|Year : 2015 | Volume
| Issue : 3 | Page : 176-182
Quantitative EEG in autistic children
Hala Elhabashy1, Omnia Raafat2, Lamia Afifi1, Hebatallah Raafat1, Khaled Abdullah1
1 Clinical Neurophysiology Unit, Cairo University, Cairo, Egypt
2 Department of Psychiatry, Cairo University, Cairo, Egypt
|Date of Submission||15-Apr-2015|
|Date of Acceptance||29-Apr-2015|
|Date of Web Publication||13-Aug-2015|
Clinical Neurophysiology Unit, Cairo University, Kasr Al Ainy St., Cairo 11562
Source of Support: None, Conflict of Interest: None
Autism spectrum disorder is a neurodevelopmental disorder that is characterized mainly by difficulties in social interaction and communication. Studies have suggested abnormal neural connectivity patterns in the brains of patients with autism.
The current work aimed to study the quantitative electroencephalography (EEG) findings in autistic children and compare it with those of normal controls.
The EEG recordings of 21 autistic children between 4 and 12 years of age were compared with those of 21 age-matched and sex-matched controls under an eyes-opened condition. Differences in cerebral functioning were examined using measurements of absolute and relative power and intrahemispheric and interhemispheric coherence.
There were statistically significant differences in EEG power between the autistic and control groups, with greater absolute of delta and theta power especially at the frontal region in autistic children. There was also global reduction in relative alpha and beta power especially in the frontal, central, and posterior regions in autistic children. In addition, there was a pattern of underconnectivity and overconnectivity when measuring the intrahemispheric and interhemispheric coherence in the autistic compared with the control group.
These results suggested regional dysfunction of the brain in autistic children, along with a pattern of abnormal neural connectivity, which could explain the autistic symptomatology.
Keywords: autism spectrum disorder, autism, coherence, power, quantitative electroencephalography
|How to cite this article:|
Elhabashy H, Raafat O, Afifi L, Raafat H, Abdullah K. Quantitative EEG in autistic children. Egypt J Neurol Psychiatry Neurosurg 2015;52:176-82
|How to cite this URL:|
Elhabashy H, Raafat O, Afifi L, Raafat H, Abdullah K. Quantitative EEG in autistic children. Egypt J Neurol Psychiatry Neurosurg [serial online] 2015 [cited 2021 Oct 27];52:176-82. Available from: http://www.ejnpn.eg.net/text.asp?2015/52/3/176/162031
| Introduction|| |
Autism spectrum disorder (ASD) and autism are both terms for a group of complex neurodevelopmental disorders characterized by varying degrees of social interaction and verbal and nonverbal communication impairments. These features appear during early childhood, tend to persist life-long, and often lead to poor outcome during adulthood . Recent epidemiological studies estimate the prevalence of ASD to be one in 88 children in the USA . Given that an autistic disorder-identifying laboratory test is not available, diagnoses are primarily based on detailed clinical interview and behavioral observation, often using formatted scales such as the Childhood Autism Rating Scale (CARS) and the Checklist for Autism in Toddlers .
Despite extensive research, there is still much debate about the morphological, functional, and neuropsychological characteristics of the 'autistic' brain and thus the neural basis of altered behaviors in ASD remains largely unclear. Several neuroimaging and neurophysiological techniques have been used to understand the correlation between brain functionality and autistic behavior .
Quantitative electroencephalography (QEEG) has attracted considerable interest in recent times and it is increasingly used in studies on neurodevelopmental disorders, especially ASD. QEEG is a neurophysiological test that utilizes computerized mathematical analysis to convert the raw EEG data into different frequency ranges. These ranges mainly include delta, theta, alpha, beta, and gamma frequencies. The absolute power, relative power, asymmetry, and brain connectivity or coherence in each frequency band can be calculated. Coherence measures the degree of coupling between signals generated by specific neuronal assemblies, which are located in proximity of the recorded electrodes. Coherently oscillating neuronal assemblies exhibit electrical activity with common spectral properties. When a coherent oscillation occurs these neural groups can effectively communicate, because their communication windows for input and for output are open at the same time . Therefore, EEG coherence provides information on the connectivity between regions underlying a pair or more of recording electrodes. Previous studies have shown that neural connectivity can be impaired in patients with ASD. There is evidence for both a pattern of hypoconnectivity and regions of hyperconnectivity in autism .
Therefore, QEEG has the ability to analyze the background activity of EEG with sophisticated statistical techniques to reveal patterns invisible to the naked eye . It has the advantage of being an easy, relatively inexpensive, and noninvasive test compared with some other neuroimaging techniques such as fMRI. Several studies have attempted to utilize QEEG as a screening method for neurodevelopmental disorders. For instance, Coben et al.  have demonstrated that autistic children can be distinguished from normal children by their EEG alone at a rate more than 88%, as they were able to show that certain patterns of regional dysfunction could be identified through the QEEG. Furthermore, it was suggested that QEEG has the potential of monitoring treatment outcomes in ASD children .
| Aim of work|| |
The goal of our research was to study the QEEG findings in autistic children and compare it with age-matched controls. Data from absolute and relative EEG powers and EEG coherence were analyzed in an attempt to understand the functional brain abnormalities and neuroelectrophysiological characteristics of children with autism. If striking differences can be found between children with ASD and normal children, it might be possible to endorse the utility of QEEG as an inexpensive screening test for ASD.
| Patients and methods|| |
This study was performed on 21 children (18 boys and three girls) between 4 and 12 years age presenting with ASD. Diagnosis of ASD was based on the Diagnostic and Statistical Manual of Mental Disorders, 5th ed. . These patients were selected from the Child Psychiatry Outpatient Clinic, the Center of Social and Preventive Medicine, Abou El-Reesh hospitals, Cairo University. Patients were excluded from the study if they had any major physical handicap such as motor, visual, or auditory impairment, major medical illness, inborn errors of metabolism, any neurological disorder, past history of repeated head trauma, history of convulsive disorders, or were taking medications during the study period. The experimental procedures used were approved by the Ethics Committee of the Faculty of Medicine, Cairo University. Informed oral or written consent was obtained from the caregivers of the autistic children participating in the study.
The present study included 21 healthy age-matched and sex-matched normal children (16 boys and five girls) between 4 and 12 years of age, with no history of physical, neurological, or psychiatric illness. This group was selected from local primary schools after written or oral consent was obtained from their parents. Children were excluded from the control group if spike wave or sharp wave activity was present in the EEG.
During the interview, parents of all participants were interviewed using a full clinical psychiatric sheet. Parents were asked about the developmental and medical history of the children, including the children's current and past medical conditions. The CARS was applied to the autistic children to assess the severity of autism .
The EEG recording was obtained under an eyes-opened resting condition for 10 min with the children seated on a comfortable chair. The EEG for both groups was recorded using Win EEG System (Novatech EEG Inc., Mesa, Arizona, USA ) and Mitsar QEEG software (Mitsar Co., Saint Petersburg, Russia) with a sampling rate of 256 Hz. The settings were set on low-frequency filters of 0.3 Hz, high-frequency filter of 30 Hz and a 50-Hz notch filter. The children were fitted with a 19-electrodes cap according to the standard 10-20 international EEG configuration. Electrode gel was applied on each electrode site. Electrode-skin impedance was maintained below 5 kΩ. All measurements were taken referentially with linked ears. After completion of the testing, all of the EEG data were analyzed to reject artifacts such as eye movements, blinks, or muscle activity. A minimum of 75 s of artifact-free EEG was available for analysis. After elimination and removal of artifacts, the record was divided into 4-s epochs, which were subjected to Fourier power spectral analysis, to determine the magnitude of each frequency band in microvolt.
The frequency bands were classified into delta (0.5-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), and beta (13-25 Hz). Data from 19 electrode sites, including FP1, FP2, F3, F4, F7, F8, Fz, T3, T4, T5, T6, C3, C4, Cz, P3, P4, Pz, O1, and O2 were analyzed. The absolute power (the amount of energy in μV 2 ) and the relative power (the percentage of total power within each frequency band) were calculated for each frequency band in each electrode site. Thereafter, the absolute and relative powers from 19 electrodes were averaged for nine regions. These regions were the left frontal (Fp1, F3, and F7), the right frontal (Fp2, F4, and F8), the left central (T3 and C3), the right central (T4 and C4), the midline frontal (Fz), the midline central (Cz), and the midline posterior (Pz).
Coherence indices were computed for six intrahemispheric (Fp1-F3, Fp2-F4, T3-T5, T4-T6, C3-P3, and C4-P4) electrode pairs and eight interhemispheric (Fp1-Fp2, F7-F8, F3-F4, C3-C4, T3-T4, T5-T6, P3-P4, and O1-O2) electrode pairs.
Data were analyzed using SPSS win statistical package, version 21 (SPSS Inc., Chicago, Illinois, USA). Numerical data were expressed as mean and SD. Qualitative data were expressed as frequency and percentage. The χ2 -test (Fisher's exact test) was used to examine the relation between qualitative variables. For quantitative data, comparison between two groups was made using either Student's t-test or the Mann-Whitney U-test (nonparametric t-test) as appropriate. All tests were two-tailed. A P-value less than 0.05 was considered significant.
| Results|| |
The patients and controls were age-matched and sex-matched without significant differences in age or sex (P = 0.49 and 0.69, respectively).
Mean absolute electroencephalography power in different brain regions
The results revealed that the autistic group demonstrated significantly higher absolute delta power in the left and right frontal regions (P = 0.003 and 0.05, respectively). The absolute theta power was also significantly higher in the left and right frontal regions (P = 0.015 and 0.039, respectively). The absolute power of alpha and beta frequency bands was reduced in the right central region (T4-C4) (P = 0.05 and 0.045, respectively) in the patient group [Table 1] and [Table 2].
|Table 1: Comparison of the mean absolute power of delta and theta frequency bands in different brain regions between the patient and control groups|
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|Table 2: Comparison of mean absolute power of alpha and beta frequency bands in different brain regions between the patient and control groups|
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Mean relative electroencephalography power in different brain regions
There was no statistically significant difference between the autistic and the control group as regards the relative EEG power of delta band. The relative theta power was greater in the autistic group compared with the control group at the right central region (P = 0.05).
There was a statistically significant difference between the autistic and control groups as regards the relative EEG power of alpha frequency band on the left frontal, right frontal, left central, right central, and right posterior regions (P-value was 0.011, 0.017, 0.001, 0.013, and 0.019, respectively) with reduced relative alpha power in autistic children.
In addition, the autistic children demonstrated significant reduction in the relative EEG power of beta band on the left frontal, right frontal, left central, right central, left posterior, and right posterior regions (P = 0.004, 0.006, 0.043, 0.012, 0.011, and 0.043, respectively) [Table 3] and [Table 4].
|Table 3: Comparison of the mean relative power of delta and theta frequency bands among different brain regions between patients and controls|
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|Table 4: The mean relative power of alpha and beta frequency bands in different brain regions among patients and controls|
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Coherence differences between the autism and control groups
The autistic children showed significantly reduced coherence in delta, theta, and alpha frequency bands (P = 0.005, 0.005, and 0.004, respectively) compared with the control group [Table 5].
|Table 5: Intrahemispheric coherence of delta, theta, alpha, and beta frequency bands|
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In the temporal regions, the autistic group showed greater interhemispheric coherence compared with the control group in the delta band, with significant difference between the two groups (P = 0.05). However, there was reduced interhemispheric coherence in the theta band in the central/parietal/occipital regions, with significant difference between the two groups (P = 0.05) [Table 6]. We did not find any difference as regards interhemispheric coherences in the alpha or theta bands between the patients and controls [Table 7].
| Discussion|| |
This study was conducted on 21 children diagnosed with ASD and 21 healthy age-matched and sex-matched children by means of QEEG. Our results revealed that children with autism demonstrated significant differences in EEG power compared with the controls. Absolute power of delta and theta frequency bands was greater in autistic children, especially over the frontal region. There was also a reduction in absolute alpha and beta frequency band power over the central region for the autistic group. In addition, we found differences in relative EEG power of autistic children compared with the age-matched control group. Relative theta band power was greater in autistic children, especially over the right central region. However, the relative EEG power of both alpha and beta frequency bands was reduced across the entire scalp, with a maximum in the frontal and posterior regions. Our findings of greater power in absolute delta and theta over the frontal cortex of autistic children are consistent with the results of previous studies that found excess delta power in the frontal cortical area in autistic children  and excess theta power ,. These findings may imply a difficulty in the functional integration of the frontal region. Previous research has supported this finding by showing impairment in frontal lobe neuronal integrity , malformation of minicolumn microcircuitry , frontal lobe enlargement, and atypical patterns of brain connectivity in autism . Excess theta activity was previously reported in children with mental activity impairments, including learning disabilities and attention deficit/hyperactivity disorder .
In the present study, there was reduction in the relative EEG power of alpha frequency band across the entire scalp maximum in the frontal and posterior regions. It was previously reported that ASD patients demonstrated significantly lower relative alpha power . As the alpha activity is related to social engagement, a reduced alpha activity in the frontal region could reflect, a lack of active engagement in social information processing. The current study revealed a significant reduction in the absolute power of beta band at the right central region and widespread reduction in relative power of beta band, especially at the frontal and posterior regions. Reduction in alpha and beta power over the posterior region in autistic children suggests that this is an area of abnormal functioning. Previous investigations have reported posterior cortical dysfunction in ASD patients, including deficits related to eye gaze , facial processing , and social cognition . Stroganova et al.  have also shown impaired visual processing using an event-related study in similar brain regions.
The coherence results in the present study revealed evidence of atypical pattern of brain connectivity. Intrahemispheric coherence was lower in autistic children than in controls, suggesting a pattern of underconnectivity within the same hemisphere in delta, theta, and alpha band coherence. Interhemispherically, delta coherence was elevated in autistic children across the temporal region (T3-T4 and T5-T6), whereas theta band coherence was lower over the central, parietal, and occipital regions (C3-C4, P3-P4, and O1-O2) compared with the control group. Similar results were reported previously by Coben et al. , who found decreased intrahemispheric delta and theta coherences in autistic children, as well as low interhemispheric coherence in the delta, theta, and beta bands, in various brain regions. The combination of higher and lower coherence in different regions of the brain for the different bands in our study was previously reported in a study carried out on ASD adults . It suggested that ASD cannot be simply described as being associated with underconnectivity or overconnectivity, but rather a form of abnormal connectivity that varies between different brain regions . High local connectivity and low long-range connectivity may occur together because of problems with synapse activity or formation . Another interesting theory suggests that there is initial overgrowth of the white matter tracts during the first 2 years of life, often followed by arrested growth and impaired connectivity patterns and myelination of white matter .
It was suggested that decreased connectivity for autistic patients, particularly in the left temporal and frontal regions could be associated with language and communication problems, whereas increased coherence in autistic patients could be explained as a compensatory mechanism of the autistic brain that establishes atypical cortical networks to replace deficit function normally associated to more localized networks .
Numerous neuroimaging studies reported widespread structural and functional abnormalities in neural networks involving the fronto-temporo-parietal cortex, limbic system, cerebellum and ventricular volume in ASD patients . Functional MRI connectivity studies have shown low frontal-to-parietal connectivity  and reduced anterior-to-posterior connectivity in general . It was shown that ASD patients do not activate the fusiform gyrus during facial processing as normal people do, suggesting that this is because of a disordered neural connectivity .
The QEEG can be helpful in the treatment of ASD through its utilization in neurofeedback. Once disturbances in functioning are identified, the patient can start biofeedback training using a computerized EEG system and a monitor that seeks to normalize the brain wave patterns. The child watches a monitor with a game-like program that moves after they produce the correct EEG patterns. After practice, the brain learns these healthy new patterns and eventually these new patterns become established leading to a change in the child's behavior . Thus, the QEEG results can be used to develop personalized treatments - for example, by using neurofeedback therapy - and evaluate the effects of therapies through quantitative measures of brain activity.
In our study, we conclude that children with ASD have QEEG dysfunctions that underlie their symptomatology. The QEEG power and coherence results are consistent with the neuropathological involvement of autism. Our data suggest regional dysfunction of the brain in ASD along with patterns of anomalies of neural connectivity. This is supported by other EEG and fMRI studies suggesting that neural connectivity abnormalities may be a major deficit leading to the symptoms of ASD. Therefore, QEEG testing, which is an easy, inexpensive procedure, can be beneficial in the assessment and diagnosis of ASD, thus paving the way for the development of tailored intervention strategies.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders
. 5th ed. Washington, DC: American Psychiatric Association; 2013.
Centers for Disease Control and Prevention. Prevalence of autism spectrum disorders - autism and developmental disabilities monitoring network, 14 sites, United States: MMWR Surveillance Summaries Atlanta, GA, USA: Centers for Disease Control and Prevention 2012; 61
McAlonan GM, Cheung V, Cheung C, Suckling J, Lam GY, Tai KS, et al
. Mapping the brain in autism. A voxel-based MRI study of volumetric differences and intercorrelations in autism. Brain 2005; 128
(Pt 2): 268-276.
Narzisi A, Muratori F, Calderoni S, Fabbro F, Urgesi C. Neuropsychological profile in high functioning autism spectrum disorders. J Autism Dev Disord 2013; 43
Fries P. A mechanism for cognitive dynamics: neuronal communication through neuronal coherence. Trends Cogn Sci 2005; 9
Murias M, Webb SJ, Greenson J, Dawson G. Resting state cortical connectivity reflected in EEG coherence in individuals with autism. Biol Psychiatry 2007; 62
Billeci L, Sicca F, Maharatna K, Apicella F, Narzisi A, Campatelli G, et al.
On the application of quantitative EEG for characterizing autistic brain: a systematic review. Front Hum Neurosci 2013; 7
Coben R, Clarke AR, Hudspeth W, Barry RJ. EEG power and coherence in autistic spectrum disorder. Clin Neurophysiol 2008; 119
Sheikhani A, Behnam H, Mohammadi MR, Noroozian M, Mohammadi M. Detection of abnormalities for diagnosing of children with autism disorders using of quantitative electroencephalography analysis. J Med Syst 2012; 36
Schopler E, Reichler RJ, DeVellis RF, Daly K. Toward objective classification of childhood autism: Childhood Autism Rating Scale (CARS). J Autism Dev Disord 1980; 10
Chan AS, Sze SL, Cheung MC. Quantitative electroencephalographic profiles for children with autistic spectrum disorder. Neuropsychology 2007; 21
Pop-Jordanova N, Zorcec T, Demerdzieva A, Gucev Z. QEEG characteristics and spectrum weighted frequency for children diagnosed as autistic spectrum disorder. Nonlinear Biomed Phys 2010; 4
Stroganova TA, Nygren G, Tsetlin MM, Posikera IN, Gillberg C, Elam M, Orekhova EV. Abnormal EEG lateralization in boys with autism. Clin Neurophysiol 2007; 118
Murphy DG, Critchley HD, Schmitz N, McAlonan G, Van Amelsvoort T, Robertson D, et al
. Asperger syndrome: a proton magnetic resonance spectroscopy study of brain. Arch Gen Psychiatry 2002; 59
Courchesne E, Pierce K. Why the frontal cortex in autism might be talking only to itself: local over-connectivity but long-distance disconnection. Curr Opin Neurobiol 2005; 15
Hill EL. Executive dysfunction in autism. Trends Cogn Sci 2004; 8
Clarke A, Barry R, McCarthy R, Selikowitz M. EEG analysis inattention-deficit/hyperactivity disorder: a comparative study of two subtypes. Psychiatry Res 1998; 81
Senju A, Tojo Y, Yaguchi K, Hasegawa T. Deviant gaze processing in children with autism: an ERP study. Neuropsychologia. 2005; 43
Critchley HD, Daly EM, Bullmore ET, Williams SR, Van Amelsvoort T. The functional neuroanatomy of social behavior: changes in cerebral blow flow when people with autistic disorder process facial expressions. J Neurotherapy 2000; 8
Pelphrey K, Adolphs R, Morris JP. Neuroanatomical substrates of social cognition dysfunction in autism. Ment Retard Dev Disabil Res Rev 2004; 10
Noonan SK, Haist F, Müller RA. Aberrant functional connectivity in autism: evidence from low-frequency BOLD signal fluctuations. Brain Res 2009; 1262
Belmonte MK, Allen G, Beckel-Mitchener A, Boulanger LM, Carper RA, Webb SJ. Autism and abnormal development of brain connectivity. J Neurosci 2004; 24
Hughes JR. Autism: the first firm finding - underconnectivity? Epilepsy Behav 2007; 11
Duffy FH, Als H. A stable pattern of EEG spectral coherence distinguishes children with autism from neurotypical controls - a large case control study. BMC Med 2012; 10:64.
Courchesne E. Brain development in autism: early overgrowth followed by premature arrest of growth. Ment Retard Dev Disabil Res Rev 2004; 10
Just MA, Cherkassky VL, Keller TA, Kana RK, Minshew NJ. Functional and anatomical cortical underconnectivity in autism: evidence from an FMRI study of an executive function task and corpus callosum morphometry. Cereb Cortex 2007; 17
Cherkassky VL, Kana RK, Keller TA, Just MA. Functional connectivity in a baseline resting-state network in autism. Neuroreport 2006; 17
Pierce K, Müller RA, Ambrose J, Allen G, Courchesne E. Face processing occurs outside the fusiform 'face area' in autism: evidence from functional MRI. Brain 2001; 124
Thompson L, Thompson M, Reid A. Functional neuroanatomy and the rationale for using EEG biofeedback for clients with Asperger's syndrome. Appl Psychophysiol Biofeedback 2010; 35
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6], [Table 7]
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