Titel;
mcDESPOT-Derived Demyelination Volume in Multiple Sclerosis Patients
Correlates with Clinical Disability and Senses Early Myelin Loss
H. H. Kitzler1,3* , J. Su2* , M. Zeineh2, C. Harper-Little3, A. Leung4, M.
Kremenchutzky5, S. C. Deoni6, and B. K. Rutt2
1 Department of Neuroadiology, Technische Universitaet Dresden, Dresden, Germany
2 Department of Radiology, Stanford University, Stanford, California, USA
3 Robarts Research Institute, University of Western Ontario, London, Ontario, Canada,
4 Department of Diagnostic Radiology and Nuclear Medicine, University of Western
Ontario, London, Ontario, Canada
5 Department of Clinical Neurological Sciences, University of Western Ontario, London,
Ontario, Canada
6 Department of Engineering, Brown University, Providence, Rhode Island, USA
(*both authors contributed equally to this work)
Running title: Whole-Brain Demyelination Quantification in MS
Total word count (text body): xxxx
Summary (400 words max; current state: 400) LEFT UNTOUCHED, LEAST TO
BE REWRITTEN AS SUGGESTED!
Conventional magnetic resonance (MRMRI) imaging is established as one of the most important surrogate
markers of Multiple Sclerosis (MS) development and treatment outcome. Based on the assumption that the
clinical course of MS is adequately reflected by focal white matter changes, many clinical trials have used lesion
volume as the principal MRMRI-derived measure; however, such measures have been recently criticized as
adding little or no independent information over and above non-imaging disability outcome measurements when
evaluated retrospectively. [reference required here?] This highlights the need to develop and validate new
quantitative WM imaging strategies that aim to characterize the invisible burden of demyelination in the brain and
establish much more sensitive and specific markers of MS that correlate strongly with clinical disability. One of
the most promising of such arising measures is myelin-selective MR imaging MRI that allows the acquisition of
Myelin-Water fraction (MWF) maps, a parameter that is correlated to the brain white matter (WM) myelination. The
aim of our study was to apply the newest myelin-selective MRMRI method, multi-component Driven Equilibrium
Single Pulse Observation of T1 and T2 (mcDESPOT) in a controlled clinical MS pilot trial. This study was designed
to assess the capabilities of this new method to explain differences in disease course and degree of disability in
subjects spanning a broad spectrum of MS disease severity. The whole-brain isotropically-resolved 3D acquisition
capability of mcDESPOT allowed for the first time the registration of 3D MWF maps to standard space, and
consequently a formalized voxel based analysis (VBA) of the data. This VBA approach combined with image
segmentation further allowed the derivation of new volumetric measures of disease severity: total demyelinated
volume (DV) in WM, DV within WM lesions, DV within dirty appearing white matter (DAWM) and DV within normal
appearing white matter (NAWM). The analysis confirmed that neither lesion burden nor lesion demyelination
correlate well with clinical disease activity measured with the extended disability status scale (EDSS) in MS
patients. In contrast, our measurements of demyelination volume in NAWM correlated significantly with the
EDSS score (R2 0.405; p<0.01). [will need to update numbers, plus decide what other data is going into this paper and to
summarize all the key results in this section here][Our most remarkable result is differentiating CIS from normals. There is
some correlation of EDSS with DV but not enough to say “wow.” The other question is whether to include the multi-variate
regression analysis.] The same measurement discriminated Clinically Isolated Syndrome (CIS) patients from a
normal control population (p<0.001), hence the technique senses very early disease-related myelin loss.
Furthermore, the same parameter discriminated patients with the secondary-progressive (SPMS) course from
relapsing-remitting MS (RRMS; p<0.01). Overall, our results demonstrate that mcDESPOT-defined demyelination
measurements show great promise to act as imaging markers of clinical disease activity in MS. Further
investigation will determine if this measure can serve as a risk factor for the conversion into definite MS and for
the secondary transition into irreversible disease progression.
Keywords: multiple sclerosis; demyelination; normal appearing white matter;
quantitative MRIMRI; myelin-selective imaging
Introduction
Multiple Sclerosis and Imaging Conventional MRI
Multiple Sclerosis (MS) is an immunologically mediated demyelinating and
axonal disease of the human central nervous system (CNS) and. It is one of the
most common disabling neurological diseases in young people adults with the
typical age-at-onset being 20 to 40 years and it is approximately twice as
common in women as in men [Platten 2006]. Depending on the location of CNS
lesions, [Hagen or others: I simplified your wording, but need to know if what I
wrote is still strictly correct] MS patients experience diverse neurological
symptoms and impairment of e.g. motor, visual, or, sensory function. Over 80%
of MS patients initially present with a relapsing disease course that eventually
transitions into permanent disability. More than 50% of patients require a walking
aid within 15 years from initial diagnosis [Weinshanker 1989].
The MS etiology [can we just say “the cause of MS”? Or is etiology a standard
term in medicine?] is believed to be the result of a complex combination of
environmental, genetic, and autoimmune factors resulting in an immune-
mediated attack on CNS myelin, [Steinman 2004]. Myelin is the basic structure
of the myelin axonal sheath of , surrounding neuronal axons cells which isand
vital for their appropriate function [Steinman 2004]. The pathology of MS ,
however, lesions is heterogeneous, including inflammatory cell infiltration,
astroglial hypertrophy, axonal loss, but demyelination is the recognized hallmark
of the disease [Lucchinetti 2001].
The application of conventional magnetic resonance imaging (MRI) techniques
has revolutionized the clinical practice in MS [Villenga 2009]. Conventional
magnetic resonance (MRMRI) imaging studies reveal focal signal deviations
lesions, traditionally called lesions or “plaques” within throughout the white matter
(WM) and less frequently also in grey matter (GM) of the brain and spinal cord in
both T2 and T1 weighted scans. This appearance of lesions on top of the
background of an inflammatory reaction throughout the central nervous system
(CNS) defineeds MS early as a multifocal inflammatory demyelinating disease.
Conventional MRI derived lesion numeric and volumetric measures are currently
used as paraclinical markers in standardized diagnostic schemes [Polman,
2005]. However, thee lesion-centered view has been challenged by studies
investigating the relationship between conventional MRI measures with clinical
MS disease severity and neuro-functional scores revealing only dissatisfying and
non-significant correlations [Fulton, 1999, more ref]. Despite the acknowledged
potential application of conventional MRI, presently available MRI technologies
have failed in meeting the critical goal of reflecting MS patient disability status
and predicting disease progression. [ref]
Such conventional MRI measures also have been widely employed as
presumptive surrogates across a broad spectrum of MS studies, ranging from
pilot trials through multi-center pivotal phase III studies. However, the presently
employed metrics fail to meet Prentice’s surrogate endpoint validation criteria for
reliable surrogate markers in predicting downstream disease activity [Prentice,
1989]; and statistically appear to offer no more valid endpoint than that already
offered by clinical outcomes of MS clinical disability as measured by the
extended disability status scale (EDSS) and the relapse rate [Daumer
2009].novel quantitative MR technologies, which have provided insights into
partial pathological aspects [not sure what you mean by this phrase "partial
pathological aspects"] of the disease. It is now thought [known?] that primary
demyelination, i.e. selective myelin destruction, is not restricted to focal MS
lesions but occurs throughout the entire CNS parenchyma. Moreover such
demyelination may be accompanied to a variable degree by remyelination and
repair. [should add strategic or key references to some of the above statements]
The current state-of-the-art treatments are disease-modifying agents that at
present are able to decrease relapse rates by 30% [Weiner 2009]. However,
despite these advances, the field of MS still lacks specific markers to predict
clinical relapses and disease progression. Novel immunotherapies are on the
rise, but to date non-invasive technologies have failed to provide accurate,
reliable tools to assess the state of myelination. Such technologies are
particularly needed for testing drug efficacy or for monitoring treatment.
Imaging technologies provide potential instruments to investigate in vivo, real
time changes that occur within the CNS over the broad spectrum of natural MS
courses as well as during treatment. The application of conventional MR imaging
techniques has already revolutionized the clinical practice in MS [Villenga 2009].
Conventional MR imaging derived numeric and volumetric measures are
currently used as paraclinical markers in standardized diagnostic schemes
[Polman, 2005]. However, despite the widely acknowledged potential application
of MRI, presently available MR technologies have failed in meeting the critical
goal of predicting disease progression and MS patient disability status. [ref?]
Conventional MR imaging measures also have been widely employed as
presumptive surrogates across a broad spectrum of MS studies, ranging
from pilot trials through multi-center pivotal phase III studies. However, the
presently employed metrics fail to meet Prentice’s surrogate endpoint
validation criteria for reliable surrogate markers in predicting downstream
disease activity [Prentice, 1989]; and statistically appear to offer no more
valid endpoint than that already offered by clinical outcomes of MS relapse
rate and clinical disability as measured by EDSS [Daumer 2009].
[Maybe a new sub-heading here?]Quantitative MRI and Imaging Myelin In
Vivo
In conventional MRI Imaging studies of MS patients, the WM tissue compartment
that does not clearly show lesions or abnormalities is referred to as the Normal
Appearing White Matter (NAWM) compartment. Moreover, lesions are not always
well-defined areas of MR signal change with sharp boundaries, but often present
ill-defined surrounding regions of signal deviation, [I don't like this term
circumjacent signal deviation: can you try something simpler / clearer? I've made
one suggestion.] the so called Dirty Appearing White Matter (DAWM). Outside of
the lesions or DAWM, [do you mean in NAWM here? If so, just say so more
clearly] a number of modern Newer, unconventional quantitative MRMRI are
often aimed at the derivation of more specific and quantifiable information about
MS pathology and its distribution. Quantitative in nature, Tthese quantitative MRI
technologies have observed alteration in parameters that may be related to
intrinsic tissue integrity myelination and axonal integrity myelination, indicative of
a process of diffuse myelin damage and axonalneuronal loss not restricted to
lesion tissue but throughout the entire CNS parenchyma [Seewann, 2009;
Vrenken, 2010].
Newer, unconventional MR imaging strategies are often aimed at the derivation
of more specific information about MS pathology and its distribution.
Progressive changes of intrinsic NAWM microstructure related to the tissue water
diffusion characteristics were detected in primary-progressive MS (PPMS)
patients in serial diffusion MR imaging study that quantified the apparent diffusion
coefficient (ADC) [Schmierer 2006]. Werring et al. investigated the dynamic
evolution of water diffusion measurements in pre-lesion NAWM in another serial
diffusion MR imaging study and found a steady and moderate increase in ADC,
followed by a rapid and marked increase at the time of lesion formation, and
even a significant but milder increase in matched NAWM regions [what does this
mean, "matched NAWM regions"?] [Werring, 2000].
Widespread tissue changes are found in NAWM of MS patients by measuring the
magnetization transfer ratio (MTR). Those changes are mainly explained in terms
of axonal damage and loss of one of the major pathological features of multiple
sclerosis [Filippi, 1998]. [I don't understand the last half of this sentence: what is
the major pathological feature you are referring to? Rewrite to make it clear] A
histological analysis of the substrate of those imaging findings revealed that not
only MTR but also T1 contrast ratio correlated strongly with axonal density, even
in NAWM. However, defining T2 lesions revealed no correlation but a range of
pathology, illustrating the low specificity of T2-weighted imaging [van
Waesberghe, 1999]. Early axonal pathology, can also be quantified with Proton
(H+) spectroscopy (S) that provides chemical composition information at the
level of metabolites. Early S studies have noted specific changes in metabolite
signatures, not only within focal T2 lesions but even a deviation from normal in
NAWM areas [Helms 2000]. A measure of ‘whole-brain’ N-acetylaspartate
(WBNAA), a marker of axonal integrity, in particular confirmed widespread axonal
pathology, largely independent of -visible inflammation in MS patients even in
Clinically Isolated Syndrome. No correlation however, was found between the T2
lesion volumes and WBNAA concentrations [is this really a concentration value
or a total integrated NAA value?] [Filippi, 2003].
Axons and their myelin sheath form an individually customized unit. [This English
doesn't make sense. I'm not even sure what you mean here. Do you mean that
the phrase "axonal loss" implies both dymelination and axonal degradation? This
definitely needs to be re-worded.] However, axonal loss is not necessarily
accompanied by demyelination, moreover both histopathologic changes seem to
contribute independently to the appearance in conventional MR imaging scans.
An imaging-histopathology case study confirmed that axonal degeneration could
occur in the absence of myelin loss as a histopathologic correlate to abnormal
MR findings in MS patients [Bjartmar, 2001]. [This last paragraph needs to be
improved. Hard to understand.]
Single component T1 relaxation time was found to be abnormal in NAWM in
established MS. When compared to MTR, quantitative T1 measurement was
more sensitive in detecting subtle pathological change. No correlation was found
between NAWM T1 changes and lesion abnormalities [what do you mean here:
lesion volume? lesion signal characteristics?] suggesting independent underlying
pathologic mechanisms [Griffin, 2002].
Fulton et al. determined the relationship between T2 lesion volume and both
neurocognitive and physical disability in untreated relapsing-remitting multiple
sclerosis. Despite some correlation to information-processing speed and verbal
long-term memory, none of ten other neurocognitive examinations or the physical
disability scales as rated according to the EDSS showed significant correlation
with total lesion volume. This challenges the view that lesion volume
measurement is a robust surrogate marker of impairment in patients with MS
[Fulton, 1999]. [Last sentence is awkward and hard to understand]
MTR vs disability correlation?
These studies point to a new direction for MS MR imaging research: to move
away from the lesion-centered view and to develop highly sensitive MR methods
that accurately and quantitatively reflect the global disease burden even in areas
that are apparently normal. Once such methods are developed, important
hypotheses can be tested; for example, that such measures reflect the subtle
underlying disease-determining pathology and will predict clinical changes in MS
disease development, as well as transition towards chronic progression.
Imaging Myelin In Vivo
It seems obvious that novel MS MR imaging MRI strategies should focus on CNS
tissue properties that are directly involved in specific disease-related pathological
processes, especially the process of demyelination. Direct quantitative myelin
assessment would have important application in a variety of inflammatory,
degenerative, and developmental disorders of the CNS as well as regeneration
from injury and trauma.
Current conventional MR imaging MRI methods do not specifically reflect myelin
content. Within the battery of unconventional MRMRI technologies,
magnetization transfer imaging (MTI), diffusion tensor imaging (DTI), and single-
component T1 and T2 relaxometry, i.e. the precise measurement of intrinsic
magnetic tissue properties, are all thought to provide information related to
myelin content. However, these measures are non-specific towards myelination.
While quantitative MTI provides an estimate of the macromolecule-bound water
fraction, this measure may also reflect inflammation processes [fix] with signal
originating from an aspect of pathology in MS other than demyelination
[Vavasour 1998, Gareau 2000]. Moreover a histological analysis of the substrate
MTI findings revealed a strong correlation with axonal density [van Waesberghe,
1999]. With regards to DTI, significant fractional anisotropy (FA) is observed
even in non-myelinated nerve tissue indicating that axonal structures may at
least in part be responsible for the signal generation [Beaulieu 2002]. [should
also mention the crossing fiber problem of DTI which is obviously unrelated to
myelination Moreover, the quantification of the integrity of myelinated WM fiber
tracts reflected by FA measures may be directly affecteddistorted in regions
where there are un-resolvable crossing fiber structures [Oochy 2007].
Finally, both T1 and T2 are influenced by a number of tissue structure and
biochemical characteristics, including free water content and the presence of
paramagnetic atoms such as iron.
Currently, multi-component relaxometric imaging (MCRI) provides the most direct
means of quantifying myelin volume in vivo. In conventional T2 MCRI, the
measured MRIMRI signal is decomposed into contributions from two or more
water pools, which in brain tissue are attributed to an intra and extra-cellular
water pool and water trapped between the hydrophobic bilayers of the myelin
sheath [Whittall 199789, Menon 1991 -> ref to be fixed]. [is it really true that
there are MCRI methods that use more than a two-pool model?] Through
appropriate data acquisition, typically comprising multiple spin-echo images
acquired over a range of echo times, and multi-exponential data analysis, maps
of the T2 characteristics and volume fractions of each water pool may be
estimated. As these volume fraction estimates show strong correlation with ‘gold-
standard’ histologic assessments [Webb 2003 -> ref to be fixed, Laule 2006],
MCRI has become the de facto standard for non-invasive myelin quantification.
Unfortunately, established MCRI methods require lengthy imaging times while
providing limited volume coverage. For example, the method of Whittall, MacKay
and colleagues [Whittall 1997, Mädler 2006 -> ref to be fixed, ISMRM
Proceedings?] requires approximately 16 minutes to acquire 16 contiguous slices
with a voxel volume of 10 mm3. These volume coverage, / spatial resolution,
and / imaging time characteristics are comparable to more recent alternative
techniques [Oh 2007 -> ref to be fixed] and make high resolution, whole-brain
investigations challenging.
An alternative to MCRI is image combination, in which imaging data acquired
with different acquisition parameters are combined so as to emphasize tissues
with specific T2 relaxation characteristics [Whittall 1991 -> ref to be fixed, Jones
20042004 -> ref to be fixed, Vidarsson 2005]. While multi-slice myelin fraction
maps may be estimated in as little as 5 minutes [Vidarsson 2005], these methods
are sensitive to T1 effects, depend on the short and long T2 selection criteria,
and can suffer from low signal-to-noise ratio (SNR) efficiency.
One of the most promising such applications is myelin-selective MR [Laule, 2007;
MacKay...add], however, these methods did not allow whole-brain high resolution
imaging in clinically practical scan times until very recently. [This paragraph
seems to stand out -- I think you've already mentioned multi-echo spin echo
methods with multi-exponential decomposition to create myelin water fraction
maps. If this is what you mean by "myelin-selective MR" then haven't you already
covered this?]
[ correlation to MTT, to characterize current practise in this area]
Multi-component Relaxometry
Multi-component driven equilibrium single pulse observation of T1/T2
(mcDESPOT) is the most recent MCRI a quantitative MR technique that allows
rapid acquisition of whole-brain data which is then processed to yield quantitative
two-pool parameters which can be used to derived quantitative myelin water
information. A series of spoiled gradient echo (SPGR) and phase-cycled steady-
state free precession (SSFP) volumetric scans are each collected each over a
broad range of flip angles (FA) at constant repetition times (TR) that allow the
extraction of a set of quantitative tissue parameters at every voxel over the whole
brain. One of these quantitative parameters is the Myelin Water Fraction (MWF),
that we and others have hypothesized to be proportional to the fraction of water
trapped between the myelin bilayers [ref-we!, ref-others!]. MWF is theoretically
specific to myelination and hitherto seems no evidence emergedt to bethat this
measure is mixed with other tissue signal components [ref!].
Extensive histologic validation has been performed validating the the method of
Whittall, MacKay and colleagues [Whittall 1997, Mädler 2006].
EXAMPLES TO FOLLOW….
[the word "seems" is weak.
WE NEED TO DISCUSS THE FOLLOWING:
I think you have to somehow sell the idea that the MWF we get from mcDESPOT
is the same quantity that McKay et al get from their T2 MCRI, and then state that
since that group has validated this MWF quantity so thoroughly that we believe
our MWF is therefore going to reflect the same strong correlation to actual myelin
content.]
We could compare ROI measurments in our data in greater fiber tracts/anatomic
regions to the data presented in:
[Is it true that mcDESPOT’s MWF looks more or less the same as T2 MCRI-
based MWF maps?] [I haven’t seen such a comparison, Brian did you?][Based
on our numbers in MSmcDESPOT-Changing to FLAIR, it would seem not. Mean
MWF in WM is around 20% for CIS.]
The novel feature of this acquisition technique is the fact that it covers the entire
brain producing isotropic datasets of the presumed myelin water fraction.
Cite other recent mcDESPOT applications here (Deoni, Kolind!)
To evaluateEvaluating specific white matter MR imaging MRI strategies like
mcDESPOT that selectively quantify myelin is of the major utmost clinical
neurological importance especially in the field of demyelinating diseases.
[“utmost” seems a little too strong for my taste, consider “major”] Therefore, the
specific aims of our study were (1) to derive myelin water fraction (MWF) maps
and our novel measurement of demyelinated volume (DV) using mcDESPOT in a
broad spectrum of MS patients, and (2) to test the hypothesis that MWF and/or
DV in normal appearing white matter (NAWM) correlates with disability in MS
thus reflecting non-lesional MS pathology that may determine disease. severity
[perhaps a mention of DV as well] Moreover, the study was driven by the
hypothesis that our new technique can quantify myelination at every voxel
throughout whole brain tissue, and therefore allows us to measure the degree of
demyelination anywhere in the brain, not just at lesion locations visible in
conventional MR data. [Somehow I think you need to state this a bit differently:
The novel feature of the mcDESPOT hereby acquisition technique is the fact that
it covers the entire brain producing isotropic datasets of the presumed myelin
water fraction. theThis whole-brain high-resolution isotropic 3D nature of the
mcDESPOT acquisition meansallows that myelin quantification canto be done
much more rigorously, using formalized voxel based analysis methods that have
found tremendous value in various voxel based morphometry applications, yet
which previous T2 MCRI / myelin mapping methods have really not been able to
achieve.
Thus rather subtle non-lesional involvement may determine disease activity and
may re-define MS as a rather global disease state that questions focal lesions in
their capability to reflect disease activity [severity?].
Materials and Methods
MS Patients & Healthy Controls
The MS group cohort (n=26) we assembled for this controlled clinical trial was
almost equally distributed across the different clinically definite MS types and the
accepted MS-precursor Clinically Isolated Syndrome (CIS): n=16 patients with
definite MS (relapsing-remitting [RRMS] n=5; secondary-progressive [SPMS]
n=6; and primary-progressive Multiple Sclerosis [PPMS] n=5) as well as n=10
patients with CIS (CIS (low risk [lr-CIS], n=5, and, high risk Clinically Isolated
Syndrome [hr-CIS], n=5).) The patient group age was (mean 49, min 19/max 68,
std. dev. ~12) Give male/male ratio (18/26 female). We also scanned an age-
matched group (n=26) of healthy controls [(HC]; n=26) (mean 42, min 23, max
66, std dev. ~12.6, 16/26 female) [don't think it was precisely age-matched] to be
used for formal statistical comparison (See demographic data for patients and
healthy controls at Tab. 1). All patients and healthy controlsparticipants were
recruited in accordance to local ethics board requirements of the University of
Western Ontario, London, Ontario, Canada. In all patients, we measured the MS
Extended Disability Status Scale (EDSS) score [ref! Kurtzke 1983], an average
scoring number derived from measures of various functions of the central
nervous system, using a scale from 0 to 10, with 10 representing greatest
disability . [is it a problem that we didn't measure EDSS in healthy controls?]
[Kurtzke 1983]. The average EDSS score for the patient group was 3.74.0 (max
8.50; min 0; std dev. 2.46). Within the entire group of patients only one lr-CIS
patient was treated with copaxone and one SPMS patient was treated with
copaxone or avonex, respectively. All other patients did not receive any immune
modulating treatment. [note that we didn’t use all the patients listed in the chart,
also be careful to use the “corrected” set of EDSS values where P001 = 2.0].
( ADD mean duration of disease, min/max, treatment, … ) [There should be a
Table that shows all relevant patient information.]
MRMRI Data Acquisition
Image acquisition used a clinical 1.5 T MRMRI scanner (GE Signa HDx, General
Electric Healthcare, Waukesha, WI) equipped with an 8-channel receive-only
radio-frequency (RF) brain array coil. We acquired isotropic nearly isotropic [1.7 x
1.7 x 2.0 mm] 3D whole-brain mcDESPOT [nearly isotropic 1.7x1.7x2mm] data
using the following imaging parameters: FOV = 22 cm, matrix = 128 x 128, slice
thickness = 1.7 mm; SPGR parameters: TE / TR = 2.1 / 6.7ms, α = {3, 4, 5, 6, 7,
8, 11, 13, 18}°; bSSFP parameters: TE / TR = 1.8 / 3.6 ms, α = {11, 14, 20, 24,
28, 34, 41, 51, 67}°, two phase cycles acquired per bSSFP flip angle. The total
mcDESPOT acquisition time was ~13 min allowing imaging within clinically-
relevant scan times. For anatomical reference and lesional tissue analysis, an
additional 2D-FLAIR sequence (TE / TR = 125 / 8800 ms, TI = 2200 ms, FOV =
22 cm, matrix = 256 x 256, slice thickness = 3 mm), as well as T1-MPRAGE
sequence (TE / TR = 3.8 / 9 ms, TI = 600 ms, FOV = 24 cm, matrix = 256 x
256, slice thickness = 1.2 mm). before and after the administration of contrast
media (Gadolinium-DTPA) was acquired. [If you aren't going to use the post-Gad
images for anything in this paper, then maybe don't bother even mentioning the
Gad injection.]
MRMRI Data Postprocessing
The processing of mcDESPOT data was accomplished using a custom MacPro
4.1 Xeon 64 Bit workstation [Jason: spell out the MacPro model correctly here]
[(2.66 GHz Quad-Core Intel Xeon, 6GB RAM) , MacPro 4.1 for macDESPOT,
2x2.8GHz Quad-Core Intel Xeon, 2GB RAM, MacPro3.1 for octopus] and
specialized in-house Python scripts to automate usage of the FMRIMRIB
Software Library (FSL) for brain extraction and intra-subject co-registration of
scans. [citeSmith et al.] [what’s the 2nd part about CNS parench. water dist?]. [not
just python scripts, since there are some underlying C programs right? Jason,
please write this up to correctly describe the software][how detailed should I be,
e.g. registration target is SPGR FA 18 deg., trilinear interpolation, specific
parameters used for FSL programs?] [maybe not down to specific parameters
used for FSL programs, but some kind of "block diagram" description.] what’s the
2nd part about CNS parench. water dist?]. The registered images are then
processed with the mcDESPOT multi-exponential fitting code, which uses
stochastic region of contraction to search for the model parameters [cite!]. This
produces 7 maps corresponding to T1 and T2 maps in the fast and slow relaxing
pools, off-resonance, residence time, and MWF. The myelin water fraction
(MWF) was obtained as the fractional short T2 component of the total T2
distribution and Myelin water fraction (MWF ) maps were derived from the data
for each subject from the mcDESPOT data [Deoni 2008].
Since the mcDESPOT method produces nearly isotropic 3D data over the entire
brain, any resulting quantitative map can be subsequently be warped to a brain
standard space. Thus, aAll MWF maps were non-linearly registered using FSL
to the MNI (Montreal Neurological Institute, Montreal, QC, Canada) [cite!]
standard brain space as defined by the non-linear MNI152 1 mm3 isotropic
resolution brain using FSL. A linear image registration with a common
framework of an iterative transformation process was used to overlay a base
image by a distorted floating image accurately. We used … [Jason][???, I have
no clue what this is about. A description of how FLIRT works?]
Subsequently, voxel-based analysis (VBA) was applied to these maps, whereby
mean and standard deviation volumes were computed from the healthy controls
MWF datamaps. On a patient-by-patient basis, each voxel's MWF value was
statistically compared to the healthy control distribution, to produce a z-score
value for that voxel. Those voxels that fell in the range of z-score < -4, i.e. that
had a MWF value at least 4 standard deviations below the mean healthy control
value, were marked and defined as significantly demyelinated .[Deoni 2008].
(more background VBA/VBM theory?) [this is a fairly intuitive idea proposed by
Sean, I’ve not seen it used anywhere else] Finally, these demyelinated voxels
were summed and scaled by the voxel volume to produce a total demyelinated
volume for each patient. We selected this measure as the main outcome
measure derived from our mcDESOT approach, and termed this quantity the
Demyelinated Volume (DV). This measure was computed for each subject,
regardless of whether they were an MS patient or normal control.
Tissue Compartment Segmentation
For subsequent voxel by voxel comparisons the conventional MRI data was
divided into fundamental WM tissue compartments for further analysis. Initially
bBrains were segmented into gray matter (GM), WM and cerebrospinal fluid
(CSF) mapps. First a selective WM map was obtained through probabilistic
segmentation from T1-weighted MPRAGE data via the Statistical Parametric
Mapping software package (SPM8; Welcome Department of Imaging
Neuroscience, UCL, London, UK) [cite!]. The probabilistic maps were converted
into binary masks by first median filtering to alleviate some errors due to grainy
noise in the MPRAGE scan and then thresholding at the 0.5 level. [is
“thresholding” too obscure a term?][no I think it is fine]… [Jason]. The resulting
WM masks were inspected by a trained radiologist and all selection below
pontine levels was excluded to standardize the analyzed brain parenchyma since
medulla oblongata and cervical spinal cord were unequally included in the
scanned volume between subjects. Some minor manual correction was applied
in areas of mixed GM and WM like the basal ganglia, in cerebrospinal plexus,
cortical vessels and the sellar region to exclude false positively selected areas
based on neuro-anatomical considerations.
Normalized Brain Volume (NBV) was also achieved as GM+WM volume
normalized by GM+WM+CSF volume [cite!]. [I thought we were going with the
term Parenchymal Volume Fraction? Still need to decide if volume loss is an
accepted clinical measure. If not, probably shouldn’t include.] We computed
NBV for each patient.Subsequently, all WM hyperintensities determined to be MS
lesions were identified identified as well-defined focal areas of elevated MRI
signal intensity in the FLAIR imagesdata, a T2-weighted MRI sequence type with
CSF water signal suppression, known to be favorable to detect MS lesions [cite!].
[Jason]
We applied a semi-automatic segmentation approach similar to the process used
to derive demyelinated volume from MWF maps. The FLAIR volume was linearly
registered to the mcDESPOT data volume and then the previously calculated
non-linear warp parameters were applied to bring this FLAIR volume into
standard space. Then Vvoxel-wise mean and standard deviation maps for
healthy control subjects were calculated, on the basis of FLAIR signal intensity.
However, conventional MRI images have arbitrary intensity scales with intensity
values that depend on various acquisition factors. Therefore a correction for
signal intensity inconsistency between subjects was applied to prepare data for
quantitative image analysis. This was accomplished by dividing all voxel
intensities by the “robust maximum” (the signal value at the 98%th percentile ) in
each skull-extracted brain for normal controls and patients. The robust maximum
is the signal value at the 98% percentile. With this correction, we then computed
z-score maps for each MS patient based on the healthy control population mean
and standard deviation maps. Lesions were then identified as clusters of voxels
whose intensities were abnormally brightelevated, greater than 4four>+4
standard deviations above the mean among normals. [not really sure I'm
understanding this as I read this: was the "robust maximum" normalization step
not applied to the healthy control volumes? It doesn't sound like it. If not, why
not?]
The technique spares hyperintense caps around the anterior and posterior horn
and trigonum of the lateral ventricles even if lesions are present. Those changes
are known as subependymal gliosis related to normal aging, i.e are not MS
lesions, and therefore our method dealt with these hyperintensities correctly.
False positive selection in non-brain tissue occurred mainly in the skull marrow
which as higher signal variation across the group, especially within the
subarachnoid vessels (flow void) at the brain surface and in the choroid plexus
and intra-ventricular vessels (flow void). (- details necessary?)
[Sorry I wrote things that kind of overlap with what you say below. Feel free to
keep whatever works best.]
TheA subsequent comparative analysis of individual patient data with healthy
control data means produced lesion segmentation masks depicting voxels
deviating from the population signal intensity by a z-score of +4 [image].
Subsequently a manual adjustment by an experienced MS neuroradiologist
([HHK)] cleared the preselected lesion masks from false positively selected
voxels in non-brain tissue, at the brain surface, and intra-ventricular locations
based on anatomical considerations. The ITK-Snap [Yushkevich 2006cite!]
software package provided a 3D seeding function to eliminate adjacent voxel
clusters of ambiguous origin when at least one featured a distinct localization
beyond brain tissue. [I think this is a little too wordy: ambiguous origin, distinct
localization. I’m unsure of the intended meaning.] Additionally the same
experienced later accomplished a conservative selection of MS lesion tissue in
individual patient space [Not sure what this means either.] (any comparison?).
[Is this last paragraph necessary, or is the previous text sufficient?] This
approach allowed producing maps of well-defined WM T2 lesions.
This technique selected the core lesion. DAWM areas surrounding lesions with
slight increases of WM signal were not selected by the method. IIn a second
step, another set of segmentation masks was produced depicting the regions
with ill-defined borders of intermediate signal intensity between that of lesions on
T2-weighted imaging and that of normal WM. This defines a tissue compartment
of non-lesional pathology, called Dirty-Appearing White Matter (DAWM). The z-
score maps were selected using a threshold of >+2. For each patient, selected
areas in this resulting mask were compared against the edited core lesion masks
using MATLAB [MathWorks, Natick, MA, USAcite]. Those 3D regions that
contained a core lesion were kept as DAWM, the rest were rejected as false
positives. (figure X)[image]. Thus only DAWM surrounding a core lesion was
included. This segmentation was reviewed by an experienced neuroradiologist.
No further correction editing was had to be applied to this unbiased automated
technique. [Not sure what this last sentence means -- sounds like you are saying
that the radiologist did not actually edit anything even though he reviewed it.]
Explain WM-DAWM-core lesions=NAWMIn addition, the WM compartment that
does not harbor any lesions or non-lesional abnormalities on T2 weighted
imaging referred to as the Normal Appearing White Matter (NAWM)
The normal-appearing white matter (NAWM) tissue compartment, which should
describe white matter tissue that looks ordinary on a FLAIR scan, was then
defined as all WM voxels minus core lesion plusand DAWM voxels (WM – core
lesions – DAWM = NAWM) for each patient (figure X). Subsequently tThe WM,
NAWM, DAWM, and lesion segmentations map registration to the standard MNI
space masks were applied to the quantitative maps in patient space to allowed
further compartment-specific study of MWF (Table x) and DV (Table x).
Brain Volume Measurements
The brain parenchymal volume fraction (BPFPVF) is a measure of global
atrophy. It is achieved as GM+WM (brain parenchymal) volume normalized by
GM+WM+CSF (intracranial) volume [Kalkers 2002]. We computed NBV for each
patient using MATLAB [Jason! Correct?]. Additionally the ventricular fraction (VF)
defined as ventricular volume/intracranial volume as a measure of central, mainly
WM atrophy [not sure if we’ll be able to get this measure, not sure what approach
to take to get it, maybe take the CSF mask and keep only the CSF regions
surrounded by WM]
Voxel Based Statistical Analysis
Performing all of the processing above in standard space allowed formalized
statistical testing on a voxel by voxel basis. The age-matched group of healthy
controls was used for direct statistical comparison using a z-score analysis.
Using segmentation results as a matrix for specific quantitative analysis allowed
defining demyelinated volumes within different WM tissue compartments being
NAWM, DAWM, and core lesion. [this last sentence very awkward and difficult to
understand]
[alternate paragraph]
Moreover, tThe above processing enabled us to develop our new measure of
demyelinated volume as the sum of voxels in patient space with a significantly
lower MWF than normal controls in a variety of tissue compartments including
total WM, NAWM, DAWM, and core lesions. The tissue compartment masks
were aligned to each subject’s MWF map using a linear registration and
thresholding at the 0.5 levelnearest neighbor interpolation. Now, with multiple
DV scores for each WM tissue compartment and for each subject, [you should
spell out exactly what you mean by the multiple DV scores] we performed a
battery of Wilcoxon rank-sum tests to examine the ability of this measure to
differentiate between healthy controls normals and different classes of MS. We
also correlated EDSS with DV after a log transformation in an attempt to see if it
is indicative of disability.
(What about T1-Lesions???)
[I was under the impression that T1-lesions show up on T2 FLAIR as well, is this
incorrect?] [correct, it is assumed that T1 hypointense lesions indicate
myelin destruction AND axonal decay, hence not all T2 lesion are also seen
in T1, and that creates a subpopulation T2+/T1+, as well as the Gad
enhancing ‘acute’ T1 lesions, Maybe not in this paper, we should think
about completing MWF and DV measurments in those two lesion
types/compartments also, open for discussion]
Multiple linear regression models an outcome as the combination of
several predictors under some assumptions. These include that the
outcome is linear in any of the predictors after accounting for the others,
the residuals are normally distributed, and theyalso are constant variance
with zero mean. Using a measure like R2, these types of models will always
appear better the more predictors that are included. In fact, even adding
random noise as predictor would seem to improve the model. A better
criterion for evaluating models would be one that not only rewards good
predictions of the outcome but also penalizes for using a large number of
predictors. Mallow’s Cp is such a criterion. The goal in model selection is
to find the model that best explains the outcome parsimoniously. It
attempts to determine the true underlying factors behind an outcome.
Using the R software environment [cite], an exhaustive search was
conducted for all possible combinations of the following predictors: age,
disease duration, PVF, log-DV in whole brain, log-DV in WM, log-DV in
NAWM , log-DV in lesions, T2 lesion load, mean MWF in whole brain, mean
MWF in WM, mean MWF in NAWM, mean MWF in lesions, and gender.
Results
The mean myelin water fraction was initially compared in different white matter
compartments in both the MS patients and the healthy controls (Fig. xx). A
constant drop of MWF in CIS, RRMS and SPMS in global WM, NAWM and
DAWM and MS lesions was noted descending from MS precursor CIS via RRMS
to the progressive courses SPMS and PPMS. Further, the WM lesion MWF in all
courses was lower than in non-lesional WM tissue defined by conventional MRI
confirming lesions to be the focus of inflammatory demyelination. However, the
drop of MWF was non-signifcantlysignificantly higher in CIS lesion compared to
RRMS lesions. Discrimination analysis for MWF in WM was done using
Wilcoxon rank sum testing for each MS subclass vs. normal controls.
Additionally, RRMS vs. SPMS was tested with the somewhat small sample size
of 5 and 6 patients in each group respectively.(… Jason: discrimination of
different classes)[will fill in when WM masks complete]
Consecutively oOur newly introduced voxel-basedquantitative measure,
Demyelinated Volume, was investigated in the same WM compartments (Fig.
xx). Here, the expected increase in demyelination was not uniformly increasing
over the different MS courses, rather a similar amount of demyelinated volume in
lesional and non-lesional pathologic WM tissue, as well as normal appearing WM
was found in both CIS and RRMS. In contrast progressive MS patients revealed
a much higher amount of demyelinated brain tissue volume with even higher
values in PPMS compared to SPMS patients in all tissue classes with a constant
increase of DV from lesions to DAWM and NAWM. However, the DAWM
demyelination in CIS and RRMS was lower than their lesion tissue
demyelination, with RRMS DAWM DV even significantly lower in comparison to
CIS patients. The mean DV in controls however was non-zero value. This is due
to the nature of DV being defined as the sum of voxels with a MWF z-score <-4.
By random chance, normals will have a limited amount of demyelinated voxels.
reflecting not disease related demyelination to be found in healthy individuals
(Necessarly true???). (Would’nt it make sense to compare the percentage of DV
in the WM compartment volume to compare the proportional demyelination to
estimated the severity of tissue degradation in compartment of different sizes
NAWM >>>lesions???)[Yes, this would partially explain why DV increases from
lesions to DAWM and NAWM. It does make more sense intuitively to discuss
these numbers instead but we probably won’t be using them for our regressions
since they correlate poorly.]
Rank sum testing for significance for DV in WM compartments revealed that all
classes cancould be distinguished from healthy controls with p < 0.001 for DV in
global WM (here still in whole brain). Notably, PVF fails to distinguish CIS (p =
0.68) and RR patients (p = 0.76) from controls while DV can. RRMS patients
had significantly lower DV than SPMS patients with p < 0.05 and PVF with p <
0.01.
Notably, PVF fails to distinguish CIS and RR patients. Progressive patients were
also significantly different from normals in PVF with p < 0.01. (Do we display this
somewhere else?)[Since PVF only applies to whole brain, we can’t really
generate another bar chart for it. Perhaps a table of values.]
Next a straightforwardsimple linear regression analysis of the clinical MS
disability score EDSS with MRI retrieved measures was executed . We found an
expected low Pearson correlation of EDSS with the white matter T2 lesion load
the ‘burden of disease’ (R2 = 0.28) reflecting its criticized poor predictive value of
disease related disability (Fig. xx). In contrast, demyelinated volume in normal
appearing WM revealed (have to explain the nawm hypothesis here again) a
much higher correlation to EDSS (R2 = 0.40) suggesting a more direct causative
association to disease-defining neurological impairment (Fig. xx). However, the
parenchymal volume volume fraction correlated highest with EDSS (R2 = 0.56)
indicating the accepted association of brain tissue loss with functional decline
depicted by this brain atrophy indicator (Fig. xx).
Because the concept of the linear regression analysis (R2 ) does not assume a
linearity of the correlation and because EDSS is not a linear scale, alternative
statistics that measures how well the relationship between two variables can be
described by a monotonic function and assuming a monotonic correlation may be
more appropriate to the data presented because of its charactersitics. Such
measure is tThe Spearman Rank Correlation Coefficient (Rrs) is such a measure.
We repeated our regression analysis using this a non-parametric testingmethod.
Hereby w W e found the lowest correlation of EDSS with lesion load (rs = 0.49; p
0.012) and, again, repeatedly a much higher correlation of greater significance
of EDSS with demyelinated volume in normal appearing WM (rs = 0.6059; p
0.0013) and againthe highest correlation EDSS with PVF (rs = 0.6073; p <
0.0001).
A better method to predict EDSS would be to incorporate multiple predictors
rather than just one. We used an exhaustive search to find the best multiple
linear regression model evaluated by Mallow’s Cp. The best model for EDSS
wais determined to contain disease duration (β = 0.0957, p << 0.001),
parenchymal volume volume fraction (β = -19.498, p < 0.001 ), and mean myelin
water fraction in lesions (β = -12.076, p = 0.102). This model hads the lowest
value for Mallow’s Cp among all the possibilities. It explaineds 80.7% of the (R2 )
variance in EDSS and hads an adjusted R2 of 78.1%. However, there were a few
other models ranked closely behind this one that switchedexchanged MWF in
lesions with MWF in WM or log-DV in NAWM. Disease duration and PVF were
still kept. This suggests that these two predictors present vital information for
determining EDSS. It should be noted that all of the third predictors failed an F-
test at the 5% significance level. [these numbers will change]
However Several influencing factors
Criterion helps to choose larger smaller models
importance or not
constant offset = intercept
choose any possible correlation
How large are residuals how trade off between values
Model selection testing fitting to predict EDSS
Linear model for EDSS measured by Mallow’s Cp
Assumption what the model is? ->Diagnostics quality testing
(Combinatorial ?)
best model with lowest mallows Cp
what is improven predictive value of our model
predictive power and fewer terms
mallows cp measuring the fit
full model means all terms tested
The 3 lowest points in descending order are:
Duration, PVF
Duration, PVF, log(DVnawm)
Duration, PVF, MWFwm
model with and w/o DV
analysis of variance (ANOVA) nested models
significantly better fit WITH DV nawm
An exhaustive search for the best linear model for EDSS as measured by Mallow’s CpFull model has terms for:Age, Disease Duration, PVF, log(DVbrain), log(DVwm), log(DVnawm), DVlesion, Lesion Load, MWFbrain, MWFwm, MWFnawm, MWFlesion, Male
The best model is chosen to include:Disease Duration, PVF, MWFwm
Disease Duration, PVF, , log(DVnawm) is a very close second
pvf vs DVnawm 0,24/rs -0.62
dv predicting one type to the other but not for predicting edss development
The correlation [what kind of correlation?] between measurements of
demyelinated volume in various tissue compartments and the MS clinical disease
score EDSS was calculated. Regions of significantly demyelinated voxels were
extracted from the MWF fraction map by computing with the above-mentioned
procedure, visualized and overlayed on an anatomical reference as shown in
representative result images in Fig. 1.
The plain myelin water fraction distribution measured is shown in Table 1.
Initially the traditional measurement of the burden of disease, i.e. the total volume
of hyperintense lesions seen in T2 FLAIR images (Fig.3) was quantified. [should
we include normalized lesion volume measures?] [If it’s a standard metric, then
yes.]
This T2 lesion load did not show a relevant correlation to the EDSS (r2 0.26) and
was subsequently compared to the measurement of demyelination volume within
the same lesions compartment (Fig. 4). This lesion demyelination (DVLesions)
showed an even weaker correlation to EDSS (r2 0.13; p < 0.073) [p-value
indicates significance, easy to misinterpret non-significant].
Before selectively analyzing different WM compartments the total brain
demyelinated volume (DVtotal) was found (Fig.2). Every MS type category differed
significantly in the amount of demyelinated volume compared to the healthy
controls. In the progressive MS-courses (SPMS, and PPMS; mean volume, SD)
a much larger demyelinated volume was observed compared to the non-
progressive categories RRMS and CIS; mean volume, SD). A series of Wilcoxon
rank sum tests showed that the demyelinated volume in any of the MS sub-
classes was significantly higher than the healthy control population, testing at the
5% level. In other words, all MS and CIS patients appear different from normals
in the DV metric.
Particular pairs of patient sub-groups revealed significant differences in
demyelination tested with Wilcoxon rank sum test for significance. The group of
RRMS patients was significantly less demyelinated than the SPMS patients (p <
0.01). The groups of high risk and low risk Clinically Isolated Syndrome were not
significantly separated from each other (p = XXX, do we need to mention?),
which questions the importance of this clinical categorization.[that's a pretty
strong statement to make] However, the entire CIS-group of patients, i.e. low
risk and high risk CIS patients (n=10) can be clearly discriminated from the
healthy control group (p << 0.001).
Within Normal Appearing White Matter compartment demyelinated volume
(DVNAWM), we see a higher correlation with EDSS. These results confirm previous
findings by other investigators that lesions are not the most important disease–
determining marker in Multiple Sclerosis, and provides intriguing evidence that
the invisible burden of demyelination in NAWM is a much more important disease
marker.
Dirty Appearing White Matter compartment demyelinated volume (DVNAWM)
results (to write more about this).
Within the study cohort, correlations were also found between NBV and EDSS (r²
= 0.51, p<0.001), and between DV and NBV (r² = 0.51, p<0.001). NBV was not
significantly different for the CIS subgroup than for the age-matched normal
control group, which was the case for the DV measure. NBV is not able to
distinguish between normals and CIS patients.
Regions of significantly demyelinated voxels were extracted from the MWF
fraction map with the previously described procedure. The DV pattern
substantially deviates from thethr conventional T2 lesion pattern (Fig. xx).
andImportantly, it progresses into the NAWM, showing that it is sensitive to the
invisible burden of disease. [we should probably describe this qualitative part
earlier than the correlation analyses]
MZ suggested: volume vs myelin / BR suggested: pat only vol vs myel meas
MZ suggested: multivariate analysis Rosenberg 3 columns for independent
contribution? NBV/PVF - MWF – Lesion
BR suggested:
Mean/SD across normals, any
correlation to edds all patients together
HK: To be discussed:
(normalized brain volume (NBV), … correlations? )
correlation DV to EDSS subscores, sub-combinations!!
Is Demyelinated Volume whole brain corrected for NBV?
Correlation total brain DV vs lesion load?
Conclusion
[ highlight mcDESPOT as new acquisition technique!]
We have demonstrated that myelin-selective mcDESPOT allows the assessment
of whole-brain isotropic tissue volumes in clinically relevant scan times.
Subsequently, this enabled the quantification of whole-brain and compartmental
demyelination using voxel-based analysis with MWF measurements. The
compartments of interest were WM, NAWM, DAWM, and lesions. Moreover, this
enabled allowed the use ofsubsequent formalized statistics for group comparison
using the Wilcoxon rank sum test. This has not been possible with other
quantitative myelin selective imaging MRI techniques up to date. For the first
time mcDESPOT was used in a controlled clinical study with a cohort of different
disease courses of MS. This current pilot study sought to determine the
prevalence and severity of subtle demyelination in NAWM in different courses of
MS and its relationship to the severity of clinical symptoms and disease related
disability. The mcDESPOT-defined demyelination measurements showed great
promise to act as new markers of clinical disease activity in MS.
[ highlight image processing differences compared to other studies!]
Based on the important mcDESPOT feature of a whole-brain isotropic
acquisition, we were able, for the first time, to combine VBA with whole-brain
MWF measurements. Meyers et al. had already compared traditional
quantitative imaging MRI with region of interest (ROI) and voxel-based analysis
methods to determine the optimal method of analysis of MWF derived from
alternative multi-echo T2 data. Their results of scan-rescan reproducibility
indicated that MWF VBA was the most consistent between scans. For the VBA
method, mean MWF was found to be more reproducible than median MWF
[Meyers, 2009].
Another important quality criteria of quantitative imaging MRI measurements are
their variability. We found (more).
We also have applied a novel strategy to segment MS lesions and important WM
compartments. The standardized segmentation of "FLAIR hyperintensities"
reduced a priori bias of the selecting neuradiologist in comparison to manual only
segmentation. Based onWith this study population based core lesion
segmentation we were subsequently able to provide important WM
compartments for selective MWF and demyelination analysis (more). The
classification of both DAWM and lesions made it possible to define NAWM.
Although subtle demyelination is recognized as pathological features of MS, it is
less clear how early this occurs and how it correlates with MRI-visible lesion
burden. The analysis of the traditional MS surrogate marker T2 lesion volume or
even if specifically myelin is quantifiedlesion demyelination confirmed the known
fact that neither lesion burden nor lesion demyelination correlate well with clinical
disease activity in MS patients. The correlational study results more or less imply
that brain atrophy (PVF) and the invisible burden of disease areis more important
factors towards disability than the lesion tissue.
Measurements of demyelinated volume in NAWM discriminated CIS-patients
from a control population. This highly significant difference represented one of
the most important findings of this study, since many other quantitative imaging
metrics would not show the same sensitivity to early MS. [I love these lines!]
This fact in particular demonstrated the potentially high sensitivity of mcDESPOT
in detecting early and conventionally invisible disease-related myelin loss. It will
need further longitudinal investigation to determine if this measure will serve as a
risk factor for conversion into definite MS in a follow-up study.
Demyelinated volume measurements in NAWM can also discriminate patients
with a secondary-progressive course from relapsing-remitting MS though with our
sample, PVF appears better at this. (more) These findings suggest that these
metrics have merit in predicting conversion into secondary progressive MS.
[ highlight new scientific finding!]
The high correlations to clinical and atrophy measures means that this new
imaging method has strong potential to act as a surrogate measure of disease
severity. Moreover, our results show that mcDESPOT is sensitive to brain tissue
changes even at the pre-MS stage, and well before established volumetric
measures register significant changes. Brain volume loss is an established
clinical marker in MS, however MWF VBA analysis provides some more specific
information over that.
The mcDESPOT method may discern the relationship between multiple sclerosis
and subtle micro-structural demyelinating changes that likely occur in brain tissue
well before lesions can be detected with conventional MRI. Furthermore, it
possibly may directly quantify and visualize the substrate of disability in MS. It is
our specific hypothesis that the development and validation of this new MRI
method designed to measure myelin content over the whole brain and the use of
this method to quantify myelin content in brain regions outside of lesions will yield
more accurate prognostic markers of disease progression in further studies.
We hereby postulate a new MRI derived measure that is able to discriminate
fdifferent MS courses by their amount of demyelination.
T2 weighting is particularly sensitive, but unspecific, to a wide range of brain
pathologies.
MTR shows many aspects of lesional and non-lesional pathology in MS but non-
lesional activity may not be evident.
MTR is sensitive to physiological changes to myelin induced by
inflammation,while the short T2 component is a more specific indicator of myelin
content in tissue. But in MTR non-lesional activity may not be evident
[citations needed]
[Discussing multi-center setting aspects]
An important aspect is the ability to use a prospective myelin imaging method
robustly in a multi-center setting. Besides the lack of specificity (discussed
above) both MTI and DTI feature significant technical limitations. MTI
measurments are semiquantitative, sensitive to machine performance, scanner
and sequence variations. DTI, however, suffers from inherently low signal
intensity (or signal-to-noise ratio [SNR]), and image distortion [Bodini 2009]. The
hereby applied mcDESPOT technique however allows …
[Discussing reproducibility aspects]
Vavasour_2006_NeuroIma_MWFReproducability
[Discussing results in DAWM]
DAWM was addressed to result mainly from Wallerian degeneration originating
from lesions [Kutzelnigg 2005]. Compared to NAWM, DAWM showed reduction
in MWF in MS brain tissue specimen, corresponding to a reduction of Luxol fast
blue (LFB) for myelin phospholipids staining and Bielschowsky silver
impregnation for axons. This suggests that DAWM is characterized by loss of
myelin phospholipids and axonal reduction [Moore 2008].
[Discussing outlook towards remyelinating therapies]
The current state-of-the-art treatments are disease-modifying agents that at
present are able to decrease relapse rates by 30% [Weiner 2009]. However,
despite these advances, the field of MS still lacks specific markers to predict
clinical relapses and disease progression. Novel immunotherapies are on the
rise, but to date non-invasive technologies have failed to provide accurate,
reliable tools to assess the state of myelination. Such technologies are
particularly needed for testing drug efficacy or for monitoring treatment. Imaging
technologies provide potential instruments to investigate in vivo, real time
changes that occur within the CNS over the broad spectrum of natural MS
courses as well as during treatment. ]]]
Discussing Model Selection and Statistical Analysis
Other papers have used non-exhaustive search methods like stepwise
regression when it was not necessary [Korteweg 2008]. These are prone to
finding local minima rather than the globally optimum model. It should be noted
that Mallow’s Cp is itself a random variable dependent on the data. Based on
our limited sample size, it is unclear which model is the most accurate but it is
reasonable to conclude that both disease duration and PVF are significant
players in predicting EDSS since they are used in all of the contending best
models. Less is known about a third predictor as is evident in its high p-value,
which indicates that we cannot reject the null hypothesis that it has no
contribution to EDSS.
BR: critical n=5, subgroup analysis cautious!
1 reas high corr edss
2 early meas dist normals to early MS
3 non versus progressive
Acknowledgements
Thank you to all our participants and their families. Thank you to all of our
research support staff; in particular D. Greer for intensive recruitment and C.
Harper-Little for scanning support.
Funding
XXX
Tables and Figures
Demographic Data lr-CISn=5
hr-CIS
n=5
RRMSn=5
SPMSn=6
PPMSn=5
MS (all)n=26
Mean age, yr (SD) 47 (11) 35 (10) 48 (12) 58 (8) 55 (7) 49 (12)
male/female ratio 2/3 1/4 1/4 0/6 4/1 8/18
Mean disease duration, yr (SD) 2 (2) 3 (2) 15 (10) 18 (15) 12 (9) 10 (11)
Mean EDSS score (SD) 2.0 (1.0) 1.4 (0.8) 2.0 (1.7) 6.4 (1.1) 5.6 (1.1) 4.0 (2.4)
Table 1 Demographic Data of Patients with MS and Reference Subjects: MS =
multiple sclerosis; lr-CIS = low risk Clinically Isolated Syndrome; hr-CIS = high
risk Clinically Isolated Syndrome; RRMS = relapsing-remitting MS; SPMS =
secondary progressive MS; SD = standard deviation; EDSS= Expanded
Disability Status Scale.
Mean MWF Mean (SD)
Normals All Pat lrCIS hrCIS RRMS SPMS PPMS
Total WM MWF0.216
(0.031)
0.178 (0.068)
0.194 (0.065)
0.200 (0.061)
0.185 (0.070)
0.162 (0.075)
0.153 (0.068)
WM w/o lesions0.179
(0.068)
0.194 (0.065)
0.200 (0.061)
0.185 (0.070)
0.164 (0.074)
0.156 (0.067)
NAWM0.180
(0.068)
0.194 (0.065)
0.200 (0.061)
0.185 (0.070)
0.165 (0.074)
0.158 (0.067)
DAWM 0.160
(0.053)
0.177 (0.045)
0.181 (0.059)
0.171 (0.041)
0.152 (0.057)
0.123 (0.064)
Core T2 lesions0.069
(0.058)
0.079 (0.063)
0.074 (0.065)
0.089 (0.069)
0.066 (0.059)
0.038 (0.036)
Table 21 Myelin Water Fraction Distribution: Total WM MWF drops continuously
over the disease spectrum. Between groups we found two important facts.
RRMS and SPMS differed significantly (statistics?) from each other. In PPMS the
MWF drop in lesions is greater than in SPMS. (to be edited)
[Do we need WM w/o Lesions (lesions extracted)?]
Mean DV (SD) Normals All Pat lrCIS hrCIS RRMS SPMS PPMS
Total WM MWF
WM w/o lesions
NAWM
DAWM
T2 lesions
Table 3 Demyelated Volume Distribution
ADDITIONAL ANALYSIS [Suggestion: we should insert a table displaying DV in
different compartments, I assume we might see important differences in DV lesion
in different MS types ]
Table 2 Demyelinated Volume Distribution: The MWF
Percentage of DV in WM sub-compartments in different MS types
Figure 1 WM Segmentation and Voxel-based Analysis: Exemplary display of the
retrieval of WM segmentation, MWF maps, and VBA results [A] Probabilistic WM
map, [B] WM compartments resulting from segmentation, [C] MWF map resulting
from mcDESPOT analysis, [D] Demyelination map resulting from study
population defined VBA, and, [E] selective compartment-specific demyelination
map addressing VBA results to specific compartments.
Figure XX Mean myelin water fraction in different white matter compartments in
different MS patients and the healthy controls (dashed line needs to be included
for HC mean MWF)
Figure XX Demyelinated Volume in different WM compartments. Demyelinated
Volume is plot as log(DV) on the base of 10.
CHANGE new editing WM in the normals!
Figure 2 Demyelination Group Comparison: Total brain Demyelinated Volume
(DVtotal) in the different MS subtypes in comparison to the healthy control group
results, tested with Wilcoxon’s rank sum test for significance. Healthy controls
and all CIS patients, and RRMS and SPMS reveal significant group differences
noted above graph bar.
Figure 3 EDSS Correlation Analysis: T2-lesion-load (volume) vs EDSS. This
confirms again that lesion metrics such as volumetric measurement of affected
tissue conventional MRI techniques only weakly correlate with MS disease
activity. [Did we correct this for brain volume???]
Figure 4 EDSS Correlation Analysis: Lesion Compartment Demyelination
(DVLesions) vs EDSS
Figure 5 EDSS Correlation Analysis: NAWM Compartment Demyelination
(DVNAWM)
Figure 6 EDSS Correlation Analysis: DAWM Compartment Demyelination
(DVDAWM)
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Excluded modules
Diffusion imaging allows retrieving information about the intrinsic WM
microstructure related to the tissue water diffusion characteristics. Werring et al.
investigated the dynamic of the evolution of water diffusion measurements in pre-
lesion WM in relapsing-remitting MS (RRMS) in a serial diffusion MRI study and
found a steady and moderate increase in the apparent diffusion coefficient
(ADC),, a measure of tissue water diffusion restriction, followed by a rapid and
marked increase at the time of lesion formation, and even a significant but milder
increase in WM regions not harboring lesions [Werring, 2000]. Such progressive
changes of normal appearing white matter (NAWM) were also detected in
primary-progressive MS (PPMS) patients in a longitudinal diffusion MRI study
that also quantified ADC [Schmierer 2006].
Widespread tissue changes are found in NAWM of MS patients by measuring the
magnetization transfer ratio (MTR), . Those changes are mainly explained in
terms of axonal damage and axon loss as one of the major pathological features
accompanying demyelination in MS [Filippi, 1998]. A histological analysis of the
substrate of those imaging findings revealed that not only MTR but also T1
contrast ratio correlated strongly with axonal density, even in NAWM [van
Waesberghe, 1999].
Early axonal pathology, can also be quantified with Proton (H+) MRI
spectroscopy (MRIS) that provides chemical composition information at the level
of metabolites. Early MRIS studies have noted specific changes in metabolite
signatures, not only within focal T2 lesions but even a deviation from normal in
NAWM areas [Helms 2000]. A measure of ‘whole-brain’ N-acetylaspartate
(WBNAA), a marker of axonal integrity, in particular confirmed widespread axonal
pathology, largely independent of MRI-visible inflammation in MS patients even
in Clinically Isolated Syndrome. No correlation however, was found between the
T2 lesion volumes and WBNAA concentrations [is this really a concentration
value or a total integrated NAA value?] [Filippi, 2003].
Axons and their myelin sheath form an individually customized unit.. However,
axonal loss is not necessarily accompanied by demyelination, moreover both
histopathologic changes seem to contribute independently to the appearance in
conventional MRI scans. An imaging-histopathology case study confirmed that
axonal degeneration can occur in the absence of myelin loss as a histopathologic
correlate to abnormal MRI findings in MS patients [Bjartmar, 2001]. [This last
paragraph needs to be improved. Hard to understand.]
Single component T1 relaxation time was found to be abnormal in NAWM in
established MS. When compared to MTR, quantitative T1 measurement was
more sensitive in detecting subtle pathological change. No correlation was found
between NAWM T1 changes and lesion signal characteristics suggesting
independent underlying pathologic mechanisms [Griffin, 2002].
MTR vs disability correlation?
These studies point to a new direction for MS MRIresearch: to move away from
the lesion-centered view and to develop highly sensitive MRI methods that
accurately and quantitatively reflect the global disease burden even in areas that
are apparently normal. Once such methods are developed, important hypotheses
can be tested; for example, that such measures reflect the subtle underlying
disease-determining pathology and will predict clinical changes in MS disease
development, as well as transition towards chronic progression.
In conclusion novel quantitative MRI technologies, have provided in vivo insights
into the pathology of the disease and revealed that primary demyelination, i.e.
selective myelin destruction, is not restricted to focal MS lesions but occurs
throughout the entire CNS parenchyma [key references]. Moreover such
demyelination may be accompanied to a variable degree by remyelination and
repair [key references].
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