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Applied Psychophysiology and Biofeedback, Vol. 26, No. 2, 2001

A Psychophysiological Marker of Attention

Deficit/ Hyperactivity Disorder (ADHD)Defining the

EEG Consistency Index

Boris Kovatchev,1,5 Daniel Cox,1 Rebecca Hill,2 Ronald Reeve,2 Raina Robeva,3

and Tim Loboschefski4

This study continues our research to further validate the idea that ADHD (AttentionDeficit/Hyperactivity Disorder) interferes with transition from one task to another andthis interference can be quantified by a Consistency Index (CI) derived from a specificmathematical representation of EEG data. We reanalyze 32 previously reported data setspresent new data for 35 boys and girls, ages 712, ADHD or control. Each data set con-tains EEG, recorded and digitized while participants perform consecutive 10-min tasks:video, reading, and math. For boys, the CI in ADHD was four times lower than in controls,p < .005, for girls this difference was two times, p < .05. ADHD/control classificationbased on the CI coincided with the DSM-IV criteria for 88% of the boys and for 67% of the

girls. Post hoc analysis indicated that the classification utility of the CI diminished with age.A CI below 40% could be a discriminating, reliable, and reproducible marker of ADHD inyoung boys.

KEY WORDS: attention-deficit/hyperactivity disorder (ADHD); electroencephalography (EEG); mathematicalmodeling.

Attention Deficit/Hyperactivity Disorder (ADHD), is the most common developmen-

tal disorder of childhood, affecting 37% of children in the United States, and often contin-

uing into adulthood (Barkley, 1998). ADHD is usually reflected by a pattern of increased

impulsivity, high levels of motor activity, and attentional problems that impair function

in home, school, and social settings (American Psychiatric Association [APA], 1994). Al-though affecting a relatively small percentage of children overall, the diagnosis of ADHD

accounts for one-third to one-half of all child referrals for mental health services (Popper,

1988). The long-term consequences of childhood ADHD include lower educational achieve-

ments and increased risk for antisocial behavior and drug abuse (Weiss & Hechtman, 1993).

1Center for Behavioral Medicine Research, University of Virginia Health System, Charlottesville, Virginia.2University of Virginia School of Education, Charlottesville, Virginia.3Department of Mathematical Sciences, Sweet Briar College, Sweet Briar, Virginia.4Department of Psychology, Sweet Briar College, Sweet Briar, Virginia.5Address all correspondence to Boris Kovatchev, PhD, Center for Behavioral Medicine Research, University of

Virginia Health System, Box 800137, Charlottesville, Virginia 22908; e-mail: [email protected].

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128 Kovatchev, Cox, Hill, Reeve, Robeva, and Loboschefski

Despite its significance as the predominant developmental disability in this country, the true

nature of ADHD remains puzzling. At present there is little understanding of the neurobio-

logical basis of ADHD. The current diagnostic criteria rest exclusively on historyand current

manifestations of behaviors reflecting ADHD symptoms (APA, 1994; Barkley, 1998). For

a diagnostic classification of ADHD individuals must sustain a majority (6 of 9) of target

behaviors, either for hyperactivity-impulsivity or inattention, for a period of 6 months or

longer. These symptoms must be considered maladaptive and inconsistent with the childs

current developmental level. In addition, these behaviors must be independently observed

in two or more settings and result in some clinically significant impairment in academic,

social, or occupational functioning. Consequently, an unambiguous diagnosis is often dif-

ficult to make and misdiagnoses can and do occur (Shaywitz, Fletcher, & Shaywitz, 1994,

1995).

As the diagnostic label implies, a major assumption of ADHD is that diagnosed chil-

dren have some type of deficit in attention, but the precise nature of this deficit remains

unknown. Although a number of hypotheses abound for this attentional difference, one

which seems tenable is that ADHD children are highly distractible, having great difficultyin selectively narrowing their attentional focus when confronted with multiple competing

stimuli. In addition to the core clinical symptoms of ADHD, high levels of comorbidity

have been found with learning, oppositional defiant, conduct, mood, and anxiety disorders

(Biederman et al., 1994; Biederman, Newcorn, & Sprich, 1991). As a consequence, ADHD

children appear less able to focus attention, maintain attention, or plan for future actions.

The consequences of these problems include poor scholastic achievement, behavioral man-

agement problems, poor peer relations, and low self-esteem, which can be long lasting and

quite serious (Barkley, Anastopoulos, Guevremont, & Fletcher, 1991). It is estimated that

the majority of children diagnosed with ADHD exhibit significant behavioral problems

during adolescence (Barkley, Fisher, Edelbrock, & Smallish, 1990; Gittleman, Mannuzza,Shenker, & Bonagura, 1985; Mannuzza et al., 1991) and manifest continuing functional

deficits and psychopathology into adulthood (Weiss & Hechtman, 1993).

Identifying ADHD as a distinct clinical syndrome with a multifactorial etiology,

Zametkin and Rapoport (1987) proposed 11 separate neuroanatomical hypotheses for the

etiology of ADHD. A recent study, using high resolution SPECT (single photon emission

computerized tomography) found that while engaged in passive watching tasks, no sub-

stantial or identifying differences between ADHD children and their matched controls were

present. However, when the experimental task required focused attention and concentration,

differences between ADHD individuals and controls began to emerge in the brain centers

responsible for governing attentional control and planning. Specifically, a substantial major-

ity (65%) of ADHD children and adolescents showed decreased perfusion in the prefrontalcortices when they were asked to engage in an intellectually demanding task as compared to

a nondemanding task, whereas only 5% of controls showed this decreased perfusion (Amen

& Carmichael, 1997). In similar research endeavors ADHD children showed cerebral hy-

poperfusion (Lou, Henriksen, & Bruhn, 1984) and in ADHD adults a lower cerebral glucose

metabolism was demonstrated (Zametkin et al., 1990). Functional MRI reveals differences

between children with ADHD and healthy controls in their frontal-striatal function and its

modulationby methylphenidate (Vaidya et al., 1998).Unfortunately, although neuroanatom-

ical findings lend support to the notion that ADHD is a distinct clinical syndrome adding to

our understanding of the etiology of ADHD, neuroimaging techniques are currently neither

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A Psychophysiological Marker of Attention Deficit / Hyperactivity Disorder 129

sensitive/specific, nor practical (in terms of availability and expense) enough for routine

assessment and diagnosis (Barkley, Grodzinsky, & DuPaul, 1992; Grodzinsky & Diamond,

1992). For this reason, EEG studies are of interest.

In terms of EEG, as far back as the late 1920s, it was known that brain wave charac-

teristics changed dramatically depending upon the task at hand (Berger, 1929). In passive,

mentally unchallenging situations, slow waves (i.e., theta and alpha) tend to dominate EEG

recordings, whereas faster waves (i.e., beta and high beta) tend to evidence themselves

when subjects were asked to focus attention on a more demanding task. Current computer

technology allowed for evaluation of stationary EEG characteristics, such as the power

of brain waves at different frequencies (theta, alpha, beta), associated with various tasks.

When ADHD children were compared to controls, different patterns of these stationary

characteristics emerged. Control individuals demonstrated power increases of beta and de-

creases of theta when involved in a variety of tasks, whereas ADHD children reportedly

had relatively higher power of the low frequencies despite the need for increased mental

activity (Crawford & Barabasz, 1996; Lubar et al., 1985; Mann, Lubar, Zimmerman, Miller,

& Muencher, 1991). In one of the largest studies procured to date, Chabot and Serfontein(1996) tested 407 children with attention deficits with and without hyperactivity, with and

without learning problems, children with attention problems who failed to reach DSM-III

criteria for the disorder, and 310 controls (ages 617). They employed spectral analysis and

observed patterns of excess theta in frontal regions and increased alpha (relative power) in

the posterior regions for the clinical groups versus controls. They then employed coherence

analysis and reported that one-third of the noncontrol children showed signs of interhemi-

spheric dysregulation characterized by this pattern of excessive theta/alpha power in the

right temporal and premotor (frontal) areas.

Lazzaro et al. (1998) explored the possibility of maturational lag and cortical under-

arousal as causes of ADHD. EEG activity of 26 adolescent (mean age of 13.4) unmedicatedADHD males and 26 age and gender matched controls were examined in a resting eyes open

condition. In the ADHD subjects, increased levels of theta activity in the anterior region of

the brain and reduced levels of beta activity in the posterior region were discovered. Lazzaro

et al. suggested that these results show evidence of a maturational lag (due to the presence of

theta in the anterior regions of the brain and reduction of beta in the posterior regions) and

reduced cortical arousal in ADHD. Monastra et al. (1999) reported similar results from their

study on 482 participants (ages 630) classified into the three groups of ADHD combined-

type, ADHD predominantly inattentive-type, and controls. They recorded from a single,

midline, prefrontal electrode (CZ) to test the hypothesis that prefrontal cortical slowing

(presence of excess theta) can differentiate ADHD subtypes from controls. Monastra et al.

report that their results showed cortical slowing that identified ADHD subjects regardlessof sex or age. Specifically, consistent with their hypothesis, statistical analysis revealed that

the ADHD groups (inattentive and combined type) displayed significantly higher levels of

slow-wave (theta) relative to fast-wave (beta) EEG activity, a ratio that is the inverse of a

previously reported Engagement Index developed to measure operators engagement with

automated and fast changing tasks (Pope & Bogart, 1991; Pope, Comstock, Bartolome,

Bogart, & Bardette, 1994). This thetabeta power ratio was significantly larger for the

ADHD groups than for the normal control group. In addition, the thetabeta ratios were

significantly larger for the younger ADHD children (ages 611). This is the only of the

reviewed studies to provide information regarding sensitivity and specificity.

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In summary, the situations in which identifiable differences commonly have been

found between ADHD and control children are those in which children are asked to focus

attention to specific tasks during an experiment. In addition, the regions of the brain that

show affected blood flow are those regions that are utilized in both attentional control and

planning for future activities. This observation generated our idea for research focusing not

only on differences between ADHD patients and controls within a more or less demanding

situation, but also on contrasts between contiguous tasks.

METHODS

Participants

The study was approved by the Institutional Review Board; 18 boys and 17 girls were

recruited through newspaper and television advertisement and were assigned into exper-

imental (ADHD) and control group. The ADHD group consisted of 9 boys and 8 girls,the rest of the participants were controls. The inclusion criteria for the experimental group

were (a) male and female children between the ages of 7 and 12, (b) had a previous diag-

nosis of ADHD with or without hyperactivity, (c) were taking any type of psychostimulant

medication (with the exception of Cylert) to treat their attentional difficulties, (d) were not

taking medication to treat anxiety or depression, (e) scored above the 84th percentile on

the Attention Problems scale on either the Child Behavior Checklist or the Teacher Report

Form, (f) both parents and teachers were interviewed, and the patients had to have either

6/9 hyperactivity-inpulsivity or 6/9 Inattentive symptoms as defined by the DSM-IV, and

(g) did not have any major health problems that might affect the brain (e.g., Cerebral Palsy).

Participants in the control group were age- and gender-matched to the experimental group.Children were included in the control group if they (a) had no known history of a disruptive-

behavior disorder, (b) had never taken stimulant medication, (c) had no more than three of

either the hyperactive-impulsive or the inattentive DSM-IVsymptoms, (d) were not taking

medication to treat anxiety or depression, and (e) did not have any major health problems

that might affect the brain.

In addition, three previously reported data sets were reanalyzed. Data set I: Four boys,

ages 610, with ADHD and four age-matched control boys tested at two 30-min trials

(video and reading) separated by a 5-min break (Cox et al., 1999). Data Set II: Six ADHD

males and six non-ADHD males, ages 1825, participated in a double-blind, placebo versus

methylphenidate controlled crossover design study. The participants were given four tasks

of the Gordon Diagnostic System, two easy (auditory and visual) and two hard (auditoryand visual) (Merkel et al., 2000). Data Set III: Six female college students with ADHD

and six controls were engaged in a series of short-term concentration exercises with shorter

resting intervals. EEG was recorded throughout the test (Loboschefski, Robeva, Kirkwood,

Cox, & Kovatchev, 1999).

Procedure

The participants and their parents met with a researcher, and were asked to sign an

informed-consent form. Prior to the EEG test parents completed three ADHD behavioral

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rating scales: Conners Parent Rating Scale (Goyette, Conners, & Ulrich, 1978),

Achenbachs Child Behavior Checklist (Achenbach & Edelbrock, 1983) and the ADHD

Symptom Inventory (Cox et al., 1999). Teachers were interviewed and completed report

forms. The evening before testing, medication of all ADHD participants was discontin-

ued. During testing an appropriately sized EEG cap (Electrode Cap International, Inc.) was

placed over participants heads. Ten electrode sites were prepared: a ground just in front of

Cz, an earlobe reference electrode, and Cz, Pz, P3, P4, F3, F4, T3, T4. Impedance criteria

was 10 k, as measured by the Prep-Check electrode impedance meter. EEG signals were

amplified and processed by a Lexicor Neurosearch-24 system, loaned by NASA. EEG was

digitized at a rate of 128 samples/s. Standard time series smoothing using a Tukey-Hanning

window was applied to the data from each EEG channel and the power spectrum was es-

timated using a standard Fast Fourier Transformation algorithm. After this preprocessing

of the data, the relative power was computed for four EEG bands: Theta (48 Hz), Alpha

(813 Hz), Beta (1322 Hz), and High Beta + EMG (2232 Hz). The residual power was

carried by the frequencies below 4 Hz and above 32 Hz. This resulted in 32 EEG parameters

(4 bands 8 channels) recorded into a data file every two seconds.The participants were tested for 35 min while performing three 10-min tasks. During

the initial 10 min participants watched a video. These 10 min were treated as a period for

adjustment of the subject to the cap and were not included in further analyses. After a

few seconds break the participants were required to read a book for 10 min This task was

followed by a 35 min structured break, and then the participants were required to solve

mathematical problems for the next 10 min In order to ensure similar task difficulty for

all participants, they were asked to bring in reading material of their choice and examples

of their math homework, i.e., participants selected test material of personal interest and

acceptable difficulty level.

With the above preprocessing rate (Fourier transformation of 8 EEG time series everysecond, aggregated in 4 bands), there were approximately 300 32 data points recorded

for each subject during each 10-min task. The two data matrices acquired during 10-min of

reading and 10-min of mathematics, were used for further computation.

Defining the EEG Consistency Index (CI)

The computation of the CI for each subject is based on a model that mathematically

incorporates our idea that individuals with ADHD would differ from controls when con-

trasting EEG power during two attention-demanding consecutive tasks. In essence, each

person has a three-dimensional EEG power spectrum representation: one dimension is fre-quency, another is spatialthe location of the electrode on his/her head, and the third istime. We will now explain this idea in detail, relying on our past experience that probably

the best way to present this rather complex mathematical paradigm is to use a series of

figures depicting the sequential steps of the computation of the CI.

EEG Frequency Dimension

Figure 1 presents the basis of our idea of consistent EEG change in the frequency

dimension. The black line is the averaged power spectrum of a subject performing a task,

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Fig. 1. Consistent (panel A) versus inconsistent (panel B) EEG power change between two contiguoustasks in the frequency dimension. The black lines represent the averaged power spectra during one task,whereas the gray lines represent the power spectra during a contiguous task.

the gray line is the power spectrum of the same subject while performing a contiguous

task. In Fig. 1(A) the black line is above the gray line at lower frequencies and mostly

below the gray line at higher frequencies (above 16 Hz). This shows that a change from one

task to another (from black to gray) results in a consistent increase of higher frequencies

and consistent decrease of lower frequencies, i.e., the subject becomes more alert while

performing the second task. In contrast, in Fig. 1(B) no consistent change in the frequency

distribution over the spectrum is observed. In other words, Fig. 1(A) presents a consistent

change in the frequency dimension, whereas Fig. 1(B) present an inconsistent change.

Figure 2 presents the averaged difference between the EEG power spectra across the

two tasks, i.e., the [point-by-point numerical] difference between the black and the gray

line in Fig. 1. In Fig. 2(A) this difference is mostly positive at lower frequencies and mostly

negative at higher frequencies. In Fig. 2(B) the power differences are scattered below and

above the frequency axis. Visually, a consistent change between two tasks will be presented

by an uninterrupted domain [Fig. 2(A)], whereas an inconsistent change would result insporadic power changes along the EEG spectrum, Fig. 2(B).

EEG Spatial DimensionThe Location of Electrodes

As opposed to the frequency dimension, the presentation of spatial EEG consistency

is based on a discrete presentation of the power spectrum at several EEG channels. Figure 3

presents an 8-channel (electrode) setting and spatially consistent/inconsistent changes be-

tween two tasks. The power spectrum of each electrode is discretized in four basic frequency

arrays: theta, alpha, beta, and high beta. Each bar in the figure represents the relative power

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Fig. 2. Consistent(panel A) versus inconsistent (panelB) EEG powerdifference between two contiguoustasks. The gray area is visually continuous during a consistent power change and interrupted during aninconsistent power change.

registered by an electrode at a particular frequency. A consistent change would mean that

at a particular frequency array most channels will display similar, unidirectional readings

[Fig. 3(A)], whereas an inconsistent change will result in scattered power changes across

the electrode sites [Fig. 3(B)].

Fig. 3. Consistent (panel A) versus inconsistent (panel B) EEG power change between two contiguous

tasks in the spatial dimensionthe location of the electrodes.

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Algorithmic Definition of the CI

The concept of EEG consistency (Figs. 13) is used as a base for the development of

an algorithm and software that computes the CI. The algorithm works as follows:

1. Discrete spectra, including residual power, are calculated for all EEG channels.2. Power change distances (PCD) between two contiguous tasks are computed for

each EEG band and channel, using the following formula:

PCD =M1 M2SD12

N1+ SD2

2

N2

,

where M1 and M2 are the mean powers at two contiguous tasks, S D1 and S D2 are

their standard deviation, and N1 and N2 are the epoch counts at these tasks. This

normalization of the power changes between tasks allows one channel/frequencyband to be directly comparable to another.

3. PCD undergo filtering to eliminate changes below a noise threshold. The noise

threshold works as follows: The PCD that are larger by an absolute value than

the threshold are marked by 1 or 1 depending on their direction, whereas all

PCD below threshold are marked by zero. This filtering transforms the PCD into

a sequence of 1, 0, 1 that indicates, for each EEG band and channel, whether a

significant power change was observed while the person shifted from one task to

another. Throughout this manuscript, a threshold of 3.5 is used for the computation

of the CI.

4. Intuitively, the shift from Task 1 to Task 2 would be consistent if most of the filteredPCD below some cutoff frequency are positive, whereas most of the indicators

above this cutoff frequency are negative, or vice versa. In contrast, the shift would

be inconsistent if the PCD vary greatly by magnitude and/or sign. Thus, Fig. 4(A)

and (B) present a consistent and an inconsistent EEG at a cutoff frequency between

beta and high beta.

5. The final pass of the computation is a simple addition of the filtered PCD below

and above the cutoff value. The CI is defined as the absolute value of the differ-

ence between these two sums, expressed as a percentage, i.e., computed using the

formula:

CI = 100

1N

below cutoff

i

above cutoff

j

% where i , j = 1, 0, 1

For example in Fig. 4(A) we have a sum of 13 below the cutoff and a sum of5 above the

cutoff. Thus, the CI of the consistent shift presented in Fig. 4(A) will be 18. In contrast the CI

of the inconsistent shift in Fig. 4(B) will be 1 (0 below the cutoff and 1 above the cutoff).

The maximum CI equals the number of EEG channels multiplied by the number of EEG

bands used during spectrum discretization. For example, with 8-channel EEG equipment

and 4 bands the CI ranges from 0 to 32. In order to make the results comparable across

different experiments, the CI will be expressed in terms of percentage from its maximum

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Fig. 4. EEG power change indicators during a consistent (panel A) and inconsistent (panel B) change

between contiguous tasks. Computation of the Consistency Index.

value. For example, the CI in Fig. 4(A) will be 56.25%, whereas in Fig. 4(B) it will be

3.125%.

RESULTS

A blind rater evaluated a video of each child during testing. The mean rating of

percent time on task for controls and ADHD participants while reading was 97% versus

89%(ns)andformathitwas96%versus85%(t= 1.90, p = .04), respectively, thus ADHD

participants displayed similar engagement with reading and slightly diminished engagement

with mathematics.

Detailed analyses of all power bands and beta/ theta power ratios, including 144 plots

of power changes across time, were performed in order to scan for suggested in the literature

increase of theta associated with ADHD (Crawford & Barabasz, 1996; Lubar et al., 1985;

Mann et al., 1991). The result of these efforts was generally negative. The only marginally

significant result included a slight increase in the average power of theta over the first

10 min of the test for ADHD patients compared to controls, F= 4.5, p = .04. There

were no significant ADHD-control differences (t= 0.2, p = .84) in the theta/beta ratio

previously reported as an indicator for ADHD (Monastra et al., 1999). Similarly, there were

no significant differences in the participants Engagement Indices, t= 0.3, p = .79 (Pope

& Bogart, 1991; Pope et al., 1994).

CI Results for Boys

Table I presents the CI of 9 control and 9 ADHD boys switching from reading to

mathematics with a 35 min break. The CI is presented by its value (column CI) and as a

percentage from its maximum value of 32 (CI%). Statistically, a t-test demonstrated that

the average CI of control boys is significantly higher than the average CI of ADHD boys,t= 4.0, p = .0008. Clinically, there was very good separation between the two groups:

only one ADHD boy (#130) had a CI above 40%, and only one control boy (#127) had a

CI below 40%. Therefore, an ADHD/Control classification based on the CI coincides with

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Table I. Consistency Index for 9 Control and 9 ADHD Boys

Control ADHD

ID CI CI% Average SD; % ID CI CI% Average SD; %

106 29 90.6 102 8 25

107 24 75 111 6 18.8108 13 40.6 116 1 3.1109 15 46.9 20.3 9.9; 63.5% 123 7 21.9 5.7 4.5; 17.7%112 30 93.8 124 1 3.1115 18 56.3 125 9 28.1117 24 75 130a 14 43.8127a,b 0 0 132 5 15.6128 30 93.8 136 0 0

Group comparison: t= 4.0; p = .0008

a The classification based on the CI does not coincide with the DSM-IVcriteria.bClassified as ADHD by the teacher, but not by the parents.

the DSM-IVdiagnostic criteria for 88% of the boys. In addition, a review of the diagnosis

showed that the false-positive subject #127, was classified as ADHD by the teacher but

not the parents.

There was significant correlation between the CI and the scores of several behavioral

ADHD scales: ADHD Symptom Inventory, r= .66, p = .002; attentional subscale of the

ADHD Symptom Inventory, r= .67, p = .002; Achenbachs Child Behavior Checklist,

r= .66, p = .002; Conners Parent Rating Scale, r= .44, p = .05.

CI Results for Girls

Statistically, the average CI of ADHD girls was 20.7% versus 45.1% for controls. This

group difference was significant, t= 2.1, p < .05. Clinically, the differentiation between

ADHD and control girls was not so clear as with boys: there were 2 ADHD girls with CI

above 40% and 4 control girls with CI below 40%.

Meta-Analysis of the Current and Previous Data

Previously reported data for 32 subjects were added to the current data set and re-

analyzed to address age and gender effects. The total sample consisted of 38 males and

29 females, 33 ADHD and 34 controls; 43 subjects were younger than 16 years. The de-mographic characteristics of all subjects are presented in Table II.

To accommodate across-studies meta-analysis the parameters of the CI were unified

across and the CI was expressed in percentages from 0 to 100%. Analysis of variance with

independent factors ADHD versus Control, Gender and Age group revealed that (1) The

average CI of ADHD subjects is 29% versus 50% for controls, F= 43.7, p < .0001;

(2) There was a significant Gender effect with males having a higher CI, F= 4.1, p < .05;

(3) There was an age trend with younger subjects having higher CI, F= 3.7, p = .06,

and (4) There was a significant interaction between ADHD-control and Gender effects

with males displaying stronger CI differences between ADHD and controls, F= 5.6,

p < .05.

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Table II. Demographic Characteristics of the Participants Included in Meta-Analysis

Participants younger Participants olderthan 16 years (N= 43) than 16 years (N= 24)

Male/female 26/17 12/12

ADHD/control 21/22 12/12Average age (years)a 10.6 20.0

Male/female 10.0/11.4 20.9/18.9ADHD/control 10.6/10.6 20.1/19.9

a There were no significant differences in age between males and females (p = .86)and between ADHD and controls (p = .88).

On the basis of the CI, a logistic regression model classified correctly 82% of all

ADHD subjects and 77% ofall control subjects with an overall classification accuracy of

80%. This model was statistically significant, p < .0001. The classification power of the

logistic model increased to 90% if only younger male subjects were included in the analysis.

In addition, a Boolean decision-making rule based on the CI classified all but one of theADHD boys versus their agegender-matched controls, a 96% correct classification.

DISCUSSION

The DSM-IV (APA, 1994) states The essential features of Attention Deficit/

Hyperactivity Disorder (ADHD) is a persistent pattern of inattention and/or hyperactivity-

impulsivity that is more frequent and severe than is typically observed in individuals in a

comparable level of development. Evidence of six of nine inattentive behaviors and/or six

of nine hyperactive-impulsive behaviors must have been present before age 7, and must

clearly interfere with social, academic and/or occupational functioning. Consequently, the

diagnosis of ADHD is highly dependent on a retrospective report of a patients past be-

havior and subjective judgment of degree of relative impairment, which presents a serious

diagnostic dilemma and reduces the precision of the ADHD assessment. Thus, consistent

with the recent NIH Consensus Statement (NIH Consensus Statement [NIH], 1998), we

can conclude that ADHD (1) Is difficult to diagnose; (2) Is considered a common problem;

(3) Is associated with many negative consequences, both for the patient and society; and

(4) Has been inconsistently associated with neuroimaging and EEG anomalies that have

been nondiagnostic in nature.

We built our idea of investigating EEG contrasts between contiguous tasks on the ba-

sis of multiple indirect physiologic and behavioral evidence that point in that direction. Asreviewed, investigators examining a variety of neuroimaging and EEG data have repeatedly

found ADHDnon-ADHD differences, but the specific differences have been inconsistent.

We believe that this apparent perplexity only reflects the complexity of the underlying prob-

lem. It is quite possible that there are various manifestations of ADHD reflecting different

physiologic and behavioral routes merging into a common pathway. So, we tried to find a

commonground between the diversity of previously unrelatedobservations and documented

ADHDnon-ADHD differences. Physiologically, blood flow studies demonstrated that on

an intellectually demanding task a majority of ADHD children and adolescents showed

decreased functioning in the frontal lobe regions of the brain (Amen & Carmichael, 1997;

Barkley et al., 1992; Grodzinsky & Diamond, 1992). Because these regions are utilized in

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attentional control and planning for future activities, we would expect that ADHD subjects

would manifest a decreased ability to control their attentional shifting when transitioning

between two contiguous tasks of different attentional or intellectual demands.Behaviorally,

a common pathway discussed by some researchers involves deficits in response inhibitory

control and reengagement. These deficits have been documented to differentiate ADHD

from non-ADHD children, are greater among those children with more severe ADHD, and

have been remedied with methylphenidate (Schachar, Tannok, & Logan, 1993; Schachar,

Tannok, Mariott, & Logan, 1995; Tannok, Schachar, & Logan, 1995). Again, the diffi-

culty with response inhibition and reengagement may be most obvious when children are

transitioning from one task to another. In addition, although various studies have exam-

ined ADHD children performing various tasks (e.g., reading and arithmetic), none have

examined contrast between consecutive tasks.

This report introduces a new marker for ADHD that is based on EEG contrasts between

tasks. The computation of the CI is based on a mathematical model of EEG consistency.

Before formulating this model, we made every effort to replicate reports suggesting that

ADHD individuals display in their EEG an increased power of theta and decreased powerof beta frequencies (Lubar et al., 1985). As we reported in the results section, the only

marginally significant result (out of 144 tests) was that the average power of theta over

the first 10 min of EEG (introductory video) was higher for ADHD participants than for

controls, F= 4.5, p = .04. However, the power of beta was not higher for controls. On

the contrary, it was tending to be lower, p = .13, primarily due to control boys. As a result,

the Engagement Index did not differentiate ADHD boys, or girls, from their corresponding

controls. In summary, no significant ADHD-control (or gender) effects were observed using

any standard EEG measure. In contrast, the CI differentiated ADHD boys from Controls

not only statistically, but also with almost complete separation of the groups. Furthermore,

one of the two false-positive control participants (#127 in Table I) was classified as ADHDby the teacher but not by the parent ratings. In addition, the CI correlated significantly with

the scores of various ADHD behavioral rating scales.

Our data collection served exclusively to confirm (or reject) that the CI maybe a reliable

and reproducible marker of ADHD. No training procedures were attempted or tested in this

study, therefore, this study cannot add empirical evidence to the issue of additional ADHD

treatment parameters that might be derived from EEG. Besides its highly significant results,

we consider this study to be a pilot research. The relatively small sample size (N= 35)

prevented us from counterbalancing the experimental designthe test always proceeded

with reading first, and then mathematics. However, it should be pointed out that the CI

is symmetric with respect to the performed tasks, i.e., it does not depend on their order

but only on the EEG power change from one task to another. In other words, greaterpower in the low frequency range in the second task, or lower power in the low frequency

range in the second task as presented in Fig. 1, would result in similar consistency indices.

Consequently we should expect that a counterbalanced design would not alter the results.

Another observed phenomena that needs further investigation is the relative failure of the CI

to clearly differentiate girls with and without ADHD (although the effect was statistically

significant). A gender optimization of the CI will be required in a larger future study.

Since the CI is a result of a rather complex mathematical model, its computation is not

straightforward. No standard statistical procedures can be applied and the CI cannot be

calculated without specialized software. The results in this report were obtained by a pilot

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version of a special algorithm and software that needs further refinement before being

available for a general use.

In summary, after analyzing four independent data sets, collected over 2 years by

different technicians with different EEG equipment/ laboratory, we conclude that the CI is a

significant discriminant of ADHD versus control participants. In addition, with the specific

subgroup of younger males, the CI works extremely well on case-by-case basis classifying

accurately almost 100% of these participants. Given that younger males below age of 16

are the predominant ADHD population, we consider the CI to be a promising finding and

a good base for future research.

ACKNOWLEDGMENTS

This research was supported by a grant from the University of Virginias Childrens

Medical Center, a grant from the Thomas F. Jeffress and Kate Miller Jeffress Memorial

Trust, and, in its biomathematical part, by the National Institutes of Health grant RO1 DK-51562. We acknowledge NASA Langley Research Center for providing EEG equipment

and consultation by Dr. Alan Pope under Technology Transfer Space Act Agreement #221.

REFERENCES

Achenbach, T. M., & Edelbrock, C. (1983). Manual for the child behavior checklist. Burlington, VT: Universityof Vermont Department of Psychiatry.

Amen, D. G., & Carmichael, B. D. (1997). High-resolution SPECT imaging in ADHD. Annals of Clinical Psy-chiatry, 9, 8186.

American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders (4th ed.).Washington, DC: American Psychiatric Association.

Barkley, R. A. (1998). Attention deficit hyperactivity disorder: A handbook for diagnosis and treatment (2nd ed.).New York: Guilford Press.

Barkley, R. A., Anastopoulos, A. D., Guevremont, D. C., & Fletcher, K. E. (1991). Adolescents with ADHD:Patterns of behavioral adjustment, academic functioning, and treatment utilization. Journal of the American

Academy of Child and Adolescent Psychiatry, 30, 752761.Barkley, R. A., Fischer, M., Edelbrock, C. S., & Smallish, L. (1990). The adolescent outcome of hyperactive

children diagnosed by research criteria: I. An 8-year prospective follow-up study. Journal of the AmericanAcademy of Child and Adolescent Psychiatry, 29, 546557.

Barkley, R. A., Grodzinsky, G., & DuPaul, G. J. (1992). Frontal lobe functions in attention deficit disorder with andwithout hyperactivity: A review and research report. Journal of Abnormal Child Psychology, 20, 163188.

Berger, H. (1929). Uber das elektrenkephalogramm des menschen. Archiv fur Psychiatrie und Nervenkrankheiten[European Archives of Psychiatry and Neurological Sciences], 87, 527570.

Biederman, J., Lapey, K. A., Milberger, S., Faraone, S. V., Reed, E. D., & Seidman, L. J. (1994). Motor preference,major depression and psychosocial dysfunction among children with attention deficit hyperactivity disorder.

Journal of Psychiatric Research, 28, 171184.Biederman, J., Newcorn, J., & Sprich, S. (1991). Comorbidity of attention deficit hyperactivity disorder with

conduct, depressive, anxiety, and other disorders. American Journal of Psychiatry, 148, 564577.Chabot, R., & Serfontein, G. (1996). Quantitative electroencephalographic profiles of children with attention

deficit disorder. Biological Psychiatry, 40(10), 951963.Cox, D. J., Kovatchev, B. P., Morris, J. B., Phillips, C., Hill, R., & Merkel, L. (1999). Electroencephalographic and

psychometric differences between boys with and without attention-deficit/hyperactivity disorder (ADHD):A pilot study. Applied Psychophysiology and Biofeedback, 23, 179188.

Crawford, H., & Barabasz, M. (1996). Quantitative EEG magnitudes in children with and without attention deficitdisorder during neurological screening and cognitive tasks. Child Study Journal, 26, 7186.

Gittleman, R.,Mannuzza, S.,Shenker, R.,& Bonagura,N. (1985). Hyperactive boys almostgrownup: I. Psychiatricstatus. Archives of General Psychiatry, 42, 937947.

8/3/2019 11305341

14/16


Goyette, C. H., Conners, C. K., & Ulrich, R. F. (1978). Normative data on revised conners parent and teacherrating scales. Journal of Abnormal Child Psychology, 6, 221236.

Grodzinsky, G. M., & Diamond, R. (1992). Frontal lobe functioning in boys with attention-deficit hyperactivitydisorder. Developmental Neuropsychology, 8, 427445.

Lazzaro, I., Gordon, E., Whitmont, S., Plahn, M., Li, W., Clarke, S., Dosen, A., & Meares, R. (1998). QuantifiedEEG activity in adolescent attention deficit hyperactivity disorder. Clinical Electroencephalography, 29(1),

3442.Loboschefski, T. W., Robeva, R. S., Kirkwood, B., Cox, D. J., & Kovatchev, B. P. (1999, April). The transition

coherence model: An EEGmarkerfor ADHD? Proceedings of the Society forResearch in Child Development,Biennial Meeting, Albuquerque, NM.

Lou, H. C., Henriksen, L., & Bruhn, P. (1984). Focal cerebral hypoperfusion in children with dysphasia and/orattention deficit disorder. Archives of Neurology, 41, 825829.

Lubar, J. F., Bianchini, K. I., Calhoun, W. H., Lambert, E. W., Brody, Z. H., & Shabsin, H. S. (1985). Spectralanalysis of EEG differences between children with and without learning disabilities. Journal of Learning

Disabilities, 18, 403408.Mann, C. A., Lubar, J. F., Zimmerman, A. W., Miller, C. A., & Muenchen, R. A. (1991). Quantitative analysis

of EEG in boys with attention-deficit-hyperactivity disorder: Controlled study with clinical implications.Pediatric Neurology, 8, 3036.

Mannuzza, S., Klein, R. G., Bonagura, N., Malloy, P., Giampino, T. L., & Addalli, K. A. (1991). Hyperactive boysalmost grown up. V. Replication of psychiatric status. Archives of General Psychiatry, 48, 7783.

Merkel, R. L., Cox, D. J., Kovatchev, B. P., Morris, J., Seward, R., Hill, R., & Reeve, R. (2000). The EEG CI as ameasure of attention deficit/ hyperactivity disorder and responsiveness to medication: A double blind placebocontrolled pilot study. Applied Psychophysiology and Biofeedback, 25, 133142.

Monastra, V. J., Lubar, J. F., Linden, M., Van Deusen, P., Green, G., Wing, W., Phillips, A., & Fenger, T. N.(1999). Assessing attention deficit hyperactivity disorder via quantitative electroencephalography: An initialvalidation study. Neuropsychology, 13(3), 424433.

NIH Consensus Statement. (1998, November 1618). Diagnosis and treatment of attention deficit hyperactivitydisorder(vol. 16, No. 2).

Pope, A. T., & Bogart, E. H. (1991). Extended attention span training system. Proceedings of Technology 2001Conference, 1, 368374. (NASA CP3136).

Pope, A. T., Comstock, J. R., Bartolome, D. S., Bogart, E. H., & Burdette, D. W. (1994). Biocybernetic systemvalidates index of operator engagement in automated task. In M. Mouloua, & R. Parasuraman (Eds.), Human

performance in automated systems: Current research and trends. (pp. 300306) Hillsdale, NJ: LawrenceErlbaum Associates.

Popper, C. W. (1988). Disorders usually first evident in infancy, childhood, or adolescence. In J. A. Talbot, R. E.Hales,& S. C. Yudofsky (Eds.), Textbook of psychiatry (pp. 649735). Washington, DC: American PsychiatricPress.

Schachar, R.,Tannock, R.,& Logan, G. (1993). Inhibitorycontrol, impulsiveness,and attention deficit hyperactivitydisorder. Clinical Psychology Review, 13, 721739.

Schachar, R., Tannock, R., Marriott, M., & Logan, G. (1995). Deficient inhibitory control in attention deficithyperactivity disorder. Journal of Abnormal Child Psychology, 23, 411437.

Shaywitz, S. E., Fletcher, J. M., & Shaywitz, B. A. (1994). Issues in the definition and classification of attentiondeficit disorder. Topics in Language Disorders, 14, 125.

Shaywitz, B. A., Fletcher, J. M., & Shaywitz, S. E. (1995). Defining and classifying learning disabilities andattention-deficit/hyperactivity disorder. Journal of Child Neurology, 10(Suppl. 1), 50 57.

Tannock, R.,Schachar, R., & Logan, G. (1995). Methylphenidate and cognitive flexibility: Dissociated dose effectsin hyperactive children. Journal of Abnormal Child Psychology, 23, 235266.

Vaidya, C. J., Austin, G., Kirkorian, G., Rudlehuber, H. W., Desmond, J. E., Glover, G. H., & Gabrieli, J. D. E.,(1998). Selective effects of methylphenidate in attention deficit hyperactivity disorder: A functional magneticresonance study. Proceedings of the National Academy of Sciences, USA, 95, 1449414499.

Weiss, G., & Hechtman, L. (1993). Hyperactive children grown up (2nd ed.). New York: Guilford Press.Zametkin, A. J., Nordahl, T. E., Gross, M., King, A. C., Semple, W. E., Rumsey, J., Hamburger, S., & Cohen,

R. M. (1990). Cerebral glucose metabolism in adults with hyperactivity of child onset. New England Journalof Medicine, 323, 13611366.

Zametkin, A. J., & Rapoport, J. L. (1987). Neurobiology of attention deficit disorder with hyperactivity: Wherehave we come in 50 years? Journal of the American Academy of Child and Adolescent Psychiatry, 26,676686.

8/3/2019 11305341

15/16

8/3/2019 11305341

16/16

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