Correlation of PET and qEEG in normal subjects

www.elsevier.com/locate/psychresns

Psychiatry Research: Neuroim

Correlation of PET and qEEG in normal subjects

Kenneth R. Alpera,b,*, E. Roy Johna,c, Jonathan Brodied,

Wilfred Gunthere, Raoul Daruwalaa, Leslie S. Prichepa,c

aBrain Research Laboratories, Department of Psychiatry, New York University Medical Center, 550 First Avenue, New York, NY 10016, USAbComprehensive Epilepsy Center, Department of Neurology, New York University Medical Center, NY, USA

cNathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USAdDepartment of Psychiatry, New York University Medical Center, New York, NY, USA

eDepartment of Psychiatry, University of Munich, Munich, Germany

Received 5 May 2005; received in revised form 15 June 2005; accepted 15 June 2005

Abstract

Positron emission tomography (PET) and quantitative electroencephalography (qEEG) were obtained in 15 normal male

subjects with eyes closed at rest. Correlations between qEEG variables and regional metabolism were examined as an approach to

investigating the metabolic and neuroanatomical basis of the generation of the EEG. Analogous to the neurometric approach to

qEEG, a normative 2-fluoro-deoxyglucose voxel data base was developed for the PET image. The PET image was transformed to

an idealized cylindrical set of coordinates to allow registration with the Talairach stereotactic atlas. PET regions of interest for the

thalamus, the left and right temporal lobes, the medial frontal cortex and the dorsolateral prefrontal cortex were defined using

Talairach coordinates and correlated to the QEEG. Salient findings included a negative correlation of thalamic metabolism to alpha

power and a positive correlation of medial frontal cortical metabolism to delta EEG power. The significance of these findings is

discussed with reference to the existing literature on the physiology of the generation of the EEG.

D 2006 Elsevier Ireland Ltd. All rights reserved.

Keywords: Human; Positron Emission Tomography (PET); Electroencephalogram (EEG); Alpha EEG power; Delta EEG power; EEG generator;

Thalamus

1. Introduction

A relationship of electroencephalographic (EEG)

activity to brain metabolism is expected because the

summed inhibitory and excitatory post-synaptic poten-

tials that are thought to account for most of the EEG are

the product of modulations of ionic conductances that

0925-4927/$ - see front matter D 2006 Elsevier Ireland Ltd. All rights rese

doi:10.1016/j.pscychresns.2005.06.008

* Corresponding author. Brain Research Laboratories, Department

of Psychiatry, New York University Medical Center, 550 First Ave-

nue, New York, NY 10016, USA. Tel.: +1 212 263 6287; fax: +1 212

263 6457.

E-mail address: [email protected] (K.R. Alper).

require metabolic energy (Pilgreen, 1995). One would

expect that there would be distinctive relationships

between regional metabolism and different EEG fre-

quency bands. However, the power in different EEG

frequency bands may be correlated with metabolism in

different brain regions. Early studies involving mea-

surement of global cerebral blood flow reported a gen-

erally positive correlation with mean EEG frequency

(Ingvar et al., 1979; Kuschinsky, 1993). However, a

simple overall correspondence between mean EEG fre-

quency and global metabolic activity might be more

characteristic of structural neurological pathology such

as primary dementia or cerebrovascular disease. For

aging 146 (2006) 271–282

rved.

K.R. Alper et al. / Psychiatry Research: Neuroimaging 146 (2006) 271–282272

example, in two studies comparing normal controls and

demented patients (Nagata et al., 1994; Obrist et al.,

1963) and in two other studies making comparisons

between hemispheres within subjects with unilateral

infarcts (Nagata, 1989; Tolonen and Tournoux, 1981),

the amount of variance in mean EEG frequency

explained by cerebral blood flow (CBF) or metabolism

was relatively greater in the demented subjects or the

infarcted hemisphere. On the other hand, studies of

schizophrenic patients, who evidence less structural

pathology than patients with primary dementia or cere-

bral infarcts, report both negative and positive correla-

tions of positron emission tomography (PET)

metabolism to specific EEG power bandwidths depend-

ing on the anatomical region examined (Alper et al.,

1998; Gunther, 1992). Okyere et al. (1986) reported a

significant positive correlation of quantitative EEG am-

plitude with CBF in normal subjects and did not find a

significant correlation of CBF to quantitative electro-

encephalographic (qEEG) frequency. It would appear

from the limited available data that metabolic activity

may be correlated with both qEEG amplitude and

frequency in humans. Further, the relationship of me-

tabolism to qEEG power spectral composition may vary

differentially with respect to QEEG topography and

metabolic regions of interest.

A limited amount of research has been reported on

the simultaneous measurement of metabolic and EEG

data in normal, awake humans. Much of this research

has involved either correlations of EEG to metabolism

in subjacent cortex or a specific focus on the relation-

ship of EEG alpha to metabolic activity in the thalamus.

Buchsbaum et al. (1984) reported on correlations of

EEG magnitude (the square root of absolute power)

with metabolism measured utilizing PET in cortical

areas subjacent to EEG electrodes in a 16-lead array

in the left hemisphere of six normal subjects at rest with

eyes closed. Statistically significant negative correla-

tions with metabolism were found for delta and alpha

magnitude (the square root of power) in the temporal

lobe, and for alpha in the occipital lobe. Also looking at

the relationship of EEG to metabolism measured utiliz-

ing PET in cortical regions subjacent to EEG leads,

Cook et al. (1998) studied six subjects with a behav-

ioral protocol that involved sequential intervals of rest

versus motor task performance with eyes open and eyes

closed, and computed both absolute and relative EEG

power with three different types of montages: linked

ears referential, bipolar, and source-derivation. Results

differed as a function of choice of montage and absolute

versus relative power, but shared the common attribute

of generally positive correlations of metabolism to

power in the frequencies corresponding approximately

to the delta, theta and beta bands; with negative corre-

lations in the alpha band.

More recently, in a study on a combined group of 12

normal and 17 depressed subjects in which PET metab-

olism and EEG were co-registered using low frequency

electromagnetic topographic analysis (LORETA), Pizza-

galli et al. (2003) found voxelwise correlations between

normalized PET metabolism in the rostral anterior cin-

gulate gyrus and theta absolute power that were positive

in the rostral anterior cingulate cortex and middle and

superior right frontal and temporal gyri, and negative in

occipital cortex. These subjects were evaluated while at

rest with eyes closed during 50% of the uptake period,

and with eyes open during the other 50% of the uptake

period. In this study, significant voxelwise correlations

involving seven EEG bands between 1.5 and 44 Hz were

limited to the theta bandwidth. A subsequent analysis of

the 12 normal subjects found a general relationship of a

greater number of positive correlations to metabolism

with increasing frequency and negative correlations with

the low alpha (Oakes et al., 2004), consistent with earlier

work indicating a general correlation of global cerebral

blood flow or metabolism with overall EEG mean fre-

quency (Ingvar et al., 1979; Kuschinsky, 1993).

Some studies have focused on thalamic metabolic

regions of interest in studies intended to focus on the

investigation of the generation of the alpha EEG

rhythm. Larson et al. (1998) found a negative corre-

lation of thalamic metabolism measured by PET to

alpha EEG power in a combined group of 8 normal

and 19 depressed subjects. Alpha EEG power was a

single measure derived from all leads over the entire

head. As in the studies above by Pizzagalli et al.

(2003) and Oakes et al. (2004), the subjects were

evaluated while at rest with eyes closed during 50%

of the uptake period, and eyes open during the other

50% of the uptake period. In a subsequent report

involving a larger group of subjects, the negative

correlation of thalamic metabolism and EEG alpha

activity was significant only in the group of 13 normal

subjects, and not the depressed subjects. On the other

hand, Danos et al. (2001) found a positive correlation

of thalamic metabolism measured utilizing PET and

alpha EEG magnitude in 13 normal subjects during a

visual continuous performance task (CPT). In this

study the QEEG measures were derived from a 32-

lead array from which a subset of three occipital leads

were selected for the analysis. Sadato et al. (1998)

also found a positive correlation of thalamic PET

metabolism with alpha absolute power in eight normal

subjects, evaluated with eyes open during a condition

K.R. Alper et al. / Psychiatry Research: Neuroimaging 146 (2006) 271–282 273

that combined sequential periods of rest and listening

to music. Alpha absolute power was a single measure

derived from the four posterior leads of a 12-lead

array. Goldman et al. (2002) found a positive correla-

tion of thalamic metabolism measured utilizing func-

tional magnetic resonance imaging with a single

measure of alpha absolute power averaged from a

subset of four bipolar pairs containing the occipital

electrodes.

The above studies have generally either focused on

the relationship of metabolism in cortex with voxelwise

co-registration with EEG or immediately subjacent to

EEG leads and EEG power, or the relationship of the

occipital cortical alpha EEG rhythm to a thalamic met-

abolic region of interest. The restriction of the data

analysis to a small number of variables in many of

these studies contributes statistical power, but has

resulted in the exclusion from the analysis of data

regarding non-alpha EEG bandwidths or the correlation

of EEG to non-local metabolic regions of interest. The

discarding of significant amounts of information of

potential interest itself raises statistical issues of con-

cern. Useful preliminary information regarding the neu-

rophysiological basis of the generation of the EEG may

be derived from examination of the topography of the

correlations of EEG spectral power with different met-

abolic regions of interest with all four bands of the EEG

over the full complement of the 19 standard leads of the

10–20 system.

In the present study, we report on the correlation of

qEEG power and regional metabolism in normal sub-

jects. The primary objective of this preliminary inves-

tigation is to generate images of the tomography of PET

EEG correlations in normal subjects. The metabolic

regions of interest, namely the medial frontal cortex

(MFC) (medial prefrontal, orbital, and anterior cingu-

late), the dorsolateral prefrontal cortex (DLPFC), the

thalamus (Thal), and the left and right temporal lobes

(LT and RT) were selected as metabolic regions of

interest for the prominence of their involvement in

neuropsychiatric disorders, and in addition the thalamus

was selected because of the attention focused on it in

prior studies quoted above correlating metabolism and

EEG. The intended significance of these images relates

to the understanding of the metabolic and neuroana-

tomical basis of the generation of the EEG. This study

used the statistical probability image (SPI) approach,

which involves voxel-by-voxel Z transformation of

PET data with reference to a normative data set and

allows co-registration of the PET image and the Talair-

ach coordinate system (John et al., 1994b; Talairach and

Tournoux, 1988).

2. Materials and methods

2.1. Subjects

Written informed consent was obtained from all

subjects. The index subjects used to compute these

PET–EEG relationships were 15 right-handed normal

males with a mean age of 25.2F5.3 years. The index

subjects were compared with a control group of 25

right-handed normal males that formed the voxel nor-

mative database described in John et al. (1994b). Iden-

tical selection criteria were used for both the index

subjects and the subjects from the voxel normative

database. Subjects were included who evidenced no

significant abnormality on physical examination, med-

ical history, routine blood chemistry and urine analysis,

and MRI of the head. Subjects were excluded who had

past histories of head trauma with loss of conscious-

ness, seizure, neurological or other medical conditions

known to affect the brain. Also excluded were subjects

with histories of substance abuse, psychopharmacolog-

ic treatment, psychiatric hospitalization, or evidence of

a current DSM-III-R disorder or past DSM-III-R mood

anxiety or psychotic disorder.

2.2. PET data acquisition

All subjects were scanned at the Department of

Chemistry, Brookhaven National Laboratory, on a

CTI 931 Tomograph (15 slices; spatial resolution of

6�6�6.5 mm full width at half maximum).

The subjects were positioned in the gantry accord-

ing to methods described elsewhere (Bartlett et al.,

1988), and they received a 68Ge/68Ga transmission

scan that provided attenuation correction and defined

the skull for image reconstruction and PET/MRI co-

registration. A 5–6 mCi bolus of [18F]2-fluoro-2-

deoxy-d-glucose (FDG) was prepared (Hamacher et

al., 1986) and administered (John et al., 1994a; Bar-

tlett et al., 1988) as described elsewhere. Scanning

began 35 min after FDG administration and continued

for 20 min. The subjects were scanned with their

eyes closed and ears unoccluded in a quiet, dimly

lit room.

The adequacy of counts and plasma input function

were ascertained for all scans. To minimize the effect of

region-independent global changes in glucose utiliza-

tion, relative metabolic values were calculated as the

ratio of voxel /whole brain activity (John et al., 1994b).

This reports the correlation of qEEG with relative PET

metabolism utilizing the approach to defining PET

regions of interest (ROIs) that is described below.


2.3. EEG data acquisition

The extremely high test–retest reliability of the

qEEG power bands recorded from resting normal sub-

jects has been repeatedly confirmed (Kaye et al., 1981;

Fein et al., 1983, 1984; Kondacs and Szabo, 1999).

PET and QEEG were recorded in two separate sessions,

and EEG data were obtained within 1 week of the PET

study. The subjects were seated comfortably in a light-

attenuated room, while 20 min of eyes closed resting

EEG data were collected using a Spectrum 32 acquisi-

tion system (Cadwell Laboratories) from the 19 mono-

polar electrode sites of the International 10/20 system,

referred to linked earlobes. A differential eye channel

was used for the detection of eye movement. All elec-

trode impedances were below 5000\ohm. The EEG

amplifiers had a bandpass from 0.5 to 70 Hz (3-dB

points), with a 60-Hz notch filter. Data were sampled at

200 Hz with 12-bit resolution.

2.4. PET data analysis

The Statistical Probability Image (SPI) approach of

John et al. (1994b) was used in the analysis of PET

data. Key elements of SPI include:

1. Shape and size normalization of the PET image;

2. Z-transformation of the PET data with reference to a

normative data set;

3. Registration of normalized PET image with the

Talairach system of coordinates (Talairach and Tour-

noux, 1988).

As the first step of shape normalization of the PET

image, the edges of the image are defined on the basis

of large changes in the variance of density of voxels

(step 0). A centroid is then computed for the area within

the set of edges (Step 1). The centroid becomes the

origin for an idealized circle with radius R that circum-

scribes the slice (Step 2).

A line ri is drawn from the centroid origin to the

edge of the slice along each of 720 radii, each succes-

sively rotated from the vertical axis to an angle h. Theselines are then linearly stretched by an amount R / ri to

dilate each line to a bstandard circleQ with radius R.

Each voxel can now be identified at its location rijwhere rj indicates the jth interval along the radius riat angle hj (Step 3).

In Step 4, each individual expanded radius, R(h)i, isdivided into N sectors of Drj(hi). For each sector in the

standard circle, the ¯Drj; hijDrj; hij , and standard deviation

ADrj,hi, of the distribution of relative metabolic activity

for each sector of every slice were determined across a

group of 25 normal subjects (John et al., 1994b).

These distributions were tested for Gaussianity, and

deviations from normality corrected by appropriate

transforms. The value of each index was compared to

the control group normative data by computing the

difference between the index subject value and the

control group mean value and dividing by the control

group standard deviation (Z-transform). To reconstruct

the entire brain, the individual PET slices transformed

to standard circles, with thickness 6 mm, were stacked

to form the standard cylinder. The standard cylinder

achieves the conformed mapping of individual brains to

the standard cylindrical space, in order to facilitate

statistical comparisons by minimizing the effect of

individual anatomical variations.

To use the Talairach coordinate system to define

PET ROIs, it was necessary to bring the PET image

into registration with the standard stereotaxic atlas

(Talairach and Tournoux, 1988). The standard Talairach

stereotaxic atlas was modified by the superimposition

of a grid on each coronal section, dividing the propor-

tional scale into 10 horizontal intervals at each of 13

vertical levels. Each coronal slice was thus represented

as 120 rectangles. Each rectangle is about 8�14 mm,

or about 4� the standard deviation of localization in the

standard circle. The standard circle representing all the

slices from the whole brain can be stacked into a

standard cylinder from which transaxial, sagittal or

coronal sections can be computed. By slicing the Z-

transformed PET images, stacked into the standard

cylinder, into 16 coronal sections, about 16 slices�10

horizontal intervals�13 vertical levels=2080 ROIs can

be defined. Each of these ROIs can be thought of as

analogous to a volume element of a bMercatorQ projec-tion of the brain. Norms are computed for each bmulti-

voxel ROIQ instead of for each voxel. Regions of inter-

est subsuming multiple bmulti-voxel ROIsQ were de-

fined in Talairach coordinates.

Table 1 summarizes the Talairach coordinates of the

PET regions of interest used in the present study. The

origin of the coordinate system of Table 1 is the inter-

section of the anterior commissure–posterior commis-

sure (AC–PC) line, the vertical line traversing the

posterior margin of the anterior commissure (AC) ori-

ented perpendicular to the AC–PC line, and the midline

(Talairach and Tournoux, 1988). Distances from the

origin are expressed in centimeters. For Z coordinates,

values are negative for regions inferior to the AC–PC

line and positive for regions superior to the AC–PC

line. For Y coordinates, values are negative for regions

posterior to the AC and positive for regions anterior to

Table 1

Talairach coordinates for PET regions of interest

Region X coordinates Y coordinates Z coordinates

Thalamus (Thal) F(0.5 to 1.7) �2.1 to �0.7 +4 to +16

F(1.0 to 2.0) �2.9 to �2.1 +4 to +12

Left temporal

(LT) or right

temporal (RT)

F(3.4 to 5.6) �4.2 to 1.8 �20 to �12F(3.4 to 6.0) �5.5 to �0.8 �12 to �1F(4.0 to 6.0) �6.2 to �1.5 �1 to +16

Medial frontal

cortex (MFC)

F(0.3 to 3.8) +5.3 to +6.6 �20 to �12F(0.3 to 1.9) +12.0 to +5.3 �24 to �14F(1.9 to 3.8) +5.3 to +6.6 �24 to�20

Dorsolateral

prefrontal

cortex

(DLPFC)

F(0 to 2.1) +5.0 to +5.6 +28 to +35

F(0.1to 1.9) +5.3 to +6.6 0 to +26

F(3.4 to 4.0) +3.6 to +5.0 +28 to +35

F(4.0 to 5.4) +3.7 to +5.3 +4 to +24

F(4.8 to 5.9) 0 to +1.8 +18 to +32

F(4.6 to 5.2) +2.3 to +4.1 +6 to +24

F(5.0 to 5.3) 0 to +1.8 +35 to +40


the AC. For X coordinates, values to the left of the

midline are positive and those to the right are negative.

The ROIs thalamus (Thal), medial frontal cortex

(MFC), and dorsolateral prefrontal cortex (DLPFC)

are each composed of two symmetrical left and right

halves with identical Y and Z coordinates. Left temporal

(LT) and right temporal lobe (RT) are each considered

separately and are the mirror images of one another.

In selecting coordinates to define the ROIs, an

attempt was made to circumscribe rectangular volumes

entirely within the structure of interest and to mini-

mize the influence of white matter. For the LT and RT,

the coordinates subsumed Brodmann areas 21 through

24, 28, 37, 38, 41 and 42. MFC was defined to

subsume the medial prefrontal and orbital cortex, and

anterior cingulate, and consisted of the medial portion

of area 10, and areas 11, 12, 32 and 33. DLPFC

consisted of the lateral portion of area 10, and areas

9, 44 and 46.

2.5. QEEG data analysis

A computerized artifact-detection algorithm and vi-

sual inspection were used to obtain 48 epochs (2.5 s

each, for a total of 2 min) of artifact-free data from 20

min of continuous raw EEG for quantitative analysis.

Power spectral analysis was performed using Fast Four-

ier Transform (FFT). For each of the 19 monopolar

derivations, absolute and relative (%) power, and

mean frequency, were computed for the delta (1.5–3.5

Hz), theta (3.5–7.5 Hz), alpha (7.5 to 12.5 Hz), and beta

(12.5–25 Hz) frequency bands. Using Neurometrics,

quantitative electrophysiologic features are log-trans-

formed to obtain Gaussianity, age-regressed, and Z-

transformed relative to population norms. The impor-

tance of each of these steps in enhancing the sensitivity

and specificity of electrophysiological data has been

discussed in detail elsewhere (John et al., 1987,

1988). Independent replications have confirmed the

Neurometric qEEG norms (Matousek and Petersen,

1973; Ahn et al., 1980; John et al., 1987, 1989; Gasser

et al., 1982; Jonkman et al., 1985; Alvarez et al., 1987;

Harmony et al., 1987; Diaz de Leon et al., 1988;

Veldhuizen et al., 1993; Duffy et al., 1993, 1995),

and provide evidence of high test retest reliability

(Kaye et al., 1981; Fein et al., 1983, 1984; Kondacs

and Szabo, 1999).

In addition to the univariate spectral features, multi-

variate regional composite features are also computed.

These features represent multivariate abnormalities for

specific spectral features across a set of topographic

regions. In the context of this study, these composites

were considered to be similar to the ROIs used in the

PET data analysis.

2.5.1. Data reduction

Data reduction of the qEEG was required to reduce

the large number of extracted features for the purpose

of input to the canonical correlation analyses. To

achieve the desired reduction and feature selection,

Pearson r correlations were computed between each

ROI and the qEEG variables, separately for each mea-

sure set. These correlations were topographically dis-

played and used to aid in the selection of qEEG bROIsQ,those regions with maximum correlations to the PET

ROI, and are shown in Figs. 1 and 2. This approach

follows that described by others (Weiner and Dunn,

1966; Oken and Chiappa, 1986), using a t-test or

ANOVA model where variables are selected which

maximize adjusted multiple correlation coefficients be-

tween qEEG and dependent variables, minimizing the

residual sum of squares (RSS).

2.6. Correlation of PET and EEG

PET ROIs were defined by Talairach coordinates

that are presented in Table 1. Relative PET metabolism

for each of the above ROIs was correlated with qEEG

absolute and relative power and mean frequency. The

Pearson r correlations computed between each ROI and

the qEEG variables are displayed in topographic maps

in Figs. 1 and 2. The color-coded scale goes from a

correlation of F0.5 through zero, where for N =15

(df =13), at P=0.05, r =0.51.

The SAS/STATR CANCORR procedure was used

to compute canonical correlations. With this method,

the significance of the relationship between sets of

Fig. 1. Correlations of PET metabolism with qEEG for delta, theta, alpha and beta frequencies (successive columns in each panel) for absolute

power (top row each panel), relative power (middle row each panel) and mean frequency (bottom row each panel) for different ROIs. Each panel is

the color-coded topographic map of the correlation values for: Medial Frontal Cortex (top left panel), Left Temporal Cortex (bottom left panel),

Dorsolateral Prefrontal Cortex (top right panel) and Right Temporal Cortex (bottom right panel) and Thalamus (bottom left panel). The color scale

for these images go from �1 (blue/light green), through zero (black) to +1 (red/yellow), for N =15 (df =13), p =0.05, r =0.51.


qEEG variables and each PET ROI was computed.

CANCORR uses an F approximation in its calculation.

This method can be considered an extension of multiple

regression and correlation analysis, where sets of vari-

ables can be used. Using CANCORR, the linear com-

binations of the sets of qEEG variables that maximize

the correlation with the PET ROI are sought.

3. Results

Maps of the correlations of each of the PET ROIs

with EEG absolute and relative power and mean fre-

quency are presented in Figs. 1 and 2. Each of the panels

in these figures depicts a single PET ROI correlating

with absolute and relative power and mean frequency

(successive rows in each panel) for the delta, theta, alpha

and beta EEG bandwidths (successive columns in each

panel). In general, it appears that correlations of PET

metabolism with qEEG show different patterns that may

be global or localized to either subjacent or distant leads.

The top panels of Fig. 1 depict the qEEG correla-

tions with PET MFC and DLPFC ROIs (left and right

panel, respectively). MFC activity is positively corre-

lated with delta power maximally at Fz and delta mean

frequency at O1. The topography of correlations for

MFC and DLPFC appears to differ. In contrast to MFC,

the strongest correlations with DLPFC activity are neg-

ative and occur for absolute power in the alpha and beta

Table 2

PET ROI, canonical correlation with qEEG variables, Wilk’s Lambda

F and probability obtained in these analyses

PET ROI CAN CORR Wilk’s Lambda Approx F PrNF

DPFC 0.8350 0.3038 5.7295 0.012

THAL 0.9205 0.1125 5.9182 0.022

AMFC 0.6375 0.5939 4.1029 0.044

LTEMP 0.9759 0.0476 8.0103 0.029

RTEMP 0.7551 0.4363 7.7531 0.007

Fig. 2. Correlation of PET metabolism with qEEG as in Fig. 1 above, for the Thalamus. Color coding as described for Fig. 1.


bandwidths in parietal–occipital leads. There is a pos-

itive correlation with relative delta power in F7.

The bottom panels of this figure map the correlations

of qEEG and PET LT and RT ROIs (left and right panel,

respectively). Possibly the most striking aspect of these

two sets of images is the marked difference of the

pattern of correlation of qEEG with the right versus

the left side. RT metabolic activity is correlated nega-

tively with beta absolute power frontally and delta

absolute power centrally. Positive correlations of RT

metabolism are seen with theta mean frequency in the

left temporal and central leads, and with beta absolute

power frontally. The LT region differs considerably

from the RT, with a negative relationship with theta

absolute and relative power and mean frequency tend-

ing to be maximal in right posterior temporal and

occipital leads, and generally weaker correlations than

those seen in RT.

Fig. 2 maps the correlations of qEEG and the PET

THAL ROI. It indicates a global negative relationship

between thalamic metabolism and alpha absolute and

relative power and beta mean frequency, as well as a

positive correlation with theta and beta relative power

in frontal leads. Thalamic metabolism is also correlated

with delta absolute and relative power at F7.

Using qEEG variables selected from the correlation

analyses described above, the canonical correlations

between qEEG ROIs and PET ROIs were computed.

As can be seen in Table 2, there were significant

canonical correlations for all PET ROIs. For the

DLPFC, the qEEG variables contributing most to the

significant canonical correlations were as follows: ab-

solute power in alpha for multivariate posterior regions

and in beta for posterior temporal and multivariate

anterior regions. For the THAL, the qEEG variables

contributing most to the significant canonical correla-

tion were relative power in alpha for all frontal regions

and the multivariate for posterior regions. For the

AMFC, the qEEG variables contributing most to the

significant canonical correlations were as follows: the

multivariates for anterior regions and posterior regions

in the delta band. For the LT, the qEEG variables

contributing most to the significant canonical correla-

tions were as follows: absolute power in theta in the

central and posterior regions and the multivariate ante-

rior regions. For the RTEMP, the qEEG variables con-

tributing most to the significant canonical correlations

were as follows: absolute power in beta in the multi-

variate anterior regions and mean frequency in theta in

the central regions.

4. Discussion

The most significant findings in this study appear

to include (1) a negative correlation of thalamic me-

,

GZ

Highlight

GZ

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GZ

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tabolism to alpha power, (2) a positive correlation of

medial frontal cortical metabolism to delta EEG abso-

lute power and beta mean frequency, (3) generally

negative correlations of DLPFC to absolute power in

all leads except for a positive correlation of delta

absolute and relative power at F7, and (4) generally

different topographic patterns of correlations of me-

tabolism to EEG in the right versus left temporal

lobes, RT correlating positively with prefrontal beta

and beta mean frequency, and LT negatively correlat-

ing with theta in posterior leads. The correlations of

PET metabolism to EEG are both global and relatively

localized and not confined to subjacent leads. While

this is a preliminary study involving a relatively small

number of subjects, previous studies using this SPI

approach (John et al., 1994b) showed significant sen-

sitivity and specificity when applied to a comparably

small clinical sample.

In addition to the relatively small number of sub-

jects, the non-simultaneity of the PET and EEG eva-

luations, which were conducted up to a week apart, is

a factor that might decrease the significance of the

PET–EEG correlations was reported here. However,

evidence for a high degree of test–retest reproducibil-

ity has been reported for both PET (Bartlett et al.,

1988) and qEEG (John et al., 1983; Kaye et al., 1981;

Fein et al., 1983, 1984; Kondacs and Szabo, 1999),

and specifically between EEG obtained in the EEG

laboratory and during the PET scanning procedure

(Alper et al., 1998).

The choice of EEG electrode montage has been

suggested to affect the robustness of PET–EEG correla-

tions. In a study on six normal subjects that compared

the use of referential, bipolar, and source derivation

montages, the source derivation produced higher corre-

lations between PET and qEEG than the referential or

bipolar montages (Cook et al., 1998). However, much

of the research on qEEG correlations with metabolism

in normal subjects has used a referential montage

(Buchsbaum et al., 1982; Danos et al., 2001; Lindgren

et al., 1999). The monopolar linked-ears montage is the

convention in the Neurometric methodology, which

offered the methodological and statistical advantages

of a large normative database and the ability to compute

QEEG measures as Z scores referenced to normative

values. A referential montage is also most suitable for

the purpose of generating maps of PET ROIs correlated

with a set of qEEG variables that most fully samples the

topography spanned by the 19 channel electrode loca-

tions, rather than limiting the analysis to pairing EEG

leads with immediately subjacent cortical metabolic

regions.

The scan for Thal (Fig. 2) and MFC (Fig. 1), seem

statistically and physiologically plausible, and indi-

cates a negative relationship between thalamic metab-

olism and EEG alpha absolute and relative power,

consistent with a similar negative relationship of

EEG alpha power to thalamic metabolism that has

been reported elsewhere in the literature (Larson et

al., 1998; Lindgren et al., 1999). A correlation of

thalamic metabolism with alpha might be expected

in view of the evidence that the thalamus is important

in the generation of alpha activity (Nunez, 1995a;

Lopes da Silva, 1991; Steriade et al., 1990). The

fact that the correlation of thalamic metabolism with

alpha power is negative is consistent with a model of

generation of the alpha EEG rhythm that emphasizes

the dynamic interaction of cortex and thalamus (Llinas

and Ribary, 2001; John, 2002; Nunez, 1995a). This

view differs distinctly from the idea of the thalamus as

a bpacemakerQ of the awake alpha rhythm, and in-

stead, involves the selective activation of different

resonant modes or bEigenstatesQ of cerebral cortex.

The frequency at which the thalamus activates the

cortex is strongly dependent on the state of membrane

polarization of the reticular nucleus of the thalamus

(RNT) (Nunez, 1995b; Silberstein, 1995); however,

the frequency with which the cortex becomes engaged

in the resulting thalamocortical oscillation also

depends on the intrinsic oscillatory modes of the

cortex (Silberstein, 1995). The rhythmic cortical mode

then becomes entrained on the thalamus reflecting

thalamic bfrequency plasticity,Q resulting in matching

of the resonant frequencies of the thalamus and cortex

in a stable and energetically conservative oscillatory

state.

According to this model, resonance of thalamus and

cortex in the alpha frequency range corresponds to a

state of relative hyperpolarization of thalamic relay

cells by RNT and decreased transfer of signal from

the periphery through thalamus to cortex. Under these

conditions, a negative correlation between thalamic

metabolism and alpha activity might be expected due

to a lower energy requirement of the thalamus during

generation of the global cortical alpha rhythm with

decreased throughput to cortex. Conversely, the rela-

tively higher energetic requirement of a depolarized

bias of thalamic relay cells by the RNT, with increased

transfer of signal via specific thalamic afferents to

cortex , would be expected to correspond to higher

frequency local cortical resonances and diminished

alpha.

This model can be reconciled with the fMRI study

of Goldman et al. (2002), which reported a positive


correlation between alpha power and thalamic metab-

olism. When metabolism is measured with PET in

subjects at rest with eyes closed as in the present

study, the temporally dominant feature during the

continuous radiolabeled glucose uptake period would

presumably be the energetically conservative thalamo-

cortical oscillatory state, in which lower thalamic

metabolism would be expected to correspond to great-

er alpha power. Functional magnetic resonance imag-

ing, in contrast to PET, does not measure the baseline

energetically conservative thalamocortical oscillatory

state corresponding to the alpha rhythm itself, but

instead measures the changes in cerebral blood flow

corresponding to the modulation of the alpha rhythm.

Some studies have reported a positive correlation

between thalamic metabolism measured utilizing

PET and alpha EEG power (Danos et al., 2001).

This may be due to the fact that subjects in these

studies were presented with sensory stimuli such as

the visual continuous performance task (Danos et al.,

2001) or music, as opposed to the quiet resting

condition of the studies, including the present one,

in which a negative relationship of thalamic metabo-

lism was observed (Lindgren et al., 1999; Larson,

1998).

Fig. 1 shows a positive correlation between MFC

metabolism and delta EEG activity. We have previ-

ously reported a similar positive correlation between

delta EEG power and frontal metabolic activity mea-

sured by PET in a small group of schizophrenic sub-

jects (Alper et al., 1998). Pizzagalli et al. (2003)

reported a positive correlation between theta and me-

tabolism in the anterior cingulate and temporal corti-

ces, without significant correlations to delta; however,

that study differed from the present one in a number

of respects including the definition of the delta and

theta bandwidths and regions of interest. Although

there is a tendency in the neurological literature to

ascribe only pathological significance to delta EEG

activity, there is strong evidence for delta as a corre-

late of normal mental activity in awake humans. In

multiply replicated EEG norms, delta activity accounts

for more than 20% of the raw total EEG power in

anterior leads (Matousek and Petersen, 1973; Gasser et

al., 1982; Jonkman et al., 1985; Alvarez et al., 1987;

Harmony et al., 1987; John et al., 1987). Studies using

dipole modeling have located the major delta genera-

tor deep in prefrontal cortex (Michel et al., 1992,

1993). A variety of behavioral studies of delta activity

in normal awake humans show a positive relationship

of delta activity to mental effort. Increased frontal

delta has been observed in normal subjects performing

calculations (Fernandez et al., 1995; Harmony et al.,

1996, 2004), delayed match from sample (John et al.,

1996; Harmony et al., 2004), word and figure catego-

rization (Harmony et al., 2001), reaction time tasks

(Van Dijk et al., 1992), abstract thought (Michel et al.,

1993) and an omitted stimulus paradigm (Basar-Ero-

glu et al., 1992), and to covary positively with P300

amplitude (Schurmann et al., 2001; Basar et al., 1984;

Intriligator and Polich, 1994). Fernandez et al. (1995)

hypothesize that normal physiologic delta might be a

functional correlate of the gating of peripheral input in

order to facilitate binner concentration,Q or the alloca-

tion of attention to an internally represented task.

Delta activity can also be viewed as a global rhythm

of association cortex that serves the purpose of inte-

gration by synchronizing coherent activity and phase

coupling across widely spatially distributed neural

assemblies (Nunez, 1995a).

An important issue that should be addressed in

future work correlating PET and EEG is the question

of the underlying dimensionality of the PET and EEG

measure sets. In the present study, the dimensionality

of the PET measure set was reduced by defining a

priori anatomical regions of interest. The EEG mea-

sure set in this study was defined by the descriptors

consisting of the leads of the 10–20 system and the

traditional four EEG bandwidths. The dimensionality

of the data sets can also be defined empirically by

factor analysis of either the PET or EEG data sets. The

general agreement between multiple laboratories re-

garding the results of factor analysis of PET data

(Volkow et al., 1986; Schroeder et al., 1994; Zuendorf

et al., 2003) is evidence for a replicable underlying set

of dimensions of the PET metabolic image. There are

multiple replications of small sets of factors resulting

from principal components analysis of EEG data

(Andresen, 1993; Arruda et al., 1996). In addition to

data reduction, an appreciation of the factor structure

of PET and EEG might aid the understanding of the

metabolic basis of the neurophysiologic generation

of the EEG, and might potentially add statistical

power in detecting abnormality or change associated

with neuropsychiatric disorders or psychopharmaco-

logical effects.

In summary, this preliminary study found correla-

tions between regional metabolism and EEG spectral

power. The findings of a negative correlation be-

tween thalamic metabolism and alpha power, and a

positive correlation of frontal cortical metabolism and

delta power appear plausible given the present un-

derstanding of the neurophysiological basis of these

two EEG rhythms.


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