close

Вход

Забыли?

вход по аккаунту

?

Detecting white matter injury in sickle cell disease using voxel-based morphometry.

код для вставкиСкачать
Detecting White Matter Injury in Sickle Cell
Disease Using Voxel-Based Morphometry
Torsten Baldeweg, MD,1,2 Alexandra M. Hogan, PhD,1,2 Dawn E. Saunders, MD,3 Paul Telfer, MD,4
David G. Gadian, DPhil,2,5 Faraneh Vargha-Khadem, PhD,1,2 and Fenella J. Kirkham, MBBChir2,6
Objective: Sickle cell disease (SCD) is associated with cerebrovascular disease, cerebral infarction, and cognitive dysfunction. This study aimed to detect the presence and extent of white matter abnormalities in individuals with SCD using
voxel-based morphometry (VBM). Methods: Thirty-six children and adolescents with SCD (age range, 9 –24 years) and
31 controls (8 –25 years) underwent magnetic resonance investigations using T1- and T2-weighted protocols. White and
gray matter density maps were obtained from three-dimensional magnetic resonance imaging (MRI) data sets. Using
VBM, we compared the maps between controls and SCD individuals with silent white matter infarct lesions (SCDⴙL;
n ⴝ 16), and those without visible abnormality (SCDⴚL; n ⴝ 20). Results: In comparison with controls, intelligence
quotients (IQs) were lower in both SCD groups irrespective of presence of visible lesions. VBM showed widespread
bilateral white matter abnormalities in the SCDⴙL group, extending beyond the regions of focal infarction in the deep
anterior and posterior white matter borderzones. Bilateral white matter abnormalities were also observed in the SCDⴚL
group, in locations similar to those in the SCDⴙL group. Interpretation: VBM is sensitive to detection of widespread
white matter injury in SCD patients in borderzones between arterial territories even in the absence of evidence of
infarction. Those changes may contribute to cognitive deficits in this population.
Ann Neurol 2006;59:662– 672
As mortality for sickle cell disease has decreased,1 attention has increasingly focused on the chronic adverse
effects that significantly impair quality of life. In addition to a high incidence of overt stroke with a peak in
childhood2 and a high recurrence rate in those who are
not chronically transfused,3 covert infarction, in the
absence of neurological symptoms and signs, affects up
to a quarter of children with sickle cell disease (SCD)
screened with magnetic resonance imaging (MRI).4,5 It
has also become increasingly clear that some patients
show cognitive deficits and are at risk of intellectual
decline.6 Compared with patients without infarcts,
those with overt or covert infarcts are at greater risk for
such decline.6,7 Lesions are often unilateral, small, and
focal, yet the cognitive deficits are widespread, involving different functional domains, including verbal and
nonverbal intelligence. In view of these deficits, we hypothesize that there may well be additional, relatively
extensive damage that is not seen on conventional imaging. We further suspect that such damage may extend to both hemispheres; otherwise, given that the le-
sions are acquired early in life when the
reorganizational capacity of the brain is high, we might
expect rescue of function by the contralateral hemisphere.8,9 Cognitive deficits have also been found in
children without obvious lesions,6,10 but whether some
have brain abnormality beyond the resolution of current T2-weighted imaging remains to be addressed.
Previous reports have documented subtle abnormalities in gray matter T1 on quantitative MRI,11 but the
distribution across the whole brain has not yet been
reported, and there are few quantitative data on white
matter abnormality, although this is the site of most of
the visible covert infarcts. This study was conducted to
examine the distribution of covert infarcts in children
with SCD using voxel-based morphometry (VBM)
analysis of MRI scans. This technique can identify
group differences in white and gray matter across the
whole brain.12 The VBM technique has been successfully applied to MRI data sets to reveal subtle abnormalities of white and gray matter density not visible on
conventional imaging in pediatric patient groups, such
1
Developmental Cognitive Neuroscience Unit, Institute of Child
Health, University College; 2Great Ormond Street Hospital for
Children and 3Department of Radiology, Great Ormond Street
Hospital for Children; 4Department of Paediatric Haematology and
Oncology, Queen Elizabeth Children’s Service, The Royal London
Hospital; 5Radiology and Physics Unit, Institute of Child Health
and 6Neurosciences Unit, Institute of Child Health, University College, London, United Kingdom.
Current address for Dr Hogan: Developmental Brain-Behaviour
Unit, University of Southampton, Southampton, United Kindgom.
Received Oct 12, 2005, and in revised form Nov 16. Accepted for
publication Dec 2, 2005.
662
Published online Jan 31, 2006, in Wiley InterScience
(www.interscience.wiley.com). DOI: 10.1002/ana.20790
Address correspondence to Dr Baldeweg, Institute of Child Health,
30 Guilford Street, London WC1N 1EH, UK.
E-mail: t.baldeweg@ich.ucl.ac.uk
© 2006 American Neurological Association
Published by Wiley-Liss, Inc., through Wiley Subscription Services
as those with hypoxia,13 autism,14 and temporal lobe
epilepsy15 and in children born very prematurely.16
Hence, we investigated the efficacy of this method in
identifying white and gray matter abnormalities in patients with SCD, with or without covert infarction,
and explored the possible relationship of this abnormality to intellectual abilities.
Subjects and Methods
Participants
The patients with SCD were recruited as part of a longitudinal follow-up study of the East London cohort.7,17 Ethical
permission was granted by the Great Ormond Street Hospital Research and Ethics Committee and fully informed consent was obtained from each participant. SCD was diagnosed
by hemoglobin electrophoresis either on cord-blood screening at birth (n ⫽ 14) or after clinical symptoms emerged
later in childhood. Patients with SCD and controls originally
recruited to the study at the Institute of Child Health were
invited to return for 1 day of neuropsychological and MRI
assessments. A total of 108 patients (89 with hemoglobin
[Hb] SS, 14 with HbSC, and 5 with Hb S␤ thalassemia)
had undergone a baseline neuropsychology assessment between 1992 and 2000,7 of whom 51 (22 female, 44 HbSS, 7
HbSC) agreed to return for follow-up assessments between
2001 and 2003. Of the 24 sibling controls originally recruited, 12 agreed to undergo MRI examination during the
follow-up assessment. This group was supplemented by the
recruitment of 19 additional healthy nonsibling, white controls, so that the total was 31.
Neuropsychology
All subjects were assessed by a single researcher (A.M.H.) on
the day of the MR investigation. Measures of intelligence
were obtained using the Wechsler Intelligence Scale for Children–3rd edition UK (WISC-III18), for those children aged
6 years and older (12 patients, 16 controls), and the Wechsler Adult Intelligence Scales (WAIS19) for adults aged from
16 years (24 patients, 15 controls).
Magnetic Resonance Imaging
STRUCTURAL MAGNETIC RESONANCE IMAGING. The
MRI investigation was performed on a 1.5-Tesla Siemens Vision system (Siemens AG, Erlangen, Germany) and included
sagittal and coronal T1-weighted (TR, 570 milliseconds; TE,
14 milliseconds), axial TSE T2-weighted (TR, 3,458 milliseconds; TE ⫽ 96 milliseconds), and coronal turbo-fluid attenuated inversion recovery (FLAIR) T2-weighted imaging
(TR, 9,999 milliseconds; TE, 119 milliseconds; inversion
time TI, 2,210 milliseconds). Volume T1-weighted scans
were acquired using a three-dimensional FLASH sequence
(TR, 16.8 milliseconds; TE, 5.7 milliseconds; flip angle, 21
degrees; voxel size, 0.8 ⫻ 0.8 ⫻ 1.0mm).
All images were evaluated by a consultant pediatric neuroradiologist, who was unaware of the status of each participant. MRI abnormalities then were classified by the neurologist (F.J.K.) as overt or covert infarction, respectively, if
there was an area of increased signal intensity on T2weighted MRI with or without a history of a neurological
event lasting more than 24 hours. All MRIs of SCD cases
without infarct lesions (SCD⫺L) were reviewed a second
time, jointly with a second consultant pediatric neuroradiologist, to confirm the accuracy of the initial evaluation.
For the illustration of lesion sites
across the group, lesion overlap maps were created using
MRIcro software (C. Rorden, www.mricro.com). For this
purpose, location and size of lesions (as volume of interest)
in each case was first manually transcribed from individual
axial and coronal T2-weighted images onto the same set of
12 coronal MRI slices. These slices were obtained from the
Montreal Neurological Institute (MNI) single-subject T1 image (available as a stereotactic template image within SPM99
software [SPMs]) by sectioning this three-dimensional image
in the coronal plane at an angulation of ⫺20 degrees with a
slice thickness of 1cm. The white matter lesions of each individual patient then were transcribed into these 12 slices,
and region of interest images were created for each individual. Finally, a group lesion overlap map was created by averaging across all 16 individual patient region of interest images and by color-coding areas of lesion overlap. Because the
same template brain was used for both the lesion overlap
maps and SPM analysis, the lesion coordinates correspond
approximately to those used in SPM.
LESION OVERLAP MAPS.
VBM analysis
was performed using SPM99 software (Wellcome Department of Imaging Neuroscience, www.fil.ion.ucl.ac.uk). Scans
were normalized to the MNI template using a 16-parameter
affine transformation and 4 ⫻ 5 ⫻ 4 nonlinear bias function. Images then were classified into gray matter, white matter, and nonbrain tissue, including cerebrospinal fluid, and
three other background classes. A modified mixture model
cluster analysis technique was used to identify voxel intensities matching particular tissue types, combined with an a priori knowledge of the spatial distribution of these tissues in
normal subjects.20 This information is in the form of prior
probability images, provided by the Montreal Neurological
Institute, which have been derived from MRIs of 152
healthy subjects. The segmentation requires an iterative algorithm that begins with assigning starting estimates for the
belonging probabilities of tissue classes based on these prior
probability images. The segmentation step also incorporates
an image intensity nonuniformity correction to address
image intensity variations that arises for various reasons in
MRI. Segmented gray and white matter images were
smoothed using a 12mm full-width half-maximum Gaussian
kernel, which renders data more normally distributed12 for
subsequent statistical analysis on a voxel-by-voxel basis. The
voxel values represent the amount of tissue per unit volume
that is classified as white or gray matter. We refer to this as
white or gray matter density. Data were normalized by global
white or gray matter to account for any differences among
the participants that are simply caused by brain size. Therefore, the resultant density measure reflects group differences
in local but not global white or gray matter. Data were presented on glass brain projections in the form of SPMs showing regions where the local amounts of white or gray matter
differ between two groups. SPMs were also overlaid onto
VOXEL-BASED MORPHOMETRY ANALYSIS.
Baldeweg et al: White Matter Injury in SCD
663
white or gray matter segments obtained by averaging across
all control subjects.
The statistical threshold of p ⬍ 0.05, family-wise errorcorrected for multiple comparisons across the whole brain,21
was used for regional differences where there was no prior
hypothesis. Regions where there was an a priori hypothesis
that white matter reductions would be present (ie, in
periventricular regions) were evaluated at threshold of p ⬍
0.001, uncorrected for multiple comparisons. No threshold
for cluster size was applied. SPMs were displayed in all figures at the threshold of p ⬍ 0.001, uncorrected for multiple
comparisons (details on the SPM coordinates of significant
group differences at a threshold of p ⬍ 0.05, corrected for
multiple comparisons, are available upon request). To visualize the distribution of individual subject data points across
groups, we extracted mean parameter estimates (in arbitrary
units corresponding to the likelihood that a voxel belongs to
a particular tissue compartment, ie, white matter) in selected
clusters of maximal z values after extracting the first eigenvariate of all voxels belonging to that cluster using a standard
SPM99 function.
Results
Clinical Characteristics
The total sample size available for the VBM analysis
was 67 children divided into three groups (31 controls,
including 12 siblings, 20 SCD⫺L, and 16 SCD⫹L).
Of the 51 SCD patients recruited for this study, 13
did not complete the MRI examination or there were
movement artifacts in the three-dimensional data sets.
Another two children with overt stroke and large corticosubcortical infarcts were excluded from further
analysis. Of the remaining 36 patients with SCD, 3
had had anterior transient ischemic attacks (TIAs), 7
had had posterior TIAs, 2 seizures, 16 headaches,
and/or learning and behavioral difficulties, and 9 had
been neurologically asymptomatic.
Table 1. Demographic Characteristics of the Sample Included
in the VBM Analysis
SCD
Silent
Infarct
(⫹L)
Characteristic
Controls
No Infarct
(⫺L)
N
Age (SD)
Sex (No. female)
Hb status: SC/SS
Neuropsychology
(SD)
VIQ
PIQ
FSIQ
31a
15.7 (3.6)
13
—
20
17.1 (4.1)
6
6/20
16
18.2 (4.4)
8
0/16
102 (11)
101 (14)
101 (11)
93 (15)b
92 (14)
92 (14)b
84 (14)b
82 (13)b
82 (13)b
a
Including 12 siblings.
Post hoc difference from control value significant at p⫽0.05.
b
VBM ⫽ voxel-based morphometry; SCD ⫽ sickle cell disease;
SD ⫽ standard deviation;VIQ ⫽ verbal; PIQ ⫽ performance;
FSIQ ⫽ full scale IQ.
derzone areas of the anterior and posterior cerebral circulation.
Neuropsychological Data
Intelligence quotients (IQs) were significantly lower in
both groups with SCD compared with controls (see
Table 1), with effects on verbal (VIQ) (F[2,64] ⫽
10.6; p ⬍ 0.001), performance (PIQ) (F[2,64] ⫽ 10.3;
p ⬍ 0.001), and full-scale IQ (FSIQ) (F[2,69] ⫽ 13.7;
p ⬍ 0.001). Significant post hoc differences ( p ⬍
0.05) were found for VIQ and FSIQ between the conTable 2. Magnetic Resonance Imaging Findings
in the SCD Groups
SCD
Identification of Lesions on Conventional Magnetic
Resonance Imaging
The 31 controls (“Control” group) and 20 of the patients with SCD had normal MRI scans on conventional neuroradiological assessment (SCD⫺L group).
The “Lesion” group (SCD⫹L) consisted of 16 participants with covert lesions confined to white matter
only (Table 1). There were no significant differences
between the three groups in mean age (F[2,64] ⫽ 2.2,
p ⫽ 0.12) and gender (␹2 ⫽ 1.542, p ⫽ 0.463). The
distribution of lesions and presence of other MRI findings are shown in Table 2. In most cases, the silent
infarct lesions clustered in the deep frontal and parietal
periventricular white matter, often bilaterally, as illustrated in the lesion overlap maps (Fig 1). The MRIs
from one of the cases with overt stroke are shown in
Figure 2. It is of note that the white matter destruction
in this case mirrors the distribution of covert infarct
lesions shown in Figure 1, which cluster along the bor-
664
Annals of Neurology
Vol 59
No 4
April 2006
Finding
No Infarct
(⫺L)
N
20
MRA abnormalities present
5
Cortical atrophy
None
19
Mild
1
Moderate
0
Severe
0
Distribution of silent infarct lesions
Frontal
Unilateral
Bilateral
Posterior
Parietal
Occipital
Frontal and posterior
Basal ganglia
Silent Infarct
(⫹L)
16
5
12
2
2
0
11
4
6
23
6
2
SCD ⫽ sickle cell disease; MRA ⫽ magnetic resonance angiography.
Fig 1. Lesion overlap maps show the average location of white matter lesions in the sickle cell disease with silent white matter infarct lesions (SCD⫹L) group. Color coding indicates the number of cases overlapping in one location and slice. In each case, the
location and size of the lesion was manually transcribed from individual axial and coronal T2-weighted images onto coronal slices
of a single-subject T1 image. Sections are shown as indicated schematically on the right.
trol and SCD⫺L group as well as on all three measures
between the SCD⫺L, and SCD⫹L groups. Furthermore, intellectual abilities of the sibling control group
(n ⫽ 12) were not different from those of the nonsibling control participants (FSIQ: 103 [standard deviation (SD) 8], 101 [12] respectively; t[df ⫽ 29] ⫽ 0.56;
p ⫽ 0.583), ensuring that group differences cannot be
attributed to factors such as ethnicity and family environment. The same group differences in IQ scores, as
described above, were observed when the statistical
comparison of cases with SCD was restricted to the
group of sibling controls only. IQ scores were significantly lower in both groups with SCD compared with
sibling controls, affecting both VIQ (F[2,45] ⫽ 8.4;
Fig 2. T2-weighted images of a 15-year-old boy who had suffered an extensive bilateral stroke involving the white matter and the
overlying cortical tissue of the frontal and parietal lobes. It is of note that the white matter destruction in this case mirrors the distribution of silent infarct lesions shown in Figure 1, which cluster (indicated by arrows) along the borderzone areas of the anterior and posterior
cerebral circulation.
Baldeweg et al: White Matter Injury in SCD
665
Fig 3. Voxel-based morphometry comparison of white matter density between controls and the sickle cell disease with silent white
matter infarct lesions (SCD⫹L) group. Regions of reduced white matter density in SCD⫹L are displayed on the mean white matter segment (A) and in a glass brain view (B). Standardized parameter estimates for a cluster of maximal T value (6.50) in the
right frontal white matter (SPM coordinates indicated by crosshair in (A): x ⫽ 22, y ⫽ 36, z ⫽ 12) are shown in boxplots and
as individual data points (C). SCD⫹L patients were grouped according to location of lesions in either frontal or posterior regions.
“F⫹P” indicates that lesions were found in both frontal and posterior areas.
p ⬍ 0.001) and PIQ (F[2,59] ⫽ 6.2; p ⫽ 0.004). Hemoglobinopathy did not significantly influence IQ
scores in the SCD⫺L group (VIQ: 91 [SD 12], 96
[21]; PIQ: 90 [13], 96 [18]; p ⫽ 0.588 and p ⫽
0.363, for individuals with HbSS and HbSC, respectively).
Voxel-Based Morphometry
First, VBM was used
to evaluate the extent of white matter abnormalities in
the group with identified covert infarct lesions compared
with controls. Significant decreases in white matter density (ie, the amount of tissue per unit volume classified
as white matter; see Subjects and Methods) were found
extending along the ventricles bilaterally from the anterior frontal to parietooccipital white matter (Fig 3). The
anterior frontal white matter density decreases were in a
similar locations to those indicated by the lesion overlap
maps (slices 3–5 in Fig 1). The decrease of anterior frontal white matter density did not appear to be dependent
on the presence of lesions in frontal white matter, because similar changes were seen in those cases with exclusively posterior lesions (see boxplot in Fig 3C). Furthermore, white matter density was also decreased along
WHITE MATTER ABNORMALITIES.
666
Annals of Neurology
Vol 59
No 4
April 2006
the whole extent of the corpus callosum, where no overt
lesions had been detected. Nevertheless, the frontal
white matter changes were more pronounced in those
patients with presence of both frontal and posterior lesion (see Fig 3C).
Second, to explore the possibility that similar white
matter changes could be detected in the absence of MRI
evidence of infarction, an additional VBM analysis was
conducted comparing the SCD⫺L with the control
group. Indeed, similar bilateral white matter density decreases ( p ⬍ 0.001 for each hemisphere, uncorrected)
were also observed in this group (Fig 4). Although the
spatial extent of white matter change was less compared
with that found in the SCD⫹L group, there was considerable similarity in the location of change, showing an
anterior to posterior extension along the ventricles bilaterally. In contrast with the lesion group, there was much
less change in the deep frontal white matter, likely because of the absence of infarcts in those regions.
An additional VBM analysis was performed using
only the data available from 12 sibling controls and 12
age-matched SCD⫺L cases. In agreement with the previous analysis (see Fig 4), significant white matter density reductions were found in similarly extended re-
Fig 4. Voxel-based morphometry comparison of white matter density between controls and the sickle cell disease without visible abnormality (SCD⫺L) group. Regions of reduced white matter density in SCD⫺L are displayed on the mean white matter segment (A) and
in a glass brain view (B). Standardized parameter estimates for a cluster of maximal T value (7.80) in the right central white matter
(SPM coordinates indicated by crosshair in (A): x ⫽ 26, y ⫽ ⫺20, z ⫽ 42) are shown in boxplots and as individual data points (C).
gions along the ventricles, suggesting that the observed
changes are not related to factors other than SCD (data
available upon request).
Furthermore, to explore if the degree of white matter
abnormality is directly related to the level of intellectual disability in this cohort, we computed a correlation analysis between IQ scores and white matter density (Fig 5). Although these correlations did not reach
corrected levels of significance, their distribution is indeed very similar to the regions identified in the group
comparisons (see Figs 3 and 4). Similar but less extensive clusters of positive correlations were also found
when the analysis was restricted to the SCD groups
only (see Fig 5B).
GRAY MATTER ABNORMALITIES. Because the focal distribution of gray matter abnormalities11 across the
whole brain has not yet been reported, changes in gray
matter density were evaluated here using VBM (Fig 6).
In the lesion group, areas of extensive gray matter density decrease were found along the medial wall of the
frontal and parietal lobes, surrounding the cingulate
sulcus and extending posteriorly into the precuneus
(see Fig 6A). At a threshold of p ⬍ 0.001, uncorrected,
these changes also extended onto the lateral surface of
the frontal and parietal lobes. In the SCD⫺L group,
some gray matter density reductions were also visible at
p ⬍ 0.001 uncorrected along the medial frontal surface, with changes in small regions of the right lateral
frontal lobe reaching a threshold of p ⫽ 0.05, after
correction for the whole-brain volume.
Discussion
This study examined the distribution of brain abnormalities in children and adolescents with SCD using
VBM. Compared with controls, we have shown focal
abnormality in white matter density (ie, the amount of
tissue per unit volume classified as white matter) in a
distribution compatible with the borderzones between
arterial territories in patients with sickle cell disease.
Together with changes in gray matter density in focal
regions of the frontal lobes, these MRI changes may
contribute to the spectrum of cognitive difficulties in
this population.
Previous applications have demonstrated that VBM
is a powerful tool to demonstrate the severity and localization of atrophy of gray matter and white matter
in neurodegenerative disorders as well as of hypoplasia
in developmental conditions13–16,22,23 There are, however, few data in populations at risk of chronic ischemic brain damage, in part, because VBM is currently
not the most appropriate technique for characterizing
Baldeweg et al: White Matter Injury in SCD
667
Fig 5. Correlation analysis between verbal (VIQ, left side) and performance intelligence quotients (PIQ, right side) and white matter density using SPM. Clusters of significant positive correlations (threshold at p value less than 0.05, two-tailed, uncorrected for
multiple comparisons) are seen in distributed regions of the frontoparietal white matter: (A) when all subjects in the study (n ⫽
67) were included as well as when (in B) the analysis was restricted to sickle cell disease individuals (n ⫽ 36). Peak correlations
in the anterior deep frontal regions reached p ⬍0.0001 in both analyses.
focal infarcts.24 Nevertheless, VBM showed focal gray
matter loss in children with hypoxic brain injury.13
Lesion Location
The distribution of white matter changes on VBM is
compatible with the anatomical location of borderzone
areas of the cerebral circulation between the anterior
and middle cerebral arteries, and to some degree is also
indicative of changes in the posterior borderzone areas
between the middle and posterior cerebral arteries.
Neuropathological25 and imaging studies26,27 have
shown that most overt and covert ischemic strokes occur in those borderzone areas. This finding was replicated here, as shown in the lesion overlap maps in Figure 1, and the cerebral distribution of those at risk
areas is amply demonstrated in the case of severe frontoparietal stroke shown in Figure 2. It is sometimes
assumed that the borderzone has a uniform, wedge-like
shape between the anterior and middle artery distributions (eg, Mantyla and colleagues28). However, our
data are in agreement with the view that this wedgelike
volume can be differentiated into two separate borderzones, a deep (medullary) and a superficial (leptomeningeal) borderzone.29 The superficial borderzone is
composed of the relatively strong anastomoses between
the capillaries of the cortical branches of the anterior,
middle, and posterior cerebral arteries. In contrast, the
deep borderzone includes the deep ganglionic branches
arising from the proximal portions of the vessels and
668
Annals of Neurology
Vol 59
No 4
April 2006
the associated communicating arteries that supply the
deep central portion of the thalamus and cerebral
hemispheres and these anastomoses are weak.29 This
vascular weakness may account for the predominant
clustering of covert infarct lesions as well as VBM
changes along the deep borderzone.
The incidence of covert infarction was 22% in the
cooperative study of children with SCD aged 6 to 19
years30; the majority have abnormal MRI by the age of
6 years, but some children sustain injury later and recurrent covert and overt stroke lesions are common.
Previous single-center studies have reported a prevalence of MRI abnormalities as high as 46%,31 and in
the current study of a population selected through
clinic attendance in 1992 to 2000 and their willingness
to return for a follow-up covert infarction was found in
44% (16/36).
Cognitive Impairments
Since the early 1990s, several studies have associated
cognitive impairment with the presence of infarction in
children with SCD.32–34 Although some of these studies have reported the results of large batteries of neuropsychological assessments7,34,35 or looked specifically
for deficits in attention33,36 and working memory,37
most have focused on intelligence (IQ). Covert infarcts
in the frontal lobes are associated with lowered IQ, but
deficits have also been found in children without infarction10,38 (for review see Schatz and colleagues39).
Fig 6. Voxel-based morphometry comparison of gray matter density between controls and the sickle cell disease with silent white
matter infarct lesions (SCD⫹L) (A) and sickle cell disease without visible abnormality (SCD⫺L) (B) groups. Regions of reduced
gray matter density in the SCD groups are displayed on the mean gray matter segment (top) and in a glass brain view (bottom).
SPM coordinates (x, y, z) of the displayed sections as indicated by arrow in the glass brains are for A: 5, ⫺5, 53, and for B: 1,
⫺13, 57.
Our findings confirm those reports, including the lowered intellectual abilities in children without infarction,
and also suggest that environmental factors are less important than sickle cell disease.
QUANTITATIVE MAGNETIC RESONANCE IMAGING STUDIES. Our findings of more extensive cerebral abnor-
mality than is visualized on conventional T2-weighted
imaging complements previous studies using quantitative MRI. Steen and colleagues40,41 used a precise and
accurate inversion recovery (PAIR) method for parametric T1 mapping in a single transverse slice acquired
at the level of the basal ganglia. Lower T1 values were
found in the gray matter of the basal ganglia, thalamus,
and cortex in a cohort of children and adolescents with
SCD. The position of the acquisition slice, however,
was more ventral than the level at which white matter
changes were seen on VBM in the current study, that
is, in the centrum semiovale and corona radiata. This
may explain why no such white matter deficits were
observed in the studies of Steen and colleagues.
The severity and extent of VBM changes is broadly
commensurate with the degree of cognitive impairment
in this cohort. The extensive bilateral, frontoparietal
changes in both white and gray matter in the lesion
group could be caused by secondary (Wallerian) degeneration of white matter tracts after infarction. However, white matter density was also decreased in the
corpus callosum, where no lesions were found. In addition, the presence of deep frontal white matter
changes after exclusively posterior lesions (see Fig 3C)
and the white matter changes observed in the contralateral hemisphere in cases with unilateral infarcts
(data not shown) suggest that a more diffuse process is
involved. Such diffuse brain injury was previously suspected on the basis of cognitive data alone.31,38 Indeed,
the more modest cognitive impairments and VBM
changes in the group without infarct lesions may be
related to chronic processes, such as sustained or intermittent ischemia or hypoxemia that may be present
from early infancy. Cognitive deficits previously have
been associated with subclinical cerebral vasculopathy40
and low hemoglobin level.11,42
If so, this suggests that infarcts in children with
SCD might be more appropriately viewed as an additional burden on an already compromised system.
VBM demonstrates an anatomical basis for the functional abnormalities which might allow cause to be in-
Baldeweg et al: White Matter Injury in SCD
669
vestigated. The observed distribution of white and gray
matter changes as well as of correlations with IQ scores
is compatible with the functional anatomy of distributed medial and lateral frontal as well as posterior regions implicated in supporting fluid intelligence.43,44
On VBM, the density of gray and white matter in bilateral frontal, temporal, and parietal areas is associated
with higher IQ in healthy adults.45 In children, who
were born preterm, the decline in verbal IQ was correlated to the density in bilateral deep frontal white
matter.16
In contrast
with the increase in understanding of overt arterial
ischemic stroke, the pathophysiology of covert infarction remains unclear. Clinical risk factors associated
with silent infarction are a low pain event rate, history of seizures, high leukocyte count, and the SEN
␤s globin gene haplotype.46 Possible mechanisms include (1) large vessel disease and relative hypotension47 leading to perfusion failure in the borderzones,
as for overt stroke, (2) small vessel disease or (3) microembolization, from diseased cerebral vessels or perhaps paradoxically via a patent foramen ovale, as well
as the residua of (4) sinovenous thrombosis48 or posterior leukencephalopathy.49 Our data suggest that
ischemic injury occurs in a borderzone distribution
even in patients with no visible covert infarcts. The
distribution of injury demonstrated on VBM is consistent with a hemodynamic mechanism, with critically reduced blood flow in the borderzones (for further discussion of the borderzone concept see Pavlakis
and colleagues26).
The neuropathological basis for the white matter
changes in the SCD⫺L group is currently not known.
A likely cause of lower white matter density could be a
low T1-weighted signal intensity, as observed in focal
areas of leukomalacia (eg, Fig. 1 in Steen and colleauges40), resulting in a reduced likelihood of voxels
being classified as white matter.
PATHOPHYSIOLOGICAL CONSIDERATIONS.
LIMITATIONS. Although the age of our study participants spanned a range in which maturational changes
are seen,50,51 when age was included as a covariate in
the analyses, the current findings were not altered.
However, it is likely that such analysis lacked the
power to detect differences in maturation between the
healthy and children with SCD, as suggested by other
studies.52,53
Furthermore, the presence of small lesions, although
poorly visible on T1-weighted images, may have affected the spatial normalization and segmentation of
scans. Therefore, great care was taken to visually check
the resulting images and to exclude all scans with subtle artifacts, largely caused by subject motion. How-
670
Annals of Neurology
Vol 59
No 4
April 2006
ever, these factors cannot account for the strikingly
similar findings in the SCD group without lesions.
The present VBM findings are independent of possible differences in brain size and global gray matter
and white matter volume. A previous investigation observed no differences in white matter volume but a significant reduction in cortical and subcortical gray matter volume in SCD children compared with controls.54
Together with the current data both studies suggest
that there are global as well as local gray matter
changes in the frontal lobes, whereas white matter
changes appear of focal nature.
Finally, we emphasize that our VBM analysis is
based on group comparisons, and attempts at characterizing lesions in individual data sets may lack the sensitivity and accuracy24 required to make it a diagnostically useful tool.
In summary, VBM was found sensitive to widespread white matter abnormalities in the borderzone
territories in children and adolescents with SCD. This
is in keeping with their neuropsychological profile
which also suggests more widespread dysfunction of
both verbal and nonverbal cognition. The distribution
of white matter changes is in agreement with the prediction that such bilateral changes severely constrain
the reorganizational capacity of the developing brain.
Even more importantly, our data also suggest that the
VBM method is able to detect changes in white matter
density in the absence of any MRI evidence of infarction. The effects of covert pathology (anemia, silent infarction, relative systemic hypotension, pulmonary hypertension) on cognition is an important issue for the
appropriate management of children with SCD, and
VBM has considerable potential to address these. Further research should identify the age range during
which VBM changes emerge in childhood, whether
they are predictive of silent infarct or stroke and if they
can be modified by other risk factors or reversed with
treatment. The identification of children at risk of
stroke and silent infarction using noninvasive techniques is a major goal of current research in sickle cell
disease and a validated technique might be of considerable use as a surrogate marker in clinical trials.
This study was funded by Action Medical Research (UK) and the
Wellcome Trust and was undertaken at Great Ormond Street Hospital for Children NHS Trust, which received a proportion of its
funding from the NHS Executive.
We thank Drs K. Chong and T. Cox for neuroradiological expertise, J. Ho and H. Ducie for conducting the MRI investigations, Dr
J. Evans for referring her patients for this study. We are grateful to
all participants for their time and cooperation.
References
1. Platt OS, Brambilla DJ, Rosse WF, et al. Mortality in sickle cell
disease. Life expectancy and risk factors for early death. N Engl
J Med 1994;330:1639 –1644.
2. Ohene-Frempong K, Weiner SJ, Sleeper LA, et al. Cerebrovascular accidents in sickle cell disease: rates and risk factors. Blood
1998;91:288 –294.
3. Powars D, Wilson B, Imbus C, et al. The natural history of
stroke in sickle cell disease. Am J Med 1978;65:461– 471.
4. Pegelow CH, Macklin EA, Moser FG, et al. Longitudinal
changes in brain magnetic resonance imaging findings in children with sickle cell disease. Blood 2002;99:3014 –3018.
5. Bernaudin F, Verlhac S, Freard F, et al. Multicenter prospective
study of children with sickle cell disease: radiographic and psychometric correlation. J Child Neurol 2000;15:333–343.
6. Wang W, Enos L, Gallagher D, et al. Neuropsychologic performance in school-aged children with sickle cell disease: a report from the Cooperative Study of Sickle Cell Disease. J Pediatr 2001;139:391–397.
7. Watkins KE, Hewes DK, Connelly A, et al. Cognitive deficits
associated with frontal-lobe infarction in children with sickle
cell disease. Dev Med Child Neurol 1998;40:536 –543.
8. Vargha-Khadem F, Isaacs E, Muter V. A review of cognitive
outcome after unilateral lesions sustained during childhood.
J Child Neurol 1994;9(suppl 2):67–73.
9. Hogan AM, Kirkham FJ, Isaacs E. Intelligence after Stroke in
Childhood: a review of the literature and suggestions for future
research. J Child Psychol 2000;15:325–332.
10. Steen RG, Miles MA, Helton KJ, et al. Cognitive impairment
in children with hemoglobin SS sickle cell disease: relationship
to MR imaging findings and hematocrit. AJNR Am J Neuroradiol 2003;24:382–389.
11. Steen RG, Xiong X, Mulhern RK, et al. Subtle brain abnormalities in children with sickle cell disease: relationship to
blood hematocrit. Ann Neurol 1999;45:279 –286.
12. Ashburner J, Friston KJ. Voxel-based morphometry—the methods. Neuroimage 2000;11:805– 821.
13. Gadian DG, Aicardi J, Watkins KE, et al. Developmental amnesia associated with early hypoxic-ischaemic injury. Brain
2000;123:499 –507.
14. Salmond CH, Ashburner J, Connelly A, et al. The role of the
medial temporal lobe in autistic spectrum disorders. Eur J Neurosci 2005;22:764 –772.
15. Cormack F, Gadian DG, Vargha-Khadem F, et al. Extrahippocampal grey matter density abnormalities in paediatric
mesial temporal sclerosis. Neuroimage 2005;27:635– 643.
16. Isaacs EB, Edmonds CJ, Chong WK, et al. Brain morphometry
and IQ measurements in preterm children. Brain 2004;127:
2595–2607.
17. Kirkham FJ, Hewes DK, Prengler M, et al. Nocturnal hypoxaemia and central-nervous-system events in sickle-cell disease.
Lancet 2001;357:1656 –1659.
18. Wechsler D. Wechsler Intelligence Scale for Children. 3rd ed.
Sidcup, Kent: The Psychological Corporation, 1991.
19. Wechsler, D. Wechsler Adult Intelligence Scale. 3rd ed.
London: The Psychological Corporation, 1997.
20. Ashburner J, Friston K. Multimodal image coregistration and
partitioning—a unified framework. Neuroimage 1997;6:
209 –217.
21. Friston KJ, Holmes A, Poline JB, et al. Detecting activations in
PET and fMRI: levels of inference and power. Neuroimage
1996;4:223–235.
22. Belton E, Salmond CH, Watkins KE, et al. Bilateral brain abnormalities associated with dominantly inherited verbal and
orofacial dyspraxia. Hum Brain Mapp 2003;18:194 –200.
23. Chung MK, Dalton KM, Alexander AL, et al. Less white matter concentration in autism: 2D voxel-based morphometry.
Neuroimage 2004;23:242–251.
24. Mehta S, Grabowski TJ, Trivedi Y, et al. Evaluation of voxelbased morphometry for focal lesion detection in individuals.
Neuroimage 2003;20:1438 –1454.
25. Rothman SM, Fulling KH, Nelson JS. Sickle cell anemia and
central nervous system infarction: a neuropathological study.
Ann Neurol 1986;20:684 – 690.
26. Pavlakis SG, Bello J, Prohovnik I, et al. Brain infarction in
sickle cell anemia: magnetic resonance imaging correlates. Ann
Neurol 1988;23:125–130.
27. Adams RJ, Nichols FT, McKie V, et al. Cerebral infarction in
sickle cell anemia: mechanism based on CT and MRI. Neurology 1988;38:1012–1017.
28. Mantyla R, Aronen HJ, Salonen O, et al. The prevalence and
distribution of white-matter changes on different MRI pulse sequences in a post-stroke cohort. Neuroradiology 1999;41:
657– 665.
29. Bryan RN, Whitlow WD, Levy LM. Cerebral infarction and
ischemic disease. In: Atlas SW, ed. Magnetic resonance imaging
of the brain and spine. New York: Raven Press, 1996:411– 437.
30. Miller ST, Macklin EA, Pegelow CH, et al. Silent infarction as
a risk factor for overt stroke in children with sickle cell anemia:
a report from the Cooperative Study of Sickle Cell Disease.
J Pediatr 2001;139:385–390.
31. Steen RG, Xiong X, Langston JW, et al. Brain injury in children with sickle cell disease: prevalence and etiology. Ann Neurol 2003;54:564 –572.
32. Hariman LM, Griffith ER, Hurtig AL, et al. Functional outcomes of children with sickle-cell disease affected by stroke.
Arch Phys Med Rehabil 1991;72:498 –502.
33. Craft S, Schatz J, Glauser TA, et al. Neuropsychologic effects of
stroke in children with sickle cell anemia. J Pediatr 1993;123:
712–717.
34. Cohen MJ, Branch WB, McKie VC, et al. Neuropsychological
impairment in children with sickle cell anemia and cerebrovascular accidents. Clin Pediatr 1994;33:517–524.
35. DeBaun MR, Schatz J, Siegel MJ, et al. Cognitive screening
examinations for silent cerebral infarcts in sickle cell disease.
Neurology 1998;50:1678 –1682.
36. Brown RT, Davis PC, Lambert R, et al. Neurocognitive functioning and magnetic resonance imaging in children with sickle
cell disease. J Pediatr Psychol 2000;25:503–513.
37. White D, Salorio C, Schatz J, DeBaun MR. Preliminary study
of working memory in children with stroke related to sickle
cell disease. J Clin Exp Neuropsychol 2000;22:257–264.
38. Steen RG, Fineberg-Buchner C, Hankins G, et al. Cognitive
deficits in children with sickle cell disease. J Child Neurol
2005;20:102–107.
39. Schatz J, Finke RL, Kellett JM, et al. Cognitive functioning in
children with sickle cell disease: a meta-analysis. J Pediatr Psychol 2002;27:739 –748.
40. Steen RG, Reddick WE, Mulhern RK, et al. Quantitative MRI
of the brain in children with sickle cell disease reveals abnormalities unseen by conventional MRI. J Magn Reson Imaging
1998;8:535–543.
41. Steen RG, Langston JW, Ogg RJ, et al. Diffuse T1 reduction
in gray matter of sickle cell disease patients: evidence of selective vulnerability to damage? Magn Reson Imaging 1999;17:
503–515.
42. Kirkham FJ, Hogan AM. Risk factors for arterial ischemic
stroke in childhood. CNS Spectrums 2004;9:451– 464.
43. Gray JR, Chabris CF, Braver TS. Neural mechanisms of general
fluid intelligence. Nat Neurosci 2003;6:316 –322.
44. Duncan J, Seitz RJ, Kolodny J, et al. A neural basis for general
intelligence. Science 2000;289:457– 460.
Baldeweg et al: White Matter Injury in SCD
671
45. Haier RJ, Jung RE, Yeo RA, et al. Structural brain variation
and general intelligence. Neuroimage 2004;23:425– 433.
46. Kinney TR, Sleeper LA, Wang WC, et al. Silent cerebral infarcts in sickle cell anemia: a risk factor analysis. The Cooperative Study of Sickle Cell Disease. Pediatrics 1999;103:
640 – 645.
47. Pegelow CH, Colangelo L, Steinberg M, et al. Natural history
of blood pressure in sickle cell disease: risks for stroke and death
associated with relative hypertension in sickle cell anemia. Am J
Med 1997;102:171–177.
48. Sebire G, Tabarki B, Saunders DE, et al. Cerebral venous sinus
thrombosis in children: risk factors, presentation, diagnosis and
outcome. Brain 2005;128:477– 489.
49. Henderson JN, Noetzel MJ, McKinstry RC, et al. Reversible
posterior leukoencephalopathy syndrome and silent cerebral infarcts are associated with severe acute chest syndrome in children with sickle cell disease. Blood 2003;101:415– 419.
672
Annals of Neurology
Vol 59
No 4
April 2006
50. Steen RG, Ogg RJ, Reddick WE, et al. Age-related changes in
the pediatric brain: quantitative MR evidence of maturational
changes during adolescence. AJNR Am J Neuroradiol 1997;18:
819 – 828.
51. Paus T. Mapping brain maturation and cognitive development
during adolescence. Trends Cogn Sci 2005;9:60 – 68.
52. Steen RG, Schroeder J. Age-related changes in the pediatric
brain: proton T1 in healthy children and in children with sickle
cell disease. Magn Reson Imaging 2003;21:9 –15.
53. Steen RG, Hunte M, Traipe E, et al. Brain T1 in young
children with sickle cell disease: evidence of early abnormalities in brain development. Magn Reson Imaging 2004;22:
299 –306.
54. Steen RG, Emudianughe T, Hunte M, et al. Brain volume in
pediatric patients with sickle cell disease: evidence of volumetric
growth delay? AJNR Am J Neuroradiol 2005;26:455– 462.
Документ
Категория
Без категории
Просмотров
0
Размер файла
953 Кб
Теги
base, sickle, using, morphometric, voxel, matter, detecting, white, disease, injury, cells
1/--страниц
Пожаловаться на содержимое документа