Scientifi c Journal of Multiple Sclerosis
1 like98 views
This review article discusses the use of magnetic resonance imaging (MRI) in diagnosing and monitoring multiple sclerosis (MS). MRI plays a key role in MS diagnosis by detecting lesions in the brain and spinal cord to demonstrate dissemination of the disease in space and time. Conventional MRI markers described include hyperintense lesions on T2-weighted, proton density, and fluid-attenuated inversion recovery sequences, which indicate demyelination and inflammation. Hypointense lesions on T1-weighted sequences ("black holes") correlate with axonal loss. Lesion number, location and volume provide information on prognosis and can predict disability and disease progression. Non-conventional MRI techniques discussed provide additional insights into MS pathology.
![Scientific Journal of Multiple Sclerosis
SCIRES Literature - Volume 1 Issue 1 - www.scireslit.com Page -009
ABBREVIATIONS
Axial Diffusivity- AD; blood oxygenation level dependent- BOLD;
Central Nervous System- CNS; Cerebrospinal Fluid- CSF; Choline-
Cho; Clinically Isolated Syndrome- CIS; Creatine- Cr; Diffusion
Tensor Imaging- DTI; Dissemination In Space- DIS; Dissemination
In Time- DIT; Expanded Disability Status Scale- EDSS; Fluid-
Attenuated Inversion Recovery- FLAIR; Fractional Anisotropy-
FA; Functional MRI- Fmri; Gamma-Amino Butyric Acid- GABA;
Glutamate- Glu; Glutamine- Gln; Gradient Echo- GRE; Gray Matter-
GM; Lactate- Lac; Magnetic Resonance Imaging- MRI; Magnetic
Resonance Spectroscopy- MRS; Magnetization Transfer Ratio- MTR;
Mean Diffusivity- MD; Multiple Sclerosis- MS; Magnetic Resonance-
MR; Myoinositol- Mi; N-Acetyl Aspartate- NAA; Normal-Appearing
White Matter- NAWM; Primary Progressive Multiple Sclerosis-
PPMS; Progressive Multifocal Leukoencephalopathy- PML; Proton
Density- PD; Quantitative Magnetic Susceptibility- QS; Radial
Diffusivity- RD; Relapsing-Remitting Multiple Sclerosis- RRMS;
Secondary Progressive Multiple Sclerosis – SPMS; White Matter-
WM
INTRODUCTION
Multiple sclerosis (MS) is a chronic and inflammatory disease of
the central nervous system and one of the most common causes of
non-traumatic neurological disability in young adults. Approximately
2.5 million persons worldwide are living with MS. For the past few
decades, magnetic resonance imaging (MRI) has played a crucial
role in MS diagnosis, monitoring and treatment, understanding
MS natural history/ progression and pathophysiology, and as an
outcome measure in clinical trials [1,2]. This article will provide a
brief overview of MS and describe the core features and uses of MRI
for MS.
OVERVIEW OF MS
It has been shown that both genetic and environmental risk
factors contribute to the development of MS [3,4]. While MS is
known to be an immune-mediated disease, the exact underlying
cause is not yet fully understood. Histopathologically, demyelination
and axonal loss are evident [5,6]. The clinical course can be relapsing
or progressive. Eighty-five percent of patients are initially diagnosed
with relapsing-remitting MS (RRMS). In the RRMS subtype, patients
experience episodes of neurological disturbance (relapses) defined
as being longer than 24 hours duration without fever or infection,
and followed by periods of (partial) recovery [7]. More than half of
these patients enter a secondary-progressive phase (median time to
conversion = 10 years) (secondary progressive MS- SPMS), which
is characterized by progressive neurologic decline with or without
occasional relapses, minor remissions, and plateaus [7]. About 15%
show a primary progressive disease type (primary progressive MS-
PPMS) with steady and progressive accrual of disability from first
symptom onset on (without any signs of recovery). PPMS occurs
more commonly in older patients, with the mean age of disease onset
being 40 as compared to 30 years of age in RRMS [8-10]. Visual and
sensory dysfunctions are the most common symptoms at disease
onset in the RRMS type, while progressive gait disturbance dominates
in PPMS. Over the course of the disease, symptoms may vary and can
include muscle spasticity, weakness, dizziness and vertigo, fatigue,
and cognitive dysfunction, amongst other symptoms. The overall
life expectancy for adult-onset MS patients is only 6 years lower than
the rest of the population [11,12]. Therefore, the burden of disease
lies primarily in the reduction in quality of life and accumulation of
physical disability.
So far, no single test is used to diagnosis MS and the diagnosis
relies on the pattern of clinical and supporting paraclinical results
yielding evidence of dissemination of the disease in time and space.
Exclusion of other possible disease mimics is also mandatory. This
article will now describe how MRI is utilized in MS diagnosis, and
describe features of MS from conventional and nonconventional MRI
indices.
DIAGNOSIS OF MS
Diagnosis of definite RRMS relies on meeting the criteria of both,
dissemination in space (DIS) and dissemination in time (DIT) [2].
DIT can be either confirmed clinically by two relapses involving
different structures of the central nervous system (clinical DIS) at
separate time points (clinical evidence of DIT), or with the help of
MRI. On MRI, DIS can be based on one or more T2
lesion(s) (or
plaques) in at least two of four following areas of the central nervous
system (CNS): periventricular, juxtacortical, infratentorial, and spinal
cord [2]. The 2010 McDonald MRI criteria [2] for demonstration of
DIT include: (1) a new T2
and/or T1
gadolinium-enhancing lesion(s)
on a follow-up MRI, with reference to a baseline scan, irrespective
of the timing of the baseline MRI, or (2) simultaneous presence
of asymptomatic T1
gadolinium-enhancing and non-enhancing
lesions at any time. MS can be diagnosed at the time of first clinical
presentation if the MRI features confirm both DIS and DIT, or can
be diagnosed over time based on clinical and/or MRI evidence of
new lesions. More recently in 2016, the MAGNIMS MRI criteria
for the diagnosis of MS were introduced [13]. As these criteria
are new, they are not as commonly used in clinical practice yet as
ABSTRACT
Magnetic resonance imaging (MRI) is an essential tool for multiple sclerosis (MS) diagnosis and treatment, understanding MS
natural history and pathophysiology, and as an outcome measure in clinical trials. This review will provide descriptions of the features,
pathophysiological substrates, and clinical utility of MRI measures of MS including T2
-weighted, proton density (PD), and fluid-attenuated
inversion recovery (FLAIR) hyperintense lesions, T1
-weighted hypointense lesions, gadolinium-enhancing lesions, and measures of
brain atrophy. Lesion presence and atrophy within both the brain and spinal cord will be described. This review will also provide a
description of non-conventional MRI markers including diffusion tensor imaging (DTI), functional MRI (fMRI), magnetization transfer ratio
(MTR) imaging, relaxometry/ quantitative magnetic susceptibility (QS) mapping, and magnetic resonance spectroscopy (MRS). Basic
descriptions of how these measures are obtained, the pathological substrates, clinical correlates (e.g. with physical disability, cognition,
fatigue, etc.) and advantages/ drawbacks of each technique will be reviewed. Conclusions will be drawn on the overall clinical utility and
future directions for use of MRI in MS.
Keywords: multiple sclerosis (MS); magnetic resonance imaging (MRI); neuroimaging](https://image.slidesharecdn.com/sjms-id13-180818084254/85/Scientifi-c-Journal-of-Multiple-Sclerosis-2-320.jpg)
![Scientific Journal of Multiple Sclerosis
SCIRES Literature - Volume 1 Issue 1 - www.scireslit.com Page -010
the 2010 McDonald criteria [2]. Major modifications to the 2010
McDonald criteria include that periventricular involvement must
be confirmed by three or more lesions in this region versus one, and
that the optic nerve is another area that can be used to confirm DIS,
thus increasing the number of regions to confirm DIS from four
to five. In addition, the area of juxtacortical was expanded to also
include cortical. Another modification includes that no distinction
needs to be made between asymptomatic and symptomatic lesions.
Therefore with regards to DIT, the simultaneous presentation of a
T1
gadolinium-enhancing and non-enhancing lesions at any time can
include either asymptomatic or symptomatic lesions. Diagnosis of
primary progressive PPMS [2] rests on the presentation of one year of
disability progression (retrospectively or prospectively determined)
and meeting two of the following criteria: (a) DIS based on one or
more T2
lesions in at least one of the following areas characteristic for
MS: periventricular, juxtacortical, or infratentorial, (b) DIS based on
twoormoreT2
lesionsinthespinalcord,and(c)positivecerebrospinal
fluid (evidence of oligoclonal band and/or elevated IgG index). The
recent 2016 MAGNIMS criteria have changed the DIS criteria for
PPMS to be the same as that for RRMS. Furthermore, cerebrospinal
fluid can be used to confirm uncertain cases. The different types of MS
lesions and their characteristics will now be described.
CONVENTIONAL MRI MARKERS IN MS
Hyperintense lesions on T2
-weighted, Proton Density
(PD) and Fluid-Attenuated Inversion Recovery (FLAIR)
Sequences
The hallmark of MS is focal demyelination within the central
nervous system. This demyelination is due to local inflammation
and destruction [14]. The demyelination tends to occur in a
perivenous location, and is typically distributed around the ventricles
(periventricular), adjacent to the cortex, infratentorially (brainstem
and cerebellum), within the spinal cord, and involving the subcortical
U-fibers. T2
-weighted, PD, and FLAIR hyperintense lesions (Figure
1a,b,c), also known as plaques, are markers of demyelination
and also can represent inflammation (e.g. influx of inflammatory
cytokines, B-cell follicles), edema (high water content), gliosis (tissue
scarring, astrogliosis), demyelination, iron deposition, ischemia, and
remyelination [15]. Thus, the pathological substrate of MS lesions is
not specific. Lesions can occur in both the white matter (WM) and
gray matter (GM), and can represent demyelination in either tissue
type [16,17]. WM lesions, however, contain more infiltrating immune
cells compared to GM lesions [18]. The microstructural features of
GM lesions suggest that they are markers of neuronal damage and
microglial activation [19].
Visually, lesions appear as areas of clearly defined, oval
hyperintensities on T2
-weighted, PD, and FLAIR images (Figure 1).
Supratentorial lesions are better visualized using a FLAIR sequence,
which suppresses the T2
-hyperintense signal coming from the
cerebrospinal fluid (CSF). Periventricular and juxtacortical lesions
tend to be bigger than infratentorial lesions, and morphologically are
ovoid or round. On sagittal views, periventricular lesions tend to be
oriented ninety degrees to the ventricles, which appear as fingerlike
projections (Dawson’s fingers). Corpus callosal lesions are also
common to MS and are best visualized on a sagittal FLAIR sequences
as punctate lesions along the septo-callosal margin. Approximately
83% of individuals with MS demonstrate spinal cord abnormalities
(focal lesions or diffuse abnormalities) [20]. Visualization of T2
-
hyperintense lesions in the spinal cord is more challenging than in the
brain. Spinal cord lesions show inherently a lower contrast, tend to
be less than three vertebral bodies in length and most often affect the
posterior column and corticospinal tract [21,22]. Furthermore, the
thin anatomical structure of the cord and its susceptibility to artifacts
hamper magnetic resonance (MR) imaging in this region.
In addition to their role in diagnosing MS, the presence and
extent of lesions can be used to predict prognosis in individuals with
clinically isolated syndrome(CIS), and in individuals with confirmed
MS. CIS patients usually present with optic neuritis, brainstem or
spinal cord syndrome, and approximately 60% will be diagnosed with
MS within 5 years [23]. In CIS, clinically silent T2
brain lesions at
baseline are associated with high likelihood of developing MS [24]. A
20-year follow-up study found that T2
lesion volume at 0, 5, 10, 14, and
20 years and its change within the first 5 years correlated moderately
with 20-year physical disability level as measured by the Expanded
Disability Status Scale (EDSS) score and with MS Functional
Composite score [25], which incorporates a measure of cognitive in
addition to physical function. Furthermore, the rate of lesion growth
was three times higher in patients who developed SPMS rather than
RRMS. The presence of spinal cord lesions is also associated with risk
of subsequent MS and disability accrual in CIS [26,27].
The correlations between lesions and clinical outcomes such as
cognition [28,29], pain [28], and depression [30,31], quality of life
[28,32], are moderate at best suggesting other factors must play a
role. This is known as the clinico-radiological paradox in MS [33].
For example, recent meta-analysis found a correlation of only r=-0.3
between cognitive function and T2
lesion burden [34]. It has been
suggested, however, that this paradox can be overcome by taking into
account the localization/ spatial pattern of lesions [35], though this
has yet to be further confirmed. Cortical GM lesions appear to have
stronger clinical correlations than WM lesions. One study found that
the correlation with EDSS score appears more robust with cortical
Figure 1: Example of MRI sequences depicting MS lesions. lesions appear
bright (hyperintense) on the (a) fluid-attenuated Inversion Recovery (FLAIR)
Image, (b) T2
-weighted and (c) proton density (PD) images. (d) T1
hypointense
lesions (black holes) appear dark in the T1
-weighted image, with examples
indicated by red arrows.](https://image.slidesharecdn.com/sjms-id13-180818084254/85/Scientifi-c-Journal-of-Multiple-Sclerosis-3-320.jpg)
![Scientific Journal of Multiple Sclerosis
SCIRES Literature - Volume 1 Issue 1 - www.scireslit.com Page -011
lesions rather than WM lesion volume [36]. This study also found
that the extent of leukocortical (traversing white and gray matter)
lesions was associated with cognitive function. The number and
volume of cortical GM lesions have also been shown to correlate
with longer disease duration, progressive forms of MS and greater
disability [37,38], as well as cognitive impairment [37,39].
As a marker of disease activity, MRI lesions can be used as an
outcome in clinical trials in addition to traditional outcomes such
as disability. A meta-analysis which included 18,901 RRMS patients
demonstrated that MRI lesions within a short follow-up (6-9 months)
can be used to predict later relapse rate (12-24), providing strong
support for inclusion of MRI as a clinical outcome measure [40].
Hypointense lesions on T1-weighted sequences
Areas of focal T2
-hyperintensity are seen as hypointense lesions
on unenhanced T1
-weighted images and are commonly called
“black holes” [41] (Figure 1d). Some lesions become isointense as
the inflammatory component settles. However, in 10-30% more
extensive demyelination and axonal loss is taking place leading to
persistent black holes [42]. Port-mortem studies have shown that
black holes are correlated with axonal density [43,44] rather than
degree of demyelination or number of reactive astrocytes [44]. In the
spinal cord, conventional MRI sequences seem to be less sensitive to
demonstrate black holes [45].
Compared to other imaging techniques, T1
-weighted black holes
demonstrate high correlations with disability level and disability
progression [46,47]. Patients with SPMS also have a higher T1
lesion
load than those with RRMS [46,47]. Such data therefore suggest that
T1
-weighted black holes are markers of more severe chronic disease
and less demonstrable of the clinical-radiological paradox in MS.
Gadolinium-enhancing lesions on T1
-weighted sequences
MS begins with an inflammatory event marked by the infiltration
of immune cells into the brain and spinal cord due to disruption
of the blood-brain barrier [48]. This disruption to the blood-brain
barrier can be visualized with the intravenous administration of the
paramagnetic contrast agent, gadolinium. Gadolinium-enhancement
appears hyperintense (bright) on T1
-weighted images. If gadolinium
has leaked across the blood-brain barrier to within a lesion, this
indicatesthatthelesionisnew,havingdevelopedwithinthelastmonth
[49]. The duration of contrast enhancement is approximately 3-weeks
[50]. Enhancement can be either solid or ring-like, with an open
ring pattern being more specific for MS, compared to infectious or
neoplastic lesions. The enhancement pattern may provide insight into
the underlying pathology of the lesion. Ring enhancing lesions with
central pallor, with the open segment often medially situated, arise
from areas of previous demyelination or areas of rapidly expanding
inflammation. They tend to be larger than homogenously enhancing
lesions but only weakly predict the development of T1
hypointense
lesions (black holes), compared to homogenous enhancement [51].
The value in obtaining gadolinium-enhanced images lies in its
ability to monitor disease activity [52] and detect new inflammatory
disease activity which could be missing on other sequences such as
FLAIR. Gadolinium-enhancement can be present in individuals who
appear clinically stable [53]. As a marker of inflammatory disease
activity, the number of gadolinium-enhancing lesions was able
to predict relapse rate in a meta-analysis of 307 MS patients [54].
However, it is not a good predictor of disability progression over 2
years [54]. Gadolinium is essential in patients with CIS who have an
atypical clinical presentation or unusual brain features as a way to
rule out other conditions [55].
Lesions can also be used for safety monitoring in patients with
MS. Gadolinium is essential prior to and for safety monitoring of
high-risk drugs such as natalizumab [55]. Lesions are also useful for
identifying progressive multifocal leukoencephalopathy (PML), a
potentially life-threatening infection associated with use of traditional
disease-modifying therapies for MS, especially natalizumab. Punctate
lesions, which can be either T2
-weighted hyperintense or gadolinium-
enhancing, can be used to differentiate PML from other MS related
symptom [56]. Diagnosis of PML before clinical symptoms present is
associated with better outcomes [57] thus advocating for the use of
MRI for detection of pre-clinical PML activity.
Theidentificationoflesionsisdependentonaccuratesegmentation
which can be difficult to obtain. For example, partial volume effects
(signal loss due mixture of tissue types) can affect segmentation. Also,
intensity thresholds must be set precisely. One study found that the
correlation of black hole volume with EDSS score varied depending
on the intensity values used to segment black holes [58]. Given such
complexity, lesion segmentation should be done by trained personnel,
checked across raters, and if used for clinical management, take other
clinical results into consideration.
Brain atrophy
Brain atrophy can be quantified using different metrics including
absolute volumes of total brain or specific structures, and/or relative
or normative values such as infratentorial/ supratentorial ratio,
which can account for inter-individual variability in head size.
There have been a multitude of different algorithms developed
to measure brain volume in MS (e.g. SIENAX, NeuroQuant,
MSmetrix), making it difficult to compare across studies [59].
Volume loss is likely due to several factors including demyelination,
axonal loss (e.g. due to Wallerian degeneration), or failure of re-
myelination [60]. The lack of association with lesion volume suggests
different pathological substrates whereby lesion volume is more
representative of inflammation and atrophy is more representative
of neurodegeneration. Infratentorial/ supratentorial ratio has been
shown to associate with disease duration, EDSS score, and is closely
related to T1
-weighted black hole lesion load/ volume suggesting
similar pathological substrates of irreversible tissue destruction and
axonal loss [61].
A meta-analysis including more than 13,500 RRMS patients
found that treatment effects on brain atrophy (change in brain volume
from month 6-12 to month 24) is associated with treatment effects
disability progression [62]. The treatment effects on active (“new”
or “new enlarging”) T2
lesions were also correlated with treatment
effects on disability progression. Furthermore, the regression model
including both brain atrophy and active lesions were able to explain
75% of the variance in treatment effect on disability. Overall, this
meta-analysis suggested that both measures of brain atrophy and
active lesions are independent contributors to the treatment effect on
disability in RRMS patients.
Gray matter volume has been shown to correlate with disability
[63] and cognitive impairment [64] in MS. Deep GM volume [65],
and especially thalamic volume [66-68], has consistently been
reported as the highest brain MRI correlate of cognitive dysfunction
in MS. Cortical thickness of the posterior parietal cortex [69], regional
atrophy of the cerebral cortex, WM, and caudate head [70], and](https://image.slidesharecdn.com/sjms-id13-180818084254/85/Scientifi-c-Journal-of-Multiple-Sclerosis-4-320.jpg)
![Scientific Journal of Multiple Sclerosis
SCIRES Literature - Volume 1 Issue 1 - www.scireslit.com Page -012
thalamic and cerebellar atrophy [71] have been shown to associate
with MS fatigue. Depression, another common symptom of MS, has
been shown to associate with total brain volume and neocortical
gray matter volume [72]. Another study also reported ventricular
enlargement and frontal atrophy in depressed versus non-depressed
MS patients [73]. In 2016, a consensus panel supported the use of
brain volume as a measure for monitoring disease progression and
cognitive function in patients with MS [74].
Spinal cord atrophy
Individuals with MS demonstrate smaller spinal cord volume
(cross sectional area) than controls [75] at the upper, lower cervical,
and upper thoracic levels but not in the lower thoracic and lumbar
sections. The extent of spinal cord volume is associated with disease
duration, disease type [76] and does not correlate with lesion size.
The lack of correlation with lesion size suggests that tissue loss within
lesions is not what accounts for spinal cord atrophy but rather axonal
degeneration. Indeed, axonal loss as a pathological substrate of spinal
cord volume loss was shown using a post-mortem study [77]. Cervical
cord area has been shown to have the highest correlation with EDSS
score, followed by thoracic cord area [76]. Another study found that
total cord area is correlated with EDSS score [78], providing further
support of spinal cord atrophy as a marker of physical disability.
NON-CONVENTIONAL MRI MARKERS IN MS
Diffusion Tensor Imaging
Diffusion tensor imaging (DTI) is a technique used to infer
the diffusion of water within brain tissue. By sampling diffusivity
across multiple directions, a three-dimensional representation of
diffusion, modelled as an ellipsoid, can be computed at each voxel.
The diffusion ellipsoid allows for calculation of axial diffusivity
(AD), radial diffusivity (RD), mean diffusivity (MD) and fractional
anisotropy (FA), the three most commonly reported DTI measures.
axial diffusivity indicates diffusivity parallel to the main axis of WM
tracts and is indicative of axonal loss [79,80]. Radial diffusivity
represents diffusivity perpendicular to the main axis and is related to
demyelination [81,82]. mean diffusivity (MD) is the average diffusion
regardless of direction and could be due to factors such as vasogenic
edema [83]. fractional anisotropy (FA) incorporates both AD and
RD and indicates what fraction of the diffusion tensor ellipsoid
displays anisotropic (directionally dependent) diffusion. FA is a
dimensionless unit with values ranging from 0-1 with higher values
representing increased tissue microstructural integrity [84,85]. In the
case of water within WM tracts, diffusion would occur preferentially
parallel to the long axis of the axon, rather than perpendicular to it
due to restriction from cell membranes, thereby resulting in higher
FA values. Examples of the three different types of DTI images,
obtained from an MS patient is shown in Figure 2.
DTI offers a method to evaluate whether “normal-appearing” (i.e.
non-lesional) tissue is indeed normal, as damage to lesional tissue is
already assumed. As expected, higher MD and lower FA is observed
in lesional tissue compared to normal-appearing WM (NAWM)
[83,86]. Compared to the WM of healthy controls, the NAWM of
patients with MS shows higher MD and lower FA, suggesting tissue
damage to be present even in non-lesional tissue [83,86]. With respect
to clinical correlations, higher MD within lesional tissue has been
shown to associate with higher EDSS scores, especially so in patients
with SPMS to SPMS [83]. DTI-derived measures of corticospinal
tract damage have been shown to correlate with walking performance
[87] and EDSS scores [88]. Another study [89] found that cervical
spinal cord DTI measures independently explained variability in
hip flexion strength, vibration sensation threshold, and EDSS score.
DTI can also be used to interrogate the microstructural integrity
of GM. Kearney and colleagues [90] observed that spinal cord GM
RD was associated with EDSS score, 9-hole peg test and timed walk
performance, particularly in SPMS. Associations between cognitive
performance and DTI derived measures have also been shown within
NAWM on such tests as the Paced Auditory Serial Addition Test [91],
in comparing cognitively impaired to unimpaired patients [92], and
in predicting overall cognitive dysfunction [93]. Another study found
that MD within the hippocampus was associated with episodic verbal
memory performance [94]. With respect to fatigue, lower FA in the
anterior internal capsule is associated with increased self-reported
fatigue [95]. Finally, MS patients reporting depression have been
shown to have lower FA and higher MD in the left anterior temporal
NAWM and NAGM compared to non-depressed patients [96].
There are certain drawbacks, however, to use of DTI which
include crossing fibers (fibers of different orientations within the same
voxel) and partial volume effects (mixture of tissue types within the
same voxel) [97]. In such cases, modelling of the diffusion ellipsoid
may fail. Another problem inherent in the diffusion ellipsoid model
is that it assumes diffusion propagates in time and space in a Gaussian
manner. In considering diffusion where water molecules encounter
different viscosities and obstacles (e.g. cell membranes) it thus may
be problematic to consider this diffusion as Gaussian. Despite these
limitations, the utility of DTI to interrogate microstructural damage
in MS is still widely accepted.
Functional MRI
Functional MRI (fMRI) can be used to measure the impact of
MS on neural patterns of activation through resting-state and task-
based paradigms. Resting-state fMRI is used to identify “networks”,
or groups of brain regions that show functional connectivity,
or temporal synchronization of activation, with one another. A
consistent pattern observed in MS is one of heightened resting-state
connectivity in RRMS patients compared to healthy controls. This has
been demonstrated for numerous resting-state networks including
the default-mode [98-101], frontoparietal [99], salience [99], visual
processing [99], and sensorimotor [98] networks. However, in RRMS
patients with cognitive impairment, lower functional connectivity of
the default-mode [102, 103], fronto-parietal [102,103], salience [102]
and dorsal attention [103] networks has been observed compared to
Figure 2: Examples of the three different types of DTI images. The same
slice is shown across the three different DTI images. (a) FA image in which
areas of high FA are brighter than surrounding tissue and occur along WM
tracts. Periventricular lesions are present in this image which have lower FA
and appear darker. (b) Axial diffusivity and (c) Radial diffusivity images in
which areas of higher diffusivity appear bright, such as in cerebrospinal fluid
and periventricular lesions.](https://image.slidesharecdn.com/sjms-id13-180818084254/85/Scientifi-c-Journal-of-Multiple-Sclerosis-5-320.jpg)
![Scientific Journal of Multiple Sclerosis
SCIRES Literature - Volume 1 Issue 1 - www.scireslit.com Page -013
cognitively intact MS patients. In contrast to patients with RRMS,
patients with progressive forms of MS demonstrate lower resting-
state connectivity compared to healthy controls [104].
Task-based fMRI analysis involves modelling the blood oxygen
level-dependent (BOLD) response elicited by stimulus presentation.
The BOLD response is due to a combination of changes to cerebral
blood flow, cerebral metabolic rate of oxygen, and cerebral blood
volume in relation to greater energy demands of more active neurons
[105]. It has been reported, however, that there are alterations in
the BOLD response of persons with MS, namely reductions in
peak amplitude, that are related to individual differences in WM
microstructural integrity (FA) [106]. Thus, WM microstructural
damage should be taken into account when assessing BOLD
activation differences between MS and healthy control groups. In
task-based BOLD studies to date, there has been much variability as
to whether MS patients demonstrate higher versus lower activation
during the performance of motor, cognitive, and fatigue-inducing
tasks. Very early on in the disease, task-based fMRI has identified
patterns of increased activation during tasks of working memory and
attention [107-111] in patients with MS compared to healthy controls.
Such pattern is more evident in patients who perform at the same
(versus worse) level as healthy controls behaviourally on the fMRI
tasks utilized in these studies [108-110], suggesting a compensatory
mechanism in order to maintain task performance. However, at
higher levels of task difficulty [112-113] and in patients with cognitive
impairment [114], increases in activation are less evident. A broad
conceptualization of the resting-state and task-based fMRI findings in
MS is that RRMS patients experience heightened activation early-on
until structural damage (lesion volume, brain atrophy) reaches a level
where activation can no longer compensate. At this point, activation
reaches a peak and subsequently declines, followed by concomitant
cognitive disability. The reason for the inconsistency within the MS
fMRI literature can be attributed to the variability in terms of sample
characteristics (e.g. time since diagnosis, extent of structural damage,
baseline cognitive/ clinical status), due to specific fMRI paradigms
used, and/or different analytic programs and methods used (e.g.
independent components analysis, seed-based correlations).
Magnetization Transfer Ratio Imaging
In MS brain WM lesions, there is a decrease in total myelin lipid
and protein concentration, primarily as a result of autoimmune
neuroinflammation. Consequently, a common goal of quantitative
MRI techniques used in MS research is to first visualize and then
quantify myelin loss in both WM lesions and NAWM. Standard MRI
techniques, applied at the clinically-accessible field strengths of 1.5
and 3 Tesla (T), cannot directly detect signal from myelin. However,
magnetization transfer ratio (MTR) imaging is a specialized MRI
technique where the myelin signal can be indirectly detected
through the transfer of magnetization signal from myelin to higher
concentration free water molecules [115-117].
MTR is sensitive to the relative degree of myelination in brain
tissue [115,118]. Demyelinated MS WM lesions show significantly
reduced MTR (Figure 3), while comparatively smaller reductions are
observed in NAWM [119-120]. In MS lesions, the MTR value may
partially recover to baseline healthy WM values during remyelination.
The partial MTR recovery is believed to be a measure of the reduced
capacity of oligodendrocytes to remyelinate axons after an initial
inflammatory event [120]. Because of the capacity of MTR to track
demyelination and subsequent remyelination over the course of
many months, it may serve as a useful longer-term measure of WM
tissue integrity in MS.
A consistent pattern of MTR reduction in NAWM has been
observed around the left and right lateral ventricles [121]. The pattern
is observed in both RRMS and SPMS, and supports a CSF mediated
pathogenesis [121]. Patients with cognitive impairment also show
lower whole brain MTR [122]. In addition, diffuse brain damage
(measured by reduced normal-appearing brain tissue MTR) at the
onset of MS has been shown to be a predictor of change in memory
performance over 7 years [123].
While most MTR studies of MS have focused on WM damage,
cortical sub-pial GM damage can also be quite extensive [124,125].
This is particularly true in chronic disease. Recent research has
demonstrated that the pattern of sub-pial cortical damage in MS
can be monitored using MTR imaging [126]. Evidence from multi-
laminar, cortical surface analysis of the MTR signal suggests focal
areas of cortical surface MTR reduction, indicative of diffuse sub-pial
pathology, are most prevalent along the outer cortical surface [127].
Post-mortem histopathology has revealed that cortical GM lesions
show reduced T-cell inflammation and little disruption of the blood–
brain barrier [128-130]. This, however, is contrasted with some early
stage cortical lesion biopsy studies demonstrating a rim of activated
microglia that could be interpreted as a sign of active inflammation
[131]. Additional longitudinal, in vivo human imaging and animal
studies are needed to clarify the complete underlying mechanism of
cortical pathology in MS.
Overall, MTR has shown significant promise as an effective
tool for monitoring pathology in MS. However, linking MTR signal
directly to myelin loss in both WM and cortical GM is subject to
some confounds. MTR is an indirect measure that is weighted
by the T1
relaxation time of water molecules, the homogeneity of
radiofrequency excitation, as well as MRI sequence parameters [132].
Figure 3: Example of white matter (yellow arrows) and cortical gray matter
(red arrows) MS lesions visible on 3T MRI using (a) T1
-weighted,, (b) MTR,
(c) T2
-weighted and (d) 3D FLAIR-based MRI (1 mm3
isotropic MRI spatial
resolution).](https://image.slidesharecdn.com/sjms-id13-180818084254/85/Scientifi-c-Journal-of-Multiple-Sclerosis-6-320.jpg)
![Scientific Journal of Multiple Sclerosis
SCIRES Literature - Volume 1 Issue 1 - www.scireslit.com Page -014
Ongoing advances in specificity of the MTR contrast in relation to
myelin and other pathological substrates of MS remain necessary
[132-134].
Relaxometry/Quantitative Magnetic Susceptibility Imag-
ing
MRI-based relaxometry encapsulates a family of techniques
that measure changes in water proton density, longitudinal (T1
),
transverse (T2
) and apparent transverse (T2
*
) relaxation times of water
molecules. T2
mapping, in particular, has been applied extensively in
MS research and can be related to the breakdown of myelin, gliosis
and edema in WM [135]. The T2
multi-exponential signal decay curve
measured using MRI can be mathematically modeled to yield maps
of (i) intra-axonal/extra-axonal water and (ii) water trapped between
the myelin bilayers (myelin water)[136-138]. The ratio of the myelin
water to the total water content is referred to as the myelin water
fraction and is considered a surrogate measure of myelin content
[137]. Support for this is given by the strong quantitative correlation
between luxol fast blue histochemical myelin staining and the
amplitude of the myelin water fraction MRI signal [139].
Because of the longer MRI acquisition times generally associated
with measurements of T2
, other forms of relaxometry, including
measurement of the longitudinal relaxation time (T1
) and the
apparent transverse relaxation time (T2
*
), have been explored for
mappingthepathologicalcharacteristicsofMSbrainandspinaltissue.
Measurement of T1
offers a relatively stable and consistent measure of
WM myelin content [140]. T1
maps also provide a measure of intra-
cortical myelin content that can be applied for precise identification of
myeloarchitecture across cortical depths and brain regions [141]. In
post-mortem fixed MS brain tissue, T1
has shown a strong correlation
with cortical neuronal cell density [142]. This relationship is most
consistent in cortical GM lesions where a significant reduction in
neuronal density and phosphorylated neurofilament are present
[142]. The loss of cortical neuronal cell bodies results in a concurrent
increase in free water which mediates longer T1
relaxation times
associated with cortical pathology in MS [143].
In studies comparing MRI to post-mortem myelin staining,
increased T2
*
has been linked to more severe loss of myelin in WM
[144]. T2
*
mapping can be performed using a multi-echo gradient
echo sequence which allows reduced acquisition times compared
to standard T1
and T2
mapping techniques. Recent high field MRI
measurements of the multi-component T2
*
signal show elevated
concentrations of interstitial and axonal water in lesions compared
to NAWM of RRMS subjects [145]. This feature is apparent in both
enhancing and non-enhancing lesions.
Quantitative magnetic susceptibility (QS) is an MRI property
associated with relaxometry (Figure 4). Tissue magnetic susceptibility
results in a magnetic field perturbation in an imaging voxel and this
feature can be linked to the amount of iron and myelin in tissue
[146,147]. QS maps can be efficiently derived from the phase and
magnitude information collected with a standard gradient echo
(GRE) MRI sequence. GRE MR imaging is fast and allows whole-
brain coverage in shorter acquisition times compared to conventional
spin-echo imaging employed for traditional T2
mapping.
QS maps have proven useful in monitoring the complex sequence
of inflammatory and neurodegenerative processes within MS
WM lesions [145,148,149]. During the pathogenesis of MS lesion
formation, demyelination in the lesion rim and core, as well as
iron deposition caused by infiltrating microglia and macrophages,
increase the magnetic susceptibility [150-152]. This increase can be
measured based on QS images. QS and T2
-weighted images have
been used together for differentiating acute-active lesions from
chronic smoldering ones. Gold standard validation of this technique
based on post-mortem antibody staining studies has revealed that
the hyperintense, paramagnetic QS rim around WM lesions can
be partially attributed to M1 microglia/macrophages, which cause
smoldering inflammation that exists even after the blood brain
barrier seals [149,152].
Equally relevant, QS maps have demonstrated an increased
sensitivity to changes in subcortical iron associated with both MS
disease related processes and normal ageing [153-155]. While iron
is an essential trace element for neuronal functioning, evidence
exists that excessive brain iron levels may exert toxic effects via the
formation of free radicals [156]. In such cases, iron may be released
within the CNS due to cellular degeneration, which in turn may
enhance oxidative stress–induced neurodegenerative processes
[157]. Region-of-interest analysis of QS MR images has revealed that
the mean QS in subcortical GM is strongly correlated with clinical
disability as defined by the EDSS [154].
In summary, relaxometry is a quantitative MRI metric that can
be linked to demyelination, gliosis and axonal loss in MS. Both post-
mortem and in vivo studies have shown correlations between myelin,
axonal staining and relaxometry metrics. Nevertheless, future work
is needed to (i) reduce the total acquisition time necessary for whole-
brain relaxometry imaging and (ii) enhance the specificity of these
measures to early pathological changes and response to therapy in
MS. The QS mapping technique offers an interesting alternative for
rapid imaging of demyelination in lesions and NAWM. It also allows
imaging of iron accumulation in the subcortical nuclei. Further
research is essential to improve the signal processing and associated
Figure 4: Representative 7T MR images of an MS white matter lesion.
MR images correspond to one imaging slice from a 45-year-old patient
with relapsing-remitting MS: (A) T1
-weighted magnetization-prepared rapid
acquisition gradient-echo image, (B) T2
-weighted magnetization-prepared
fluid attenuated inversion-recovery image, (C) R2* maps and (D) QS maps.
Magnified views of a white matter lesion are displayed in the red-outlined
insets at the top right-hand corner of each individual image.](https://image.slidesharecdn.com/sjms-id13-180818084254/85/Scientifi-c-Journal-of-Multiple-Sclerosis-7-320.jpg)
![Scientific Journal of Multiple Sclerosis
SCIRES Literature - Volume 1 Issue 1 - www.scireslit.com Page -015
biophysical models relating the QS signal to pathological substrates
of MS.
Magnetic Resonance spectroscopy
Standard MRI is not specific to biochemically-relevant substrates
of cellular metabolism which are altered in MS. In contrast, magnetic
resonance spectroscopy (MRS) is a technique whereby the relative
concentration of metabolites in the CNS can be measured. In
particular, proton (H1
) MRS is usually used for detection of a group
of CNS metabolites: N-acetyl aspartate (NAA), choline (Cho),
creatine (Cr), myoinositol (Mi), glutamate (Glu), glutamine (Gln)
and lactate (Lac). which exist in brain and spinal cord tissue in low
concentrations (1-10 mM).
The (NAA) MRS signal, located at 2.02 ppm in the proton MRS
spectrum, has in vivo concentrations on the order of 7.8 mM and
is of major interest in MS [158,159]. NAA is a biochemical amino
acid derivative produced in neurons and transported along axonal
projections [160]. Early MRS studies revealed that NAA in MS
NAWM is consistently reduced, compared to other metabolites
[161,162]. The standard interpretation of this finding is that the
reduced concentration of NAA is a result of neuronal loss or
dysfunction. In acute active gadolinium-enhancing lesions, an initial
reduction of NAA at the onset of white lesions is seen. After the acute
phase, this reduction can be partially restored and followed on serial
MRS studies [161,163]. The restoration of NAA suggests that the
initial decrease in values of NAA during demyelination does not only
represent neuronal and axonal loss, but also identifies signatures of
edema resolution.
Choline (Cho), which exists in the proton NMR spectrum at 3.2
ppm, and has in vivo concentrations on the order of 1.3 mM, serves
as a marker of myelin phospholipid bilayer metabolism [158,159].
During active demyelination, breakdown and release of membrane
phosphocholine and glycerol-phosphocholine are believed to lead to
the observed elevated choline MRS signal in both lesions and NAWM
[164-168]. An increase in the choline MRS signal can also be observed
up to 12 months prior to WM lesion formation [169]. This finding
is consistent with a localized pre-lesional phospholipid membrane
breakdown.
Glutamate (Glu), is an excitatory neurotransmitter converted
to a Glutamine (Gln) and Gamma-Amino Butyric Acid (GABA)
in neurons following synaptic release [170]. Glutamate, with
approximately 10 mM in vivo concentrations, and glutamine, with
approximately 5 mM in vivo concentrations, are linked to neuro-
axonal energy supply [158,159]. Patients with SPMS, in particular,
show reduced GABA levels in the hippocampus and sensorimotor
cortex compared to healthy subjects [171]. These MRS measures may,
thus, prove useful in monitoring more advanced neuroprotective
therapies for SPMS.
From a clinical standpoint, the NAA/Cr ratio in NAWM of MS
patients has been shown to correlate with EDSS [172, 173], suggesting
WM neuronal dysfunction and gliosis can be linked to clinical
disability. In normal-appearing cortical GM, NAA deficits have also
been found in SPMS patients relative to age-matched controls [174].
However, these NAA deficits do not correlate significantly with EDSS.
In addition, NAA/Cr ratio has been shown to be significantly lower in
MS patients reporting high fatigue [175].
A number of technical issues remain before the MRS family of
techniques is more applicable to monitoring MS pathology. First,
MRS is associated with reduced signal-to-noise ratio (SNR) and long
acquisition times when compared to standard imaging. Secondly,
the lower SNR results in associated reduced spatial resolution.
These issues have led to MRS not being routinely used in MS clinical
practice. Nonetheless, improved SNR associated with high field
MRI, combined with advances in rapid imaging and compressed
sensing techniques are now making whole-brain MRS imaging more
accessible to MS researchers.
SUMMARY AND CONCLUSIONS
MRI is a crucial tool in the diagnosis and management of MS.
MRI can also be used to elucidate the pathophysiological substrates of
brainandspinalcordabnormalitiesinMS.Specificpathophysiological
markers are better identified using certain techniques over another.
For example, breakdown of the blood-brain and active inflammation
are best identified using gadolinium enhancement, whereas MTR
can be used to measure extent of re-myelination. Conventional
MRI sequences such as T2
and T1
weighted sequences are routinely
acquired in clinical practice and used primarily for MS diagnosis
and monitoring. Non-conventional measures such as DTI, fMRI,
and MRS are not required for clinical diagnosis and management.
but offer unique insights into the pathophysiology of MS and are
becoming more widely recognized for use not solely in the realm of
research investigations.
Despite advances in all of the MRI techniques described in this
review, each is associated with drawbacks that must be taken into
consideration. For example, lesion segmentation is dependent on
the definition of intensity thresholds and atrophy measurements
required accurate anatomical delineation of brain regions. Though
many automated programs exist to obtain volumes, etc. outputs
should be checked by ideally more than one personnel. Further
complications arise because of variability across sites with respect
to type of scanner and scanning parameters used. It is advisable that
standard MRI protocols be adopted and there have been efforts to
this end as pertains to imaging sequences acquired/ MRI protocols
[55,176-178] and analytical tools [179, 180]. This will also help
facilitate multi-site collaborations and address inconsistencies in the
literature. The clinico-radiological paradox should also be further
investigated in MS. For patients with heavy disease burden as shown
on MRI but minimal overt clinical symptoms, potential protective
factors should be explored which could include aspects such cognitive
reserve, exercise, and diet or vitamin supplementation.
While current MRI techniques are being further refined and new
techniques are emerging, MRI will continue to help further elucidate
the pathogenesis of MS which will help in earlier detection and guide
the development of treatments. As an outcome measure in clinical
trials, MRI is also recognized as an important tool for monitoring
treatment success and patient safety. For these any other reasons, MRI
will continue to play an absolutely essential role in the identification
and management of MS.
REFERENCES
1. Paty DW, Oger JJ, Kastrukoff LF, Hashimoto SA, Hooge JP, Eisen AA, et al.
MRI in the diagnosis of MS: a prospective study with comparison of clinical
evaluation, evoked potentials, oligoclonal banding, and CT. Neurology. 1988;
38: 180-185. https://goo.gl/2469BM
2. Polman C, Reingold S, Banwell B, Clanet M, Cohen J, Filippi M, et al.
Diagnostic criteria for multiple sclerosis: 2010 Revisions to the McDonald
criteria. Ann Neurol. 2011; 69: 292-302. https://goo.gl/cjcZfZ
3. Sadovnick AD, Baird PA. The familial nature of multiple sclerosis: age-
corrected empiric recurrence risks for children and siblings of patients.
Neurology. 1988; 38: 990-991. https://goo.gl/rxYbv1](https://image.slidesharecdn.com/sjms-id13-180818084254/85/Scientifi-c-Journal-of-Multiple-Sclerosis-8-320.jpg)
![Scientific Journal of Multiple Sclerosis
SCIRES Literature - Volume 1 Issue 1 - www.scireslit.com Page -019
sclerosis: lesion characterization with magnetization transfer imaging.
Radiology. 1992; 182: 483-491. https://goo.gl/tTBZQc
119. Ropele S, Strasser-Fuchs S, Augustin M, Stollberger R, Enzinger
C, Hartung HP, et al. A comparison of magnetization transfer ratio,
magnetization transfer rate, and the native relaxation time of water protons
related to relapsing-remitting multiple sclerosis. AJNR Am J Neuroradiol.
2000; 21: 1885-1891. https://goo.gl/ABGHXw
120. Brown RA, Narayanan S, Arnold DL. Imaging of repeated episodes of
demyelination and remyelination in multiple sclerosis. Neuroimage Clin.
2014; 6: 20-25. https://goo.gl/zccB3b
121. Liu Z, Pardini M, Yaldizli O, Sethi V, Muhlert N, Wheeler-Kingshott CA, et
al. Magnetization transfer ratio measures in normal-appearing white matter
show periventricular gradient abnormalities in multiple sclerosis. Brain.
2015; 138: 1239-1246. https://goo.gl/hJeUCK
122. Faiss JH, Dahne D, Baum K, Deppe R, Hoffmann F, Kohler W, et al.
Reduced magnetisation transfer ratio in cognitively impaired patients at
the very early stage of multiple sclerosis: a prospective, multicenter, cross-
sectional study. BMJ Open. 2014; 4: 004409. https://goo.gl/Jzmvg3
123. Deloire MS, Ruet A, Hamel D, Bonnet M, Dousset V, Brochet B. MRI
predictors of cognitive outcome in early multiple sclerosis. Neurology.
2011; 76: 1161-1167. https://goo.gl/ASTUAh
124. Kutzelnigg A, Lucchinetti CF, Stadelmann C, Bruck W, Rauschka H,
Bergmann M, et al. Cortical demyelination and diffuse white matter injury
in multiple sclerosis. Brain. 2005; 128: 2705-2712. https://goo.gl/a6sTZg
125. Mainero C, Louapre C, Govindarajan ST, Gianni C, Nielsen AS, Cohen-
Adad J, et al. A gradient in cortical pathology in multiple sclerosis by in vivo
quantitative 7 T imaging. Brain. 2015; 138: 932-945. https://goo.gl/PYSCnt
126. Schmierer K, Scaravilli F, Altmann DR, Barker GJ, Miller DH. Magnetization
transfer ratio and myelin in postmortem multiple sclerosis brain. Ann
Neurol. 2004; 56: 407-415. https://goo.gl/6HFpB5
127. Rudko DA, Derakhshan M, Maranzano J, Nakamura K, Arnold DL,
Narayanan S. Delineation of cortical pathology in multiple sclerosis using
multi-surface magnetization transfer ratio imaging. Neuroimage Clin. 2016;
12: 858-868. https://goo.gl/aECcFH
128. Kidd D, Barkhof F, McConnell R, Algra PR, Allen IV, Revesz T. Cortical
lesions in multiple sclerosis. Brain. 1999; 122: 17-26. https://goo.gl/oFuq77
129. Peterson JW, Bo L, Mork S, Chang A, Trapp BD. Transected neurites,
apoptotic neurons, and reduced inflammation in cortical multiple sclerosis
lesions. Ann Neurol. 2001; 50: 389-400. https://goo.gl/UnW2Pi
130. Bo L, Vedeler CA, Nyland H, Trapp BD, Mork SJ. Intracortical multiple
sclerosis lesions are not associated with increased lymphocyte infiltration.
Mult Scler. 2003; 9: 323-331. https://goo.gl/Kim5nF
131. Lucchinetti CF, Popescu BF, Bunyan RF, Moll NM, Roemer SF, Lassmann
H, et al. Inflammatory cortical demyelination in early multiple sclerosis. N
Engl J Med. 2011; 365: 2188-2197. https://goo.gl/3mrTjx
132. Helms G, Dathe H, Kallenberg K, Dechent P. High-resolution maps of
magnetization transfer with inherent correction for RF inhomogeneity and
T1 relaxation obtained from 3D FLASH MRI. Magn Reson Med. 2008; 60:
1396-1407. https://goo.gl/km2jue
133. Levesque I, Sled JG, Narayanan S, Santos AC, Brass SD, Francis SJ,
et al. The role of edema and demyelination in chronic T1 black holes: a
quantitative magnetization transfer study. J Magn Reson Imaging. 2005;
21: 103-110. https://goo.gl/hYWMem
134. Levesque IR, Giacomini PS, Narayanan S, Ribeiro LT, Sled JG, Arnold DL,
et al. Quantitative magnetization transfer and myelin water imaging of the
evolution of acute multiple sclerosis lesions. Magn Reson Med. 2010; 63:
633-640. https://goo.gl/PizLDr
135. Schmierer K, Wheeler Kingshott CA, Tozer DJ, Boulby PA, Parkes HG,
Yousry TA, et al. Quantitative magnetic resonance of postmortem multiple
sclerosis brain before and after fixation. Magn Reson Med. 2008; 59: 268-
277. https://goo.gl/8TbURT
136. Poon CS, Henkelman RM. Practical T2 quantitation for clinical applications.
J Magn Reson Imaging. 1992; 2: 541-553. https://goo.gl/a6oPH4
137. MacKay A, Whittall K, Adler J, Li D, Paty D, Graeb D. In vivo visualization
of myelin water in brain by magnetic resonance. Magn Reson Med. 1994;
31: 673-677. https://goo.gl/mwiRq7
138. Whittall KP, MacKay AL, Graeb DA, Nugent RA, Li DK, Paty DW. In vivo
measurement of T2 distributions and water contents in normal human
brain. Magn Reson Med. 1997; 37: 34-43. https://goo.gl/aakc8J
139. Laule C, Kozlowski P, Leung E, Li DK, Mackay AL, Moore GR. Myelin
water imaging of multiple sclerosis at 7 T: correlations with histopathology.
Neuroimage. 2008; 40: 1575-1580. https://goo.gl/xw4Rvo
140. Vrenken H, Geurts JJ, Knol DL, van Dijk LN, Dattola V, Jasperse B, et
al. Whole-brain T1 mapping in multiple sclerosis: global changes of
normal-appearing gray and white matter. Radiology. 2006; 240: 811-820.
https://goo.gl/T8vTgA
141. Tardif CL, Schafer A, Waehnert M, Dinse J, Turner R, Bazin PL. Multi-
contrast multi-scale surface registration for improved alignment of cortical
areas. Neuroimage. 2015; 111: 107-122. https://goo.gl/9XC3sM
142. Schmierer K, Parkes HG, So PW, An SF, Brandner S, Ordidge RJ,
et al. High field (9.4 Tesla) magnetic resonance imaging of cortical
grey matter lesions in multiple sclerosis. Brain. 2010; 133: 858-867.
https://goo.gl/gR6dMF
143. Vavasour IM, Li DK, Laule C, Traboulsee AL, Moore GR, Mackay AL.
Multi-parametric MR assessment of T(1) black holes in multiple sclerosis
: evidence that myelin loss is not greater in hypointense versus isointense
T(1) lesions. J Neurol. 2007; 254: 1653-1659. https://goo.gl/xuYNX8
144. Yao B, Bagnato F, Matsuura E, Merkle H, van Gelderen P, Cantor FK, et al.
Chronic multiple sclerosis lesions: characterization with high-field-strength
MR imaging. Radiology. 2012; 262: 206-215. https://goo.gl/wuwy7b
145. Li X, van Gelderen P, Sati P, de Zwart JA, Reich DS, Duyn JH.
Detection of demyelination in multiple sclerosis by analysis of [Formula:
see text] relaxation at 7 T. Neuroimage Clin. 2015; 7: 709-714.
https://goo.gl/m5CAfZ
146. Weisskoff RM, Kiihne S. MRI susceptometry: image-based measurement
of absolute susceptibility of MR contrast agents and human blood. Magn
Reson Med. 1992; 24: 375-83. https://goo.gl/yVRSqq
147. de Rochefort L, Liu T, Kressler B, Liu J, Spincemaille P, Lebon V, et al.
Quantitative susceptibility map reconstruction from MR phase data using
bayesian regularization: validation and application to brain imaging. Magn
Reson Med. 2010; 63: 194-206. https://goo.gl/VbkWPb
148. Mehta V, Pei W, Yang G, Li S, Swamy E, Boster A, et al. Iron is a sensitive
biomarker for inflammation in multiple sclerosis lesions. PLoS One. 2013;
8: 57573. https://goo.gl/QaXPTA
149. Zhang Y, Gauthier SA, Gupta A, Comunale J, Chia-Yi Chiang G,
Zhou D, et al. Longitudinal change in magnetic susceptibility of new
enhanced multiple sclerosis (MS) lesions measured on serial quantitative
susceptibility mapping (QSM). J Magn Reson Imaging. 2016; 44: 426-432.
https://goo.gl/huYWXM
150. Chen W, Gauthier SA, Gupta A, Comunale J, Liu T, Wang S, et al.
Quantitative susceptibility mapping of multiple sclerosis lesions at various
ages. Radiology. 2014; 271: 183-192. https://goo.gl/BHEPDi
151. Taetzsch T, Levesque S, McGraw C, Brookins S, Luqa R, Bonini MG, et al.
Redox regulation of NF-kappaB p50 and M1 polarization in microglia. Glia.
2015; 63: 423-440. https://goo.gl/1wtEHQ
152. Wisnieff C, Ramanan S, Olesik J, Gauthier S, Wang Y, Pitt D. Quantitative
susceptibility mapping (QSM) of white matter multiple sclerosis lesions:
Interpreting positive susceptibility and the presence of iron. Magn Reson
Med. 2015; 74: 564-570. https://goo.gl/ddzoMm
153. Langkammer C, Liu T, Khalil M, Enzinger C, Jehna M, Fuchs S, et al.
Quantitative susceptibility mapping in multiple sclerosis. Radiology. 2013;
267: 551-559. https://goo.gl/Ng2f18
154. Rudko DA, Solovey I, Gati JS, Kremenchutzky M, Menon RS. Multiple
sclerosis: improved identification of disease-relevant changes in gray and
white matter by using susceptibility-based MR imaging. Radiology. 2014;
272: 851-864. https://goo.gl/JAU2k5
155. Cobzas D, Sun H, Walsh AJ, Lebel RM, Blevins G, Wilman AH. Subcortical
gray matter segmentation and voxel-based analysis using transverse
relaxation and quantitative susceptibility mapping with application
to multiple sclerosis. J Magn Reson Imaging. 2015; 42: 1601-1610.
https://goo.gl/bBSsv4](https://image.slidesharecdn.com/sjms-id13-180818084254/85/Scientifi-c-Journal-of-Multiple-Sclerosis-12-320.jpg)
Scientifi c Journal of Multiple Sclerosis
- 1. Review Article Magnetic Resonance Imaging of Multiple Sclerosis - Nadine Akbar1 *, David A Rudko2 and Katrin Parmar3 1 School of Rehabilitation Therapy, Faculty of Health Sciences, Queen’s University, Kingston, Ontario, Canada 2 Department of Neurology and Neurosurgery and Department of Biomedical Engineering, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada 3 Department of Neurology, University Hospital Basel, Basel, Switzerland *Address for Correspondence: Nadine Akbar, Queen’s University, School of Rehabilitation Therapy, Louise D Acton Building, 31 George Street, Kingston, Ontario, Canada, K7L 3N6, Tel : +1- 613- 537- 7874; ext. 77874; E-mail: Submitted: 10 October 2017; Approved: 14 November 2017; Published: 16 November 2017 Cite this article: Akbar N, Rudko DA, Parmar K. Magnetic Resonance Imaging of Multiple Sclerosis. Sci J Mult Scler. 2017; 1(1): 008-020. Copyright: © 2017 Akbar N, et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Scientific Journal of Multiple Sclerosis
- 2. Scientific Journal of Multiple Sclerosis SCIRES Literature - Volume 1 Issue 1 - www.scireslit.com Page -009 ABBREVIATIONS Axial Diffusivity- AD; blood oxygenation level dependent- BOLD; Central Nervous System- CNS; Cerebrospinal Fluid- CSF; Choline- Cho; Clinically Isolated Syndrome- CIS; Creatine- Cr; Diffusion Tensor Imaging- DTI; Dissemination In Space- DIS; Dissemination In Time- DIT; Expanded Disability Status Scale- EDSS; Fluid- Attenuated Inversion Recovery- FLAIR; Fractional Anisotropy- FA; Functional MRI- Fmri; Gamma-Amino Butyric Acid- GABA; Glutamate- Glu; Glutamine- Gln; Gradient Echo- GRE; Gray Matter- GM; Lactate- Lac; Magnetic Resonance Imaging- MRI; Magnetic Resonance Spectroscopy- MRS; Magnetization Transfer Ratio- MTR; Mean Diffusivity- MD; Multiple Sclerosis- MS; Magnetic Resonance- MR; Myoinositol- Mi; N-Acetyl Aspartate- NAA; Normal-Appearing White Matter- NAWM; Primary Progressive Multiple Sclerosis- PPMS; Progressive Multifocal Leukoencephalopathy- PML; Proton Density- PD; Quantitative Magnetic Susceptibility- QS; Radial Diffusivity- RD; Relapsing-Remitting Multiple Sclerosis- RRMS; Secondary Progressive Multiple Sclerosis – SPMS; White Matter- WM INTRODUCTION Multiple sclerosis (MS) is a chronic and inflammatory disease of the central nervous system and one of the most common causes of non-traumatic neurological disability in young adults. Approximately 2.5 million persons worldwide are living with MS. For the past few decades, magnetic resonance imaging (MRI) has played a crucial role in MS diagnosis, monitoring and treatment, understanding MS natural history/ progression and pathophysiology, and as an outcome measure in clinical trials [1,2]. This article will provide a brief overview of MS and describe the core features and uses of MRI for MS. OVERVIEW OF MS It has been shown that both genetic and environmental risk factors contribute to the development of MS [3,4]. While MS is known to be an immune-mediated disease, the exact underlying cause is not yet fully understood. Histopathologically, demyelination and axonal loss are evident [5,6]. The clinical course can be relapsing or progressive. Eighty-five percent of patients are initially diagnosed with relapsing-remitting MS (RRMS). In the RRMS subtype, patients experience episodes of neurological disturbance (relapses) defined as being longer than 24 hours duration without fever or infection, and followed by periods of (partial) recovery [7]. More than half of these patients enter a secondary-progressive phase (median time to conversion = 10 years) (secondary progressive MS- SPMS), which is characterized by progressive neurologic decline with or without occasional relapses, minor remissions, and plateaus [7]. About 15% show a primary progressive disease type (primary progressive MS- PPMS) with steady and progressive accrual of disability from first symptom onset on (without any signs of recovery). PPMS occurs more commonly in older patients, with the mean age of disease onset being 40 as compared to 30 years of age in RRMS [8-10]. Visual and sensory dysfunctions are the most common symptoms at disease onset in the RRMS type, while progressive gait disturbance dominates in PPMS. Over the course of the disease, symptoms may vary and can include muscle spasticity, weakness, dizziness and vertigo, fatigue, and cognitive dysfunction, amongst other symptoms. The overall life expectancy for adult-onset MS patients is only 6 years lower than the rest of the population [11,12]. Therefore, the burden of disease lies primarily in the reduction in quality of life and accumulation of physical disability. So far, no single test is used to diagnosis MS and the diagnosis relies on the pattern of clinical and supporting paraclinical results yielding evidence of dissemination of the disease in time and space. Exclusion of other possible disease mimics is also mandatory. This article will now describe how MRI is utilized in MS diagnosis, and describe features of MS from conventional and nonconventional MRI indices. DIAGNOSIS OF MS Diagnosis of definite RRMS relies on meeting the criteria of both, dissemination in space (DIS) and dissemination in time (DIT) [2]. DIT can be either confirmed clinically by two relapses involving different structures of the central nervous system (clinical DIS) at separate time points (clinical evidence of DIT), or with the help of MRI. On MRI, DIS can be based on one or more T2 lesion(s) (or plaques) in at least two of four following areas of the central nervous system (CNS): periventricular, juxtacortical, infratentorial, and spinal cord [2]. The 2010 McDonald MRI criteria [2] for demonstration of DIT include: (1) a new T2 and/or T1 gadolinium-enhancing lesion(s) on a follow-up MRI, with reference to a baseline scan, irrespective of the timing of the baseline MRI, or (2) simultaneous presence of asymptomatic T1 gadolinium-enhancing and non-enhancing lesions at any time. MS can be diagnosed at the time of first clinical presentation if the MRI features confirm both DIS and DIT, or can be diagnosed over time based on clinical and/or MRI evidence of new lesions. More recently in 2016, the MAGNIMS MRI criteria for the diagnosis of MS were introduced [13]. As these criteria are new, they are not as commonly used in clinical practice yet as ABSTRACT Magnetic resonance imaging (MRI) is an essential tool for multiple sclerosis (MS) diagnosis and treatment, understanding MS natural history and pathophysiology, and as an outcome measure in clinical trials. This review will provide descriptions of the features, pathophysiological substrates, and clinical utility of MRI measures of MS including T2 -weighted, proton density (PD), and fluid-attenuated inversion recovery (FLAIR) hyperintense lesions, T1 -weighted hypointense lesions, gadolinium-enhancing lesions, and measures of brain atrophy. Lesion presence and atrophy within both the brain and spinal cord will be described. This review will also provide a description of non-conventional MRI markers including diffusion tensor imaging (DTI), functional MRI (fMRI), magnetization transfer ratio (MTR) imaging, relaxometry/ quantitative magnetic susceptibility (QS) mapping, and magnetic resonance spectroscopy (MRS). Basic descriptions of how these measures are obtained, the pathological substrates, clinical correlates (e.g. with physical disability, cognition, fatigue, etc.) and advantages/ drawbacks of each technique will be reviewed. Conclusions will be drawn on the overall clinical utility and future directions for use of MRI in MS. Keywords: multiple sclerosis (MS); magnetic resonance imaging (MRI); neuroimaging
- 3. Scientific Journal of Multiple Sclerosis SCIRES Literature - Volume 1 Issue 1 - www.scireslit.com Page -010 the 2010 McDonald criteria [2]. Major modifications to the 2010 McDonald criteria include that periventricular involvement must be confirmed by three or more lesions in this region versus one, and that the optic nerve is another area that can be used to confirm DIS, thus increasing the number of regions to confirm DIS from four to five. In addition, the area of juxtacortical was expanded to also include cortical. Another modification includes that no distinction needs to be made between asymptomatic and symptomatic lesions. Therefore with regards to DIT, the simultaneous presentation of a T1 gadolinium-enhancing and non-enhancing lesions at any time can include either asymptomatic or symptomatic lesions. Diagnosis of primary progressive PPMS [2] rests on the presentation of one year of disability progression (retrospectively or prospectively determined) and meeting two of the following criteria: (a) DIS based on one or more T2 lesions in at least one of the following areas characteristic for MS: periventricular, juxtacortical, or infratentorial, (b) DIS based on twoormoreT2 lesionsinthespinalcord,and(c)positivecerebrospinal fluid (evidence of oligoclonal band and/or elevated IgG index). The recent 2016 MAGNIMS criteria have changed the DIS criteria for PPMS to be the same as that for RRMS. Furthermore, cerebrospinal fluid can be used to confirm uncertain cases. The different types of MS lesions and their characteristics will now be described. CONVENTIONAL MRI MARKERS IN MS Hyperintense lesions on T2 -weighted, Proton Density (PD) and Fluid-Attenuated Inversion Recovery (FLAIR) Sequences The hallmark of MS is focal demyelination within the central nervous system. This demyelination is due to local inflammation and destruction [14]. The demyelination tends to occur in a perivenous location, and is typically distributed around the ventricles (periventricular), adjacent to the cortex, infratentorially (brainstem and cerebellum), within the spinal cord, and involving the subcortical U-fibers. T2 -weighted, PD, and FLAIR hyperintense lesions (Figure 1a,b,c), also known as plaques, are markers of demyelination and also can represent inflammation (e.g. influx of inflammatory cytokines, B-cell follicles), edema (high water content), gliosis (tissue scarring, astrogliosis), demyelination, iron deposition, ischemia, and remyelination [15]. Thus, the pathological substrate of MS lesions is not specific. Lesions can occur in both the white matter (WM) and gray matter (GM), and can represent demyelination in either tissue type [16,17]. WM lesions, however, contain more infiltrating immune cells compared to GM lesions [18]. The microstructural features of GM lesions suggest that they are markers of neuronal damage and microglial activation [19]. Visually, lesions appear as areas of clearly defined, oval hyperintensities on T2 -weighted, PD, and FLAIR images (Figure 1). Supratentorial lesions are better visualized using a FLAIR sequence, which suppresses the T2 -hyperintense signal coming from the cerebrospinal fluid (CSF). Periventricular and juxtacortical lesions tend to be bigger than infratentorial lesions, and morphologically are ovoid or round. On sagittal views, periventricular lesions tend to be oriented ninety degrees to the ventricles, which appear as fingerlike projections (Dawson’s fingers). Corpus callosal lesions are also common to MS and are best visualized on a sagittal FLAIR sequences as punctate lesions along the septo-callosal margin. Approximately 83% of individuals with MS demonstrate spinal cord abnormalities (focal lesions or diffuse abnormalities) [20]. Visualization of T2 - hyperintense lesions in the spinal cord is more challenging than in the brain. Spinal cord lesions show inherently a lower contrast, tend to be less than three vertebral bodies in length and most often affect the posterior column and corticospinal tract [21,22]. Furthermore, the thin anatomical structure of the cord and its susceptibility to artifacts hamper magnetic resonance (MR) imaging in this region. In addition to their role in diagnosing MS, the presence and extent of lesions can be used to predict prognosis in individuals with clinically isolated syndrome(CIS), and in individuals with confirmed MS. CIS patients usually present with optic neuritis, brainstem or spinal cord syndrome, and approximately 60% will be diagnosed with MS within 5 years [23]. In CIS, clinically silent T2 brain lesions at baseline are associated with high likelihood of developing MS [24]. A 20-year follow-up study found that T2 lesion volume at 0, 5, 10, 14, and 20 years and its change within the first 5 years correlated moderately with 20-year physical disability level as measured by the Expanded Disability Status Scale (EDSS) score and with MS Functional Composite score [25], which incorporates a measure of cognitive in addition to physical function. Furthermore, the rate of lesion growth was three times higher in patients who developed SPMS rather than RRMS. The presence of spinal cord lesions is also associated with risk of subsequent MS and disability accrual in CIS [26,27]. The correlations between lesions and clinical outcomes such as cognition [28,29], pain [28], and depression [30,31], quality of life [28,32], are moderate at best suggesting other factors must play a role. This is known as the clinico-radiological paradox in MS [33]. For example, recent meta-analysis found a correlation of only r=-0.3 between cognitive function and T2 lesion burden [34]. It has been suggested, however, that this paradox can be overcome by taking into account the localization/ spatial pattern of lesions [35], though this has yet to be further confirmed. Cortical GM lesions appear to have stronger clinical correlations than WM lesions. One study found that the correlation with EDSS score appears more robust with cortical Figure 1: Example of MRI sequences depicting MS lesions. lesions appear bright (hyperintense) on the (a) fluid-attenuated Inversion Recovery (FLAIR) Image, (b) T2 -weighted and (c) proton density (PD) images. (d) T1 hypointense lesions (black holes) appear dark in the T1 -weighted image, with examples indicated by red arrows.
- 4. Scientific Journal of Multiple Sclerosis SCIRES Literature - Volume 1 Issue 1 - www.scireslit.com Page -011 lesions rather than WM lesion volume [36]. This study also found that the extent of leukocortical (traversing white and gray matter) lesions was associated with cognitive function. The number and volume of cortical GM lesions have also been shown to correlate with longer disease duration, progressive forms of MS and greater disability [37,38], as well as cognitive impairment [37,39]. As a marker of disease activity, MRI lesions can be used as an outcome in clinical trials in addition to traditional outcomes such as disability. A meta-analysis which included 18,901 RRMS patients demonstrated that MRI lesions within a short follow-up (6-9 months) can be used to predict later relapse rate (12-24), providing strong support for inclusion of MRI as a clinical outcome measure [40]. Hypointense lesions on T1-weighted sequences Areas of focal T2 -hyperintensity are seen as hypointense lesions on unenhanced T1 -weighted images and are commonly called “black holes” [41] (Figure 1d). Some lesions become isointense as the inflammatory component settles. However, in 10-30% more extensive demyelination and axonal loss is taking place leading to persistent black holes [42]. Port-mortem studies have shown that black holes are correlated with axonal density [43,44] rather than degree of demyelination or number of reactive astrocytes [44]. In the spinal cord, conventional MRI sequences seem to be less sensitive to demonstrate black holes [45]. Compared to other imaging techniques, T1 -weighted black holes demonstrate high correlations with disability level and disability progression [46,47]. Patients with SPMS also have a higher T1 lesion load than those with RRMS [46,47]. Such data therefore suggest that T1 -weighted black holes are markers of more severe chronic disease and less demonstrable of the clinical-radiological paradox in MS. Gadolinium-enhancing lesions on T1 -weighted sequences MS begins with an inflammatory event marked by the infiltration of immune cells into the brain and spinal cord due to disruption of the blood-brain barrier [48]. This disruption to the blood-brain barrier can be visualized with the intravenous administration of the paramagnetic contrast agent, gadolinium. Gadolinium-enhancement appears hyperintense (bright) on T1 -weighted images. If gadolinium has leaked across the blood-brain barrier to within a lesion, this indicatesthatthelesionisnew,havingdevelopedwithinthelastmonth [49]. The duration of contrast enhancement is approximately 3-weeks [50]. Enhancement can be either solid or ring-like, with an open ring pattern being more specific for MS, compared to infectious or neoplastic lesions. The enhancement pattern may provide insight into the underlying pathology of the lesion. Ring enhancing lesions with central pallor, with the open segment often medially situated, arise from areas of previous demyelination or areas of rapidly expanding inflammation. They tend to be larger than homogenously enhancing lesions but only weakly predict the development of T1 hypointense lesions (black holes), compared to homogenous enhancement [51]. The value in obtaining gadolinium-enhanced images lies in its ability to monitor disease activity [52] and detect new inflammatory disease activity which could be missing on other sequences such as FLAIR. Gadolinium-enhancement can be present in individuals who appear clinically stable [53]. As a marker of inflammatory disease activity, the number of gadolinium-enhancing lesions was able to predict relapse rate in a meta-analysis of 307 MS patients [54]. However, it is not a good predictor of disability progression over 2 years [54]. Gadolinium is essential in patients with CIS who have an atypical clinical presentation or unusual brain features as a way to rule out other conditions [55]. Lesions can also be used for safety monitoring in patients with MS. Gadolinium is essential prior to and for safety monitoring of high-risk drugs such as natalizumab [55]. Lesions are also useful for identifying progressive multifocal leukoencephalopathy (PML), a potentially life-threatening infection associated with use of traditional disease-modifying therapies for MS, especially natalizumab. Punctate lesions, which can be either T2 -weighted hyperintense or gadolinium- enhancing, can be used to differentiate PML from other MS related symptom [56]. Diagnosis of PML before clinical symptoms present is associated with better outcomes [57] thus advocating for the use of MRI for detection of pre-clinical PML activity. Theidentificationoflesionsisdependentonaccuratesegmentation which can be difficult to obtain. For example, partial volume effects (signal loss due mixture of tissue types) can affect segmentation. Also, intensity thresholds must be set precisely. One study found that the correlation of black hole volume with EDSS score varied depending on the intensity values used to segment black holes [58]. Given such complexity, lesion segmentation should be done by trained personnel, checked across raters, and if used for clinical management, take other clinical results into consideration. Brain atrophy Brain atrophy can be quantified using different metrics including absolute volumes of total brain or specific structures, and/or relative or normative values such as infratentorial/ supratentorial ratio, which can account for inter-individual variability in head size. There have been a multitude of different algorithms developed to measure brain volume in MS (e.g. SIENAX, NeuroQuant, MSmetrix), making it difficult to compare across studies [59]. Volume loss is likely due to several factors including demyelination, axonal loss (e.g. due to Wallerian degeneration), or failure of re- myelination [60]. The lack of association with lesion volume suggests different pathological substrates whereby lesion volume is more representative of inflammation and atrophy is more representative of neurodegeneration. Infratentorial/ supratentorial ratio has been shown to associate with disease duration, EDSS score, and is closely related to T1 -weighted black hole lesion load/ volume suggesting similar pathological substrates of irreversible tissue destruction and axonal loss [61]. A meta-analysis including more than 13,500 RRMS patients found that treatment effects on brain atrophy (change in brain volume from month 6-12 to month 24) is associated with treatment effects disability progression [62]. The treatment effects on active (“new” or “new enlarging”) T2 lesions were also correlated with treatment effects on disability progression. Furthermore, the regression model including both brain atrophy and active lesions were able to explain 75% of the variance in treatment effect on disability. Overall, this meta-analysis suggested that both measures of brain atrophy and active lesions are independent contributors to the treatment effect on disability in RRMS patients. Gray matter volume has been shown to correlate with disability [63] and cognitive impairment [64] in MS. Deep GM volume [65], and especially thalamic volume [66-68], has consistently been reported as the highest brain MRI correlate of cognitive dysfunction in MS. Cortical thickness of the posterior parietal cortex [69], regional atrophy of the cerebral cortex, WM, and caudate head [70], and
- 5. Scientific Journal of Multiple Sclerosis SCIRES Literature - Volume 1 Issue 1 - www.scireslit.com Page -012 thalamic and cerebellar atrophy [71] have been shown to associate with MS fatigue. Depression, another common symptom of MS, has been shown to associate with total brain volume and neocortical gray matter volume [72]. Another study also reported ventricular enlargement and frontal atrophy in depressed versus non-depressed MS patients [73]. In 2016, a consensus panel supported the use of brain volume as a measure for monitoring disease progression and cognitive function in patients with MS [74]. Spinal cord atrophy Individuals with MS demonstrate smaller spinal cord volume (cross sectional area) than controls [75] at the upper, lower cervical, and upper thoracic levels but not in the lower thoracic and lumbar sections. The extent of spinal cord volume is associated with disease duration, disease type [76] and does not correlate with lesion size. The lack of correlation with lesion size suggests that tissue loss within lesions is not what accounts for spinal cord atrophy but rather axonal degeneration. Indeed, axonal loss as a pathological substrate of spinal cord volume loss was shown using a post-mortem study [77]. Cervical cord area has been shown to have the highest correlation with EDSS score, followed by thoracic cord area [76]. Another study found that total cord area is correlated with EDSS score [78], providing further support of spinal cord atrophy as a marker of physical disability. NON-CONVENTIONAL MRI MARKERS IN MS Diffusion Tensor Imaging Diffusion tensor imaging (DTI) is a technique used to infer the diffusion of water within brain tissue. By sampling diffusivity across multiple directions, a three-dimensional representation of diffusion, modelled as an ellipsoid, can be computed at each voxel. The diffusion ellipsoid allows for calculation of axial diffusivity (AD), radial diffusivity (RD), mean diffusivity (MD) and fractional anisotropy (FA), the three most commonly reported DTI measures. axial diffusivity indicates diffusivity parallel to the main axis of WM tracts and is indicative of axonal loss [79,80]. Radial diffusivity represents diffusivity perpendicular to the main axis and is related to demyelination [81,82]. mean diffusivity (MD) is the average diffusion regardless of direction and could be due to factors such as vasogenic edema [83]. fractional anisotropy (FA) incorporates both AD and RD and indicates what fraction of the diffusion tensor ellipsoid displays anisotropic (directionally dependent) diffusion. FA is a dimensionless unit with values ranging from 0-1 with higher values representing increased tissue microstructural integrity [84,85]. In the case of water within WM tracts, diffusion would occur preferentially parallel to the long axis of the axon, rather than perpendicular to it due to restriction from cell membranes, thereby resulting in higher FA values. Examples of the three different types of DTI images, obtained from an MS patient is shown in Figure 2. DTI offers a method to evaluate whether “normal-appearing” (i.e. non-lesional) tissue is indeed normal, as damage to lesional tissue is already assumed. As expected, higher MD and lower FA is observed in lesional tissue compared to normal-appearing WM (NAWM) [83,86]. Compared to the WM of healthy controls, the NAWM of patients with MS shows higher MD and lower FA, suggesting tissue damage to be present even in non-lesional tissue [83,86]. With respect to clinical correlations, higher MD within lesional tissue has been shown to associate with higher EDSS scores, especially so in patients with SPMS to SPMS [83]. DTI-derived measures of corticospinal tract damage have been shown to correlate with walking performance [87] and EDSS scores [88]. Another study [89] found that cervical spinal cord DTI measures independently explained variability in hip flexion strength, vibration sensation threshold, and EDSS score. DTI can also be used to interrogate the microstructural integrity of GM. Kearney and colleagues [90] observed that spinal cord GM RD was associated with EDSS score, 9-hole peg test and timed walk performance, particularly in SPMS. Associations between cognitive performance and DTI derived measures have also been shown within NAWM on such tests as the Paced Auditory Serial Addition Test [91], in comparing cognitively impaired to unimpaired patients [92], and in predicting overall cognitive dysfunction [93]. Another study found that MD within the hippocampus was associated with episodic verbal memory performance [94]. With respect to fatigue, lower FA in the anterior internal capsule is associated with increased self-reported fatigue [95]. Finally, MS patients reporting depression have been shown to have lower FA and higher MD in the left anterior temporal NAWM and NAGM compared to non-depressed patients [96]. There are certain drawbacks, however, to use of DTI which include crossing fibers (fibers of different orientations within the same voxel) and partial volume effects (mixture of tissue types within the same voxel) [97]. In such cases, modelling of the diffusion ellipsoid may fail. Another problem inherent in the diffusion ellipsoid model is that it assumes diffusion propagates in time and space in a Gaussian manner. In considering diffusion where water molecules encounter different viscosities and obstacles (e.g. cell membranes) it thus may be problematic to consider this diffusion as Gaussian. Despite these limitations, the utility of DTI to interrogate microstructural damage in MS is still widely accepted. Functional MRI Functional MRI (fMRI) can be used to measure the impact of MS on neural patterns of activation through resting-state and task- based paradigms. Resting-state fMRI is used to identify “networks”, or groups of brain regions that show functional connectivity, or temporal synchronization of activation, with one another. A consistent pattern observed in MS is one of heightened resting-state connectivity in RRMS patients compared to healthy controls. This has been demonstrated for numerous resting-state networks including the default-mode [98-101], frontoparietal [99], salience [99], visual processing [99], and sensorimotor [98] networks. However, in RRMS patients with cognitive impairment, lower functional connectivity of the default-mode [102, 103], fronto-parietal [102,103], salience [102] and dorsal attention [103] networks has been observed compared to Figure 2: Examples of the three different types of DTI images. The same slice is shown across the three different DTI images. (a) FA image in which areas of high FA are brighter than surrounding tissue and occur along WM tracts. Periventricular lesions are present in this image which have lower FA and appear darker. (b) Axial diffusivity and (c) Radial diffusivity images in which areas of higher diffusivity appear bright, such as in cerebrospinal fluid and periventricular lesions.
- 6. Scientific Journal of Multiple Sclerosis SCIRES Literature - Volume 1 Issue 1 - www.scireslit.com Page -013 cognitively intact MS patients. In contrast to patients with RRMS, patients with progressive forms of MS demonstrate lower resting- state connectivity compared to healthy controls [104]. Task-based fMRI analysis involves modelling the blood oxygen level-dependent (BOLD) response elicited by stimulus presentation. The BOLD response is due to a combination of changes to cerebral blood flow, cerebral metabolic rate of oxygen, and cerebral blood volume in relation to greater energy demands of more active neurons [105]. It has been reported, however, that there are alterations in the BOLD response of persons with MS, namely reductions in peak amplitude, that are related to individual differences in WM microstructural integrity (FA) [106]. Thus, WM microstructural damage should be taken into account when assessing BOLD activation differences between MS and healthy control groups. In task-based BOLD studies to date, there has been much variability as to whether MS patients demonstrate higher versus lower activation during the performance of motor, cognitive, and fatigue-inducing tasks. Very early on in the disease, task-based fMRI has identified patterns of increased activation during tasks of working memory and attention [107-111] in patients with MS compared to healthy controls. Such pattern is more evident in patients who perform at the same (versus worse) level as healthy controls behaviourally on the fMRI tasks utilized in these studies [108-110], suggesting a compensatory mechanism in order to maintain task performance. However, at higher levels of task difficulty [112-113] and in patients with cognitive impairment [114], increases in activation are less evident. A broad conceptualization of the resting-state and task-based fMRI findings in MS is that RRMS patients experience heightened activation early-on until structural damage (lesion volume, brain atrophy) reaches a level where activation can no longer compensate. At this point, activation reaches a peak and subsequently declines, followed by concomitant cognitive disability. The reason for the inconsistency within the MS fMRI literature can be attributed to the variability in terms of sample characteristics (e.g. time since diagnosis, extent of structural damage, baseline cognitive/ clinical status), due to specific fMRI paradigms used, and/or different analytic programs and methods used (e.g. independent components analysis, seed-based correlations). Magnetization Transfer Ratio Imaging In MS brain WM lesions, there is a decrease in total myelin lipid and protein concentration, primarily as a result of autoimmune neuroinflammation. Consequently, a common goal of quantitative MRI techniques used in MS research is to first visualize and then quantify myelin loss in both WM lesions and NAWM. Standard MRI techniques, applied at the clinically-accessible field strengths of 1.5 and 3 Tesla (T), cannot directly detect signal from myelin. However, magnetization transfer ratio (MTR) imaging is a specialized MRI technique where the myelin signal can be indirectly detected through the transfer of magnetization signal from myelin to higher concentration free water molecules [115-117]. MTR is sensitive to the relative degree of myelination in brain tissue [115,118]. Demyelinated MS WM lesions show significantly reduced MTR (Figure 3), while comparatively smaller reductions are observed in NAWM [119-120]. In MS lesions, the MTR value may partially recover to baseline healthy WM values during remyelination. The partial MTR recovery is believed to be a measure of the reduced capacity of oligodendrocytes to remyelinate axons after an initial inflammatory event [120]. Because of the capacity of MTR to track demyelination and subsequent remyelination over the course of many months, it may serve as a useful longer-term measure of WM tissue integrity in MS. A consistent pattern of MTR reduction in NAWM has been observed around the left and right lateral ventricles [121]. The pattern is observed in both RRMS and SPMS, and supports a CSF mediated pathogenesis [121]. Patients with cognitive impairment also show lower whole brain MTR [122]. In addition, diffuse brain damage (measured by reduced normal-appearing brain tissue MTR) at the onset of MS has been shown to be a predictor of change in memory performance over 7 years [123]. While most MTR studies of MS have focused on WM damage, cortical sub-pial GM damage can also be quite extensive [124,125]. This is particularly true in chronic disease. Recent research has demonstrated that the pattern of sub-pial cortical damage in MS can be monitored using MTR imaging [126]. Evidence from multi- laminar, cortical surface analysis of the MTR signal suggests focal areas of cortical surface MTR reduction, indicative of diffuse sub-pial pathology, are most prevalent along the outer cortical surface [127]. Post-mortem histopathology has revealed that cortical GM lesions show reduced T-cell inflammation and little disruption of the blood– brain barrier [128-130]. This, however, is contrasted with some early stage cortical lesion biopsy studies demonstrating a rim of activated microglia that could be interpreted as a sign of active inflammation [131]. Additional longitudinal, in vivo human imaging and animal studies are needed to clarify the complete underlying mechanism of cortical pathology in MS. Overall, MTR has shown significant promise as an effective tool for monitoring pathology in MS. However, linking MTR signal directly to myelin loss in both WM and cortical GM is subject to some confounds. MTR is an indirect measure that is weighted by the T1 relaxation time of water molecules, the homogeneity of radiofrequency excitation, as well as MRI sequence parameters [132]. Figure 3: Example of white matter (yellow arrows) and cortical gray matter (red arrows) MS lesions visible on 3T MRI using (a) T1 -weighted,, (b) MTR, (c) T2 -weighted and (d) 3D FLAIR-based MRI (1 mm3 isotropic MRI spatial resolution).
- 7. Scientific Journal of Multiple Sclerosis SCIRES Literature - Volume 1 Issue 1 - www.scireslit.com Page -014 Ongoing advances in specificity of the MTR contrast in relation to myelin and other pathological substrates of MS remain necessary [132-134]. Relaxometry/Quantitative Magnetic Susceptibility Imag- ing MRI-based relaxometry encapsulates a family of techniques that measure changes in water proton density, longitudinal (T1 ), transverse (T2 ) and apparent transverse (T2 * ) relaxation times of water molecules. T2 mapping, in particular, has been applied extensively in MS research and can be related to the breakdown of myelin, gliosis and edema in WM [135]. The T2 multi-exponential signal decay curve measured using MRI can be mathematically modeled to yield maps of (i) intra-axonal/extra-axonal water and (ii) water trapped between the myelin bilayers (myelin water)[136-138]. The ratio of the myelin water to the total water content is referred to as the myelin water fraction and is considered a surrogate measure of myelin content [137]. Support for this is given by the strong quantitative correlation between luxol fast blue histochemical myelin staining and the amplitude of the myelin water fraction MRI signal [139]. Because of the longer MRI acquisition times generally associated with measurements of T2 , other forms of relaxometry, including measurement of the longitudinal relaxation time (T1 ) and the apparent transverse relaxation time (T2 * ), have been explored for mappingthepathologicalcharacteristicsofMSbrainandspinaltissue. Measurement of T1 offers a relatively stable and consistent measure of WM myelin content [140]. T1 maps also provide a measure of intra- cortical myelin content that can be applied for precise identification of myeloarchitecture across cortical depths and brain regions [141]. In post-mortem fixed MS brain tissue, T1 has shown a strong correlation with cortical neuronal cell density [142]. This relationship is most consistent in cortical GM lesions where a significant reduction in neuronal density and phosphorylated neurofilament are present [142]. The loss of cortical neuronal cell bodies results in a concurrent increase in free water which mediates longer T1 relaxation times associated with cortical pathology in MS [143]. In studies comparing MRI to post-mortem myelin staining, increased T2 * has been linked to more severe loss of myelin in WM [144]. T2 * mapping can be performed using a multi-echo gradient echo sequence which allows reduced acquisition times compared to standard T1 and T2 mapping techniques. Recent high field MRI measurements of the multi-component T2 * signal show elevated concentrations of interstitial and axonal water in lesions compared to NAWM of RRMS subjects [145]. This feature is apparent in both enhancing and non-enhancing lesions. Quantitative magnetic susceptibility (QS) is an MRI property associated with relaxometry (Figure 4). Tissue magnetic susceptibility results in a magnetic field perturbation in an imaging voxel and this feature can be linked to the amount of iron and myelin in tissue [146,147]. QS maps can be efficiently derived from the phase and magnitude information collected with a standard gradient echo (GRE) MRI sequence. GRE MR imaging is fast and allows whole- brain coverage in shorter acquisition times compared to conventional spin-echo imaging employed for traditional T2 mapping. QS maps have proven useful in monitoring the complex sequence of inflammatory and neurodegenerative processes within MS WM lesions [145,148,149]. During the pathogenesis of MS lesion formation, demyelination in the lesion rim and core, as well as iron deposition caused by infiltrating microglia and macrophages, increase the magnetic susceptibility [150-152]. This increase can be measured based on QS images. QS and T2 -weighted images have been used together for differentiating acute-active lesions from chronic smoldering ones. Gold standard validation of this technique based on post-mortem antibody staining studies has revealed that the hyperintense, paramagnetic QS rim around WM lesions can be partially attributed to M1 microglia/macrophages, which cause smoldering inflammation that exists even after the blood brain barrier seals [149,152]. Equally relevant, QS maps have demonstrated an increased sensitivity to changes in subcortical iron associated with both MS disease related processes and normal ageing [153-155]. While iron is an essential trace element for neuronal functioning, evidence exists that excessive brain iron levels may exert toxic effects via the formation of free radicals [156]. In such cases, iron may be released within the CNS due to cellular degeneration, which in turn may enhance oxidative stress–induced neurodegenerative processes [157]. Region-of-interest analysis of QS MR images has revealed that the mean QS in subcortical GM is strongly correlated with clinical disability as defined by the EDSS [154]. In summary, relaxometry is a quantitative MRI metric that can be linked to demyelination, gliosis and axonal loss in MS. Both post- mortem and in vivo studies have shown correlations between myelin, axonal staining and relaxometry metrics. Nevertheless, future work is needed to (i) reduce the total acquisition time necessary for whole- brain relaxometry imaging and (ii) enhance the specificity of these measures to early pathological changes and response to therapy in MS. The QS mapping technique offers an interesting alternative for rapid imaging of demyelination in lesions and NAWM. It also allows imaging of iron accumulation in the subcortical nuclei. Further research is essential to improve the signal processing and associated Figure 4: Representative 7T MR images of an MS white matter lesion. MR images correspond to one imaging slice from a 45-year-old patient with relapsing-remitting MS: (A) T1 -weighted magnetization-prepared rapid acquisition gradient-echo image, (B) T2 -weighted magnetization-prepared fluid attenuated inversion-recovery image, (C) R2* maps and (D) QS maps. Magnified views of a white matter lesion are displayed in the red-outlined insets at the top right-hand corner of each individual image.
- 8. Scientific Journal of Multiple Sclerosis SCIRES Literature - Volume 1 Issue 1 - www.scireslit.com Page -015 biophysical models relating the QS signal to pathological substrates of MS. Magnetic Resonance spectroscopy Standard MRI is not specific to biochemically-relevant substrates of cellular metabolism which are altered in MS. In contrast, magnetic resonance spectroscopy (MRS) is a technique whereby the relative concentration of metabolites in the CNS can be measured. In particular, proton (H1 ) MRS is usually used for detection of a group of CNS metabolites: N-acetyl aspartate (NAA), choline (Cho), creatine (Cr), myoinositol (Mi), glutamate (Glu), glutamine (Gln) and lactate (Lac). which exist in brain and spinal cord tissue in low concentrations (1-10 mM). The (NAA) MRS signal, located at 2.02 ppm in the proton MRS spectrum, has in vivo concentrations on the order of 7.8 mM and is of major interest in MS [158,159]. NAA is a biochemical amino acid derivative produced in neurons and transported along axonal projections [160]. Early MRS studies revealed that NAA in MS NAWM is consistently reduced, compared to other metabolites [161,162]. The standard interpretation of this finding is that the reduced concentration of NAA is a result of neuronal loss or dysfunction. In acute active gadolinium-enhancing lesions, an initial reduction of NAA at the onset of white lesions is seen. After the acute phase, this reduction can be partially restored and followed on serial MRS studies [161,163]. The restoration of NAA suggests that the initial decrease in values of NAA during demyelination does not only represent neuronal and axonal loss, but also identifies signatures of edema resolution. Choline (Cho), which exists in the proton NMR spectrum at 3.2 ppm, and has in vivo concentrations on the order of 1.3 mM, serves as a marker of myelin phospholipid bilayer metabolism [158,159]. During active demyelination, breakdown and release of membrane phosphocholine and glycerol-phosphocholine are believed to lead to the observed elevated choline MRS signal in both lesions and NAWM [164-168]. An increase in the choline MRS signal can also be observed up to 12 months prior to WM lesion formation [169]. This finding is consistent with a localized pre-lesional phospholipid membrane breakdown. Glutamate (Glu), is an excitatory neurotransmitter converted to a Glutamine (Gln) and Gamma-Amino Butyric Acid (GABA) in neurons following synaptic release [170]. Glutamate, with approximately 10 mM in vivo concentrations, and glutamine, with approximately 5 mM in vivo concentrations, are linked to neuro- axonal energy supply [158,159]. Patients with SPMS, in particular, show reduced GABA levels in the hippocampus and sensorimotor cortex compared to healthy subjects [171]. These MRS measures may, thus, prove useful in monitoring more advanced neuroprotective therapies for SPMS. From a clinical standpoint, the NAA/Cr ratio in NAWM of MS patients has been shown to correlate with EDSS [172, 173], suggesting WM neuronal dysfunction and gliosis can be linked to clinical disability. In normal-appearing cortical GM, NAA deficits have also been found in SPMS patients relative to age-matched controls [174]. However, these NAA deficits do not correlate significantly with EDSS. In addition, NAA/Cr ratio has been shown to be significantly lower in MS patients reporting high fatigue [175]. A number of technical issues remain before the MRS family of techniques is more applicable to monitoring MS pathology. First, MRS is associated with reduced signal-to-noise ratio (SNR) and long acquisition times when compared to standard imaging. Secondly, the lower SNR results in associated reduced spatial resolution. These issues have led to MRS not being routinely used in MS clinical practice. Nonetheless, improved SNR associated with high field MRI, combined with advances in rapid imaging and compressed sensing techniques are now making whole-brain MRS imaging more accessible to MS researchers. SUMMARY AND CONCLUSIONS MRI is a crucial tool in the diagnosis and management of MS. MRI can also be used to elucidate the pathophysiological substrates of brainandspinalcordabnormalitiesinMS.Specificpathophysiological markers are better identified using certain techniques over another. For example, breakdown of the blood-brain and active inflammation are best identified using gadolinium enhancement, whereas MTR can be used to measure extent of re-myelination. Conventional MRI sequences such as T2 and T1 weighted sequences are routinely acquired in clinical practice and used primarily for MS diagnosis and monitoring. Non-conventional measures such as DTI, fMRI, and MRS are not required for clinical diagnosis and management. but offer unique insights into the pathophysiology of MS and are becoming more widely recognized for use not solely in the realm of research investigations. Despite advances in all of the MRI techniques described in this review, each is associated with drawbacks that must be taken into consideration. For example, lesion segmentation is dependent on the definition of intensity thresholds and atrophy measurements required accurate anatomical delineation of brain regions. Though many automated programs exist to obtain volumes, etc. outputs should be checked by ideally more than one personnel. Further complications arise because of variability across sites with respect to type of scanner and scanning parameters used. It is advisable that standard MRI protocols be adopted and there have been efforts to this end as pertains to imaging sequences acquired/ MRI protocols [55,176-178] and analytical tools [179, 180]. This will also help facilitate multi-site collaborations and address inconsistencies in the literature. The clinico-radiological paradox should also be further investigated in MS. For patients with heavy disease burden as shown on MRI but minimal overt clinical symptoms, potential protective factors should be explored which could include aspects such cognitive reserve, exercise, and diet or vitamin supplementation. While current MRI techniques are being further refined and new techniques are emerging, MRI will continue to help further elucidate the pathogenesis of MS which will help in earlier detection and guide the development of treatments. As an outcome measure in clinical trials, MRI is also recognized as an important tool for monitoring treatment success and patient safety. For these any other reasons, MRI will continue to play an absolutely essential role in the identification and management of MS. REFERENCES 1. Paty DW, Oger JJ, Kastrukoff LF, Hashimoto SA, Hooge JP, Eisen AA, et al. MRI in the diagnosis of MS: a prospective study with comparison of clinical evaluation, evoked potentials, oligoclonal banding, and CT. Neurology. 1988; 38: 180-185. https://goo.gl/2469BM 2. Polman C, Reingold S, Banwell B, Clanet M, Cohen J, Filippi M, et al. Diagnostic criteria for multiple sclerosis: 2010 Revisions to the McDonald criteria. Ann Neurol. 2011; 69: 292-302. https://goo.gl/cjcZfZ 3. Sadovnick AD, Baird PA. The familial nature of multiple sclerosis: age- corrected empiric recurrence risks for children and siblings of patients. Neurology. 1988; 38: 990-991. https://goo.gl/rxYbv1
- 9. Scientific Journal of Multiple Sclerosis SCIRES Literature - Volume 1 Issue 1 - www.scireslit.com Page -016 4. Koch-Henriksen N, Sorenson PS. The changing demographic pattern of multiple sclerosis epidemiology. Lancet Neurol. 2010; 9: 520-532. https://goo.gl/W6BX6b 5. Hauser SL, Oksenberg JR. The neurobiology of multiple sclerosis: genes, inflammation, and neurodegeneration. Neuron. 2006; 52: 61-76. https://goo.gl/T45XY2 6. Lassmann H, Bruck W, Lucchinetti CF. The immunopathology of multiple sclerosis: an overview. Brain Pathol. 2007; 17: 210-218. https://goo.gl/nz1P5S 7. Lublin FD, Reingold SC. Defining the clinical course of multiple sclerosis: results of an international survey. Neurology. 1996; 46: 907-911. https://goo.gl/BL4tHj 8. Miller DH, Leary SM. Primary-progressive multiple sclerosis. Lancet Neurol. 2007; 6: 903-912. https://goo.gl/1kFpSU 9. Tremlett H, Paty D, Devonshire V. Disability progression in multiple sclerosis is slower than previously reported. Neurology. 2006; 66: 172-177. https://goo.gl/wL1sWg 10. Confavreux C, Vukusic S. Natural history of multiple sclerosis: A unifying concept. Brain. 2006; 129: 606-616. https://goo.gl/BGMSeU 11. Kaufman DW, Reshef S, Golub HL, Peucker M, Corwin MJ, Goodin DS, et al. Survival in commercially insured multiple sclerosis patients and comparator subjects in the U.S. Mult Scler Relat Disord. 2014; 3: 364-371. https://goo.gl/uRV6Lc 12. Sadovnick D, Ebers GC, Wilson R, Paty DW. Life expectancy in patients attending multiple sclerosis clinicstle. Neurology. 1992; 42: 991-994. https://goo.gl/6Wi6xk 13. Filippi M, Rocca MA, Ciccarelli O, De Stefano N, Evangelou N, Kappos L, et al. MRI criteria for the diagnosis of multiple sclerosis: MAGNIMS consensus guidelines. Lancet Neurol. 2016; 15: 292-303. https://goo.gl/6pHbFC 14. Lassmann H. Mechanisms of demyelination and tissue destruction in multiple sclerosis. Clin Neurol Neurosurg. 2002; 104: 168-171. https://goo.gl/wPAbQE 15. Matthews PM, Roncaroli F, Waldman A, Sormani MP, De Stefano N, Giovannoni G, et al. A practical review of the neuropathology and neuroimaging of multiple sclerosis. Pract Neurol. 2016; 16: 279-287. https://goo.gl/NidVRe 16. Kutzelnigg A, Lucchinetti CF, Stadelmann C, Bruck W, Rauschka H, Bergmann M, et al. Cortical demyelination and diffuse white matter injury in multiple sclerosis. Brain. 2005; 128: 2705-2712. https://goo.gl/v2ttfA 17. Geurts JJG, Barkhof F. Grey matter pathology in multiple sclerosis. Lancet Neurol. 2008; 7: 841-851. https://goo.gl/bbB8Z3 18. Prins M, Schul E, Geurts J, van der Valk P, Drukarch B, van Dam AM. Pathological differences between white and grey matter multiple sclerosis lesions. Ann N Y Acad Sci. 2015; 1351: 99-113. https://goo.gl/bh14e8 19. Filippi M, Preziosa P, Pagani E, Copetti M, Mesaros S, Colombo B, et al. Microstructural magnetic resonance imaging of cortical lesions in multiple sclerosis. Mult Scler. 2013; 19: 418-426. https://goo.gl/KCDjv1 20. Bot JCJ, Barkhof F, Polman CH, Nijeholt GJL a, de Groot V, Bergers E, et al. Spinal cord abnormalities in recently diagnosed MS patients: Added value of spinal MRI examination. Neurology. 2004; 62: 226-233. https://goo.gl/X4rFwC 21. Weier K, Mazraeh J, Naegelin Y, Thoeni A, Hirsch JG, Fabbro T, et al. Biplanar MRI for the assessment of the spinal cord in multiple sclerosis. MultScler. 2012; 18:1560-1569. https://goo.gl/q2CPZU 22. Weier K, Mazraeh J, Naegelin Y, Thoeni A, Hirsch JG, Fabbro T, et al. Biplanar MRI for the assessment of the spinal cord in multiple sclerosis. Mult Scler. 2012; 18: 1560-1569. https://goo.gl/xEbNTk 23. Kuhle J, Disanto G, Dobson R, Adiutori R, Bianchi L, Topping J, et al. Conversion from clinically isolated syndrome to multiple sclerosis: A large multicentre study. Mult Scler. 2015; 21: 1013-1024. https://goo.gl/pXjqp2 24. Miller D, Barkhof F, Montalban X, Thompson A, Filippi M. Clinically isolated syndromes suggestive of multiple sclerosis, part 2: non-conventional MRI, recovery processes, and management. Lancet Neurol. 2005; 4: 341-348. https://goo.gl/A8MGTM 25. Fisniku LK, Chard DT, Jackson JS, Anderson VM, Altmann DR, Miszkiel KA, et al. Gray matter atrophy is related to long-term disability in multiple sclerosis. Ann Neurol. 2008; 64: 247-254. https://goo.gl/5xBKzT 26. Arrambide G, Rovira A, Sastre-Garriga J, Tur C, Castillo J, Rio J, et al. Spinal cord lesions: A modest contributor to diagnosis in clinically isolated syndromes but a relevant prognostic factor. Mult Scler. 2017. https://goo.gl/RLKPjD 27. Sombekke MH, Wattjes MP, Balk LJ, Nielsen JM, Vrenken H, Uitdehaag BMJ, et al. Spinal cord lesions in patients with clinically isolated syndrome: A powerful tool in diagnosis and prognosis. Neurology. 2013; 80: 69-75. https://goo.gl/rjJ5tD 28. Fritz NE, Roy S, Keller J, Prince J, Calabresi PA, Zackowski KM. Pain, cognition and quality of life associate with structural measures of brain volume loss in multiple sclerosis. NeuroRehabilitation. 2016; 39: 535-544. https://goo.gl/9f8Sx6 29. Papadopoulou A, Muller Lenke N, Naegelin Y, Kalt G, Bendfeldt K, Kuster P, et al. Contribution of cortical and white matter lesions to cognitive impairment in multiple sclerosis. Mult Scler. 2013; 19: 1290-1296. https://goo.gl/yojd8K 30. Rojas JI, Sanchez F, Patrucco L, Miguez J, Besada C, Cristiano E. Brain structural changes in patients in the early stages of multiple sclerosis with depression. Neurol Res. 2017; 39: 596–600. https://goo.gl/KV566g 31. Bakshi R, Czarnecki D, Shaikh ZA, Priore RL, Janardhan V, Kaliszky Z, et al. Brain MRI lesions and atrophy are related to depression in multiple sclerosis. Neuroreport. 2000; 11: 1153-1158. https://goo.gl/Mq4CGs 32. Mowry EM, Beheshtian A, Waubant E, Goodin DS, Cree BA, Qualley P, et al. Quality of life in multiple sclerosis is associated with lesion burden and brain volume measures. Neurology. 2009; 72: 1760-1765. https://goo.gl/QXpZqq 33. Barkhof F. The clinico-radiological paradox in multiple sclerosis revisited. Curr Opin Neurol. 2002; 15: 239-245. https://goo.gl/CYNeMD 34. Mollison D, Sellar R, Bastin M, Mollison D, Chandran S, Wardlaw J, et al. The clinico-radiological paradox of cognitive function and MRI burden of white matter lesions in people with multiple sclerosis: A systematic review and meta-analysis. PLoS Comput Biol. 2017; 12: 0177727. https://goo.gl/iPa3T4 35. Hackmack K, Weygandt M, Wuerfel J, Pfueller CF, Bellmann-Strobl J, Paul F, et al. Can we overcome the “clinico-radiological paradox” in multiple sclerosis? J Neurol. 2012; 259: 2151-2160. https://goo.gl/viKdCN 36. Harrison DM, Roy S, Oh J, Izbudak I, Pham D, Courtney S, et al. Association of Cortical Lesion Burden on 7-T Magnetic Resonance Imaging With Cognition and Disability in Multiple Sclerosis. JAMA Neurol. 2015; 72: 1004. https://goo.gl/xpiWNK 37. Calabrese M, Agosta F, Rinaldi F. Cortical lesions and atrophy associated with cognitive impairment in relapsing-remitting multiple sclerosis. Arch Neurol. 2009; 66: 1144-1150. https://goo.gl/ovCpiD 38. Filippi M, Preziosa P, Pagani E, Copetti M, Mesaros S, Colombo B, et al. Microstructural magnetic resonance imaging of cortical lesions in multiple sclerosis. Mult Scler. 2013; 19: 418-426. https://goo.gl/XjNm6q 39. Mike A, Glanz BI, Hildenbrand P, Meier D, Bolden K, Liguori M, et al. Identification and clinical impact of multiple sclerosis cortical lesions as assessed by routine 3T MR imaging. Am J Neuroradiol. 2011; 32: 515-521. https://goo.gl/SEzt6Q 40. Sormani MP, Bruzzi P. MRI lesions as a surrogate for relapses in multiple sclerosis: A meta-analysis of randomised trials. Lancet Neurol. 2013; 12: 669- 676. https://goo.gl/QWW52S 41. Sahraian MA, Radue EW, Haller S, Kappos L. Black holes in multiple sclerosis: definition, evolution, and clinical correlations. Acta Neurol Scand. 2010; 122: 1-8. https://goo.gl/JoPa1y 42. Filippi M, Rocca MA, Barkhof F, Brück W, Chen JT, Comi G, et al. Association between pathological and MRI findings in multiple sclerosis. Lancet Neurol. 2012; 11: 349-360. https://goo.gl/iffJSv 43. van Waesberghe JHTM, Kamphorst W, De Groot CJA, Van Walderveen MAA, Castelijns JA, Ravid R, et al. Axonal loss in multiple sclerosis lesions: Magnetic resonance imaging insights into substrates of disability. Ann Neurol. 1999; 46: 747-754. https://goo.gl/9wcBwB 44. van Walderveen MAA, Kamphorst W, Scheltens P, van Waesberghe JHTM, Ravid R, Valk J, et al. Histopathologic correlate of hypointense lesions on
- 10. Scientific Journal of Multiple Sclerosis SCIRES Literature - Volume 1 Issue 1 - www.scireslit.com Page -017 T1-weighted spin-echo MRI in multiple sclerosis. Neurology. 1998; 50: 1282– 1288. https://goo.gl/bU59pW 45. Gass A, Filippi M, Rodegher ME, Schwartz A, Comi G, Hennerici MG. Characteristics of chronic MS lesions in the cerebrum, brainstem, spinal cord, and optic nerve on T1-weighted MRI. Neurology. 1998; 50: 548-550. https://goo.gl/6uwfrd 46. van Walderveen MAA, Lycklama á Nijeholt GJ, Ader HJ, Jongen PJH, Polman CH, Castelijns JA, et al. Hypointense lesions on T1-weighted spin- echo magnetic resonance imaging: Relation to clinical characteristics in subgroups of patients with multiple sclerosis. Arch Neurol. 2000; 58: 76-81. https://goo.gl/PscfzS 47. Truyen L, van Waesberghe JH, van Walderveen MA, van Oosten BW, Polman CH, Hommes OR, et al. Accumulation of hypointense lesions (“black holes”) on T1 spin-echo MRI correlates with disease progression in multiple sclerosis. Neurology. 1996; 47: 1469-1476. https://goo.gl/AQZQFk 48. Kermode AG, Thompson AJ, Tofts P, Macmanus DG, Kendall BE, Kingsley DPE, et al. Breakdown of the blood-brain barrier precedes symptoms and other mri signs of new lesions in multiple sclerosis: Pathogenetic and clinical implications. Brain. 1990; 113: 1477-1489. https://goo.gl/SZvuWT 49. Maggi P, Cummings Macri SM, Gaitán MI, Leibovitch E, Wholer JE, Knight HL, et al. The formation of inflammatory demyelinated lesions in cerebral white matter. Ann Neurol. 2014; 76: 594-608. https://goo.gl/YXyEBr 50. Cotton F, Weiner HL, Jolesz FA, Guttmann CRG. MRI contrast uptake in new lesions in relapsing-remitting MS followed at weekly intervals. Neurology. 2003; 60: 640-646. https://goo.gl/A6GkLe 51. Van Waesberghe JHTM, Van Walderveen MAA, Castelijns JA, Scheltens P, Lycklama A Nijeholt GJ, Polman CH, et al. Patterns of lesion development in multiple sclerosis: Longitudinal observations with T1-weighted spin-echo and magnetization transfer MR. Am J Neuroradiol. 1998; 19: 675-683. https://goo.gl/GJaJ93 52. McFarland HF, Frank JA, Albert PS, Smith ME, Martin R, Harris JO, et al. Using gadolinium-enhanced magnetic resonance imaging lesions to monitor disease activity in multiple sclerosis. Ann Neurol. 1992; 32: 758-766. https://goo.gl/3cPxHE 53. Harris JO, Frank J a, Patronas N, McFarlin DE, McFarland HF. Serial gadolinium-enhanced magnetic resonance imaging scans in patients with early, relapsing-remitting multiple sclerosis: implications for clinical trials and natural history. Ann Neurol. 1991; 29: 548-555. https://goo.gl/h2cCJS 54. Kappos L, Moeri D, Radue EW, Schoetzau A, Schweikert K, Barkhof F, et al. Predictive value of gadolinium-enhanced magnetic resonance imaging for relapse rate and changes in disability or impairment in multiple sclerosis: a meta-analysis. Lancet. 1999; 353: 964-969. https://goo.gl/QAFSyq 55. Traboulsee A, Létourneau-Guillon L, Freedman MS, O’Connor PW, Bharatha A, Chakraborty S, et al. Canadian Expert Panel Recommendations for MRI Use in MS Diagnosis and Monitoring. Can J Neurol Sci. 2015; 42: 159-167. https://goo.gl/MEBVmU 56. Hodel J, Darchis C, Outteryck O, Verclytte S, Deramecourt V, Lacour A, et al. Punctate pattern: A promising imaging marker for the diagnosis of natalizumab-associated PML. Neurology. 2016; 86: 1516-1523. https://goo.gl/Ckra1T 57. Dong-Si T, Richman S, Wattjes MP, Wenten M, Gheuens S, Philip J, et al. Outcome and survival of asymptomatic PML in natalizumab-treated MS patients. Ann Clin Transl Neurol. 2014; 1: 755-764. https://goo.gl/MgjE8i 58. Tam RC, Traboulsee a, Riddehough a, Sheikhzadeh F, Li DKB. The impact of intensity variations in T1-hypointense lesions on clinical correlations in multiple sclerosis. Mult Scler. 2011; 17: 949-957. https://goo.gl/zSTfLx 59. Rocca MA, Battaglini M, Benedict RHB, De Stefano N, Geurts JJG, Henry RG, et al. Brain MRI atrophy quantification in MS. Neurology. 2017; 88: 403- 413. https://goo.gl/QwYsS5 60. Miller DH, Barkhof F, Frank JA, Parker GJM, Thompson AJ. Measurement of atrophy in multiple sclerosis: pathological basis, methodological aspects and clinical relevance. Brain. 2002; 125: 1676-1695. https://goo.gl/NJ7cqa 61. Paolillo A, Pozzilli C, Gasperini C, Giugni E, Mainero C, Giuliani S, et al. Brain atrophy in relapsing-remitting multiple sclerosis: Relationship with “black holes”, disease duration and clinical disability. J Neurol Sci. 2000; 174: 85- 91. https://goo.gl/4HhgZ6 62. Sormani MP, Arnold DL, De Stefano N. Treatment effect on brain atrophy correlates with treatment effect on disability in multiple sclerosis. Ann Neurol. 2014; 75: 43-49. https://goo.gl/SxYALJ 63. Fisher E, Lee JC, Nakamura K, Rudick RA. Gray matter atrophy in multiple sclerosis: a longitudinal study. Ann Neurol. 2008; 64: 255-265. https://goo.gl/mpaEpY 64. Calabrese M, Agosta F, Rinaldi F. Cortical lesions and atrophy associated with cognitive impairment in relapsing-remitting multiple sclerosis. Arch Neurol. 2009; 66: 1144-1150. https://goo.gl/Xy6Mgk 65. Daams M, Steenwijk MD, Schoonheim MM, Wattjes MP, Balk LJ, Tewarie PK, et al. Multi-parametric structural magnetic resonance imaging in relation to cognitive dysfunction in long-standing multiple sclerosis. Mult Scler. 2016; 22: 608-619. https://goo.gl/fg3GjG 66. Gouveia A, Dias SP, Santos T, Rocha H, Coelho CR, Ruano L, et al. Cognitive impairment and magnetic resonance imaging correlates in primary progressive multiple sclerosis. Acta Neurol Scand. 2017; 136: 109-15. https://goo.gl/MQx11L 67. Houtchens MK, Benedict RH, Killiany R, Sharma J, Jaisani Z, Singh B, et al. Thalamic atrophy and cognition in multiple sclerosis. Neurology. 2007; 69: 1213-1223. https://goo.gl/NkWGkM 68. Benedict RH, Bruce JM, Dwyer MG, Abdelrahman N, Hussein S, Weinstock- Guttman B, et al. Neocortical atrophy, third ventricular width, and cognitive dysfunction in multiple sclerosis. Arch Neurol. 2006; 63: 1301-1306. https://goo.gl/ohD6yu 69. Pellicano C, Gallo A, Li X, Ikonomidou VN, Evangelou IE, Ohayon JM, et al. Relationship of Cortical Atrophy to Fatigue in Patients With Multiple Sclerosis. Arch Neurol. 2010; 67: 447-453. https://goo.gl/SJ2Rae 70. Andreasen AK, Jakobsen J, Soerensen L, Andersen H, Petersen T, Bjarkam CR, et al. Regional brain atrophy in primary fatigued patients with multiple sclerosis. Neuroimage. 2010; 50: 608-615. https://goo.gl/gDTZU4 71. Nourbakhsh B, Azevedo C, Nunan-Saah J, Maghzi AH, Spain R, Pelletier D, et al. Longitudinal associations between brain structural changes and fatigue in early MS. Mult Scler Relat Disord. 2016; 5: 29-33. https://goo.gl/PZC2pQ 72. Rojas JI, Sanchez F, Patrucco L, Miguez J, Besada C, Cristiano E. Brain structural changes in patients in the early stages of multiple sclerosis with depression. Neurol Res. 2017; 39: 596-600. https://goo.gl/Yq2rgq 73. Bakshi R, Czarnecki D, Shaikh ZA, Priore RL, Janardhan V, Kaliszky Z, et al. Brain MRI lesions and atrophy are related to depression in multiple sclerosis. Neuroreport. 2000; 11: 1153-1158. https://goo.gl/eAiZ5W 74. Alroughani R, Deleu D, El Salem K, Al-Hashel J, Alexander KJ, Abdelrazek MA, et al. A regional consensus recommendation on brain atrophy as an outcome measure in multiple sclerosis. BMC Neurol. 2016; 16: 240. https://goo.gl/SXnSME 75. Evangelou N, DeLuca GC, Owens T, Esiri MM. Pathological study of spinal cord atrophy in multiple sclerosis suggests limited role of local lesions. Brain. 2005; 128: 29-34. https://goo.gl/5bgkhd 76. Schlaeger R, Papinutto N, Zhu AH, Lobach I V., Bevan CJ, Bucci M, et al. Association Between Thoracic Spinal Cord Gray Matter Atrophy and Disability in Multiple Sclerosis. JAMA Neurol. 2015; 72: 897. https://goo.gl/EqGryP 77. Ganter P, Prince C, Esiri MM. Spinal cord axonal loss in multiple sclerosis: A post-mortem study. Neuropathol Appl Neurobiol. 1999; 25: 459-467. https://goo.gl/mV6JJG 78. Losseff NA, Webb SL, Riordan JI, Page R, Wang L, Barker GJ, et al. Spinal cord atrophy and disability in multiple sclerosis. Brain. 1996; 119: 701-708. https://goo.gl/kiFTpS 79. Aung WY, Mar S, Benzinger TL. Diffusion tensor MRI as a biomarker in axonal and myelin damage. Imaging Med. 2013; 5: 427-440. https://goo.gl/8caYk4 80. Song S-K, Sun S-W, Ramsbottom MJ, Chang C, Russell J, Cross AH. Dysmyelination revealed through MRI as increased radial (but unchanged axial) diffusion of water. Neuroimage. 2002; 17: 1429-1436. https://goo.gl/DF8vp3 81. Klawiter EC, Schmidt RE, Trinkaus K, Liang HF, Budde MD, Naismith RT, et al. Radial diffusivity predicts demyelination in ex vivo multiple sclerosis spinal cords. Neuroimage. 2011; 55: 1454-1460. https://goo.gl/H5P2KN
- 11. Scientific Journal of Multiple Sclerosis SCIRES Literature - Volume 1 Issue 1 - www.scireslit.com Page -018 82. Song SK, Yoshino J, Le TQ, Lin SJ, Sun SW, Cross AH, et al. Demyelination increases radial diffusivity in corpus callosum of mouse brain. Neuroimage. 2005; 26: 132-140. https://goo.gl/gezfdm 83. Filippi M, Cercignani M, Inglese M, Horsfield MA, Comi G. Diffusion tensor magnetic resonance imaging in multiple sclerosis. Neurology. 2001; 56: 304- 311. https://goo.gl/ucD2sz 84. Le Bihan D, Mangin JF, Poupon C, Clark C a, Pappata S, Molko N, et al. Diffusion tensor imaging: concepts and applications. J Magn Reson Imaging. 2001; 13: 534-546. https://goo.gl/cxnKCJ 85. Mori S, Zhang J. Principles of diffusion tensor imaging and its applications to basic neuroscience research. Neuron. 2006; 51: 527-539. https://goo.gl/VA3A1j 86. Gratsias G, Kapsalaki E, Kogia S, Dardiotis E, Tsimourtou V, Lavdas E, et al. A quantitative evaluation of damage in normal appearing white matter in patients with multiple sclerosis using diffusion tensor MR imaging at 3 T. Acta Neurol Belg. 2015; 115: 111-116. https://goo.gl/9LeFdd 87. Hubbard EA, Wetter NC, Sutton BP, Pilutti LA, Motl RW. Diffusion tensor imaging of the corticospinal tract and walking performance in multiple sclerosis. J Neurol Sci. 2016; 363: 225-231. https://goo.gl/YbvJ3E 88. Tovar-Moll F, Evangelou IE, Chiu AW, Auh S, Chen C, Ehrmantraut M, et al. Diffuse and Focal Corticospinal Tract Disease and Its Impact on Patient Disability in Multiple Sclerosis. J Neuroimaging. 2015; 25: 200-206. https://goo.gl/FeJv6a 89. Oh J, Zackowski K, Chen M, Newsome S, Saidha S, Smith S a, et al. Multiparametric MRI correlates of sensorimotor function in the spinal cord in multiple sclerosis. Mult Scler. 2013; 19: 427-435. https://goo.gl/WfScHL 90. Kearney H, Schneider T, Yiannakas MC, Altmann DR, Wheeler-Kingshott CAM, Ciccarelli O, et al. Spinal cord grey matter abnormalities are associated with secondary progression and physical disability in multiple sclerosis. J Neurol Neurosurg Psychiatry. 2015; 86: 608-614 https://goo.gl/8TT3Ti 91. Sbardella E, Petsas N, Tona F, Prosperini L, Raz E, Pace G, et al. Assessing the Correlation between Grey and White Matter Damage with Motor and Cognitive Impairment in Multiple Sclerosis Patients. PLoS One. 2013; 8. https://goo.gl/x2zuh1 92. Akbar N, Lobaugh NJ, O’Connor P, Moradzadeh L, Scott CJM, Feinstein A. Diffusion tensor imaging abnormalities in cognitively impaired multiple sclerosis patients. Can J Neurol Sci. 2010; 37: 608-614. https://goo.gl/ai4pr4 93. Daams M, Steenwijk MD, Schoonheim MM, Wattjes MP, Balk LJ, Tewarie PK, et al. Multi-parametric structural magnetic resonance imaging in relation to cognitive dysfunction in long-standing multiple sclerosis. Mult Scler. 2016; 22: 608-619. https://goo.gl/fqo6w8 94. Planche V, Ruet A, CoupeP, Lamargue-Hamel D, Deloire M, Pereira B, et al. Hippocampal microstructural damage correlates with memory impairment in clinically isolated syndrome suggestive of multiple sclerosis. Mult Scler. 2017; 23: 1214-1224. https://goo.gl/91RwKu 95. Genova HM, Rajagopalan V, Deluca J, Das A, Binder A, Arjunan A, et al. Examination of cognitive fatigue in multiple sclerosis using functional magnetic resonance imaging and diffusion tensor imaging. PLoS One. 2013; 8: 78811. https://goo.gl/mwpRqv 96. Feinstein A, O’Connor P, Akbar N, Moradzadeh L, Scott CJ, Lobaugh NJ. Diffusion tensor imaging abnormalities in depressed multiple sclerosis patients. Mult Scler. 2010; 16: 189-196. https://goo.gl/tLHM2F 97. Assaf Y, Pasternak O. Diffusion tensor imaging (DTI)-based white matter mapping in brain research: A review. J Mol Neurosci. 2008; 34: 51-61. https://goo.gl/J49Z66 98. Basile B, Castelli M, Monteleone F, Nocentini U, Caltagirone C, Centonze D, et al. Functional connectivity changes within specific networks parallel the clinical evolution of multiple sclerosis. Mult Scler. 2014; 20: 1050-1057. https://goo.gl/k3Mmj5 99. Faivre A , Zaaraoui W, Crespy L, Reuter F, Wybrecht D, Soulier E, et al. Assessing brain connectivity at rest is clinically relevant in early multiple sclerosis. Mult Scler. 2012; 18: 1251-1258. https://goo.gl/UMfPmE 100. Hawellek DJ, Hipp JF, Lewis CM, Corbetta M, Engel AK. Increased functional connectivity indicates the severity of cognitive impairment in multiple sclerosis. Proc Natl Acad Sci USA. 2011; 108: 19066-19071. https://goo.gl/U5ZzDE 101. Zhou F, Zhuang Y, Gong H, Wang B, Wang X, Chen Q, et al. Altered inter- subregion connectivity of the default mode network in relapsing remitting multiple sclerosis: a functional and structural connectivity study. PLoS One. 2014; 9: 101198. https://goo.gl/4zZtNk 102. Cruz-Gomez AJ, Ventura-Campos N, Belenguer A, Avila C, Forn C. The link between resting-state functional connectivity and cognition in MS patients. Mult Scler. 2014; 20: 338-348. https://goo.gl/cjB6tY 103. Louapre C, Perlbarg V, Garcia-Lorenzo D, Urbanski M, Benali H, Assouad R, et al. Brain networks disconnection in early multiple sclerosis cognitive deficits: An anatomofunctional study. Hum Brain Mapp. 2014; 35: 4706- 4717. https://goo.gl/Q4x44t 104. Rocca MA, Valsasina P, Absinta M, Riccitelli G, Rodegher ME, Misci P, et al. Default-mode network dysfunction and cognitive impairment in progressive MS. Neurology. 2010; 74: 1252-1259. https://goo.gl/jM3qkm 105. Buxton RB. Introduction to functional magnetic resonance imaging: Principles and techniques. Cambridge: Cambridge University Press. 2002; 402. https://goo.gl/H47YpU 106. Hubbard NA, Turner M, Hutchison JL, Ouyang A, Strain J, Oasay L, et al. Multiple sclerosis-related white matter microstructural change alters the BOLD hemodynamic response. J Cereb Blood Flow Metab. 2016; 36: 1872-1884. https://goo.gl/BFtYXe 107. Audoin B, Ibarrola D, Ranjeva JP, Confort-Gouny S, Malikova I, Ali Cherif A, et al. Compensatory cortical activation observed by fMRI during a cognitive task at the earliest stage of MS. Hum Brain Mapp. 2003; 20: 51- 58. https://goo.gl/HGsFPh 108. Audoin B, Van Au Duong M, Ranjeva J, Ibarrola D, Malikova I, Confort Gouny S, et al. Magnetic resonance study of the influence of tissue damage and cortical reorganization on PASAT performance at the earliest stage of multiple sclerosis. Hum Brain Mapp. 2005; 24: 216-228. https://goo.gl/kreNwh 109. Mainero C, Caramia F, Pozzilli C, Pisani A, Pestalozza I, Borriello G, et al. fMRI evidence of brain reorganization during attention and memory tasks in multiple sclerosis. Neuroimage. 2004; 21: 858-867. https://goo.gl/gqfpF2 110. Penner IK, Rausch M, Kappos L, Opwis K, Radü EW. Analysis of impairment related functional architecture in MS patients during performance of different attention tasks. J Neurol. 2003; 250: 461-472. https://goo.gl/KHtEzi 111. Staffen W, Mair A, Zauner H, Unterrainer J, Niederhofer H, Kutzelnigg A, et al. Cognitive function and fMRI in patients with multiple sclerosis: evidence for compensatory cortical activation during an attention task. Brain. 2002; 125: 1275-1282. https://goo.gl/hYRabA 112. Cader S, Cifelli A, Abu-Omar Y, Palace J, Matthews PM. Reduced brain functional reserve and altered functional connectivity in patients with multiple sclerosis. Brain. 2006; 129: 527-537. https://goo.gl/kyWZco 113. Amann M, Dossegger LS, Penner I-K, Hirsch JG, Raselli C, Calabrese P, et al. Altered functional adaptation to attention and working memory tasks with increasing complexity in relapsing-remitting multiple sclerosis patients. Hum Brain Mapp. 2011; 32: 1704-1719. https://goo.gl/X8GrSN 114. Rocca MA, Valsasina P, Hulst HE, Abdel-Aziz K, Enzinger C, Gallo A, et al. Functional correlates of cognitive dysfunction in multiple sclerosis: A multicenter fMRI Study. Hum Brain Mapp. 2014; 35: 5799-5814. https://goo.gl/gfDajd 115. Filippi M, Rocca MA, Comi G. Magnetization transfer ratios of multiple sclerosis lesions with variable durations of enhancement. J Neurol Sci. 1998; 159: 162-165. https://goo.gl/wSdfky 116. Pike GB, de Stefano N, Narayanan S, Francis GS, Antel JP, Arnold DL. Combined magnetization transfer and proton spectroscopic imaging in the assessment of pathologic brain lesions in multiple sclerosis. AJNR Am J Neuroradiol. 1999; 20: 829-837. https://goo.gl/51Pvyu 117. Rocca MA, Mastronardo G, Rodegher M, Comi G, Filippi M. Long-term changes of magnetization transfer-derived measures from patients with relapsing-remitting and secondary progressive multiple sclerosis. AJNR Am J Neuroradiol. 1999; 20: 821-827. https://goo.gl/NgNSCN 118. Dousset V, Grossman RI, Ramer KN, Schnall MD, Young LH, Gonzalez- Scarano F, et al. Experimental allergic encephalomyelitis and multiple
- 12. Scientific Journal of Multiple Sclerosis SCIRES Literature - Volume 1 Issue 1 - www.scireslit.com Page -019 sclerosis: lesion characterization with magnetization transfer imaging. Radiology. 1992; 182: 483-491. https://goo.gl/tTBZQc 119. Ropele S, Strasser-Fuchs S, Augustin M, Stollberger R, Enzinger C, Hartung HP, et al. A comparison of magnetization transfer ratio, magnetization transfer rate, and the native relaxation time of water protons related to relapsing-remitting multiple sclerosis. AJNR Am J Neuroradiol. 2000; 21: 1885-1891. https://goo.gl/ABGHXw 120. Brown RA, Narayanan S, Arnold DL. Imaging of repeated episodes of demyelination and remyelination in multiple sclerosis. Neuroimage Clin. 2014; 6: 20-25. https://goo.gl/zccB3b 121. Liu Z, Pardini M, Yaldizli O, Sethi V, Muhlert N, Wheeler-Kingshott CA, et al. Magnetization transfer ratio measures in normal-appearing white matter show periventricular gradient abnormalities in multiple sclerosis. Brain. 2015; 138: 1239-1246. https://goo.gl/hJeUCK 122. Faiss JH, Dahne D, Baum K, Deppe R, Hoffmann F, Kohler W, et al. Reduced magnetisation transfer ratio in cognitively impaired patients at the very early stage of multiple sclerosis: a prospective, multicenter, cross- sectional study. BMJ Open. 2014; 4: 004409. https://goo.gl/Jzmvg3 123. Deloire MS, Ruet A, Hamel D, Bonnet M, Dousset V, Brochet B. MRI predictors of cognitive outcome in early multiple sclerosis. Neurology. 2011; 76: 1161-1167. https://goo.gl/ASTUAh 124. Kutzelnigg A, Lucchinetti CF, Stadelmann C, Bruck W, Rauschka H, Bergmann M, et al. Cortical demyelination and diffuse white matter injury in multiple sclerosis. Brain. 2005; 128: 2705-2712. https://goo.gl/a6sTZg 125. Mainero C, Louapre C, Govindarajan ST, Gianni C, Nielsen AS, Cohen- Adad J, et al. A gradient in cortical pathology in multiple sclerosis by in vivo quantitative 7 T imaging. Brain. 2015; 138: 932-945. https://goo.gl/PYSCnt 126. Schmierer K, Scaravilli F, Altmann DR, Barker GJ, Miller DH. Magnetization transfer ratio and myelin in postmortem multiple sclerosis brain. Ann Neurol. 2004; 56: 407-415. https://goo.gl/6HFpB5 127. Rudko DA, Derakhshan M, Maranzano J, Nakamura K, Arnold DL, Narayanan S. Delineation of cortical pathology in multiple sclerosis using multi-surface magnetization transfer ratio imaging. Neuroimage Clin. 2016; 12: 858-868. https://goo.gl/aECcFH 128. Kidd D, Barkhof F, McConnell R, Algra PR, Allen IV, Revesz T. Cortical lesions in multiple sclerosis. Brain. 1999; 122: 17-26. https://goo.gl/oFuq77 129. Peterson JW, Bo L, Mork S, Chang A, Trapp BD. Transected neurites, apoptotic neurons, and reduced inflammation in cortical multiple sclerosis lesions. Ann Neurol. 2001; 50: 389-400. https://goo.gl/UnW2Pi 130. Bo L, Vedeler CA, Nyland H, Trapp BD, Mork SJ. Intracortical multiple sclerosis lesions are not associated with increased lymphocyte infiltration. Mult Scler. 2003; 9: 323-331. https://goo.gl/Kim5nF 131. Lucchinetti CF, Popescu BF, Bunyan RF, Moll NM, Roemer SF, Lassmann H, et al. Inflammatory cortical demyelination in early multiple sclerosis. N Engl J Med. 2011; 365: 2188-2197. https://goo.gl/3mrTjx 132. Helms G, Dathe H, Kallenberg K, Dechent P. High-resolution maps of magnetization transfer with inherent correction for RF inhomogeneity and T1 relaxation obtained from 3D FLASH MRI. Magn Reson Med. 2008; 60: 1396-1407. https://goo.gl/km2jue 133. Levesque I, Sled JG, Narayanan S, Santos AC, Brass SD, Francis SJ, et al. The role of edema and demyelination in chronic T1 black holes: a quantitative magnetization transfer study. J Magn Reson Imaging. 2005; 21: 103-110. https://goo.gl/hYWMem 134. Levesque IR, Giacomini PS, Narayanan S, Ribeiro LT, Sled JG, Arnold DL, et al. Quantitative magnetization transfer and myelin water imaging of the evolution of acute multiple sclerosis lesions. Magn Reson Med. 2010; 63: 633-640. https://goo.gl/PizLDr 135. Schmierer K, Wheeler Kingshott CA, Tozer DJ, Boulby PA, Parkes HG, Yousry TA, et al. Quantitative magnetic resonance of postmortem multiple sclerosis brain before and after fixation. Magn Reson Med. 2008; 59: 268- 277. https://goo.gl/8TbURT 136. Poon CS, Henkelman RM. Practical T2 quantitation for clinical applications. J Magn Reson Imaging. 1992; 2: 541-553. https://goo.gl/a6oPH4 137. MacKay A, Whittall K, Adler J, Li D, Paty D, Graeb D. In vivo visualization of myelin water in brain by magnetic resonance. Magn Reson Med. 1994; 31: 673-677. https://goo.gl/mwiRq7 138. Whittall KP, MacKay AL, Graeb DA, Nugent RA, Li DK, Paty DW. In vivo measurement of T2 distributions and water contents in normal human brain. Magn Reson Med. 1997; 37: 34-43. https://goo.gl/aakc8J 139. Laule C, Kozlowski P, Leung E, Li DK, Mackay AL, Moore GR. Myelin water imaging of multiple sclerosis at 7 T: correlations with histopathology. Neuroimage. 2008; 40: 1575-1580. https://goo.gl/xw4Rvo 140. Vrenken H, Geurts JJ, Knol DL, van Dijk LN, Dattola V, Jasperse B, et al. Whole-brain T1 mapping in multiple sclerosis: global changes of normal-appearing gray and white matter. Radiology. 2006; 240: 811-820. https://goo.gl/T8vTgA 141. Tardif CL, Schafer A, Waehnert M, Dinse J, Turner R, Bazin PL. Multi- contrast multi-scale surface registration for improved alignment of cortical areas. Neuroimage. 2015; 111: 107-122. https://goo.gl/9XC3sM 142. Schmierer K, Parkes HG, So PW, An SF, Brandner S, Ordidge RJ, et al. High field (9.4 Tesla) magnetic resonance imaging of cortical grey matter lesions in multiple sclerosis. Brain. 2010; 133: 858-867. https://goo.gl/gR6dMF 143. Vavasour IM, Li DK, Laule C, Traboulsee AL, Moore GR, Mackay AL. Multi-parametric MR assessment of T(1) black holes in multiple sclerosis : evidence that myelin loss is not greater in hypointense versus isointense T(1) lesions. J Neurol. 2007; 254: 1653-1659. https://goo.gl/xuYNX8 144. Yao B, Bagnato F, Matsuura E, Merkle H, van Gelderen P, Cantor FK, et al. Chronic multiple sclerosis lesions: characterization with high-field-strength MR imaging. Radiology. 2012; 262: 206-215. https://goo.gl/wuwy7b 145. Li X, van Gelderen P, Sati P, de Zwart JA, Reich DS, Duyn JH. Detection of demyelination in multiple sclerosis by analysis of [Formula: see text] relaxation at 7 T. Neuroimage Clin. 2015; 7: 709-714. https://goo.gl/m5CAfZ 146. Weisskoff RM, Kiihne S. MRI susceptometry: image-based measurement of absolute susceptibility of MR contrast agents and human blood. Magn Reson Med. 1992; 24: 375-83. https://goo.gl/yVRSqq 147. de Rochefort L, Liu T, Kressler B, Liu J, Spincemaille P, Lebon V, et al. Quantitative susceptibility map reconstruction from MR phase data using bayesian regularization: validation and application to brain imaging. Magn Reson Med. 2010; 63: 194-206. https://goo.gl/VbkWPb 148. Mehta V, Pei W, Yang G, Li S, Swamy E, Boster A, et al. Iron is a sensitive biomarker for inflammation in multiple sclerosis lesions. PLoS One. 2013; 8: 57573. https://goo.gl/QaXPTA 149. Zhang Y, Gauthier SA, Gupta A, Comunale J, Chia-Yi Chiang G, Zhou D, et al. Longitudinal change in magnetic susceptibility of new enhanced multiple sclerosis (MS) lesions measured on serial quantitative susceptibility mapping (QSM). J Magn Reson Imaging. 2016; 44: 426-432. https://goo.gl/huYWXM 150. Chen W, Gauthier SA, Gupta A, Comunale J, Liu T, Wang S, et al. Quantitative susceptibility mapping of multiple sclerosis lesions at various ages. Radiology. 2014; 271: 183-192. https://goo.gl/BHEPDi 151. Taetzsch T, Levesque S, McGraw C, Brookins S, Luqa R, Bonini MG, et al. Redox regulation of NF-kappaB p50 and M1 polarization in microglia. Glia. 2015; 63: 423-440. https://goo.gl/1wtEHQ 152. Wisnieff C, Ramanan S, Olesik J, Gauthier S, Wang Y, Pitt D. Quantitative susceptibility mapping (QSM) of white matter multiple sclerosis lesions: Interpreting positive susceptibility and the presence of iron. Magn Reson Med. 2015; 74: 564-570. https://goo.gl/ddzoMm 153. Langkammer C, Liu T, Khalil M, Enzinger C, Jehna M, Fuchs S, et al. Quantitative susceptibility mapping in multiple sclerosis. Radiology. 2013; 267: 551-559. https://goo.gl/Ng2f18 154. Rudko DA, Solovey I, Gati JS, Kremenchutzky M, Menon RS. Multiple sclerosis: improved identification of disease-relevant changes in gray and white matter by using susceptibility-based MR imaging. Radiology. 2014; 272: 851-864. https://goo.gl/JAU2k5 155. Cobzas D, Sun H, Walsh AJ, Lebel RM, Blevins G, Wilman AH. Subcortical gray matter segmentation and voxel-based analysis using transverse relaxation and quantitative susceptibility mapping with application to multiple sclerosis. J Magn Reson Imaging. 2015; 42: 1601-1610. https://goo.gl/bBSsv4
- 13. Scientific Journal of Multiple Sclerosis SCIRES Literature - Volume 1 Issue 1 - www.scireslit.com Page -020 156. LeVine SM. Iron deposits in multiple sclerosis and Alzheimer’s disease brains. Brain Res. 1997; 760: 298-303. https://goo.gl/CYWHJu 157. Nunez MT, Urrutia P, Mena N, Aguirre P, Tapia V, Salazar J. Iron toxicity in neurodegeneration. Biometals. 2012; 25: 761-76. https://goo.gl/fGTph8 158. Leary SM, Brex PA, MacManus DG, Parker GJ, Barker GJ, Miller DH, et al. A (1) H magnetic resonance spectroscopy study of aging in parietal white matter: implications for trials in multiple sclerosis. Magn Reson Imaging. 2000; 18: 455-459. https://goo.gl/j6vHHE 159. Tofts P, John Wiley & Sons. Quantitative MRI of the brain measuring changes caused by disease. Chichester, West Sussex; Hoboken, NJ: Wiley. 2003; 617. https://goo.gl/4WQTf9 160. Govindaraju V, Young K, Maudsley AA. Proton NMR chemical shifts and coupling constants for brain metabolites. NMR Biomed. 2000; 13: 129-153. https://goo.gl/vEU97L 161. Davie CA, Hawkins CP, Barker GJ, Brennan A, Tofts PS, Miller DH, et al. Serial proton magnetic resonance spectroscopy in acute multiple sclerosis lesions. Brain. 1994; 117: 49-58. https://goo.gl/CjvEiU 162. Narayanan S, Francis SJ, Sled JG, Santos AC, Antel S, Levesque I, et al. Axonal injury in the cerebral normal-appearing white matter of patients with multiple sclerosis is related to concurrent demyelination in lesions but not to concurrent demyelination in normal-appearing white matter. Neuroimage. 2006; 29: 637-642. https://goo.gl/in2iTe 163. De Stefano N, Matthews PM, Arnold DL. Reversible decreases in N-acetylaspartate after acute brain injury. Magn Reson Med. 1995; 34: 721-727. https://goo.gl/5MiYs4 164. Matthews PM, Francis G, Antel J, Arnold DL. Proton magnetic resonance spectroscopy for metabolic characterization of plaques in multiple sclerosis. Neurology. 1991; 41: 1251-1256. https://goo.gl/SDeQHY 165. Matthews PM, Pioro E, Narayanan S, De Stefano N, Fu L, Francis G, et al. Assessment of lesion pathology in multiple sclerosis using quantitative MRI morphometry and magnetic resonance spectroscopy. Brain. 1996; 119: 715-722. https://goo.gl/RZi4Ua 166. Miller BL. A review of chemical issues in 1H NMR spectroscopy: N-acetyl-L-aspartate, creatine and choline. NMR Biomed. 1991; 4: 47-52. https://goo.gl/AVfkwj 167. Arnold DL, Matthews PM, Francis G, Antel J. Proton magnetic resonance spectroscopy of human brain in vivo in the evaluation of multiple sclerosis: assessment of the load of disease. Magn Reson Med. 1990; 14: 154-159. https://goo.gl/5tqTmb 168. Arnold DL, Riess GT, Matthews PM, Francis GS, Collins DL, Wolfson C, et al. Use of proton magnetic resonance spectroscopy for monitoring disease progression in multiple sclerosis. Ann Neurol. 1994; 36: 76-82. https://goo.gl/CMHe4r 169. Tartaglia MC, Narayanan S, De Stefano N, Arnaoutelis R, Antel SB, Francis SJ, et al. Choline is increased in pre-lesional normal appearing white matter in multiple sclerosis. J Neurol. 2002; 249: 1382-1390. https://goo.gl/mDGRY3 170. Meldrum BS. Glutamate as a neurotransmitter in the brain: review of physiology and pathology. J Nutr. 2000; 130: 1007-1015. https://goo.gl/fA5Vgp 171. Cawley N, Solanky BS, Muhlert N, Tur C, Edden RA, Wheeler-Kingshott CA, et al. Reduced gamma-aminobutyric acid concentration is associated with physical disability in progressive multiple sclerosis. Brain. 2015; 138: 2584-2595. https://goo.gl/nVQFKb 172. Sarchielli P, Presciutti O, Pelliccioli GP, Tarducci R, Gobbi G, Chiarini P, et al. Absolute quantification of brain metabolites by proton magnetic resonance spectroscopy in normal-appearing white matter of multiple sclerosis patients. Brain. 1999; 122: 513-521. https://goo.gl/zRRQYU 173. Ruiz-Pena JL, Pinero P, Sellers G, Argente J, Casado A, Foronda J, et al. Magnetic resonance spectroscopy of normal appearing white matter in early relapsing-remitting multiple sclerosis: correlations between disability and spectroscopy. BMC Neurol. 2004; 4: 8. https://goo.gl/SVjp4H 174. Adalsteinsson E, Langer-Gould A, Homer RJ, Rao A, Sullivan EV, Lima CA, et al. Gray matter N-acetyl aspartate deficits in secondary progressive but not relapsing-remitting multiple sclerosis. AJNR Am J Neuroradiol. 2003; 24: 1941-1945. https://goo.gl/qdbKZ3 175. Tartaglia MC, Narayanan S, Francis SJ, Santos AC, De Stefano N, Lapierre Y, et al. The relationship between diffuse axonal damage and fatigue in multiple sclerosis. Arch Neurol. 2004; 61: 201-207. https://goo.gl/sSB6dd 176. Vågberg M, Axelsson M, Birgander R, Burman J, Cananau C, Forslin Y, et al. Guidelines for the use of magnetic resonance imaging in diagnosing and monitoring the treatment of multiple sclerosis: recommendations of the Swedish Multiple Sclerosis Association and the Swedish Neuroradiological Society. Acta Neurol Scand. 2017; 135: 17-24. https://goo.gl/ipa5H9 177. Traboulsee A, Simon JH, Stone L, Fisher E, Jones DE, Malhotra A, et al. Revised recommendations of the consortium of MS centers task force for a standardized MRI protocol and clinical guidelines for the diagnosis and follow-up of multiple sclerosis. Am J Neuroradiol. 2016; 37: 394-401. https://goo.gl/qxFc26 178. Rovira A, Wattjes MP, Tintore M, Tur C, Yousry T a, Sormani MP, et al. Evidence-based guidelines: MAGNIMS consensus guidelines on the use of MRI in multiple sclerosis-clinical implementation in the diagnostic process. Nat Rev Neurol. 2015; 11: 1-12. https://goo.gl/uef8US 179. Carass A, Roy S, Jog A, Cuzzocreo JL, Magrath E, Gherman A, et al. Longitudinal multiple sclerosis lesion segmentation: Resource and challenge. Neuroimage. 2017; 148: 7-102. https://goo.gl/b1297g 180. Dwyer M, Silva D, Bergsland N, Horakova D, Ramasamy D, Durfee J, et al. Neurological Software Tool for REliable Atrophy Measurement (NeuroSTREAM) in Multiple Sclerosis. Neurology. 2016; 86: 769-779. https://goo.gl/YskCMq