Brain volume change

0 12 6 18 24 Time (months)

%

–2

–1.5

-1

0

–0.5

0 12 6 18 24

WM volume change

Time (months)

%

–2

–1.5

–1

0

–0.5

0.5

0 12 6 18 24

0

–1

–2

–3

GM volume change

Time (months)

%

Dashed lines delineate 95% confidence intervals

Value of corpus callosum (CC) area change for predicting conversion to CDMS

• Assessment of relationship between MRI measures and clinical status in 181 highly active RRMS patients from Avonex-Steroid-Azathioprine study (mean age 31.2 yr, disease duration 5.5 yr, EDSS 1.9) over 9 yr

• Multivariate analysis: 2 MRI markers evaluated during the 1st yr on treatment were predictive of sustained disability progression over 9 yr:

– T1 lesion volume at mo 12 (HR 1.16; P=0.009)

– CC change mo 12-0 (HR 0.92; P=0.009)

• Individual predictive accuracy (ROC analysis):

CC volume and T1 lesion volume seem to predict about half of pts who will have sustained disability progression over 9 year of interferon β

Value of corpus callosum (CC) volume change for predicting disability progression after 9 yr

Horakova D. Neurology 2014;82(10 Suppl):P6.117

9

• Affects the majority of MS patients (++ SPMS, + PPMS), progressive over time

(Stevenson, 1998; Lin, 2003; Furby, 2008, 2010; Rocca, 2011, Lukas 2013, Lukas 2014) • Moderate association with clinical status (Losseff, 1996; Nijeholt, 1998; Edwards, 1999, Furby, 2008; Rocca, 2011, Lukas 2013) • Faster in MS patients with disability progression over time (Lukas 2014) • Relevant for disability in the long term (Kearney, 2014)

Spinal cord atrophy in MS

440 MS patients

UCCA, Upper cervical cord cross-sectional area; Lukas, et al. Radiology 2013

MR imaging–derived UCCA was found to be the most significant spinal cord parameter for explaining EDSS score

UC

CA

mm

2

P=0.30

MRI parameters predicting EDSS P value

UCCA <0.001

T1 lesion volume 0.001

Diffuse spinal cord abnormalities 0.002

Number of spinal cord segments 0.004

P<0.001 Multivariate linear regression

Spinal cord atrophy in MS: correlation with clinical disability

352 MS patients (two centers)

UCCA, Upper cervical cord cross-sectional area; Lukas, et al. JNNP 2014

Spinal cord atrophy in MS: changes over time

UC

CA

mm

2

•SC atrophy develops mostly independently from brain pathology, but not to number of SC segments involved by lesions and the presence of diffuse SC abnormalities •aUCCA was significantly higher in patients with disease progression (−2.3% per year) than in stable patients (−1.2% per year; p=0.003) •aPBVC did not differ between subtypes (RRMS: −0.42% per year; SPMS −0.6% per year; PPMS: −0.46% per year) nor between progressive and stable patients ( p=0.055)

aUCCA: -2.2%

aUCCA: -1.3%

aUCCA: -1.5%

113 MS patients; 20 controls

UCCA, Upper cervical cord cross-sectional area; Schlaeger, et al. Ann Neurol 2014

Grey matter spinal cord atrophy in MS: correlation with clinical disability

PSIR control

Mild RRMS

Progressive MS

Spearman Rank Correlations between PSIR and Clinical Measures

manually segmented

3D T1w brain Dedicated 3D T1w c-spine

Spinal cord atrophy in MS: brain/spinal 3D T1-PSIR

PSIR Advantages:

• Easy to measure

• “Summary measure” of the irreversible/destructive pathological process of MS

• Whole brain atrophy is highly reproducible and sensitive to disease-related changes

• Correlates with disability, cognitive impairment and fatigue

Disadvantages:

• End-stage phenomenon

• Pseudoatrophy effect (first 6–12 months)

• Fluctuations: steroids, hydration

• Co-morbidities: smoking, alcohol, body mass…

• Technically demanding / time consuming: reimbursement?

• Not enough evidence to use atrophy measures to assess and predict individual treatment response

Brain/spinal cord atrophy assessment in MS

10

Neuroimmunology Unit MRI Neuro Unit

Special thanks to: Radiology department • Cristina Auger • Deborah Pareto • Raquel Mitjana • Elena Huerga • Juan F. Corral • Xavier Aymerich • Manel Alberich Neurology department • Xavier Montalban • Jaume Sastre-Garriga • Mar Tintore • Jordi Rio • Carmen Tur • Anka Vidal

Advanced quantitative brain MR measures Massimo Filippi Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University. E-mail address: [email protected]

Introduction

Conventional magnetic resonance imaging (MRI) sequences (i.e., dual-echo, FLAIR, pre- and post-

contrast T1-weighted scans) provide important pieces of information for diagnosing multiple

sclerosis (MS), understanding its natural history, and assessing treatment efficacy. Disappointingly,

the strength of the association between conventional MRI findings and the clinical manifestations of

the disease remains modest in patients with MS. This is likely due to the relative lack of specificity

of conventional MRI in the evaluation of the heterogeneous pathological substrates of the disease,

its inability to provide accurate estimates of damage outside focal lesions, and the fact that it cannot

provide information on central nervous system (CNS) functional reorganization after tissue injury

has occurred. Several advanced MRI techniques have been applied to estimate overall MS burden in

the different phases of the disease. Their use has allowed to grade in vivo the heterogeneity of MS

pathology in focal lesions, normal-appearing white matter (NAWM) and gray matter (GM).

Heterogeneity of WM lesions

Variable degrees of magnetization transfer ratio (MTR) reduction have been reported in acute and

chronic MS lesions, with the most prominent changes found in T1-hypointense lesions. Recently,

MRI contrast agents composed of iron particles, known as ultrasmall particles of iron oxide

(USPIO) or super-paramagnetic iron particles of oxide (SPIO), have been introduced to monitor

different aspects of the MS inflammatory process. These particles are taken by cells of the

monocyte/macrophage system. As a consequence, USPIO-enhancement reflects cellular infiltration

and may complement Gd-enhancement.[1]

High-field (3.0-4.0 Tesla) and ultra-high field (7.0 Tesla and higher) MRI scanners are becoming

progressively available and contribute to detect a greater number and a larger volume of T2 and

enhancing brain lesions. These MR scanners also provide a better definition of lesions in terms of

location in the WM and GM, morphology, architecture and association with vasculature, at a

resolution which resembles that of pathological assessment.[2-4] Due to the better definition of the

relationship between demyelinating lesions and the deep venous system, several studies have shown

that MS plaques form around the microvasculature.[4, 5] This feature has been reinforced by the

investigation of blood-brain barrier abnormalities in MS at 7.0 Tesla, which showed that the

majority of enhancing lesions are perivenular, and that the smallest lesions have a concentric pattern

of enhancement, suggesting that they grow outward from a central vein.[5] The presence of a

central small vein and a rim of hypointensity on 7.0 Tesla T2*-weighted magnitude images can also

assist in the differentiation of WM lesions found in MS patients from those of patients with other

neurological conditions that can mimic MS.

Cortical lesions

Although imaging the cortex is technically difficult, due to its morphology and location, and the

nature of the pathology within this structure, the development of specialized MRI sequences that

can suppress the signal from WM and cerebrospinal fluid simultaneously, named “double inversion

recovery” (DIR), has enabled substantial improvements in cortical lesions (CLs) detection.[6] Using

DIR, MRI studies revealed that CLs develop early in MS and increase in number and size with

progression of the disease.[7] On the other hand, they are sparse in benign forms of the disease[8]

and in children with MS.[9] CLs contribute to explain both locomotor disability and cognitive

impairment,[10] and their presence is associated with other MRI indexes of damage such as T2

lesion load and WM and GM atrophy.[11] In patients presenting with a clinically isolated syndrome

(CIS) suggestive of MS the accuracy of MRI diagnostic criteria for MS increases when considering

CLs on baseline scans.[12] Importantly, CLs have not been identified in migraine patients with

multiple brain WM hyperintensities and patients with neuromyelitis optica, thus suggesting a high

specificity for MS. Measuring CLs might also represent a valuable marker to monitor treatment

effect.[13]

Diffuse NAWM damage

Outside T2 lesions, quantitative MRI techniques allow estimating the presence and extent of

abnormalities in the NAWM of MS patients. Several studies with serial MR investigations have

shown that, at least in some cases, subtle WM changes can be seen in areas which days to weeks

later develop into classical enhancing lesions. These changes consist of a reduction of MTR,

increase in mean diffusivity (MD), and mild to moderate reduction of N-acetylaspartate. NAWM

MTR histogram-derived measures evolve at different rates in the major MS clinical phenotypes.

NAWM MTR reduction has also been shown to predict the accumulation of clinical disability over

the subsequent five years in patients with definite MS. A voxelwise assessment of the regional

distribution of abnormalities of diffusion tensor (DT) MR indexes has been shown to be a

rewarding strategy for understanding the heterogeneity of MS clinical phenotypes. In a recent

study,[14] this method has been applied to 172 MS patients with different clinical phenotypes and it

has been found that compared with healthy controls, CIS patients had significantly increased MD,

axial diffusivity, and radial diffusivity in the majority of WM tracts of the brain. Compared to

controls, patients with primary progressive (PP) MS also showed a diffuse fractional anisotropy

(FA) decrease involving the majority of WM tracts. No relevant difference in diffusivity measures

was found between CIS and relapsing-remitting (RR) MS patients. Compared with benign MS

patients, those with RRMS had reduced FA values in all WM tracts and a decreased axial

diffusivity in the majority of the tracts. Secondary progressive (SP) MS had a pronounced damage

to the majority of tracts and, compared with benign MS, a pronounced FA alteration of the tracts

relevant for motor impairment. Significant DT MRI abnormalities of brain WM tracts, which were

only partially correlated with focal WM lesions, were also found in very young pediatric MS

patients.[15]

Several approaches have been developed to investigate damage to selected WM tracts with the

ultimate goal of improving the correlation with clinical measures. In patients with CIS, diffusivity

measures of the corticospinal tract correlate with clinical measures of motor impairment.[16] In

patients with optic neuritis, reduced structural connectivity values in the optic radiations compared

with controls have been shown.[17] Diffusivity abnormalities in optic radiations have been related

to trans-synaptic degeneration secondary to optic nerve damage and Wallerian degeneration due to

local lesions in a recent study in which patients were classified according to the presence of

previous optic neuritis and lesions along these tracts.[18] In cognitively impaired MS patients

damage of specific corticothalamic tracts explained global cognitive dysfunction and impairment of

selected cognitive domains better than MRI measures of thalamic and cortical damage.[19]

Advances in DT MRI and tractography have spurred the development of brain connectivity

techniques, which allow defining and quantifying anatomical links between remote brain

regions.[20] The use of these approaches has revealed reduced network efficiency in the WM

structural networks of MS patients,[21] including those at the earliest stages of the disease.[22]

Diffuse GM damage

The application of modern MR techniques can contribute to the assessment of different aspects of

GM damage, including the presence of diffuse disease-related abnormalities (measured using

quantitative techniques such as MT and DT MRI), metabolic abnormalities (measured by means of

proton MR spectroscopy), irreversible tissue loss (atrophy) and iron deposition (quantified using

T2/T2*-weighted imaging), thought to reflect neurodegeneration. Several studies have demonstrated

reduced MTR and increased MD in the GM of patients with different MS phenotypes including

those at the earliest clinical stages of the disease.[23, 24] These abnormalities are more severe in

patients with the progressive disease phenotypes. Analogous findings have been shown when

measuring cortical atrophy.[25] Diffuse cortical damage is not stable, but tends to worsen over time,

independent of the progression of damage within the WM.[26] The clinical relevance of measuring

such a damage has been demonstrated by several studies which have shown correlations with

clinical disability and cognitive impairment.[25] A longitudinal study has found an increased rate of

cortical tissue loss in patients with progressing disability compared to those with stable disease,[27]

whereas another study demonstrated that progressive neocortical loss is relevant to MS-associated

cognitive impairment.[28, 29] A recent 13-year longitudinal study demonstrated that GM damage is

one of the key factors associated with both long-term accumulation of disability and cognitive

impairment in MS patients.[29]

Analysis of the spatial distribution of GM damage has demonstrated that different regions might

have different vulnerabilities to MS-related pathological processes. Overall, MRI studies have

agreed in identifying the frontal, temporal and parietal lobes as the most affected cortical regions in

MS patients. However, the patterns of GM loss differs between the major MS clinical

phenotypes.[30] The quantification of diffuse GM damage provides robust prognostic measures of

disease progression. In RRMS patients, GM MTR was found to be an independent predictor of the

accumulation of disability over the subsequent eight years[31] and baseline GM fraction was found

to be an independent predictors of the development of SPMS over a 13-year period,[29] while in

PPMS, GM MD predicted the accumulation of disability over a five-year period.[26]

Assessment of atrophy of strategic GM structures could contribute to explain deficits in selective

cognitive domains, as well as the occurrence of specific symptoms and disability progression.

Hippocampal atrophy has been associated with deficits in memory encoding and retrieval.[32] In

this perspective, a recent study showed that hippocampal subregions have a different vulnerability

to MS-related damage, with a relative sparing of the head of the left hippocampus.[33] Atrophy of

the frontal and parietal lobes has been correlated with the presence and severity of fatigue.[34]

Thalamic atrophy has been correlated with accumulation of disability after a eight year follow up

period in patients with relapse-onset MS[35] and after a five year follow up period in PPMS

patients.[36]

Brain functional reorganization

Studies with functional MRI (fMRI) of the visual, cognitive and motor systems have consistently

demonstrated functional cortical changes in all MS phenotypes, with altered activation of regions

normally devoted to the performance of a given task and/or the recruitment of additional areas in

comparison to healthy subjects.[37] fMRI abnormalities in MS patients occur relatively early in the

course of the disease, even in patients with CIS and pediatric MS,[38] and tend to vary over the

course of the disease, not only after an acute relapse, but also in clinically stable patients.[37]

Functional and structural MRI abnormalities in MS patients are strictly correlated,[37] suggesting

that increased recruitment of “critical” cortical networks helps to limit the functional impact of MS-

related damage. However, increased cortical recruitment cannot continue indefinitely, and a lack of,

or exhaustion of, the “classical” adaptive mechanisms has been considered as a possible factor

responsible for unfavorable clinical evolution or accelerated cognitive decline.[37, 39]

Several studies have attempted to develop sophisticated statistical approaches to establish the

strength of activations and the synchrony between specific cortical areas, through the analysis of

functional and effective connectivities.[40] The combination of measures of functional connectivity

with measures of structural damage to specific WM fiber tracts is also likely to improve our

understanding of the relationship between structural and functional abnormalities.

Recently, the analysis of brain activity at rest has shown an increased synchronization of the

majority of the resting-state networks (RSN) in patients with CIS,[41] a reduced functional

connectivity (FC) of anterior regions of the default-mode network in patients with progressive

MS[42] and cognitive impairment,[40] and a complex reorganization of the visual network in

normal-sighted patients who recovered from a previous optic neuritis.[43] Distributed abnormalities

of RS FC within and between large-scale neuronal networks have been shown in RRMS and

pediatric MS patients and have been related to the extent of T2 lesions.[44, 45] Active and RS fMRI

have also been applied to assess modifications of the patterns of activations and FC following

cognitive rehabilitation in a few single-centre studies.[46, 47] A recent study demonstrated that

changes in RS FC of cognitive-related networks contributes to the persistence of the effects of

cognitive rehabilitation after six months in RRMS patients.[48]

Graph theory is a mathematical framework which allows to describe a network as a graph

consisting of a collection of nodes (i.e., brain regions) and edges (i.e., structural and functional

connections). The application of this approach to RS fMRI data has allowed to investigate the

topological organization of functional brain network connectivity. In a recent study an impairment

of global integration, that likely reflects a reduced competence in information exchange between

distant brain areas, has been demonstrated in MS patients. Such a modification of regional network

properties contributed to cognitive impairment and phenotypic variability of MS.[49]

Conclusions

Conventional and advanced MRI techniques have been applied extensively to the study of MS and

such an effort has contributed to improve our ability to diagnose and monitor the disease, as well as

our understanding of its pathophysiology and treatment efficacy. Nevertheless, many challenges

remain. Quantitative, metabolic and functional imaging techniques need to be optimized and

standardized across multiple centres to monitor adequately disease evolution. In addition, from the

large body of available literature, it results clear that none of these quantitative techniques, taken in

isolation is able to provide a complete picture of the complexity of the MS process. A

multiparametric approach, combining aggregates of different MR quantities might improve our

ability to monitor the disease. With the increased availability of high field and ultra-high field MR

scanners, such an issue is now becoming extremely critical. Furthermore, the practical utility of

modern MR approaches from a research setting to daily-life clinical practice still needs to be

investigated.

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