Quantitative Electroencephalographic Analysis Of Children And Adolescents With Attention Deficit / Hyperactivity Disorder

Quantitative Electroencephalographic Analysis of Children and Adolescents with Attention Deficit / Hyperactivity Disorder Compared with Non-Clinical Controls: Diagnostic Implications

A Thesis Presented for the Master of Arts Degree at The University of Tennessee, Knoxville

Jared Blackburn December 2000

Acknowledgments Special thanks to: Drs. Joel Lubar, Deborah Baldwin, and Stephen Handel for serving on my advisement and thesis committees. Jon Fredrick and Phillip Vanlandingham for assisting in data analysis Joel Lubar, Sheri Brim, and others for supplying archival data Efthymios Angelakis, Tina Stathopoulou, Kerry Towler, Dorit Ben Shalom, my family, and the people and Autism Network International and #AutFriends, for their morale and emotional support during the whole process.

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Abstract The use of quantitative electroencephalography, or qEEG, in the diagnosis of psychiatric and neurological conditions is a growing field with important potential for the future. The recognition of Attention Deficit (Hyperactivity) Disorder, or ADD/HD, as a common, biologically based disability is also increasing, with such a diagnosis becoming increasingly common. The use of qEEG as to aid in the identification of ADD/HD in individuals would therefore be of great practical utility. It has already been demonstrated that qEEG can be used effectively to differentiate those with ADD/HD from non-clinical controls. However, most previous studies have used only single electrode recordings in there analysis. Thus further investigation is warranted to determine of the potential for increasing the utility of qEEG based assessment through the use of multielectrode recordings. To investigate the potential usefulness of multielectrode qEEG in identifying ADD/HD, archival data on 125 children and adolescents with a diagnosis of ADD/HD were compared to a widely accepted normative database to determine if specific differences from the database typified the group. All EEG recordings had been taken between the years using the 19 electrode international 10-20 montage with the Lexicor Neurosearch 24. Phase, coherence, asymmetry, and relative power were computed using Hudspeth’s Neurorep program and compared to Thatcher’s Lifespan Normative Database. This produced brain maps for each of the participants, which were visually

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sorted based on patterns of relative power distribution. Additionally, mathematically averaged maps were produced for each of five age-group categories. No consistent patterns were observed in the phase or coherence maps, and averaging these resulted in normal-looking maps as the individual differences “washed out.” Lateral asymmetry abnormalities were found to persist in the averaged brain maps, and to increase with age, particularly in the delta band. Many participants (about one third) were observed to have above average alpha over most of the head, and this was apparent in the averaged maps, but was not sufficiently high to classify as abnormal. However, power ratios of specific band passes were more robust, and it was found that α/β was very often high, especially in the younger ages, and that θ/β has a similar tendency to be high for children age ten or below. Also, α/β was most notably higher than the norm in the parietal regions, while θ/β was typically highest along the midline, especially at Cz and Fz. It was concluded that θ/β and α/β are the most useful qEEG measures for assessing ADD/HD. Further α/β was found to work primarily for in the parietal regions and for those age fifteen and younger, while θ/β was best at Cz and for children age ten and younger.

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Table of Contents CHAPTER

PAGE

I.

A Review of Current Literature

1

II.

Participants

17

III.

Methods

19

IV.

Results

24

V.

Discussion

36

References

44

Appendix A. Figures

58

Vita

64

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Symbols and Abbreviations Used ADD

Attention Deficit Disorder

ADHD

Attention Deficit Hyperactivity Disorder (Attention Deficit Disorder, hyperactive type or combined type in DSM-IV terminology)

AD/HD

Attention Deficit / Hyperactivity Disorder, either ADD, ADHD, or both I nclusively

Hz

Hertz, on cycle per second (1/s)

EEG

Electroencephalography

qEEG

Quantitative

Electroencephalography,

EEG

analyzed

mathematically by computer α, alpha

EEG between 7 and 13 Hz; usually high amplitude, sinusoidal, and between 8-12 Hz (alpha is often defined for 8-12 Hz only but here is slightly larger)

β, beta

EEG between

13 and 22 Hz; usually small amplitude and

asynchronous; technically, beta may be above 22 Hz, but that is the limit of the soft ware used in this analysis. θ, alpha

EEG between 3.5 and 7 Hz

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∆, δ, alpha

EEG between 0.5 and 3.5 Hz

Phase

The distance between to analogous peaks in a pear of wave forms

Coherence

The degree to which two wave forms maintain a constant phase relationship

Asymmetry

The degree of similarity between the amplitudes of two waveforms

Relative Power

The percentage of total power found within a certain band pass

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A Review of Current Literature Attention Deficit Disorder (ADD) and Attention Deficit Hyperactivity Disorder (ADHD), collectively referred to as ADD/HD, are disorders of attention which become manifest early in life and persist throughout the life span (DSM-IV, 1994). The defining features of ADD/HD include inattention, impulsiveness, and hyperactivity, with ADD presenting primarily with features of inattention, and ADHD showing primarily hyperactivity and impulsively. A combined type, which includes clinically significant levels of both hyperactive/impulsive and inattentive symptoms is also recognized, being given the same diagnostic code as the hyperactive-impulsive type, and will also be referred to as ADHD. With an incidence estimated at 3%-7%, ADD/HD has become a widely recognized syndrome in both clinical settings and the popular media, and is currently among the most commonly diagnosed disorders among children (Ross and Ross 1982). Attentional problems have been reported to apply to both internal and external stimuli, and difficulty with self-control and with the organization and completion of tasks have been reported as well (Shaffer 1994). ADD/HD has a strong a genetic basis. It has been found that first- and seconddegree relatives of ADD/HD patients are more likely than the general population to have the disorder themselves (Biederman, Faraone, Keenan, Knee, Tsuang, 1990; Faraone, Biederman, Milberger, 1993, 1994). A concordance rate of 51% for identical twins and 33% for same-sex fraternal twins has also been reported, though this was for a rather

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broadly defined concept of "hyperactivity" (Goodman and Stevenson, 1989). (Many other disorders have been found to be unusually common in those with ADD/HD. Disorders often reported as commonly comorbid with ADD/HD include mood disorders, anxiety, obsessive-compulsive disorder, substance abuse, oppositional-defiant disorder, antisocial personality and conduct disorder, and learning disabilities (Biederman et al., 1992; Shaywitz and Shaywitz 1987). Other studies, however, suggest that genetic susceptibility to antisocial behavior and alcoholism are transmitted separately from ADHD per se, though hyperactivity and impulsiveness are common in antisocial and conduct disordered populations (Stewart, de Blois, and Cummings 1980). LaHoste et al. (1996) reported having found polymorphisms specifically related to dopamine receptor D4 to be associated with ADD/HD, greatly strengthening arguments for a genetic basis for the syndrome. The degree to which genetic, biodevelopmental, and psychological factors play in producing the final syndrome of ADD/HD is complex and still debated, but the primacy of neurological dysfunction in the overall equation is widely agreed upon (Slomka 1998). A comprehensive, historically-oriented review of the prevalence of ADD/HD and related social factors in Sweden has suggested the possibility that inflexibility in the school system is positively correlated with rates of diagnosis (Rydelius 1999). This could be taken either to indicate many diagnoses are simply the result of children being placed in school before ready, as evidence that more flexibility with respect to developmental (especially cognitive and emotional) preparedness reduces diagnosability and need for

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intervention in genuine cases of ADD/HD, or a combination of those two possibilities. Purely anatomic studies of ADD/HD have yielded some interesting, though sometimes mixed, results. One recent study (Berquin et al. 1998) found that the vermis was significantly smaller in a sample of 46 boys with ADHD, as compared to 47 normal controls. Most of the size difference in this study was found in the lobules VIII, IX and X. This finding is interesting in its comparison with similar findings in autism, where vermal lobules VI and VII have often been found to be smaller (Murakami, Courchense, Press, et al. 1989; Courchense, Yeung-Courchense, Elmasian, and Grillon. 1988). The cerebellar findings in these two distinct clinical syndromes are interesting both for their similarities and their differences. Both autism and ADD/HD may involve cerebellar anatomy or physiology in at least some cases the vermis is the primary site of abnormality, and attention, particularly for attention shifting. However, the location of abnormalities differs, with the eighth through tenth lobules implicated in ADD/HD, while the sixth and seventh have been implicated in autism. The exact behavioral effects in clinical manifestation can only be guessed at; however, it would appear that abnormalities in lobules VI and VII may result in inhibited attention shifting, while abnormalities in lobules VIII through X may result in disinherited attention shifting. The nearness of these sites, the high prevalence of inattentiveness and hyperactivity as associated features in autism (DSM-IV), and some other areas of similarities and overlap (described and sited elsewhere) open questions as to the exact biodevelopmental relationship between these two syndromes. Castellanos et al. (1996) and Mostofsky et al.

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(1998), similarly reported decreased size in the cerebellum, particularly the posterior half. It has been found that children with ADD/HD have smaller than average brain sizes (Castellanos et al., 1994; Castellanos et al., 1996), generally by about 5%. Additionally, anterior corpus callosum and basal ganglia have often been cited as an area of underdevelopment, and an atypical pattern of left-right morphological asymmetry is often noted in such studies as well. Baumgardner et al. (1996) noted that the genual portion was significantly smaller in children with attention deficit disorder, while Giedd et al., note that the genual portion, and anterior corpus callosum in general were smaller in their ADD/HD participants. Hynd et al. (1993) found that 72.7% of normal children had larger left corpus callosum, as compared with the right, while those with ADD/HD tended to have a reverse pattern, with 63.6 percent show are large right corpus callosum than left. Castellanos et al. (1994) and Castellanos et al. (1996) had similar findings with respect to the caudate nucleus, finding that the right caudate was significantly larger in normal children, but not for those with ADD/HD, and that the right caudate of the ADD/HD children was significantly smaller than the normals. In the 1996 publication, Castellanos et al. also reported a smaller than in normal right globus pallidus and a smaller cerebellum. Semrud-Clikeman et al. (1994), reported that the posterior corpus callosum, especially the splenium, were smaller in a sample of 15 right-handed boys with ADHD as opposed to 15 normal, male, right-handed controls. This study did not, however, find significant differences between overall size or shape of the corpus callosum. Another Page 4

study (Lyoo, Noam, Lee, Lee, 1996) also found that significant difference in the corpus callosum between 51 participants with ADHD and 28 controls; no effects on the callosal and measures were found to be related to sex, dyslexia, or conduct disorder. This study also checked for the differences in the lateral ventricles, but none were found. Hynd et al. (1991) also found that areas of the posterior corpus callosum were significantly smaller in ADD/HD children than in controls, especially in the in the area of the splenium and genu. It was also specifically noted that, while statistically differing from normal controls, none of the ADD/HD children in their sample were rated as clinically abnormal. Exactly how to harmonize finding of a smaller corpus callosum specifically in posterior regions with the more common finding of smaller size applying specifically to the anterior regions is not obvious, though it may be that researchers' scrutiny was focused on regions of interest at the time of their research, so as to miss abnormalities in other areas. At least one other study (Filipek et al., 1997) reported smaller size of the posterior corpus callosum. Even more difficult to make sense of, are results that conflict with others. Unfortunately, there are several such findings in anatomic studies of attention deficit disorders. One such study (Aylward et al., 1996) found that the total area of the globus pallidus was significantly smaller in the clinical sample than in controls, and also that left (but not the right, as above) was smaller in both ADD/HD participants and those with Tourette's syndrome. It was also found that these findings did not differentiate between those with ADD/HD alone and those with ADD/HD and Tourette's syndrome, though it differentiated both groups from normal children. Filipek et al. likewise noted that the left

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caudate head was significantly smaller in ADD/HD participants than in controls (again, contrasting with the smaller right caudate finding reviewed above), but did report smaller total size for the right frontal region and more significantly reduced right-frontal white mater. A bilaterally smaller inferior-anterior region of the caudate was also reported. Macrocephaly, often defined as a head circumference above the 95th percentile has recently been reported in ADHD (Ghaziuddin, Zaccagnini, Tsai, Elardo, 1999). This study found that autistic individuals with macrocephaly displayed hyperactivity and impulsively while those without hyperactivity did not show macrocephaly. Further, it was found that individuals with ADHD (but not with purely inattentive ADD) also displayed macrocephaly. Thus, macrocephaly, rather than be associated with autism, as was once believed (Bailey, Phillips, Rutter 1996), appears to be associated with hyperactivity and impulsively, including that found in ADHD. This finding once again raises questions about the biodevelopmental relationship between attention-deficit disorder and the autism spectrum. However, it should be noted that in autism, a larger than typical brain size is also reported along with external macrocephaly, whereas a lesser cerebral volume has been reported with regard to ADD/HD. A common area of interest in ADD/HD research has been the frontal lobes, due to their association with executive functions, particularly executive control of attention and motor inhibition. An interesting comparison has been made between ADD/HD and Rett's Syndrome (Niedermeyer and Naidu, 1997), a genetically based disease that only afflicts girls and causes progressive CNS degeneration, presumably due to metabolic Page 6

abnormalities (van Acker, 1997). Niedermeyer and Naidu take special note of the fact that girls with Rett's syndrome develop progressive restlessness and motor inhibition, and also show a develop severe damage of the frontal lobes. They thus suggest that frontal degeneration is the cause of motor inhibition in Rett's syndrome, and that a similar explanation can be applied to ADD/HD, though in the later case it is not anatomic damage but a benign metabolic underactivity that causes the lack of inhibition. While it is the author's opinion that this analogy hold merit, it is also worth noting the concluding frontal damage is the cause of disinhibition in those with Rett's syndrome is more an assumption than a demonstrated fact, as Rett's syndrome typically involves widespread degeneration of the brain and spinal cord. In addition to the uses of brain anatomic imaging, metabolic studies, and electrophysiological studies, neuropsychological testing has been used in the attempt to uncover the biological basis of attention deficit disorders. A study by Aman et al. (1997), found that boys with a diagnosis of ADHD did significantly worse than controls on both frontal lobe and parietal lobe tests when not on medication, though the frontal lobe tests showed the most impairment. When medicated, however, these boys performed as well as controls. One of the goals of the study was to determine whether frontal or parietal lobe theories better explained ADD/HD; as both frontal and parietal tests showed impairments, this goal neither should be seen as definitively implicated, though, as noted, frontal tests showed more impairments, suggesting that a frontal dysfunction hypothesis may have more merit than the a parietal hypothesis. Mulligan et al. (1996) reported that a

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sample of 309 children with ADHD to be impaired in vestibular processing and motor planning, but to have relatively good visual perception and tactile localization. It is notable that motor praxis is also a frontal function. Comparing ADD/HD adults to schizophrenics (Oie and Rund, 1999) yielded interesting, though not surprising, results. It was reported that the group with a diagnosis of ADHD had relatively more specific deficits primarily involving frontal lobe function. Specifically, the ADHD groups showed impairment primarily in the areas of learning, verbal memory, and sustained attention. By comparison, the schizophrenics seemed to have a much more general and pervasive pattern of impairment, and to show greater deficits in visual rather than verbal memory. Neuropsychological research on ADD/HD has also looked at hemispheric dominance and asymmetry, particularly focusing on the possibility of right hemisphere dysfunction. In 1995 Carter, Krener, Chaderjian, Northcutt, and Wolfe reported a deficit in attentional orienting toward left field cues relative to right field response, thus suggesting a right hemisphere deficit. Epstein, Conners, Erhardt, March, and Swanson (1997) also reported that research participants with ADHD had an unusually long delay when switching attention to the left visual field after being subject to a right field distracter. These results in combination with anatomic and metabolic findings have been the basis of a theory of a right hemispheric control of attention that is dysfunctional in attention deficit disorders.

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Positron Emission Tomography (PET), which uses radioactive tracers to measure the distribution of a substance (often glucose) in the body, thus allowing inference about the metabolic uptake of the substance, has been used to measure brain metabolism in ADD/HD. Research using PET has found significantly lower glucose metabolism specifically in the left anterior frontal lobe; the glucose metabolism in these areas was also strongly, inversely correlated to measures of the behavioral symptoms (Zamektin et al., 1993). At least one study has reported that glucose metabolism during continuous performance testing decreases with age in ADHD women, but not in men nor in normal women (Ernst, Zametkin, Phillips, Cohen 1998). Further it was shown that cerebral glucose metabolism to decrease with age in women diagnosed with ADHD, but not in normal women nor in men (with or without ADD/HD), and that in these women, performance on attentional tasks was significantly effected by age. Only adults were tested in this study, however. An earlier study of similar nature found no significant differences in adolescents with or without ADHD when looking at males or mixed sex groups, but found that adolescent ADHD girls had 15% lower perfusion than control girls and 19.6% low perfusion than ADHD boys (Ernst et al., 1994). Ernst, Cohen, Liebenauer, Jons, and Zametkin also reported (1997) that girls with ADHD not only had significantly lower cerebral glucose metabolism than normal girls, but that parietal and subcortical regions were particularly effected, and that glucose metabolism was lower in the left hemisphere for these girls, as opposed to in the right hemisphere for controls. How to these findings of left hypoperfusion relate to other findings implicating the right

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hemisphere dysfunction is not obvious, nor is why women would differ in this way. Single-Photon Emission Computed Tomography (SPECT) has also been used in investigating brain metabolism in ADD/HD, and it has been reported (O'Tuama, 1993) that SPECT studies using technetium 99m hexamethylpropylene amine oxime and xenon133 as a tracer revealed hypoperfusion of the periventricular and striatal region. Hyperperfusion of the motor and sensory cortical areas was also reported. Amen and Carmichael (1997) found, using SPECT, that 65% of a group of a sample of 54 children and adolescents diagnosed with ADHD according to DSM-III-R standards to have decreased blood perfusion in the frontal lobes during a cognitive task, as compared to a resting baseline, while only 5% of controls had a similar drop. It was noted that this is consistent with PET and qEEG finding, in that these often show a decreased frontal activation in ADDs during cognitive load. Using SPECT, a greater blood perfusion asymmetry between ADHD and mixed psychiatric controls has also been reported, with the ADHD group showing lower perfusion in the left frontal and left parietal lobes than in the right hand homologues (Sieg, Gaffney, Preston, Hellings, 1995). It has also been reported (Lou, Henriksen, Bruhn, Borner, Nielsen, 1989) that comparison of six children with "pure" ADHD with a group of 13 who had both ADHD and other neurological symptoms revealed no differences between the two groups, but found that both had hypoperfusion in the area of the right striatum. An animal (rat) model of attention deficit disorder has been suggested which postulates a dysfunction of both serotoninergic and dopaminergic neurons (Kostrzewa, Page 10

Brus, Kalbfleisch, Perry, Fuller, 1994). The dopamine element had been tested by Ernst, Zametkin, Matochik, Jons, and Cohen (1998), using PET to measure DOPA decaboxylase activity, who reported evidence strongly suggesting that dopamine metabolism in the prefrontal cortex is less in ADHD adults than in normal controls. Additionally, they found that males with ADHD had lower dopamine than females with ADHD, while normal males had higher dopamine than normal females. This was most striking in the medial and left parts of the prefrontal area. Though they found no significant difference in either of the subcortical areas measured, specifically the striatum and midbrain, Ernst et al. postulated that the cortical dopamine deficiency was the result of subcortical abnormality. Further animal research has suggested the possibility that dopamine may not be stored effectively in the brains of those with ADD/HD (Russell, de Villiers, Sagvolden, Lamm, Taljaard, 1998). Research into the possible role of other monoamines has revealed that blood levels of 3-methoxy-4-hydroxyphenylglycol (MHPG), a metabolite of norepinephrine, was lower in children with ADHD, suggesting the cause of ADD/HD may be related to norepinephrine (Halperin et al., 1997). This does not conflict with a dopaminergic hypothesis as norepinephrine is synthesized from dopamine (Anderson and Cohen, 1991), and a dysfunction in the dopamine path way would effect norepinephrine, though relating this to vesicular storage proposed by Russell et al. is less obvious. A study looking at the serotonin level in blood platelets, found no difference between ADHD participants and normal controls (Pornnoppadol, Friesen, Haussler, Glaser, Todd, 1999). The possibility

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that some cases of ADD/HD may be caused by an allergic reaction leading to betaadrenergic and cholinergeric imbalance has also been suggested (Marshall 1989), though this was not intended as an explanation for the disorder in general and it was specifically stated that such cases would most likely account for only a small minority of referals. The most common treatment for primary symptoms of ADD/HD is the use of psychostimulant drugs, especially methylphenidate (Weiss, 1994; Hallowell and Ratey, 1994). It has been found that psychostimulants decrease the amount of motor energy expended by children with ADD/HD (Butte, Treuth, Voigt, Llorente, Heird 1999). Research on normal subjects has revealed that methylphenidate does blockade dopamine transporters, thus increasing synaptic dopamine, with the effects being dependent on the dose given (Volkow, Wang, Fowler, Gatley, Logan, et al. 1998). It has been found that a placebo will increase cerebral metabolism, but only slightly (Schmidt, Ernst, Matochik, Maisog, Pan, et al. 1996). Long term oral use of either methylphenidate or damphetamine also had only slight effects on cerebral metabolism, with none for the damphetamine, and only two of 60 areas for methylphenidate, though both did increase systolic blood pressure (Matochik, Liebenauer, King, Szymanski, Cohen, et al. 1994). Research into event-related potentials (ERPs) has been fairly prolific in the field of ADD/HD, with many such studies employing a continuous performance test as the event-producing stimulus. DeFrance et al. (1996) found that P250, P350, and P500 waves could differentiate between ADHD, inattentive ADD, and controls, with the P500 yielding the most robust results, when readings were taken during a continuous Page 12

performance test. In this study, the control and ADHD groups had symmetrically positioned P250 and P350 components, while the inattentive group had a bias towards the left hemisphere, interpreted as right hemisphere underactivation. Other researchers have found that P3b component was larger anteriorly and smaller posteriorly to target stimuli, while N2 was larger anteriorly and smaller posteriorly to non-target stimuli (Johnstone and Barry, 1996).

This was interpreted as a dysfunction of low-level attentional

processes in the posterior region with an intentionally based compensation by frontal regions. Oades et al. (1996) reported a very elaborate set of findings from an experiment in which auditory ERPs resulting from three tones of varying rarity. It was found that participants with ADHD had shorter latencies and did not show latency differences between frontal and parietal regions, suggesting faster processing. Also, the ADHD group was reported to show P3 components that were larger, shifted anteriorly, and lacked a right bias which the researchers had expected and found in normal subjects. It was concluded from this study that those with ADHD had primarily right hemisphere impairment. Others, however, report smaller than normal P3s however (Overtoom et al, 1998). Van Leeuven et al. (1998) reported decreased global field power in their ADD participants. Frank et al. (1998) reported that ERP abnormalities in ADD/HD do not change with respect to age, and also that P3 measures differentiated those with learning disabilities from those without regardless of the presence or absence of ADD/HD, but did not distinguish those with ADD/HD. The conclusion reached was that the P3 had to do with processing rather than attention, and was thus not a valid diagnostic indicator of

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ADD/HD. Subtyping psychiatric patients on the basis of quantitative electroencephalography has been attempted, with reasonable amounts of success (John, Prichep, Almas 1992). This line of research has been of particular interest in the areas of ADD/HD, where qEEG appears to be quite promising in differentiating between people with ADD/HD, normal controls, and others. Chabot and Serfontein (1996) reported a specificity of 88% and a sensitivity of 93.7% when applying discriminant analysis to the qEEG's of a sample of 407 children with a diagnosis of ADD and 310 normal controls. Additionally, 92.6% of the abnormal qEEG involved either slowing or increased activity of the frontal areas. Further studies have demonstrated that it is also possible to distinguish ADD/HD from learning disability in much the same way (Chabot, Merkin, Wood, Davenport, Serfontein 1996), and also to predict responses to medication (Levy and Ward, 1995). Mann, Lubar, Zimmerman, Miller, and Muenchen (1992) reported specifically that frontal theta, defined as activity between 4 and 7.75 Hz was elevated, while temporal and posterior beta, defined to include activity from 12.75 to 21 Hz, was less than normal, in a sample of 25 right-handed boys with ADD/HD and 27 matched controls. These differences were enhanced by cognitive engagement, specifically reading and drawing. The above findings support the central role of neural function in generating the cognitive style and behavioral symptoms associated with ADD/HD. Further, many of these data imply a decrease in active processing, particularly in the frontal regions. This led me to hypothesize that lower frequency EEG will be found, especially in frontal Page 14

areas. As it has previously been reported that qEEG, particularly spectral analysis, is useful in differentiating people with ADD/HD from non-clinical controls, the chance of finding more slow activity frontally seems all the more probable. Thus, a part of this project was the replication of these previous results. Additionally, I looked for commonly found patterns, which may represent sub-categories of ADD/HD. I will further investigate other, more complex forms of qEEG which show the probable nature of functional connection and intracortical coordination between cortical regions. The specifics of this procedure are as follows: To these ends I analysed of archival data on 125 children diagnosed with ADD, which were collected by Dr. Joel Lubar between the years 1991 and 1997. I intend for these data to be made into a diagnostic database to differentiate children with ADD/HD from normal controls. I also analyzed eyes-closed baselines with the Neurorep Display Program by comparing them with a normative database — specifically the Thatcher's Lifespan Normative Database — and prints brain maps showing differences from a normative control group. I classified these based on dominant band pass (delta, theta, alpha, or beta), and compared numbers of cases in each band pass category. I also created statistically derived composite brain maps for each of several age groups. Firstly, I predicted that qEEG measures taken using a 19-electrode electroencephalography device may be used to effectively differentiate children with

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ADD/HD from normal controls. Secondly, I predicted that children with ADD will be diverse, and will produce more than one pattern of qEEG abnormalities, but that most will be classifiable according to a few patterns on the basis of their dominant EGG frequency. I predicted that the most consistent pattern will consist of excessive alpha (7-13 Hz activity) over all or most of the head. Lastly, I predicted that when averaged, the ADD group as a whole will show higher than average relative power in lower frequencies, specifically alpha (7-13 Hz) and theta (3.5-7 Hz).

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Participants The participants were then divided into five age groups: 7-8 year olds, 9-10 year olds, 11-12 year olds, 13-15 year olds, and 16-18 year olds. Any participants who were over the age of 18 or who were female were removed from the sample, as there was insufficient data available on females and adults to perform any meaningful group analysis. This left a sample of 125 male children and adolescents, all of whom had Attention Deficit Disorder and were right handed. For the purpose of all analysis, every participant in the original sample of 173, include all 125 whose data were used, was assigned a unique, three digit identification number (ID), which was used to identify the participants in all part of the data analysis and reporting. All subjects were formally diagnosed as having an Attention Deficit Disorder by qualified professionals. Additionally, the vast majority were described as manifesting notable hyperactivity. Unfortunately, due to differences in diagnostic classification at the times of the original evaluations and EEG recordings, it cannot be certain which subjects or how many may have fell into the specific subtypes of hyperactive-impulsive, inattentive, or mixed, according to current nosology. Archival data on a second, much smaller, sample, was acquired during the course of evaluating these data. This sample consisted of 16 children, and the sorting part of the analysis was repeated on them. This group did have more complete diagnoses and

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psychometrics. Unfortunately, these subjects were a mixed group in terms of gender and handedness, and had significantly different demographic and symptomatic profiles when compared to the original sample. Compared with the original sample, these children had more good epochs, no bad electrodes, and few abnormalities, suggesting that milder symptoms, better recordings, or both, limit the ability to compare this group with the original sample. In addition, the sample size for these data was small, making statistical power poor.

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Methods Archival data on 173 children and adults were analyzed in order to extract qEEG features typical of Attention Deficit Disorder. The data was collected using the Lexicor Neurosearch 24 from Lexicor Medical Corporation. This data had been collected at a sampling rate of 128 Hz with an amplitude of 32 k. The data was previously artifact rejected and analyzed in Lexicor Neurosearch 24, with the results being exported to space delimited ASCII files (*.prn files) containing information on the mean power in various locations and in various frequency ranges. The frequency ranges used for this purpose were the standard band passes, such as delta (0-4 Hz), Theta (4-8 Hz), Alpha (8-12 Hz) and Beta (13-21 Hz). The data had been collected and processed in this way for each of six conditions: an eyes closed baseline, an eyes open baseline, during reading, during listening to a story, while copying figures from the Beery-Bender Visual-Motor Gestalt Test, and while performing Ravens Progressive Color Matrices. The data contained in the *.prn files were then imported into Microsoft® Excel™, such that each *.prn file became one spread sheet of an Excel workbook with a name equal to the ID number assigned to the participant. Seperate workbooks were created for each for each of the five age groups. At the end of each workbook was included a set of pages containing calculated statistics pertaining to the data taken from the *.prn files, and named according to the statistic on that specific sheet. The follow statistical values were calculated with respect to each age group: arithmetic mean (hence forth called the mean),

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standard deviation, standard error, 95% confidence interval, and intervals of 1, 1.5, and 2 standard deviations above and below the mean. The result of this analysis were then placed in a set of tables, each of which showed the mean relative power flanked by the bounds of the 1.5 standard deviation intervals for each location and band pass, with one table being produced per age group per task conditions. Further analysis was done using on digitalized recording of raw EEG. These digitized EEGs were saved by the Lexicor Neurosearch 24 in files with the extension of *.dat. These *.dat files contain a digitized form of the EEG waves which has not been modified beyond the act of digitization itself. As a result, these files contain the equivalent of a complete EEG reading, recorded and repayable as if being currently measure, and thus can be treated much as would a raw signal. These file were moved to a computer which was running Hudspeth's Neurorep program in Microsoft® DOS 6.22 on a 450 MHz Intel® Pentium II™ processor. The use of Hudspeth's Neurorep program was to compare the data with Thatcher's Lifespan Normative Database and generate brain maps which pictorially represent the frequency distributions and information about connectivity within the cerebral cortex. Four primary types of maps were generated: phase maps, which show the degree of lag between similar signals appearing in different locations; coherence maps, which show the degree to which the brainwaves maintain a constant phase relationship; asymmetry maps, which show the degree to which paired locations maintain similar EEG amplitudes; and relative power maps, which simply display the distributions of power within the four Page 20

traditional band passes across the scalp, and, by inference, the cortex. Phase is interpreted as an inverse measure of the speed at which informations is typically transimitted between locations.

Coherence is considered to be evidence about degree to which

locations do communinicated. Relative power is considered a measure of the amount and type of activity in a region, with higher frequencies and asynchronous wave form being associated with higher activation.

The implications of asymmetry are less well

understood, but may be related to differential development, connection to subcortical generators, or, when extremely high between a location and all other necrosis (though the later is not expected to be common in this population). Do to unalterable configurations built into the Neurorep program and characteristics of the Thatcher Lifespan Database, a different set of definition's were used for the band passes in this part of the investigation: delta, 0.5 to 3.5 Hz; theta, 3.5 to 7 Hz; alpha, 7 to 13 Hz; and beta, 13 to 22 Hz. The page containing relative power also contained a set of maps showing the ratios of several band pass pairs: delta/alpha, delta/beta, theta/alpha, theta/beta, and alpha/beta. The band pass ratios are reported only as their directly calculated numeric value, while all other values in these brain maps are reported both as actual values and as Z-score differences from Thatcher's Lifespan Normative Database. As an intermediate step in generating brain maps, Hudspeth's Neuroreport program generates a set of data files in the form of space delimited ASCII tables. These tables contain numeric data on the coherences, phases, asymmetries, and power Page 21

distribution (both relative and absolute), and is used in creating the actual brain maps which are exported for viewing. In order to gain a better picture of possible group tendencies, these intermediate tables were imported into Excel for statistical analysis, with each table being made into one spreadsheet of a workbook. Two such workbooks were made for each of the five age groups, based on the entire sample having been split into two groups for statistical purposes. The statically generated tables were then saved as space delimited tables with the same format as the original files, and were used to produce compound group brain maps for each half age group. All standard brain map types were produced for all half age groups in this way. A similar set of compound brain maps based on all the participants in each age group was generated for relative power and power ratios in the same way. As an additional measure, sign tests were also done on some of the band passes. This was to make sure results acquired in the creation of the averaged brain maps was not the result of a few outliers. Because the purpose of these test were only for checking results already acquired by the previously completed parametric test, only differences which had been significantly abnormal for both of the half the samples were analyzed with the sign test. Of these, only two measures showed up as a consistent pattern across age groups. These were the theta/beta ratios in central locations (Fz, Cz, and Pz), and the alpha/beta ratios in parietal locations (P3, Pz, and P4). Because a new version of Hudspeth's Neuroreport program had been acquired by the lab between analyzing the first half of the sample and the second, the first half had Page 22

been analyzed by version 3.0 while the second had been analyzed using 4.0. Because alpha/beta ratios were an added feature in version 4.0, this had not originally been shown for the first half of the sample. (Thatcher's Lifespan Database, however, had not changed, and remained the basis for all comparisons by the program.) As a result, the first half of the sample had to be reanalyzed at this point for use in the sign test. After halving both halves of the sample analyzed with version 4.0, they were recombined, and the number higher (+) and lower (-) ratios for in the relevant locations were manually counted and sign tests were calculated. The same division of the sample into two sub-samples that was used in averaging the data was also used here. Due to the fact that further sub-dividing the sample into age groups would make the samples for many band-passes to small for meaningful statistical analysis, no such analyses were performed for specific sub-division of age and dominant band-pass. However, the sample within specific age groups were sorted and classified according to the dominant band-pass and number in each band-pass were counted and compared. Statistical analyses were performed on the band-pass groupings without separation based on age.

Page 23

Results Tables showed the means for each age group, at each location, for each band pass, all flanked by the boundary values of 1.5 standard deviations. As there were no controls to compare these results with, no results can be claimed from these beyond the raw numbers obtained. For this reason, these results will not be further discussed here, and are not being considered part of the results of this thesis research. They were previously discussed because the analysis of this data was part of the process that led to other analyses using Thatcher's Lifespan Normative Database. Of the 125 cases that were used to produce tables, 12 had missing, corrupted, or incomplete raw EEGs. Only 113 usable raw EEGs were recoverable from there on tape back-up storage. As a result, average brain maps, brain maps sorting, and related sign tests use only 113 total cases. Averaging derived measures for each age group yielded the following results. There were no significantly abnormal coherence measures for any age groups. Phase in the beta band was found to be significantly below normal between F4 and F8 for seven and eight year olds, and was also significantly below normal in the same location and band pass for Group I (but not Group II) of the eleven and twelve year olds. This would indicate reduced time for a signal to travel between F4 and F8, and thus reveals increased speed of transmission. Based on these observations the Z-score

Page 24

difference were also compared for other all other ages and in both groups. It was found that the phase was lower than normal mean for all groups compared, and for only two of these (13 through 15 year olds, both groups; Z = -0.77) the difference was not significantly more than one standard deviation. The consistency of this finding suggests that it may be typical of at least a large subgroup the ADD/HD population, and may warrant further investigation. However, it was not universal, and the exact implications of this finding are not obvious at this time. Phase was also found to be atypical between C4 and O2 for the eleven and twelve year olds. In this case the measures being significantly higher than normal. No similar finding was found to occur consistently in other ages, and it thus suspected that this finding does not represent a difference typical of ADD/HD, but simply a chance variation in this group. While it is possible that maturation effects could explain this discrepancy, there is no reason to suspect such an abnormality would develop at this particular age and then disappear. Thus, it is the opinion of this researcher that this is finding is a chance occurrence and of no practical value in dealing with the ADD/HD population at large. An interesting pattern of phase abnormalities appeared in the sixteen through eighteen year olds. Phase was found to be significantly below normal from Fp1 to Fp2, F3, and F7. The thirteen to fifteen year olds, and the eleven and twelve year olds, had a similar trend, but it did not reach significance. It is tempting to relate this to a dysfunction of the left frontal lobe, possibly affecting executive function, and perhaps to further postulate that this gets worse with age. However, it is odd that it appears mostly at delta, Page 25

rather than a higher band, and a more likely possibility is that this is the result of residual eye artifact, with the researcher was unable to fully eliminate. The results with respect to asymmetry were more interesting and robust. All significantly abnormal asymmetries were in delta, and all were high. Among the seven and eight year olds delta asymmetry was high between C4 and F8. Among thirteen to fifteen year olds, delta had high asymmetry between F8 and T4. Finally, the sixteen through eighteen year olds showed high asymmetry in delta in the following areas: F2 to O2, F2 to C4, F1 to T5, F5 to C3, and F1 to O1. Many others came out significantly high in Group I or Group II, especially amount the thirteen to fifteen year olds and the sixteen to eighteen year olds. Further, there was a generally observable tendency for asymmetries to be high, especially in delta which increase with age. It is also notable that all significantly high delta asymmetries are lateral in nature. Some might postulate these to be the result of residual eye artifact, however, this cannot be the case, as not all of these asymmetries involve frontal locations. What is particularly interesting is that these abnormalities become more pronounced with age, as they become more common and severe in the older ages. The averaged frequency maps also showed some differences in the form of observable trends. However, there was no significant abnormalities in relative power for in any band pass, at any location, for any age group. Even if groups I and II are treat individually, there are no significant abnormalities for any averaged relative power data. This is not surprising, because abnormalities in relative powers were quite rare among in Page 26

the individual maps. Looking at trends as indicated by Hudspeth's Neurorept Program: Alpha tended to be somewhat high for the eleven and twelve year olds and the thirteen through fifteen year olds. Beta was low for seven and eight year olds, while theta was low for eleven and twelve year olds and thirteen through fifteen year olds, and delta was high for the eleven and twelve year olds and sixteen through eighteen year olds. Alpha/beta ratios tended to be high in all age groups except the sixteen through eighteen year olds; the tendency for alpha/beta ratios to be low was especially strong for the seven and eight year olds and the nine and ten year olds. Theta/beta ratios were also high in seven and eight year olds and the nine and ten year olds, being especially high in the younger children (seven and eight year olds). Delta beta was also high for the seven and eight year olds. Surprisingly, theta/beta ratios were low in the eleven and twelve year olds and the thirteen through fifteen year olds. Additionally, delta/beta was low in the eleven and twelve year olds and theta/beta was low in thirteen through fifteen year olds, though the isolate nature of these findings compared the same band pass in other age groups raises the possibility of chance occurrence in these cases. Unfortunately, statistical measures are not given for ratios in the Thatcher Lifespan Database, so it cannot be determined whether or not these differences represented statistically significant abnormalities. However, based on these results, it appear that the theta/beta ratio is diagnostically useful in younger children (ages ten and younger), while the alpha/beta ratio may be of diagnostic significance over a greater age range. Page 27

The changes in theta/beta ratio from high to low with age, and the similarly decreasing difference in alpha/beta ratios from the norm with age, raise an interesting question about development in these children. It appears that there is a notable predominance of slow over fast waves in the EEG the participants which decreases with age. While this alone would be normal and expected, the data suggest that degree is much greater in than in a normal population, with the young ADD/HD having markedly higher than normal slow wave activity which decrease greatly with age, such that the theta/beta ratio goes from high to low compare to the norm. However, it should be remember that this is not longitudinal data, and nothing is known from this study about how these particular children's EEG changed over time. Therefore, it is premature to assume that slow waves actually attenuate with age, nor that fast waves increase, as random variation within the population, coinciding with age, cannot be completely ruled out. However, this may be an important object for further research, as there is a clear suggestion in these data that that may be the case. Sign test result on theta/beta ratios yield no significant results except for the 13-15 and 16-18 year old groups, both at Pz and both showing a tendency to be lower than the normative average. Alpha/beta ratios, however, were much more robust and in the expected direction. Alpha/beta ratios were significantly more likely to be high at Pz for all age groups, and at P3 and P4 for all age groups except the 11-12 year olds. Further 10 of the 15 alpha beta tests were significant to at least a 0.01 level; specifically, all parietal location were p=0.01 or better for the 7-8 year olds, the 9-10 year olds, the 13-15 year

Page 28

olds, as was Pz for the 16-18 year olds. Pz for the 11-12 year olds, and P3 and P4 for the 16-18 year olds were significant only at a 0.05 level. Further, had significance been set to 0.1 instead of 0.05, all parietal locations for all age groups would have been significantly high for the alpha/beta ratio at that level. In order to take into consideration the possibility of multiple neurological subtypes of ADD/HD, the data was sorted according to dominant band pass (either delta, theta, alpha, beta, or mixed). The primary rationale for doing this is the possibility that more than one pattern may exist, which would be hidden by averaging the data. Band pass was picked as a criterion primarily because its diagnostic relevance is well established in this population and because band pass related patterns are often relatively simple to recognize in a brain map. The primary drawback to such a categorization with this specific data set is the lack of psychometric measures or detailed diagnostic information, making it impossible to tell if the electrophysioligical sub-types found in this sample correspond to behavioral sub-type, nor give any clue to what those behavioral sub-types would be. The latter problem has been partly resolved by the sorting a second archival sample on the same criteria. Part I of the data, when sorted into dominant band pass by the author, was divided as follows: Delta, 9%; theta, 5%; alpha, 38%; beta, 2%; and mixed (two or more) bandpasses, 48%. The mixed group could be further subdivided as follows: Delta and theta, 9%; delta and beta, 12%; theta and alpha, 7%; theta and beta, 12%; alpha and beta, 5%; and indeterminate (three or four band-passes nearly equal, or none clearly abnormal), Page 29

12%. A second researcher also sorted the data in part I according to the same criteria, and interrater reliability was calculated. Reliability was high for alpha (0.85), but was lower for the other band-passes, 0.73 for delta, 0.76 for theta, 0.36 for beta, and 0.49 for mixed. This researcher divided the data as follows: Delta, 13%; theta, 9%; alpha, 43%; beta, 13%; and mixed 22%. He did not further sub-divide the mixed group. Apparently, the a group with strongly dominant alpha rhythm exists, which makes up a little over a third of the sample, and which is readily identified by independent observers. The small numbers and generally poor interrater reliability argue against the particular validity of the other groups; although delta and theta do have more reasonable reliability than the beta and mixed groups, it is still not extremely strong and the small number in these groups suggests that they may not be specific subtypes. Considering that the delta, theta, and beta groups occur in number comparable to those of the mixed twoband types (for example, delta and theta) suggests that all these may be simply a large, heterogeneous assortment, and that the ADHD population, as represented here, my well be divided into a relatively homogeneous alpha-dominant group and a heterogeneous non-alpha group.

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Table 1: Numbers and Percentages of Cases to Display Specific Bands as Dominant, by Group and All Participants Total N

δ

θ

α

β

Mixed

N

21

7

3

5

0

6

Olds % of Total

na

33%

14%

24%

0%

29%

N

29

1

3

9

0

16

Olds % of Total

na

3%

10%

31%

0%

55%

N

24

0

0

10

2

12

Olds % of Total

na

0%

0%

42%

8%

50%

N

18

0

0

6

0

12

Olds % of Total

na

0%

0%

33%

0%

67%

N

21

0

3

8

1

9

Olds % of Total

na

0%

14%

38%

5%

43%

N

16

0

0

7

1

7

Sample % of Total

na

0%

0%

44%

6%

44%

N

129

8

9

45

4

62

And Groups % of Total

na

6%

7%

35%

3%

48%

Group

Type of Value

7-8 year

9-10 year

11-12 year

13-15 year

16-18 year

Psychometric

All Ages

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Sorting by band pass within age groups yields results very similar to sorting for the whole sample. Generally, about half the cases fell into the "mixed" category, having no single band pass clearly dominant in the sense of being unusually high while others were not. Approximately another third of each group had primarily alpha. Delta, theta, and beta were rare. Interestingly, seven of the eight children with primarily excessive delta were in the 7-8 year old age group; this is probably related to the fact that the mean EEG frequency increases with age until around puberty. Other than this, little difference was noted between age groups with respect to dominant band pass, as is shown in table table 2. Unfortunately, it is uncertain what the practical implication of these finding are, as the lack of psychometric data make it impossible to compare membership in possible behavioral subtypes with membership in the observed band-pass based categorization. An analysis of the other qEEG measures, including coherence, phase, and asymmetry of the EEG, yields no significant difference in the type, number, location, nor band-pass of abnormalities. This is true even without adjusting for the large number of measurement, and would obviously be even more true were any such statistical correction applied. All statistical tests of this type were done on the first half sample, and none were repeated on the second half as no significant findings were available to retest. A second, smaller set of data, was also categorized in terms of dominant band-

Page 32

pass, in a manner identical to that used for the primary sample of 125. This sample had been used in another research project, which involved cerebral blood flow biofeedback and audio-visual stimulation. Since pre-treatment data on this set of children was readily available and included psychometric and behavioral measure, it was sort and the results compared to diagnostic categorization, either inattentive or hyperactive-impulsive. The primary researcher sorted the data, while blind to the diagnostic status of the subjects. It was found that the alpha dominant group had a 50-50 chance of being diagnosed as either type, and 75% of these gave their biofeedback trainer the impression of being primarily hyperactive (as opposed to 25% which did not). However, the non-alpha dominant children were 80% likely to be diagnosed as inattentive and 83% likely to impress their biofeedback trainer as inattentive, while only 20% were diagnosed as hyper-active and 33% gave their biofeedback trainer such an impression. Unfortunately, these results are not significant, due to a very small sample size, and can only be considered a hint for further research, and not as a verified finding.

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Table 2: Differences Observed Between Sorted Groups: This table shows the means of several types of demographic and experimental variables, flanked by the top and bottom of a confidence interval and follows by the standard deviation. Confidence intervals were defined as two standard errors of the mean about the mean.

Include are the number or good epochs, number of good channels,

participant's age, year measures were taken, total number of abnormalities on any map (a rough measure of neuropathology), highest θ/β ratio on head, the percent who had a θ/β of at least 3/2 times Thatcher's normative database at some location, and the percentage that had a θ/β greater than Thatcher's normative database at Cz.

Significant differences are highlighted; note that the main

differences were between the psychometric sample and everyone else and primarily in demographics and recording quality, though much fewer abnormalities were evident. (Statistical normality has not been tested.)

All

Demographics

Results

# Good # Good Epochs Channels Age

Year of Total Highest % with θ/β % with θ/β Ratio 3/2 > NT >NT at Cz θ/β EEG Abnormalies

Top of CI

109.27

18.83

13.18

1994.29

31.80

3.99

—

—

Mean

101.79

18.67

12.21 1993.81

27.83

3.62

49%

56%

Bottom of CI σ

94.31 42.48

18.51 0.90

11.24 5.50

23.86 22.56

3.25 2.10

— —

— —

n=129

Psychometric

1993.33 2.74

Demographics

Results

# Good # Good Epochs Channels Age

Year of Total Highest % with θ/β % with θ/β EEG Abnormalies θ/β Ratio 3/2 > NT >NT at Cz

Top of CI

163.21

19.00

12.34

1998.30

14.60

4.39

—

—

Mean

149.94

19.00

11.18 1998.13

11.25

3.33

38%

56%

Bottom of CI σ

136.68 26.53

19.00 0.00

10.02 2.32

7.90 6.70

2.28 2.11

— —

— —

Sample n=16

1997.97 0.33

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Table 2: Differences Observed Between Sorted Groups (continued)

Alpha Group

Demographics

Results

# Good # Good Epochs Channels Age

Year of Total Highest % with θ/β % with θ/β EEG Abnormalies θ/β Ratio 3/2 > NT >NT at Cz

Top of CI

120.69

18.90

12.66

1994.90

33.97

3.38

—

—

Mean

108.96

18.56

11.65 1994.04

27.22

3.10

41%

56%

Bottom of CI σ

97.23 39.33

18.22 1.15

10.64 3.38

20.47 22.63

2.82 0.94

— —

— —

n=45

Mixed Group

1993.18 2.89

Demographics

Results

# Good # Good Epochs Channels Age

Year of Total Highest % with θ/β % with θ/β EEG Abnormalies θ/β Ratio 3/2 > NT >NT at Cz

Top of CI

105.80

18.91

12.81

1994.61

34.46

4.27

—

—

Mean

96.37

18.71

11.86 1993.91

29.00

3.71

49%

51%

Bottom of CI σ

86.94 37.13

18.51 0.79

10.91 3.73

23.54 21.48

3.15 2.20

— —

— —

n=62

High θ/β n=59

1993.21 2.74

Demographics # Good # Good Epochs Channels Age

Results Year of Total Highest % with θ/β % with θ/β θ/β Ratio 3/2 > NT >NT at Cz EEG Abnormalies

Top of CI

99.80

18.89

12.15

1994.16

32.12

5.84

—

—

Mean

89.48

18.68

11.19 1993.55

26.81

5.29

100%

100%

Bottom of CI σ

79.16 39.64

18.47 0.79

10.23 3.68

21.50 20.38

4.74 2.13

— —

— —

Low θ/β

1992.94 2.35

Demographics

Results

# Good # Good Epochs Channels Age

Year of Total Highest % with θ/β % with θ/β EEG Abnormalies θ/β Ratio 3/2 > NT >NT at Cz

Top of CI

116.72

18.89

13.60

1994.28

35.85

2.22

—

—

Mean

105.90

18.70

12.60 1993.53

29.60

2.06

0%

0%

Bottom of CI σ

95.08 40.12

18.51 0.70

11.60 3.70

23.35 23.17

1.90 0.59

— —

— —

n=55

1992.78 2.79

Page 35

Discussion Probably the most important and interesting findings were those involving averaged brain maps. The finding of low phase between F4 and F8 on some averaged maps is not likely to be important, as it was not found on other averaged maps, and showed no clear pattern of with respect to where and when it appeared the three times it was found. This was not an important finding, but more likely a result of random variation, appearing by luck in a few of the averaged maps. Much the same conclusion is reached with respect to the higher phase between C4 and O2 in the 11-12 year old group and the frontal phase found in the 16-18 year olds. The increasing number of lateral asymmetries with age, on the other hand, may be of real importance. Not only was this found across ages, but showed a clear pattern of increase when moving from younger to older age groups. This leads to the possibility of a progressive lag, in which the brain, or parts, are developing more slowly than normal, so that they fall further and further behind the norm as the child increases in age. Other possible explanations include that damage is being done progressively, whether do to inherent or behavioral causes (such as drug use or accident), or that a process of deviant development is pulling the ADD/HD children further from the norm. The later concept differs from a progressive lag in that development is not simply falling behind, but following a different path such that the ADD/HD and normal brain diverge with age, and differs from progressive damage in that it does not imply tissue death but merely deviant

Page 36

structural and metabolic development. At this time, considering previous research and current theories, a progressive lag would seem the most likely. However, regardless of the exact nature and cause of the change, it is important to note that, based on the evidence here, the ADD/HD brain becomes less, not more, normal with age. While it may be noted that there were no significant differences in the relative power maps, this may be misleading. Such difference were rarely seen even on individual maps, so not finding them on averaged maps, in which individual differences had the opportunity to cancel each other out, was to be expected. While this implies that assessment on the basis of single band relative power alone may be questionable, it does not eliminate the use of power spectral maps in conjunction with other measures as an extra piece of convergent information to help guide diagnostic decision making. The theta/beta and alpha/beta ratio results may well be the most important findings. These finding suggest that the theta/beta ratio may be useful up to around age ten, with most of their usefulness being with ages eight and below. The alpha/beta ratio, however, is at least as diagnostic of an attention deficit disorder for these ages, and remains valid into the teen years, and possibly into adulthood, though further research would be needed to confirm the later. Considering the importance often given to theta/beta ratios in diagnostic use, the alpha/beta ratio may be more consistently useful has an importance that cannot be understated. However, this research only points to alpha/beta as a useful measure, it does not, in and of itself, define a protocol for such diagnostic use, especially in the later teen years and beyond, when the mean alpha/beta Page 37

ratio is not significantly abnormal (as previously defined) but merely likely to be above average (as determined by the application of sign tests). Looking at the groups the participants were sorted into is also quite interesting. The only single band pass which commonly came out as uniquely dominant on given maps was alpha. This fits well with the findings that alpha/beta ratios were especially diagnostic of ADD/HD. It further replicates Chabot and Serfontein (1996) and Chabot et al. (1996) which also found a large alpha-dominant group. The fact that alpha is found as in the occipital cortex when the eyes are closed, is associated with hypnotic and meditative state, and is increased by activities such as shooting, archery, and golf (Andreassi 2000) may be related. Considering that "clearing" ones mind is a typical part of the targeting in the latter activities, it appears that alpha as a sort of "idling" rhythm, and its appearance in many with ADD/HD may signify a disengaged state as more common in this population. It is also worth noting that, while only alpha was commonly found to be dominant on its own, about half the sample had "mixed" patterns of EEG and these mixed patterns were themselves extremely heterogeneous. It may be, that the alpha pattern may represent a rather distinct phenotype which may be something of a "natural" form of ADD/HD, while the others may represent an eclectic assortment of attentional and inhibitory impairments of various origins. It is also possible that some "alpha-heads" made it into the later groups as the result of later insult to the brain causing a shift in their dominant EEG patterns. An interesting consideration is that the alpha groups may represent a more Page 38

genetically based and genetically homogeneous form, possibly corresponding to so-called "hunter" type which has been made maladaptive by the modern, organized world. A few things do argue against the later conclusion, however, notably that there were no significant differences observed between the alpha-dominant group and other (except, of course, for alpha levels). Further, it should be noted that alpha/beta ratios were above average for the vast majority of participants (as determined by sign tests), not just the alpha-dominant groups, the proportion showing higher than average alpha/beta being much greater than one third. Whatever the interpretation, it does appear from this research that one fairly homogeneous electrophysiological subtype exists for ADD/HD, which accounts for about a third of cases, but that a larger portion of referrals are do not conform to any group, and may be due to a less specific and consistent set of causes. Considering all the result found in this study, it is concluded that alpha rhythm EEG tends to be found in greater quantity or power in those with a diagnosis of ADD/HD, and that this may be of diagnostic significance, particularly the ratio between mean power alpha and mean beta power. It has also been found that a large portion, apparently constituting about a third of the ADD/HD population, has primarily higher than normal alpha over all or most of the head, with normal or low readings in the other three traditional band passes (delta, theta, and beta). It may be that the alpha-dominant type is more likely to be hyperactive-impulsive or represents a more inherent (rather than damaged) form of ADD/HD, but these conclusions cannot be safely made from the data at hand.

Page 39

The relative weakness of theta/beta ratios is a bit surprising, as these have been used effectively in diagnostic and biofeedback protocols. However, it should be kept in mind that theta/beta ratios were found to be high throughout middle childhood, especially in the younger portion of the sample, becoming unreliable as the teen years were approached. Theta/beta ratios may, therefore, continue to have some use in younger and middle children, but should not be considered appropriate for older children or teenagers. Alpha/beta ratios, on the other hand, appear to be useful for both younger and older children, and with teenagers to a lesser degree. It is uncertain how this measure would hold up in adulthood. However, the development of newer diagnostic and feedback protocols using alpha/beta ratios appears to be in order.

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Table 3: Overview of the Result Concerning θ/β and α/β Ratios: The table below reviews finding about the diagnostic usefulness of the θ/β and α/β ratios. Given are the percentage of participants that for whom θ/β or α/β were higher than the database at parietal locations, and sign test significance for participants being higher than the database. All significances are reported as 0.001, 0.01, 0.05, or n/a (not significant), based on the strictest commonly used level they could pass. Those labeled as "(Low)" were in the opposite direction from expected, being lower that the norm rather than higher.

Age Group Ages 7-8 Ages 9-10 Ages 11-12 Ages 13-15 Ages 16-18 % High θ/β at Fz 45.8% 57.1% 34.8% 21.7% 45.0% % High θ/β at Cz 45.8% 62.9% 30.4% 13.0% 25.0% % High θ/β at Pz 45.8% 57.1% 21.7% 4.3% 15.0% θ/β Sign Test at Fz n/a n/a n/a n/a n/a θ/β Sign Test at Cz n/a 0.05 n/a n/a n/a θ/β Sign Test at Pz n/a n.a n/a 0.01 (Low) 0.05 (Low) % High α/β at P3 75.0% 60.0% 69.6% 65.2% 80.0% % High α/β at Pz 70.8% 60.0% 73.9% 65.2% 85.0% % High α/β at P4 70.8% 57.1% 65.2% 65.2% 80.0% α/β Sign Test at P3 0.001 0.001 n/a 0.01 0.05 α/β Sign Test at Pz 0.001 0.001 0.05 0.01 0.01 α/β Sign Test at P4 0.001 0.001 n/a 0.01 0.05

Page 41

Thus, these data show (1) that alpha is by far the most common single band pass (of the traditional four) to appear alone as dominantly high in ADD/HD, (2) that alpha is high, on average for all age groups, but becomes less so with age, (3) that above average alpha is extremely common, for some ages seeming nearly universal, and (4) theta/beta ratios may be useful with younger children, but not those over the age of ten. The most appropriate areas of further research based on the current study appear to be (1) determination if alpha/beta ratio is, in fact, effective and differentiating those with ADD/HD from controls, (2) if alpha/beta is diagnostically useful in practical use, developing effective diagnostic protocols using this measure, (3) determine if alpha/beta related biofeedback protocols are effective in enhancing attention and/or reducing hyperactivity, and developing relevant treatment protocols, and (4) determining if the observed differences between "alpha-heads" and non-"alpha-heads" are meaningful in behavioral and/or cognitive terms.

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Appendix A: Figures This appendix is dedicated to a set of five figures which may be of interest, but which had no obvious location in the main text into which they could be easily fit. Specifically, this section displays the band pass ratio maps for each age group. Each of these maps shows five ratios, ∆/α, ∆/α ∆/β, ∆/β θ/α, θ/α θ/β, θ/β and α/β, α/β which are commonly used in qEEG based assessment. Particular attention should be paid to the θ/β and α/β ratio as they are commonly used in assessing ADD/HD and were notably abnormal in this sample, making them of particular importance. It appears from these data that θ/β is diagnostically useful through age ten and α/β is diagnostically useful through age fifteen.

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old participants, while below are normative values. Note especially theθ/β and α/β ratios.

Displayed are five commonly used band pass ratios. The top row shows the average ratios for the seven and eight year

This figure was created using Hudspeth’s Neurorep program and Thatcher’s Lifespan Normative Database.

Figure 1: θ/β ratios for seven and eight year olds

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vv

θ/β and α/β ratios.

This figure was created using Hudspeth’s Neurorep program and Thatcher’s Lifespan Normative Database.

Figure 2: θ/β ratios for nine and ten year olds

old

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v

gram andThatcher’s Lifespan Normative Database.

year old participants, while below are normative values. Note especially theθ/β and α/β ratios.

Displayed are five commonly used band pass ratios. The top row shows the average ratios for the eleven and twelve

This figure was created using Hudspeth’s Neurorep pro

Figure 3: θ/β ratios for eleven and twelve year olds

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θ/β and α/β ratios.

This figure was created using Hudspeth’s Neurorep program and Thatcher’s Lifespan Normative Database.

Figure 4: θ/β ratios for thirteen through fifteen year olds

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v

he average ratios for the sixteen through

eighteen year old participants, while below are normative values. Note especially theθ/β and α/β ratios.

Displayed are five commonly used band pass ratios. The top row shows t

This figure was created using Hudspeth’s Neurorep program and Thatcher’s Lifespan Normative Database.

Figure 5: θ/β ratios for sixteen through eighteen year olds

Vita Jared Blackburn was born at Ft. Ord, California on September 14, 1972. The child of a career Air Force sergeant, he was a world traveler before being a year old, and had been enrolled in seven schools by the time he started middle school in sixth grade. He graduated from Morristown-Hamblen East High-School in Morristown, Tennessee on June 5, 1992. He went on to complete an Associate of Science in elementary education at Walters State Community College in Morristown, and to graduate Cum Laude with a Bachelor of Art in psychology from the University of Tennessee, Knoxville in 1998. He then enrolled in the Master of Arts program in psychology at the University of Tennessee.

During his years at the University of Tennessee he was also an active

member of Autism Network International and a founding board member of the charitable organization AutFriends Incorporated. He received his Master of Art on December 16, 2000. He is presently working on his Doctorate of Philosophy in psychology at the University of Tennessee, Knoxville.

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