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Brain & Development 34 (2012) 806–811

Original article

EEG characteristics and visual cognitive function of childrenwith attention deficit hyperactivity disorder (ADHD)

Tongkun Shi a, Xia Li a,b, Jia Song a, Na Zhao a, Caihong Sun a, Wei Xia a,Lijie Wu a,⇑, Akemi Tomoda c

a Department of Child and Adolescent Health, Public Health College, Harbin Medical University, Harbin, PR Chinab Department of Sanitarian of Zaozhuang Maternity and Child Care Hospital, 25# East of Wenhua Road, Shizhong District, Zaozhuang,

Shandong 277100, PR Chinac Child Development Research Center, Graduate School of Medicine, University of Fukui, Fukui, Japan

Received 29 August 2011; received in revised form 16 February 2012; accepted 28 February 2012

Abstract

Using visual and auditory continuous performance tests (CPT) and EEG, cognitive function and EEG power were investigated inpatients with attention deficit hyperactivity disorder (ADHD). CPT and EEG were conducted for 44 ADHD children and 44healthy controls of comparable age and sex. The EEG power tests include relative power of theta, alpha, and beta, and theta/betaand theta/alpha ratios. ADHD patients showed significantly higher theta relative power, lower beta relative power, and highertheta/beta ratio (p < 0.05). ADHD patients showed a significantly lower score of auditory CPT (p < 0.05). The EEG power char-acteristics were correlated significantly with the visual attention function in ADHD children (p < 0.01). Higher-order level cognitivedysfunction affects ADHD pathogenesis. Cortical hypoarousal effects on several mechanisms including the fronto-striatal circuitrymay be implicated in the inhibition of prepotent and premature responses.� 2012 The Japanese Society of Child Neurology. Published by Elsevier B.V. All rights reserved.

Keywords: Attention deficit hyperactivity disorder (ADHD); Integrated visual and auditory continuous performance test (IVA-CPT); Children;EEG; Visual cognitive function

1. Introduction

Attention deficit hyperactivity disorder (ADHD) isthe most common psychiatric disorder among children[1]. As defined in DSM-IV, ADHD symptoms includedevelopmentally inappropriate levels of attention,hyperactivity, and impulsive behavior. If untreated,ADHD, which has been estimated conservatively asaffecting 3–5% of school-aged children [2], can influencechildren’s studies, family settings, and social life. Thepathogenesis of this disorder remains unknown, with alack of objective, comprehensive indicator for diagnose

0387-7604/$ - see front matter � 2012 The Japanese Society of Child Neuro

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⇑ Corresponding author. Tel./fax: +86 451 87502867.E-mail address: wulijie_123@yahoo.com.cn (L. Wu).

of ADHD. Reportedly, 45–90% of ADHD childrenshow electroencephalographic abnormalities [3].

Quantitative electroencephalographic recording is anoninvasive measurement of the baseline or the underly-ing brain state before information processing [4]. Elec-troencephalographic measurements have been regardedas highly sensitive in distinguishing ADHD patientsfrom healthy subjects in many studies [5,6]. An earlystudy that identified EEG abnormalities in children withADHD was Jasper’s study [7] (1938) of children withbehavior problems: the study examined 71 children aged2–16 years, most with IQ above 70. Results show thatover half of the subjects had EEG abnormalities, pre-dominantly increased slow wave (2–6 Hz) activity inone or more regions, and often in frontal regions. With

logy. Published by Elsevier B.V. All rights reserved.

T. Shi et al. / Brain & Development 34 (2012) 806–811 807

the application of computer aided tools, many newmethods in EEG analysis were applied, including mea-surements of amplitude, relative power, absolute power,and analysis of the dominant frequency and average fre-quency, each band power percentage, and power ratios.Clarke et al. [8] reported that 20 ADHD children’s rela-tive power and absolute power of alpha and beta waveswere lower than those of normal controls. The relativepower and absolute power of theta waves were higherthan in the control group. Monastra et al. [9] found thatthe value of theta/beta ratio was useful to distinguishADHD patients from normal ones, and found that therespective degrees of sensitivity and specificity reached86% and 98%. Clarke’s et al. [8] research also revealedthat the values of theta/alpha ratio and theta/beta ratiowere diverse in normal children and in ADHD children.In addition, this ratio was useful to distinguish inatten-tion-type patients and combined-type patients.

We used a biofeedback system to detect the levels ofrelative power of EEG and the values of the relativepower ratio, along with neuropsychological testing anal-yses to assess correlation between the visual cognitivefunction and the EEG characteristics. Thereby, we wereable to explore the relations between resting-state EEGand ADHD behavior symptoms, and to validate theEEG power in predicting the cognitive activities ofADHD children.

2. Methods

2.1. Subjects

Subjects included 44 children who were diagnosedwith ADHD (mean age 8.45; SD 2.11; range 6–14 years;36 boys and 8 girls) and 44 age-and-sex-matched (pair-wise) healthy children as a control group (mean age8.32; SD 1.99; range 6–14 years). Forty-four ADHDchildren included 34 inattentive type (IT) children, sevencombined type (CT) children and three hyperactive-impulsive type (HI) children. All ADHD children wererecruited from Child Development and BehaviorResearch Center of Harbin Medical University; healthychildren were recruited from primary school as volun-teers. Both of two groups were residents of Harbin,the capital city of Heilongjiang province.

All ADHD subjects were selected by two pediatri-cians. The ADHD diagnosis was further confirmed witha semi-structured interview based on the Diagnostic andStatistical Manual of Mental Disorders Fourth Edition,Text Revision (DSM-IV-TR) diagnostic criteria forADHD.

Exclusion criteria for both the ADHD group and thecontrol group include a personal history of physicalbrain injury, neurological disorder, genetic disorder, orother severe medical condition or a personal history ofsubstance abuse or dependency. Patients with IQ of 80

or less (Wechsler Intelligence Scale for Children, ThirdEdition; WISC-III), and those with comorbid epilepsy,progressive neurological and psychiatric disorders wereexcluded. Control group subjects were also excluded ifthey reported a family history of ADHD, psychiatricdisorder, or genetic brain disorder.

Both ADHD patients and healthy children were med-ication free (central nervous system stimulant) for atleast 48 h before testing. Moreover, for at least 2 h priorto testing, participants were asked to refrain from caf-feine intake and smoking. All subjects or their guardianswere provided a written informed consent form for par-ticipation in the research at the Child Development andBehavior Research Center at Harbin Medical Univer-sity, in accordance with National Health and MedicalResearch Council guidelines.

2.2. Procedures and tasks

2.2.1. Resting electroencephalographic condition

The EEG was recorded in an eyes-closed resting condi-tion, with subjects sitting in reclining chair. The test used abiofeedback system (VBFB 3000; Thought TechnologyLtd., Canada). Electrode placement followed the Interna-tional 10–20 system, using three electrodes. The Fouriertransformation was carried out for 600 s consisting ofone EEG record in each patient. According to the interna-tional 10–20 Electrode Placement System, Cz [10,11] isthe standard placement sites, which is the most sensitiveEEG area between ADHD and normal children. Active(record) electrode was placed at site Cz in order to collectEEG activity signals easily. Reference (A1) and Ground(A2) electrode were placed on opposite ear lobes. Sub-jects, who were told that the task would last for 10 min,were asked to rest quietly with their eyes closed duringthe recording. The record index includes the level of therelative power of theta (4–7 Hz) wave, alpha (8–12 Hz)wave, the beta (13–32 Hz) wave, and the values of thetheta/beta ratio and theta/alpha ratio.

2.2.2. Cognitive tasks

This test mainly adopted the software of IntegratedVisual and Auditory continuous performance test (IVA-CPT; Brain Train, USA). Furthermore, informationfrom the tests was collected by computer. The IVA-CPTprinciple is mainly to represent repeated auditory andvisual stimuli, and to record the response of subjects(including reaction time, omissions, endurance, repetitionfrequency, etc.) to assess the attention activity. The IVA-CPT results include six full-scale quotients and 12 inde-pendent quotients. The full scale response control quo-tient (FRCQ) is based on equal weights of the auditoryresponse control quotient (ARCQ) and the visualresponse control quotient (VRCQ), ARCQ and VRCQscores are based on equal weights (1/3) of their respectiveprudence, consistency and stamina scales.

Table 1Power of EEG between ADHD children and the control group (�x� s).

Items AD/HD Control t p

Theta % 21.75 ± 2.94 20.49 ± 1.85 2.402 .021*

Alpha % 14.53 ± 3.84 14.01 ± 4.01 0.481 .634Beta % 5.44 ± 1.24 6.41 ± 1.16 �4.302 .001**

Theta/beta 4.22 ± 1.23 3.41 ± 0.57 4.194 .001**

Theta/alpha 1.62 ± 0.49 1.54 ± 0.34 0.667 .765

808 T. Shi et al. / Brain & Development 34 (2012) 806–811

The full scale attention quotient (FSAQ) is based onequal weights of the auditory attention quotient (AAQ)and the visual attention quotient (VAQ). The AAQ andVAQ in turn are based on equal weights (1/3) of theirrespective vigilance, focus and speed scales.

During the test, subjects were asked to press themouse left key once if they saw or heard the number“1”, and not to click if they saw or heard the number“2”. Participants were asked to remember the followingfour rules: see “1” press mouse; hear “1” press mouse;see “2” do not press the mouse left key; hear “2” donot press the mouse left key. The computer recordedthe statistical analysis feedback of subjects.

2.3. Statistical analysis

2.3.1. Statistical method

A paired t-test was used to compare the results of theADHD group to those of the control group. A correla-tion analysis was used to measure the relative powerbetween the brain electrical level and cognitive taskgrades in two groups. Furthermore, as a secondary anal-ysis, a Pearson correlation matrix was computed toascertain whether the power of EEG for each bandwas related to the cognitive function in the IVA-CPT,with the significance cutoff set at p < 0.05. All statisticalanalyses were processed using software (SPSS 13.0;SPSS Inc., USA).

2.3.2. Quality controlAll testers were strictly trained to be familiar with the

use of test instruments, and to adopt a unified standard.Testers ensured the completion of the whole process foreach subject. The test environment was quiet, with nomagnetic interference, thereby ensuring the reliabilityof data collection.

3. Results

3.1. Electroencephalographic analyses

Table 1 presents the relative power in theta, alpha,and beta, and the theta/beta ratio and theta/alpha ratio

Table 2Results of IVA-CPT of the response control quotient for ADHD children a

Items AD/HD

FRCQ 80.60 ± 21.55ARCQ 83.74 ± 18.95VRAQ 81.65 ± 22.28Auditory prudence quotient 91.84 ± 20.75Visual prudence quotient 87.97 ± 17.92Auditory consistency quotient 75.37 ± 15.63Visual consistency quotient 80.08 ± 23.22Auditory stamina quotient 99.89 ± 22.82Visual stamina quotient 93.51 ± 19.55

Note: FRCQ, full scale response control quotient; ARCQ, auditory respons

for ADHD subjects and control subjects across scalpsites during eyes-closed resting activity. Using paired t-tests, significant differences were found between twogroups in theta relative power, beta relative power,and the theta/beta ratio (t = 2.402, p < 0.05;t = �4.302, p < 0.05; t = 4.194, p < 0.05). In the ADHDgroup, the level of relative power of theta and the valueof theta/beta ratio were significantly higher than in thecontrol group, and the level of relative power of betawas significantly lower than in the control group.

3.2. Cognitive task analyses

Tables 2 and 3 present differences between theADHD group and the control group in the result ofIVA-CPT for the response control quotient and atten-tion control quotient.

For the response control quotient, the ADHD groupscores were significantly lower in seven quotients ofresponse control: the full scale response control quo-tient, auditory response control quotient, visualresponse control quotient, auditory prudence quotient,visual prudence quotient, auditory consistency quotient,and visual consistency quotient (p < 0.05).

Regarding the attention control quotient, the ADHDgroup scores were significantly lower in nine quotients ofattention control: the full scale attention quotient, audi-tory attention control quotient, visual attention controlquotient, auditory vigilance quotient, visual vigilancequotient, auditory focus quotient, visual focus quotient,auditory speed quotient, and visual speed quotient(p < 0.05).

nd the control group (�x� s).

Control t p

97.34 ± 18.72 �3.274 .002**

98.24 ± 17.60 �3.436 .001**

97.70 ± 17.06 �2.996 .005**

102.92 ± 18.95 �2.456 .019*

98.78 ± 15.16 �2.932 .006**

93.92 ± 16.68 �5.125 .001**

99.49 ± 18.37 �3.849 .001**

99.50 ± 12.52 .094 .92698.05 ± 12.24 �1.130 .266

e control quotient; VRCQ, visual response control quotient.

Table 3Result of IVA-CPT of the attention quotient for ADHD children and the control group (�x� s).

Items AD/HD Control t p

FAQ 68.86 ± 23.89 98.51 ± 17.75 �5.747 .001**

AAQ 69.34 ± 24.45 97.05 ± 18.84 �5.028 .001**

VAQ 74.95 ± 23.86 100.62 ± 15.48 �5.428 .001**

Auditory vigilance quotient 66.34 ± 32.99 94.21 ± 20.58 �4.039 .001**

Visual vigilance quotient 78.11 ± 22.21 99.49 ± 13.57 �4.660 .001**

Auditory focus quotient 82.92 ± 16.53 98.68 ± 14.01 �4.745 .001**

Visual focus quotient 85.62 ± 18.07 99.73 ± 17.16 �3.252 .002**

Auditory speed quotient 94.63 ± 14.79 102.00 ± 12.34 �2.174 .036*

Visual speed quotient 94.38 ± 17.49 101.73 ± 13.31 �2.125 .041*

Note: FAQ, full scale attention quotient; AAQ, auditory attention quotient; VAQ, visual attention quotient.

T. Shi et al. / Brain & Development 34 (2012) 806–811 809

Table 4Correlation between IVA-CPT and the relative power of EEG in ADHD children.

Item Theta % Alpha % Beta % Theta/beta Theta/alpha

FRCQ �.050 .303 .094 �.043 �.261ARCQ �.03 .302 .097 �.059 �.243VRAQ �.061 .233 .189 �.093 �.189Auditory prudence quotient �.045 .238 .112 �.109 �.193Visual prudence quotient �.544** .104 .069 �.293 �.349Auditory consistency quotient �.040 .127 .090 �.093 �.007Visual consistency quotient �.078 .150 .238 �.156 �.030Auditory stamina quotient �.044 .191 .080 �.033 �.186Visual stamina quotient �.177 .212 .148 �.042 �.184FAQ �.322 .441* .066 �.114 �.184AAQ �.227 .302 .016 �.097 �.159VAQ �.267 .294 .016 �.145 �.034Auditory vigilance quotient �.102 .406* .027 �.035 �.246Visual vigilance quotient �.718** .042 .096 �.411* �.213Auditory focus quotient �.522** .142 .230 �.062 �.107Visual focus quotient �.074 .104 .281 �.235 �.010Auditory speed quotient �.647** .079 .039 �.358* �.291Visual speed quotient �.524** .003 .290 �.050 �.126

* p < 0.05.** p < 0.01.

3.3. Correlation analyses

The Pearson correlation matrix between indexes ofEEG characteristics and scores of cognitive tasks inADHD group is shown in Table 4. For ADHD subjects,a significant negative linear relation was found betweenthe level of theta relative power and each of the visualprudence quotient, visual vigilance quotient, auditoryfocus quotient, auditory speed quotient, and visualspeed quotient. Statistically significant positive linearcorrelation was found between the level of alpha relativepower and each of the full scale attention quotient andthe auditory vigilance quotient. In addition, a significantnegative linear relation was found between the level ofthe theta/beta ratio and the scores of the visual vigilanceand the auditory speed quotient.

4. Discussion

The method of recording the relative power of EEGin detection analysis, which is used widely in ADHD

studies, can distinguish the normal group from the casegroup. Particularly, EEG recording under eyes-closedconditions is an extremely reliable method [12,13]. In1996, Chabot and Serfontein [6] reported that theEEG of the children with ADHD differed from thoseof normal children, results showed that the theta abso-lute power and the relative power of electrical wasincreased, mainly concentrated in the frontal lobe, espe-cially in prefrontal regions. Furthermore, alpha wavesand beta waves changed. In 1998, Clarke et al. [14] firstapplied the DSM-IV diagnosis of ADHD and analyzedthe brain electrical power of all subjects, revealing thattest electrode parts of the theta relative power and abso-lute power are significantly higher than those of the con-trol group. The brain electrical power of alpha and betaare decreased significantly. A previous study also sup-ports the results presented above [15].

This study found that the theta relative electricalpower of ADHD children was statistically significantlyhigher than that of the control group. The beta relativeelectrical power was lower than that of the control

810 T. Shi et al. / Brain & Development 34 (2012) 806–811

group to a statistically significant degree. These findingsrevealed that the EEG characteristics of ADHD chil-dren are abnormal, which may imply that the averageexcitation of the brain is decreased, and that the inhibi-tion of the cortical nervous system is decreased. Conse-quently, the ADHD children can not maintain anappropriate degree of attention. They may not be ableto specifically examine many things, engendering behav-ior such as inattention in class and hyperactivity. Thedysfunction of specific brain areas associated with theseabnormalities might explain characteristics of clinicalsymptoms observed in ADHD patients.

In recent years, with the development of the quantita-tive electroencephalogram technology for ADHD study,it has been recognized that the ratio of theta/beta canforecast the ADHD group and the control group well.Monastra et al. [9] research showed that theta/betaratios can differ between ADHD patients and others,with specificity and sensitivity reaching 86% and 98%,respectively. Many scholars have analyzed multiplequantitative EEGs and have reported an increase inthe value of theta/beta ratio among ADHD childrenas a common property, with further conclusions show-ing that when the value of theta/beta power is 3.08, spec-ificity and sensitivity both reached 94% [16]. Our resultsdemonstrate that the mean of theta/beta ratio in ADHDchildren (age: 6–14) is 4.22 (SD = 1.26), which was sig-nificantly higher than that of the control group.

Cognitive testing tasks, which include the prudencequotient, consistency quotient, and stamina quotient,reflect the subjects’ auditory and visual response controlability, especially the prudence quotient. It can measurethe omission of ADHD children in auditory and visual.The vigilance quotient, focus quotient, and speed quotientreflect auditory and visual attention abilities of subjects,especially the vigilance quotient. It can measure the errorof ADHD children in auditory and visual tasks. The sub-ject is inferred to have cognitive function disorder if thescore is less than 85. These results demonstrate that the“omission” of the ADHD group was significantly higherthan that of the control group, and that the number of“errors” in the ADHD group was significantly greaterthan that for the control group, thereby revealing thatADHD children might have cognitive function disorder.

The results of Pearson correlation analysis showedthat the visual prudence quotient and visual vigilancequotient each shared a significantly negative linear cor-relation with the level of theta relative power when thelevel of theta relative power raised the levels of omissionand numbers of errors. This result is consistent withresults of other studies. Swartwood et al. [17] reporteda significant linear correlation between the left foreheadtheta band change and the results of continuous opera-tion test. Daniel [18] found the power of theta andresponse delayed error were significantly higher thanin the control group.

The results of correlation analyses revealed thetapower improvement and the scores of signal detectiontasks significantly correlated to the error rate, whichimplied that abnormal theta activities affect ADHD chil-dren’s performance of cognitive tasks, especially the levelof attention. Some earlier studies identified that a signifi-cant increase of theta is consistent with decreased whitematter, glucose metabolism, and total brain capacity[19–22]. Increased theta wave activity of ADHD childrencan engender changes in attention, inability to concen-trate, and other defects of behavior in class. Normalexperiments also revealed a correlation between increasedtheta activity and sleep [23]. The theta activity level,reflecting a person’s cognitive level, also decreased [24].

Additionally, correlation between alpha activity andattention defects has been shown in go/no go out of task[25]. The alpha activity level reflects a certain arousalstate. The results of the present study showed that thelevel of alpha activities and full scale attention quotientshare a significant positive correlation, implying that thealpha level affects brain resource allocation.

The results of correlation analyses conducted for brainelectrical power and cognitive function of ADHD chil-dren show that improvement of the theta/beta ratio is sig-nificantly and negatively related to performance of visualmemory and the visual vigilance quotient. Manyresearchers have reported a significant and positive corre-lation between the level of EEG power and error rate insignal stop task [26]. Martine [27] reported that thetheta/alpha ratio and theta/beta ratio are negatively cor-related to response speed in a signal-to-stop task. Theresult of the survey also showed that the level of theta/beta ratio has a significant negative correlation with audi-tory speed. This result is inconsistent with that reportedabove. The increase of the theta/beta ratio might havecertain relations with hyperactivity and impulsive behav-iors [28]. A unified account of our findings is provided bya biophysical model presented by Rowe et al. [29].

This preliminary study includes some limitations: thesamples are small, and we did not carry out stratifiedanalysis including the age and sub-type of ADHD chil-dren. Moreover, we did not analyze the electrical activ-ity characteristics of various brains areas because of thelimited number of brain electrodes. We suggest thatfuture studies can use an electrode cap, comparing dif-ferent changes of EEG among various brain areas.

Acknowledgments

The work was supported by the Child DevelopmentBehavioral Research Center, Harbin MedicalUniversity.

Funding support from NSFC (30571576) is gratefullyacknowledged, in addition to the support of Yi Fu Pri-mary School, Harbin, for data collection.

T. Shi et al. / Brain & Development 34 (2012) 806–811 811

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