EEG Characteristics of Generalized Anxiety Disorder in Childhood

Abstract

Background: Anxiety is defined as subjective sense of worry, apprehension, fear and distress. When severe, anxiety disorder can affect a child's thinking, decision-making ability, learning and concentration. The aim of this study was to analyze the power spectra and spectrum weighted frequency (brain rate) as an indicator of general mental arousal in anxious patients and to compare the results with healthy preadolescents on the same age and gender. Methodology: The diagnosis was made according to two statistical manuals (DMSIV-R and ICD-10), medical history, neuropsychological assessment and QEEG. Results from spectra power for four conditions (eyes closed, eyes open, VCPT and ACPT) were exported to brain rate software. Results and discussion: Calculating factorial ANOVA we found that there was a strong statistical significance, between results of power spectra for all four bands and brain rate for sagittal and lateral topography between control group of healthy subjects vs. the observed anxious group. Conclusions: The results indicated the presence of decreased theta, alpha and beta activity, especially in central and midline regions.The identification of these characteristics in comparison with the HBI database is very simple and easy and has important implications for mean of QEEG in the assessment of children with anxiety. However, until more research is done, these abnormal QEEG patterns, can not be considered as pathognomonic of anxiety disorder.

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EEG Characteristics of Generalized Anxiety Disorder in Childhood

Aneta DemerdzievaDepartment of Psychophysiology, University Pediatric Clinic in Skopje, Faculty of Medicine, University St. Cyril and Methodius, Republic of Macedonia

Original paper

SUMMARYBackground: Anxiety is defined as subjective sense of worry, apprehension, fear and distress. When severe, anxiety disorder can affect a childs thinking, decision-making ability, learning and concentration. The aim of this study was to analyze the power spectra and spectrum weighted frequency (brain rate) as an indicator of general mental arousal in anxious patients and to compare the results with healthy preadolescents on the same age and gender. Methodology:

The diagnosis was made according to two statisistical manuals (DMSIV-R and ICD-10), medical history, neuropsychological assessment and QEEG. Results from spectra power for four conditions (eyes closed, eyes open, VCPT and ACPT) were exported to brain rate software. Results and discussion: Calculating factorial ANOVA we found that there was a strong statistical significance, between results of power spectra for all four bands and brain rate for sagittal and lateral topography between control group of healthy subjects vs. the observed anxious group. Conclu-

sions: The results indicated the presenceof decreased theta, alpha and beta activity, especially in central and midline regions.The identification of these characteristics in comparison with the HBI database is very simple and easy and has important implications for mean of QEEG in the assessment of children with anxiety. However, until more research is done, these abnormal QEEG patterns, can not be considered as pathognomonic of anxiety disorder.

Key words: anxiety, QEEG, spectrum weighted frequency (brain rate).

1. INTRODUCTION

Anxiety is dened as subjective sense of worry, apprehension, fear and distress. Children usually have a period of at least one month with recurrence of excessive, disproportionate and intrusive anxieties. The multiple anxieties and worries occur across at least two situations or activities and onset is always below age of 18. The disorder does not occur as part of a broader disturbance of emotions, conduct, personality, or of a pervasive developmental disorder (1).

When severe, anxiety disorder can aect a childs thinking, decision-making ability, and perceptions of the environment, learning and concentration. Brain imaging and functional studies have shown some evidence of abnormal function in several regions of the brain. The eld of electrophysiology has emerged as a potent tool in the exploration of brain function. Many authors reported specic electro-physiological patterns for anxiety disorders. The basal instability in

cortical arousal, as reected in measures of quantitative electroencephalography (QEEG) is common to most of the anxiety disorders. Resting electroencephalographic (EEG) measures tends to correlate with symptom sub-patterns and be exacerbated by condition-specic stimulation (2).

Among the techniques of functional brain imaging, QEEG oers many advantages, such as ideal temporal resolution in the millisecond time domain characteristic of neuronal information processing, no ionizing radiation and relatively inexpensiveness (3).

The brain controls all mental and bodily functions through the orchestrated use of dierent brain-wave frequencies. Slower brain-waves (delta and theta) cause us to function more slowly as in sleep or in a resting state. Higher frequencies (beta) cause us to function more quickly or intensely as in study, planning or working. QEEG involves not just recording the EEG but doing measurements quanti-

fying data concerning the amount of electrical activity occurring at particular frequencies or across dened frequency bands. The quantitative EEG uses computer algorithms that transform this raw EEG into quantitative displays that assist the clinician to recognize deviations from normal.

Over the past decades, electro-physiology has substantially contributed to the understanding of brain functions during normal development, and psychiatric conditions of children and adolescents (4). There is study from Australian authors which demonstrates that the eyes-closed and eyes-open conditions provide EEG measures differing in topography as well as power levels (5). Another study investigated age-related changes and sex dierences in the EEGs of normal children. It was obvious that relative delta and theta decreased and alpha and beta increased with increasing age. All of these indicated a developmental reduction in slow wave activity. (6,7). Maturational dierenc-

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Eeg Characteristics of Generalized Anxiety Disorder in Childhood

es occurs earlier at the midline than in the two hemispheres. Females were also found to have a developmental lag in the EEG compared with males (6).

In order to better understand the mechanisms of anxiety disorders in childhood it is important to evaluate the electroencephalogram parameters (spectra power and brain rate) and compare them with same parameters of healthy children paired with respect to age, gender and maternal scholastic level. We want to investigate whether anxiety symptoms were associated with special brain electrical activity during stressful situations in middle childhood. Many authors reported frontal electroencephalogram (EEG) asymmetries (8). Also, regulatory skills and behaviors developing rapidly during childhood play a critical role in linking frontal EEG asymmetries to emotion reactivity in children (9). There are ndings that highlight the need to focus on the early stability in physiological measuresspectra band asymmetry-which may be implicated later in developing behavioral problems (10).

Aer analysis of QEEG spectra power we introduced the calculation of spectrum weighted frequency or brain rate which is correlated to brain electric and metabolic activity. In particular, brain rate in further text- fb can serve as a preliminary diagnostic indicator of general mental activation (i.e. consciousness level), in addition to heart rate, blood pressure or temperature as standard indicators of general bodily activation (11).

Brain rate is calculated by following formula:

with:

where the index i denotes the frequency band (for delta i = 1, for theta i = 2, etc.) and Vi is the corresponding mean amplitude of the

electric potential or power. Following the standard ve-band classication, one has f i = 2, 6, 10, 14 and 18 respectively (11).

According to the fact that anxiety patients have same specic spectra power results in comparison with healthy subjects we expected that there would be deviations in brain rate values in comparison with healthy controls, either. So, the aim of this study was to analyze spectra power values and brain rate in dierent brain regions as an indicators of general mental arousal in anxiety patients and to compare the results with healthy preadolescents at the same age and the same gender.

2. METHODOLOGY

First group of anxiety preadoles-cents and teenagers was comprised of 30 individuals mean age 12.5, S.D.= 3.85 years, 10 female and 20 male patient. Group of healthy controls has 30 youngs at mean age of 12.17, S.D.= 3.91 years and at same gender as anxiety group.

All subjects were patients of the Department for Psychophysiology at University Pediatric Clinic in Skopje, in the period from January 2008 until December 2010. The diagnosis was made according two statistic manuals : DMS-IV-R (12) and ICD-10 (1). Detailed medical history, neuropsychological assessment and QEEG have been realized in all preadolescents. Inclusion criteria were: age between 7 and 18 years; absence of actual neurological impairments and absence of the use of psychoactive or psychotropic substances (screened by a previous anamnesis and clinical examination).

The control group were youngs free from history of any psycho-pathological or neurological symptoms, assessed through personal interview and self report. All subjects had normal or corrected to normal vision.

Informed consent for QEEG recording has been appropriately obtained from all participants. All of them were assessed with psychological testing (personality questionnaires: GAS and EPQ in a single session that lasted approximately 2

hours). Subjects were without any medication 48- hours before testing and were asked to have good sleep night before testing. All of them must have good meal before testing to avoid eects of hypoglycemia on brain function. They were seated in a comfortable chair with a backrest and were instructed not to move their eyes during testing.

EEG was recorded with Quantitative EEG equipment (Mitsar, Ltd.) amplier[from 19 electrodes, referenced to linked ears (on the International 10-20 system) with 250 Hz sampling rate in 0.3 70 Hz frequency range in the following conditions:1. eyes opened (EO) 5 minutes,

2. eyes closed (EC) 5 minutes,3. visual continuous performance task (VCPT) 20 minutes.

4. auditory continuous performance task (ACPT) 20 minutes.

The ground electrode was placed between Fpz and Fz. The impedance levels for all electrodes were set to 5 K. We used two stimulus GO/ NOGO task developed specically for HBI (Human Brain Institute) database. VCPT consisted of 400 trials and ACPT of 1000 trials. Subjects were instructed to press a button with index nger of their right hand for GO condition and not to press a button for NOGO condition.

Recorded results were referred and analyzed as data base montage. The 19 electrode positions were allocated to three sagittal regions: Frontal Fp1, Fp2, F3, Fz, F4, F7 and F8,

Central T3, T4, C3, Cz and C4, Posterior T5, T6, P3, Pz, P4, O1 and O2

and according three lateral regions:

leFp1, F3, F7, T3, C3, T5, P3, O1,

midlineFz, Cz, Pz and rightFp2, F4, F8, T4, C4, T6, P4, O2,by averaging the power with

in each region. Scale: 50 mcV/cm, speed 30 mm/sec, time constant 0.3 sec, low frequency lter 30 Hz. The analysis was made aer

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eliminating artifacts resulting from movements, large scale muscle tension, sweat, and large eye movements. Vertical and horizontal eye movement artifact correction was done by means of Independent Component Analysis (ICA). ICA is an information maximization algorithm that derives spatial lters by blind source separation of the EEG signals into temporally independent and spatially xed components.

Then recordings for four conditions (eyes closed, eyes open, VCPT and ACPT ) were sufficient for calculation of spectra power values for all 19 electrodes. Obtained results for power spectra were exported to brain rate soware and then calculated for each region separately. The QEEG spectra power data and data for brain rate were analyzed using Statistica soware (version 7.0). A series of repeated measures analysis of varianceFactorial ANOVA was performed using the factors: sagittal topography (frontal, central and posterior region), lateral topography (le, midline and right), measurement condition (EO, EC, VCPT and ACPT) and group (anxiety vs. controls) for absolute spectra power (V2) and brain rate values. Then post hoc Boneroni test was performed to explain signicant interactions. Due to space reasons only signicant eects and interactions between groups are reported here.

3. RESULTS

All individual spectra of the anxiety subjects were analyzed and compared with the HBI database. A post hoc Bonferroni test was performed in order to determinate which bands were contributing the most to the multivariate eect in function of the QEEG characteristics of groups and condition. A summary of the signicant dierence is shown in Table1 for each of power spectra bands according group and condition.

Delta (0.5-4 Hz)

The factorial ANOVA/MANOVA results have shown that the total power of delta depends on the group qualication F(3, 174.0)=29.28, p=.000 and the highest value of delta was found in posteri-

or region and the lowest in frontal region (Fig.1, le panel). According condition again the highest value was found in posterior region for all four conditions F(9, 423.6) = 2.42, p =.011 (Fig.1, right panel) with highest value in eyes closed and lowest in eyes open condition.

Concerning the lateral topography, there was no statistical differences in delta power between groups and conditions. The lowest value of delta power was found in anxious group in midline region

and the highest in anxious group in posterior region. Results according condition for sagittal and lateral topography (Table 1) present no statistical signicance for absolute delta power.

Theta (4-8 Hz)

The amplitude of the total theta power for sagittal topography depends on groups qualication F(3, 174) =9.8165, p=.00001 (Fig.3, le panel) and from condition F(9, 423.62)=.64720, p=.75653 (Fig.3, right panel). As it can be seen from

Group effects Condition effects Normals vs.

Anxious EO vs.EC EO vs.VCPT EC vs.VCPT EC vs.ACPT Delta F 0.048540

CP 0.031479 LMR

Theta F

C 0.036668

P 0.030099 LM 0.003014R

Alpha F 0.000000 0.000044 0.000051

C 0.000992 0.000005 0.000002 0.000002 P 0.000000 0.000000 0.000000 L 0.000000 0.000000 0.000000 M 0.003179 0.000000 0.000000 0.000000 R 0.000000 0.000000 0.000000 Beta F 0.000267 0.000000 0.000027

C 0.001430 0.036097 P 0.031912L 0.006696 0.000109 0.022837 M 0.000000R 0.005309 0.000126 0.008121 F-frontal; C-central; P-parietal; L-left; M-midline; R-right; EC-eyes closed; EO-eyes opened; VCPT-visual contionous performance test; ACPT-auditory contionous performance test.

Table 1: Summary of significant post hoc Bonferroni (for spectra power) p-values between groups, conditions and regions.

Brainwaves Regions

Figure 1. Absolute delta power values for sagittal topography according group -left panel and according conditions-right panel (EC-eyes closed, EO-eyes opened, VCPTvisual continuous performance test, ACPT-auditory continuous performance test)

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Figure 2. Absolute delta power values for lateral topography according groups- left panel and according conditions-right panel (EC-eyes closed, EO-eyes opened, VCPTvisual continuous performance test, ACPT-auditory continuous performance test)

Figure 3. Absolute theta power values for sagittal topography according groups -left panel and according conditions-right panel (EC-eyes closed, EO-eyes opened, VCPTvisual continuous performance test, ACPT-auditory continuous performance test)

Figure 4. Absolute theta power values for lateral topography according groups- left panel and according conditions- right panel (EC-eyes closed, EO-eyes opened, VCPTvisual continuous performance test, ACPT- auditory continuous performance test)

Figure 5. Absolute alpha power values for sagittal topography according groups -left panel and according conditions-right panel (EC-eyes closed, EO-eyes opened, VCPTvisual continuous performance test, ACPT- auditory continuous performance test)

the Figure 3 the amplitude values of theta are the highest in posterior region and in eyes closed condition according sagittal topography.

While, concerning the lateral topography the highest value of theta was obtained at the middle region F(3, 174)=9.8165, p=.00001 (Fig.4, le panel) in group of normal individuals. Absolute value of theta power does not depend on condition classication F (9, 423.62)=.64720, p=.75653 (Fig.4, right panel). A post hoc Bonferroni test showed statistical signicant dierence in theta power between normals vs. anxious in centralconcerning sagittal distribution and in midline regionsconcerning lateral distribution (Table 1). For the condition periods we found that only for posterior regions there is higher amplitude of theta in EO vs. EC tasks, while there is no statistical dierence for the other sagittal and lateral regions (Table1).

Alpha (8-12 Hz)

The analysis of the sagittal topography of alpha shows greater power for alpha in the posterior re-

gions, which is expectable, than in the frontal and central ones F(3, 174)=11.000, p=.00000 (Fig.5, le

panel). Concerning condition F(9, 423.62)=20.406, p=0.0000 the highest value of absolute alpha power was obtained in posterior region in eyes closed condition (Fig.5, right panel).

Concerning the lateral topography the highest value of alpha is at the sides and the lowest in the middle region, F(3, 174)=7.8811, p=.00006 (Fig.6, le panel). For all three sites highest alpha was recorded in eyes closed condition F(9, 423.62)=13.935, p=0.0000 (Fig.6, right panel). A post-hoc Bonferroni test showed statistically signicant dierence of alpha power in normals vs. anxious for central and midline region (Table 1). For the condition periods we found statistically signicant dierence of alpha in EC compared to EO, VCPT and the ACPT tasks (Table 1).

Beta (12-30 Hz)

The sagittal topography results showed that there is no statistical difference for beta in sagittal topography according groups F(3, 174)=1.9737, p=.11970 (Fig.7, le panel) but according conditions there is strong statistical signicance F(9, 423.62)=7.8783, p=.00000 with highest value in frontal region during tasks -VCPT and ACPT (Figure 7, right panel). Later-

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Figure 6. Absolute alpha power values for lateral topography according groups- left panel and according conditions- right panel (EC-eyes closed, EO-eyes opened, VCPTvisual continuous performance test, ACPT- auditory continuous performance test)

Figure 7. Absolute beta power values for sagittal topography according groups -left panel and according conditions-right panel (EC-eyes closed, EO-eyes opened, VCPTvisual continuous performance test, ACPT-auditory continuous performance test)

Figure 8. Absolute beta power values for lateral topography according groups- left panel and according conditions- right panel (EC-eyes closed, EO-eyes opened, VCPTvisual continuous performance test, ACPT- auditory continuous performance test)

Group effect

al topography results showed strong statistical signicance for absolute beta power according groups F(3, 174)=22.006, p=.00000 with highest values at the sites (Figure 8, le panel) and nearly the same according conditions F(9, 423.62)=5.1106, p=.00000-especially for tasks VCPT and ACPT (Figure 7 right panel).

A post-hoc Bonferroni test showed greater dierence of beta power in normals vs. anxious only for mid-line region (Table 1). For the condition periods we found statistical signicant dierence for EC vs. EO only in posterior region and for EO vs. VCPT and EO vs. ACPT for both (lateral le and right) sites (Table 1).

Maximum values of fb for sagittal

topography were obtained in frontal and central regions, while the minimum in posterior region (Figure 9, le panel). Anyway these results are statistically signicant lower than those in healthy controls. These is in correlation with results obtained for spectra power. According conditions results were statistically signicant lower in anxious

group and maximum value for fb

were obtained in frontal region during VCPT and minimum value in posterior region during ACPT.

Maximum values of fb for lateral topography are obtained in the le and right sides (Fig.10, le panel), which indicates higher excitability of lateral regions. According conditions in lateral topography the lower fb values were obtained in midline site for all four conditions (Fig.10, right panel). Does these fb

results suggest hypoarausal in children with anxiety disorders in opposite to our expectations that these children will be hiperaraused ?

Maybe obtained results are illustrating heterogeneous and multifactorial character of anxiety disorders in childhood and dierent ways of ghting with problems in preadolescence.

4. DISCUSSION

The purpose of the present study was to determinate the QEEG characteristics of children with generalized anxiety disorder. While EEGs of the adults with anxiety have been extensively exanimated, there are just few published studies for children with the same disorder. EEG assessment is not standard procedure in evaluation of these children. Obtained results between groups have shown statistical signicant dierence in absolute delta power for anxious group in frontal and posterior region. Usually delta waves in eyes open condition and during tasks have been observed as pathological events, but Kropotov found that slow waves are present in EEG during all states from the deep sleep to the state of focused attention (13). When there is a decrease in CNS activation level, there is an decrease in mean frequency of alpha and a increase in slow activity. In our results increase of slow (del-

Normals vs. Anxious

Frontal Central Posterior Left Midline Right0.000000 0.000006 0.000595 0.000000 0.007569 0.000000

Conditioneffects EC vs. EO 0.018463 0.000826 0.009325

EO vs. VCPT 0.000215EC vs. VCPT 0.000000 0.000201 0.006644 EC vs. ACPT 0.000047 0.000000 0.000776

Table 2. Summary of significant post hoc Bonferroni (for brain rate results) p-values between groups, conditions and regions.

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Figure 9. fb values for sagittal topography topography according groups- left panel and according conditions- right panel (EC-eyes closed, EO-eyes opened, VCPTvisual continuous performance test, ACPT-auditory continuous performance test)

Figure 10. fb values for lateral topography according groups- left panel and according conditions-right panel (EC-eyes closed, EO-eyes opened, VCPTvisual continuous performance test, ACPT- auditory continuous performance test)

ta) activity was found in posterior region while in frontal region delta activity was decreased. This is opposite from attention and same aective disorders where slow activity is increased frontally (14).

Slow activity has been reported in areas of cortical dysfunction. Concerning theta power we found statistical signicant dierence between groups in central region according sagittal topography and in midline region according lateral topography. In both situations absolute theta power in anxious patients was decreased in comparison with absolute theta power in healthy subjects. For posterior region only in EO vs. EC condition dierence was statistical signicant. Kropotov (13) reported that frontal midline theta in resting state can be found in the raw EEG of small group of normal population which is low in anxiety and in neurotic scale and high in extraversion scale. Opposite to this, can decreased theta activity in central and midline region be specic for anxiety disorder? In context of these Gasser at al (15) reported that more low frequency activity was recognized at the midline than

at the two hemispheres in EEG of healthy school age children and adolescent.

Alpha rhythms are maybe the most investigated rhythms of human brain activity. Beside this the mechanisms of their generation are still poorly understood. The power of alpha activity is inversely correlated to metabolic function of the corresponding cortical area giving rise to a functional explanation of alpha rhythms as idling rhythms of the cortex. Regarding absolute alpha power statistical signicant difference between groups was found again in central region according sagittal topography and in midline region according lateral topography. The same as for absolute theta power, in both situations absolute alpha power in anxious patients was decreased in comparison with absolute alpha power in healthy subjects. As alpha band promotes mental resourcefulness and enhances overall sense of relaxation it is obvious that lower alpha will be associated with opposite conditions which are usually part of clinical presentation of anxiety. Decreased absolute alpha power, same like decreased the-

ta power in central and midline region may be a specic EEG marker for anxiety. This maybe is eect of raised metabolic function in mid-line and central regions. Of course the highest value of alpha power was obtained in eyes closed condition and between all conditions we obtained statistical signicant difference in EC vs. EO, EC vs. VCPT and EC vs. ACPT. There was no statistical signicant dierence between EO, VCPT and ACPT, which just conrms the meaning of alpha as an idling rhythm of the brain.

At start of this research, according Lubars (16) theory we expected that children with anxiety will have excess of beta especially in frontal region. At the end we found very unexpected results: mean absolute beta power for anxious children was lower than in healthy controls. Usually beta rhythm is associated with focused attention (13). From our point of view decrease of beta activity in midline region in anxious patients maybe is reason for problems with an attentionsymptom which was reported from all parents in rst interview. Mean value of absolute beta power for posterior region was higher in anxious group than in healthy controls but this dierence was not statistical signicant.

Results from brain rate were in correlation with results obtained for spectra power. The introduced concept of brain-rate, representing the weighted mean frequency of the potential/power EEG spectrum, may serve as a preliminary indicator of general mental activation (mental arousal) level (11). It could be concluded that the measurement of fb,

combined with QEEG, can be used as an objective indicator of arousal related to the level of consciousness.

The main question is if we can dene this as specic for childhood anxiety and this kind of CNS dys-function. Many authors found that dierent brain activity can be presented clinically very similarly. In one study where QEEG assessment was made to big group of patients with mental disorders the most frequent abnormality was a decrease in slow (delta and/or theta) bands, either alone, or with beta increase,

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or with alpha decrease, followed by increase in beta band. No normal subject showed delta and/or theta decrease. However, no pattern was specic of any entity, and patients within the same diagnostic may present dierent patterns (19).

Our results show that anxiety is very complex and are correlated with decreased theta, alpha and beta power. As alpha activity corresponds with relax state than decreased alpha was expected. Following Lubars theory of hypoarausal, where it was hypothesized that, if reduced beta and increased theta represents underarousal, than what can we say for anxious children who have decreased theta, alpha and beta activity? Increased beta power in eyes closed for posterior region and in midline region, compared to the eyes opened condition, can be referred as inner-arousal, already noted in previous studies of Pop-Jordanov & Pop-Jordanova and Cooper et al. (17,18). But in other conditions especially eyes open, where decreased beta is dominant can we dene this condition as hypoarausal?

At the end dening of specic EEG patterns for anxiety disorder is very important if we want to apply neurotherapy. To achieve a relaxed state oen we ask from child to increase EEG activity between 11-15 Hz high alpha and low beta and teach them to breathe diaphragmatically at a rate of about 6 breaths per minute. This protocol will help child to remain calm even under stress and remain in a state of problem solving concentration. This sustaining of focused concentration is a key to success both in school and in life (20, 21).

In our group children with anxiety have specic QEEG characteristics and they are in correlation with behavioural presentation of disease. We can conclude that anxiety is heterogeneous disorder either in clinical expression or in the electrophysiological basis and more research is needed in this direction.

5. CONCLUSION

The present results have important implications for the use of QEEG in the diagnosis of anxiety

in children. This study investigates the presence of the QEEG changes in comparison with group of healthy preadolescents at same age and same gander. The results indicated the presence of decreased theta, alpha and beta activity especially in central and midline regions. Decrease in the delta and theta bands of the QEEG can be regarded as a specic sign of brain dysfunction. It could be concluded that the measurement of fb, combined with

QEEG, can be used as an objective indicator of arousal related to the level of consciousness.

The identication of these characteristics in comparison with the HBI database is very simple and easy and has important implications for mean of QEEG in the assessment of children with anxiety. Eorts aimed at developing QEEG into a tool to identify individual specics of anxious preadolescents and this implies the importance of such tool to be reliable in clinical practice, especially when neurofeedback therapy is applied. These quantitative electroencephalogram ndings in children with anxiety have a clear relation with psychological measurements and could be due to brain immaturity. However, until more research will be done, this abnormal QEEG patterns, can not be considered as pathognomonic of anxiety disorder.

Abbreviations: QEEG-quantitative electroencephalography; fb-brain

rate; EO-eyes open; EC-eyes closed; VCPT-visual continuous performance test; ACPT- auditory continuous performance test.

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Corresponding author: Aneta Demerdzieva, MD. Department for Psychophysiology, University Pediatric Clinic, Faculty of Medicine, University St. Cyril and Methodius, Vodnjanska 17, 1000 Skopje, Macedonia.

E-mail: [email protected]

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