 |
|
Cognitive and Physiologic Correlates of Subclinical Structural Brain Disease in Elderly Healthy Control Subjects
Ian A. Cook, MD;
Andrew F. Leuchter, MD;
Melinda L. Morgan, PhD;
Elise Witte Conlee, PhD;
Steven David;
Robert Lufkin, MD;
Ashkan Babaie, MD;
Jennifer J. Dunkin, PhD;
Ruth O'Hara, PhD;
Sara Simon, PhD;
Amy Lightner, MD;
Susan Thomas, MD;
David Broumandi, MD;
Neeraj Badjatia, MD;
Laura Mickes;
Rajal K. Mody, MD;
Sanjaya Arora, MD;
Zimu Zheng, MD;
Michelle Abrams, RN;
Susan Rosenberg-Thompson, MSN
Arch Neurol. 2002;59:1612-1620.
ABSTRACT
| |
Context Healthy elderly persons commonly show 4 types of change in brain structure—cortical
atrophy, central atrophy, deep white-matter hyperintensities, and periventricular
hyperintensities—as forms of subclinical structural brain disease (SSBD).
Objectives To characterize the volumes of SSBD present with aging and to determine
the associations of SSBD, physiology, and cognitive function.
Design Cross-sectional study.
Setting University of California, Los Angeles, Neuropsychiatric Institute.
Subjects Forty-three community-dwelling healthy control subjects, aged 60 through
93 years.
Main Outcome Measures Volumetric magnetic resonance imaging, neuropsychological testing, and
quantitative electroencephalographic coherence (functional connectivity) between
brain regions.
Results Regression models demonstrated significant relationships between SSBD
volumes, age, cognitive performance, and connectivity. Cortical and central
atrophy and periventricular hyperintensities had significant associations
with age while deep white-matter hyperintensities did not. Posterior atrophy
showed stronger associations with age than did anterior atrophy. Only a subset
of subjects at older ages showed large SSBD volumes; older subjects primarily
showed increasing variance of SSBD. Although all subjects scored within the
normal range on cognitive testing, SSBD volume was inversely related to performance,
most notably on the Trail-Making Test part B and the Shipley-Hartford Abstract
Reasoning test. Coherence had significant associations with SSBD. Path analysis
supported mediation of the effects of deep white-matter hyperintensities and
periventricular hyperintensities on cognition by altered connectivity. For
several measures, cognitive performance was best explained by coherence, and
only secondarily by SSBD.
Conclusions Modest volumes of SSBD were associated with decrements in cognitive
performance within the normal range in healthy subjects. Lower coherence was
associated with greater volumes of SSBD and increasing age. Path analysis
models suggest that brain functional connectivity mediates some effects of
SSBD on cognition.
INTRODUCTION
STRUCTURAL CHANGES of the brain are widely thought to be an inherent
part of aging, with significant atrophy and white matter changes reported
in 30% to 100% of the healthy elderly population.1-2
These changes seem to be related not only to age, but also to physical illnesses
(eg, hypertension, diabetes mellitus3-4).
They reach their highest prevalence in patients who have dementia,5 depression,6 and other
neuropsychiatric disorders.7 Nevertheless,
these structural features are not invariably associated with illness and are
considered by some to be features of normal aging.1, 8
Specific changes have been identified on magnetic resonance imaging
(MRI) scans: cortical atrophy, ventricular enlargement, deep white-matter
hyperintensities (DWMHs) in subcortical white matter, and periventricular
hyperintensities (PVHs) (Figure 1). The effect of these structural changes on cognitive or functional abilities
is unclear. All 4 can be subsumed under the rubric of "subclinical structural
brain disease" (SSBD) as a shorthand to review a broad literature and develop
a paradigm for examining structural changes in the aging brain.
|
|
|
|
Figure 1. Examples of the 4 types of subclinical
structural brain disease are shown with representative magnetic resonance
images. Arrows indicate the areas of each structural change: A, cortical atrophy
(increased sulcal cerebrospinal fluid); B, central atrophy (ventricular enlargement);
C, deep white-matter hyperintensities; and D, periventricular hyperintensities.
White spots around the scalp are fiducial markers placed at the sites of the
electroencephalographic electrodes.
|
|
|
General associations between SSBD and impairment have been reported,
with large volumes of atrophy and white matter lesions found in elderly subjects
who report subjective cognitive impairments,9-10
impaired mobility,11 and mood disorders.12-13 The converse association between
structural changes and poorer cognitive function has also been reported.4, 14-17
Nevertheless, there is not good agreement on the functional consequences of
structural disease, since others have reported little or no association.18-20
Some of these inconsistencies likely reflect limitations of the measurement
techniques. Few studies have used precise methods to quantitate disease, though
volume of damage is likely to be important.21
Instead they have used semiquantitative rating scales3, 5, 10, 22-23
that yield nonvolumetric values and limit accuracy and reliability. Subjective
judgments for "thresholds" of disease on multiple point scales (ie, 0-3, 9,
or even 24 points)23 pose problems of systematizing
what differentiates, for example, a 1 from a 2 rating. Accuracy may be limited
by systematic overrating or underrating of pathologic abnormality, because
raters must determine what constitutes sufficient change to be rated greater
than 0; one study22 explicitly excluded some
types of PVHs as a "normal variant." Additionally, attention is not uniform
in evaluating all types of SSBD, such as differentiating DWMHs from PVHs,24 leading to inconsistent conclusions.5, 10, 14, 25-26
Regional differences have received variable attention: studies of global atrophy4 have reached different conclusions from investigations
that considered regions separately.27-28
A mechanism that may link SSBD to cognitive effects is disruption in
the connectivity between brain regions.7 Quantitative
electroencephalographic (QEEG) coherence can assess connections between regions29 and permit testing of this possible mechanism. Our
past work linked white matter lesions with decreased coherence in both healthy
subjects and and subjects with dementia.30-31
In this project, we combined volumetric MRI measurements in healthy
elderly subjects with neuropsychological assessments and coherence values
to clarify the cognitive correlates of SSBD and to investigate a potential
mechanism for these relationships. Figure
2 shows a path analysis model for evaluating whether SSBD's effects
on cognition arise from disruption in connectivity. Building on Inzitari's
propositions,7 we hypothesized that increasing
age would be associated with larger volumes of SSBD (arrow a), and that poorer cognitive performance would be associated with
both larger SSBD volumes (arrow b) and lower coherence
(arrow c). We further hypothesized that larger SSBD
volumes would be associated with greater disruption in connectivity (arrow d) as greater damage would be expected to produce greater
impairment in neuronal signal transmission. Finally, we used this path analysis
model to test the hypothesis that connectivity mediates the effects of SSBD
on cognitive function.
|
|
|
|
Figure 2. Path analysis allows testing of
the hypothesized relationships between subclinical structural brain disease
(SSBD), connectivity, and cognition. Arrows represent correlations among increasing
age and increasing volume of SSBD (a), increasing
volumes of SSBD and poor cognitive performance (b),
reduced connectivity and cognitive impairment (c),
and increasing disconnection with increasing SSBD volume (d).
|
|
|
SUBJECTS AND METHODS
SUBJECTS
We recruited 43 subjects from the community. All were at least 60 years
old, were in good health, and had normal findings on neurological examination.
Exclusion criteria included any history of an axis I psychiatric disorder;
any poorly controlled medical illness that could affect brain function (eg,
untreated hypothyroidism); current use of medications that could alter electroencephalographic
activity (eg, benzodiazepines); current or past drug or alcohol abuse; and
a history of head trauma, brain surgery, skull defect, stroke, or transient
ischemic attacks. This study was approved by the University of California,
Los Angeles, institutional review board; informed consent was obtained from
all subjects. Demographic characteristics are given in Table 1, including age, sex, educational level, and health status
(Cumulative Illiness Rating Scale–Geriatrics).32
Subclinical structural brain disease measures were available from all 43 subjects.
Because some subjects did not have usable QEEG recordings (eye-movement and/or
muscle-tension artifacts), or declined to complete all cognitive tests, subsets
of subjects (ranging from 28 to 43 subjects) were used for the analyses involving
QEEG data and cognitive scores; sample sizes are indicated for each analysis
(Table 2,
Table 3, and
Table 4).
Subjects with QEEG data were not statistically different on any demographic
factor or clinical rating from those without QEEG data. All subjects were
right-handed except for one left-handed woman.
|
|
|
|
Table 1. Demographic and Clinical Features for 43 Healthy Elderly Subjects*
|
|
|
|
|
|
|
Table 2. Relationship of SSBD and Cognitive Function*
|
|
|
|
|
|
|
Table 3. Regression Models of SSBD and Age as Predictors of Cognition*
|
|
|
|
|
|
|
Table 4. Correlations of Coherence with SSBD and Cognition*
|
|
|
|
|
|
|
Figure 4. Relationship among ventricular
cerebrospinal fluid volume (vCSF) (A), sulcal CSF volume (sCSF) (B), deep
white-matter hyperintensity (DWMH) volume (C), and periventricular hyperintensity
(PVH) and age for the 43 healthy control subjects. Volumes show a significant
increase with age for vCSF, sCSF, and PVH. There is a significant increase
in variability of volumes for those older than 70 years, most clearly seen
with sCSF. Volumes and regression lines are indicated separately for anterior
(solid squares, solid lines) and posterior regions (open circles, dashed lines),
and are significant for sCSF (F1,41 = 6.27,
P = .02) and vCSF
(F1,41 = 4.89, P = .03).
|
|
|
MRI METHODS
Brains were imaged using a 1.5-T scanner (Signa; GE Medical Systems,
Milwaukee, Wis). Parameters included a 256 x 256 window, 3-mm slices,
no interslice space, and a double–echo-pulse sequence with the following:
echo time, 3000 milliseconds; repetition time, 16 milliseconds; and echo time,
3000 milliseconds; repetition time, 80 milliseconds. Data were processed with
standard segmentation protocols, using the MRX software package.37
This software has shown sensitivity and reliability for detecting age-related
changes.38-39
Segmentation of brain, ventricular spaces, and lesions was performed
in 4 steps, by operators blinded to clinical and QEEG data. First, an outline
(mask) of the cerebral hemispheres was created for each scan plane, to delineate
brain parenchyma from other structures and to eliminate the latter from further
examination. Second, the operator selected sample points of each specific
tissue and fluid type: sulcal cerebrospinal fluid (sCSF), normal cortical
and subcortical gray matter, normal white matter, DWMHs, PVHs, and ventricular
fluid (vCSF). The computer then classified all volume elements (voxels) according
to these sample points via the signal intensity in both echo sequences.
Third, these automated tissue segmentations were reviewed for accuracy
and misclassifications were corrected. The operator searched for misclassifications
from partial volume effects at the boundary between segments (eg, brain and
CSF). Finally, voxels for each tissue were summed and converted to milliliter
values. Data were evaluated for the whole brain and for anterior and posterior
regions separately. These were divided by a vertical plane bisecting the line
between the genu and splenium of the corpus callosum, drawn where that distance
was smallest.
The use of the MRX software package has been investigated by Sandor
and colleagues,40 and Guttmann et al,41 who reported high interrater reliability using manually
drawn regions and good reproducibility of data from multiple scans on the
same subjects. We have verified the reproducibility in our laboratory with
values comparable to those reported by Guttmann et al.11
EEG METHODS
Recordings were performed while subjects rested in the eye-closed, maximally
alert state, as previously detailed.2, 31
Subjects were alerted by the technicians at the emergence of any sign of drowsiness.
A parietal electrode (Pz)–referential montage was used with electrodes
placed according to the 10-20 system.42 Signals
were digitally recorded (bandpass width, 0.3-70 Hz) and analyzed with the
QND system (Neurodata Inc, Pasadena, Calif). This system allowed for off-line
reformatting to bipolar channels for coherence calculations (Figure 3).2, 31 The first
20 to 32 seconds of artifact-free data were selected for processing by a technician,
with selections confirmed by a second technician (both blinded to subject
identity). Data were analyzed with a sample rate of 256 samples per second
per channel with a fast Fourier transform (1024 points) to calculate values
for coherence in 4-Hz wide bands previously examined (6-10 Hz, 10-14 Hz, and
14-18 Hz).2, 31, 43
|
|
|
|
Figure 3. Coherence was computed to detect
alterations in functional connectivity between regions connected by known
neuroanatomical pathways. Corticosubcortical connectivity was assessed in
prerolandic (A) and postrolandic (B) networks (modified from Leuchter et al2). Left hemisphere pathways are shown here; measures
were calculated separately from both hemispheres. Dots indicate electrodes;
arrows, pathways; and gray areas, electrode pairs at the ends of the pathways.
|
|
|
COHERENCE
Coherence measures the similarity between signals at different locations,
and is analogous to the square of a correlation coefficient between 2 EEG
channels.29 High values (near 1) indicate much
shared activity between the 2 channels, while low values (near 0) indicate
little shared activity. Computationally, coherence is a function of the power
spectra for 2 channels, x and y, at any given frequency f:
or the square of the cross-spectrum of the 2 channels divided by the
product of the spectra of the individual channels.
Thatcher et al44 measured the information
transmitted through corticocortical fibers by averaging coherence values among
recording sites overlying their distribution. By combining coherence values
from bipolar channels overlying known structures,2
this measure can assess functional connectivity in these areas of interest.
We previously used this approach to study disruption of connectivity in complex
networks of corticocortical and corticosubcortical fibers (eg, prerolandic,
frontal cortex [Figure 3A]) and
the projections of the visual and association cortex in the postrolandic area
(Figure 3B)2;
subjects with vascular dementia showed reductions in coherence in these networks.
In the present study, we measured coherence in the prerolandic and postrolandic
regions. As in our previous work,2 values were
multiplied by 10 and log-transformed to minimize skew and kurtosis. We limited
our examination to frequencies above 6 Hz, because these bands have shown
a consistent association between decreased coherence and impaired cognition.2
COGNITIVE MEASURES
We assessed cognition with measures previously shown to be sensitive
to structural changes. Boone et al21, 45
found that frontal measures are particularly sensitive to significant white
matter disease. The work of Heaton and colleagues46-47
with patients who have multiple sclerosis suggests that measures of attention,
incidental memory, and psychomotor function are also useful. Consequently,
we used the Trail-Making Tests48-50
to measure attention and processing speed (Trails A) and sequencing abilities
(Trails B). We used the Controlled Oral Word Association Test (FAS test, named for its stimuli)51 to
measure verbal fluency and semantic memory retrieval. The Shipley-Hartford
Abstract Reasoning test52 was used to assess
complex abstracting ability. The Boston Naming Test53
was used as a measure of confrontational naming.
STATISTICAL METHODS
Statistical analyses were performed using SPSS Analytic Software, Version
10.1 (SPSS Inc, Chicago, Ill). Continuous outcome data were analyzed with
linear regression models and t tests. Differences
in SSBD variance between age groups were examined with the Levene test for
equality of variance. The test of parallelism was used to evaluate the homogeneity
of regression slopes. Path analysis54 was used
to test whether the effects of SSBD on cognition were mediated by coherence.
Regression equations from the hypothesized path model were used to test whether
(1) the independent variable (SSBD) affected the mediator variable (connectivity),
(2) the independent variable affected the dependent variable (cognition),
and (3) the mediator variable (connectivity) affected the outcome variable.
If all 3 conditions were met, and the path coefficient of the independent
variable to the dependent variable was smaller than the path coefficient of
the mediator to the dependent variable when cognition was regressed on both
connectivity and SSBD, one could conclude that the hypothesized mediation
was present.55
RESULTS
EFFECTS OF AGE ON VOLUMES OF STRUCTURAL CHANGE
Significant linear relationships were found between age and central
atrophy (r41 = 0.47, P = .001), cortical atrophy (r41
= 0.46, P = .002), and PVHs (r41 = 0.47, P = .002), but not with
DWMHs (r41 = 0.22, P = .15) (Figure 4) (n =
43). Scatterplots revealed that, collectively, the older individuals had more
SSBD than the younger subjects, but larger volumes were not inevitable: many
subjects older than 75 years exhibited small volumes that were comparable
to those in adults younger than 75 years. A primary finding was the increased
variability in SSBD volumes for those older than 75 years, with a subgroup
of subjects showing much greater volumes than those seen in the 60- to 75-year-old
age group. Using the Levene test, this increase in variance was significant
for DWMHs (F41 = 9.17, P = .004) and PVHs
(F41 = 4.93, P = .03) but not for vCSF
(F41 = 3.67, P = .14) or sCSF (F41 = 0.02, P = .89).
Because DWMH was not associated with age, its relationship with other
factors was examined. Deep white-matter hyperintensity volume was significantly
correlated with health state (CIRS-G, r41
= 0.34, P = .01) and total PVH volume (r41 = 0.49, P<.001). Deep white-matter
hyperintensity volume was not correlated with Hachinski scores in our subject
pool, though this may reflect the limited range for the latter scale in these
subjects.
To evaluate regional differences, we regressed age against SBBD volumes
separately for the anterior and posterior regions. Different relationships
were found for the atrophy measures but not for the white matter changes (lines
in Figure 4). Regression slopes
were significantly different for anterior vs posterior sCSF and vCSF, with
age-related atrophy seen more prominently in the posterior regions. In contrast,
the slopes for DWMHs and PVHs were not significantly different for the anterior
and posterior regions. The same process was used to evaluate lateral differences
and age; no differences were found between the right and left hemispheres.
EFFECT OF SSBD ON COGNITIVE FUNCTION
Whole-brain SSBD volumes showed significant relationships with cognitive
performance; larger SSBD volumes were associated with poorer performance,
seen most strongly with Trails B performance (Table 2). A regional analysis (Table 2) revealed more similarities than differences between the
anterior and posterior regions. For example, Trails B performance was significantly
correlated with both anterior and posterior measures of PVHs, sCSF, and vCSF
but was associated only with anterior DWMHs.
JOINT RELATIONSHIP OF SSBD AND AGE WITH COGNITIVE PERFORMANCE
Regression models incorporated age and SSBD variables to predict cognitive
function (Table 3). After age
entered the model, sCSF was the most important structural variable in accounting
for the variance in Trails B performance, followed by DWMHs. In contrast,
after age had entered the model, DWMH was the best structural variable for
predicting performance on abstract reasoning (Shipley-Hartford Abstract Reasoning
test), followed by vCSF. While age clearly was important, the structural measures
further explained the variance in performance.
RELATIONSHIP OF SSBD AND FUNCTIONAL CONNECTIVITY, AND OF CONNECTIVITY
WITH COGNITION
Increasing PVH volumes were associated with significantly lower values
of coherence in prerolandic and postrolandic regions, as was the case for
DWMHs and vCSF (Table 4). In contrast,
the only significant association for sCSF was with postrolandic coherence,
the region with the greatest volumes of sCSF.
All cognitive measures showed associations with coherence, but with
differing patterns of association (Table
4). For example, Trails A performance showed significant associations
with coherence in both prerolandic and postrolandic regions, while Trails
B showed a pattern of multiple significant relationships with connectivity
in the both areas.
PATH ANALYSIS MODEL OF RELATIONSHIPS BETWEEN SSBD, CONNECTIVITY, AND
COGNITION
To link these observations, we used a path analysis model to test whether
the effects of SSBD on cognition are mediated through disrupted connectivity.
To build this model, we constructed a total white-matter disease variable
by summing PVH and DWMH measures, and a total atrophy variable by summing
sCSF and vCSF. In parallel, a total brain connectivity measure was constructed
by averaging coherence values in all bands and regions. We examined bivariate
statistics to determine which demographic variables were associated with our
most sensitive cognitive outcome variable (Trails B) and should be included
as confounders; age was significantly correlated with our cognitive measure
(r27 = 0.538, P
= .002), but none of the other parameters showed a significant association.
The central focus of this model is the potential mechanism relating structural
changes to cognitive performance; consequently, age was placed in the model
as exerting a physical influence through SSBD volumes. Paths and statistical
values are shown in Figure 5. The
relationships in this model support the hypothesis that altered connectivity
does mediate the effects of white-matter disease on cognition.
|
|
|
|
Figure 5. The mediation hypothesis was examined
using path analysis. A, The results for total white-matter disease burden
(total WMD) support a mediation role for disturbances in connectivity (total
COH or coherence) in WMD's effects on cognition (Trails B). Path a
indicates the standardized path coefficient ß = .51, P = .002;
b, ß = .45, P = .02;
c, ß = .34, P = .005; and
d, ß = .30, P = .048.
B, This mediation is not supported for the effects of atrophy
(total atrophy) on cognition (total COH). Path a
indicates ß = .45, P = .004;
b, ß = .31, P = .044;
c, ß = .41, P = .01; and
d, ß = .26, P = .08.
|
|
|
COMMENT
Our findings indicate a series of relationships between structural changes,
age, cognition, and connectivity. First, while volume of some types of SSBD
was strongly associated with increasing age, this association was not seen
uniformly across types of change or brain region. Furthermore, variance in
the volumes of SSBD increased with age, with only a subset of the oldest-old
subjects showing volumes of change significantly greater than the younger-old
age group. Second, there were detectable effects of most types of SSBD on
cognition, even though these healthy subjects had modest volumes of SSBD and
cognitive function in the normal range. Third, SSBD also affected functional
connectivity, with significant correlations with coherence. Fourth, our path
analysis models support the conclusion that effects of white-matter SSBD on
cognitive function are mediated through impairment of functional connections,
and support this mediation role at the trend level for atrophy.
The strongest relationships between SSBD and age were seen for central
and cortical atrophy and PVHs. There was a regional difference for atrophy,
with greater prominence over the posterior brain regions, both cortically
and centrally; in contrast, white-matter changes did not show a regional difference.
These findings are consistent with prior reports of atrophy and aging in healthy
subjects,56-57 but extend them
with the finding of regional differences. The regional prominence of posterior
atrophy with age in healthy subjects is, to our knowledge, a new finding and
is particularly intriguing given that Alzheimer disease is commonly associated
with atrophy and hypometabolism in posterior regions.58-61
The increased variability of SSBD in our older subjects is compatible
with prior reports from Jernigan et al62-63
and Goldstein et al.64 A clinical implication
is that increasing volumes of structural change with aging are not inevitable;
some of our most aged subjects showed small amounts of SSBD. Of note, deep
white-matter hyperintensity volume was not significantly related to age but
was related to health status. The differing patterns of association for PVHs
and DWMHs suggest that these white matter changes may be pathophysiologically
related but are not identical.
Our findings suggest that SSBD is associated with decrements in cognitive
performance even in a healthy elderly control population, seen most strongly
with the Trails B task. Trail-Making Tests are thought to reflect executive
function65-66 and Boone et al21 reported on the sensitivity of executive tasks to
white-matter disease throughout the brain. Trail-making performance has previously
been reported to be affected by age in healthy adults67
and by deterioration in the integrity of white-matter tracts,17
but without the volumetric data needed to test whether SSBD might be mediating
the effect of age. Our data do suggest a mediating role for SSBD on the decrement
in performance with aging. Our subjects had a high average level of education;
while this may be a limitation for generalizing, it suggests that even subjects
with presumably high brain reserve68-69
show detectable changes in cognition from SSBD as they age.
Volumes of SSBD showed influences on coherence, consistent with our
previous reports in other populations,30-31
and with the intrahemispheric coherence findings of Koyama et al70
and of Duffy et al71 using interhemispheric
coherence. While Geschwind and Kaplan72 and
Geschwind73 advanced the idea that a process
of "disconnexion" underlay the deficits in their clinical populations, our
data suggest that changes in connectivity may occur during asymptomatic aging.
This is also supported by recent observations by O'Sullivan et al17 using diffusion tensor imaging. Our findings support
a pathophysiological model in which the effects of SSBD produce disturbances
in information processing.
Coherence was significantly related to cognitive performance, with intriguing
differences among the tests for patterns of connectivity. The Trails B task
depends on numerous processing steps, and showed multiple associations with
connectivity variables, while the related but simpler Trails A task showed
fewer associations, suggesting that the performance of the Trails B test may
demand more complex integrative processing. A limitation of our study is that
these were resting-state EEGs: task-activated QEEG recordings might reveal
additional relationships.
The relationships between structural damage, coherence, and cognition
in the path analysis model support our hypothesis that the effects of SSBD
on cognition are mediated by disruptions in neuronal connectivity. To our
knowledge, this is the first demonstration of a mechanism that integrates
structural and functional connectivity data to explain the cognitive consequences
of subtle structural damage in normal aging. The relationship between structural
damage and disconnection is more clearly established for disturbances in white-matter
structures31-32 than for those
involving gray matter, so these findings are consistent with prior observations.
These findings are also largely consistent with previous work in dementia
subjects.31-32
In this group of healthy elderly subjects, even small amounts of SSBD
were seen to produce detectable changes in QEEG measures and decrements in
cognitive performance. For some forms of SSBD, the adverse effect on cognition
seems to be mediated via disruption in connectivity between brain regions,
though other factors are also important. We conclude that these mild degrees
of structural change can no longer be presumed to be inconsequential for cognitive
function.
AUTHOR INFORMATION
Accepted for publication January 25, 2002.
Author contributions: Study concept and design (Drs Cook, Leuchter, Dunkin, and O'Hara); acquisition of
data (Drs Cook, Witte Conlee, Lufkin, Babaie, Simon, Lightner,
Badjatia, Mody, and Arora, Mr David, and Mss Mickes, Abrams, and Rosenberg-Thompson); analysis and interpretation of data (Drs Cook, Leuchter,
Morgan, Witte Conlee, Thomas, Broumandi, Arora, and Zheng, and Mr David); drafting of the manuscript (Drs Cook, Leuchter,
Witte Conlee, Babaie, Simon, Broumandi, Badjatia, Mody, and Arora, Mr David,
and Mss Mickes, Abrams, and Rosenberg-Thompson); critical revision
of the manuscript for important intellectual content (Drs
Cook, Leuchter, Morgan, Lufkin, Dunkin, O'Hara, Lightner, Thomas, and Zheng); statistical expertise (Drs Cook, Morgan, and Witte
Conlee); obtained funding (Drs Cook and Leuchter); administrative, technical, and material support (Drs Cook, Leuchter, Babaie, O'Hara, Simon, Thomas, Broumandi, Mody, and Arora,
Mr David, and Mss Mickes, Abrams, and Rosenberg-Thompson); study supervision (Drs Cook, Leuchter, Witte Conlee, and Dunkin).
This study was supported by Career Development Award K08-MH01483 (Dr
Cook) and by grants R01-MH40705 and Research Scientist Development Award K02-MH01165
(Dr Leuchter) from the National Institute of Mental Health, Bethesda, Md.
We also acknowledge support by a Young Investigator Award (Rio Hondo Investigator)
from the National Alliance for Research in Schizophrenia and Depression, Great
Neck, NY (Dr Cook).
We thank Ron Kikinis, MD, for access to the MRX software; to Barbara
Siegman, REEGT, Mariahn Smith, REEGT, and Suzanne Hodgkin, REEGT, for recording
and processing the QEEG data; to Valerie Gauche for supervising the MRI scans;
and to Kelly Nielson for expert assistance in the preparation of the manuscript,
figures, and tables.
Corresponding author and reprints: Ian A. Cook, MD, University of
California, Los Angeles, Neuropsychiatric Institute, 760 Westwood Plaza, Los
Angeles, CA 90024-1759 (e-mail icook{at}ucla.edu).
From the University of California, Los Angeles, Neuropsychiatric Institute
and the University of California, Los Angeles, School of Medicine.
REFERENCES
| |
1. Ketonen LM. Neuroimaging of the aging brain. Neurol Clin. 1998;16:581-598.
FULL TEXT
|
ISI
| PUBMED
2. Leuchter AF, Newton TF, Cook IA, Walter DO, Rosenberg-Thompson S, Lachenbruch PA. Changes in brain functional connectivity in Alzheimer-type and multi-infarct
dementia. Brain. 1992;115:1543-1561.
FREE FULL TEXT
3. Longstreth WT Jr, Manolio TA, Arnold A, et al for the Cardiovascular Health Study. Clinical correlates of white matter findings on cranial magnetic resonance
imaging of 3301 elderly people. Stroke. 1996;27:1274-1282.
FREE FULL TEXT
4. Longstreth WT Jr, Arnold AM, Manolio TA, et al for the Cardiovascular Health Study. Clinical correlates of ventricular and sulcal size on cranial magnetic
resonance imaging of 3301 elderly people. Neuroepidemiology. 2000;19:30-42.
FULL TEXT
|
ISI
| PUBMED
5. Smith CD, Snowdon DA, Wang H, Markesbery WR. White matter volumes and periventricular white matter hyperintensities
in aging and dementia. Neurology. 2000;54:838-842.
FREE FULL TEXT
6. Kumar A, Bilker W, Jin Z, Udupa J. Atrophy and high intensity lesions: complementary neurobiological mechanisms
in late-life depression. Neuropsychopharmacology. 2000;22:264-274.
FULL TEXT
|
ISI
| PUBMED
7. Inzitari D. Age-related white matter changes and cognitive impairment. Ann Neurol. 2000;47:141-143.
FULL TEXT
|
ISI
| PUBMED
8. Fazekas F, Schmidt R, Kleinert R, Kapeller P, Roob G, Flooh E. The spectrum of age-associated brain abnormalities: their measurement
and histopathological correlates. J Neural Transm Suppl. 1998;53:31-39.
PUBMED
9. Barber R, Scheltens P, Gholkar A, et al. White matter lesions on magnetic resonance imaging in dementia with
Lewy bodies, Alzheimer's disease, vascular dementia, and normal aging. J Neurol Neurosurg Psychiatry. 1999;67:66-72.
FREE FULL TEXT
10. de Groot JC, de Leeuw FE, Oudkerk M, et al. Cerebral white matter lesions and cognitive function: the Rotterdam
Scan Study. Ann Neurol. 2000;47:145-151.
FULL TEXT
|
ISI
| PUBMED
11. Guttmann CR, Benson R, Warfield SK, et al. White matter abnormalities in mobility-impaired older persons. Neurology. 2000;54:1277-1283.
FREE FULL TEXT
12. Lenze E, Cross D, McKeel D, Neuman RJ, Sheline YI. White matter hyperintensities and gray matter lesions in physically
healthy depressed subjects. Am J Psychiatry. 1999;156:1602-1607.
FREE FULL TEXT
13. Lesser IM, Boone KB, Mehringer CM, Wohl MA, Miller BL, Berman NG. Cognition and white matter hyperintensities in older depressed patients. Am J Psychiatry. 1996;153:1280-1287.
FREE FULL TEXT
14. Baum KA, Schulte C, Girke W, Reischies FM, Felix R. Incidental white-matter foci on MRI in "healthy" subjects: evidence
of subtle cognitive dysfunction. Neuroradiology. 1996;38:755-760.
FULL TEXT
|
ISI
| PUBMED
15. Gunning-Dixon FM, Raz N. The cognitive correlates of white matter abnormalities in normal aging:
a quantitative review. Neuropsychology. 2000;14:224-232.
FULL TEXT
|
ISI
| PUBMED
16. Kuller LH, Shemanski L, Manolio T, et al. Relationship between ApoE, MRI findings, and cognitive function in
the Cardiovascular Health Study. Stroke. 1998;29:388-398.
FREE FULL TEXT
17. O'Sullivan M, Jones DK, Summers PE, Morris RG, Williams SC, Markus HS. Evidence for cortical "disconnection" as a mechanism of age-related
cognitive decline. Neurology. 2001;57:632-638.
F |
