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Psychiatry Research: Neuroimaging Section 90 Ž1999. 125]140
Relationship between brain electrical activity and
cortical perfusion in normal subjects
Andrew F. Leuchter a,b,c,U , Sebastian H.J. Uijtdehaage b, Ian A. Cook a,b,
Ruth O’Harae, Mark Mandelkern d,f
a
Quantitati¨ e EEG Laboratory, UCLA Neuropsychiatric Institute and Hospital, 760 Westwood Plaza, Los Angeles,
CA 90024, USA
b
Department of Psychiatry and Biobeha¨ ioral Sciences, UCLA School of Medicine, Los Angeles, CA, USA
c
Medication De¨ elopment Research Unit, West Los Angeles VA Medical Center, Los Angeles, CA, USA
d
Department of Nuclear Medicine, West Los Angeles VA Medical Center, Los Angeles, CA, USA
e
Department of Psychiatry and Beha¨ ioral Sciences, Stanford Uni¨ ersity, Stanford, CA, USA
f
Department of Physics, Uni¨ ersity of California, Ir¨ ine, CA, USA
Received 30 December 1998; received in revised form 25 January 1999; accepted 31 January 1999
Abstract
Cerebral glucose uptake and perfusion are accepted as tightly coupled measures of energy utilization in both
normal and diseased brain. The coupling of brain electrical activity to perfusion has been demonstrated, however,
only in the presence of chronic brain disease. Very few studies have examined the relationship between cerebral
electrical activity and energy utilization in normal brain tissue. To clarify this relationship, we performed 33
Ž
.
H 15
2 O-positron emission tomography PET scans in six normal subjects both at rest and during a simple motor task,
and acquired surface-recorded quantitative electroencephalogram ŽQEEG. data simultaneously with isotope injection. We examined the associations between cerebral perfusion directly underlying each recording electrode and
three QEEG measures Žabsolute power, relative power, and cordance.. All EEG measures had moderately strong
coupling with perfusion at most frequency bands, although the directions of the associations differed from those
previously reported in subjects with stroke or dementia. Of the three QEEG measures examined, cordance had the
strongest relationship with perfusion Žmultiple R 2 s 0.58.. Cordance and PET were equally effective in detecting
lateralized activation associated with the motor task, while EEG power did not detect this activation. Electrodes in
the concordant state had a significantly higher mean perfusion than those in the discordant state. These results
U
Corresponding author. Tel.: q1-310-825-0207; fax: q1-310-825-7642; e-mail: afl@qeeg.npi.ucla.edu
0925-4927r99r$ - see front matter Q 1999 Elsevier Science Ireland Ltd. All rights reserved.
PII: S 0 9 2 5 - 4 9 2 7 Ž 9 9 . 0 0 0 0 6 - 2
126
A.F. Leuchter et al. r Psychiatry Research: Neuroimaging Section 90 (1999) 125]140
indicate that normal brain electrical activity has a moderately strong association with cerebral perfusion. Cordance
may be the most useful QEEG measure for monitoring cerebral perfusion in subjects without chronic brain disease.
Q 1999 Elsevier Science Ireland Ltd. All rights reserved.
Keywords: Cerebral perfusion; Quantitative electroencephalogram ŽQEEG.; Positron emission tomography ŽPET.; Cordance;
Normal subjects
1. Introduction
Cerebral glucose uptake and blood flow long
ago were hypothesized to be comparable measures of energy utilization ŽRoy and Sherrington,
1890.. This hypothesis now has been tested with
imaging techniques, such as autoradiography,
positron emission tomography ŽPET., and single
photon emission computed tomography ŽSPECT..
In normal subjects, cerebral glucose uptake and
blood flow generally are accepted as tightly coupled measures of cerebral energy utilization ŽDes
Rossiers et al., 1974; Sokoloff, 1977, 1981..
Brain electrical activity represents the single
greatest demand on cerebral metabolism
ŽErecinska and Silver, 1989., suggesting that measurement of electrical energy also should be coupled to cerebral metabolism and perfusion. Berger
Ž1938. first hypothesized that the rhythmic activity
in the surface-recorded electroencephalogram
ŽEEG. closely reflected brain metabolic activity.
Interestingly, however, most previous studies have
shown that EEG reflects cerebral energy utilization accurately only under conditions of extreme
dysfunction. Animal models using blood vessel
occlusion ŽCartheuser, 1988. or metabolic suppression with medication ŽKlementavicius et al.,
1996. have demonstrated strong associations
between the cerebral metabolic rate for oxygen
and EEG power and frequency. Similarly, studies
of human subjects suffering from stroke ŽTolonen
and Sulg, 1981; Nagata et al., 1982; Nagata, 1988.,
degenerative brain diseases ŽStigsby et al., 1981;
Wszolek et al., 1992; Passero et al., 1995; Valladeres-Neto et al., 1995., or epilepsy ŽJibiki et al.,
1994. have found that cerebral perfusion and
metabolism have a negative association with
slow-wave energy and a positive association with
alpha energy.
Very few studies have examined associations
between electrical activity and cerebral perfusion
in the normal brain. Most studies asserted to
examine normal brain actually focused upon the
undamaged cerebral hemisphere in stroke
patients ŽMelamed et al., 1975; Nagata, 1989;
Nagata et al., 1989.. It now is known, however,
that the contralateral hemisphere in stroke
patients may show changes in metabolism and
perfusion ŽSerrati et al., 1994., perhaps reflecting
transcallosal fiber degeneration ŽIglesias et al.,
1996.. Some studies utilized elderly volunteers
with incompletely characterized health status
ŽObrist et al., 1963. or patient volunteers ŽIngvar
and Risberg, 1967; Ingvar et al., 1976; Ingvar,
1979. who suffered from chronic psychiatric illnesses andror conditions that are currently
recognized as risk factors for brain disease Ži.e.
alcohol abuse, atherosclerosis .. These studies reported relationships between cerebral electrical
activity and perfusion which ranged from weak
ŽObrist et al., 1963. to moderately strong ŽIngvar
and Risberg, 1967; Ingvar et al., 1976; Ingvar,
1979., but it is not clear that the subjects examined were truly representative of normal function. Only two studies have examined the association between EEG power and PET scanning
Žusing the fluorodeoxyglucose-18 technique.
ŽBuchsbaum et al., 1984. or SPECT scanning
Žusing the Xenon-133 technique. ŽOkyere et al.,
1986. obtained simultaneously in normal subjects.
Although both groups found moderately strong
associations between electrical activity Žin the alpha band. and perfusion, consistent associations
were limited to the occipital regions. Examination
of other brain regions showed a variable relationship between EEG power and metabolism, with
both positive and negative associations in the
same EEG frequency band in different brain regions ŽBuchsbaum et al., 1984..
A.F. Leuchter et al. r Psychiatry Research: Neuroimaging Section 90 (1999) 125]140
Because of inconsistencies in the methods and
results from previous studies, the relationship
between surface-recorded EEG in different frequency bands and the perfusion of underlying
brain tissue remains unclear. We performed the
current study to clarify the associations between
quantitative EEG ŽQEEG. measures and cerebral perfusion Žusing 15 O-positron emission tomography. in normal subjects at rest and while
performing a motor activation task. A secondary
aim of this study was to derive a QEEG index
predicting relative perfusion. We examined three
QEEG measures: absolute power, relative power,
and cordance. Cordance integrates absolute and
relative power, and in previous work in subjects
with brain disease Ži.e. stroke, dementia. has
shown more robust and consistent associations
with cerebral perfusion Žas measured by HMPAO-SPECT. than either power measure alone
ŽLeuchter et al., 1994a,b..
2. Materials and methods
2.1. Subjects
Six right-handed male subjects Žages 20]30,
mean age 28. with no history of psychiatric, medical, or neurologic illness were recruited from the
community. All subjects were assessed with a
clinical history, neurologic examination, and magnetic resonance imaging ŽMRI. scanning to confirm the absence of neurologic disease. All experiments were approved by the UCLA Human Subjects Protection Committee, and subjects’ consent
was obtained according to the Declaration of
Helsinki.
2.2. Acti¨ ation task
The subjects were examined in a darkened,
sound-attenuated room, in both the resting condition and while they were performing a simple
motor task Žsqueezing a foam rubber ball with the
right or left hand.. Both the resting and motor
task conditions were repeated in the eyes-open
and eyes-closed states. For the resting condition,
subjects were maintained in the maximally alert
state for 2 min through frequent re-alerting. For
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the motor task condition, subjects were instructed
to squeeze a foam rubber ball with either the
right or left hand continuously for a period of 2]3
min Žincluding a pre-recording period of 1 min..
Subjects underwent scanning in the following
order of conditions: Ž1. resting, eyes closed; Ž2.
right hand squeezing, eyes open; Ž3. left hand
squeezing, eyes open; Ž4. right hand squeezing,
eyes closed; Ž5. resting, eyes open; Ž6. right hand
squeezing, eyes closed; Ž7. right hand squeezing,
eyes open; and Ž8. left hand squeezing, eyes open.
2.3. PET scanning procedure
PET scans were performed using a Siemens
ECAT 953 scanner which has an axial acceptance
of 10.5 cm and generates 31 transaxial planes.
The patient was positioned on the gantry so
that the scan planes were parallel to the
cantho]meatal line. PET data were acquired
starting immediately after isotope injection in 30-s
frames, and data from the second frame Žstarting
30 s after injection, representing tracer uptake in
the brain. were used for processing. Fig. 1 summarizes the recording procedure during the motor task.
Exactly 25 mCi of H 2 15 O-tracer were injected
intravenously as a bolus while the subject was in
the resting state or Žfor the activation conditions.
60 s after the subject began performing the motor
task. Subjects were permitted to rest for 10 min
between motor task sessions, with eight scans
performed per subject. We performed 48 H 15
2 OPET scans, but because of a failure to obtain
simultaneous EEG data, excessive EEG artifacts
during data acquisition, or technical difficulties
with the PET scanner, 15 scans were eliminated
from the analysis for a total of 33 scans.
2.4. QEEG recording
QEEG data were acquired simultaneously with
PET scanning using a QND electroencephalograph ŽNeurodata; Pasadena, CA., with a sampling rate of 256 pointsrchannelrsecond, high
frequency filter of 70 Hz., and low frequency filter
of 0.5 Hz. Electrodes were placed according to an
enhanced montage based upon the International
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A.F. Leuchter et al. r Psychiatry Research: Neuroimaging Section 90 (1999) 125]140
Fig. 1. Sequence and time course of the PETrEEGrtask
protocol.
10]20 System ŽFig. 2. using a custom electrode
cap ŽElectroCap Inc; Eaton, OH..
EEG data were selected from the time frame
15]60 s after injection. The EEG data sampling
window started before the PET data sampling
window, since cerebral blood flow changes may
lag behind electrical activity by as much as 10 s
ŽToga et al., 1995.. The EEG data sampling window was slightly longer than that for PET, to
facilitate selection of artifact-free data. Any data
that were contaminated by drowsiness, muscle,
motion, or other artifacts were deleted.
All artifact-free EEG data recorded in the designated time frame were selected for further processing; the amount varied between 12 and 30 s
per EEG. Absolute EEG power Žmeasured in
mV 2 . was calculated in a series of overlapping
4-Hz bands between 0 and 40 Hz Ž0]4 Hz, 2]6
Hz, 4]8 Hz, etc.. using a fast Fourier transform
ŽPress et al., 1986.. Power was calculated according to the single-source Žlocal Laplacian. technique described by Hjorth Ž1975., in which voltage values from surrounding nearest-neighbor
electrodes are averaged to obtain voltage attributable to a single electrode. This montage was
selected since the local Laplacian derivation more
accurately reflects the electrical activity attributable to a given electrode than common-reference EEG data ŽGevins, 1990..
2.5. Cordance algorithm
Cordance combines absolute and relative
QEEG power measurements, characterizing both
the magnitude of and the relationship between
Fig. 2. Map showing 36 electrodes used for EEG recording.
This montage is based upon the International 10]20 System,
with additional electrodes placed along diagonals between
electrode sites to provide enhanced coverage of the frontal
and parietal association cortices. Lines indicate ‘nearest
neighbor’ electrode pairs used for EEG power and cordance
calculations. Boxes over the central area delineate those electrodes over the left and right motor strips which were used to
detect lateralized activation.
absolute and relative power at each recording
electrode. The revised cordance algorithm reported here is based upon three steps in the
processing of power, each of which is designed to
refine the association between power and cerebral perfusion. These steps are: Ž1. reattribution
of power from bipolar pairs of electrodes to individual electrodes; Ž2. spatial normalization of absolute and relative power across brain areas; and
Ž3. characterization of the association between
normalized absolute and relative power measures.
Each step in processing of power, as well as the
rationale for each step, is discussed separately
below.
2.5.1. Reattributing power
There is no single electrode reference which is
ideal for calculating power values. All referencing
methods may obscure or distort information in
the EEG signal, by underestimating power measurements from electrodes close to the reference
andror contributing local signal effects from the
A.F. Leuchter et al. r Psychiatry Research: Neuroimaging Section 90 (1999) 125]140
reference to distant electrodes. One indicator of
the capacity of a reference to reflect accurately
the activity originating under a recording electrode is to examine the association between power
calculated using different referencing methods
and the perfusion underlying each electrode. Cook
et al. Ž1998. recently compared linked-ears, reattributed amplitude Ži.e. single-source or Hjorth
transformation., and reattributed power referencing methods in terms of the strength of association between power at a recording electrode and
perfusion underlying that electrode Žusing H 15
2 Opositron emission tomography.. This study found
that the reattributed power measure showed the
strongest association with perfusion underlying
the recording electrode.
To calculate reattributed power, absolute power
was calculated for each bipolar pair of nearestneighbor electrodes shown in the grid of electrodes in Fig. 2 Ževery electrode paired with its
nearest neighbors.. Absolute power was reattributed from electrode pairs to individual electrodes, by averaging EEG power for all electrode
pairs which included that electrode ŽCook et al.,
1998.. Before averaging, any channels containing
significant muscle or other artifacts were deleted
from the grid. If the deletion of channels for
reason of artifact left any electrode with fewer
than two remaining nearest neighbors, that electrode was deleted completely. After averaging, a
square-root transformation was performed to
minimize skewness and kurtosis ŽLeuchter et al.,
1993.. Lastly, relative EEG power Žabsolute power
in a frequency band divided by total power for the
entire spectrum. was calculated based upon the
reattributed absolute power.
2.5.2. Spatial normalization of power
Absolute power is affected by factors that may
not directly be related to brain function Ži.e. interelectrode distances, characteristics of tissue
between electrode and brain. and is difficult to
compare between individuals since total power
varies widely between individuals. Absolute power
therefore commonly is normalized with respect to
the energy across the frequency spectrum Ži.e.
relative power, or the percentage of energy in a
frequency band.. While relative power controls
129
for major sources of power variation between
individuals, it also may suppress information
which be contained in the absolute power measure. Leuchter et al. Ž1993. reported that absolute
and relative power measures were complementary
measures of brain activity; while they are strongly
interrelated, they also contain unique information
regarding brain function.
In order to retain information from both absolute and relative power, they each were normalized independently across electrode sites Žspatial
normalization. using z-scores. Mean absolute and
relative power was calculated across all electrode
sites in each frequency band f. Z-scores then
were calculated for each electrode site s in each
frequency band. This process yielded normalized
absolute and relative power values for each electrode site w A norm Ž s, f . and R norm Ž s, f . , respectivelyx.
2.5.3. Relating normalized power measures
It previously has been reported that absolute
and relative power can be affected differently by
brain dysfunction. Either absolute or relative
power may be little changed by brain dysfunction;
conversely, absolute power in a band over an area
of dysfunction may decrease while relative power
in that same band may increase ŽLeuchter et al.,
1994a..
In order to retain the information content of
both absolute and relative power, A norm and R norm
first are summed to yield a single value that
indicates the cordance value w ZŽ s, f . x at each electrode site:
ZŽ s, f . s A normŽ s, f . q R normŽ s, f .
Next, A norm and R norm are examined to determine if they are above or below their respective mean power values. One of four conditions
exists at each electrode site in each frequency
band: Ž1. A norm and R norm are both above their
respective means; Ž2. A norm is below but R norm is
above their respective means; Ž3. A norm and R norm
both are below their respective means; or Ž4.
A norm is above but R norm is below their respective
mean values for that frequency band. Each of
these conditions corresponds to one of the quadrants in Fig. 3. If an electrode is characterized by
130
A.F. Leuchter et al. r Psychiatry Research: Neuroimaging Section 90 (1999) 125]140
Fig. 3. Diagram showing the methods by which cordance relates normalized absolute and relative power. Quadrant in the above
figure is indicated both by polar coordinates Ž08, 908, 1808, 2708. and by quadrant numbers ŽI, II, III, IV.. After normalization of the
absolute and relative power values described in the methods, the normalized values w A norm Ž s, f . and R norm Ž s, f . x are summed to
create the cordance value w ZŽ s, f . x. In addition to a cordance value, each electrode also has a categorical value indicating whether
A norm Ž s, f . and R norm Ž s, f . both are above or below their respective means Žthe concordant state, Fig. 3a., or whether one is above
while the other is below its respective mean Žthe discordant state, Fig. 3b..
Z values both of which are above or below the
mean values Ži.e. quadrant I or III, or polar
coordinates between 0 and 908 or 180 and 2708,
respectively., that electrode shows concordance
Ži.e. absolute and relative power are positively
associated; see Fig. 3a.. If conversely either A norm
or R norm is above while the other is below their
respective mean values Ži.e. quadrant II or IV, or
polar coordinates between 90 and 1808 or 270 and
3608, respectively., that electrode shows discordance Ži.e. absolute and relative power are negatively associated; see Fig. 3b..
Each electrode site in each frequency band
therefore has a cordance value ZŽ s, f . , as well as a
categorical descriptor indicating whether it is in
the concordant or discordant state.
2.6. Integrating QEEG data and PET images
The technique for measuring cerebral perfusion under each electrode has been described in
detail elsewhere ŽCook et al., 1998. and is described only briefly here. This technique allowed
for accurate and consistent spatial registration of
each EEG electrode with the immediately under-
lying cerebral cortex. Prior to PET scanning, Lucite markers containing 22 NaCl were placed over
each EEG electrode recording site to permit visualization of the sites on PET images ŽFig. 3..
Images were processed on a Tatung microCOMPstation5 SPARC-compatible workstation ŽTatung
Science and Technology, Milpitas, CA. running
AVS software ŽAdvanced Visualization System,
Waltham, MA.. With the AVS software, a threedimensional PET image was reconstructed based
upon the summation of the axial image data
across slices.
The centers of the 22 NaCl fiducial markers
were identified, and lines were automatically
drawn from each electrode location to a center
point in the brain; this point is defined as the
mid-point in the x- and y-directions in the axial
plane best showing the basal ganglia. Each line
served as the central axis for a cylindrical volume
of cerebral cortex under each electrode site. The
outer boundary of this volume was a 2.5-cm2
circular region of interest ŽROI. centered on the
line segment, placed at the external level of the
cortex Žwhere the pixel count exceeded background levels.; 2.5 cm2 is the cortical surface area
A.F. Leuchter et al. r Psychiatry Research: Neuroimaging Section 90 (1999) 125]140
which has been shown to generate the unique
activity recorded at a surface EEG electrode
ŽGevins, 1990.. The inner boundary of the cylinder was the corresponding circle at a depth of 1.0
cm below the cortical surface. These 2.5-cm2 =
1.0-cm cylindrical volumes therefore spanned the
cortical ribbon and constituted a digital sample of
cortex underlying each electrode. The actual PET
counts within these regions were summated to
determine the perfusion value for each electrode
site. These automatically computed volumes obviated the need for hand drawing of ROIs and the
inaccuracies that would arise from estimating the
cross-sections as these cylindrical ROIs traversed
sequential image slices. To mitigate the slight
fluctuations of the 25 mCi H 15
2 O tracers, PET
values were scaled from 0 Žthe lowest count. to 1
Žthe highest count.. The resulting PET values
therefore reflected relative perfusion.
2.7. Association between QEEG measures and
perfusion
Each combined EEG-PET recording resulted
in an array of paired observations. Each pair
consisted of an estimate of relative perfusion and
a QEEG measure. For each 4 Hz band, relative
perfusion was regressed separately on EEG power
and ZŽ s, f . . The number of electrodes available for
analysis in each individual scan was utilized as a
weight in the regression equation. Each regression analysis was based on a total of 670 observations. Residuals were systematically examined,
and no outliers Ždefined as standardized residuals
) 2. or other violations of the regression model
were observed. Data from all scans were pooled
and relative perfusion was regressed on dummycoded subject and condition variables to account
for subject and condition effects. In addition, the
mean relative perfusion values for electrodes
showing concordance and discordance were calculated separately, and differences in mean perfusion were examined using t-tests.
2.8. Deri¨ ation of a QEEG measure of perfusion
A tentative QEEG measure of relative perfusion was derived by determining the optimal com-
131
bination of frequency bands predicting PET values. In addition, the accuracy with which perfusion and the EEG measures each detected activation during the motor tasks was assessed. Two
regions of interest were defined over the motor
strip bilaterally, defined by the following EEG
electrodes Žleft: T3, C3, FC5, FC1; right: T4, C4,
FC6, FC2. Žregions are indicated by the rectangular boxes in Fig. 2.. Corresponding PET regions
underlying these electrodes also were identified.
For each individual motor activation session, we
determined whether PET, cordance, and EEG
power accurately detected lateralized activation
Ždefined as a maximum activation value in the
region of interest contralateral to the hand
squeezing.. The proportion of tests showing accurate lateralization for each measure was calculated and examined using a binomial test to determine if accuracy in lateralization exceeded
chance levels.
3. Results
3.1. Associations between QEEG and perfusion
The strength of the associations between power
or cordance and relative perfusion is displayed in
Fig. 4, where the magnitude of the partial correlation coefficients is graphed as a function of frequency. In most frequency bands, power and cordance w ZŽ s, f . x showed significant associations with
relative perfusion. For all EEG measures the
relationship with perfusion was triphasic: positive
associations were seen in the 4-Hz bands which
had a lower bound below 6 Hz; negative associations were seen in the bands which had a lower
bound between 6 and 10 Hz; and positive associations were seen in the bands which had a lower
bound at or above 12 Hz Žexcept for absolute
power, which had a negligible association between
12 and 18 Hz..
Among the EEG measures, the strongest associations with perfusion were obtained with ZŽ s, f . .
The associations between ZŽ s, f . and relative perfusion were similar in all conditions ŽFig. 5.. Only
the resting eyes-open condition yielded a deviant
pattern of partial correlations, none of which
were significant. Importantly, from all EEGs in
132
A.F. Leuchter et al. r Psychiatry Research: Neuroimaging Section 90 (1999) 125]140
Fig. 4. Plot showing the partial correlation coefficient between EEG power and cordance values w ZŽ s, f . x, and relative perfusion as a
function of frequency band. Statistical significance is indicated by horizontal lines representing the magnitude at which a correlation
coefficient attains significance: dashed line Ž- - - -. for Ps 0.05; dotted line Ž? ? ?. for Ps 0.01; dotted]dashed line Ž ] ? ] . for
Ps 0.001.
this condition more than 12 electrodes were removed due to artifacts. As we established in a
later stage of the analyses Žsee below., the validity
of ZŽ s, f . became questionable if more than 12
electrodes were removed.
A stepwise regression analysis was used to find
the optimal combination of frequency bands to
predict relative perfusion. ZŽ s, f . values for the
respective 4-Hz band were offered as predictors
of perfusion Žadjusted for subject and condition
effects .. The results revealed that ZŽ s, f . for the
8]12-Hz Ž‘alpha’. band and for the 20]24-Hz
band proved to be relatively strong predictors of
perfusion wmultiple R s 0.51, F Ž11, 658. s 21.07
P- .001x. The association between the cordance
measures Ž8]12 and 20]24 Hz. and perfusion was
further enhanced if the scans for the eyes closed
conditions were analyzed separately from those
for the eyes-open condition weyes closed: R s 0.58,
F Ž8, 295. s 16.70, P- 0.0001; eyes open: R s
0.49, F Ž8, 357. s 14.74, P- 0.0001, respectivelyx.
We performed additional analyses examining
the associations between perfusion and power for
individual electrodes, and for groups of up to 8
electrodes clustered on a regional basis. None of
the correlations were significant Ž r F 0.15.. This
finding probably reflected the limitations of this
dataset. First, there was a limited number of
subjects. Second, there was a limited range of
relative perfusion values in these normal subjects.
Third, some electrodes had to be deleted from
the montage in each recording because of problems with muscle artifact. An analysis of these
data on an electrode-by-electrode, or even region-by-region basis, further limited the number
of data points and diminished the range of the
PET values in any one analysis.
Electrodes were grouped by whether they were
A.F. Leuchter et al. r Psychiatry Research: Neuroimaging Section 90 (1999) 125]140
133
Fig. 5. Plot showing the partial correlation coefficient between Z and relative perfusion for each condition Žresting state vs. hand
squeezing, eyes open vs. eyes closed.. Key: RH ec s right hand squeezing, eyes closed; RH eo s right hand squeezing, eyes open;
Rest eo s resting state, eyes open; Rest ec s resting state, eyes closed; Left eo s left hand squeezing, eyes open.
concordant or discordant in the 20]24-Hz frequency band Ž n s 484., which had the strongest
association with perfusion. The concordant electrodes had significantly higher mean relative perfusion than the discordant electrodes Ž0.58 vs.
0.42. Ž t s 3.21, P- .001..
Eyes closed:
3.2. Deri¨ ation of formulae to predict perfusion
Eyes open:
Relative Perfusion s 0.49516
y0.0472 w Z Ž 8]12Hz.x
q0.04025 w Z Ž 20]24Hz.x
Relative Perfusion s 0.504821
Regression equations were calculated for each
individual scan with ZŽ s, f . for the 8]12- and
20]24-Hz bands as predictors. An optimized regression formula was derived by calculating the
weighted mean of the intercepts and of the regression coefficients with the number of electrodes in each scan as weights. The eyes-open and
eyes-closed conditions were analyzed separately
and yielded the following preliminary equations:
y0.02309 w Z Ž 8]12Hz.x
q0.032559w Z Ž 20]24Hz.x
where ZŽ8]12 Hz. and ZŽ20]24 Hz. are ZŽ s, f . for
the 8]12-Hz band and the 20]24-Hz band, respectively.
For each individual scan, we estimated perfusion of all available electrode sites using the
formulae Žsome electrodes were deleted from the
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A.F. Leuchter et al. r Psychiatry Research: Neuroimaging Section 90 (1999) 125]140
montage due to artifacts .. Thus, we obtained pairwise values of estimated perfusion and ‘real’ perfusion as rendered by the PET. The correlation
coefficient between those two sets of values was
the validity coefficient. We found that if more
than 12 electrodes were deleted from the 36-electrode montage, the validity coefficient dropped
sharply for many recordings Ž- 0.2.. We therefore recalculated the perfusion equations holding
out EEG recordings from which more than 12
electrodes were removed. The resulting equations
were:
Eyes closed:
Relative Perfusion s 0.50406
y0.04836 w Z Ž 8]12Hz.x
q0.03913 w Z Ž 20]24Hz.x
Fig. 6. Distribution of the validity coefficients of the cordance-estimated perfusion values.
A.F. Leuchter et al. r Psychiatry Research: Neuroimaging Section 90 (1999) 125]140
Table 1
Accuracy of detecting lateralized activation Ždefined as a
maximum activation value in the region of interest contralateral to hand squeezing.
Misses
P Žbinomial
distribution.
Measure
Hits
PET
Cordance
QEEG power
Ž8]12 Hz.
QEEG power
Ž20]24 Hz.
15
16
9
8
0.07
0.05
11
13
0.15
12
12
0.16
Eyes open:
Relative Perfusion s 0.48500
y0.03103w Z Ž 8]12Hz.x
q0.03901 w Z Ž 20]24Hz.x
where ZŽ8]12 Hz. and ZŽ20]24 Hz. are ZŽ s, f . for
the 8]12-Hz band and the 20]24-Hz band, respectively.
A jackknifed validation procedure yielded
strong correlations between the EEG measure
and perfusion ŽFig. 6.. For the eyes-closed condition, a median correlation of 0.58 Žrange:
0.44]0.70. was found. The eyes-open condition
yielded a lower correlation coefficient Žmedian s
0.45, range: 0.30]0.71..
3.3. Detection of lateralized acti¨ ation
We next compared the accuracy of PET, the
cordance estimate of perfusion, and absolute and
relative power measures in detecting lateralized
activation over the motor strip. Both PET and
cordance detected lateralized activation with accuracy exceeding chance levels of 0.10, while
power did not exceed chance levels Ž P) 0.10.
ŽTable 1..
4. Discussion
These results show that surface-recorded
QEEG does reflect cerebral energy utilization in
normal subjects, as evidenced by the moderately
strong associations with relative perfusion. These
135
associations were stable across several conditions
Ži.e. resting state vs. motor task, right-hand vs.
left-hand activity. and across all brain regions,
although the strength of the association was different in the eyes-open and eyes-closed conditions.
In these normal subjects at most frequencies,
there was a positive association between QEEG
power and cerebral perfusion. The strength of the
association varied significantly across the frequencies, with the strongest positive associations
between QEEG measures and perfusion in these
maximally alert subjects seen in a portion of the
beta band between 20 and 28 Hz. This finding is
consistent with the fact that beta activity above 20
Hz is characteristic of the activated state and is
accentuated by visual attention ŽLopes da Silva et
al., 1970., accurate task performance ŽFreeman
and Van Dijk, 1988., and fine motor movements
ŽMurthy and Fetz, 1992..
The only frequency bands in which there was a
strong negative association between power and
perfusion were those with a lower bound between
6 and 10 Hz, centering on the alpha band Ž8]12
Hz.. This finding is consistent with the fact that
alpha activity is conceived of as ‘inhibitory’: it is
characteristic of the resting state and is suppressed by arousal ŽSteriade et al., 1990. or activation during tasks ŽPfurtscheller and Neuper,
1992.. These findings also are consistent with the
previous report which examined metabolism and
alpha power in normal subjects, and found a
negative association ŽBuchsbaum et al., 1984..
These results indicate that the association
between electrical energy and cerebral perfusion
differs between normal and diseased brain. The
direction of the association between EEG power
and cerebral perfusion in these normal subjects is
opposite of that reported in previous studies of
subjects with stroke or dementia, which reported
a positive association between alpha power and
perfusion, as well as a negative association
between slow-wave power and perfusion ŽStigsby
et al., 1981; Nagata et al., 1984, 1989; Nagata,
1988, 1989; Sloan et al., 1995; Valladeres-Neto et
al., 1995.. These findings suggest that brain damage results in a fundamental alteration in the
136
A.F. Leuchter et al. r Psychiatry Research: Neuroimaging Section 90 (1999) 125]140
coupling between slow-wave or alpha-band energy
and perfusion in diseased brain. It is known that
structural alterations in the brain may lead to a
partial uncoupling of metabolism and perfusion
ŽFink et al., 1996., even in brain tissue somewhat
distant from the site of injury ŽMarchal et al.,
1996.. Basic neurophysiologic studies highlight
differences in the origins of normal and pathologic slow-wave activity. Normal slow waves are
generated locally throughout the brain by layers
II, III and V of the cerebral cortex, representing
the firing of GABAergic interneurons inhibiting
large pyramidal cells ŽBall et al., 1977; Steriade
and Buzsaki, 1990. under the influence of thalamocortical projections ŽSteriade et al., 1990..
Pathologic slow waves are generated by these
same layers, but in cells which have been altered
by partial deafferentation Ži.e. a partial loss of
modulating influences from the thalamus.. The
partial loss of subcortical influences alters the
nature of synaptic activity in layers II, III, and V
of the cortex, and possibly the nature of the
coupling between EEG power and perfusion.
In addition to altering the nature of the associations between power and perfusion, brain damage also obscures the origin of slow waves in
the EEG. Pathologic slow waves frequently do
not arise locally, but instead represent a projected
rhythm from distant Ždamaged. brain areas
ŽSteriade et al., 1990.. Differences in the origins
of normal and pathologic areas may alter or obscure the relationship between local brain electrical activity and metabolism or perfusion. This
hypothesis is consistent with the findings of Valladeres-Neto et al. Ž1995., who examined the relationship between EEG activity and metabolism in
patients with Alzheimer’s disease. They found
that local EEG activity was much more strongly
associated with hippocampal metabolism than
with metabolism underlying the recording electrode.
The negative association that we report between
alpha frequency power and perfusion is the opposite of that reported in the presence of brain
disease, but is entirely consistent with our understanding of the functional significance of alpha
activity. Since this activity is inhibitory, it should
be negatively associated with perfusion. It is pos-
sible that the positive association seen between
alpha activity and perfusion in diseased brain is
peculiar to brain disease. Those areas that are
capable of producing alpha activity are probably
less affected by pathological processes and therefore have higher perfusion than areas in which
alpha activity is not seen.
The association between cordance and perfusion was stronger and more consistent than the
association between power and perfusion. This
reflects the fact that cordance combines measures
of absolute and relative power, both of which
have independent associations with perfusion
ŽCook et al., 1998.. All scalp-recorded EEG waves
represent the summed post-synaptic potentials of
pyramidal cells firing in the cortex ŽSteriade et al.,
1990., but we hypothesize that absolute and relative power are sensitive to different aspects of
cortical cellular activity. Absolute power, for example, provides information about the energy
produced by an ensemble of cells firing at a given
frequency. Relative power additionally relates the
energy from an ensemble of cells firing at a
particular frequency to those firing at other frequencies, possibly providing information about
the relative synchronization of cells under a given
electrode or the relative number of cells firing at
different frequencies. The cordance algorithm
also entails an additional step of spatial normalization, relating the energy output from one electrode to all other electrodes on the scalp. There is
insufficient knowledge about the basic neurophysiology of relative power to speculate further about
the mechanisms which account for the enhanced
association observed between cordance and perfusion.
The current results indicate that we have identified two different ‘states’ of the cerebral cortex,
based upon the association between absolute and
relative power: concordance Žin which the mean
absolute and relative power at that electrode are
both above or below the respective global means.
and discordance Žmean absolute power is below
while relative power is above the respective global
means, or vice versa.. These results show that
electrodes in the concordant state, at least in the
beta frequency band, are associated with higher
perfusion than electrodes in the discordant state.
A.F. Leuchter et al. r Psychiatry Research: Neuroimaging Section 90 (1999) 125]140
This finding is also consistent with our previous
reports that show that in subjects with brain disease, discordance in the beta and theta frequency
bands is seen over areas of brain dysfunction
ŽLeuchter et al., 1994a,b.. It is important to note,
however, that discordance is not synonymous with
brain dysfunction, since all these normal subjects
had a significant number of electrodes in the
discordant state. Rather, concordance in most
frequency bands is a categorical indicator seen at
electrodes over brain areas in a higher relative
perfusion state, and may be useful as an indicator
of relatively enhanced brain activity. Conversely,
discordance at most frequencies is a categorical
indicator seen over areas in a lower relative perfusion state, possibly indicating relatively suppressed brain activity. Further studies to examine
cordance in different cognitive, emotional, or
treatment conditions could clarify this point.
The current results also highlight the effects of
technical factors in affecting the apparent relationship between EEG and perfusion measures.
Some of the differences between the current results and those of previous studies may be explained by technical differences in recording and
analysis methods. Many previous studies
processed data from a referential montage
ŽNagata et al., 1984, 1989; Nagata, 1988, 1989;
Valladeres-Neto et al., 1995., which our previous
work has shown produces substantially weaker
associations with perfusion than the local Laplacian transformation ŽCook et al., 1998.. In other
studies, associations were calculated between
QEEG and perfusion data gathered from different brain regions ŽTolonen and Sulg, 1981;
Wszolek et al., 1992., at different times ŽNagata
et al., 1989; Jibiki et al., 1994., or while the
subjects were in different mental states ŽNagata
et al., 1984.. All of these factors have been demonstrated profoundly to influence brain activity
Žcf. Posner et al., 1988.. Furthermore, the present
study shows that the association between EEG
and perfusion is consistently strongest in the beta
bands, which frequently have not been examined
by previous studies. It is important to note, however, that the usefulness of beta frequency measures was limited by the presence of artifacts,
137
which necessitated the elimination of several
recordings from this study.
The methods used in this study permitted us to
detect associations between EEG power and perfusion that were significant across brain regions.
It would be valuable to examine the topographic
variability in these associations. A technical limitation of the current study is that the limited
range of relative perfusion values Žwhich were
clustered in a small range in these normal subjects. and the limited number of subjects, as well
as deletion of electrodes because of artifacts, did
not permit us to examine individual electrodes
separately. Previous studies which examined subjects with brain disease found a broader range of
perfusion values, which make correlations easier
to examine. Although the associations in this study
were significant for the brain as a whole, it is
possible that some regions could show weaker or
stronger associations than those reported overall.
Future studies of normal subjects should examine
possible regional variability in these QEEG measures.
The methods of this report, aimed at the comparison of different modalities of brain imaging,
are inherently fraught with difficulties and limitations. This problem is particularly marked when
comparing methods as disparate as PET scanning
Žbased upon tomographic reconstructions of
brain. and QEEG Žmeasured from outside the
scalp.. Even beyond the inherent technical limitations of this approach, no two imaging techniques
ever can be entirely comparable. These techniques all contribute some similar and some
unique information regarding brain function, and
the correlational approach of this study only can
estimate shared information.
The goal of comparing two QEEG measures is
less fraught with limitations. We previously reported that in patients with brain disease, cordance had stronger associations with cerebral
perfusion Žmeasured with HMPAO-SPECT. than
EEG power measures. The current study extends
those findings and indicates that cordance has
stronger associations with cerebral perfusion than
QEEG power in normal subjects as well. The
stronger associations seen with cordance may re-
138
A.F. Leuchter et al. r Psychiatry Research: Neuroimaging Section 90 (1999) 125]140
flect the fact that cordance integrates absolute
and relative power data, which are complementary measures of brain function ŽLeuchter et al.,
1993.. The enhanced association with perfusion
was most evident in the high-frequency beta band
Žabove 20 Hz., a frequency range which is closely
associated with mental effort and task activation.
Furthermore, cordance detected the neurophysiologic processes of lateralized activation with
greater accuracy than conventional EEG power
measures. These findings suggest that cordance
may offer particular advantages as a measure of
cerebral activity in subjects without structural
brain disease
Acknowledgements
This work was supported by research grant
1RO1 MH40705 and Research Scientist Development Award 1KO2 MH01165 from the National
Institute on Mental Health ŽNIMH., and the
Medication Development Research Unit contract
1YO1 DA50038 from the National Institute on
Drug Abuse to the Department of Veterans Affairs ŽAFL., a NARSAD Young Investigator
Award and training grant T32 MH17140 from the
NIMH ŽIAC., and a fellowship from the Brookdale Foundation ŽROH.. The views in this
manuscript represent those of the authors and do
not necessarily represent those of the Department of Veteran Affairs. The authors gratefully
acknowledge the expert consultation of Lynn
Fairbanks, Ph.D., with the statistical analyses, as
well as the assistance of Mariahn Smith,
R.EEG.T., in the collection and processing of the
EEG data, Michelle Abrams, R.N., in subject
recruitment and scheduling, and Mychelle Blake,
M.S.W., in the preparation of this manuscript.
References
Ball, C.J., Gloor, P., Schaul, N., 1977. The cortical electromicrophysiology of pathological delta waves in the electroencephalogram of the cat. Electroencephalography and Clinical Neurophysiology 43, 346]361.
Berger, H., 1938. Das elektroenkephalogramm des menschen.
Nova Acta Leop 6, 173]309.
Buchsbaum, M.S., Kessler, R., King, A., Johnson, J., Cappelletti, J., 1984. Simultaneous cerebral glucography with
positron emission tomography and topographic electroencephalography. In: Pfurtscheller, G., Jonkman, E.J., Lopes
da Silva, F.H. ŽEds.., Brain Ischemia: Quantitative EEG
and Imaging Techniques, Progress in Brain Research, vol.
62. Elsevier Science Publishers, New York, pp. 263]269.
Cartheuser, C.F., 1988. Progressive hypoxia until brain electrical silence: a useful model for studying progressive interventions. Canadian Journal of Physiology and Pharmacalogy 66, 1398]1406.
Cook, I.A., O’Hara, R., Uijtdehaage, S.H., Mandelkern, M.,
Leuchter, A.F., 1998. Assessing the accuracy of topographic
EEG mapping for determining local brain function. Electroencephalography and Clinical Neurophysiology 107,
408]414.
Des Rossiers, M.H., Kennedy, C., Patlak, C.S., 1974. Relationship between local cerebral blood flow and glucose utilization in the rat. Neurology 24, 389.
Erecinska, M., Silver, I.A., 1989. ATP and brain function.
Journal of Cerebral Blood Flow and Metabolism 9, 2]19.
Fink, G.R., Pawlik, G., Stefan, H., Pietrzyk, U., Wienhard, K.,
Heiss, W.D., 1996. Temporal lobe epilepsy: evidence for
interictal uncoupling of blood flow and glucose metabolism
in temporomesial structures. Journal of Neurological Sciences 137, 28]34.
Freeman, W.J., Van Dijk, B.W., 1988. Spatial patterns of
visual cortical fast EEG during conditioned reflex in a
rhesus monkey. Brain Research 422, 267]276.
Gevins, A., 1990. Dynamic patterns in multiple lead data. In:
Rohrbaugh, J., Johnson, R., Parasuraman, R. ŽEds..,
Event-Related Brain Potentials: Basic Issues and Applications. Oxford University Press, New York, pp. 44]56.
Hjorth, B., 1975. Technical contribution. An on-line transformation of EEG scalp potentials into orthogonal source
derivations. EEG and Clinical Neurophysiology 39,
526]530.
Iglesias, S., Marchal, G., Rioux, P., Beaudouin, V., Hauttement, A.J., de la Sayette, V., Le Doze, F., Derlon, J.M.,
Viader, F., Baron, J.C., 1996. Do changes in oxygen
metabolism in the unaffected cerebral hemisphere underlie
early neurological recovery after stroke? A positron emission tomography study. Stroke 27, 1192]1199.
Ingvar, D.H., Sjolund,
B., Ardo, A., 1976. Correlation between
¨
dominant EEG frequency, cerebral oxygen uptake and
blood flow. EEG and Clinical Neurophysiology 41, 268]276.
Ingvar, D.H., 1979. ‘Hyperfrontal’ distribution of the cerebral
grey matter flow in resting wakefulness; on the functional
anatomy of the conscious state. Acta Neurologica Scandinavica 60, 12]25.
Ingvar, D.H., Risberg, J., 1967. Increase of regional cerebral
blood flow during mental effort in normals and in patients
with focal brain disorders. Experimental Brain Research 3,
195]211.
Jibiki, I., Kurokawa, K., Fukushima, T., Kido, H., Yamaguchi,
A.F. Leuchter et al. r Psychiatry Research: Neuroimaging Section 90 (1999) 125]140
N., Matsuda, H., Hisada, K., 1994. Correlations between
quantitative EEG and regional cerebral blood flow ŽSPECT.
in patients with partial epilepsy. Neuropsychobiology 30,
46]52.
Klementavicius, R., Nemoto, E.M., Yonas, H., 1996. The Q10
ratio for basal cerebral metabolic rate for oxygen in rats.
Journal of Neurosurgery 85, 482]487.
Leuchter, A.F., Cook, I.A., Newton, T.F., Walter, D.O.,
Rosenberg-Thompson, S., Lachenbruch, P.A., Weiner, H.,
1993. Regional differences in brain electrical activity in
dementia: use of spectral power and spectral ratio measures. Electroencephalography and Clinical Neurophysiology 87, 385]393.
Leuchter, A.F., Cook, I.A., Lufkin, R.B., Dunkin, J., Newton,
T., Cummings, J.L., Mackey, K., Walter, D., 1994a. Cordance: a new method for assessment of cerebral perfusion
and metabolism using quantitative electroencephalography.
Neuroimage 1, 208]219.
Leuchter, A.F., Cook, I.A., Mena, I., Dunkin, J., Cummings,
J.L., Newton, T., Migneco, O., Lufkin, R., Walter, D.,
Lachenbruch, P., 1994b. Assessment of cerebral perfusion
using quantitative EEG cordance. Psychiatry Research:
Neuroimaging 55, 141]152.
Lopes da Silva, F.H., Van Rotterdam, A., Storm van Leeuwen,
W., Tielen, A.M., 1970. Dynamic characteristics of visual
evoked potentials in the dog. II. Beta frequency selectivity
in evoked potentials and background activity. EEG and
Clinical Neurophysiology 29, 260]268.
Marchal, G., Furlan, M., Beaudouin, V., Rioux, P., Hauttement, J.L., Serrati, C., de la Sayette, V., Le Doze, F.,
Viader, F., Derlon, J.M., Baron, J.C., 1996. Early spontaneous hyperperfusion after stroke. A marker of favourable
tissue outcome? Brain 119, 409]419.
Melamed, E., Lavy, S., Portnoy, Z., Sadan, S., Carmon, A.,
1975. Correlation between cerebral blood flow and EEG
frequency in the contralateral hemisphere in acute cerebral
infarction. Journal of the Neurological Sciences 26, 21]27.
Murthy, V.N., Fetz, E.E., 1992. Coherent 25]35 Hz oscillations in the sensorimotor cortex of the awake behaving
monkey. Proceedings of the National Academy of Science
USA 89, 5670]5674.
Nagata, K., 1988. Topographic EEG in brain ischemia }
correlation with blood flow and metabolism. Brain Topography 1, 97]106.
Nagata, K., 1989. Topographic EEG mapping in cerebrovascular disease. Brain Topography 2, 119]128.
Nagata, K., Mizukami, M., Araki, G., Kawase, T., Hirano, M.,
1982. Topographic electroencephalographic study of cerebral infarction using computed mapping of the EEG. Journal of Cerebral Blood Flow and Metabolism 2, 79]88.
Nagata, K., Tagawa, K., Hiroi, S., 1989. Electroencephalographic correlates of blood flow and oxygen metabolism
provided by positron emission tomography in patients with
cerebral infarction. Electroencephalography and Clinical
Neurophysiolgy 72, 16]30.
139
Nagata, K., Yunoki, K., Araki, G., Mizumi, M., 1984. Topographic electroencephalographic study of transient ischemic attacks. Electroencephalography and Clinical Neurophysiology 58, 291]301.
Obrist, W.D., Sokoloff, L., Lassen, N.A., et al., 1963. Relation
of EEG to cerebral blood flow and metabolism in old age.
Electroencephalography and Clinical Neurophysiology 15,
610]619.
Okyere, J.G., Ktonas, P.Y., Meyer, J.S., 1986. Quantification
of the alpha EEG modulation and its relation to cerebral
blood flow. IEEE Transactions on Biomedical Engineering
BME33]7, 690]696.
Passero, S., Rocchi, R., Vatti, G., Burgalassi, L., Battistini, N.,
1995. Quantitative EEG mapping, cerebral blood flow, and
neuropsychological function in Alzheimer’s disease. Dementia 6, 148]156.
Pfurtscheller, G., Neuper, C., 1992. Simultaneous EEG 10 Hz
desynchronization and 40 Hz synchronization during finger
movements. Neuroreport 3 Ž12., 1057]1060.
Posner, M.I., Petersen, S.E., Fox, P.T., Raichle, M.E., 1988.
Localization of cognitive operations in the human brain.
Science 240, 1627]1631.
Press, W.H., Flannery, B.P., Teukolsky, S.A., Vetterling, W.T.,
1986. Numerical Recipes: The Art of Scientific Computing.
Cambridge University Press, Cambridge, England.
Roy, C.W., Sherrington, C.S., 1890. On the regulation of the
blood supply of the brain. Journal of Physiology ŽLondon.
11, 85]108.
Serrati, C., Marchal, G., Rioux, P., 1994. Contralateral cerebellar hypometabolism: a predictor for stroke outcome?
Journal of Neurology, Neurosurgery and Psychiatry 57,
174]179.
Sloan, E.P., Genton, G.W., Kennedy, N.S., MacLennan, J.M.,
1995. Electroencephalography and single photon emission
computed tomography in dementia: a comparative study.
Psychological Medicine 25, 631]638.
Sokoloff, L., 1977. Relationship between physiological function and energy metabolism in the central nervous system.
Journal of Neurochemistry 29, 13]26.
Sokoloff, L., 1981. Relationships among local functional activity, energy metabolism, and blood flow in the central nervous system. Federation Proceedings 40, 2311]2316.
Steriade, M., Buzsaki, G., 1990. Parallel activation of thalamic
and cortical neurons by brainstem and basal forebrain
cholinergic systems. In: Steriade, M., Biesold, D. ŽEds..,
Brain Cholinergic Systems. Oxford, New York, pp. 3]62.
Steriade, M., Gloor, P., Llinas, R.R., Lopes da Silva, F.H.,
Mesulam, M.M., 1990. Basic mechanisms of cerebral
rhythmic activities. Electroencephalography and Clinical
Neurophysiology 76, 481]508.
Stigsby, B., Johannesson, G., Ingvar, P.H., 1981. Regional
EEG analysis and regional cerebral blood flow in
Alzheimer’s and Pick’s diseases. Electroencephalography
and Clinical Neurophysiology 51, 537]547.
Toga, A.W., Cannestra, A.F., Black, K.L., 1995. The tem-
140
A.F. Leuchter et al. r Psychiatry Research: Neuroimaging Section 90 (1999) 125]140
poralrspatial evolution of optical signals in human cortex.
Cerebral Cortex 6, 561]565.
Tolonen, U., Sulg, I.A., 1981. Comparison of quantitative
EEG parameters from four different analysis techniques in
evaluation of relationships between EEG and CBF in brain
infarction. Electroencephalography and Clinical Neurophysiology 51, 177]185.
Valladeres-Neto, D.C., Buchsbaum, M.S., Evans, W.J., Nguyen,
D., Nguyen, P., Siegel, B., Stanley, J., Starr, A., Guich, S.,
Rice, D., 1995. EEG delta positron emission tomography
and memory deficit in Alzheimer’s disease. Neuropsychobiology 31, 173]181.
Wszolek, Z.K., Herkes, G.K., Lagerlund, T.D., Kokmen, E.,
1992. Comparison of EEG background frequency analysis,
psychologic test scores, short test of mental status, and
quantitative SPECT in dementia. Journal of Geriatric Psychiatry and Neurology 5, 22]30.
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