ORIGINAL RESEARCH
PEDIATRICS
Assessment of Disease Severity in Late Infantile Neuronal
Ceroid Lipofuscinosis Using Multiparametric MR Imaging
J.P. Dyke, D. Sondhi, H.U. Voss, D.C. Shungu, X. Mao, K. Yohay, S. Worgall, N.R. Hackett, C. Hollmann, M.E. Yeotsas, A.L. Jeong,
B. Van de Graaf, I. Cao, S.M. Kaminsky, L.A. Heier, K.D. Rudser, M.M. Souweidane, M.G. Kaplitt, B. Kosofsky, R.G. Crystal, and D. Ballon
ABSTRACT
BACKGROUND AND PURPOSE: LINCL is a uniformly fatal lysosomal storage disease resulting from mutations in the CLN2 gene that
encodes for tripeptidyl peptidase 1, a lysosomal enzyme necessary for the degradation of products of cellular metabolism. With the goal
of developing quantitative noninvasive imaging biomarkers sensitive to disease progression, we evaluated a 5-component MR imaging
metric and tested its correlation with a clinically derived disease-severity score.
MATERIALS AND METHODS: MR imaging parameters were measured across the brain, including quantitative measures of the ADC, FA,
nuclear spin-spin relaxation times (T2), volume percentage of CSF (%CSF), and NAA/Cr ratios. Thirty MR imaging datasets were prospectively acquired from 23 subjects with LINCL (2.5– 8.4 years of age; 8 male/15 female). Whole-brain histograms were created, and the mode
and mean values of the histograms were used to characterize disease severity.
RESULTS: Correlation of single MR imaging parameters against the clinical disease-severity scale yielded linear regressions with R2 ranging
from 0.25 to 0.70. Combinations of the 5 biomarkers were evaluated by using PCA. The best combination included ADC, %CSF, and NAA/Cr
(R2 ⫽ 0.76, P ⬍ .001).
CONCLUSIONS: The multiparametric disease-severity score obtained from the combination of ADC, %CSF, and NAA/Cr whole-brain MR
imaging techniques provided a robust measure of disease severity, which may be useful in clinical therapeutic trials of LINCL in which an
objective assessment of therapeutic response is desired.
ABBREVIATIONS: FA ⫽ fractional anisotropy; GM ⫽ gray matter; LINCL ⫽ late infantile neuronal ceroid lipofuscinosis; MRIDSS ⫽ MR imaging disease-severity
score; PCA ⫽ principal component analysis
L
incl, a form of Batten disease, is a progressive uniformly fatal
lysosomal storage disorder resulting from mutations in the
CLN2 gene with predominantly neurologic symptoms.1 The
CLN2 gene encodes for tripeptidyl peptidase 1 (TPP-I), a lysosomal protease.2 In affected children, undegraded products of
cellular metabolism accumulate within lysosomes over several
Received April 19, 2012; accepted after revision June 14.
From the Departments of Radiology (J.P.D., H.U.V., D.C.S., X.M., L.A.H., D.B.), Genetic
Medicine (D.S., S.W., N.R.H., C.H., M.E.Y., A.L.J., B.V.d.G., I.C., S.M.K., R.G.C.), Pediatrics (K.Y., S.W., B.K.), and Neurological Surgery (M.M.S., M.G.K.), Weill Cornell Medical College, New York, New York; and Division of Biostatistics (K.D.R.), Clinical and
Translational Science Institute, University of Minnesota, Minneapolis, Minnesota.
This work was supported in part by grants from the National Institutes of Health
(R01NS061848, UL1RR024996, and U54NS065768) and from the Partnership for
Cures, Chicago, Illinois.
Please address correspondence to Jonathan P. Dyke, PhD, Department of Radiology, Weill Cornell Medical College, 1300 York Ave, Box 234, New York, NY 10021;
e-mail: jpd2001@med.cornell.edu
Indicates open access to non-subscribers at www.ajnr.org
Indicates article with supplemental on-line table.
http://dx.doi.org/10.3174/ajnr.A3297
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years, eventually causing cell death.3 Histologically, the lysosomes are characterized by the presence of autofluorescent
ceroid lipofuscin. Neurologic symptoms of the disease begin to
appear at 2– 4 years of age and progressively worsen, leading to
death by of 8 –12 years of age. Diagnosis is traditionally made
after the appearance of one of several clinical features, including retinopathy, motor abnormalities, epilepsy, or dementia.4
Definitive diagnosis of LINCL is made through enzymatic testing of skin biopsies or blood lymphocytes and mutation identification through molecular genetic testing.5,6
LINCL is currently incurable. The current standard of care is
palliative in response to clinical symptoms, though research into
novel therapeutic strategies, including gene transfer at our institution, is ongoing.6,7 Assessment of disease severity is made during subject examination via neurologic rating systems. One such
scale developed at our institution assigns individual scores ranging from 0 to 3 to each of 4 neurologic functions, including feeding, motor, gait, and language development, with higher scores
indicative of greater functionality in each area.8 The sum total
of the 4 scores is used as a marker for overall disease severity, with
the maximum score of 12 indicative of a healthy individual.
Previous MR imaging studies of LINCL displayed marked cortical atrophy and CNS volume loss with disease progression. Severe cerebellar atrophy, along with loss of cortical neurons, axons,
and white matter myelin, was also documented.9 MR spectroscopy showed decreased NAA/Cr metabolite ratios and increased
myo-inositol/creatine ratios compared with healthy controls.10
We have previously shown that increased ventricular volume
and increased apparent whole-brain–water self-diffusion diffusion coefficients (ADC) are associated with a decreasing LINCL
score.8,11
To develop more quantitative biomarkers of disease progression that could also be used to assess the efficacy of gene transfer
for the CNS manifestations of LINCL, we directed the present
study toward a comprehensive evaluation of a total of 5 quantitative MR imaging biomarkers. While information was available on
a voxel-by-voxel basis for each of the methods, we evaluated an
automated objective assessment of disease progression via analysis of whole-brain histograms. Each of the 5 quantitative MR imaging techniques interrogated different aspects of brain morphometry or metabolism and included the following: 1) the ADC
of water, a measure of tissue integrity related to the restriction of
the free molecular motion of water by cellular membranes; 2)
diffusion FA, a measure of the anisotropic diffusion of water molecules, which is related to the degree of myelination in white matter independent of the ADC; 3) T2 relaxation times, an important
basis for brain contrast in clinical MR imaging; 4) the volume
percentage of CSF (%CSF), a measure of brain atrophy; and 5)
NAA/Cr metabolite ratios, a marker for neuronal function.12-14
Each of the 5 MR imaging biomarkers allows intersubject comparison or assessment of serial studies on a single subject. The goal
of this study was to determine whether the combination of MR
imaging– derived biomarkers was feasible to routinely acquire in a
single scanning session in this patient population and whether it
was relevant to calculating an overall disease-severity score.
MATERIALS AND METHODS
Study Population
This study was conducted under a research protocol approved by
the institutional review board at Weill Cornell Medical College.
Parents or guardians signed informed consent. All subjects were
diagnosed both by phenotypic findings and genetic analysis.
Thirty MR imaging datasets were acquired from 23 subjects (2.5–
8.4 years of age; median, 4.8 years; 8 male/15 female). Seven subjects were scanned at 2 time points as part of a separate therapeutic
trial for LINCL but were untreated at the time of the scans. To
participate in this study, the subjects’ genotypes included at least 1
of the most common CLN2 mutant genes as outlined in On-line
Table 1.15,16 The subjects’ LINCL scores (0 –12 scale) were 6.0 ⫾
2.5 on average and ranged from 1.5 to 11. All subjects were
evaluated by using the Weill Cornell LINCL Disease Severity
Scale as described above by 4 observers.8 Clinical evaluations
were performed within 2 days of the corresponding imaging
examinations.
MR Imaging Acquisition Techniques
All imaging data were acquired by using a 3T HDx MR imaging
system (GE Healthcare, Milwaukee, Wisconsin) with an 8-channel head resonator. Standard-of-care clinical imaging was appended to the research study and included axial T1-weighted, T2
FLAIR, and coronal T2-weighted imaging series. The research
sequences included single-shot axial diffusion-weighted echoplanar imaging for the ADC acquisition with b-values of 0 and
1000 s/mm2, a matrix size of 128 ⫻ 128, a 20.0-cm FOV, a 4.0-mm
section thickness, and a 0.4-mm section gap. Diffusion tensor
imaging for FA acquisition was performed by using an echo-planar pulse sequence with a b-value of 800 s/mm2 and 33 gradient
directions. A matrix size of 128 ⫻ 128, a 25.6-cm FOV, and a
2.0-mm section thickness resulted in isotropic voxels with dimensions of 2.0 ⫻ 2.0 ⫻ 2.0 mm. For T2 mapping, a multiecho spinecho sequence was used with TE of 20, 40, 60, and 80 ms. The
pulse sequence TR was set at 1000 ms to limit the overall acquisition time of the series. Images at each TE were used for exponential fitting of the T2 value on a voxel-by-voxel basis. A total of 32
axial sections with a matrix size of 256 ⫻ 192, a 20.0-cm FOV, a
section thickness of 4.0 mm, and a section gap of 0.4 mm were
used to cover the whole brain. A sagittal high-resolution isotropic
3D BRAin VOlume imaging (BRAVO) sequence was applied with
1.0 ⫻ 1.0 ⫻ 1.0 mm spatial resolution for the calculation of %CSF
volume. This sequence used a fast 3D-gradient echo technique
with a TR of 12 ms, a TE of 5 ms, a TI of 450 ms, and an acceleration factor of 2.
Finally, for NAA/Cr measurements, proton spectroscopic imaging data were acquired with water suppression by using a spinecho– based 4-section chemical shift imaging sequence and a TR
of 2300 ms with a TE of 280 ms.17 A FOV of 20.0 cm and a
chemical shift imaging matrix of 24 ⫻ 24 resulted in a voxel size of
0.83 ⫻ 0.83 ⫻ 1.5 cm (1.0 mL) in 16 minutes of scanning time.
Pericranial lipid contamination was reduced by using octagonal
outer volume suppression bands. All subjects were maintained
under general anesthesia as a standard of care throughout the
imaging procedures and were continuously monitored by an anesthesiologist. All of the above methods were applied to each subject in a total image examination time of approximately 75
minutes.
MR Imaging Analysis Techniques
Parametric images of the ADC were calculated by using processing software provided with the scanner. Fractional anisotropy values were calculated for each voxel also by using vendor-supplied
software. Image segmentation into GM, white matter, and CSF
components was performed by using the fMRI of the Brain Software Library (http://www.fmrib.ox.ac.uk/fsl/).18,19 The BrainExtraction Tool was used for skull-stripping followed by segmentation by using FMRIB’s Automated Segmentation Tool with 8
iterative passes for bias field correction. Coregistration of the FA
and T2 maps with the segmentation masks was achieved by aligning the FA b⫽0 s/mm2 and T2 TE ⫽ 20 ms images with the 3D
BRAVO scans, respectively. Spectroscopic image acquisition used
N-acetylaspartate at 2.02 ppm as a reference peak. A susceptibility
correction used a point-by-point cross-correlation between subsequent spectra and the reference spectrum for alignment. Peak
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areas were integrated by using the XSOS spectroscopic analysis
software package developed in-house (D.C.S., X.M.), and the
NAA/Cr ratio was calculated for all voxels.
An image mask based on a low-signal-intensity threshold was
used for ADC measurements to create histograms containing all
voxels within the brain while discriminating against regions containing only noise. The contribution from CSF to the ADC maps
was prevented because the CSF and brain parenchyma compartments were easy to discern in the histograms as we described
previously.11
The mode of the parenchymal peak in the histogram was used
as the relevant variable for the ADC histogram. To reduce the CSF
contamination from the FA and T2 histograms, we isolated contributions from gray and white matter. White matter FA histograms were produced by taking the product of the binary white
matter segmentation mask and FA maps. Gray matter T2 maps
were calculated from voxels identified via the product of the gray
matter segmentation mask and T2-weighted images. Application
of the white and gray matter masks to the FA and T2 images
greatly reduced CSF contamination in these measures, allowing
the mean of the histogram to be used for characterization. For
spectroscopy, all voxels in the brain excluding those in the lateral
ventricles were combined to produce a histogram of NAA/Cr
values.
Statistical Methods
PCA was performed on the MR imaging dataset by using Matlab
(R2011a; MathWorks, Natick, Massachusetts). PCA determines
the direction of greatest variability of the data in the MR imaging
biomarker variable space using a linear combination of the biomarkers. The data for each component were first standardized by
subtracting the mean and dividing by the SD, thereby allowing
combination of biomarkers with different units. Next, the singular value decomposition method was used to solve for the principal components. All combinations of n biomarkers were tested
with n ⱕ 5. The output of the analysis was an n-dimensional-unit
vector of coefficients for the direction of greatest variability
(PC1), followed by an n-dimensional-unit vector designating the
direction of next greatest variability orthogonal to PC1 (PC2),
and so on until a complete set of n-orthogonal-basis vectors was
specified. We then defined an MR imaging– based disease-severity
score to be equal to the linear combination of the n-biomarkers
weighted by the PC1 coefficients. This scoring system had a scale
that was determined by minimizing the sum of squared differences with the clinical LINCL scores and thus was similar to the
0 –12 range of the clinical LINCL scale. Finally, we determined the
linear correlation of this score with the clinical LINCL score by
calculating Pearson correlation coefficients (R2).
RESULTS
Representative images are shown in Fig 1 for a subject (BD-04) at
5.2 years of age with a clinical LINCL score equal to 3.0, showing
enlarged ventricles. In general, the image data were of high quality
and specifically free of major motion artifacts, owing to the general anesthesia that was administered throughout the study. In Fig
2, examples are shown of histograms from the ADC and NAA/Cr
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Pearson correlation coefficients (R 2) for individual MRI
biomarkers (top row) versus the LINCL score and biomarker
combinations performed using PCAa
ADC
WM FA
GM T2
%CSF
NAA/Cr
0.62
0.40
0.25
0.70
0.63
X
X
X
X
X
X
X
X
X
X
X
X
PCA
0.70
0.75
0.76
Note:—X indicates that this specific MRI biomarker was included in that combination
of PCA analysis to produce the R2 quoted in the last column.
a
Only the best 3 combinations are shown.
biomarkers for subjects with LINCL scores of 3 and 9 exhibiting
marked differences due to disease progression.
In general, the ADC (R2 ⫽ 0.62, P ⬍ .001), gray matter T2
relaxation time (R2 ⫽ 0.25, P ⬍ .005), and %CSF volume (R2 ⫽
0.70, P ⬍ .001) all increased with disease progression, while the
white matter FA (R2 ⫽ 0.40, P ⬍ .001) and NAA/Cr (R2 ⫽ 0.63,
P ⬍ .001) decreased with increasing disease severity. Most surprising, FA and T2 were more weakly correlated with LINCL disease severity than the other components, even when segmented
into gray and white matter contributions (Fig 3A-E). Indeed, we
found the highest correlation between the MR imaging– based
score and clinical disease severity when combining only 3 components: ADC, %CSF, and NAA/Cr with R2 ⫽ 0.76, P ⬍ .001 (Table). Therefore we did not include FA or T2 in our final diseaseseverity score (Fig 3F).
A plot of PC1 versus PC2 for all 30 subjects is shown in Fig 4.
For the ADC, %CSF, and NAA/Cr combination, PC1 accounted
for 86.0% of the variability in the data, with PC2 accounting for an
additional 9.8%. The vector of coefficients for PC1 with n ⫽ 30
subjects, normalized to unit length, was equal to 0.56, 0.59, ⫺0.58
and was used to define the MRIDSS as
1)
MRIDSS ⬅ ⫺1.0 *A * {PC1} ⫹ B ⫽
⫺1.0 *A * {0.56*[(ADC-0.94)/0.06]
⫹ 0.59*关共%CSF-31.4兲/6.8兴
⫺ 0.58*关共NAA/Cr ⫺ 1.58兲/0.26兴} ⫹ B
with the mean and SD of each biomarker given by ADC (0.94 ⫾
0.06 ⫻ 10⫺3 mm2/s), %CSF (31.4% ⫾ 6.8%), and NAA/Cr
(1.58 ⫾ 0.26). The factor ⫺1.0 was inserted in the first equation so
that the MRIDSS and clinical LINCL score both assigned higher
scores to healthier subjects. The additive factor B was chosen so
that the MRIDSS had the same mean value as a parent population
of clinical LINCL scores, that is, B ⫽ 6. Next, note that application
of the factor A is equivalent to linearly scaling the unit vector of
coefficients of PC1. Because we chose a scaling factor such that A
minimized the square of the differences between the MRIDSS and
the clinical LINCL scores, we found A ⫽ 1.33. Equation 1 with the
above coefficients is plotted in Fig 3F, and all numeric data for
individual subjects is provided in On-line Table 1. The predictive
accuracy of the MRIDSS as estimated by the root mean squared
predictive error was 1.49, based on the SD of the residuals of
equation 1 with the clinical LINCL score.
DISCUSSION
Routine T1- and T2-weighted MR imaging pulse sequences are
frequently insensitive to pathophysiologic changes in neurode-
allowed us to sidestep an important problem in handling multiparametric imaging
datasets, namely how to compare results
from different data-acquisition series that
are optimally acquired with a range of
spatial resolution or section orientations.
In our previous work, by using the ADC
as a measure of disease severity, we developed a detailed analysis of multiple histogram peaks, as are evident in Fig 2A, that
included consideration of the important
problem of partial volume averaging in
imaging.11,26 For the present work, we
simplified the analysis, calculating only
the modes and means of histograms, with
the aim of enhancing the utility of the
methods in clinical trials. Specifically, the
image analysis is automated and is, for example, independent of user-specified region-of-interest analysis. Characterization of histograms without advanced
FIG 1. Representative parametric images from each of the MR imaging biomarkers for subject modeling tools or sophisticated user inBD-04 at 5.2 years of age with a clinical LINCL score of 3.0. A, ADC. B, FA. C, T2 relaxation time. tervention should facilitate broader use of
D, CSF segmentation for %CSF volume calculation. E and F, Spectroscopic imaging grid and
the technique.
representative spectrum. Peak assignments are Cho, Cr, and NAA.
Each of the 5 MR imaging biomarkers
in this study contains complementary information. A significant correlation of
each MR imaging biomarker was found
with clinical LINCL scores, with P ⬍ .005
in all cases. The imaging metric obtained
from the combination of whole-brain
ADC, %CSF, and NAA/Cr variables resulted in a generalized measure of disease
severity and offers a time-savings of approximately 20 minutes in image acquisition relative to inclusion of all 5 biomarkFIG 2. Whole-brain histograms comparing a subject in the early stages of LINCL (BD-02, clinical ers. The metric is a noninvasive objective
LINCL score ⫽ 9, solid lines) with one with more advanced disease (BD-09, clinical LINCL
score ⫽ 3, dotted lines). A, ADC; note the bimodal distribution of the LINCL ⫽ 3 subject, measure requiring no introduced MR imindicative of increased ventricular volume and tissue atrophy; the parenchymal peak is on the aging contrast agents. It may prove useful
left. B, N-acetylaspartate-to-creatine ratio.
in future serial assessments of subjects undergoing treatment.
Note that since the clinical score is
generative diseases.20 Conversely, several studies using quantitasubjective and not a definitive standard, for example, as one might
tive measurements of each of the 5 MR imaging biomarkers choobtain from histology, it is unclear that a given discrepancy in the
sen for this work have shown independent utility in assessing
correlation is necessarily attributable to deficiency in the comunderlying physiologic parameters in these diseases.11-14,21,22
bined MR imaging score or any of the individual components. In
Multiparametric MR imaging has been applied to various disease
an attempt to minimize clinical variability, we had multiple expestates, primarily focusing on oncologic applications.23-25 Validation
rienced examiners evaluate the LINCL score for each patient and
of multiparametric datasets has typically relied on binary categorizathen average their scores. Aside from being an objective measure
tion of histologic tissue type as containing either benign or malignant
of disease severity, a key advantage of our MR imaging method is
cells. Relying on this dichotomization of tissue, receiver operating
the potential ability to refine the evaluation to specific brain areas
characteristic curves and specific cutoff thresholds are typically dein ways that are beyond the capability of clinical examinations.
fined for each MR imaging biomarker to calculate specificity or senAlso, the fact that 3 independent biomarkers were used in combisitivity, depending on the disease state. Application of multiparametnation is likely to make the MRIDSS more robust with respect to
ric MR imaging has been limited in assessing continuous progression
variability due to different scanner platforms.
of severity such as seen in neurodegenerative diseases.
With regard to the comparison of the calculated regression
lines for the MR imaging variables in subjects with LINCL relative
The histogram analysis methods described in the present study
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FIG 3. The results of histogram analysis are shown for all MR imaging biomarkers and all subjects including the ADC (A); white matter (solid
diamonds, B) and gray matter (hollow diamonds) FA; gray matter (solid diamonds) and white matter (hollow diamonds) T2 relaxation time (C);
%CSF (D); NAA/Cr ratio (E); and MRIDSS calculated from the combination of ADC, %CSF, and NAA/Cr, each plotted versus the clinical LINCL
score (F).
seen with progression of LINCL.27 Likewise, WM FA increased
with normal aging in the pediatric population as opposed to
decreases in WM FA seen with LINCL disease progression.27
Whole-brain T2 histograms decreased with age in pediatric
populations as opposed to the increase in gray matter T2 values
found with advanced LINCL.28 NAA/Cr ratios did not change
significantly with age during early childhood and adolescence,
while significant decreases were found with advancing LINCL
severity.29 CSF volume remained constant at values ranging from
7% to 9% from early childhood to adolescence in healthy subjects
compared with 3– 4-fold increases in the %CSF seen in advanced
LINCL.30
Finally, the methods developed for this work are, of course,
quite general and, specifically, are not limited to any single brain
pathology. The measurement of multiple independent biomarkers via noncontrast MR imaging in an acceptable scanning time of
ⱕ75 minutes, combined with a simple automated histogram and
principal component analysis, may provide a robust methodology
for assessment of disease severity in multicenter clinical therapeutic trials of a variety of brain diseases.
FIG 4. PC1 versus PC2 for all n ⫽ 30 data points for the combination
of ADC, %CSF, and NAA/Cr. The numeric values for PC1 and PC2 are
provided in On-line Table 1.
to the trends in age-matched normative data, there are significant
differences according to the literature.27-30 Normative measures
of ADC showed a decrease with pediatric age as opposed to that
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CONCLUSIONS
A quantitative noninvasive MR imaging– based disease severity
score for late infantile neuronal ceroid lipofuscinosis has been
presented. The metric combines data from brain-water apparent
diffusion coefficients, the volume percentage of CSF, and
N-acetylaspartate-to-creatine metabolite ratios. The methods
used can be adapted to run on multiple scanner platforms in a
straightforward manner.
Disclosures: Stephen M. Kaminsky—RELATED: Grant: NIH.* Barry Kosofsky—RELATED: Grant: NINDS.* Kyle D. Rudser—RELATED: Grant: NIH, Comments: partial support from NIH grant U54NS065768.* Dikoma C. Shungu—UNRELATED: Consultancy:
Roche Product Limited, Florida Hospital. Stefan Worgall—RELATED: Grant: NIH.*
Kaleb Yohay—RELATED: Grant: NINDS.* *Money paid to the institution.
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