Applied Psychophysiology and Biofeedback, Vol. 23. No. 3.1998

Electroencephalographic and Psychometric Differences Between Boys with and Without Attention-Deficit/ Hyperactivity Disorder (ADHD): A Pilot Study Daniel J. Cox,1,2 Boris P. Kovatchev, James B. Morris, Jr., Cheralee Phillips, Rebecca J. Hill, and Larry Merkel

Attention-Deficit/Hyperactivity Disorder (ADHD) is reported to have an incidence of 3-5%, and is associated with a variety of interpersonal, academic, and social problem behaviors. There is controversy as to whether ADHD is a learned behavioral or brain dysfunction. Research has explored a variety of measures to assess behavioral and brain dysfunctions in this population, with no consistent and clearly diagnostic results. We investigated whether a new psychometric and a new electroencephalographic procedure would clearly differentiate ADHD. The psychometric was based on DSM-IV criteria and the EEG measure was based on the assumption that ADHD interferes with cognitive transition from one discrete task to another. Parents of four ADHD boys (ages 8-12) and four age- and interest-matched non-ADHD boys completed the ADHD Symptom Inventory, while their sons' EEG was monitored during viewing of a video and reading of a book. For the ADHD boys, this was repeated a second time, 3 months later, to assess test-retest reliability. Both the psychometric and the EEG measures clearly differentiated the two samples (p's < .01) with no overlap in scores, were reliable over 3 months (r = .87), and were significantly correlated with one another (r = .85). While a small sample size, these robust, related and reliable findings suggest that both the psychometric and the psychophysiological EEG measures deserve further replication and exploration. KEY WORDS: attention deficit; hyperactivity; ADHD; psychometrics; EEG; electroencephalography; assessment; diagnosis.

INTRODUCTION DSM-IV (APA, 1994) estimates the prevalence of Attention-Deficit/Hyperactivity Disorder (ADHD) in school-age children as 3% to 5%. Of all children referred for mental health services, one-third to one-half have been attributed to ADHD (Popper, 1988). Despite these high reported rates of prevalence, ADHD remains a controversial diagnosis. Critics 1University

of Virginia Health Sciences Center, Charlottesville, Virginia 22908. correspondence should be addressed to Daniel J. Cox, Behavioral Medicine Center, Box 223, University of Virginia Health Sciences Center, Charlottesville, Virginia 22908.

2 All

179 1090-0586/98/0900-O179$15.00/0 © 1998 Plenum Publishing Corporation

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have proposed that ADHD is not a distinct clinical syndrome, but merely an excuse for poor behavior or an attempt by schools to medicate children in order to obtain compliance (Kohn, 1989; McGinnis, 1997, Schrag & Divoky, 1975). On the other hand, researchers are rinding mounting evidence that ADHD is primarily a brain dysfunction, and misbehavior is a symptom of this disorder (Teeter & Semrud-Clikeman, 1995; Zametkin & Rapoport, 1987). At the center of this debate is a lack of an objective diagnostic criteria. Currently, the diagnosis of ADHD is based on a clinical evaluation, assessing whether a person shows six of nine hyperactivity and/or six of nine attention-deficit behaviors, for at least 6 months, to a degree that is maladaptive and inconsistent with developmental level, in two or more settings, leading to clinically significant impairment in social, academic, or occupational functioning, that were evident before age seven (APA, 1994). Regardless of how ADHD is conceptualized, it is clear that the children who are given this diagnostic label exhibit significant behavioral problems. The hallmarks of ADHD are hyperactivity, impulsivity, and an inability to sustain attention. In addition to these core clinical symptoms of ADHD, high levels of comorbidity have been found with learning, oppositional-defiant, conduct, mood, and anxiety disorders (Biederman, Newcorn, & Sprich, 1991). Furthermore, it is estimated that the majority of children diagnosed with ADHD exhibit significant behavioral problems during adolescence (e.g., Barkley, Fischer, Edelbrock, & Smallish, 1990; Mannuzza et al., 1991) and manifest continuing functional deficits (Klein & Mannuzza, 1991) and psychopathology (Pelham, 1982) into adulthood. The etiology of ADHD remains methodologically difficult to study (Barkley, 1990). Most investigators, however, suggest a multifactorial etiology that includes neurobiology as an important factor. As such, brain activity in individuals with diagnosed ADHD has been extensively investigated. Zametikin and Rapoport (1987) identified 11 separate neuroanatomical hypotheses that have been proposed for the etiology of ADHD. Cerebral blood flow studies have demonstrated decreased metabolic activity in the mesial frontal areas (Lou, Henriksen, & Bruhn, 1984) and the right-sided frontal striatal system for children with ADHD (Heilman, Voeller, & Nadeu, 1991). Furthermore, children with ADHD showed increased metabolic activity in cerebral areas believed to be involved in maintaining attention (Heilman, Voeller, & Nadeu, 1991). Researchers using Magnetic Resonance Imaging (MRI) have found that children with ADHD differ in the width of their right frontal lobes when compared to nondiagnosed children (Hynd, Semrud-Clikeman, Lorys, Novey, & Eliopolus, 1990) as well as having smaller corpus callosa (Hynd, Semrud-Clikeman, Lorys, Novey, and Eliopolus, & Lyytinen, 1991). Garcia-Sanchez, Estevez-Gonzalez, Suarez-Romero, & Junque (1997) found decreased performance on visuospatial tasks for children with ADHD during extensive neuropsychological testing, demonstrating functional differences that are consistent with right-hemisphere deficits. Neuroimaging techniques, however, have not yet been demonstrated to be of diagnostic significance, and would not be practical for most clinical situations. EEG data would be more accessible, and some studies have reported EEG differences between children with and without ADHD. These differences, however, have not been consistently documented and remain somewhat controversial (Goldstein & Ingersoll, 1993). As long ago as 1938, Jasper, Solomon, & Bradley, reported EEG abnormalities in children with minimal brain dysfunction (an outdated term used to describe children with hyperactivity and poor attentiveness as well as learning disabilities and conduct disorder). In 1976, Joel Lubar began using EEG biofeedback to attempt behavioral changes in hyperkinetic

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children (Lubar & Shouse, 1976). There have been several recent studies exploring EEG differences of children with ADHD. Mann, Lubar, Zimmerman, Miller, & Muenchen (1992) performed quantitative analysis of EEG on 25 boys with ADHD, ages 9-12, and 27 ageand grade-matched controls. They found increased theta (4-7.75 Hz) and decreased beta 1 (12.75-21 Hz) in the ADHD group. The differences were generalized, but more prominent for theta in frontal regions (e.g., F3 and F4) and for beta in temporal regions (e.g., T3 and T4). When the participants were performing tasks (reading and drawing), the differences between the groups were greater than when they were at rest (visual fixation). Crawford and Barabasz (1996) found that seven children with ADHD had more low alpha activity in their right than left hemispheres at frontal sites (FP1, FP2, F7, F8) when listening to a story with eyes closed and at temporal (T3, T4) and central sites (C3, C3) while doing arithmetic. The seven control children showed no differences between hemispheres. Risser and Bowers (1993) found that ADHD children (n = 10) had significant differences on cognitive measures (e.g., Benton Visual Retention Test Revised) and had elevated levels of polyspike EEG activity compared to control children (n = 10). Kuperman, Johnson, Amdt, Lindgren, and Wolraich (1996) examined boys grouped on the basis of DSM-III-R diagnostic criteria. The three groups consisted of 16 boys with ADD, 12 with undifferentiated attention-deficit disorder (UADD), and 12 controls with no disruptive-behavior disorder. During an eyes-open condition, using combined hemisphere data (18 sites), when compared to controls, the ADHD participants had increased beta band Relative to Percent Power (RPP) while UADD participants had decreased delta band RPP (p < .01) and increased beta band RPP (p < .01). Also, UADD subjects showed interhemispheric asymmetry, with less lefthemisphere delta band (p < .03) and more left-hemisphere beta band (p < .05) RPP. One study explored the capacity of EEG data to predict Ritalin response (Bawden, 1993), but included only 3 case studies. In summary, the literature addressing the capacity of EEG data being used to differentiate children with ADHD from children without ADHD, includes some studies that demonstrate differences. Typically, these studies have contrasted activity of various bands (e.g., theta and beta) while the child is performing a specified task. Unfortunately, however, the differences reported are not consistent from one study to another. Identification of a specific brain dysfunction could not only help resolve the controversy of whether ADHD is primarily a behavioral or a physiological problem, but could be a significant aid in the objective diagnosis of the condition, possible quantification of responsiveness to treatment if the physiological processes reverse following treatment, could assess whether a child is "growing out of" this condition, and could also serve as a foundation for a self-regulatory biofeedback intervention. Existing studies have evaluated brain function while subjects performed a fixed task, such as listening to a story with eyes closed or solving math problems (e.g., Crawford & Barabasz, 1996). Clinical and research observations, however, suggest that many individuals with ADHD have a primary problem transitioning from task to task (Schachar, Tannock, & Logan, 1993; Schachar, Tannock, Marriott, & Logan, 1995). For this reason, we hypothesized that relative to control children without ADHD, children with ADHD would show less consistency in their EEG data when transitioning from one task to another task, and this would be reliable over 3 months. Further, we hypothesized that simply having parents rate the extent to which their child displayed the 18 DSM-IV criteria would serve as a useful screening tool to differentiate ADHD.

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METHODS Subjects Four boys diagnosed with ADHD, who were currently prescribed and regularly taking Ritalin, and four age-matched boys known not to have a disruptive-behavior disorder and who had never taken Ritalin, were recruited from the same Cub Scout Pack. Ages for the matched pairs of ADHD/Control participants were 8/8, 9/9, 9/9, 12/12. The ADHD boys were taking, on average, 20 mg of Ritalin per day. Two of the boys with ADHD were Caucasians, one African American, and one Indian. The control children were all Caucasian. Procedures All testing occurred on weekends. None of the boys with ADHD regularly took Ritalin on weekends, and did not take a morning dose of Ritalin. The examiner was blind to the diagnostic category of each boy. The first two and last two boys tested were controls, and the middle four boys tested were ADHD participants. Parents signed an informed consent and completed a questionnaire consisting of: (1) the

10 items of the Hyperactivity Index subscale and the 4 items of the Impulsivity-Hyperactivity Scale of the Conners' Parent Rating Scale (Goyette, Conners, & Ulrich, 1978), (2) the 11 items of the Attention-Problems subscale from Achenbach's Child Behavior Checklist (Achenbach & Edlebrook, 1983) and (3) the ADHD Symptom Inventory (ADHD-SI), which is an 18-item scale developed from DSM-IV and is shown in Table I. Mean scores for these scales appear in Table II. Boys were seated in a recliner, and an appropriately sized EEG cap (Electrode Cap International, Inc.) was placed over their heads. Six electrode sites were prepared: a ground just in front of Cz, a right earlobe reference electrode, and CHz, PZ, P3, P4- Impedance criteria was 10 K Ohms, as measured by a Prep-Check electrode impedance meter. These EEG sites and impedance criteria were recommended by NASA research investigating attention of pilots (Pope & Bogart, 1993; Pope, Bogart, & Bartolome, 1995). EEG signals were amplified and processed by the Biopac system and on-line data analysis was accomplished using CREW (Crew Response Evaluation Window-NASA) system. A video camera filmed head and eye movement during testing. EEG was digitized from the four input channels at a rate of 200 samples per second into a circular buffer. Data were taken from the buffer in four data arrays, 512 data points each, i.e., at this sample rate 2.56 sec of data were analyzed at a time for each input channel. Standard time series techniques were used for this initial data retrieval: each array was smoothed using a Tukey-Hanning window and the power spectrum was estimated using a Fast Fourier transformation (Box & Jenkins, 1976). Then the total power was computed for each of four EEG bands: Theta 4-8 Hz; Alpha 8-13 Hz; Beta 13-22 Hz and High Beta + EMG 22-40 Hz. The residual power was carried by the frequencies 1.6-4 Hz and 40-55 Hz. The band powers were normalized to produce percent power for each band and percent residual power. This procedure resulted in 16 EEG parameters—the percentage power in four bands for four electrode sites, computed on 2.56-sec data chunks. The 16 EEG parameters were recorded on line while the boys watched a self-selected Disney video for 30 min and then while reading a book of their choice for 30 min. This

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Table I. Items and Instructions from the ADHD-Symptom Inventory Instructions: For each item below, please circle the number that best describes your child over the last six months (If they are taking medications, describe their behavior when they are not taking medications). 0 = Not True: 1 = Sometimes True, or True, but for less than 6 Months; 2 = Often True 1 . Fails to give close attention to details or makes careless mistakes in schoolwork, work, or other activities. 2. Has difficulty sustaining attention in tasks or play activities. 3. Does not seem to listen when spoken to directly. 4. Does not follow-through on instructions and fails to finish schoolwork or chores. 5. Has difficulty organizing tasks and activities. 6. Avoids, dislikes, or is reluctant to engage in tasks that require sustained mental effort (such as schoolwork or homework). 7. Loses things necessary for tasks or activities (e.g., toys, school assignments, pencils, books, or tools.) 8. Is easily distracted by extraneous stimuli. 9. Is forgetful in daily activities. 10. Fidgets with hands or feet or squirms in seat. 11 . Leaves seat in classroom or in other situations in which remaining seated is expected. 12. Runs about or climbs excessively in situations in which it is inappropriate. 13. Has difficulty playing or engaging in leisure activities quietly. 14. Is "on the go" or acts as if "driven by a motor." 15. Talks excessively. 16. Blurts out answers before questions have been completed. 17. Has difficulty awaiting turn. 18. Interrupts or intrudes on others (e.g., butts into conversations or games).

Table II. Results of the ADHD Scales, Where the Mean Equals SO and the Standard Deviation Equals 10 Imp-Hyp a

43 51 43 35 x43 51 51 72 39 x53 a

Hyp-Index

44 55 37 36 43 57 62 80 58 61.5

Alt-Prob

ADHD-SI

NonADHD subjects 50 2(2/0) 50 2(2/0) 50 0(0/0) 50 0(0/0)

50

1

ADHD subjects 54 8(2/6) 54 22(13/9) 65 23(11/12) 57 10(10/0)

57.5

15.8

ADHD-SI retest 0(0/0) 2(2/0) 0(0/0) 1(1/0) 0.75 10(9/8) 22(13/9) 24(10/14) 14(13/1) 17.5

Imp-Hyp. = T -scores from Impulsive-Hyperactive subscale of Conners'; HypIndex = T -scores from Hyperactivity Index of Conners'; Att-Prob = T-scores from Attention Problems subscale of Achenbach's Child Behavior Checklist; ADHD-SI = Total raw scores from the ADHD Symptom Inventory (raw score from the attention/hyperactive and impulsive items are in brackets).

0

1

0 0 0

1 2 1 2 1 2

0 0

1 1

2 2

0

1

2

0 0 0 0

1 1 1 1

2 2 2 2

0 0 0 0

1 1 1 1 1 1 1

2 2 2 2 2 2 2

0 0 0

2

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yielded about 1,400 readings on 16 parameters for each boy. The elapsed time between these two tasks was approximately 4 min, as the EEG system was reset. Following the 60-min data collection, the EEG cap was removed, the boys were given a $25 gift certificate, and the session concluded. The four ADHD boys repeated this data sampling 3 months later, again on Saturday morning while off Ritalin, to assess test-retest reliability. Given that our initial data indicated that similar results were found whether we analyzed the first 5, 10, or the entire 30 min of data, the repeat testing employed only 5 min of EEG monitoring while watching the video and while reading. Computation of a Consistency Index The Consistency Index (CI) is a composite measure of the homogeneity of the EEG data shift when a participant transitions from performance during Task A to performance during Task B. The CI was computed for each boy on the basis of his approximately 1,400 readings of 16 EEG parameters (4 EEG bands x 4 Channels) across the two tasks. The computation is a three-step procedure that works as follows: At Step 1, the mean power for each of the 16 EEG parameter was calculated for each task. The standardized distances between the 16 pairs of means were calculated by subtracting Task A-Task B means and dividing by the subject's pooled standard deviation. This is similar to a computation of the Student t-score. Step 2, the 16 distances for each boy were converted into categories, labeled by —1, 0 or +1. A distance below minus 1.65 was labeled as —1, a distance in the range —1.65, 1.65 was labeled as 0 and a distance above 1.65 was labeled as +1. This is approximately equivalent to making t-tests using a two-tail probability of 0.1 because, with about 1,400 readings involved, the inverse Student distribution function of a probability of 0.1 is approximately equal to — 1.65. However, no conclusions based on t-tests or any other parametric techniques are applied to the data. Step 3, the absolute value of the sum of each boy's 16 categories is a number between 0 and 16 that was named Consistency Index. Thus the higher the CI, the more of the 16 EEG parameters made consistent EEG power shifts. The closer the CI is to 0, the more inconsistent the EEG was during the two tasks. RESULTS Psychometrics As seen in Table III, correlations among the four scales were high. The ADHD-SI correlated .72, .88, and .82 with the Hyperactivity Index, Attention Problems scale, and Impulsivity-Hyperactivity scale, respectively. Additionally, the ADHD-SI discriminated between diagnostic status the most strongly (t = 3.7, p <0.01) among the four psychometrics, with no overlap in scores (see Table II). This suggests that while related to the traditional scales, the ADHD-SI may be a better screening device for children with ADHD.

EEG As a liberal test of any differences between the two groups for the four band passes, separate t-tests were performed for percent theta, alpha, beta, and high beta. All comparisons

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EEG/Psychometric Differences with ADHD Table III. Correlations of Diagnostic Status and ADHD Symptom Scales

Diagnosis

ADHD-SIa

Hyp-Index

Att-Prob

Imp-Hyp

.835 p = .010

.782 p = .022 .881 p = .004

.763 p = .028 .817 p = .013

.482 p = .226 .719 p = .044

.885 p = .003

.860 p = .006

ADHD-SI Hyp-Index Att-Prob a

.759 p = .029

ADHD-SI = ADHD Symptom Inventory, derived from DSM IV criteria; Hyp-Index = Hyperactivity Index of Conners'; Att-Prob = Attention Problems subscale of Achenbach's Child Behavior Checklist; Imp-Hyp. = Impulsive-Hyperactive subscale of Conners'.

were nonsignificant (p's > 0.2). Thus, the two groups did not differ in terms of average percentage power on any of the four EEG bands while performing a single task, as well as across tasks. Also, there was no difference between the four electrode sites in terms of total (theta + alpha + beta + high beta) power. Subsequently, we computed the CI for each boy (Table IV—Column CI). The mean/ standard deviation CI for ADHD boys was 4.25/2.1, (range 2-7) and 13.75/2.1 (range 12-16) for the control boys (2 = 2.3, p < .02). The CI was unrelated to age (r =0.02, p = .5), but highly related to the ADHD-SI scores (r = +.85, p < .001). Reliability As seen in Table IV, the ADHD-SI was reliable over three months for the eight boys (r = .87, p < .005) and the CI was highly stable for the ADHD boys with the mean CI being 4.2 and 4.0 for the two testings, for the two no-Ritalin conditions separated by 3 months.

Table IV. Consistency Index, Age, and ADHD-SI Score for Each Subject Consistency Index,

ID

Age

101 102 107 108

8 9 12 9

103 104

12 8 9 9

Test 1

Test 2

ADHD-SI Test 1

Subjects with ADHD

16

2

12

0 0 2

12 15 Subjects with ADHD

a

105

106* a

Black subject. Indian subject.

b

7 4 2 4

6 4 1 5

10 8 23 22

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Cox, Kovatchev, Morris, Phillips, Hill, and Merkel Table V. Movement of Children During First 5 min of Each Task Viewing Video

Reading

S# 101 102 107 108 103 104 105 106

Actual Group

Assigned Group

Body Mvmnt

Control Control Control Control

Control Control ADHD ADHD ADHD Control ADHD Control

1 2 3 3 3 3 1 1

ADHD ADHD ADHD ADHD

Head-Eye Mvmnt

Pulling Cap

1

0 0 0 0 0 0 0 0

4

4 2 2 4 4 1

Body Mvmnt

Head Mvmnt

Pulling Cap

0

0 3 5 5 3 3 1 4

0 0 0 0 6 0 0 0

4

4 5 1 3 5 2

Movement Artifact One concern about the study was possible movement artifact. In spite of an automatic artifact rejection procedure based upon parameters computed from eyes-open and eyesclosed baselines, we were concerned that some of our findings could be related to greater movement in the children with ADHD. Eye movement during reading was not considered a problem, since it was similar for both groups of boys. Since all children were videotaped, eye, head and, body movements, and pulling at the cap were observed in 30-sec intervals and rated on a 0 to 2 scale by a rater blind to the child's diagnostic category. These ratings were summed for each child, during each task, for each movement rated. The rater also attempted to determine which children had ADHD based upon the video presentation. The results of these tabulations for the eight children, along with their actual and estimated diagnostic status are shown in Table V. Because there was no variance (all children were rated 0) for pulling at the cap during reading, pulling at the cap was excluded from a MANOVA comparing the two groups. No differences in the two groups were found (F = 0.061, p = .993). A univariate ANOVA for pulling the cap during video viewing also showed no group differences (F = 1.000, p = .356). Additionally, none of these parameters correlated with CI. Children were not able to be correctly assigned to a diagnostic group based on observing them during the two tasks. Two children were correctly and two children were incorrectly assigned to each group (p = .50). DISCUSSION This sample size was small and homogeneous in terms of age, gender and interest, but not race, making generalization speculative. However, the nonoverlapping differences between ADHD and non-ADHD boys in terms of both the ADHD-SI and the CI, the high degree of agreement between the two different (behavioral and physiological) sets of data, and the stability of the findings over 3 months, makes this pilot data both interesting and deserving of further investigation. This is despite the fact that there were no behavioral differences between the two groups while on task, in part due to the structure, lack of distractions, adult supervision and the electrode cap and cables discouraging excessive movement. While the ADHD-SI is an impressive common sense approach to screening ADHD children, it may not be as sensitive in different situations, such as with girls, younger or older children, among different socioeconomic populations, etc. Separate follow up

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studies of 18- to 25-year-old males (Merkel, Cox, Kovatchev, Morris, Seward, Hill, & Reeve, 1999) and 6- to 12-year-old boys and girls (Hill, 1998) found the ADHD-SI similarly discriminating. However, all of these studies involved patients who were pre-selected for ADHD, which may have biased parental symptom reporting. The ADHD-SI requires further verification and replication to determine its sensitivity and specificity, both as a screening and diagnostic device. While it is assumed that the EEG data is probably independent of socioeconomic status, it may be influenced by race, gender and age. Merkel et al. (1999) controlled for race, investigated older males on different tasks (continual visual and auditory attention tasks), with a 2 (ADHD vs nonADHD) x 2 (Ritalin versus Placebo) double blind, randomized, cross-over design. In this replication of the current study, the CI under placebo conditions differentiated ADHD from control males, and this difference disappeared when participants were under the influence of Ritalin, i.e., Ritalin increased the CI to the normal range. The ADHD participants in the present study were tested a third time on Ritalin. The CI was doubled in two, showed no change in one boy who was sleep deprived, and equipment failure prevented analysis of the forth boy. The current data did not support the view that ADHD children have relatively less beta and more theta. However, the present approach differs from traditional approaches in several ways: evaluating relative change in EEG within participants when transitioning across tasks, EEG sites, data reduction, and data analysis. Consequently, this approach is still very preliminary, and requires further replication and investigation across different research centers, sexes, sub-types of ADHD and the influence of different co-morbidities. While both the CI and the ADHD-SI could serve as diagnostic tools, the simple and quick ADHD-SI may serve as a useful screening tool, while the CI may be useful in exploring the underlying physiological mechanisms of ADHD and its varied treatments. ACKNOWLEDGMENTS This research report was supported by NIH grant RO1 HD 28160. We would like to acknowledge NASA Langley Research Center for providing the CREW technology and consultation by Dr. Alan Pope under Technology Transfer Space Act Agreement #221.

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