Department of Psychiatry, Brain Research Laboratories, New York University School of Medicine,
462 First Avenue, OBV—Room 884, New York, NY 10016, USA
bDepartment of Neuroscience, Tor Vergata University, IRCSS, 00133 Rome, St. Lucia, Italy
cNathan Kline Institute for Psychiatric Research, Orangeberg, NY 10962, USA
This article focuses on computerized methods of quantifying electroencephalography
(EEG) and the clinical use of comparing EEG features obtained from
specific patients with psychiatric and neurologic disorders to values obtained
from a population of normal individuals. The current status of quantitative EEG
(qEEG) studies is reviewed with the goal of extracting information that would be
useful to the practicing clinician. Although the major focus of this article is the
use of qEEG in child and adolescent psychiatric disorders, preliminary sections of
this article summarize qEEG findings from relevant adult psychiatric and
neurologic disorders. The qEEG studies that involved children and adolescents
have been, with a few exceptions, limited to individuals with attention or learning
problems. Many qEEG studies of adult psychiatric populations have
implications that can impact on our knowledge of childhood disorders and are
summarized. Initial sections also present a discussion of the development of
qEEG, controversial issues surrounding its clinical usage, and a summary of
important methodologic issues.
The clinical uses of qEEG were described in a position paper of the American
Medical Electroencephalographic Society [1]. These uses include the detection of
an organic disorder as the underlying cause of brain dysfunction, roles in making
1056-4993/05/$ – see front matter D 2004 Elsevier Inc. All rights reserved.
doi:10.1016/j.chc.2004.07.005 childpsych.theclinics.com
* Corresponding author.
E-mail address: Robert.chabot@med.nyu.edu (R.J. Chabot).
Child Adolesc Psychiatric Clin N Am
14 (2005) 21– 53
differential diagnosis, and epileptic source localization. We add possible roles in
determining appropriate medication selection, following treatment response, and
delineating the underlying cause of specific psychiatric disorders. Sections of this
article examine the current status of qEEG and how it can impact on these
outstanding issues.
The greatest body of evidence regarding replicable neurophysiologic indices
of psychiatric and developmental disorders has been provided by qEEG studies.
Electrophysiologic assessment is also the most practical and economic neuroimaging
method, because it uses relatively simple, inexpensive equipment that
can be used in space readily available in clinics, hospitals or private offices.
Special purpose qEEG analytic algorithms are widely available from commercial
sources, training workshops with continuing medical education accreditation in
collection, analysis, and interpretation of data are regularly presented by
professional societies and equipment manufacturers, and certification examinations
are administered by the American Medical EEG Association and the
American Board of Clinical Neurophysiology.
Important technical terms are defined as follows:
! The four commonly used EEG frequency bands used include (1) delta (1.5–
3.5 Hz), (2) theta (3.5 –7.5 Hz), (3) alpha (7.5–12.5 Hz), and (4) beta (12.5–
25 Hz). Total power represents the frequency range of 1.5 to 25 Hz.
! Absolute power: The average amount of power (mV2 ) in each frequency
band and in the total frequency spectrum of the EEG recorded from each
electrode site.
! Relative power: The percentage of the total power contributed by each
frequency band in the spectrum from each electrode site. These features
define the frequency composition of the electrical signal independent of its
total power. For example, relative alpha power is the ratio of total alpha
power/total power at each electrode site.
! Power asymmetry: Interhemispheric: The ratio of absolute power between
corresponding (homologous) regions of the two hemispheres in each frequency
band and for the total power across all frequency bands. Intrahemispheric:
The ratio of absolute power between regions within a hemisphere in
each frequency band and for the total power. This addresses the question,
‘‘How similar is the observed activity between/or within hemispheres?’’
! Coherence: Interhemispheric: The amount of synchronization of electrical
events in corresponding brain regions, separately for each frequency band
and for the entire frequency spectrum. Intrahemispheric: The amount of
synchronization of electrical events between regions within a hemisphere in
each frequency band and for the entire frequency spectrum. This addresses
the question, ‘‘How synchronized is the observed activity?’’
! Mean frequency: The frequency within each band, or for the entire spectrum,
above and below which there is the same amount of power. This
addresses the question, ‘‘Where in each frequency band—or in the entire
frequency spectrum—is the concentration of power?’’
R.J. Chabot et al / Child Adolesc Psychiatric Clin N Am 14 (2005) 21–53 22
Historical perspective: origins of the electroencephalogram
Research about the origins of the various EEG frequency bands makes it clear
that anatomically complex regulatory systems are involved in the generation of
the EEG power spectrum. Brain stem, thalamic, and cortical processes mediate
this regulation using all the major neurotransmitters [2–5]. The EEG power
spectrum can be argued to be characteristic for human beings, resulting from the
coordination of brain processes normally produced in healthy individuals. These
facts suggest that EEG frequency measures can be sensitive to brain dysfunctions
believed to be abnormal in psychiatric disorders. Numerous twin and family
studies have been conducted on normal variation in the human EEG. A recent
review concluded that most EEG parameters are to a large extent genetically
determined [6]. The effect size of genetic determination is between 76% and 89%
for the four EEG frequency bands [7], and about 60% of the variance in theta,
alpha, and beta coherence was explained by genetic factors. Environmental
factors did not influence variation in coherence [8].
Initial qEEG studies showed systematic changes with maturation from birth to
adulthood in the average power in the delta, theta, alpha, and beta frequency
bands [9]. Replication studies not only confirmed these systematic changes with
age but they also found no significant differences between the EEGs of normally
functioning Swedish children and white or black US children [10]. Cultural
independence and replication of qEEG findings has been extended to studies from
Barbados, China, Cuba, Germany, Holland, Japan, Korea, Mexico, Netherlands,
Sweden, United States, and Venezuela [11–24].
The independence from cultural and ethnic factors of normative qEEG
descriptors makes possible objective assessment of brain integrity in persons of
any age, origin, or background. The incidence of positive findings different from
the normative database in healthy, normally functioning individuals repeatedly
has been shown to be within the chance levels, with high test-retest reliability.
Normative data have been extended to cover the age range from 1 to 95 years of
age for each of the electrode positions in the standardized international 10/20
system and broadened to include measures of absolute power, relative power,
mean frequency, coherence, and symmetry [25–27].
Controversial issues
The limited acceptance of qEEG in US psychiatry can be attributed largely to
two major factors. First, most papers that report the results of qEEG studies of
psychiatric patients have not appeared in journals widely read by psychiatrists but
rather in specialized electrophysiologic or brain research publications. Reports of
qEEG abnormalities in psychiatric patients have been regarded as nonspecific
and are not included in the curriculum of medical students or psychiatric residents.
Second, since 1989, skeptical statements about the use of qEEG in
R.J. Chabot et al / Child Adolesc Psychiatric Clin N Am 14 (2005) 21–53 23
psychiatry have appeared in professional journals [28] and in position statements
by committees from some professional organizations, such as the American EEG
Society, the American Academy of Neurology, and the American Psychiatric
Association. These position statements indicated that published qEEG findings
were promising but required further research before clinical use could be established.
These negative conclusions were repeated in a report by subcommittees of
the American Academy of Neurology, the American Clinical Neurophysiology
Society, and a panel of experts [29].
Findings from a large number of excellent studies not reviewed by these
committees and from numerous studies completed since the time of most of these
reviews provide substantial additional support for the validity and clinical use of
qEEG in several areas of child psychiatry, however. The American Medical EEG
Association recently issued a positive position statement about the clinical value
of qEEG in psychiatry [1], and the American Psychiatric Electrophysiological
Association established a committee to assess the current use of qEEG examinations
in the management of various psychiatric disorders. After a thorough
review of more than 500 qEEG and conventional EEG studies of psychiatric
patients published in the last 20 years, this positive report was adopted by the
Steering Committee of the American Psychiatric Electrophysiological Association
in May 1996.
The specificity of qEEG findings recently was questioned in a study that
compared the EEGs of 100 normal controls with those obtained from an independent
sample of 67 controls and 340 patients with 22 different psychiatric or
neurological diagnosis [30]. The authors conclude that while decreases in delta
and theta absolute and relative power are specific signs of brain dysfunction that
correlate with cortical atrophy, no specific qEEG patterns could be found that
were pathognomonic for any specific disorder. While this is an interesting study,
there is a fatal flaw that invalidates their conclusion. The group sizes for any
specific disorder were highly limited, with the largest group at 57 patients and
with nine of the disorders having less than 10 patients. Clearly these numbers are
too small to expect anything but non-specific findings. Interestingly, abnormal
qEEG findings were reported in 11.9% of their normal controls, suggesting the
inadequate number of individuals in their normal database. Furthermore, an
editorial appeared in the same journal issue in support of their findings [31]. This
editorial made general statements that reiterated potential problems with qEEG
research. These included problems with EEG filter settings, artifact inclusion or
exclusion, drowsiness, age effects, medication effects, and statistical problems
due to the large number of qEEG variables often available for study compared to
the size of the patient populations under study. It is interesting that this editorial
praises the described study since it suffers from a major problem of small sample
sizes. In the following methodological section of this article we address each of
these criticisms. We also argue that the use of an appropriate normal database and
method of collecting and analyzing qEEG can effectively make such criticism
a non-issue. The present article reassess the current status of qEEG research
findings in lieu of the criticisms described above.
R.J. Chabot et al / Child Adolesc Psychiatric Clin N Am 14 (2005) 21–53 24
It is the goal of this article to provide an up-to-date and comprehensive review
of all of the relevant research published to date to allow for an informed
consideration of the scientific knowledge base on the clinical value of qEEG in
child and adolescent psychiatry.
Quantitative electroencephalographic methodologic issues
A brief description of the development, replication, validation, and sensitivity
of the neurometric qEEG methodology follows. The neurometric qEEG
normative database has been published, and findings using this technique have
been replicated widely. Neurometrics is the only qEEG technology that has
published normative data and been approved by the US Food and Drug
Administration. Complete details have been published elsewhere [11,25,26,32].
The neurometric analytic method enables objective evaluation of brain function
based on qEEG. Its initial development was supported by program grants from
the Research Applied to National Needs Program of the National Science
Foundation and the Bureau of Educational Handicapped of the US Office of
Education. An understanding of the important methodologic issues that follow is
necessary to offset the criticisms of qEEG that were described previously.
Normative database
The neurometric normative database contains the EEG records and features
derived from 650 individuals, aged 6 to 90 years, with function confirmed
as normal by multidisciplinary examinations [25]. The number of subjects
required for reliability at each age was statistically determined and increased until
consistent split half replications were obtained. This sample requirement was
dynamic in that different ages required different Ns. For example, in the ages
from 6 to 13, in which brain maturation changes are rapid, the Ns were greater,
as were those in later adolescence, in which findings indicated that the frontal
regions of the brain were maturing to adult levels [33].
Quantitative features were extracted from artifact-free data by spectral analysis
of the EEG (qEEG), log transformed to obtain normal (Gaussian) distributions,
age regressed, and evaluated statistically relative to the distributions of every
feature in the qEEG database [27,34]. Great care was taken to include only
artifact-free EEG and guard against changes in patient state, such as drowsiness.
All features were transformed to Z scores and expressed in standard deviations
from the normative values. This allows objective assessment of the statistical
probability that the measurements obtained from an individual lie outside the
normal limits for his or her age. The importance of selecting artifact-free EEG
segments for analysis and the use of log transformation must be stressed, because
the failure to follow these procedures validates the criticisms described
previously. For example, qEEG normal control groups often rely on a reference
sample of data obtained from individuals whose ages span one or several
R.J. Chabot et al / Child Adolesc Psychiatric Clin N Am 14 (2005) 21–53 25
decades. In neurometrics, the use of age-regression techniques yields an estimate
of the range expected from persons exactly the same age as the subject.
Computation of the Z score for the difference between the predicted normative
value and the value obtained from the individual estimates the probability that
such a value might be obtained by chance from a healthy peer. Using only
significant Z values in feature selection for further statistical analyses acts as a
preliminary step in data reduction. Test-retest reliability of neurometric qEEG has
been confirmed by intensive short- and long-term follow-up studies in a large
sample [35]. Although concern about normative databases can be valid, the
widespread independent replications described previously provide confidence in
the use of the neurometric normative database. Statistical evaluation of
distributions of features by gender revealed small differences within the normal
population compared with between-population variance (eg, normal versus
abnormal). Neurometric qEEG contains combined gender norms, considered to
be a more conservative approach.
Distinctive patterns of qEEG abnormalities have been described in diverse
psychiatric disorders (eg, depression, schizophrenia, dementia, and attention
deficit hyperactivity disorder [ADHD]). This allows differentiation of these
disorders from normal and, where appropriate, from each other [36]. A large body
of peer-reviewed published data from independent laboratories reports the
sensitivity of neurometrics in varied clinical populations, including head injury
[37], stroke and transient ischemic attack [13,38], schizophrenia [39], depression
[40], marijuana abuse [41], and ADHD [42,43].
Importance of reduction of quantitative electroencephalographic feature set
Criticisms of qEEG studies often focus on the abundance of qEEG features
available for study, which can lead to spurious findings if appropriate measures
are not undertaken. Statistically guided data reduction is fundamental. Conventional
methods of data reduction, such as feature selection from t-tests and
analysis of variance (ANOVAs), used to identify variables significantly related to
dependent variables of interest, should be used [44,45]. Variables should be
selected that maximize adjusted multiple correlation coefficients between qEEG
and dependent variables, minimizing the residual sum of squares with each
feature set considered independently and appropriate corrections for multiple
tests applied (eg, Bonferroni, Tukey, or Greenhouse-Geisser). In parallel, factor
and discriminant analysis can be used to reduce the dimensionality of the variable
set to better address specific hypotheses. Selected qEEG features can be pruned
further by using stepwise procedures and split-half or jackknifed replications,
always maintaining the conservative rule of 10:1 subject-to-variable ratios. These
methods allow one to identify variables that independently account for the
maximum variance in the model under study. In this way, the likelihood of
spurious findings can be minimized and the sensitivity and specificity of qEEG
findings increased.
R.J. Chabot et al / Child Adolesc Psychiatric Clin N Am 14 (2005) 21–53 26
Quantitative electroencephalographic source localization
Knowledge about the neuroanatomic generators of EEG frequency components
has important implications for the generation of models of the neurophysiology
of the EEG and the neuropathology of psychiatric disorders. For
reviews of this literature, see Hughes and John [46] and Alper et al [47]. The
major qEEG source localizations method currently available is variable resolution
electromagnetic tomography (VARETA) [48]. Correlations of VARETA maps of
broadband spectral parameters with radiologic studies in patients with spaceoccupying
lesions have shown that EEG delta power is correlated with the
volume of the lesion and EEG theta power is correlated with the volume of
edema surrounding such lesions [49–52]. Recent research has further tested the
accuracy of VARETA in a group of patients with various space-occupying lesions,
evaluating the Z correspondence of VARETA solutions in the delta and
theta frequency domains to the volume of brain edema and the centroid of the
mass [53]. The authors concluded that VARETA achieved accurate location of
brain lesions. Using LORETA analyses (a source localization algorithm mathematically
akin to VARETA), Pascual-Marqui [54] reported further validation of
such methods by demonstrating low error of sources and correct localization of
primary sensory cortices of evoked potential data. In a recent study using
LORETA, Saletu et al [55] found different representative drugs to induce
different changes in different brain regions, which they interpreted as supporting
the use of such methods for studying the mode of action of psychotropic drugs.
Differences between specific drug-free patient groups and normal individuals
were found to be opposite to the observed changes induced by the respective
drugs. In a subsequent section of this article we demonstrate how VARETA can
be used in the development of a neuroanatomic model of attention deficit disorder
(ADD) in children and adolescents.
Relevant quantitative electroencephalographic studies in adult psychiatric
disorders
Dementias
Studies that use qEEG in dementia patients are in agreement with conventional
EEG findings and report increased delta or theta power [56–70], decreased
mean frequency [68,71–73], decreased beta power [74,75], and decreased
occipital dominant frequency [60,65]. Many studies regard increased slow
activity before reduction of alpha power as the earliest electrophysiologic indicator
that appears in Alzheimer’s disease [57,65,69,70,76,77]. The amount of
theta activity shows the best correlation with cognitive deterioration [70,78,79]
and clinical outcome in longitudinal follow-up [66,69,70,76,80]. Increased delta
seems to be a correlate of severe advanced dementia, subsequent to increased
theta [67,70,80,81]. Multiple studies report accurate discrimination of patients
R.J. Chabot et al / Child Adolesc Psychiatric Clin N Am 14 (2005) 21–53 27
with Alzheimer’s disease from depressed patients and normal controls using
qEEG measures of slow activity [26,56,71,82]. Several qEEG studies of dementia
patients report high correlations between the severity of cognitive impairment and
amount of EEG slowing. These features are absent in depression and are localized
in multi-infarct dementia, which enables these disorders to be differentiated from
Alzheimer’s dementia.
Alcohol and substance abuse
Several recent studies of substance abuse have used qEEG. Replicated reports
have appeared of increased beta relative power in alcohol dependence [26,83–
86]. Increased alpha power, especially in anterior regions, has been reported in
withdrawal and after acute exposure to cannabis [41,87]. Increased alpha and
decreased delta and theta have been reported in crack cocaine users in withdrawal
[88–92]. Use of qEEG reveals marked abnormalities in alcohol and substance
abuse. The effects vary depending on the drug. Either increased slow activity
with lower alpha and beta or the converse has been reported, which reflects
diversity of substances studied and the differences in anatomic regions or states
focused on. There is a consensus regarding increased beta relative power in
alcoholism and increased alpha in chronic cannabis or crack cocaine users.
In studies from our laboratory [93], a chronic crack cocaine–dependent
population was divided by age of first use (age b20 or _20 years) (young onset,
n = 52; adult onset, n = 48) to explore the consequences of use during adolescence.
The qEEGs contained significantly more theta excess in individuals who
started using as adolescents, which suggests enhanced vulnerability for such
effects on brain function. Of note, theta excess characterized the group of cocaine
abusers who relapsed most quickly [94]. A significantly larger (Pb0.04)
proportion of the group who began using as adolescents was found to have a
history or current signs of ADHD. Clear differences were reported between crack
cocaine–dependent subjects who began using as adolescents and subjects who
began using as adults.
Schizophrenia
Numerous qEEG studies have been performed on carefully evaluated groups
of patients with schizophrenia. A deficit in alpha power is consistently reported
[26,95–100] with altered alpha mean frequency or diminished alpha responsiveness
[101–103]. Numerous studies have reported increased beta activity in
schizophrenia [98,104–107]. Neuroleptic medication typically increases alpha
power [107–109] and reduces beta power [110,111], which suggests possible
normalization of deviant features by medication. Increased delta or theta activity
also has been reported in a large number of studies [95,98,99,106,112–119].
Increased slow activity apparently can result from long-term neuroleptic
treatment [120,121], although there are reports of increased delta in patients
off medication for several weeks [86,95,98] and reduction of delta or theta after
R.J. Chabot et al / Child Adolesc Psychiatric Clin N Am 14 (2005) 21–53 28
resumption of medication [108,118,122]. Patients with schizophrenia can be
discriminated from controls by the presence of increased amounts of delta activity
in the left anterior temporal area [123].
Heterogeneity within schizophrenia has been documented in a large sample of
medicated, nonmedicated, and never-medicated persons with schizophrenia using
cluster analysis based on qEEG variables. Five subtypes were described, with
qEEG profiles characterized by (1) delta plus theta excess, (2) theta excess with
decreased alpha and beta, (3) theta plus alpha excess with beta deficit, (4) alpha
excess with decreases in delta, theta, and beta, and (5) beta excess [124]. Patients
who were never medicated were classified into three of these subtypes.
Individuals with schizophrenia with qEEG profiles that corresponded to some
of the groups identified by this cluster analysis have been reported to display
differential responses to treatment with haloperidol [39] or risperidone [125].
Heterogeneity in the schizophrenic population has been presented in other qEEG
studies [126,127]. In the cluster analysis just cited, qEEG asymmetry was found
in every frequency band for all five subtypes [124]. Increased coherence within
cerebral hemispheres in anterior regions also has been consistently reported
[115,124,128–130].
Mood disorders
Numerous qEEG studies have found increased alpha or theta power in
depressed patients [26,71,131–137]. Asymmetry within cerebral hemispheres,
especially in anterior regions, has been reported repeatedly [138–142], as
has decreased coherence [26,115,143]. In bipolar illness, in contrast to unipolar
depression, alpha activity is reduced [135,144] and beta activity increased
[26,145]. This difference may serve to separate unipolar from bipolar patients
who are evaluated while in a state of depression without prior history of mania
[143,145].
Available qEEG studies suggest a high incidence of abnormalities in patients
with anxiety, panic, and obsessive-compulsive disorder [146–150]. Diminished
alpha activity has been found in anxiety disorder [151,152], and increased theta
activity has been reported in obsessive-compulsive disorder [153,154]. Two
subtypes of patients with obsessive-compulsive disorder have been described.
One, with increased alpha relative power, responded positively (82%) to serotonergic
antidepressants, whereas the second, with increased theta relative power,
failed to improve (80%) [155]. Recent reports stated that a qEEG measure called
cordance may play a role in predicting clinical response to different antidepressants
[156–158]. A qEEG was obtained before treatment and 48 hours
and 1 week after initiation of treatment with fluoxetine, venlafaxine, or placebo,
with treatment response evaluated out to 8 weeks. No baseline qEEG differences
were noted, whereas responders to placebo showed increased prefrontal cordance
and medication responders showed decreased prefrontal cordance within 48 hours
of treatment initiation. Nonresponders showed no change in cordance values.
These results may indicate a role for the prefrontal cortex in mediating treatment
R.J. Chabot et al / Child Adolesc Psychiatric Clin N Am 14 (2005) 21–53 29
response, with changes in cordance values preceding favorable behavioral
response. Currently, this research has not been replicated beyond this group of
51 patients.
Mild head injury or concussion syndrome
Patients with complaints of cognitive, memory, or attention deficit after mild
head injury without loss of consciousness frequently present for psychiatric
evaluation for worker’s compensation and disability benefits. Objective evidence
of brain dysfunction in such cases is critical. Numerous qEEG studies of severe
(Glasgow Coma Scale 4–8) and moderate head injury (Glasgow Coma Scale
9–12) have agreed that increased theta and decreased alpha power or decreased
coherence and increased asymmetry are found in such patients. Changes in these
measures provide the best predictors of long-term outcome [159–162]. The qEEG
abnormalities that persist after mild or moderate head injury are similar in type to
those found after severe head injury, namely increased power in the theta band,
decreased alpha, low coherence, and increased asymmetry. It is noteworthy that
similar EEG abnormalities have been reported in boxers [163] and professional
soccer players who were ‘‘headers’’ [164]. There is a broad consensus that
increased focal or diffuse theta, decreased alpha, decreased coherence, and
increased asymmetry are common EEG indicators of postconcussion syndrome.
There are multiple reports of discriminant functions based on qEEG variables that
successfully separated normal individuals from patients with a history of mild to
moderate head injury years after apparent clinical recovery [37,165]. Thatcher
et al [166] argued that qEEG findings meet all criteria for admissibility into the
federal court system.
Quantitative electroencephalography in adult attention deficit hyperactivity
disorder
A single qEEG study compared qEEG findings among normal controls, adults
with ADD, and adults with attention problems that do not reach criteria for
ADHD [167]. Results indicated that adults with ADHD show increased theta
absolute and relative power in comparison to both control groups. This finding is
consistent with that described later in children and adolescents with ADHD.
Adults with attention problems but not ADHD showed reduced relative theta
and increased relative beta power in comparisons to normal controls and adults
with ADHD.
Quantitative electroencephalography: sensitivity to signs of cortical dysfunction
We have published several qEEG studies that attest to the sensitivity of qEEG
in the documentation of signs of cortical dysfunction in various disorders. These
studies attest to the use of qEEG to document brain dysfunction and evaluate the
effectiveness of treatment of these abnormalities.
R.J. Chabot et al / Child Adolesc Psychiatric Clin N Am 14 (2005) 21–53 30
The use of qEEG was found to be a sensitive indicator of brain dysfunction in
patients with systemic lupus erythematosus who present with or without
neuropsychiatric manifestations of their illness [19]. In a sample of 52 such
patients, qEEG was found to have a sensitivity of 87% and a specificity of 75% in
documenting a neurophysiologic disorder. The qEEG profiles described varied
with the severity and type of neuropsychiatric problem manifested. Patients with
signs of memory and cognitive problems showed qEEG profiles similar to that
described in dementia, whereas patients with clinical signs of depression showed
qEEG findings similar to that seen in mood disorders. In 6 patients tested before
and after treatment, qEEG changes mirrored changes in clinical state. The qEEG
also was found to be useful in documenting the effects of Lyme disease on brain
function [168]. Abnormal qEEG was seen in 75% of patients with active Lyme
disease and was found to normalize after successful treatment. Use of qEEG also
has been shown to be a sensitive indicator of cortical dysfunction caused by
cerebral ischemia [169,170]. Signs of pre-existing cortical dysfunction were
noted in 40% of 38 patients before undergoing cardiopulmonary bypass surgery,
with the degree of abnormality predictive of the development of postoperative
neuropsychological test performance deficits. A comparison of preoperative and
1-week postoperative qEEG showed a positive correlation with neuropsychological
function 3 months after surgery. These results—in addition to the qEEG
findings reported in mild head injury—are compatible with the notion that qEEG
could provide useful information about brain function in situations in which
unexplained changes in cognitive function occur in children and adolescents.
Quantitative electroencephalographic studies in childhood and adolescent
disorders
Autism
Several studies have used varying types and degrees of EEG quantification to
describe differences between autistic children and matched normal controls [171].
Studies that used different EEG recording conditions (normal waking, stage II
sleep, and during cognitive activation) reported findings of hemispheric differences
in normal controls and a lack of hemispheric differences in autism [172–
174]. The largest such study examined qEEG in autistic children, normal
controls, mental age-matched toddlers, and age-matched mentally handicapped
individuals [175]. The autistic children showed increased frontal/temporal and
left temporal total power and decreased power asymmetry when compared with
normal or mentally handicapped controls. The autistic children and mental agematched
toddlers showed greater within-and-between cerebral hemispheric EEG
coherence than the other two groups. The autistic children’s EEG findings
indicated decreased cerebral hemispheric and topographic differentiation, which
suggested a severe maturational lag [176]. No qEEG studies that compared large
numbers of autistic children with children with other psychiatric disorders have
R.J. Chabot et al / Child Adolesc Psychiatric Clin N Am 14 (2005) 21–53 31
been published. The qEEG measurements of the degree of maturational lag and
amount of EEG slowing in individual autistic children might prove useful in the
development of educational intervention strategies [177].
Quantitative electroencephalography in children and adolescents with diabetes
Three qEEG studies of the effects of diabetes and hypoglycemia on brain
function have been conducted. The first study examined qEEG in 44 persons with
insulin-dependent diabetes and age-matched controls. A significant correlation
was found between hemoglobin A1c concentrations and decreased alpha relative
power. A positive history of ketoacidotic episodes was associated with increased
delta-theta and decreased alpha relative power [178]. An examination of qEEG in
28 children with type 1 diabetes and 28 age- and sex-matched controls revealed
a relationship between severe hypoglycemic episodes and increased theta in
frontal/central regions and increased delta in occipital regions. Nonlocalized
decreases in alpha power also were found [179]. A recent study examined the
effects of a controlled reduction in plasma glucose concentration in 19 children
with diabetes and 17 children without. Decreased glucose was associated with
increased delta and theta activity in both groups but was more pronounced in the
children with diabetes [180]. The authors concluded that improvement in glucose
metabolism is an important factor in preventing the development of qEEG
abnormality in children with diabetes.
Specific developmental disorders
The qEEG studies of eyes-closed resting EEG in dyslexia have resulted in
inconsistent findings, including decreased and elevated alpha or beta power and
increased theta power [181]. These inconsistencies most likely reflect small
sample sizes, varying methods of defining dyslexia, and differences in qEEG
recording and analysis techniques. For example, no differences were reported
between normal controls and a highly screened sample of boys with pure dyslexia
[14,182].
Several studies documented qEEG abnormalities in less selective samples of
children with learning disorders (LDs). Children with severe spelling disorders
showed decreased alpha and beta absolute and relative power in parietal and
occipital regions and increased temporal-parietal/occipital power ratios—both
signs of decreased topographic cortical differentiation [181]. Data that suggested
that the nature of qEEG abnormalities in LDs may change with age also have
been published [183]. Although 8- and 9-year-old children showed decreased
alpha, the topographic distribution was different, and 10-year-old children
showed focal theta excess. This age effect has not been replicated. The work of
John et al [25] would suggest that when age-regression qEEG equations are used
to compare normal children and children with LD, age effects disappear. The
finding of increased theta and decreased alpha in children with LD has been
replicated. Children with LDs without hyperactivity but with attention problems
R.J. Chabot et al / Child Adolesc Psychiatric Clin N Am 14 (2005) 21–53 32
showed increased theta and low alpha power [184]. Hyperactive children and
children with learning disorders have been shown to have decreased alpha and
beta power in comparison to normal controls [185]. The discrepant results of
these studies most likely reflect differences in patient selection criteria and the
location of recording electrodes. An examination of qEEG abnormalities across a
wide topographic distribution of recording sites and a large sample of children
with LDs reveal most of the qEEG abnormalities described previously are an
indication of the heterogeneity of LDs [25].
John [186] used the neurometric approach to qEEG to examine children with
LDs. Samples of 155 children with generalized LD and 155 children with specific
LD (SLD) had their qEEGs compared with the neurometric normal database.
Abnormal qEEGs were found in 32.7% of the children with SLD and 38.1% of
the children with LD, whereas only 5.5% of an independent sample of normal
children had abnormal qEEGs. The percentage of children who showed various
types of qEEG frequency abnormality also was presented and included increased
delta or theta and decreased alpha relative power. A discriminant function that
compared these groups of children to each other achieved sensitivity and specificity
levels that were well above chance levels [25].
Using qEEG techniques similar to those just described, Harmony and
associates [177,187] elucidated the nature of neurophysiologic abnormalities in
children with documented LDs. Children with LDs were shown to have different
patterns of brain maturation than normal controls. Within normal controls, there
was an increase of posterior/vertex EEG coherence and a decrease in coherence
among frontal recordings with increased age, which indicated increased differentiation
of frontal cortical regions and increased communication across basic
sensory and association cortex. These changes were not seen in children with
LDs. Instead, these children showed no change in posterior/vertex coherence with
age, and levels of frontal coherence remained high across all ages. Brain
maturation as indexed by changes in EEG coherence indicated a developmental
deviation in children with learning problems [177]. This finding was replicated
using different but converging qEEG feature sets. Decreased spatial differentiation
of the EEG was reported in children with spelling problems [181], and
the structure of the parietal/temporal and occipital EEG could be explained by a
single factor in children with specific reading disorders, whereas three factors
were required in normal controls [188]. VARETA images of the qEEG of
46 children with LD and 25 control children showed increased theta in the frontal
lobes of the children with LD and more alpha activity in the occipital lobes of
the controls [189]. Coherence differences also were reported between children
with dyslexia and a control population. Coherence between cerebral hemispheres
was greater in the control children, which indicated a greater disconnection of
cerebral hemispheres in the children with dyslexia [190].
The qEEG findings presented herein and our own research indicates that
children with LDs represent a heterogeneous population. Harmony et al [187]
showed that the nature of the qEEG abnormality present was directly related to
academic performance in reading and writing. Increased power in delta or
R.J. Chabot et al / Child Adolesc Psychiatric Clin N Am 14 (2005) 21–53 33
decreased alpha power was associated with a poor educational evaluation,
increased theta or decreased alpha was associated with mildly abnormal
evaluations, and increased alpha and decreased theta were associated with good
evaluations. Theta excess with alpha deficit was described as reflecting
maturational lag, whereas delta excess indicated cerebral dysfunction. qEEG
can be used to indicate which children with learning problems present with an
underlying neurophysiologic dysfunction. This information may be useful for
determining resource allocation and designing remediation programs.
The role that environmental and cultural factors may play in brain development
recently was examined [191]. A comparison was made between the qEEGs
of children at high and low risk of developing learning problems caused by
residing in economically, socially, and culturally disadvantaged environments.
These children were tested at 18 to 30 months, 4 years, and 5 to 6 years of age.
High-risk children were found to have increased delta and theta in frontal regions
and decreased alpha in posterior regions. Although these qEEG differences
decreased with age, frontal theta excess and posterior alpha deficits persisted.
This study indicates that sociocultural effects contribute to EEG maturation.
Likewise, Ito et al [192] reported that severely abused children have EEGs
characterized by increased interhemispheric coherence, which indicates delayed
brain development.
Quantitative electroencephalographic studies of attention deficit disorders
The greatest amount of qEEG information available in children and
adolescents involves those with ADD and ADHD. We examine this information
in detail and conclude with a neurophysiologic model of these disorders. Many
early studies conducted in children with attention deficit disorder had small
samples of children with ADD or ADHD with recordings of eyes-open EEG from
2 to 3 leads within the central, parietal, or occipital regions. The results from
these studies were relatively consistent despite these shortcomings. Hyperactive
children were reported to show decreased alpha activity and increased intrahemisphere
coherence [193], decreased alpha and beta activity [185], and
decreased alpha and beta absolute power [194]. These studies suggested that
central and parietal/occipital deficits of alpha and beta may characterize the eyesopen
EEG of hyperactive children.
When the number of recording channels is increased or larger samples of
children are tested, more consistent patterns of qEEG abnormality emerge.
Samples of 21 Japanese, 41 Chinese, and 29 Korean children with ADHD were
found to have eyes-closed resting EEGs characterized by increased delta and fast
theta with decreased alpha activity over left central or occipital regions when
compared with age-matched normal controls and children with conduct disorders
[24]. Regional differences in ADHD/normal qEEG findings also have been
reported [195]. Eyes-open resting EEG was recorded from 16 channels in 25 boys
with ADD without hyperactivity or concomitant learning problems and 25 agematched
normal controls. The qEEG of these boys with ADD was characterized
R.J. Chabot et al / Child Adolesc Psychiatric Clin N Am 14 (2005) 21–53 34
by generalized theta excess and beta deficit, with the theta excess greater in
frontal/temporal regions and the beta deficit greatest in temporal and posterior
regions. The size of these differences increased when the EEG was recorded
while reading or drawing. Similarly, El-Sayed reported that the amount of qEEG
slowing in frontal regions and the degree of beta deficit increased in children with
attention problems as the amount of attention load was increased while EEG was
recorded during performance of a continuous performance task [233].
Several recently published studies examined qEEG in various subgroups of
Australian children and adolescents with attention problems. A review of these
and other relevant findings also was published [196]. Their initial study examined
eyes-closed resting qEEG in 8- to 12-year-old children with ADHD and children
with ADHD of predominantly inattentive type. Although both groups showed
increased theta and decreased alpha and beta, the inattentive subgroup results
were less severe [197]. The qEEG coherence differences were then examined
between these subgroups of children with ADHD and normal controls. At shorter
electrode distances, children with ADHD had increased intrahemispheric theta
coherence and decreased lateral coherence differences. At longer distances,
children with ADHD showed decreased alpha intrahemispheric coherence,
whereas in frontal regions they showed increased theta and delta and decreased
alpha interhemispheric coherence. Children of the inattentive subgroup with
ADHD had less severe abnormality than those in the ADHD hyperactivity
subgroup [196]. The authors concluded that these findings indicated reduced
cortical differentiation and specialization in ADHD. Clarke et al [198] used
cluster analysis of qEEG to document the existence of three ADHD subtypes in a
sample of 184 boys with ADHD and 40 age- and gender-matched controls.
Subtype 1 showed increased total power, increased relative theta, and decreased
relative delta and beta waves; type 2 showed increased relative theta and decreased
relative alpha and increased central/posterior relative delta. Subtype 3
showed increased relative beta and decreased relative alpha activity. Gender
differences also were examined. They used cluster analysis to examine the
qEEGs of 100 girls with ADHD and 40 age- and gender-matched controls [199].
Two clusters were identified. The largest subtype showed increased total power
and increased relative theta and decreased relative delta and beta power in
comparison to the control population. The second subtype showed increased high
amplitude theta and decreases in delta, alpha, and beta. The relatively small
number of normal controls may have influenced these results (see later
discussions regarding our ADHD research).
The clinical use of qEEG as a possible diagnostic tool for ADHD has been
examined using discriminant analyses techniques. A discriminant function was
developed that correctly classified 80% of 25 children with ADHD and 74% of
27 normal controls [195]. These discriminant results were similar to those
reported by Lubar et al [184,200] in children with ADD without hyperactivity but
with reading disorders. The eyes-open resting EEG of this sample of children was
characterized by an increased theta-beta power ratio, especially in frontal/
temporal regions, with 79.2% correct identification of 69 children with ADD
R.J. Chabot et al / Child Adolesc Psychiatric Clin N Am 14 (2005) 21–53 35
against 34 normal controls. More recently, Monastra et al [201,202] recorded
eyes-open qEEG and used the theta-beta power ratio from the midline central
region to distinguish 176 children, adolescents, and young adults with ADD and
221 children adolescents, and young adults with ADHD from 85 normal controls.
They reported sensitivity rates from 86% to 90% and specificity rates between
94% and 98%.
Studies of medication effects in children with attention deficit hyperactivity
disorder
Several studies have examined the relationships between pretreatment EEG
and treatment response to methylphenidate or d-amphetamine. In an early study,
it was reported that 6- to 9-year-old boys with minimal brain dysfunction were
more likely to respond to methylphenidate if abnormal conventional EEG and
neurologic soft signs were present versus if they were absent [203]. These
findings have not been replicated. Halperin et al [204] reported that the presence
or absence of conventional EEG abnormalities did not predict response to
methylphenidate. The qEEG differences have been reported between ADHD
responders and nonresponders to stimulants. Responders to d- or l-amphetamine
showed predrug qEEGs characterized by increased predominant peak beta
frequency and nonsignificant increases in theta and alpha power when compared
with nonresponders. Increased visual evoked potential values of N220 more than
250 msec and increased average beta frequency more than 13 Hz correctly
identified 100% of responders and 70% of nonresponders [205]. Age-regressed
qEEG features extracted from eyes-closed resting EEG collected before
medication with methylphenidate were used to develop a discriminant function
to distinguish 16 responding from 12 nonresponding boys with ADHD.
Responders were correctly identified 81% of the time and nonresponders were
identified 83% of the time. Responders’ qEEGs were characterized by significant
developmental deviation (qEEG findings abnormal at any age), whereas
nonresponders were characterized by significant maturational lag (qEEG findings
normal at a younger age) [206]. These findings increase the accuracy of the
discriminant results over those found by Steinhausen et al [207], who correctly
predicted methylphenidate response in 73.3% using qEEG features that had not
been age regressed. Children with ADHD who responded to methylphenidate
(n = 10) were reported to have less frontal theta and alpha and more frontal
beta activity than nonresponders [208].
Three reports suggest that methylphenidate or d-amphetamine leads to a
normalization of the qEEG of boys with ADHD who respond to treatment
[199,209,210]. In a second study, however, Clarke et al [211] found that stimulant
medication did not lead to a normalization of the qEEGs of boys with ADHD
whose qEEG was characterized by beta excess. In each of these studies, the
sample sizes involved were small (nb 25), which makes these conclusions
premature. Suffin and Emory [43] examined qEEG in 100 patients diagnosed
with either attention or affective disorders. They reported that 13 of 15 patients
R.J. Chabot et al / Child Adolesc Psychiatric Clin N Am 14 (2005) 21–53 36
with attention disorder and 9of 10 patients with affective disorder with a frontal
alpha excess responded to antidepressants; 7of 7 patients with attention disorder
with a frontal theta excess responded to stimulants; 17of 20 patients with
affective disorder and 5 of 5 patients with attention disorder with frontal alpha
excess plus frontal hypercoherence responded to an anticonvulsant plus lithium,
and 2 of 2 patients with affective disorder and 2 of 3 patients with attention
disorder with a frontal theta excess plus hypercoherence responded to an
anticonvulsant. Although interesting, these results are based on small samples,
diagnostic criteria are not presented, and the results have yet to be replicated and
are not in agreement with our studies (described later), which show that theta
and alpha excess subtypes may respond to stimulant medication.
Neurometric quantitative electroencephalography in learning and attention
disorders
Most studies cited previously indicated that qEEG can play a significant role
in the diagnosis and evaluation of children with learning and attention problems.
Despite this evidence, the clinical use of qEEG has been questioned for want of
knowledge of the sensitivity and specificity of qEEG measures in mixed clinical
populations [212,213]. It is the purpose of this section to provide this information
using the child psychiatric qEEG database developed at the Brain Research
Laboratories over the past 25 years. Within this section we review the study of
John et al [25], who used qEEG to evaluate brain function in children with LDs,
review our recent studies of qEEG in children with ADD and ADHD [214–216],
and present a comparison of the normal children and children with LD and ADD/
ADHD from these two studies [42]. We also present evidence that qEEG can be
useful for optimizing medication selection during pharmacologic intervention in
ADD and ADHD. We propose that this information and the cited research are
sufficient to justify the routine clinical use of qEEG in the diagnosis and
treatment of learning and attention disorders.
All children with ADD and ADHD were referred to the Developmental
Pediatrics and Learning Disorders Clinic in Sydney, Australia. A sample of 407
children was evaluated between June 1991 and December 1992. All children
were examined by a pediatric neurologist and had neuropsychological and qEEG
evaluations. None of the children received medication at the time of testing.
Children with histories of epilepsy, drug abuse, head injury, or psychotic disorders
were excluded. Diagnostic and Statistical Manual-III criteria were used
for clinical classification [214]. This sample included children from the ages of
6 to 17 years (mean age, 10.8 years), with 78% having normal IQ scores. Within
this sample, 43.9% had ADHD, 40.5% had ADD, and 15.6% did not reach
criteria for ADD. This later group rated high in attention problems but showed no
impulsivity or hyperactivity and is called the attention subgroup-ATT. A reading
disorder was present in 58% of the entire sample.
Treatment response data were available on 152 of these children, with 42.8%
showing a positive response to dexamphetamine, 53.3% to methylphenidate, and
R.J. Chabot et al / Child Adolesc Psychiatric Clin N Am 14 (2005) 21–53 37
3.9% to thioridazine. The choice of medication was based on the clinical
presentation of the child and a challenge paired-associate learning task given
before medication at the time of initial evaluation and repeated after a trial dose of
dexamphetamine or methylphenidate. An adverse reaction (decreased memory
performance) resulted in new testing and placement on the other medication in
13 children initially tested on methylphenidate and 18 initially tested on
dexamphetamine. All 6 children who responded to thioridazine had either
adverse reaction or no change in paired-associate performance to dexamphetamine
and methylphenidate. Treatment response was evaluated 6 months after
treatment initiation. This evaluation included parent and teacher ratings of
changes in learning or in behavior and parent/teacher ratings on the Connors and
Diagnostic and Statistical Manual-III rating scales.
The populations with LD included 127 children with SLD (mean age,
11.4 years) whose LD occurred in only one academic area and who had normal
full-scale IQ scores and 115 children with LD (mean age, 11.8 years) whose LD
spanned two or more academic areas and who had full-scale IQ scores between
65 and 84 [25]. Although these children with LD and SLD were not specifically
screened for ADD or ADHD, children with hyperactivity were excluded, and all
had been selected by their respective school systems because of learning
problems. No known neurologic disorder was noted in these children.
The normal controls included 310 children between the ages of 6 and 17 years.
Details concerning the collection and validation of this normal sample have been
published [10]. Statements about the reliability and validity of these normal
databases were described in initial sections of this article.
The following section represents a summary of our previously published
research involving children with attention and learning problems [42,214–216].
Most children with ADHD and ADD in the normal and low IQ groups showed
qEEG abnormalities when compared with the normal database. The qEEG
frequency abnormality occurred in more than 80% of the 407 children in this
population, with theta and alpha excess the most prevalent abnormal finding.
Frontal and central regions were the most likely to be involved, and when the
abnormality was generalized, its magnitude was usually greatest in these regions.
Inter- and intrahemispheric abnormality was present in approximately 35% and
included (1) increased coherence of theta or alpha activity between left and right
frontal recordings and between frontal and temporal recordings within each
hemisphere, (2) decreased coherence between left and right posterior temporal
and parietal regions, (3) frontal/posterior power asymmetry within each hemisphere
reflecting increased frontal power, and (4) left/right hemisphere power
asymmetry in posterior temporal and parietal regions, with the right hemisphere
most likely to show a power excess. Two major subtypes of qEEG abnormality
were identified that involved theta or alpha excess accompanied by either normal
or decreased alpha mean frequency. Beta excess was present in approximately
10% of these children.
Stepwise multivariate discriminant procedures were used to examine the
sensitivity and specificity of several two-way comparisons. Normal controls were
R.J. Chabot et al / Child Adolesc Psychiatric Clin N Am 14 (2005) 21–53 38
distinguished from children with ADHD/ADD with a sensitivity of 93.7% and a
specificity of 88.0%. The qEEG differences between the normal children and
children with low IQ and ADHD or ADD and between the children with ADHD
and ADD were present but minimal in comparison to the differences between the
normal population and population with ADD or ADHD. The presence or absence
of a secondary LD did not contribute to any of the qEEG differences observed.
When the population with ADHD or ADD was compared with the population of
children with LDs not secondary to an attention problem, qEEG differences were
observed. Children with ADHD or ADD could be distinguished from children
with LDs with a sensitivity of 97% and a specificity of 84.2%.
A qEEG also proved useful in the management of treatment response to
stimulant medication. The qEEG differences were found between individuals
who showed a short-term (initial response to one dose) positive response to
treatment with dextroamphetamine or methylphenidate and individuals who did
not benefit. Although the sensitivity and specificity levels of this discriminant
function were modest (68.7% and 67.5%, respectively), the function was accurate
(84.8%) in classifying children who had initially shown a negative response to
either dextroamphetamine or methylphenidate. Pretreatment qEEG and behavioral
measures showed a sensitivity of 83.1% and a specificity of 88.2% in
predicting long-term treatment response to either dextroamphetamine or
methylphenidate. Within the population with ADHD, 93.7% of the alpha excess
(n = 16), 83.3% of the beta excess (n = 6), and 75% of the theta excess (n = 40)
children showed a positive long-term response to stimulants. None of the children
with ADHD with an alpha or beta excess showed a negative response to either
stimulant, whereas 17.5% of the children with a theta excess showed a negative
response to treatment with dextroamphetamine. Within the population of children
with ADD, 66.7% of the beta excess (n = 6), 54.5% of the alpha excess (n = 11),
and 33.3% of the theta excess (n = 27) children showed a positive response to
stimulant therapy. None of the children with ADD with beta excess showed a
negative response to either stimulant. One of eight children with an alpha excess
treated with methylphenidate showed a negative long-term response. In contrast,
the likelihood of a negative response to either dextroamphetamine or
methylphenidate reached 30% for the children with theta excess.
Attention deficit hyperactivity disorder and attention deficit disorder:
maturational lag or developmental deviation?
The neurometric qEEG features of maturational lag (qEEG normal at a
younger age) and developmental deviation (qEEG abnormal at any age) indicated
that a developmental deviation was present in 35% of our sample localized
mainly to frontal and central regions, with signs of maturational lag mainly in
posterior regions present in 7%. To seek further evidence of maturational lag as
the underlying neurophysiologic mechanism involved in ADHD and ADD, the
qEEGs of our population with ADHD or ADD were assessed as a function of
R.J. Chabot et al / Child Adolesc Psychiatric Clin N Am 14 (2005) 21–53 39
age. Multiple ANOVAs were used to compare relative power, absolute power,
mean frequency, power asymmetry, and coherence values across four age ranges
(5–7, 8–10, 11–13, and 14–17 years). The degree of qEEG abnormality remained
stable, with no significant systematic decreases in the degree of abnormality
occurring across this age span. When qEEG values are age regressed, the pattern
of normal ADHD and ADD differences remains constant from the early school
years into late adolescence. The elevated frontal theta activity seen in children
with ADHD also has been reported in adults with ADHD, although the beta
deficit has been found to decrease with age [167,217].
Neurophysiologic subtypes in attention deficit hyperactivity disorder and
attention deficit disorder and learning disability
Cluster analyses procedures were used to identify the major neurophysiologic
subtypes within samples of 344 children with ADHD or ADD and 245 children
with LD or SLD. To comply with the statistical assumptions underlying cluster
analyses, we preselected qEEG features and limited the number entered into the
analyses in a systematic fashion. The qEEG variables chosen were those for
which the highest ANOVA values were obtained when comparing the children
with ADHD and ADD to normals, the children with LD and SLD to normals, and
the children with ADHD or ADD to the children with LD or SLD. We selected
variables that showed the greatest variance across the entire population of
children. Cluster analyses were performed using 35 qEEG variables that met
these criteria. An iterative approach was taken as we examined cluster solutions
starting at three clusters and progressing until the next new cluster solution failed
to further subdivide the population into clusters with more than ten members. The
five-cluster solution showed the most clearly defined cluster structure. The cluster
analyses were performed on split-half replications of our database and the entire
database. The split-half results were optimal for five clusters and replicated
each other.
Cluster one was characterized by generalized excess of alpha and deficit of
delta absolute and relative power, increased frontal theta coherence and alpha
coherence, and parietal and posterior temporal power asymmetry. Cluster two
was characterized by generalized excess of theta absolute and relative power,
decreased alpha mean frequency, and increased frontal theta coherence. Cluster
three was characterized by a generalized deficit of theta, alpha, and beta absolute
power, a generalized excess of delta and deficit of alpha relative power, and
decreased frontal alpha coherence. Cluster four was characterized by excess
frontal/central delta and theta, a generalized deficit of alpha absolute power,
generalized delta and theta excess and alpha deficit of relative power, decreased
theta and alpha mean frequency, decreased frontal and central alpha coherence,
and frontal, central, and temporal power asymmetry. Cluster five was
characterized by essentially normal qEEG findings. In this five-cluster solution,
more than 98% of the children with ADHD or ADD were placed into clusters one
or two. The children with LDs were evenly distributed among the five clusters.
R.J. Chabot et al / Child Adolesc Psychiatric Clin N Am 14 (2005) 21–53 40
Long-term stimulant treatment response data were available on 49 children with
ADD or ADHD from cluster one and 59 children with ADD or ADHD from
cluster two. Within cluster one, 75.5% showed a positive response to stimulants,
18.4% showed no measurable change, and 6.1% showed a negative response.
Within cluster two, 50.8% showed a positive response to stimulants, 33.9%
showed no change, and 15.2% showed a negative response.
VARETA images were calculated for the five patients with ADHD or ADD
closest to the centroid of clusters 1 and 2. Currently, technical problems prevent
us from examining the VARETA results for the children with LD or SLD. The
VARETA images associated with cluster one (alpha excess) at 11 Hz show
primarily cortical abnormalities that are maximal and seem to originate in right
parietal cortical regions. VARETA images of cluster two (theta excess at 5.4 Hz)
show primarily temporal cortical and hippocampal abnormalities. VARETA
images at the 5.4-Hz band for cluster one and at the 11-Hz band for cluster two
were within normal limits.
Proposed neurophysiologic model of attention deficit hyperactivity
disorder/attention deficit disorder
The results of the cluster analyses described previously indicate that the major
qEEG frequency abnormalities seen in ADHD and ADD involve excess of theta
or alpha absolute or relative power [218–220]. Evidence exists that two different
but interconnected neural systems are involved in the generation of EEG within
the theta and alpha frequency bands [3,5]. Theta seems to be generated within the
septal-hippocampal pathway, whereas the alpha frequency involves thalamocortical
and cortical-cortical circuitry. Within the theta-generating septal-hippocampal
pathway, the septal nucleus and the nucleus accumbens receive inhibitory
modulation through dopaminergic innervation from the ventral tegmental area via
D2 receptors [221,222]. Cholinergic efferents modulate hippocampal and
cingulate cortex, with these hippocampal pathways acting to regulate the septal
nucleus. Theta excess can occur with overactivation of the septal-hippocampal
pathway or secondary via disinhibition from negative dopaminergic regulation
[223].
Several different alterations in the thalamocortical alpha-generating pathway
can result in alpha excess. The thalamic pathway receives positive modulation
from the midbrain reticular formation via acetylcholine and negative regulation
through nucleus reticularis of the thalamus via gamma-aminobutyric-acid with
further modulation via the dopaminergic striatal/nigral system. Alterations in the
regulation of this system can lead to alpha excess by overactivation of the
thalamus that may be caused by decreased modulation via the dopaminergic
nigral system or underactivation of the prefrontal cortex and a resulting disinhibition
from nucleus reticularis. A theta or alpha excess might result from low
dopamine levels, and our qEEG findings are in agreement with the dopaminergic
theory of ADHD expressed by Levy [224], which conceptualizes ADHD as a
R.J. Chabot et al / Child Adolesc Psychiatric Clin N Am 14 (2005) 21–53 41
disorder of the polysynaptic dopaminergic circuits between prefrontal and striatal
centers of activity. These findings are also compatible with the neurophysiologic
model of ADHD proposed by Niedermeyer and Naidu [225], which also
emphasizes prefrontal, frontal and striatal, and thalamic interconnections. The
previously mentioned model also is supported by MRI and positron emission
tomographic imaging studies and by behavioral, pharmacologic, and neuroanatomic
studies on the nature of cortical and subcortical disturbances in function
that characterizes children with attention and learning problems [226–232].
In our opinion, ADD cannot be conceptualized as a single disease entity with a
narrow phenotype and a distinct cause. Rather, ADD represents a spectrum of
disorders that may be represented by different neurophysiologic subtypes present
within the population of children with attention and learning problems. qEEG
may prove to be the most clinically relevant imaging technique for use in children
with attention and learning problems. Of all neuroimaging techniques, qEEG is
less expensive, less invasive, and easier to perform and has the largest patient
database, which indicates the presence of different subtypes of attention and LDs
that may be differentially amenable to various treatment approaches. The
emergence of EEG biofeedback treatment techniques offers a direct application of
qEEG for determining qEEG biofeedback treatment parameters and may offer
effective treatment that is not medication oriented.
We believe that these findings justify the clinical use of qEEG in the initial
screening and treatment evaluation stages of children with ADD, ADHD, and
LD. A qEEG can act as an adjunct to clinical evaluation and behavioral
testing and play several of the roles set forth in the introduction to this article. A
qEEG can aid in the detection of organicity as the cause of brain dysfunction
in children who present with learning and attention problems. It also can aid
in the differential diagnosis of ADD or ADHD and LD. A qEEG can play a
role in optimizing pharmacologic, remediation, or psychological intervention.
Finally, qEEG-based models may help explain the pathophysiology of
these disorders.

References

[1] Duffy FH, Hughes JR, Miranda F, Bernard P, Cook P. Status of quantitative EEG (qEEG)
in clinical 1994. Clin Electroencephalogr 1994;25:6–22.
[2] Llinas R. The intrinsic properties of mammalian neurons: insights into central nervous
system function. Science 1988;242:1654– 64.
[3] Lopes Da Silva FH. The generation of electric and magnetic signals the brain by local networks.
In: Greger R, Windhorst U, editors. Comparative human physiology. Berlin7 Springer-Verlag;
1996. p. 509– 31.
[4] McCormick DA. Neurotransmitter actions in the thalamus and cerebral cortex and their
role in neuromodulation of thalamocortical activity. Prog Neurobiol 1992;39:337– 88.
[5] Steriade M, Gloor P, Llinas RR, Lopes Da Silva F, Mesulam MM. Basic mechanisms
of cerebral rhythmic activities. Electroencephalogr Clin Neurophysiol 1990;76:481– 508.
[6] van Beijsterveldt CEM, Boomsma DI. Genetics of the human electroencephalogram (EEG)
and event-related brain potentials (ERPs): a review. Hum Genet 2000;94(4):319–30.
R.J. Chabot et al / Child Adolesc Psychiatric Clin N Am 14 (2005) 21–53 42
[7] van Beijsterveldt CE, Molenaar MPC, de Geus EJ, Boomsma DI. Heritability of human
brain functioning as assessed by electroencephalography. Am J Hum Genet 1996;58(3):562–73.
[8] van Beijsterveldt CEM, Molenaar PC, de Geus EJ, Oomsma DI. Genetic and environmental
influences on EEG coherence. Behav Genet 1998;28:6443–53.
[9] Matousek M, Petersen I. Norms for the EEG. In: Kellaway P, Petersen I, editors. Automation of
clinical electroencephalography. New York7 Raven Press; 1973. p. 75– 102.
[10] John ER, Ahn H, Prichep LS, Trepetin M, Brown D, Kaye H. Developmental equations for
the electroencephalogram. Science 1980;210:1255– 8.
[11] Ahn H, Prichep LS, John ER, Baird H, Trepetin M, Kaye H. Developmental equations reflect
brain dysfunction. Science 1980;210:1259 –62.
[12] Gasser T, Bacher P, Mochs J. Transformation towards the normal distribution of broadband
spectral parameters of the EEG. Electroencephalogr Clin Neurophysiol 1982;53:119– 24.
[13] Jonkman EJ, Poortvliet DCJ, Veering MM, DeWeerd AW, John ER. The use of neurometrics
in the study of patients with cerebral ischemia. Electroencephalogr Clin Neurophysiol
1985;61:333–41.
[14] Yingling CD, Galin D, Fein G, Peltzman D, Davenport L. Neurometrics does not detect
pure dyslexics. Electroencephalogr Clin Neurophysiol 1986;63:426–30.
[15] Harmony T, Alvarez A, Pascual-Marqui R, Ramos A, Marosi E, Diaz de Leon A, et al. EEG
maturation of children with different economic and psychosocial characteristics. Int J Neurosci
1987;31:103–13.
[16] Alvarez A, Pascual R, Valdes P. EEG development equations confirmed for Cuban school
children. Electroencephalogr Clin Neurophysiol 1987;67(4):330–2.
[17] Diaz de Leon AE, Harmony T, Marosi E, Becker J, Alvarez A. Effect of different factors
on EEG spectral parameters. Int J Neurosci 1988;43:123– 31.
[18] Oken BS, Chiappa KH. Short-term variability in EEG frequency analysis. Electroencephalogr
Clin Neurophysiol 1988;69:191–8.
[19] Ritchlin CT, Chabot RJ, Alper K, Buyon J, Belmont HM, Roubey R, et al. Quantitative
electroencephalography: a new approach to the diagnosis of cerebral dysfunction in systemic
lupus erythematosus. Arthritis Rheum 1992;35(11):1330–42.
[20] Valdes-Sosa P, Bosch J, Gray F, Hernandez J, Riera J, Pascual R, et al. Frequency domain
models of the EEG. Brain Topogr 1992;4(4):309– 19.
[21] Veldhuizen RJ, Jonkman EJ, Poortvliet JDC. Sex differences in age regression parameters
of healthy adults-normative data. Electroencephalogr Clin Neurophysiol 1993;86(6):377– 84.
[22] Duffy FH, Albert MS, McAnulty GB. The pattern of age-related differences in electrophysiological
activity of healthy subjects. Neurobiol Aging 1993;14:73 – 4.
[23] Duffy FH, Jones K, McAnulty GB, Albert M. Spectral coherence in normal adults: unrestricted
principal components analysis. Clin Electroencephalogr 1995;26:30 – 46.
[24] Matsuura M, Okubo Y, Toru M, Kojima T, He Y, Hou Y, et al. A cross-national study of
children with emotional and behavioral problems: a WHO collaborative study in the Western
Pacific region. Biol Psychiatry 1993;34:59– 65.
[25] John ER, Prichep LS, Ahn H, Easton P, Fridman J, Kaye H. Neurometric evaluation of
cognitive dysfunctions and neurological disorders in children. Prog Neurobiol 1983;21:239–90.
[26] John ER, Prichep LS, Friedman J, Easton P. Neurometrics: computer assisted differential
diagnosis of brain dysfunctions. Science 1988;293:162–9.
[27] John ER, Prichep LS. Principles of neurometrics and neurometric analysis of EEG and evoked
potentials. In: Niedermeyer E, Lopes Da Silva F, editors. EEG: basic principles, clinical
applications and related fields. Baltimore7 Williams and Wilkins; 1993. p. 989– 1003.
[28] Fisch BJ, Pedley TA. The role of quantitative topographic mapping on ‘‘neurometrics’’ in
the diagnosis of psychiatric and neurological disorders: the cons. Electroencephalogr
Clin Neurophysiol 1989;73:5– 9.
[29] Nuwer M. Assessment of digital EEG, quantitative EEG and brain mapping: mapping: report
of the American Academy of Neurology and the American American Clinical Neurophysiology
Society. Neurology 1997;49:277–92.
R.J. Chabot et al / Child Adolesc Psychiatric Clin N Am 14 (2005) 21–53 43
[30] Coutin-Churchman P, Anez Y, Uzctegui M, Alvarez L, Vergara F, Mendez L, et al. Quantitative
spectral analysis if EEG in psychiatry revisited: drawing signs out of numbers in a clinical
setting. Clin Neurophysiol 2003;114:2294– 306.
[31] Nuwer M. Clinical use of qEEG. Clin Neurophysiol 2003;114:2225.
[32] John ER, Prichep LS, Easton P. Normative data banks and neurometrics: basic concepts,
methods and results of norm construction. In: Gevins AS, Remond A, editors. Handbook of
electroencephalography and clinical neurophysiology. Amsterdam7 Elsevier; 1987. p. 449– 95.
[33] Hudspeth WJ, Pribram KH. Psychophysiological indices of cerebral maturation. Int J
Psychophysiol 1992;12(1):19 – 29.
[34] Halikas JA, Kuhn KL, Crosby R, Carlson G, Crea F. The measurements of craving in cocaine
patients using the Minnesota Cocaine Craving Scale. Compr Psychiatry 1991;32:22 –7.
[35] Kondacs A, Szabo M. Long-term intra-individual variability of the background EEG in
normals. Clin Neurophysiol 1999;110:1708–16.
[36] Prichep LS, John ER. qEEG profiles of psychiatric disorders. Brain Topogr 1992;4(4):249 – 57.
[37] Thatcher RW, Walker RA, Gerson I, Geisler FH. EEG discriminant analyses of mild
head trauma. Electroencephalogr Clin Neurophysiol 1989;73:94 – 106.
[38] de Weerd AW, Veldhuizen RJ, Eering MM, Poortvliet DC. Long-term reconstructive vascular
surgery for cerebral ischemia. Acta Neurol Scand 1989;79:311 – 5.
[39] Czobor P, Volavka J. Pretreatment EEG predicts short-term response to haloperidol treatment.
Biol Psychiatry 1991;30:927–42.
[40] Roemer RA, Shagass C, Dubin W, Jaffe R, Katz R. Relationship between pretreatment EEG
coherence measures and subsequent response to ECT. Neuropsychobiology 1991;24:121–4.
[41] Struve FA, Straumanis JJ, Patrick G. Persistent topographic quantitative EEG sequelae of
chronic marijuana use: a replication study and initial discriminant function analysis. Clin
Electroencephalogr 1994;25(2):63 – 75.
[42] Chabot RJ, Merkin H, Wood LM, Davenport TL, Serfontein G. Sensitivity and specificity
of qEEG in children with attention deficit or specific developmental learning disorders. Clin
Electroencephalogr 1996;27(1):26 – 34.
[43] Suffin SC, Emory WH. Neurometric subgroups in attentional and affective disorders their
association with pharmacotherapeutic outcome. Clin Electroencephalogr 1995;26(2):76 – 83.
[44] Weiner JM, Dunn OJ. Elimination of variates in discrimination problems. Biometrics
1966;22:268– 75.
[45] Oken BS, Chiappa KH. Statistical issues concerning computerized analysis of brainwave
topography. Ann Neurol 1986;19:493– 4.
[46] Hughes JR, John ER. Conventional and quantitative electroencephalography in psychiatry.
J Neuropsychiatry Clin Neurosci 1999;11(2):190–208.
[47] Alper KR, Prichep LS, Kowalik SC, Rosenthal M. Neuroanatomical localization of
qEEG abnormality related to treatment failure in cocaine dependence. In: Harris LS, editor.
Problems of drug dependence 1998. In Proceedings of the 60th Annual Scientific Meeting.
NIDA Research Monograph No. 179. National Institute of Drug Abuse; 1998.
[48] Valdes-Sosa P, Garcia F, Casanova R. Variable resolution electromagnetic tomography. In:
Wood C, editor. Proceedings of the 10th International Conference on Biomagnetism. Santa Fe:
Biomag; 1996.
[49] Harmony T, Fernandez-Bouzas A, Marosi E. Correlation between computed tomography and
voltage and current source density spectral EEG parameters in patients with brain lesions.
Electroencephalogr Clin Neurophysiol 1993;87:196– 205.
[50] Harmony T, Fernandez-Bouzas A, Marosi E, Fernandez T, Valdes P, Bosch J, et al. Frequency
source analysis in patients with brain lesions. Brain Topogr 1995;8(2):109 –17.
[51] Fernandez-Bouzas A, Harmony T, Bosch J, Aubert E, Fernandez T, Valdes P, et al. EEG source
spectra in localized brain lesions. In press.
[52] Fernandez-Bouzas A, Casanova R, Harmony T, Valdes-Sosa P, Aubert-Vazquez E, Silva-
Pereyra J, et al. EEG frequency domain distributed inverse solutions in lesions. Electroencephalogr
Clin Neurophysiol 1997;103(1):195.
R.J. Chabot et al / Child Adolesc Psychiatric Clin N Am 14 (2005) 21–53 44
[53] Fernandez-Bouzas A, Harmony T, Bosch JAE, Fernandez T, Valdes P, Silva J, et al. Sources
of abnormal EEG activity in the presence of brain lesions. Clin Electroencephalogr 1999;30(2):
1–7.
[54] Pascual-Marqui RD. Low resolution brain electromagnetic tomography (LORETA): methodological
considerations [abstract]. Int J Neuropsychopharmacol 2000;3(1):S84.
[55] Saletu B, Anderer G, Saletu-Zyhlarz RD, Pascual-Marqui R. Identifying target regions of
psychotropic drugs in mental disorders by means of LORETA [abstract]. Int J Neuropsychopharmacol
2000;3(1):S85.
[56] Duffy FH, Albert MS, McNulty G. Brain electrical activity in patients with presenile and
senile dementia of the Alzheimer type. Ann Neurol 1984;16:439– 48.
[57] Coburn KA, Danziger WL, Storandt M. A longitudinal EEG study of mild senile of Alzheimer
type: changes at 1 year and at 2.5. Electroencephalogr Clin Neurophysiol 1985;61:101– 12.
[58] Leuchter AF, Spar JE, Walter DO, Weiner H. Electroencephalographic spectra and coherence
in the diagnosis of Alzheimer’s-type and multi-infarct dementia. Arch Gen Psychiatry 1987;
44:993– 8.
[59] Breslau J, Starr A, Sicotte N, Higa JBMS. Topographic EEG changes with normal aging
and SDAT. Electroencephalogr Clin Neurophysiol 1989;72:281– 9.
[60] Prinz PN, Vitiello MV. Dominant occipital (alpha) rhythm frequency in early stage Alzheimer’s
disease and depression. Electroencephalogr Clin Neurophysiol 1989;73:427–32.
[61] Soininen H, Partanen J, Laulumaa V. Longitudinal EEG spectral analysis in early stage of
Alzheimer’s disease. Electroencephalogr Clin Neurophysiol 1989;72:290– 7.
[62] Coburn KA, Danziger WL, Berg L. Replication of a study of frequency of resting awake
EEG in mild probable Alzheimer’s disease. Electroencephalogr Clin Neurophysiol 1990;75:
148– 54.
[63] Rice DM, Buchsbaum MS, Starr A, Auslander L. Abnormal EEG slow activity in left temporal
areas in senile dementia of the Alzheimer’s type. J Gerontol 1990;45:145– 51.
[64] Streletz LJ, Reyes PF, Zolewska M. Computer analysis of EEG activity in dementia of
the Alzheimer type and Huntington’s disease. Neurobiol Aging 1990;11:15– 20.
[65] Williamson PC, Merskey H, Morrison S, Rabheru H, Fox H, Wands K, et al. Quantitative
electrophysiologic correlates of cognitive decline in normal elderly subjects. Arch Neurol 1990;
47:1185– 8.
[66] Heikala EL, Laulumaa V, Soikkeli R, Partanen J, Soininen H, Riekkinen PJ. Slow wave
activity in the spectral analysis the electroencephalogram is associated with cortical dysfunction
in patients Alzheimer’s disease. Behav Neurosci 1991;105:409– 15.
[67] Hier DB, Mangone CA, Ganellen R, Warach RV, VanEgeren SJ, Perlik SJ, et al. Quantitative
measurement of delta activity in Alzheimer’s disease. Clin Electroencephalogr 1991;22:178–82.
[68] Saletu B, Anderer P, Paulus E, Grunberger J, Wicke L, Neuhold A, et al. EEG brain mapping
in diagnostic and therapeutic assessment of dementia. Alzheimer Dis Assoc Disord 1991;
5(1):S57– 75.
[69] Soininen H, Partanen J, Laulumaa V, Paakkonen A, Helkala EL, Riekkinen PJ. Serial EEG
in Alzheimer’s disease: 3 year follow-up and clinical outcome. Electroencephalogr Clin
Neurophysiol 1992;79:342– 8.
[70] Prichep LS, John ER, Ferris HS, Reisberg B, Alper KR, Cancro R. Quantitative EEG correlates
of cognitive deterioration in the elderly. Neurobiol Aging 1994;15(1):85– 90.
[71] Brenner RP, Ulrich RF, Spiker DG, Sclabassi RJ, Reynolds CF, Marin RS, et al. Computerized
EEG spectral analysis in elderly normal, demented and depressed subjects. Electroencephalogr
Clin Neurophysiol 1986;64:483–92.
[72] Visser SL, Van Tilburg V, Hooijer C, Jonker C, de Rijke W. Visual evoked potentials (VEPs)
in senile dementia (Alzheimer type) and in non-organic behavioural disorders in the elderly:
comparison with EEG parameters. Electroencephalogr Clin Neurophysiol 1985;60:115– 21.
[73] Giannitrapani D, Collins J, Vassiliadis D. The EEG spectra of Alzheimer’s disease. Int J
Psychophysiol 1991;10:259–69.
[74] Soininen H, Partanen VJ, Helkala EL, Riekkinen PJ. EEG findings in senile dementia
and normal aging. Acta Neurol Scand 1981;65:59–70.
R.J. Chabot et al / Child Adolesc Psychiatric Clin N Am 14 (2005) 21–53 45
[75] Albert MS, Duffy FH, McAnulty GB. Electrophysiologic comparisons between two groups
of patients with Alzheimer’s disease. Arch Neurol 1990;47:857– 63.
[76] Soininen H, Partanen J, Paakkonen A, Koivisto E, Riekkinen P. Changes in absolute
power values of EEG spectra in the follow-up of Alzheimer’s disease. Acta Neurol Scand 1991;
83:133–6.
[77] Brenner RP, Reynolds CF, Ulrich RF. EEG findings in depressive pseudodementia and
dementia with secondary depression. Electroencephalogr Clin Neurophysiol 1989;72:298– 304.
[78] Dierks T, Perisic I, Frolich L, Ihl R, Maurer K. Topography of the quantitative electroencephalogram
in dementia the Alzheimer type: relation to severity of dementia. Psychiatr Res
1991;40:181– 94.
[79] Richards M, Folstein M, Albert M, Miller L, Bylsma F, Lafleche G, et al. Multicenter study
of predictors of disease course Alzheimer disease (the predictors study). II. Neurological,
psychiatric and demographic influences on baseline measures of disease. Alzheimer Dis Assoc
Disord 1993;7:22–32.
[80] Rae-Grant A, Blume W, Lau C, Hachinski VC, Fisman M, Merskey H. The electroencephalogram
in Alzheimer-type dementia: a sequential study correlating the electroencephalogram
with psychometric and quantitative pathologic data. Arch Neurol 1987;44:50– 4.
[81] Hughes JR, Shanmugham S, Wetzel LC. The relationship between EEG changes and cognitive
functions in dementia: a study in a VA population. Clin Electroencephalogr 1989;20:77– 85.
[82] O’Connor KP, Shaw JC, Ongley CO. The EEG and differential diagnosis in psychogeriatrics.
Br J Psychiatry 1979;135:156– 62.
[83] Coger RW, Dymond AM, Serafetinides EA. EEG signs of brain impairment in alcoholism.
Biol Psychiatry 1978;13:729–39.
[84] Bauer LO, Hesselbrock VM. EEG autonomic and subjective correlates for alcoholism. J Stud
Alcohol 1993;54:577– 89.
[85] Gabrielli WF, Mednick SA, Volavka J. EEGs in children of alcoholic fathers. Psychophysiology
1982;19:404– 7.
[86] Coger RW, Dymond AM, Serafetinides EA. Electroencephalographic similarities between
chronic alcoholics and chronic non-paranoid schizophrenics. Arch Gen Psychiatry 1979;
36:91–4.
[87] Struve FA, Straumanis JJ, Patrick G, Price L. Topographic mapping of quantitative EEG
variables in chronic heavy marijuana users: empirical findings with psychiatric patients.
Clin Electroencephalogr 1989;20(1):6– 23.
[88] Alper KR, Chabot RJ, Kim AH, Prichep LS, John ER. Quantitative EEG correlates of
crack cocaine dependence. Psychiatr Res 1990;35:95– 106.
[89] Alper KR, Chabot R, Prichep LS, John ER. Crack cocaine dependence: discrimination
from major depression using qEEG variables. In: Mauer K, editor. Imaging of the brain
in psychiatric and related fields. Berlin7 Springer-Verlag; 1993. p. 289– 93.
[90] Cornwell A, Roemer RA, Jackson P, Dew D. Paroxysmal-like EEG abnormalities associated
with chronic polydrug abuse. Biol Psychiatry 1994;35:692–3.
[91] Prichep LS, Alper K, Kowalik SC, John ER, Merkin HA, Tom M, et al. Quantitative
electroencephalographic characteristics of crack cocaine dependence. Biol Psychiatry 1996;
40(10):986– 93.
[92] Roemer RA, Cornwell A, Jackson P, Dewart D. Quantitative EEG measures: correlations
with lifetime exposure to cocaine, alcohol, and marijuana in chronic polydrug. Biol Psychiatry
1994;35:624– 5.
[93] Prichep LS, Howard B, Merkin H, Rausch L, Alper K. Adolescent substance abuse intensifies
brain dysfunction. Ann N Y Acad Sci 2003.
[94] Prichep LS, Alper KR, Sverdlov L, Kowalik SC, John ER, Merkin HTM, et al. Outcome
related electrophysiological subtypes of cocaine dependence. Clin Electroencephalogr 2002;
33(1):8– 20.
[95] Fenton GW, Fenwick PBC, Dollimore J, Dunn TL, Hirsh SR. EEG spectral analysis in
schizophrenia. Br J Psychiatry 1980;136:445–55.
R.J. Chabot et al / Child Adolesc Psychiatric Clin N Am 14 (2005) 21–53 46
[96] Koukkou M. EEG states of the brain, information processing, and schizophrenic primary
symptoms. Psychiatry Res 1982;6:235 – 44.
[97] Stevens JR, Livermore A. Telemetered EEG in schizophrenia: spectral analysis during
abnormal behavior episodes. J Neurol Neurosurg Psychiatry 1982;45:385–95.
[98] Morihisa JM, Duffy FH, Wyatt RJ. Brain electrical activity mapping (BEAM) in schizophrenic
patients. Arch Gen Psychiatry 1983;40:719– 28.
[99] Dierks T, Maurer K, Ihl R, Schmidtke A. Evaluation and interpretation of topographic
EEG data in schizophrenic patients. In: Maurer K, editor. Topographic brain mapping and
evoked potentials. Berlin7 Springer-Verlag; 1989. p. 507– 17.
[100] Merrin EL, Floyd TC. Negative symptoms and EEG alpha activity in schizophrenic patients.
Schizophr Res 1992;8:11 – 20.
[101] Small JG, Milstein V, Sharpley PH, Lapper M, All IF. Electroencephalographic findings in
relation to diagnostic constructs in psychiatry. Biol Pyschiatry 1984;19:471– 87.
[102] Itil TM, Freyhan FA, Ban JH, editors. Psychotropic drugs and the human EEG. New York7
Karger, S. Inc.; 1974.
[103] Columbo C, Gambini O, Macciardi F. Alpha reactivity in schizophrenia and in schizophrenic
spectrum disorders: demographic, clinical and hemispheric assessment. Int J Psychophysiol
1989;7:47– 54.
[104] Laurian S, Bryois C, Gaillard JM. Some aspects of brain electrical activity in schizophrenia.
Recent Adv Biol Psychiatry 1984;15:60– 8.
[105] Kemali D, Maj M, Galderisi S. Clinical, biological and neuropsychological features associated
with lateral ventricular enlargement in DSM III schizophrenic disorder. Psychiatry Res 1986;
21:137– 49.
[106] Karson CN, Coppola R, Morihisa JM, Weinberger DR. Computerized electroencephalographic
activity mapping in schizophrenia. Arch Gen Psychiatry 1987;44:514– 7.
[107] Galderisi S, Maj M, Mucci A, Bucci P, Kemali D. qEEG alpha 1 changes after a single dose
of high-potency neuroleptics as a predictor of short-term response to treatment in schizophrenic
patients. Biol Psychiatry 1994;35:367–74.
[108] Saletu B, Kufferle B, Grunberger J, Foldes P, Topitz A, Anderer P. Clinical, EEG mapping
and psychometric studies in negative schizophrenia: comparative and psychometric trials
with amisulpride and fluphenazine. Neuropsychobiology 1994;29:125–35.
[109] Schellenberg R, Milch W, Schwarz A, Schober F, Dimpfel W. Quantitative EEG and BPRS
data following haldol-decanoate administration in schizophrenics. Int Clin Psychopharmacol
1994;9:17– 24.
[110] Niedermeyer E. EEG and clinical neurophysiology. In: Niedermeyer E, Lopes Da Silva F,
editors. Electroencephalography: basic principles, clinical applications and related fields.
Baltimore7 Urban & Schwarzenberg; 1987. p. 97– 117.
[111] Herrmann WM, Schaerer E. Pharmaco-EEG: computer EEG analysis to describe the projection
of drug effects on a functional cerebral level in humans. In: Lopes Da Silva FH, Storm van
Leeuwen W, Remond A, editors. Handbook of electroencephalography and clinical neurophysiology.
New York7 Elsevier Science; 1986. p. 385– 445.
[112] Kemali D, Vacca L, Marciano T, Celani T, Nolfe G, Iorio G. Computerized EEG in
schizophrenics. Neuropsychobiology 1980;6:260– 7.
[113] Etevenon P, Peron-Magnan P, Rioux P, Pidoux B, Bisserbe JC, Verdeauz G, et al. Schizophrenia
assessed by computerized EEG. Recent Adv Biol Psychiatry 1981;6:29–34.
[114] Gunther W, Breitline D. Predominant sensorimotor area left hemisphere dysfunction in
schizophrenia measured by brain electrical activity mapping. Biol Psychiatry 1985;20:515–32.
[115] Ford MR, Goethe JW, Dekker DK. EEG coherence and power in the discrimination of
psychiatric disorders and medication effects. Biol Psychiatry 1986;21:1175– 88.
[116] Muller HF, Achim A, Aur A, Burchbinder A. Topography and possible physiological significance
of EEG amplitude variability in psychosis. Acta Psychiatr Scand 1986;73:665–7.
[117] Williamson P, Mamelak M. Frontal spectral EEG findings in acutely ill schizophrenics. Biol
Psychiatry 1987;22:1021–4.
R.J. Chabot et al / Child Adolesc Psychiatric Clin N Am 14 (2005) 21–53 47
[118] Galderisi S, Mucci A, Mignone ML, Maj M, Kemali D. qEEG mapping in drug-free
schizophrenics: differences from healthy subjects and changes induced by haloperidol treatment.
Schizophr Res 1992;6:15–24.
[119] Primavera A, Fonti A, Novello P, Roccatagliata G, Cocito L. Epileptic seizures in patients
with catatonic syndrome. J Neurol Neurosurg Psychiatry 1994;57(11):1419–22.
[120] Westphal KP, Grozinger B, Diekmann V, Reess W, Leibing U, Kornhuber HH. Slower theta
activity over the midfrontal cortex in schizophrenic patients. Acta Psychiatr Scand 1990;
81:132– 8.
[121] Matsuura M, Yoshino M, Ohta K, Onda H, Nakajima K, Kojima T. Clinical significance of
diffuse delta EEG activity chronic schizophrenia. Clin Electroencephalogr 1994;25(3):115– 21.
[122] Lifshitz K, Lee KL, Susswein S. Long-term replicability of EEG spectra and auditory potentials
in schizophrenic and normal subjects. Neuropsychobiology 1988;18:205–11.
[123] Gattaz WF, Mayer S, Ziegler P, Platz M, Gasser T. Hypofrontality on topographic EEG
in schizophrenia: correlations with neuropsychological and psychopathological parameters.
Eur Arch Psychiatry Clin Neurosci 1992;241:328– 32.
[124] John ER, Prichep LS, Alper KR, Mas F, Cancro R, Easton P, et al. Quantitative electrophysiological
characteristics and subtyping of schizophrenia. Biol Psychiatry 1994;36:
801– 26.
[125] Czobor P, Volavka J. Quantitative EEG effect of risperidone in schizophrenic patients. J Clin
Psychopharmacol 1993;13(5):332–42.
[126] Gunther W, Steinberg R, Petsch RSP, Kugler J. EEG mapping in psychiatry: studies of type I/II
schizophrenia using motor activation. In: Maurer K, editor. Topographic brain mapping and
evoked potentials. Berlin7 Springer-Verlag; 1989. p. 438– 50.
[127] Shagass C, Roemer RA. Evoked potential topography in unmedicated and medicated
schizophrenics. Int J Psychophysiol 1991;10:213– 24.
[128] Merrin EL, Floyd TC, Fein G. EEG coherence in unmedicated schizophrenic patients. Biol
Psychiatry 1989;25:60 – 6.
[129] Pockberger H, Thau K, Lovrek A, Petsche H, Pappelsberger P. Coherence mapping reveals
differences in the of EEG psychiatric patients and healthy persons. In: Maurer K, editor.
Topographic brain mapping of EEG and evoked potentials. Berlin7 Springer-Verlag; 1989.
p. 451–7.
[130] Nagase Y, Okubo Y, Matsuura M, Kojima T, Toru M. EEG coherence in unmedicated
schizophrenic patients: topographical study. Biol Psychiatry 1992;32:1028– 34.
[131] Alper K. Quantitative EEG and evoked potentials in adult psychiatry. In: Pankseep J, editor.
Advances in biological psychiatry. Greenwich (CT)7 JAI Press; 1995. p. 65– 112.
[132] Monakhov K, Perris C. Neurophysiological correlates of depressive symptomatology. Neuropsychobiolgoy
1980;6:268–79.
[133] Itil TM. The discovery of antidepresssant drugs by computer-analyzed human cerebral bioelectrical
potentials (CEEG). Prog Neurobiol 1983;20:185– 249.
[134] Nystrom C, Matousek M, Hallstrom T. Relationships between EEG and clinical characteristics
in major depressive disorder. Acta Psychiatr Scand 1986;73:390– 4.
[135] Knott VJ, Lapierre YD. Computerized EEG correlates of depression and antidepressant
treatment. Prog Neuropsychopharmacol Biol Psychiatry 1987;11:213–21.
[136] Pollock VE, Schneider LS. Topographic quantitative EEG in elderly subjects with depression.
Psychophysiology 1990;27(4):438– 44.
[137] Nieber D, Schlegel S. Relationships between psychomotor retardation and EEG power
spectrum in major depression. Biol Psychiatry 1992;25:20 – 3.
[138] Schaffer CE, Davidson RH, Saron C. Frontal and parietal electroencephalogram asymmetry
in depressed and nondepressed subjects. Biol Psychiatry 1982;18:753–62.
[139] Kemali D, Vacca L, Marciano F, Nolfe G, Galderi S, Salvati A. CEEG findings in schizophrenics,
depressives, obsessives, heroin addicts and normals. Recent Adv Biol Psychiatry
1981;6:17–28.
[140] Henriques JB, Davidson RJ. Regional brain electrical asymmetries discriminate between
previously depressed and healthy control subjects. J Abnorm Psychol 1990;99(1):22 – 31.
R.J. Chabot et al / Child Adolesc Psychiatric Clin N Am 14 (2005) 21–53 48
[141] Henriques JB, Davidson RJ. Left frontal hypoactivation in depression. J Abnorm Psychol
1991;100(4):535– 45.
[142] Allen JJ, Iacono WG, Depue RA, Arbisi P. Regional electroencephalographic asymmetries
in bipolar seasonal affective disorder before and after exposure to light. Biol Psychiatry 1993;
33:642– 6.
[143] Prichep LS, John ER, Essig-Peppard T, Alper KR. Neurometric subtyping of depressive
disorders. In: Cazzullo CL, Invernizzi G, editors. Plasticity and morphology of the CNS.
London7 MTP Press; 1990.
[144] Clementz BA, Sponheim SR, Iacono W. Resting EEG in first-episode schizophrenia patients,
bipolar psychotic patients, and their first-degree relatives. Psychophysiology 1994;31:486–94.
[145] Prichep LS, John ER, Alper K, Gomez-Mont F, Essig-Peppard T, Flitter M. Quantitative
EEG in depressive disorders. In: Shagass C, Josiassen RC, Roemer RA, editors. Brain electrical
potentials and psychopathology. Amsterdam7 Elsevier; 1986. p. 223–44.
[146] Small JG. Psychiatric disorders and EEG. In: Niedermeyer E, Lopes Da Silva F, editors.
Electroencephalography: basic principles, clinical applications, and related fields. Baltimore7
Williams & Wilkins; 1993. p. 581– 96.
[147] Jenike MA, Brotman AW. The EEG in obsessive-compulsive disorder. J Clin Psychiatry 1984;
45:122– 4.
[148] Abraham HD. Stimulants, panic, and BEAM EEG abnormalities. Am J Psychiatry 1989;
146(7):947–8.
[149] Louie AK, Lannon RA, Ketter TA. Treatment of cocaine-induced panic disorder. Am J
Psychiatry 1989;146:40–4.
[150] Abraham HD, Duffy F. Computed EEG abnormalities in panic disorder with and without
drug abuse. Biol Psychiatry 1991;29:687– 90.
[151] Buchsbaum MS, Hazlett E, Sicotte N, Stein J, Wu J, Zetin M. Topographic EEG changes
with benzodiazepine administration in generalized anxiety disorder. Biol Psychiatry 1985;20:
832– 42.
[152] Siciliani O, Schiavon M, Tansella M. Anxiety and EEG alpha activity in neurotic patients.
Acta Psychiatr Scand 1975;52:116–31.
[153] Perros P, Young ES, Ritson JJ, Price GW, Mann P. Power spectral EEG analysis and
EEG variability in obsessive-compulsive disorder. Brain Topogr 1992;4:187– 92.
[154] Silverman JS, Loychik SG. Brain-mapping abnormalities in a family with three obsessivecompulsive
children. J Neuropsychiatry Clin Neurosci 1990;2:319 –22.
[155] Prichep LS, Mas F, Hollander E, Liebowitz M, John ER, Almas M, et al. Quantitative
electroencephalographic (qEEG) subtyping of obsessive compulsive disorder. Psychiatry Res
1993;50(1):25 – 32.
[156] Cook IA, Leuchter AF. Prefrontal changes and treatment response prediction in depression.
Semin Clin Neuropsychiatry 2001;6(2):113– 20.
[157] Cook IA, Leuchter AF, Morgan M, Witte E, Stubbeman WF, Abrams MT, et al. Early changes
in prefrontal activity characterize clinical responders to antidepressants. Neuropsychopharmacology
2002;27(1):120 –31.
[158] Leuchter AF, Cook IA, White EA, Morgan M, Abrams M. Changes in brain function of
depressed subjects during treatment with placebo. Am J Psychiatry 2002;159:133–9.
[159] Bricolo A, Turazzi S, Faccioli F, Odorizzi F, Sciaretta G, Erculiani P. Clinical application
of compressed spectral array in EEG monitoring of comatose patients. Electroencephalogr
Clin Neurophysiol 1978;45:211 – 25.
[160] Steudel WI, Krqger J. Using the spectral analysis of the EEG prognosis of severe brain injuries
in the first post-traumatic week. Acta Neurochir 1979;28(Suppl):40–2.
[161] Rimel RW, Giordani B, Barth JT, Jane JA. Moderate head injury: completing the clinical
spectrum of trauma. Neurosurgery 1982;11:344– 51.
[162] Thatcher RW, Cantor DS, McAlaster R. Comprehensive predictions of outcome in closed
head-injured patients. Ann N Y Acad Sci 1983;82:82 – 101.
[163] Ross RJ, Cole M, Thompson JS, Kim KH. Boxers: computer tomography, EEG, and
neurological. JAMA 1983;249:211 – 3.
R.J. Chabot et al / Child Adolesc Psychiatric Clin N Am 14 (2005) 21–53 49
[164] Tysvaer AT, Stroll OV, Bachen I. Soccer injuries to the brain: a neurologic electroencephalographic
study of former players. Acta Neurol Scand 1989;80:151– 6.
[165] Randolph C, Miller MH. EEG and cognitive performance following closed head injury.
Neuropsychobiology 1988;20:43 – 50.
[166] Thatcher RW, Biver CJ, North DM. Quantitative EEG and the Frye and Daubert standards
of admissibility. Clin Electroencephalogr 2003;34(2):39 – 53.
[167] Bresnahan SM, Barry RJ. Specificity of quantitative EEG analysis in adults with attention
deficit hyperactivity disorder. Psychiatry Res 2002;112(2):133– 44.
[168] Chabot RJ, Sigal LH. qEEG and evoked potentials in central nervous system Lyme disease.
Clin Electroencephalogr 1995;26(3):137– 45.
[169] Gugino LD, Chabot RJ, Aglio LS, Maddi R, Gosnell J, Aranki S. qEEG and neuropsychological
profiles of patients prior undergoing cardiopulmonary bypass surgical
procedures. Clin Electroencephalogr 1997;28(2):87 – 97.
[170] Chabot RJ, Gugino LD, Aglio LS, Maddi R, Cote W. qEEG and neuropsychological profiles
of patients after cardiopulmonary bypass surgical procedures. Clin Electroencephalogr
1997;28:98–105.
[171] Tsai LY. Neurophysiologic studies of infantile autism. Adv Dev Behav Pediatr 1987;235– 60.
[172] Small JH, Small IF, Milstein V, et al. Familial associations with EEG variants in manicdepressive
disease. Arch Gen Psychiatry 1975;32:43 –8.
[173] Dawson G, Warrenburg S, Fuller P. Cerebral laterialization in individuals diagnosed as autistic
early childhood. Brain Lang 1982;15:353– 68.
[174] Ogawa T, Sugiyama A, Ishiwa S, Sizuki M, Ishihara T, Sato K. Ontogenic development
of EEG-asymmetry in early infantile. Brain Dev 1982;4:439 – 49.
[175] Cantor DS, Thatcher RW, Hrybyk M. Computerized EEG analyses of autistic children. J
Autism Dev Disord 1986;16:169–87.
[176] Kinsbourne M. Hemispheric specialization and the growth of human. Am Psychol 1982;
37:411 – 20.
[177] Marosi E, Harmony T, Sanchez L, Becker J, Bernal J, Reyes A, et al. Maturation of the
coherence of EEG activity normal and learning-disabled children. Electroencephalogr Clin
Neurophysiol 1992;83:350– 7.
[178] Hauser E, Strohmayer C, Seidl R, Birnbacher R, Lischka A, Schober E. Quantitative EEG
in young diabetics. J Child Neurol 1995;10(4):330– 4.
[179] Bjorgaas M, Sand T, Gimse R. Quantitative EEG in type 1diabetic children with and without
episodes of severe hypoglycemia: a controlled, blind study. Acta Neurol Scand 1996;
93(6):398– 402.
[180] Bjorgaas M, Sand T, Vik T, Jorde R. Quantitative EEG during controlled hypoglycemia
in diabetic and non-diabetic children. Diabet Med 1998;15(1):30 –7.
[181] Byring RF, Salmi T, Sainio K, Orn H. EEG in children with spelling disabilities.
Electroencephalogr Clin Neurophysiol 1991;79:247–55.
[182] Galin D, Herron J, Johnstone J, Fein G, Yingling C. EEG alpha asymmetry in dyslexics
during speaking block design tasks. Brain Lang 1988;35:241– 53.
[183] Colon EJ, Notermas SLH, de Weerd JPC. The discriminating role of EEG power spectra
in dyslexic children. J Neurol 1979;221:257–62.
[184] Lubar JF, Bianchini KJ, Calhoun WH. Spectral analysis of EEG differences between
children with and without learning disabilities. J Learn Disabil 1985;18:403– 8.
[185] Dykman RA, Holcomb PJ, Oglesby DM. Electrocortical frequencies in hyperactive, learningdisabled,
mixed and normal children. Biol Psychiatry 1982;17:675–85.
[186] John ER. Neurometric evaluation of brain function related to learning disorders. Acta Neurol
Scand 1981;64(Suppl 89):87– 100.
[187] Harmony T, Hinojosa G, Marosi E, Becker J, Rodriguez M, Reyes A, et al. Correlation between
EEG spectral parameters and an evaluation. Int J Neurosci 1990;54:147– 55.
[188] Mechelse K, Gemund JJ, Nije J, Burg L, Laurs J. Visual and quantitative analysis of
EEGs of school children, children, and school children with specific reading diability.
Electroencephalogr Clin Neurophysiol 1975;39:106– 8.
R.J. Chabot et al / Child Adolesc Psychiatric Clin N Am 14 (2005) 21–53 50
[189] Fernandez T, Harmony T, Fernandez-Bouzas A, Silva J, Herrera W, Santiago-Rodriguez E,
et al. Sources of EEG activity in learning disabled children. Clin Electroencephalogr 2002;
33(4):160–4.
[190] Leisman G. Coherence of hemispheric function in developmental dyslexia. Brain Cogn 2002;
48(2–3):425– 31.
[191] Otero GA, Pliego-Rivero FB, Fernandez T, Ricardo J. EEG development in children with
sociocultural disadvantages: a follow-up study. Clin Neurophysiol 2003;114(10):1918– 25.
[192] Ito Y, Teicher MH, Glod CA, Ackerman E. Preliminary evidence for aberrant cortical
development in abused children: a quantitative EEG study. J Neuropsychiatry Clin Neurosci
1998;10:298– 307.
[193] Montagu JD. The hyperkinetic child: a behavioural, electrodermal and EEG investigation.
Dev Med Child Neurol 1975;17:299–305.
[194] Callaway E, Halliday R, Naylor H. Hyperactive children’s event related potentials fail to
support underarousal and maturational-lag theories. Arch Gen Psychiatry 1983;40:1243–8.
[195] Mann CA, Lubar J, Zimmerman A, Miller C, Muenchen R. Quantitative analysis of EEG
in boys with deficit hyperactivity disorder: controlled study with clinical implications.
Pediatr Neurol 1992;8:30–6.
[196] Barry RJ, Clarke AR, Johnstone SJ. A review of electrophysiology in attention-deficit/
hyperactivity disorder: I. Qualitative and quantitative electroencephalography. Clin Neurophysiol
2003;114(2):171– 83.
[197] Clarke AR, Barry RJ, McCarthy R, Selikowitz M. EEG analysis in attention-deficit/
hyperactivity disorder: a comparative study of two subtypes. Psychiatry Res 1998;81(1):19 –29.
[198] Clarke AR, Barry RJ, McCarthy R, Selikowitz M. EEG-defined subtypes of children
with attention-deficit/hyperactivity disorder. Clin Neurophysiol 2001;112:2098–105.
[199] Clarke AR, Barry RJ, McCarthy R, Seilkowitz M, Brown CR, Croft RJ. Effects of stimulant
medications on the EEG of children with attention-deficit/hyperactivity disorder predominantly
inattentive type. Int J Psychophysiol 2003;47(2):129– 37.
[200] Lubar JF. Discourse on the development of EEG diagnostics biofeedback for attention-deficit/
hyperactivity disorders. Biofeedback Self Regul 1991;16:201–24.
[201] Monastra VJ, Lubar JF, Linden M, VanDeusen P, Green G, Wing W, et al. Assessing attention
deficit-hyperactivity disorder via quantitative electroencephalography: an initial validation
study. Neuropsychology 1999;13(3):424–33.
[202] Monastra VJ, Lubar JF, Linden M. The development of a quantitative electroencephalographic
scanning process for attention deficit-hyperactivity disorder: reliability and validity studies.
Neuropsychology 2001;15(1):136– 44.
[203] Satterfield J. EEG issues in children with minimal brain. Semin Psychiatry 1973;5:35–46.
[204] Halperin JM, Gittleman R, Katz S. Relationship between stimulant effect, electroencephalogram,
and clinical neurological findings in hyperactive children. J Am Acad Child Psychiatry
1986;25:820– 5.
[205] McIntyre H, Firemark M, Cho A. Computer analyzed EEG in amphetamine responsive
hyperactives. Psychiatry Res 1981;4:189– 97.
[206] Prichep LS, John ER. Neurometric studies of methylphenidate responders and nonresponders.
In: Pavlidis GT, editor. Perspectives on dyslexia. New York7 John Wiley &
Sons; 1990. p. 133– 9.
[207] Steinhausen H, Romahn G, Gobel D. Computer analyzed EEG methylphenidate responsive
hyperactive children. Neuropediatrics 1984;15:28 –32.
[208] Loo SK, Teale PD, Reite ML. EEG correlates of methylphenidate response among children
with ADHD: a preliminary report. Biol Psychiatry 1999;45(12):1657–60.
[209] Craggs MD, Wright JJ, Werry JS. A pilot study of the effects of methylphenidate on
the vigilance-related EEG in hyperactivity. Electroencephalogr Clin Neurophysiol 1980;48:
34– 42.
[210] Surwillo WW. Changes in the electroencephalogram accompanying the use stimulant
drugs (methylphenidate and dextroamphetamine) in hyperactive children. Biol Psychiatry
1977;12:787– 99.
R.J. Chabot et al / Child Adolesc Psychiatric Clin N Am 14 (2005) 21–53 51
[211] Clarke AR, Barry RJ, McCarthy R, Selikowitz M, Clarke DC, Croft RJ. Effects of stimulant
medications on children with attention-deficit/hyperactivity disorder and excessive beta
activity in their EEG. Clin Neurophysiol 2003;114(9):1729–37.
[212] Levy F. Neurometrics: review and comments. ADHD Report 1994;2:35798.
[213] Levy F, Ward P. Neurometrics, dynamic brain imaging and attention deficit. J Paediatr Child
Health 1995;31:279– 83.
[214] Chabot RJ, Serfontein G. Quantitative EEG profiles of children with attention deficit disorder.
Biol Psychiatry 1996;40:951– 63.
[215] Chabot RJ, Orgill AA, Crawford G, Harris MJ, Serfontein G. Behavioral and electrophysiological
predictors of treatment response stimulants in children with attention disorders.
J Child Neurol 1999;14(6):343– 51.
[216] Chabot RJ, diMichele F, Prichep L, John ER. The clinical role of computerized EEG in
the evaluation and treatment of learning and attention disorders in children and adolescents.
J Neuropsychiatry Clin Neurosci 2001;13:171– 86.
[217] Bresnahan SM, Anderson JW, Barry RJ. Age-related changes in quantitative EEG in attentiondeficit/
hyperactivity disorder. Biol Psychiatry 1999;46(12):1690– 7.
[218] Riddle MA, Scahill L, King RA, Hardin MT, Anderson GM, Ort SI, et al. Double-blind,
crossover trial of fluoxetine and placebo in children and adolescents with obsessive-compulsive
disorder. J Am Acad Child Adolesc Psychiatry 1992;31(6):1062– 9.
[219] Noble EP, Blum K, Khalsa ME, Ritchie T, Montgomery A, Wood RC. Allelic association
of the D2 dopamine receptor gene with cocaine dependence. Drug Alchol Depend 1993;33:
271– 85.
[220] Ebstein RP, Gritsenko I, Nemanov L, Frish A, Osher Y, Belmaker RH. No association between
the serotonin transporter gene regulatory polymorphism and the tridimensional
personality questionnnaire (TPQ) temperament of harm avoidance. Mol Psychiatry 1997;
2:224– 6.
[221] DeBoer P, Abercrombie E. Physiological release of striatal acetylcholine in vivo: modulation
of d1 and d2 dopamine receptor subtypes. J Pharmacol Exp Ther 1996;277:775– 83.
[222] Icarashi Y, Takahashi H, Aral T, Maruyama Y. Suppression of cholinergic activity via the
dopamine d2 receptor in the rat stratum. Neurochem Int 1997;30:191–7.
[223] Russel V, de Villiers A, Sagvolden T, et al. Altered dopaminergic function in the prefrontal
cortex, nucleus accumbens and caudate-putamen of an animal model of attention deficit
hyperactivity disorder. Brain Res 1995;676:343–51.
[224] Levy F. The dopamine theory of attention deficit hyperactivity disorder. Aust N Z J Psychiatry
1991;25:277–83.
[225] Niedermeyer E, Naidu SB. Attention deficit disorder and frontal motor cortex disconnection.
Clin Electroencephalogr 1998;28:130–6.
[226] Barkley RA, Grodzinsky G, DuPaul GJ. Frontal lobe functions in attention deficit disorder
and without hyperactivity: a review and research report. J Abnorm Child Psychol 1992;20(2):
163– 88.
[227] Hynd G, Semrud-Clikeman M, Lorys A, Novey E, Eliopulos D, Lyytinen H. Corpus callosum
morphology in attention deficit disorder: morphometric analysis of MRI. J Learning Disord
1991;24:141–6.
[228] Kim BN, Lee JS, Cho SC, Lee DS. Methylphenidate increased regional cerebral flow in
subjects with attention deficit/hyperactivity disorder. Yonsei Med J 2001;42(1):19 –29.
[229] Lou H, Henriksen L, Bruhn P, Borner H, Nielsen J. Striatal dysfunction in attention deficit
and hyperkinetic. Arch Neurol 1989;46:48– 52.
[230] Robbins TW, Roberts AC, Owen AM, Sahakian BJ, Everitt BJ, Wilkinson L, et al.
Monoaminergic-dependent cognitive functions in the prefrontal cortex monkey and man.
Motor and Cognitive Functions of the Prefrontal Cortex. 1994. p. 94– 111.
[231] Shue KL, Douglas VI. Attention deficit hyperactivity disorder and the frontal syndrome.
Brain Cogn 1992;20:104– 24.
[232] Zametkin AJ, Liebenauer LL, Fitzgerald GA, King AC, Minkunas DV, Herscovitch P, et al.
R.J. Chabot et al / Child Adolesc Psychiatric Clin N Am 14 (2005) 21–53 52
Brain metabolism in teenagers with attention-deficit hyperactivity disorder. Arch Gen
Psychiatry 1993;50:333–40.
[233] El-Sayed E, Larsson JO, Persson HE, Rydelius PA. Altered cortical activity in children with
attention-deficit/hyperactivity disorder during attentional load task. J Am Acad Child Adolesc
Psychiatry 2002;41:811– 9.
R.J. Chabot et al / Child Adolesc Psychiatric Clin N Am 14 (2005) 21–53 53