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Bicaudate Ratio as a Magnetic Resonance Imaging Marker of Brain Atrophy in Multiple Sclerosis
Robert A. Bermel;
Rohit Bakshi, MD;
Christopher Tjoa;
Srinivas R. Puli, MD;
Lawrence Jacobs, MD
Arch Neurol. 2002;59:275-280.
ABSTRACT
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Context Brain atrophy has emerged as a useful surrogate marker of disease involvement
in multiple sclerosis (MS). The relationship between whole-brain or regional
atrophy and cognitive dysfunction is poorly understood.
Objectives To determine whether the bicaudate ratio (BCR)—the minimum intercaudate
distance divided by brain width along the same line—is increased in
MS and to compare the ability of the BCR, whole-brain atrophy, and other magnetic
resonance imaging markers to predict cognitive dysfunction.
Design Case-control study.
Setting University-affiliated clinic.
Participants Sixty patients with MS and 50 age- and sex-matched control subjects.
Main Outcome Measures Bicaudate ratio, whole-brain atrophy, T2 lesion load, T1 ("black hole")
lesion load, and caudate volume were measured quantitatively using fluid-attenuated
inversion recovery, T1-weighted, and gradient-echo magnetic resonance imaging
scans. Symbol Digit Modalities Test was used to assess cognitive function.
Results The BCR (mean [SD]) was higher in patients with MS (0.11 [0.03]) than
in controls (0.09 [0.02]) (P<.001), suggesting
subcortical atrophy in MS. The BCR was related to total T2 (r = 0.56, P<.001) and T1 (r = 0.40, P<.002) lesion volumes, but not
caudate volume in patients with MS. Regression modeling selected BCR (P<.05), but not whole-brain atrophy, T1 or T2 lesion
volume, or caudate volume as predictive of Symbol Digit Modalities Test score
in patients with MS.
Conclusions The BCR is increased in MS and is more closely associated with cognitive
dysfunction than are other magnetic resonance imaging surrogate markers including
whole-brain atrophy. Increased BCR is best explained by frontal horn ventricular
enlargement due to atrophy of deep frontal subcortical white matter. This
highlights the close relationship between subcortical atrophy and cognitive
impairment in patients with MS.
INTRODUCTION
TOTAL HYPERINTENSE T2 lesion load is an established magnetic resonance
imaging (MRI) measure used to assess the disease progression of multiple sclerosis
(MS). Hyperintense lesions on T2-weighted images, however, are nonspecific
in defining underlying pathologic abnormalities that includes a range of tissue
changes and has poor sensitivity for detection of important microscopic disease.1 Hyperintense T2 lesions correlate poorly with physical
disability and long-term disease course in patients with MS.2
Because the principal clinical challenge in treating MS is to monitor and
suspend chronic disease progression, current research has investigated other
methods to assess the global MS disease process. Advancing brain atrophy has
been identified as a possible surrogate marker of this long-term progression.3 It has been suggested that atrophy in MS represents
destructive and irreversible pathologic processes, making it a more reliable
indicator of disease progression than the nonspecific T2 lesion load assessment.4
Using computer algorithms of volume reconstruction, whole-brain atrophy
has been identifed in patients with relapsing-remitting and secondary progressive
MS.3, 5-6 Several
research groups showed that whole-brain atrophy correlated with physical disability
and predicted disease progression in patients with MS.3-4,6-8
These studies also demonstrated that whole-brain atrophy begins early in the
disease process of MS.
Atrophy appears to be widespread, affecting the cerebral cortex, corpus
callosum, deep central regions, brainstem, and cerebellum, but the involvement
is heterogeneous among regions.7 While quantitative
methods are available to measure global atrophy, the best methods to assess
regional atrophy7 in MS are unknown. The bicaudate
ratio (BCR) is a reliable measure that reflects subcortical atrophy and increases
with normal aging.9-10 The BCR
is quantitative and readily obtainable from MRI scans. It is useful in identifying
regional atrophy in neurologic diseases11-12
but, to our knowledge, it has never been applied to MS. In this study, we
evaluated whether the BCR differed between patients with MS and control subjects.
We also assessed whether the BCR would correlate with caudate volume and clinical
measures of disease involvement in patients with MS. We compared BCR to T1
("black hole") lesion load, total hyperintense T2 lesion load, and whole-brain
atrophy for relative value in predicting clinical variables.
SUBJECTS AND METHODS
SUBJECTS
A consecutive series of 60 patients with MS and 50 controls were scanned
using the same MRI unit following the same MRI protocol at a tertiary care
university hospital. All patients with MS were clinically confirmed13 and were treated at a university-affiliated MS clinic.
None of the patients with MS had any other major medical illnesses, were younger
than 20 years or older than 60 years, had a history of substance abuse, had
acute exacerbations, or had used a corticosteroid within the previous 4 weeks
before clinical and MRI testing. Using established definitions of clinical
course,14 42 patients had relapsing-remitting
MS and 18 had secondary progressive MS. Physical disability was assessed within
1 week of undergoing MRI by a single experienced neurologist (R.B.), who was
blind to the MRI findings. Using the Expanded Disability Status Scale (EDSS),15 physical disability ranged from 0 (best) to 8.0 (worst)
(mean [SD], 3.7 [1.9]). The patients' disease duration of MS ranged from 0.5
to 38 years (mean [SD] disease duration, 10.6 [9.4] years). Controls included
healthy volunteers recruited from hospital staff (n = 8) and consecutive patients
referred to the MRI center (Imaging Services, Kaleida Health, Buffalo, NY)
for dizziness, headaches, and new-onset reactive or idiopathic seizures (not
chronic epilepsy) who had normal neurologic test results and no abnormalities
on MRIs and in whom MS was excluded clinically. An experienced observer (R.B.)
reviewed the MRI scans of the controls to ensure that there were no abnormalities.
Patients with MS (41 women [68%]) and controls (35 women [70%]) were sex-matched.
Patients with MS (mean [SD] age, 42 [9] years) and controls (mean age [SD],
42 [10] years) were also age-matched. Lifetime corticosteroid use in the MS
group was moderate overall, as detailed separately.16
Different aspects of these patients and controls have been presented recently
as part of a separate study.16
COGNITIVE TESTING
The Symbol Digit Modalities Test (SDMT) is a commonly used neuropsychological
test of information processing speed.17 Subjects
are asked to write the digits that substitute for geometric symbols shown
in a key at the top of the page. The score is the number of correct responses
recorded in 90 seconds. Subjects responded by writing their answers; none
of the patients had significant dominant upper extremity weakness that would
compromise their ability to write. Twenty-three randomly chosen patients with
MS (15 patients with relapsing-remitting MS and 8 patients with secondary
progressive MS) took the SDMT within 1 week of MRI scanning. Patients who
took the SDMT were demographically similar to those who did not take the SDMT
for age (P = .58), sex (P
= .67), and clinical course (P = .49). Mean (SD)
EDSS score in the group that took the SDMT (3.2 [1.6] points) was slightly,
but significantly, lower than in the group that did not take the SDMT (4.1
[1.8] points) (P = .04). The remaining controls or
patients with MS did not take the SDMT. A modified version of the brain parenchymal
fraction6 was measured on the MRIs of these
same 23 patients as described in the "MRI and Variables" subsection, to correct
for whole-brain atrophy in the patients who took the SDMT.
MRI AND VARIABLES
T1-weighted (repetition time, 585 milliseconds; echo time, 20 milliseconds;
and number of signal averages, 1) and fast spin-echo fluid-attenuated inversion
recovery (FLAIR) images (repetition time, 8000 milliseconds; echo time, 120
milliseconds; number of signal averages, 2; and inversion time, 2200 milliseconds)
were obtained in the axial plane using 5-mm contiguous, interleaved slices
at 1.5 T, with an in-plane resolution of approximately 1 mm. Fast FLAIR included
an echo train length (number of echoes) 20; echo spacing, 12 milliseconds;
field of view, 24 cm; acquisition matrix, 189 x 256 pixels; and bandwidth,
217.3 Hz/pixel. A presaturation band was placed caudal to the image volume.
Detailed imaging parameters of our FLAIR protocol were published previously.18 A subset of the patients with MS (n = 24; 16 patients
with relapsing-remitting MS and 8 patients with secondary progressive MS)
underwent an additional protocol that was tailored to the detailed depiction
of anatomical structures. This was a 3-dimensional (nongapped) gradient-echo
volume study performed in the coronal plane, with a repetition time of 24
milliseconds, echo time of 7 milliseconds, slice thickness of 1.8 mm, acquisition
matrix size of 256 x 256 pixels, and an in-plane resolution of 0.977
mm. Images were transferred by computer network to Unix-based workstations
on which images were analyzed quantitatively (EasyVision, Release 2.1.2; Philips
Medical Systems, Best, the Netherlands; Sun Ultra 10; Sun Microsystems, Mountainview,
Calif). A trained observer (R.A.B.) who was blinded to clinical information
performed the quantitative MRI analysis. The BCR was the minimum intercaudate
distance divided by brain width along the same line (Figure 1).11 The BCR was measured
in the FLAIR axial slice where the heads of the caudate nuclei were most visible
and closest to one another (Figure 1).
Total hyperintense T2 parenchymal plaque lesion load was determined by manual
tracing of lesions on FLAIR images. Total parenchymal lesion volume (lesion
load) was the sum of the volume (area multiplied by slice thickness) of each
lesion seen on each interleaved axial slice; artifacts and other normal hyperintensities
seen in the normal population on FLAIR images were avoided.18
Since hypointense T1 lesions are difficult to delineate manually, the analysis
of T1 lesion volume was performed using a semiautomated edge finding and local
thresholding technique (Java Image, Version 1.0; Xinapse Systems, Leicester,
England; also available at: http://www.xinapse.com). The operator
clicks on the edge of the hypointense area and the program examines a region
5 x 5 pixels around the mouse click and computes the maximum intensity
gradient within that region. The pixel with the highest intensity gradient
is then used as the starting point for contour following, thus outlining the
region where the intensity is locally lower than at the starting pixel. A
black hole lesion was defined as a lesion appearing visibly hypointense to
the surrounding white matter on T1 images, detectable by the edge finding
software, and having a corresponding hyperintensity on FLAIR images.
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Figure 1. Fluid-attenuated inversion-recovery
magnetic resonance imaging scan of a patient with multiple sclerosis showing
the technique of determining the bicaudate ratio (BCR). The BCR is the minimum
intercaudate distance (solid line) divided by brain width along the same line
(dashed line).
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To correct for whole-brain atrophy, the ratio of brain parenchymal volume
to the total volume within the surface contour was measured on FLAIR images,
modified from a previously described method.6
The brain parenchymal volume was defined as the intracranial central nervous
system tissue with signal intensity of brain parenchyma after the cerebrospinal
fluid (subarachnoid space and ventricles) was extracted. Whole-brain atrophy
measurement was semiautomated, using computer edge finding to isolate brain
tissue from the skull, and intensity thresholding to measure the volume of
cerebrospinal fluid space including the sulci and ventricular system (Java
Image, 1.0). Caudate volume was measured on coronal gradient-echo images by
manually tracing the caudate nuclei bilaterally in each slice where they were
visible using an image analysis program (Java Image, 1.0 ). The posterior
boundaries of the caudate nuclei were defined as the point in the tail of
the caudate nuclei at which they became too small (or positioned in long axis)
and could not be seen. Caudate volume was calculated by summing the number
of voxels occupied by the caudate nuclei in each slice, multiplied by slice
thickness.
RELIABILITY OF MRI MEASUREMENTS
The same individual (R.A.B.) reanalyzed the MRI scans of 10 randomly
chosen patients with MS at least 2 weeks after the initial analysis. Mean
intraobserver coefficients of variation were 2.3% for the BCR, 1.2% for total
hyperintense T2 parenchymal plaque lesion load, 1.7% for T1 black hole volume,
0.32% for whole-brain atrophy, and 1.64% for caudate volume. A second individual
(S.R.P.) measured the BCR in 10 randomly chosen patients with MS, showing
a mean interobserver coefficient of variation of 4.2% for the BCR. Two healthy
volunteers underwent the MRI protocol twice (1 week apart) to determine the
effect of head position and slice selection on the BCR and to assess test-retest
reliability. A single observer (R.A.B.) analyzed these serial data from the
2 healthy volunteers, showing a mean interstudy coefficient of variation of
4.36% for the BCR.
STATISTICAL METHODS
We used descriptive statistics and plots to determine whether the variables
were normally distributed. All variables were normally distributed, except
for total hyperintense T2 parenchymal lesion load, which was skewed to the
right, and was converted to the logarithm prior to statistical analysis. Differences
between groups and correlations before adjusting for age were assessed using
the independent sample t test and the Pearson product
moment correlation test or Spearman rank correlation test. Age-adjusted correlations
and other multivariate comparisons were made using logistic or linear regression
modeling. Each variable was assessed using a separate model that also contained
the covariates of interest. The validity of all statistical models was assessed
by analysis of variance; any model at P>.05 was discarded.
After model validity was established, only assessed variables with P<.05 were considered statistically significant in that model. To
correct for the effect of aging on BCR, age was forced into all regression
models. To correct caudate volume for head size, total brain volume was forced
into the regression model with caudate volume as the other independent variable,
based on a previously described method.19 Statistical
analysis was performed using the SPSS software package (Version 10.0; SPSS
Inc, Chicago, Ill).
RESULTS
The BCR was positively correlated with age in patients with MS (Pearson r = 0.48, P<.001) and controls
(Pearson r = 0.33, P = .02);
therefore, all correlations involving BCR were age adjusted. To verify that
no effects were created as an artifact of age adjustment and to quantitate
their relative strengths, all correlations are also reported without age adjustment,
using the Spearman rank or Pearson product moment correlation test. However,
statistical age adjustment did not greatly affect the correlation results
when comparing the patients with MS with the controls, most likely because
the 2 cohorts were age-matched. Because all of the patients who took the SDMT
were younger than 55 years, age was not expected to affect the SDMT scores20 and our analysis confirmed that SDMT performance
was not related to age (Pearson r = 0.19, P = .39).
The BCR was higher in patients with MS (mean [SD], 0.11 [0.03]) than
the controls (0.09 [0.02]) (P<.001), indicating
subcortical atrophy in patients with MS. This difference persisted after adjusting
for age (P<.001). The difference in BCR between
controls and the patients with MS was attributable to differences in the intercaudate
distance (numerator of the BCR). The mean (SD) distance between the caudate
nuclei in patients with MS (11.88 [3.94] mm) was significantly higher than
in the controls (9.67 [2.67] mm) (P<.001), while
the brain width along the same line (denominator of the BCR) did not differ
between the 2 groups (P = .89). Figure 2 shows an increased BCR in patients with MS compared with
controls. The BCR (mean [SD]) did not differ between the patients with relapsing-remitting
MS (0.11 [0.04]) and the patients with secondary progressive MS (0.11 [0.03])
(P = .39). The mean (SD) SDMT score in the 23 patients
with MS was 43.70 points (10.99). The BCR was significantly inversely correlated
with SDMT score both before (Pearson r = -0.424, P = .04) and after (P<.01)
age adjustment, indicating that greater subcortical atrophy was associated
with poorer cognitive function (Figure 3).
This association between BCR and SDMT score remained statistically significant
after adjusting for whole-brain atrophy (P<.03).
Further regression modeling with age adjustment indicated that the BCR was
significantly predictive of the SDMT score (P<.02),
while neither T1 black hole lesion volume (P = .65)
nor total hyperintense T2 parenchymal lesion load (P
= .33) were predictive of the SDMT score. Also, regression modeling selected
BCR as the better predictor of the SDMT score (P
= .03) over caudate volume (P = .45) after adjusting
for age and total brain volume. Thus, the BCR was independently predictive
of the SDMT score, after considering all of the other MRI measures. The BCR
also correlated positively and significantly with the total hyperintense T2
parenchymal lesion load (Pearson r = 0.56, P<.001; P<.001) and with
T1 black hole lesion volume (Pearson r = 0.40, P<.002; P<.005) both before
and after age adjustment. However, BCR (Spearman r
= 0.20, P = .12; P = .47,
respectively), total hyperintense T2 parenchymal lesion load (Spearman r = 0.22, P = .08), or T1 black
hole lesion volume (Spearman r = 0.20, P = .14) did not significantly correlate with the EDSS score. Similarly,
BCR (P = .86 by logistic regression), T1 black hole
lesion volume (P = .82 by individual samples t test), and total hyperintense T2 parenchymal lesion load
(P = .21 by individual samples t test) did not differ between the patients with relapsing-remitting
MS and those with secondary progressive MS. No significant correlation was
found between the BCR and the caudate volume either with (P = .08) or without (Pearson r = -0.39, P = .06) adjustment for total brain volume in the subgroup
of 24 patients with MS. Table 1
summarizes associations between BCR and clinical and/or MRI variables in the
patients with MS.
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Figure 2. Box plots showing bicaudate ratio
(BCR) in 60 patients with multiple sclerosis (MS) vs 50 age- and sex-matched
healthy control subjects. Boxes indicate 1 SD from the mean. Vertical bars
indicate the total range of scores. The BCR was higher in patients with MS
(mean [SD] 0.11 [0.03]) than in the controls (0.09 [0.02]) (P<.001), indicating subcortical atrophy in the patients with MS.
This difference persisted after adjusting for age (P<.001).
The difference in BCR between controls and the patients with MS was attributable
to differences in the intercaudate distance (numerator of the BCR). The mean
distance between the caudate nuclei in the patients with MS (11.88 mm [3.94
mm]) was significantly higher than in the controls (9.67 mm [2.67 mm]) (P<.001), while the brain width along the same line (denominator
of the BCR) did not differ between the 2 groups (P=
.89).
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Figure 3. Scatterplot of bicaudate ratio
(BCR) vs Symbol Digit Modalities Test (SDMT) score of cognitive function in
23 patients with multiple sclerosis. The BCR was significantly inversely correlated
with the SDMT score both before (Pearson r= -0.42, P= .04) and after (P<.01) age
adjustment, indicating that greater subcortical atrophy was associated with
poorer cognitive function.
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Associations Between Bicaudate Ratio and Clinical and Magnetic Resonance
Imaging Variables in Patients With Multiple Sclerosis (MS)
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COMMENT
This study indicates that increased BCR is associated with MS, is related
to delayed cognitive processing speed, and correlates with the MRI disease
burden of T1 and T2 parenchymal plaque lesion load volumes. The increased
BCR in patients with MS is caused by the widening of the intercaudate distance,
but is not significantly related to caudate volume. Thus, the BCR may be closely
related to frontal horn ventricular enlargement due to atrophy of deep frontal
subcortical white matter. This atrophy appears in patients with relapsing-remitting
MS and patients with secondary progressive MS and across a range of physical
disabilities, corroborating recent findings that atrophy begins early in the
disease process of MS.4, 7 Most
importantly, increased BCR shows a close relationship to cognitive dysfunction,
a relationship that exists despite accounting for caudate volume, total brain
T1 and T2 MRI parenchymal plaque lesion load, and whole-brain atrophy. Thus,
increased BCR appears to show clinical relevance independent of general MRI
measures. This highlights the close relationship between subcortical atrophy
and cognitive dysfunction in patients with MS.
The BCR was shown to correlate with caudate volume in diseases primarily
affecting the caudate nuclei, such as Huntington disease. However, it has
shown only a weak association with caudate volume in normal aging and in patients
with certain psychiatric illnesses, such as autism and obsessive-compulsive
disorder.9-10,12
Thus, increases in BCR may represent different processes across disease states.
In this study, BCR was not significantly related to caudate volume. In light
of this finding, it is important to consider what the increased BCR may represent
in patients with MS and how this relates to the clinical findings. We found
that the difference in BCR between the controls and the patients with MS could
be accounted for by a larger numerator of the BCR (ie, a larger intercaudate
distance) in patients with MS. This suggests that atrophy of white matter
tracts and ventricular enlargement in the vicinity of the caudate nuclei are
responsible for the increased BCR in MS. Conversely, the lack of difference
in BCR denominator between the controls and the patients with MS suggests
that increased BCR in MS is not due to a lower brain width at this level,
such as might be caused by operculofrontal cortical atrophy. It is likely
that the increased BCR represents the caudate nuclei moving apart due to adjacent
white matter atrophy and ventricular enlargement present in MS. However, the
correlation between BCR and caudate volume was tested in a limited subset
of 24 patients with MS; therefore, it is possible that a relationship between
caudate volume and BCR was not detected because of the sample size and other
methodological limitations of the current study.
Atrophy of white matter axonal tracts in the frontal subcortical and
periventricular region, including those connecting to the caudate nuclei,
could explain the link between increased BCR and cognitive dysfunction. Frontal-subcortical
circuits, many of which eventually relay through the caudate nucleus, are
known to play a role in informational processing speed that contributes to
the SDMT.21 Interruption of the dorsolateral-prefrontal
circuit has been demonstrated in diseases affecting the caudate, including
stroke, Huntington disease, and neuroacanthocytosis.21
Disruptions of this pathway have produced deficiencies in retrieval and verbal
fluency.
None of the MRI variables examined were associated with physical disability
(EDSS score) or a relapsing-remitting vs secondary progressive clinical course.
The poor relationship between conventional MRI findings and physical disability
has been reviewed in 1999.2 Possible reasons
for this poor correlation include the heavy weighting of EDSS toward motor
symptoms, its nonlinearity, and the pathologic nonspecificity of lesions on
conventional MRI scans. The lack of correlation between BCR and EDSS score
could be explained by some of the reasons outlined earlier. Interruption of
the subcortical circuits that are pertinent to increased BCR may not cause
visual, motor, sensory, and gait impairments that contribute to the EDSS score.
Whole-brain atrophy and other general atrophy measures have shown better correlations
with the EDSS score.3, 6-8
Recent studies have demonstrated that atrophy in MS occurs early in the disease
course and is present in a large number of patients with relapsing-remitting
MS.4, 6-7 Patients
with relapsing-remitting MS who have only mild physical disability suffer
continual brain volume loss that underlies their otherwise episodic disease
course.6 In the present study BCR did not differ
significantly between patients with relapsing-remitting MS and those with
secondary progressive MS, in agreement with these previous findings that atrophy
is not limited to late-stage disease.
The SDMT scores in this study are similar to the scores in a larger
cohort (N = 103) of patients with MS from a different geographical area,22 suggesting external validity of our patient sample.
Our MRI measurements were highly reliable and were obtained quantitatively.
Our controls showed a high degree of internal validity in comparison to the
patients with MS and were derived from the same population, demography, and
MRI protocol as the patients with MS. However, since most of these individuals
were referred patients rather than healthy volunteers, it is possible that
they might differ from a healthy control population, even though the MRI scans
showed no abnormality and there was no clinical evidence of MS. It is likely
that any bias introduced by our control group would lead to an underestimation
rather than an overestimation of subcortical atrophy in patients with MS.
The BCR is an easily obtained measure of subcortical atrophy that can
be performed without complex computer-assisted techniques. In this study,
we have shown that the BCR reveals information about cognitive dysfunction
in MS that is not predicted by parenchymal plaque lesion load or whole-brain
atrophy. The BCR demonstrates high reliability and excellent scan-rescan reproducibility.
Future work should determine the sensitivity of BCR in longitudinal studies
and the use of BCR compared with other regional atrophy measures, such as
cortical atrophy,7 corpus callosum area, or
third ventricular width.4 While the recent
trend has been toward measuring whole-brain atrophy in MS, regional atrophy
measures may be useful for understanding specific clinical aspects of the
disease. The BCR may be useful as a surrogate marker to complement parenchymal
plaque lesion load assessments and whole-brain atrophy measures, helping to
understand cognitive dysfunction in MS.
AUTHOR INFORMATION
Accepted for publication September 24, 2001.
Author contributions: Study concept and design (Mr Bermel and Drs Bakshi and Jacobs); acquisition of data (Messrs Bermel and Tjoa and Dr Puli); analysis and interpretation
of data (Mr Bermel and Dr Bakshi); drafting of the
manuscript (Mr Bermel and Dr Bakshi); critical revision
of the manuscript for important intellectual content (Mr
Bermel and Drs Bakshi and Jacobs); statistical expertise (Mr Bermel and Dr Bakshi); obtaining funding (Mr
Bermel and Drs Bakshi and Jacobs); administrative, technical, and material
support (Mr Tjoa and Dr Puli); study supervision (Drs Bakshi and Jacobs).
This study was supported in part by a Student Interest Group in Neurology
(SIGN) American Academy of Neurology Summer Scholarship, American Academy
of Neurology, St Paul, Minn (Mr Bermel), and grant 1 K23 NS42379-01 from the
National Institute of Neurological Disorders and Stroke, National Institutes
of Health, Bethesda, Md (Dr Bakshi).
This work was presented in preliminary form at the American Society
of Neuroimaging 2001 annual meeting, Las Vegas, Nev, January 26, 2001; as
the Medical Student Essay G. Milton Shy Award presentation at the 2001 American
Academy of Neurology annual meeting, Philadelphia, Pa, May 9, 2001; and at
the 2001 annual meeting of the American Neurological Association, Chicago,
Ill, October 1, 2001.
We thank Jacek Dmochowski, PhD, Department of Social and Preventative
Medicine, Division of Biostatistics, University at Buffalo, State University
of New York, for assistance with the statistical analysis. We are also grateful
to Shelton D. Caruthers, PhD, for assistance with the MRI computer analysis.
Technical support was provided by the Center for Computational Research and
Science and Engineering Node Services of the State University of New York,
Buffalo. We thank Mark Horsfield, PhD, for developing and installing the neuroimaging
analysis software.
Corresponding author and reprints: Rohit Bakshi, MD, Buffalo Neuroimaging
Analysis Center, Jacobs Neurological Institute, 100 High St, Buffalo, NY 14203
(e-mail: rbakshi{at}buffalo.edu).
From the Buffalo Neuroimaging Analysis Center, Jacobs Neurological
Institute (Messrs Bermel and Tjoa and Drs Bakshi, Puli, and Jacobs); Department
of Neurology, Kaleida Health and the University at Buffalo, State University
of New York (Drs Bakshi and Jacobs); and the Department of Imaging Services,
Kaleida Health (Dr Bakshi), Buffalo, NY.
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