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Electroencephalographic studies in children with autism spectrum disorders
Research in Autism Spectrum Disorders 8 (2014) 317–323
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Research in Autism Spectrum Disorders Journal homepage: http://ees.elsevier.com/RASD/default.asp
Review
Electroencephalographic studies in children with autism spectrum disorders Jolanta Strzelecka * Department of Pediatric Neurology, The Children Hospital, Professor Dr. Bogdanowicz Warsaw, Warsaw, Poland
A R T I C L E I N F O
A B S T R A C T
Article history: Received 30 July 2013 Received in revised form 15 November 2013 Accepted 27 November 2013
An important factor in the diagnosis and treatment of Autism spectrum disorder (ASD) is prescribed Electroencephalography (EEG). EEG changes may show the following: slowing, asymmetry, sharp waves or spikes, sharp and slow waves, generalized sharp and slow waves, or generalized polyspikes in a distributed or general area, multifocal or focal, unilateral or bilateral, and they may be located in many different areas of the brain. There is a need to look for a EEG phenotype typical of patients with ASD. The importance of gamma waves, rhythm mu, mirror neurons, and their role in patients with ASD was discussed. Epilepsy is reported to occur in one third of ASD patients. In ASD, seizures and EEG paroxysmal abnormalities could represent an epiphenomenon of a cerebral dysfunction independent of apparent lesions. This article reviews ASD and EEG abnormalities and discusses the interaction between epileptiform abnormalities and cognitive dysfunction. ß 2013 Elsevier Ltd. All rights reserved.
Keywords: Autism EEG Gamma rhythm Mu waves Phenotype Epilepsy
Contents 1. 2. 3. 4. 5. 6. 7. 8. 9.
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . The phenotype of autism. . . . . . . . . . . . . . . . . . Abnormal EEG power. . . . . . . . . . . . . . . . . . . . . Qualitative electroencephalography (QEEG) . . . Abnormal brain connectivity . . . . . . . . . . . . . . . The mirror neurons and mu rhythm in autism The importance of sleep in autism . . . . . . . . . . Epilepsy in autism . . . . . . . . . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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1. Introduction Autism spectrum disorder (ASD) is a complex neurological disorder that has been observed, defined, and diagnosed for many years (Griffin & Westbury, 2011). The prevalence of ASD in the general population is around 1 in 88. (Bagasra, Golkar,
* Correspondence to: Department of Pediatric Neurology, The Children Hospital, Professor Dr. Bogdanowicz Warsaw, Niekłan´ska 4/24, Warsaw, Poland. Tel.: +48 22 50 98 260. E-mail address: [email protected] 1750-9467/$ – see front matter ß 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.rasd.2013.11.010
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Garcia, Rice, & Pace, 2013). It is a disorder of the brain and is manifested in three areas: impaired social interaction, difficulties with verbal and nonverbal communication, and a limited number of interests and activities. Symptoms usually appear during the first three years of life and can persist throughout a patient’s lifetime. Recent researches on autism have led to the development of the concept of ASD, which allows diagnoses to include individuals with varying degrees of impairment and levels of functioning (Bluestone, 2005). Ancillary symptoms may encompass obsessive-compulsive disorder, sleep disturbances, hyperactivity, attentional problems, mood disturbances, gastrointestinal symptoms, selfinjurious behavior, ritualistic behavior, and sensory integration disorders. ASD is generally considered a lifelong disability of yet undetermined etiology without an established confirmatory laboratory test and, to date, without universally established, curative pharmacological or behavioral therapies (Duffy & Als, 2012). Many studies suggest that ASD is a connectivity disorder (Assaf, Jagannathan, Calhoun, et al., 2010; Belmonte, Allen, Beckel-Mitchener, et al., 2004). Furthermore, changes in brain development are known in at least some cases to precede observable changes in behavior. It is thus reasonable to conjecture that electroencephalography (EEG) signals may demonstrate discernible patterns, reflecting information about the underlying neural networks that precede changes in behavior (Bosl, Tierney, TagerFlusberg, & Nelson, 2011a, 2011b). These data, appropriately, have spawned much research into the exploration of potential etiologies as well as the development of diagnostic tests, particularly in terms of neuroimaging and EEG (Bluestone, 2005; Duffy & Als, 2012). Early detection of abnormalities in EEG signals may be an early marker for the development of cognitive impairment. The differences seem to be the greatest between the ages of 9–12 months. Using several machine-learning algorithms with assessment of the size of the entropy as a feature vector, infants were classified with over 80 percent accuracy with the control group and high risk of autism at the age of 9 months. Classification accuracy for boys was close to 100 percent at the age of 9 months and remained high (70–90 percent) at ages 12 and 18 months. For girls, the classification accuracy was highest at the age of 6 months but declined in subsequent years (Coben, Clarke, Hudspeth, & Barry, 2008; Linden, 2006; Sohal, Zhang, Yizhar, & Deisseroth, 2009).
2. The phenotype of autism Endophenotypes are biological markers associated with a given disorder and provide insight to its origins. One characteristic of endophenotypes is that they are often present in the first-degree relatives of affected individuals (Tierney, Gabard-Durnam, Vogel-Farley, et al., 2012). Endophenotypes have been identified in family members of individuals with a variety of neuropsychiatric disorders such as depression, schizophrenia (Turetsky, Calkins, Light, Olincy, et al., 2007), bipolar disorder, and Attention Deficit Hyperactivity Disorder (ADHD) (Castellanos & Tannock, 2002). Inheritance of cognitive disorders such as schizophrenia, ADHD, and autism ranges from 50 to 80 percent. It is difficult to identify the actual genetic variants responsible for this inheritance. In the case of schizophrenia and autism, few genetic variants have been identified. The first endophenotype found in ASD is paroxysmal EEG epileptiform activity. This endophenotype has an incidence of approximately 35–40 percent. With this subtype, the abnormality often appears on the left temporal lobe where speech and language occur. The second type is characterized by the presence of the phenotype of the mu rhythm. The EEG pattern in the form visible in the central area is neurologically normal. This pattern is normally seen only when the frontal lobes’ mirror neuron system is not engaged and disappears when the mirror neuron system is engaged (Neubrander, Linden, Gunkelman, & Kerson, 2011). The subtype of the third pattern is a high beta, which can be observed in the EEG results of patients with ASD (Johnstone, Gunkelman & Lunt, 2005). This subtype is characterized by easily ignited mood change or irritability. This may be associated with sensory hypersensitivity, in regard to the sensory areas of the brain, but it can also be associated with impulsivity and explosiveness looking frontally, in particular to the right. The fourth endophenotype is a coherence dysregulation (Johnstone et al., 2005; Neubrander et al., 2011; Pop-Jordanova, Zorcec, Demerdzieva, & Gucev, 2010). It is known that there are no brain tasks that happen in a single part of the brain, and a larger percentage of the brain is needed for individual tasks. Identified patterns of hyperconnectivity in the bilateral frontotemporal regions and between the left and right side of the hemisphere include hypercoherence (too many connections), which often relates to obsessiveness, and hypocoherence (too little connectivity), which is related to inattention and cognitive difficulties (Thatcher, 2001). The fifth autism subtype is very high delta activity, which represents significant cortical slowing and often corresponds to extreme activity (hyperactivity), impulsive behaviors, and inattention (Linden, 2006). High delta can activate overlaps or occur in combination with theta activity (which represents inattention, impulsivity, and hyperactivity). The sixth pattern is characterized by very low-voltage EEG and dominated by slower wave activity (Linden, 2006; Neubrander et al., 2011). This low-voltage, slow EEG is identified in diffuse encephalopathies and specifically suggests that toxic or metabolic etiologies should be ruled out. EEG endophenotypes can help us understand where in the brain, in which stage, and during what type of informationprocessing these genetic variants play a role. With increased understanding of how genes affect the brain, combinations of genetic risk scores and brain endophenotypes may become part of the future classification of psychiatric disorders and ASD (Moskvina, Craddock, Holmans, et al., 2009).
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3. Abnormal EEG power Several authors have reported a reduction of alpha activity in autism (Cantor & Chabot, 2009; Cantor, Thatcher, Hrybyk, & Kaye, 1986). Chan reported that ASD individuals demonstrated significantly lower relative alpha and higher relative delta and delta–alpha ratio. This author considered that such abnormality in relative alpha among ASD children is not restricted only to a single and specific location of the brain but on the contrary is a widespread pattern across the brain, possibly reflecting the neurophysiological abnormality associated with ASD (Chan & Leung, 2006). Differences in EEG power, particularly in the frontal lobes, are functionally related to cognition, which may be relevant to ASD. Bosl et al. (2011a, 2011b) suggest that a measure of EEG complexity can be used to detect, with very high accuracy, infants at high risk for autism. Nonlinear complexity of the EEG signal is believed to contain information about the architecture of neural networks in the brain in multiple scales. Abnormalities in the brain that lead to autistic traits may not be immediately apparent by checking the corresponding activity in the brain (Owen, Williams, & O’Donovan, 2009; Weiss, Arking, Daly, & Chakravarti, 2009). Several authors have proposed that high frequency rhythms (12–80 Hz) are generated in neuronal networks connecting excitatory pyramidal cells and inhibitory gamma-aminobutyric acid (GABA)-ergic interneurons (Kopell, Ermentrout, Whittington, & Traub, 2000). Gamma-frequency synchronization between neural assemblies was suggested to play a role in integration of sensory information, and beta oscillations are currently considered not limited to the motor system, but more generally are involved in sensorimotor integration and top-down signaling (Wang, 2010). Gamma waves have a decisive impact on the most important functions of the mind-consciousness, memory, and attention. The gamma-band range of oscillatory EEG activity (30–80 Hz) has received significant attention in recent years because of its long-postulated association with perceptual binding and connectivity-related concepts that have been proposed as dysfunctional in autism (Frith & Frith, 1999; Wilson, Rojas, Reite, Teale, & Rogers, 2007). This rhythm appears when the brain begins to focus on a task. In the cerebral cortex, there are neurons that generate 40 Hz gamma waves. These neurons are called parvalbumin (PV); the name comes from the protein parvalbumin. It is believed that the brains of people with autism are not working properly because they have fewer parvalbumin cells than normal brains (Sohal et al., 2009). The power of gamma was negatively associated with language skills and general intellectual abilities (Moskvina et al., 2009). Recent studies suggest that mu rhythms (frequency band 8–13 Hz) reflect the modulation of the motor cortex by the prefrontal mirror neurons, the cells that may play a central role in the learning of imitation and the ability to understand the actions of others (Oberman, Ramachandran, & Pineda, 2008; Palau-Baduell, Valls-Santasusana, & Salvado´-Salvado´, 2011; Pineda, 2005). It is suggested that mu rhythms are an important feature of information-processing in the transformation of ‘‘seeing’’ and ‘‘hearing’’ into ‘‘doing.’’ The existing evidence suggests that mu and other alpha-like rhythms are independent phenomena due to differences in the production source, sensitivity to sensory events, bilateral consistency, frequency, and power. 4. Qualitative electroencephalography (QEEG) The human brain is bilaterally symmetrical and problems in certain brain regions tend to cause specific problems in functioning. EEG can be measured quantitatively. This means that in the brain, activity can be tested through various tasks and evaluated with a more complete perspective. Using qualitative electroencephalography (QEEG) methodology has identified distinctive electrophysiological profiles associated with different psychiatric disorders (de Geus, 2010). Researches have shown high accuracy in repeated independent trials dividing many populations of psychiatric patients from normal people and from one another, including major affective disorder, schizophrenia, dementia, alcoholism, and learning disabilities, as well as highly accurate discrimination of known subtypes of depression. EEG has long been used to record and study the electrical activity of the outermost layer of the brain–the cerebral cortex (Prichep & John, 1992). In a routine EEG, a neurologist or electroencephalographer visually examines the traces of the oscilloscope, which show the brain’s electrical activity in the form of a line with repetitive wave-like activity (Gunkelman, Johnstone, & Lunt, 2005; Suffin & Emory, 1995). The speed of this EEG waveform, measured as the number of times per second, reflects the degree of activation of the area of the brain beneath the electrode. Slower waveform activity as in the theta or delta traces indicates reduced blood flow and glucose use in that part of the brain. Faster activity as in the beta trace shows increased brain activity. These types of electrical brain activity also reflect the level of arousal of the person (Demos, 2005; Thatcher, Walker, Gerson, & Geisler, 1989). QEEG has demonstrated the ability to reveal aspects of the functioning of the brain important for understanding different neurodevelopmental disorders including ADHD, learning disabilities, obsessive-compulsive disorder, eating disorders, and addictive disorders. QEEG measures of the relative degree of activation in the left versus the right frontal cortex are good indicators of the current mood state of the person being monitored and of mood traits such as a tendency toward negative emotion or mood (depression, anxiety) or positive emotion or mood (Demos, 2005). In addition, QEEG measures have allowed for the reliable prediction of response to psychiatric medications. QEEG is also revealing information about brain function that is useful for understanding neurodevelopmental problems, and QEEG results can be directly applicable in formulating treatment approaches to train the brain toward improved functioning (Neubrander et al., 2011). Studied children with autism by quantitative EEG spectral and coherence analysis during three experimental conditions: basal, watching a cartoon with audio (V-A), and with muted audio band (VwA). Significant reductions were found for the
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absolute power spectral density (PSD) in the central region for delta and theta, and in the posterior region for sigma and beta bands, lateralized to the right hemisphere. Significant decrements of absolute PSD for delta, theta and alpha, and increments for the beta and gamma bands were found. In children with autism, VwA versus V-A tended to show lower coherence values in the right hemisphere. The authors concluded that impairment of visual and auditory sensory integration in these children might explain their results. 5. Abnormal brain connectivity Research conducted by Wilson in patients with autism has indicated reduced functional connectivity during both cognitive tasks and at rest (Wilson et al., 2007). These data suggest that long-range connectivity may be compromised in this disorder, and current neurological theories of autism contend that disrupted inter-regional interactions may be an underlying mechanism explaining behavioral symptomatology. However, it is unclear whether deficient neuronal communication is attributable to fewer long-range tracts or more of a local deficit in neural circuitry. In Neubrander’s experience (Johnstone et al., 2005; Neubrander et al., 2011) the high beta subtype and coherence abnormalities are the most common. The estimates of the prevalence of the subtypes in children with ASD are as follows: high beta subtypes (70 percent); coherence abnormalities (70 percent); abnormal EEG subtype (33 percent); delta/theta subtype (30 percent); metabolic/toxic (low voltage/low frequency) subtype (10 percent). Six main areas of dysfunction in autism that can be identified using QEEG are (1) the amygdala with connections to the orbital and medial frontal areas of the brain; (2) the fusiform gyrus; (3) the superior temporal gyrus with the auditory cortex in the temporal lobe; (4) the anterior insula and the anterior cingulated, both parts of the limbic system (the emotional brain); (5) the frontal and parietal-temporal mirror neuron areas; and (6) the prefrontal cortex (Johnstone et al., 2005; Linden, 2006; Pop-Jordanova et al., 2010). Coben et al. (2008) examined differences in the functioning of the brain in children diagnosed with autism compared to controls matched for sex ratio, age, and intelligence. There were group differences in power and in intrahemispheric and interhemispheric coherences. Findings included excessive theta, primarily in the right posterior regions, in those with autism. There was also a pattern of deficient delta over the frontal cortex and excessive midline beta. More significantly, there was a pattern of underconnectivity in those with autism compared to the controls. This included decreased intrahemispheric delta and theta coherences across short to medium and long interelectrode distances. Interhemispherically, delta and theta coherences were low across the frontal region. Delta, theta, and alpha hypocoherences were also evident over the temporal regions. Finally, there were low delta, theta, and beta coherence measurements across posterior regions. These results suggest the dysfunctional integration of frontal and posterior brain regions in those with autism, along with a pattern of neural underconnectivity. The EEG coherence among 14 scalp points during intermittent photic stimulation at 11 fixed frequencies of 3–24 Hz was studied in boys with autism, aged 6–14 years, with relatively intact verbal and intellectual functions, and normally developing boys The number of interhemispheric coherent connections pertaining to the 20 highest connections of each individual was significantly lower in autistic patients than in the control group. 6. The mirror neurons and mu rhythm in autism Recent years have seen the discovery of mirror neurons (Iacoboni, Molnar-Szakacs, Gallese, et al., 2005), which are of great importance in the theory of autism. Mirror neurons are cells found in the brain that are responsible for the imitation of action, the ability to read other people’s thoughts, and feelings of empathy for others. Recently, biologists at the University of California at Los Angeles were for the first time able to show that these neurons also exist in the human brain (Oberman & Ramachandran, 2007). The researchers speculate that mirror neuron malfunction is closely linked to autism, accompanied by communication disorders and a reduced ability to feel empathy. The mirror neuron system develops before 12 months of age, and this system may help human infants understand other people’s actions (Palau-Baduell et al., 2011). The mu rhythm may be reactive in the normal population (inhibition mu), both independent movements and the movements made by others. These reactivities are considered to be related to the activity of mirror neurons. Patients with ASD show significant inhibition of mu to movement, but they do not respond to the movements performed by others. These results support the hypothesis of a dysfunctional mirror neuron system in individuals with ASD. In addition, the mirror neuron dysfunction would be related to social and communication impairments, cognitive deficits associated with ASD. 7. The importance of sleep in autism Markers of sleep architecture and sleep state can be objectively and non-invasively measured and referenced to developmental norms. Polysomnography is a reliable, non-invasive tool used to study the basic mechanisms of sleep, and it has proven useful to neuropsychiatric medicine by serving to identify trait markers for diseases such as narcolepsy and depression (Oberman et al., 2008). Previous studies in people with autism have identified various abnormalities in non-rapid eye movement sleep (NREM) including immature organization, decreased quantity, abnormal twitches, undifferentiated sleep, and rapid eye movement sleep (REM), Sleep Behavior Disorder, which is characterized by the absence of muscle atonia,
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is normal during REM Sleep (Diomedi, Curatolo, Scalise, et al., 1999; Tanguay, Ornitz, Forsythe, & Ritvo, 1976). There have been relatively few exclusively pediatric, polysomnographic studies of autism to date. Existing studies have consistently confirmed various abnormalities of sleep, but these are often difficult to compare due to dissimilar exclusion and inclusion criteria, different ages of the groups, and a small number of patients (Buckley, Rodriguez, Jennison, et al., 2010). Primary cholinergic deficiency may simultaneously produce deficits in rapid eye movement sleep in autism and contribute to the social-emotional deficits that are at the core of the autism phenotype, both directly and indirectly, through the lack of appropriate developmental support provided by rapid eye movement sleep early in development (Buckley et al., 2010; Steriade, Datta, Pare, Oakson, & Curro Dossi, 1990). Research was conducted on a group of children with normal development and autism. Rapid eye movement sleep was measured without drugs in recorded physiological sleep. When the time at night from which data was collected was held constant, healthy children showed a significant association between age and the organization of eye movements in separate batches. When children with autism were compared to an agecontrolled group, they showed immaturity in these phenomena. The results were similar to those in children below 18 months of age. This may be due to the immaturity of dysfunction at a number of different levels and places in the central nervous system (Daoust, Limoges, Bolduc, et al., 2004; Tanguay et al., 1976). Sleeping performs many important functions; nonrapid eye movement sleep helps the body to recover (Bluestone, 2005; Tanguay et al., 1976). Rapid eye movement sleep recovers the brain processes related to maintaining fluidity, structure, information storage, and creative possibilities. It was established that people with ASD have a significantly reduced amount of paradoxical sleep time (REM). This means that features such as the organization of mental processing and the development of creativity, which take place during REM sleep, are unavailable in persons with ASD. Rapid eye movement sleep will not occur if the organism is highly stressed, and it is known that most people with ASD are very stressed. 8. Epilepsy in autism Autism is one of the risk factors for epilepsy (Hara, 2007). Epilepsy is reported to occur in one-third of ASD patients, but the exact prevalence is unknown, with the literature reporting a wide range of estimates from 5 to 46 percent (Spence & Schneider, 2009). Among the general population, epilepsy is reported in only 1 percent. The developing brain is more susceptible to seizures than the mature brain, which is why epilepsy occurs primarily in children (Elsayed & Sayyah, 2012). The fact that the epilepsy rate is much higher in ASD patients suggests that ASD and epilepsy might share a common pathophysiological basis. Mental retardation is often associated with ASD. It is important to address the question of whether EEG abnormalities can be considered a biomarker of cortical dysfunction in this population and whether they have a causal relationship with each the phenotype of autism. There are no known, specific EEG patterns of the epileptiform discharges in patients with autism to date. These EEG changes can show the following: slowing, asymmetry, sharp waves or spikes, sharp and slow waves, generalized sharp and slow waves, or generalized polyspikes in a distributed or general area, multifocal or focal, unilateral or bilateral, and they may be located in many different areas of the brain. Chez found that common-location epileptiform abnormalities were situated in the right temporal region (Chez, Chang, Krasne, et al., 2006). Previous studies indicate that EEG abnormalities and seizures are much more frequent in autistic subjects with comorbid ¨ nal et al., 2009). Almost 15–20 percent of patients with autism without epilepsy show EEG intellectual disability (U paroxysmal abnormalities. The clinical importance of epileptiform discharges without seizures is not clear, but they may cause behavioral and cognitive problems. There are no differences in gender, autistic disorder/atypical autism, and past history of febrile seizures between groups of patients with epilepsy and those without epilepsy (Hara, 2007). Spence suggested an increased risk of seizures in women as opposed to men (Spence & Schneider, 2009). Recent meta-analysis found a male-to-female ratio in autism with epilepsy close to 2:1 versus 3.5:1 in autism without epilepsy, and a higher cumulative incidence of epilepsy in women than in men (34.5 percent versus 18.5 percent) (Amiet et al., 2008). It is not clear whether this is an effect of the female sex related to women may having more of the other risk factors, such as lower intelligence quotient and idiopathic autism. (Akshoomoff, Farid, Courchense, & Hass, 2007; Gabis, Pomeroy, & Andriola, 2005). Kanemura, Sano, Tando, Sugita, and Aihara (2012) conducted a study of EEG paroxysmal abnormalities in children with ASD and the occurrence of subsequent development of epilepsy. The incidence and spikes arrangement and the relationship to the later development of epilepsy were assessed. Paroxysmal EEG abnormalities were found in 52.4 percent of patients, and 28.6 percent of them had epilepsy. The presence of paroxysms in the frontal area of the brain was significantly associated with later development of epilepsy compared with centrotemporal paroxysms. There are two peaks in the age of onset of epilepsy in ASD patients, one in early childhood and another in adolescence (Chez et al., 2006). A noticeable loss of skills or regression of development appears in one-third of the children with autism. The period elapsing between regressions is usually at the age of 18–24 months and may be present in those who develop earlier or have delayed development (Lee, Kang, Kim, Kim, & Hung, 2011). Epileptiform abnormalities in EEG studies of patients with ASD predict epileptic seizures in adolescence (Goulden, Shinnar, Koller, et al., 1991; Lee et al., 2011). The diagnosis of seizure activity of a person with autism is difficult because the behavior disorders associated with complex partial seizures and their absence can also be attributed to autistic behaviors, such as watching but not responding to external stimulation (Chez et al., 2006). Elsayed and Sayyah (2012) recommend an EEG study for all children with autism, especially moderate and severe cases. It should be understood that these abnormalities may occur in children without seizures, and their presence should not be
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considered evidence of epilepsy. EEG epileptiform abnormalities are highly correlated to the severity of autism could serve as a prognostic tool for those children. Nordin and Gillberg (1997) concluded that epilepsy is considered a negative prognostic factor for the outcome of autism. It is difficult to decide whether to treat the epileptiform EEG abnormalities if an autistic child with no clinical seizure events has a history of regression (Gabis et al., 2005). Early intervention for seizure control is suggested when an autistic patient shows clinical seizure activity or neuropsychological problems. In this case, EEG should be performed to confirm the presence of any epileptiform EEG abnormalities. It is standard to treat EEG abnormalities with clinical seizures, but there is controversy regarding the treatment of epilepsy-related epileptiform EEG without clinical seizures. Children with ASD can sometimes experience regression with no known cause or because the treatment that used to be effective is no longer effective. Currently there are no guidelines for the treatment of clinical epileptiform EEG without seizures, with the exception of treatment for Landau–Kleffner syndrome (Tsuru, Mori, Mizuguchi, & Momoi, 2000). Epileptic seizure waves usually develop within the frontal and central lobes. The prevalence of spike discharges in other areas, including the temporal, parietal, and occipital lobe, was observed. Less than 10 percent were multifocal spikes (Yasuhara, 2010). 9. Conclusions In children with ASD, EEG changes may show the following: slowing, asymmetry, sharp waves or spikes, sharp and slow waves, generalized sharp and slow waves, or generalized polyspikes in a distributed or general area, multifocal or focal, unilateral or bilateral, and they may be located in many different areas of the brain. Autism is one of the risk factors for epilepsy. In patients with epilepsy, ASD is reported to occur in one-third of patients. Epilepsy is one of the negative factors for cognitive, adaptive, and behavioral/emotional outcomes for individuals with autism. The literature indicates several features that appear to be associated with the presence of epilepsy in ASD including IQ, additional neurogenetic disorders, developmental regression, age, and gender. In ASD, seizures and EEG paroxysmal abnormalities could represent an epiphenomenon of a cerebral dysfunction independent of apparent lesions. Early detection of abnormalities in EEG signals may be an early marker for the development of cognitive impairment in patients with ASD. References Akshoomoff, N., Farid, N., Courchense, E., & Hass, R. (2007). Abnormalities on the neurological examination and EEG in young children with pervasive developmental disorders. Journal of Autism and Developmental Disorders, 37(5), 887–893. 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Contents lists available at ScienceDirect
Research in Autism Spectrum Disorders Journal homepage: http://ees.elsevier.com/RASD/default.asp
Review
Electroencephalographic studies in children with autism spectrum disorders Jolanta Strzelecka * Department of Pediatric Neurology, The Children Hospital, Professor Dr. Bogdanowicz Warsaw, Warsaw, Poland
A R T I C L E I N F O
A B S T R A C T
Article history: Received 30 July 2013 Received in revised form 15 November 2013 Accepted 27 November 2013
An important factor in the diagnosis and treatment of Autism spectrum disorder (ASD) is prescribed Electroencephalography (EEG). EEG changes may show the following: slowing, asymmetry, sharp waves or spikes, sharp and slow waves, generalized sharp and slow waves, or generalized polyspikes in a distributed or general area, multifocal or focal, unilateral or bilateral, and they may be located in many different areas of the brain. There is a need to look for a EEG phenotype typical of patients with ASD. The importance of gamma waves, rhythm mu, mirror neurons, and their role in patients with ASD was discussed. Epilepsy is reported to occur in one third of ASD patients. In ASD, seizures and EEG paroxysmal abnormalities could represent an epiphenomenon of a cerebral dysfunction independent of apparent lesions. This article reviews ASD and EEG abnormalities and discusses the interaction between epileptiform abnormalities and cognitive dysfunction. ß 2013 Elsevier Ltd. All rights reserved.
Keywords: Autism EEG Gamma rhythm Mu waves Phenotype Epilepsy
Contents 1. 2. 3. 4. 5. 6. 7. 8. 9.
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . The phenotype of autism. . . . . . . . . . . . . . . . . . Abnormal EEG power. . . . . . . . . . . . . . . . . . . . . Qualitative electroencephalography (QEEG) . . . Abnormal brain connectivity . . . . . . . . . . . . . . . The mirror neurons and mu rhythm in autism The importance of sleep in autism . . . . . . . . . . Epilepsy in autism . . . . . . . . . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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1. Introduction Autism spectrum disorder (ASD) is a complex neurological disorder that has been observed, defined, and diagnosed for many years (Griffin & Westbury, 2011). The prevalence of ASD in the general population is around 1 in 88. (Bagasra, Golkar,
* Correspondence to: Department of Pediatric Neurology, The Children Hospital, Professor Dr. Bogdanowicz Warsaw, Niekłan´ska 4/24, Warsaw, Poland. Tel.: +48 22 50 98 260. E-mail address: [email protected] 1750-9467/$ – see front matter ß 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.rasd.2013.11.010
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Garcia, Rice, & Pace, 2013). It is a disorder of the brain and is manifested in three areas: impaired social interaction, difficulties with verbal and nonverbal communication, and a limited number of interests and activities. Symptoms usually appear during the first three years of life and can persist throughout a patient’s lifetime. Recent researches on autism have led to the development of the concept of ASD, which allows diagnoses to include individuals with varying degrees of impairment and levels of functioning (Bluestone, 2005). Ancillary symptoms may encompass obsessive-compulsive disorder, sleep disturbances, hyperactivity, attentional problems, mood disturbances, gastrointestinal symptoms, selfinjurious behavior, ritualistic behavior, and sensory integration disorders. ASD is generally considered a lifelong disability of yet undetermined etiology without an established confirmatory laboratory test and, to date, without universally established, curative pharmacological or behavioral therapies (Duffy & Als, 2012). Many studies suggest that ASD is a connectivity disorder (Assaf, Jagannathan, Calhoun, et al., 2010; Belmonte, Allen, Beckel-Mitchener, et al., 2004). Furthermore, changes in brain development are known in at least some cases to precede observable changes in behavior. It is thus reasonable to conjecture that electroencephalography (EEG) signals may demonstrate discernible patterns, reflecting information about the underlying neural networks that precede changes in behavior (Bosl, Tierney, TagerFlusberg, & Nelson, 2011a, 2011b). These data, appropriately, have spawned much research into the exploration of potential etiologies as well as the development of diagnostic tests, particularly in terms of neuroimaging and EEG (Bluestone, 2005; Duffy & Als, 2012). Early detection of abnormalities in EEG signals may be an early marker for the development of cognitive impairment. The differences seem to be the greatest between the ages of 9–12 months. Using several machine-learning algorithms with assessment of the size of the entropy as a feature vector, infants were classified with over 80 percent accuracy with the control group and high risk of autism at the age of 9 months. Classification accuracy for boys was close to 100 percent at the age of 9 months and remained high (70–90 percent) at ages 12 and 18 months. For girls, the classification accuracy was highest at the age of 6 months but declined in subsequent years (Coben, Clarke, Hudspeth, & Barry, 2008; Linden, 2006; Sohal, Zhang, Yizhar, & Deisseroth, 2009).
2. The phenotype of autism Endophenotypes are biological markers associated with a given disorder and provide insight to its origins. One characteristic of endophenotypes is that they are often present in the first-degree relatives of affected individuals (Tierney, Gabard-Durnam, Vogel-Farley, et al., 2012). Endophenotypes have been identified in family members of individuals with a variety of neuropsychiatric disorders such as depression, schizophrenia (Turetsky, Calkins, Light, Olincy, et al., 2007), bipolar disorder, and Attention Deficit Hyperactivity Disorder (ADHD) (Castellanos & Tannock, 2002). Inheritance of cognitive disorders such as schizophrenia, ADHD, and autism ranges from 50 to 80 percent. It is difficult to identify the actual genetic variants responsible for this inheritance. In the case of schizophrenia and autism, few genetic variants have been identified. The first endophenotype found in ASD is paroxysmal EEG epileptiform activity. This endophenotype has an incidence of approximately 35–40 percent. With this subtype, the abnormality often appears on the left temporal lobe where speech and language occur. The second type is characterized by the presence of the phenotype of the mu rhythm. The EEG pattern in the form visible in the central area is neurologically normal. This pattern is normally seen only when the frontal lobes’ mirror neuron system is not engaged and disappears when the mirror neuron system is engaged (Neubrander, Linden, Gunkelman, & Kerson, 2011). The subtype of the third pattern is a high beta, which can be observed in the EEG results of patients with ASD (Johnstone, Gunkelman & Lunt, 2005). This subtype is characterized by easily ignited mood change or irritability. This may be associated with sensory hypersensitivity, in regard to the sensory areas of the brain, but it can also be associated with impulsivity and explosiveness looking frontally, in particular to the right. The fourth endophenotype is a coherence dysregulation (Johnstone et al., 2005; Neubrander et al., 2011; Pop-Jordanova, Zorcec, Demerdzieva, & Gucev, 2010). It is known that there are no brain tasks that happen in a single part of the brain, and a larger percentage of the brain is needed for individual tasks. Identified patterns of hyperconnectivity in the bilateral frontotemporal regions and between the left and right side of the hemisphere include hypercoherence (too many connections), which often relates to obsessiveness, and hypocoherence (too little connectivity), which is related to inattention and cognitive difficulties (Thatcher, 2001). The fifth autism subtype is very high delta activity, which represents significant cortical slowing and often corresponds to extreme activity (hyperactivity), impulsive behaviors, and inattention (Linden, 2006). High delta can activate overlaps or occur in combination with theta activity (which represents inattention, impulsivity, and hyperactivity). The sixth pattern is characterized by very low-voltage EEG and dominated by slower wave activity (Linden, 2006; Neubrander et al., 2011). This low-voltage, slow EEG is identified in diffuse encephalopathies and specifically suggests that toxic or metabolic etiologies should be ruled out. EEG endophenotypes can help us understand where in the brain, in which stage, and during what type of informationprocessing these genetic variants play a role. With increased understanding of how genes affect the brain, combinations of genetic risk scores and brain endophenotypes may become part of the future classification of psychiatric disorders and ASD (Moskvina, Craddock, Holmans, et al., 2009).
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3. Abnormal EEG power Several authors have reported a reduction of alpha activity in autism (Cantor & Chabot, 2009; Cantor, Thatcher, Hrybyk, & Kaye, 1986). Chan reported that ASD individuals demonstrated significantly lower relative alpha and higher relative delta and delta–alpha ratio. This author considered that such abnormality in relative alpha among ASD children is not restricted only to a single and specific location of the brain but on the contrary is a widespread pattern across the brain, possibly reflecting the neurophysiological abnormality associated with ASD (Chan & Leung, 2006). Differences in EEG power, particularly in the frontal lobes, are functionally related to cognition, which may be relevant to ASD. Bosl et al. (2011a, 2011b) suggest that a measure of EEG complexity can be used to detect, with very high accuracy, infants at high risk for autism. Nonlinear complexity of the EEG signal is believed to contain information about the architecture of neural networks in the brain in multiple scales. Abnormalities in the brain that lead to autistic traits may not be immediately apparent by checking the corresponding activity in the brain (Owen, Williams, & O’Donovan, 2009; Weiss, Arking, Daly, & Chakravarti, 2009). Several authors have proposed that high frequency rhythms (12–80 Hz) are generated in neuronal networks connecting excitatory pyramidal cells and inhibitory gamma-aminobutyric acid (GABA)-ergic interneurons (Kopell, Ermentrout, Whittington, & Traub, 2000). Gamma-frequency synchronization between neural assemblies was suggested to play a role in integration of sensory information, and beta oscillations are currently considered not limited to the motor system, but more generally are involved in sensorimotor integration and top-down signaling (Wang, 2010). Gamma waves have a decisive impact on the most important functions of the mind-consciousness, memory, and attention. The gamma-band range of oscillatory EEG activity (30–80 Hz) has received significant attention in recent years because of its long-postulated association with perceptual binding and connectivity-related concepts that have been proposed as dysfunctional in autism (Frith & Frith, 1999; Wilson, Rojas, Reite, Teale, & Rogers, 2007). This rhythm appears when the brain begins to focus on a task. In the cerebral cortex, there are neurons that generate 40 Hz gamma waves. These neurons are called parvalbumin (PV); the name comes from the protein parvalbumin. It is believed that the brains of people with autism are not working properly because they have fewer parvalbumin cells than normal brains (Sohal et al., 2009). The power of gamma was negatively associated with language skills and general intellectual abilities (Moskvina et al., 2009). Recent studies suggest that mu rhythms (frequency band 8–13 Hz) reflect the modulation of the motor cortex by the prefrontal mirror neurons, the cells that may play a central role in the learning of imitation and the ability to understand the actions of others (Oberman, Ramachandran, & Pineda, 2008; Palau-Baduell, Valls-Santasusana, & Salvado´-Salvado´, 2011; Pineda, 2005). It is suggested that mu rhythms are an important feature of information-processing in the transformation of ‘‘seeing’’ and ‘‘hearing’’ into ‘‘doing.’’ The existing evidence suggests that mu and other alpha-like rhythms are independent phenomena due to differences in the production source, sensitivity to sensory events, bilateral consistency, frequency, and power. 4. Qualitative electroencephalography (QEEG) The human brain is bilaterally symmetrical and problems in certain brain regions tend to cause specific problems in functioning. EEG can be measured quantitatively. This means that in the brain, activity can be tested through various tasks and evaluated with a more complete perspective. Using qualitative electroencephalography (QEEG) methodology has identified distinctive electrophysiological profiles associated with different psychiatric disorders (de Geus, 2010). Researches have shown high accuracy in repeated independent trials dividing many populations of psychiatric patients from normal people and from one another, including major affective disorder, schizophrenia, dementia, alcoholism, and learning disabilities, as well as highly accurate discrimination of known subtypes of depression. EEG has long been used to record and study the electrical activity of the outermost layer of the brain–the cerebral cortex (Prichep & John, 1992). In a routine EEG, a neurologist or electroencephalographer visually examines the traces of the oscilloscope, which show the brain’s electrical activity in the form of a line with repetitive wave-like activity (Gunkelman, Johnstone, & Lunt, 2005; Suffin & Emory, 1995). The speed of this EEG waveform, measured as the number of times per second, reflects the degree of activation of the area of the brain beneath the electrode. Slower waveform activity as in the theta or delta traces indicates reduced blood flow and glucose use in that part of the brain. Faster activity as in the beta trace shows increased brain activity. These types of electrical brain activity also reflect the level of arousal of the person (Demos, 2005; Thatcher, Walker, Gerson, & Geisler, 1989). QEEG has demonstrated the ability to reveal aspects of the functioning of the brain important for understanding different neurodevelopmental disorders including ADHD, learning disabilities, obsessive-compulsive disorder, eating disorders, and addictive disorders. QEEG measures of the relative degree of activation in the left versus the right frontal cortex are good indicators of the current mood state of the person being monitored and of mood traits such as a tendency toward negative emotion or mood (depression, anxiety) or positive emotion or mood (Demos, 2005). In addition, QEEG measures have allowed for the reliable prediction of response to psychiatric medications. QEEG is also revealing information about brain function that is useful for understanding neurodevelopmental problems, and QEEG results can be directly applicable in formulating treatment approaches to train the brain toward improved functioning (Neubrander et al., 2011). Studied children with autism by quantitative EEG spectral and coherence analysis during three experimental conditions: basal, watching a cartoon with audio (V-A), and with muted audio band (VwA). Significant reductions were found for the
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absolute power spectral density (PSD) in the central region for delta and theta, and in the posterior region for sigma and beta bands, lateralized to the right hemisphere. Significant decrements of absolute PSD for delta, theta and alpha, and increments for the beta and gamma bands were found. In children with autism, VwA versus V-A tended to show lower coherence values in the right hemisphere. The authors concluded that impairment of visual and auditory sensory integration in these children might explain their results. 5. Abnormal brain connectivity Research conducted by Wilson in patients with autism has indicated reduced functional connectivity during both cognitive tasks and at rest (Wilson et al., 2007). These data suggest that long-range connectivity may be compromised in this disorder, and current neurological theories of autism contend that disrupted inter-regional interactions may be an underlying mechanism explaining behavioral symptomatology. However, it is unclear whether deficient neuronal communication is attributable to fewer long-range tracts or more of a local deficit in neural circuitry. In Neubrander’s experience (Johnstone et al., 2005; Neubrander et al., 2011) the high beta subtype and coherence abnormalities are the most common. The estimates of the prevalence of the subtypes in children with ASD are as follows: high beta subtypes (70 percent); coherence abnormalities (70 percent); abnormal EEG subtype (33 percent); delta/theta subtype (30 percent); metabolic/toxic (low voltage/low frequency) subtype (10 percent). Six main areas of dysfunction in autism that can be identified using QEEG are (1) the amygdala with connections to the orbital and medial frontal areas of the brain; (2) the fusiform gyrus; (3) the superior temporal gyrus with the auditory cortex in the temporal lobe; (4) the anterior insula and the anterior cingulated, both parts of the limbic system (the emotional brain); (5) the frontal and parietal-temporal mirror neuron areas; and (6) the prefrontal cortex (Johnstone et al., 2005; Linden, 2006; Pop-Jordanova et al., 2010). Coben et al. (2008) examined differences in the functioning of the brain in children diagnosed with autism compared to controls matched for sex ratio, age, and intelligence. There were group differences in power and in intrahemispheric and interhemispheric coherences. Findings included excessive theta, primarily in the right posterior regions, in those with autism. There was also a pattern of deficient delta over the frontal cortex and excessive midline beta. More significantly, there was a pattern of underconnectivity in those with autism compared to the controls. This included decreased intrahemispheric delta and theta coherences across short to medium and long interelectrode distances. Interhemispherically, delta and theta coherences were low across the frontal region. Delta, theta, and alpha hypocoherences were also evident over the temporal regions. Finally, there were low delta, theta, and beta coherence measurements across posterior regions. These results suggest the dysfunctional integration of frontal and posterior brain regions in those with autism, along with a pattern of neural underconnectivity. The EEG coherence among 14 scalp points during intermittent photic stimulation at 11 fixed frequencies of 3–24 Hz was studied in boys with autism, aged 6–14 years, with relatively intact verbal and intellectual functions, and normally developing boys The number of interhemispheric coherent connections pertaining to the 20 highest connections of each individual was significantly lower in autistic patients than in the control group. 6. The mirror neurons and mu rhythm in autism Recent years have seen the discovery of mirror neurons (Iacoboni, Molnar-Szakacs, Gallese, et al., 2005), which are of great importance in the theory of autism. Mirror neurons are cells found in the brain that are responsible for the imitation of action, the ability to read other people’s thoughts, and feelings of empathy for others. Recently, biologists at the University of California at Los Angeles were for the first time able to show that these neurons also exist in the human brain (Oberman & Ramachandran, 2007). The researchers speculate that mirror neuron malfunction is closely linked to autism, accompanied by communication disorders and a reduced ability to feel empathy. The mirror neuron system develops before 12 months of age, and this system may help human infants understand other people’s actions (Palau-Baduell et al., 2011). The mu rhythm may be reactive in the normal population (inhibition mu), both independent movements and the movements made by others. These reactivities are considered to be related to the activity of mirror neurons. Patients with ASD show significant inhibition of mu to movement, but they do not respond to the movements performed by others. These results support the hypothesis of a dysfunctional mirror neuron system in individuals with ASD. In addition, the mirror neuron dysfunction would be related to social and communication impairments, cognitive deficits associated with ASD. 7. The importance of sleep in autism Markers of sleep architecture and sleep state can be objectively and non-invasively measured and referenced to developmental norms. Polysomnography is a reliable, non-invasive tool used to study the basic mechanisms of sleep, and it has proven useful to neuropsychiatric medicine by serving to identify trait markers for diseases such as narcolepsy and depression (Oberman et al., 2008). Previous studies in people with autism have identified various abnormalities in non-rapid eye movement sleep (NREM) including immature organization, decreased quantity, abnormal twitches, undifferentiated sleep, and rapid eye movement sleep (REM), Sleep Behavior Disorder, which is characterized by the absence of muscle atonia,
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is normal during REM Sleep (Diomedi, Curatolo, Scalise, et al., 1999; Tanguay, Ornitz, Forsythe, & Ritvo, 1976). There have been relatively few exclusively pediatric, polysomnographic studies of autism to date. Existing studies have consistently confirmed various abnormalities of sleep, but these are often difficult to compare due to dissimilar exclusion and inclusion criteria, different ages of the groups, and a small number of patients (Buckley, Rodriguez, Jennison, et al., 2010). Primary cholinergic deficiency may simultaneously produce deficits in rapid eye movement sleep in autism and contribute to the social-emotional deficits that are at the core of the autism phenotype, both directly and indirectly, through the lack of appropriate developmental support provided by rapid eye movement sleep early in development (Buckley et al., 2010; Steriade, Datta, Pare, Oakson, & Curro Dossi, 1990). Research was conducted on a group of children with normal development and autism. Rapid eye movement sleep was measured without drugs in recorded physiological sleep. When the time at night from which data was collected was held constant, healthy children showed a significant association between age and the organization of eye movements in separate batches. When children with autism were compared to an agecontrolled group, they showed immaturity in these phenomena. The results were similar to those in children below 18 months of age. This may be due to the immaturity of dysfunction at a number of different levels and places in the central nervous system (Daoust, Limoges, Bolduc, et al., 2004; Tanguay et al., 1976). Sleeping performs many important functions; nonrapid eye movement sleep helps the body to recover (Bluestone, 2005; Tanguay et al., 1976). Rapid eye movement sleep recovers the brain processes related to maintaining fluidity, structure, information storage, and creative possibilities. It was established that people with ASD have a significantly reduced amount of paradoxical sleep time (REM). This means that features such as the organization of mental processing and the development of creativity, which take place during REM sleep, are unavailable in persons with ASD. Rapid eye movement sleep will not occur if the organism is highly stressed, and it is known that most people with ASD are very stressed. 8. Epilepsy in autism Autism is one of the risk factors for epilepsy (Hara, 2007). Epilepsy is reported to occur in one-third of ASD patients, but the exact prevalence is unknown, with the literature reporting a wide range of estimates from 5 to 46 percent (Spence & Schneider, 2009). Among the general population, epilepsy is reported in only 1 percent. The developing brain is more susceptible to seizures than the mature brain, which is why epilepsy occurs primarily in children (Elsayed & Sayyah, 2012). The fact that the epilepsy rate is much higher in ASD patients suggests that ASD and epilepsy might share a common pathophysiological basis. Mental retardation is often associated with ASD. It is important to address the question of whether EEG abnormalities can be considered a biomarker of cortical dysfunction in this population and whether they have a causal relationship with each the phenotype of autism. There are no known, specific EEG patterns of the epileptiform discharges in patients with autism to date. These EEG changes can show the following: slowing, asymmetry, sharp waves or spikes, sharp and slow waves, generalized sharp and slow waves, or generalized polyspikes in a distributed or general area, multifocal or focal, unilateral or bilateral, and they may be located in many different areas of the brain. Chez found that common-location epileptiform abnormalities were situated in the right temporal region (Chez, Chang, Krasne, et al., 2006). Previous studies indicate that EEG abnormalities and seizures are much more frequent in autistic subjects with comorbid ¨ nal et al., 2009). Almost 15–20 percent of patients with autism without epilepsy show EEG intellectual disability (U paroxysmal abnormalities. The clinical importance of epileptiform discharges without seizures is not clear, but they may cause behavioral and cognitive problems. There are no differences in gender, autistic disorder/atypical autism, and past history of febrile seizures between groups of patients with epilepsy and those without epilepsy (Hara, 2007). Spence suggested an increased risk of seizures in women as opposed to men (Spence & Schneider, 2009). Recent meta-analysis found a male-to-female ratio in autism with epilepsy close to 2:1 versus 3.5:1 in autism without epilepsy, and a higher cumulative incidence of epilepsy in women than in men (34.5 percent versus 18.5 percent) (Amiet et al., 2008). It is not clear whether this is an effect of the female sex related to women may having more of the other risk factors, such as lower intelligence quotient and idiopathic autism. (Akshoomoff, Farid, Courchense, & Hass, 2007; Gabis, Pomeroy, & Andriola, 2005). Kanemura, Sano, Tando, Sugita, and Aihara (2012) conducted a study of EEG paroxysmal abnormalities in children with ASD and the occurrence of subsequent development of epilepsy. The incidence and spikes arrangement and the relationship to the later development of epilepsy were assessed. Paroxysmal EEG abnormalities were found in 52.4 percent of patients, and 28.6 percent of them had epilepsy. The presence of paroxysms in the frontal area of the brain was significantly associated with later development of epilepsy compared with centrotemporal paroxysms. There are two peaks in the age of onset of epilepsy in ASD patients, one in early childhood and another in adolescence (Chez et al., 2006). A noticeable loss of skills or regression of development appears in one-third of the children with autism. The period elapsing between regressions is usually at the age of 18–24 months and may be present in those who develop earlier or have delayed development (Lee, Kang, Kim, Kim, & Hung, 2011). Epileptiform abnormalities in EEG studies of patients with ASD predict epileptic seizures in adolescence (Goulden, Shinnar, Koller, et al., 1991; Lee et al., 2011). The diagnosis of seizure activity of a person with autism is difficult because the behavior disorders associated with complex partial seizures and their absence can also be attributed to autistic behaviors, such as watching but not responding to external stimulation (Chez et al., 2006). Elsayed and Sayyah (2012) recommend an EEG study for all children with autism, especially moderate and severe cases. It should be understood that these abnormalities may occur in children without seizures, and their presence should not be
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considered evidence of epilepsy. EEG epileptiform abnormalities are highly correlated to the severity of autism could serve as a prognostic tool for those children. Nordin and Gillberg (1997) concluded that epilepsy is considered a negative prognostic factor for the outcome of autism. It is difficult to decide whether to treat the epileptiform EEG abnormalities if an autistic child with no clinical seizure events has a history of regression (Gabis et al., 2005). Early intervention for seizure control is suggested when an autistic patient shows clinical seizure activity or neuropsychological problems. In this case, EEG should be performed to confirm the presence of any epileptiform EEG abnormalities. It is standard to treat EEG abnormalities with clinical seizures, but there is controversy regarding the treatment of epilepsy-related epileptiform EEG without clinical seizures. Children with ASD can sometimes experience regression with no known cause or because the treatment that used to be effective is no longer effective. Currently there are no guidelines for the treatment of clinical epileptiform EEG without seizures, with the exception of treatment for Landau–Kleffner syndrome (Tsuru, Mori, Mizuguchi, & Momoi, 2000). Epileptic seizure waves usually develop within the frontal and central lobes. The prevalence of spike discharges in other areas, including the temporal, parietal, and occipital lobe, was observed. Less than 10 percent were multifocal spikes (Yasuhara, 2010). 9. Conclusions In children with ASD, EEG changes may show the following: slowing, asymmetry, sharp waves or spikes, sharp and slow waves, generalized sharp and slow waves, or generalized polyspikes in a distributed or general area, multifocal or focal, unilateral or bilateral, and they may be located in many different areas of the brain. Autism is one of the risk factors for epilepsy. 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