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Electrophysiological Research on Autism 

Electrophysiological Research on Autism

Electrophysiological Research on Autism

Sara Jane Webb

, Raphael Bernier

, Karen Burner

, and Michael Murias

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Subscriber: null; date: 16 August 2017

Points of Interest

  • EEG activity is exquisitely sensitive to neural timing, on a resolution that is not matched by other methodologies, providing the ability to scrutinize physiological changes at a unique level of analysis.

  • EEG/ERP paradigms can be designed for use both across the age continuum and across variable cognitive functioning.

  • ERP components enable one to disentangle and differentiate sensory processing from integration and higher order cognitive abilities, which may be important in understanding autism.

  • A failure or delay in activating neural structures that are normally specialized for a given process could result in a failure to integrate social information.

Electroencephalography (EEG) is the measurement of electrical activity produced by the brain. More specifically, scalp recorded EEG is a noninvasive method of measuring postsynaptic activity that is rhythmic and continuous, transient and episodic. While scalp EEG is not a direct measurement of brain activation, the recordings reflect the propagation of electrical activity to the scalp arising from the synchronous activation of a population of neurons that have a similar spatial organization. These generators or sources of scalp recorded activity are located parallel to each other and oriented radially to the scalp.

EEG can be examined in many different ways, providing valuable information about the functioning of the brain including: (1) EEG oscillatory activity, which is the frequency and amplitude of synchronized neural activity and characterizes the “state” of the brain; (2) evoked potentials (EPs) or event-related potentials (ERPs), which reflect measurements that are time-locked to the presentation of an external stimulus; and (3) coherence, in which functional connectivity in brain networks can be inferred from statistical relations between neurophysiological signals measured over spatially separated neuronal regions.

The benefits of this methodology are easy to identify. EEG is noninvasive, only requiring the participant to wear an electrode hat for the length of the experiment. In contrast to other neuroimaging methodologies, EEG generally does not necessitate adherence to strict behavioral requirements. Participant movement and compliance can be evaluated in real time and can be tolerated to a greater extent than in other imaging methods. EEG also has exquisite temporal resolution, with recordings reflecting electrical activity changes on the scale of milliseconds. Limitations have also been recognized including: poorer spatial resolution than other imaging modalities, increased sensitivity to generators that are closer to the surface, and insensitivity to sources that are located tangential to the skull, located in sulci, or located in deep structures (e.g., hippocampus).

Why Utilize Electrophysiology Measures for the Study of Autism?

EEG has been used to study both typical and atypical brain processes since its first recording in humans by Hans Berger in the 1920s; the first (published) reports focusing on autism emerged in the 1960s. The usefulness of EEG measurements in autism arises from both theoretical and methodological considerations. Theoretically, EEG allows the evaluation of hypotheses with respect to the timing of brain functioning, alterations in resting and active brain states, and the potential under- and overfunctional connectivity of the brain in individuals with autism. Methodologically, EEG paradigms can be created that reduce demands for behavioral compliance or manual/vocal responses. The implementation of passive paradigms, such as passive viewing of faces or listening to phonemes, allows the same paradigm to be used both across the age continuum and across variable cognitive functioning. This approach provides useful information about the whole of the autism spectrum. As well, some EEG technologies allow for the application of the scalp electrodes quickly and for recordings to be conducted over short time periods, accommodating limited attention or behavioral compliance.

Electrophysiology is a broad topic; we have chosen to focus on EEG and ERPs in response to visual stimuli during perceptual and attention processes, social processes, and background neural processes.

Event Related Potentials to Perceptual and Attention Processes

Theory and Methods

ERPs in response to visual stimulation can be recorded within a short time of stimulus onset. Basic level processing to black and white checkerboards can be characterized within milliseconds of stimulus onset over occipital electrodes and likely reflects the activity of the extrastriate visual cortex. More complex stimuli, which evoke more complicated “higher-level” perceptual and cognitive processes, likely reflect the activity and contributions of many different neural systems that overlap in time and spatial distribution. The resulting waveform is composed of multiple potentials, each with a characteristic latency range and spatial distribution.

A careful analysis of ERPs enables one to disentangle and differentiate sensory processing from integration and higher-order cognitive abilities, which may be important in understanding autism. If a sensory system is limited in its ability to perceive the environment, this would be represented in abnormal sensory ERPs such as brain stem evoked potentials or early visual evoked potentials. If autism, however, is better represented by a failure of integration or a limitation in overall capacity for information processing, this would be represented in abnormal endogenous, later cognitive components of the signal, or decreased coherence.

Visual Processing

While not specifically captured in the diagnostic criteria for autism, the existence of altered perceptual processing has been proposed as an important phenotype of the disorder. The early visual component C1 peaks approximately 60–100 ms after stimulus onset and is thought to be generated by the primary visual cortex (V1, area 17) (e.g., Clark et al., 1995; di Russo et al., 2001). During perception of Gabor patches, sinusoidal luminance patterns, children with pervasive developmental disorder (PDD) demonstrated shorter latency to the peak of C1 compared to matched typical controls. Higher Childhood Autism Rating Scale scores were correlated with faster latency (Milne, Scope, Pascalis, Buckley, & Makeig, 2009). Given the simplicity of the stimuli, the authors concluded that the faster neural response was consistent with the behavioral findings of faster visual detection in individuals with PDD and suggested that this behavior may be due to varying mechanisms of perception rather than attention.

In another simple visual paradigm using gratings, Boeschoten, Kenemnas, van Engeland, and Kemner (2007) found increased N80 responses to high spatial frequencies in children with PDD compared to controls but a failure to show differential sources of activity for high and low spatial frequencies (also see Milne et al., 2006). The P1, thought to be generated by the extrastriate cortex V2 (e.g., Mangun, Buonocore, Girelli, & Jha, 1998; Di Russo, Martinez, Sereno, Pitzalis, & Hillyard, 2002), was smaller in children with PDD than in controls, and the inferior medial sources were weaker with increased supplementary source activity in the superior lateral area. The authors concluded that this represented a failure of anatomical separation for the N80, decreased specialized processing of frequencies, and decreased extrastriate activity.

Visual boundary detection mechanisms, but not surface segregation, have been found to be abnormal in adults with ASD compared to typical controls. ERP results in adults with ASD suggested reduced perception of boundary detection starting 121 ms after stimulus presentation consistent with impaired (behavioral) identification of boundaries (Vandenbroucke, Scholte, van Engeland, Lamme, & Kemner, 2008). This was hypothesized to reflect a deficit in horizontal connections within visual areas; intact surface segregation was interpreted as associated with normal recurrent or feedback processes from higher-level areas.

While the number of early visual processing reports is relatively small, there are some general conclusions that can be made. First, activity within the primary visual cortex seems to be intact or potentially enhanced. Second, ERPs located originating from the extrastriate cortex demonstrate a pattern suggestive of perceptual impairment. Area V2 receives direct projections from V1 and shares many similar properties but is also modulated by more complex properties including figure/ground separation. Bertone et al. (2005) have suggested that individuals with autism have reduced efficiency of neurointegrative mechanisms within the perceptual systems, which would invariably impact the relatively more integrative processes of the extrastriate cortex. Both Milne et al. (2009) and Vandenbroucke et al. (2008) are consistent with this interpretation.


Central Attention: Target Processing

ERPs can be collected during selective attention paradigms in which a rare, novel, infrequent, or unattended stimulus is compared to a standard, frequent, or attended stimulus. This type of experiment results in a N1, P2, N2, and P3 complex (or LPC). The P3 is thought to reflect neural generators in the temporal, parietal, and frontal areas, and amplitude of the P3 is thought to be related to the amount of attention and processing capacity. Smaller P3 amplitudes are thought to reflect decreased processing capacity or allocation of resources, abnormal executive functions (e.g., Halgren, Marinkovic, & Chauvel, 1998), working memory (e.g., Donchin & Coles, 1988), or completion of perceptional processes and associated release of neural inhibition that follows task resolution (e.g., Kutas, McCarthy, & Donchin, 1977; Verleger, 1988). Of note, individuals with a wide range of conditions demonstrate abnormal P3 responses, including but not limited to schizophrenia, depression, ADD, dyslexia, alcoholism, multiple sclerosis, and normal aging (Picton, 1992; Polich & Criado, 2006).

During auditory/visual divided attention tasks, for example, when the participant is required to attend to a stimulus in a primary modality (e.g., tone) and ignore the presence of irrelevant (unattended) probe a in a second modality (e.g., square), reduced P3 amplitude to the attended stimuli is thought to reflect attention trade-offs. Several studies suggest that individuals with autism fail to reduce activation to the attended stimulus (Ciesielski et al., 1990; Hoeksma, Kemner, Verbaten, & van Engeland, 2004). For example, Hoeksma et al. (2004) found that when the task load was increased, children and adolescents with PDD showed increased early responses to unattended (visual) probes and failed to show normal reduction of processing to attended (auditory) probes. The authors suggested this represented abnormal allocation of attention but not necessarily a decreased processing capacity. This pattern of results was clearly found in the children but was less characteristic of the adolescents. It is unclear if this represents delayed development of the attention system or potentially subtle differences in subgroups of individuals with ASD.

Central Attention: Novelty Detection

Some findings suggest that late visual evoked parietal P3b to novel stimuli is abnormal in individuals with ASD (Novick et al., 1979; Verbaten, Roelofs, van Engeland, Kenemans, & Slangen, 1991), but that these responses to visual stimuli may be less impaired than responses to auditory stimuli (Courchesne et al., 1989; Courchesne, Lincoln, et al., 1985). Lincoln, Courchesne, Harms, and Allen (1993) propose that the decreased P3b amplitudes noted in ASD may reflect difficulty in changing expectancies in response to contextually relevant information. Furthermore, the authors suggest that a basic disturbance in the habituation process results in difficulties in discriminating novel information. An alternative hypothesis is that a failure to extract relevant information in order to create a “standard” category results in impairments in differentiating novel stimuli to the same degree as controls (Gastgeb, Strauss, & Minshew, 2006).

Shifting Attention from Central to Peripheral Stimuli

Behaviorally, individuals with autism demonstrate a number of impairments in orienting. Children with ASD, when attending to targets in the periphery, demonstrate reduced LPC amplitude and smaller P3b amplitudes accompanied by high variability in performance (Verbaten, Roelofs, van Engeland, Kenemans, & Slangen, 1991). Similarly, Townsend et al. (2001) found reduced accuracy to targets in the visual periphery, delayed or missing early LPC during attention to peripheral visual fields, and smaller amplitudes over the parietal cortex during conditions when context updating was of paramount importance. The authors interpreted this as a disruption of spatial attention networks and consistent with abnormalities in the cerebellar-frontal/parietal spatial attention systems. When the central stimulus overlapped with the peripheral target, Kawakubo et al. (2007) found increased amplitude activity during the presaccade period in adults with ASD as compared to neurotypical adults, which was interpreted as a difficulty in disengagement during visuospatial attention.


The studies reviewed here suggest that individuals with ASD may have subtle impairments in the integrative stages of visual processing and attention allocation. Abnormalities in these early stages of information processing have the potential to disrupt integrative cognitive processes that require complex information. For example, if visual stimuli are being encoded without correct figure-ground information or the correct balance of high/low spatial frequency information, then systems that must utilize this to identify and respond accordingly would organize around incorrect or degraded information. This might result in reduced attention to and differentiation of social stimuli.

Electrophysiology and Social Processes

Theory and Methods

Much of what we think of as social represents a dynamic interaction between two or more people. One of the constraints of ERP methodology is that social processes must be decomposed into time locked segments. These simple component parts can be presented as static images and contrasted to nonsocial categories or perceptual matches. However, EEG recordings in general do not have this temporal constraint, and more recent work has focused on changes in the state of brain activity during dynamic protocols.

Face Processing

A sizable amount of literature exists regarding the exploration of the neural circuitry of face processing via ERPs and other neuroimaging methods of individuals with typical development. One ERP component, called the N170, has been identified as a face sensitive component because it is greater in amplitude and shorter in latency to face stimuli relative to other types of stimuli (e.g., Bentin et al., 1996). Recorded over the posterior temporal region and peaking between 130 and 170 ms in response to faces, the N170 is larger in amplitude to eyes than inverted faces and upright faces. Likewise, these stimuli result in a larger N170 amplitude than the presentation of noses or mouths (e.g., Bentin et al., 1996).

In individuals with ASD, reports suggest abnormalities in the precursor N170 in 3- to 4-year-old children with ASD, and the N170 in adolescents and adults with ASD, as well as in parents of children with ASD (Dawson et al., 2005; McPartland, Dawson, Webb, Panagiotides, & Carver, 2004; Webb, Dawson, Bernier, & Panagiotides, 2006). Three-to 4-year-old children with autism showed a faster response to objects than faces (Webb et al., 2006). In contrast, children with typical development demonstrated a faster precursor N170 to faces than objects in the right hemisphere, while children with developmental delay (chronologic and mental age controls) failed to demonstrate any differential responses.

Adults with autism spectrum disorder (ASD) also show disruptions in face processing measured via ERPs. McPartland et al. (2004) found that 9 adolescents and adults with ASD had slower N170 responses to faces than objects (also O’Connor et al., 2007). O’Connor et al. (2005) found that the group with Asperger’s (ASP) had slower P1 and N170 responses to facial expressions of emotions compared to controls Additionally, the ASP group had reduced amplitude at the N170 compared to the typical group. O’Connor et al. (2005, 2007) interpreted these results as reflecting impaired holistic and configural processing of faces, potentially due to decreased attention to internal features or a failure of expertise processing. In contrast, in a recent report that explicitly directed the subject’s attention toward the eye region of the face, Webb et al. (2009) found that 32 high-functioning adults with ASD demonstrated P1 and N170 responses to faces that were greater in amplitude and faster in latency than to houses. This pattern is similar to that found in neurotypical adults. Adults with ASD, however, failed to show any inversion differences. Namely, they failed to differentiate upright and inverted faces at the ERP component level. This failure to differentiate upright and inverted faces in the temporal domain was related to self-reported social skills. Specifically, a faster response to upright vs. inverted faces, was correlated to less social anxiety and distress, greater social competence, and fewer autism social symptoms. Similar to Jemel et al. (2006), we concluded that if face processing was a pervasive and encompassing deficit in ASD, we would expect the results to be similar across reports.

It has been hypothesized that altered face processing ability might be an endophenotypic trait associated with autism. Parents of children with ASD failed to show a differential latency of the N170 to faces versus nonface stimuli and failed to show a right-lateralized N170 distribution (Dawson et al., 2005). During a task involving the processing of a facial emotion, again, parents of children with ASD failed to demonstrate differential latency of the N170 to neutral and happy faces versus fear faces and N170 (amplitude) hemispheric differences. Within the parent group, atypical hemispheric activation was associated with poorer performance on the Reading the Mind in the Eyes task (Dawson et al., 2008). These ERP findings parallel ERP responses to emotional faces in young children with autism (Dawson et al., 2004).

Face Memory

Several studies with infants and children have evaluated face and object memory processes using ERPs (e.g., Carver et al., 2003; de Haan & Nelson, 1997, 1999; Webb, Long & Nelson, 2005). In these studies a highly familiar stimulus, such as a picture of the child’s mother or favorite object, is compared to a picture of an unfamiliar face or an unfamiliar object, respectively. In this paradigm, both image categories, familiar and unfamiliar, are presented in the same manner within the experimental setting, but differ in the child’s a priori experience with them. By 45 to 54 months, typical children show a faster latency and increased amplitude response to unfamiliar faces than to familiar faces. In contrast, responses to familiar and unfamiliar objects are similar (Carver et al., 2003).

In 3- to 4-year-old children with ASD, the Nc does not differ in response to the mother’s face versus an unfamiliar face, but does differ between a favorite toy as compared to an unfamiliar toy (Dawson et al., 2002). Chronologically age-matched typical children, demonstrated a greater Nc amplitude response to both the unfamiliar face compared to the familiar face and the unfamiliar object compared to the familiar object. By 6 years of age, both children with ASD and chronologically and mentally age matched children show differential temporal processing of familiar and unfamiliar faces at the Nc as well as the pr-N170. As seen in Figure 37-1, children with ASD continue to show delays in latency when processing face stimuli compared to control children—both children with developmental delay and neurotypical development (Webb, Dawson, Bernier, & Panagiotides, 2008).

Figure 37–1. Latency (in milliseconds) of pr-N170 response to faces and objects (left) and familiar and novel face (right) in 6-year-old children with Asd or controls. Both groups demonstrate a faster response to faces than objects, and a faster response to familiar than novel face. However, the ASD group’s responses remain significantly slower to all categories compared to those by the control group.

Figure 37–1.
Latency (in milliseconds) of pr-N170 response to faces and objects (left) and familiar and novel face (right) in 6-year-old children with Asd or controls. Both groups demonstrate a faster response to faces than objects, and a faster response to familiar than novel face. However, the ASD group’s responses remain significantly slower to all categories compared to those by the control group.


Many researchers have suggested that the ability to use or understand information from faces is a core deficit in autism (e.g., Baron-Cohen, 1994; Dawson et al., 2002; Frith, 1989). Given that emotion is often displayed in the face, differentiating a deficit in face processing from impairments in understanding and recognizing facial expressions is difficult. However, studies have shown intact recognition of facial expressions despite significant deficits in facial recognition in patients with prosopagnosia (Shuttleworth et al., 1982). On the other hand, patients who underwent an amygdalectomy demonstrate the reverse pattern, i.e., exhibiting a deficit in expression recognition but retaining facial recognition processes (Adolphs et al., 1994). These findings suggest that the two abilities can be separated at the neural and theoretical level (Bruce & Young, 1986).

Differential ERPs to distinct facial expressions of emotion have also been shown in infants (Nelson & de Haan, 1996) and young children (Batty & Taylor, 2003), suggesting that some discrimination of facial expressions may occur at early processing stages. In preschoolers aged 3 to 4 years, with ASD or typical development, only the typical group displayed a faster and larger early (300 ms) ERP response and a larger slow wave amplitude ERP response to the fear face than the neutral face. In contrast, children with ASD did not differentiate the fear face at either stage of processing. These findings suggest that ASD is associated with abnormal processing of facial expressions of emotion and that these abnormalities originate during early stages of processing. Given that the processing of facial expression requires facial processing, it is likely that any abnormalities in the initial stages of face processing would disrupt further processing of the emotion displayed on the face.

Conflicting evidence exists for impairment in processing emotional expressions in children with ASD. In older children, differential processing of emotions via ERPs has not shown pervasive impairments in ASD. Wong et al. (2008) found normal patterns of ERP and behavioral responses to emotional expressions. However, using source localization, the children with autism displayed slower and weaker responses to emotional expressions in regions responsible for face perception and emotion processing (Wong, Fung, Chua, & McAlonan, 2008). Supporting this finding, another ERP study failed to find a difference in emotional face processing between high-functioning, 9-year-old children with ASD compared with mental-aged-matched typically developing children (Burner, Webb, & Dawson, 2008). In summary, these results suggest that further examination of factors such as age and verbal abilities in individuals with ASD is warranted in the area of facial emotion processing.

Eye Gaze

Eye contact and eye gaze serve as important functions in social interaction and communication. Individuals with autism often display atypical eye contact (Baranek, 1999; Charman et al., 1997; Osterling, Dawson, & Munson, 2002), atypical gaze fixation patterns (Klin, Jones, Schultz, Volkmar, & Cohen, 2002), and eye gaze processing impairments (Mundy et al., 1986; Dawson et al., 1998) that may contribute to their social cognitive deficits. These behavioral observations of eye gaze behavior have led to the examination of the neural basis of eye gaze processing in individuals with autism, which has produced inconsistent results. A few studies have found a larger response (measured by the N2, a face specific occipitotemporal component) to direct gaze than to averted gaze in children with ASD (Grice et al., 2005; Kylliainen, Braeutigam, Hietanen, Swithenby, & Bailey, 2006) while others have failed to find a difference in the N2 between direct and averted gaze in individuals with ASD (Senju, Hasegawa, & Tojo, 2005). In addition, response to eye gaze in the N2 was bilaterally distributed in children with ASD, whereas the response was lateralized on the right side in typically developing children (Senju et al., 2005).

Developmental and contextual factors may account for these different results since the Senju et al. (2005) study included older children and required attention to be paid to the direction of the gaze. Another possible interpretation of these contrasting findings comes from a recent behavioral examination of eye gaze detection. The study found that similarly to typically developing children, children with autism detect direct gaze faster and more efficiently than averted gaze. However, children with ASD tended to use featural information to detect direct gaze whereas typically developing children relied on configural information (Senju, Kikuchi, Hasegawa, Tojo, & Osanai, 2008). The authors interpreted these findings in the context of previous neuropsychological research showing that direct gaze elicits a larger ERP response than averted gaze in children with autism, but not typically developing children (Grice et al., 2005; Kyllianinen et al., 2006). This is possibly because the children with autism are using a featural strategy that may rely on low-level psychophysiological features in eye gaze detection.


In the 30-year history of imitation research in autism, imitative deficits in individuals with ASD have consistently been observed, and several researchers have suggested that imitation deficits are one of the core impairments of autism (e.g., Rogers & Pennington, 1991; Williams, Whiten, Suddendorf & Perrett, 2001). Despite the variability in imitation testing methodologies, sample characteristics, and control groups employed, 19 of 21 well-designed studies have found imitative deficits in autism. This imitation impairment is most marked by deficits in imitation of nonmeaningful gestures and reversal errors (Williams et al., 2004).

The EEG mu rhythm, first observed by Gastaut and Bert in 1954, is believed to reflect activity of an execution/observation matching system—the mirror neuron system (Pineda, 2005; Muthukumaraswamy & Johnson, 2004). The EEG mu rhythm band falls between 8 and 13 Hz (generally the alpha frequency band) but is recorded from central electrodes. Underlying neurons fire synchronously when an individual is at rest, but during the execution and observation of an action, the underlying neurons activate, and this results in the attenuation of the mu rhythm amplitude. This attenuation pattern of the mu wave has been consistently observed in adults and children (e.g., recent papers include Babiloni et al., 2003; Cochin et al., 2001; Lepage & Theoret, 2006; Martineau & Cochin, 2003; Muthukumaraswamy, Johnson, & McNair, 2004).

Recent work analyzing the EEG mu rhythm in individuals with ASD suggests differential activation of mu related to action execution and observation (Bernier et al., 2007; Oberman et al., 2005; Oberman, Ramachandran, & Pineda, 2008). Oberman and colleagues reported on 10 males with ASD ranging from 6 to 47 years of age and 10 age- and gender-matched controls. Participants executed a simple hand movement or watched videos of a moving hand, two bouncing balls, or television static (Oberman et al., 2005). In the typical group, as expected, the authors found characteristic mu attenuation during the execution and observation of the hand movements but not during the two control conditions. However, the ASD group failed to show attenuation of the mu rhythm during the observation condition. In a second study of adults with ASD utilizing the EEG mu rhythm, Bernier and colleagues replicated Oberman’s findings, using a paradigm in which participants executed a grasp of a wooden block and observed the experimenter grasping the block (Bernier et al., 2007). In this study of adults with ASD compared to age and cognitive ability matched adults with typical development, the typical adults showed attenuation of the mu rhythm during both the conditions of execution and observation. The adults with ASD showed mu attenuation only during the execution of the simple hand action. Additionally, the adults with ASD showed significant impairments in imitative ability behaviorally. However, imitation ability was also significantly correlated with degree of mu wave attenuation. As imitative ability increased so did the degree of mu rhythm attenuation when observing movement. This correlation was strongest for mu attenuation and the ability to imitate facial expressions (Figure 37-2).

Figure 37–2. Mu suppression (difference in mu power between observe and baseline) and face summary score for the behavioral imitation measure. Greater mu suppression is correlated with higher (better) face imitation scores.

Figure 37–2.
Mu suppression (difference in mu power between observe and baseline) and face summary score for the behavioral imitation measure. Greater mu suppression is correlated with higher (better) face imitation scores.


Impairments in multiple aspects of face processing and imitation have been documented in individuals with ASD using both behavioral and EEG methods. Recent results examining the N170 suggest that early stages of face processing may be relatively more impaired in young children with autism, with variable impairments in adults. This pattern would suggest that performance of the face processing system may be influenced by associated processing related to the type of task (e.g., attention) and ultimately more amenable to developmental mechanisms or intervention. Similarly, our recent finding that mu attenuation during observation of action is correlated with behavioral performance suggests that there may be important neural variability within the autism phenotype. Given the heterogeneity within the social symptom domain in individuals with ASD, addressing this source of variability will be important.

Background Neural Processes

Theory and Methods

Most EEG research in ASD focuses on scalp recordings in awake human subjects. Cortical neural populations that exhibit a high degree of oscillatory synchrony over relatively large areas (on the order of at least 100–1000 mm2) can generate electrical potentials that are measurable with electrodes placed on the scalp (Lopes da Silva & Pfurtsceller, 1999; Nunez & Srinivasan, 2006). This neuronal activity is spatially low-pass filtered by the poorly conducting skull, limiting the spatial resolution of scalp recorded EEG. Estimates of spatial resolution are dependant on the density of the electrode array employed, ranging from 20 cc^3 or more in low density (19 electrode arrays) to 6–8 cm cc^3 in higher density (128 channel) arrays (Ferree et al., 2001).


A striking feature of ASD is the increased but variable risk of epilepsies among affected individuals. Seizure occurrence is common, with prevalence estimates ranging from 5% to 39% (Tuchman et al., 2002). These EEG instabilities are proposed to result from atypical cellular, molecular, and local excitatory-inhibitory neuronal circuits (Rubenstein & Merzenich, 2003) that result in imbalanced cortical function due either to increased excitatory glutamatergic signaling, or reduced inhibitory GABAergic activity. Proposed disruptions in interneuron development (Levitt, 2005) that contribute to seizure activity may reduce neural synchrony, as coupling among interneurons underlies the generation of oscillatory and synchronous activity in the cerebral cortex (Buzsáki & Chrobak, 1995). At a larger scale, cortical minicolumns appear narrower and more numerous in ASD, with constituent neurons more dispersed, and a suspected coincident deficit of GABAergic inhibition (Casanova et al., 2002), which may serve to diminish local connectivity. Brain stem abnormalities may also play a role in atypical electrical activity. Welsh et al. (2005) suggested ASD may be associated with disruptions in synchronization among neural networks in the inferior olive, in which rhythmical oscillation in membrane potentials, mediated by altered connexin36 receptors, could contribute to reduced neural synchrony in ASD.

Spontaneous EEG Rhythms in ASD

Spontaneous EEG generally refers to recordings made without time locking to an external stimulus, often in an awake but resting subject (similar to “default mode”) (e.g., Raichle, 2001). Spontaneous activity is typically interpreted across predefined frequency bands that represent the speed of neural oscillations: delta (< 4 Hz), theta (4–8 Hz), alpha (8–12 Hz), beta 1 (12–20 Hz), beta 2 (20–30 Hz), and gamma (30–80 Hz). These wide frequency bands are functionally defined in adults. Understanding the development of these rhythms poses a significant challenge for pediatric research, as functional reactivity in any particular frequency range can differ according to age. Spontaneous EEG is generally reported in terms of absolute or relative power. Relative power measures, which express amplitudes in a particular frequency band as a percentage of the wider power spectrum, serve to normalize the EEG and facilitate comparisons between subjects without bias from individual amplitude differences. However, relative power creates interdependencies across frequency bands. Optimally, both relative and absolute measurements should be analyzed.

Delta, Theta, and Alpha Range EEG in ASD

Accumulating evidence suggests elevated power in the theta range is frequently associated with ASD. Murias et al. (2007) found significantly increased relativez power in ASD adults compared to controls at theta range (3–6 Hz) frequencies. This appears consistent with Daoust et al.’s (2004) observations of elevated absolute frontal theta power among ASD subjects during sleep and waking states. Similarly, Coben et al. (2008) found relative, but not absolute, theta was greater and reduced relative delta amplitudes in the ASD group. Murias et al. (2007) found decreased relative power in the alpha (9–10 Hz) range, consistent with pediatric findings of Cantor et al. (1986) and Dawson et al. (1995), both of which noted decreased absolute alpha power in frontal regions among children with ASD.

Higher Frequency Rhythms

The higher frequency EEG bands beta 1, beta 2 and gamma span 12–80 Hz and are thought to be generated in neuronal networks that include excitatory pyramidal cells and inhibitory gamma-aminobutyric acid (GABA)-ergic interneurons (Whittington et al., 2000). Murias et al. (2007) discerned that a lower beta range (13–17 Hz) relative power increase existed at posterior scalp locations in adults with ASD. Orekhova et al. (2007) demonstrated a pathological increase of gamma (24.4–44.0 Hz) in two separate populations of boys with ASD. Consistent with the theory of imbalanced excitatory/inhibitory mechanisms in ASD (Rubenstein & Merzenich, 2003), the abnormally high levels of spontaneous gamma activity in autism suggests high levels of excitability, reflecting noisy cortical networks.


In participants with ASD, Dawson et al. (1982) found atypical patterns of cerebral lateralization, involving right-hemisphere dominance for both verbal and spatial functions, suggesting selective impairment of the left cerebral hemisphere. Stroganova et al. (2007) found abnormal lateralization in children with ASD, especially in the right temporal cortex. Resting anterior EEG asymmetry in high-functioning children with ASD who displayed right frontal asymmetry displayed more symptoms of social impairments and better visual analytic skills than did children who displayed left frontal asymmetry (Sutton et al., 2005).


Theoretical conceptions of ASD have postulated abnormalities in connections among distributed neural systems (Belmonte et al., 2004; Rippon et al., 2007; Courchesne & Pierce, 2005; Just et al., 2004). Anatomical support for disordered cortical connectivity includes the observation of increased white matter in ASD, with frontal lobe white matter showing the greatest increase (Herbert et al., 2004). Decreased volumes of the corpus callosum have been observed in adults (Hardan et al., 2000) and children (Boger-Megiddo et al., 2006; Manes et al., 1999; Vidal et al., 2006) with ASD. This suggests that the symptoms of ASD may be related to impaired interactions within brain networks, rather than impaired function of specialized cortical regions. Other factors beside white matter contribute to connectivity, for example, reduced cortical inhibition in ASD would tend to decrease the degree of synchrony of widely distributed regions (Courchesne & Pierce, 2005).

The levels of synchronization between neural populations can be estimated from EEG recordings via coherence measurements. The coherence statistic is a squared correlation coefficient that provides a measure of the linearity of the relation between two EEG electrodes at one particular frequency. High coherence between two EEG signals indicates the contribution of synchronized neuronal oscillations to each electrode, suggesting functional integration between neural populations, while low coherence suggests functional segregation. EEG coherence is primarily a measure of phase correlation and with a sufficient density of recording electrodes is believed to reflect functional cortical connectivity on a centimeter scale (Nunez & Srinivasan, 2006; Srinivasan et al., 1998) either directly via corticocortical fiber systems or indirectly through networks that include other cortical or subcortical structures. The dynamics of EEG coherence may be more sensitive to changes in the developing brains than power measures. Srinivasan et al. (1999) differentiated genuine spatial correlations from volume conduction and reference electrode effects, and found cortical areas contributing to the alpha rhythm to be far more weakly correlated with each other in preadolescent children than in adults.

In adults with ASD with the eyes closed in a resting state, Murias et al. (2007) reported globally reduced EEG coherence in the 8–10 Hz frequency range (Figure 37-3). Coherence was markedly diminished within frontal electrode sites, and between frontal and temporal, parietal, and occipital sites. In contrast to the globally reduced alpha rhythm, this study found local ASD increases in theta range (3–6 Hz) coherence in temporal regions that were independent of frontal lobe power findings. The nature of these findings suggests that the frontal lobe has weak functional connections with the rest of the cortex in the alpha frequency range and appears consistent with metabolic studies showing reduced correlated blood flow between frontal and other regions (Horwitz et al., 1988; Villalobos et al., 2005). Coben et al. (2008) reported decreased coherences in children with ASD in delta, theta, and alpha ranges.

Figure 37–3. Eyes closed, resting EEG averages from 18 high functioning adults with autism, and 18 age- and IQ-matched controls. The power spectrum of frontal electrode Fz, shown at left, shows that groups do not differ in peak frequency of the alpha rhythm (10 Hz), but peak alpha amplitude is significant reduced in the ASD group. The coherence spectra between two frontal electrodes is shown at right, showing reduced coherence in the ASD group.

Figure 37–3.
Eyes closed, resting EEG averages from 18 high functioning adults with autism, and 18 age- and IQ-matched controls. The power spectrum of frontal electrode Fz, shown at left, shows that groups do not differ in peak frequency of the alpha rhythm (10 Hz), but peak alpha amplitude is significant reduced in the ASD group. The coherence spectra between two frontal electrodes is shown at right, showing reduced coherence in the ASD group.



Similar to other scientific domains, differences in analytical methods employed by different investigators make comparisons across EEG and ERP studies difficult. For EEG, of particular importance is the definition of frequency bands. Reports of EEG spectra averaged over broad frequency bands provide only coarse frequency resolution and allow for the possibility that frequency-specific effects within bands cancel out or go otherwise undetected.

Any choice of reference electrode placement distributes the signal at the reference site throughout the array of electrodes. For example, the use of either one ear potential or the average of two ear potentials as a reference confounds coherence estimates by redistributing the potentials at the reference site (Srinivasan et al., 1998; Nunez & Srinivasan, 2006). Historically, many ERP studies have shifted from low-density arrays using ear, nose, or vertex references to high-density average reference arrays. Specifically, recordings made from sparse electrode arrays with a linked-ears reference strategy limit interpretation of prior literature in developmental psychopathology. Depending on the nature of the (unknown) signals at “recording” and “reference” sites, changes in power or phase at the reference location can easily be reflected as changes in coherence or amplitude between two other recording electrodes. With a sufficiently large number of recording electrodes the average reference approximates reference independence.

Volume conduction (the passive flow of current across the scalp, skull, and cerebrospinal fluid) also strongly influences scalp potential EEG coherence (Srinivasan et al., 1998; Nunez & Srinivasan, 2006), and has been shown in electrical models of the head to introduce artificial coherence between electrodes separated by less than 10–12 centimeters. Thus, EEG potential coherence measurements are only meaningful with regard to widely spaced electrode pairs. Coherence between closely spaced EEG electrodes is elevated even when the underlying brain sources are entirely uncorrelated, such that increases in the strength of one cortical source region will increase coherence between two electrodes located within 10 cm of the source region, confounding source strength with coherence. Spatial enhancement methodologies such as the Laplacian derivation (Nunez & Srinivasan, 2006) and finite element deblurring (Gevins & Illes, 1991) avoid spatial filtering by volume conduction, but may be insensitive to low spatial frequency source dynamics, especially those generated in broadly distributed cortical or subcortical regions.

Functioning of Subjects

Individuals with ASD have substantial adaptive functioning difficulties, including variable cognitive performance and sensory abnormalities. Behaviors related to verbal understanding, tactile hypersensitivity, hyperactivity, and inattention will impact compliance, which in turn can impede recording of quality data and lead to subject selection bias that can reduce the representativeness of the findings. Appropriate methodological and behavioral strategies in the EEG lab can mitigate these issues (Foote, 2004). Training in methods for desensitizing individuals with ASD to the EEG lab and methods for monitoring attention and compliance is essential. However, practical problems such as motion artifact and short recording times contribute substantially to difficulties in interpreting across studies.

Benefits and Risks of EEG/ERP

To a large degree, the risks and benefits of utilizing EEG/ERP methodologies to elucidate autism profiles are similar to other methodological domains. As stated previously, EEG (and MEG) activity is exquisitely sensitive to neural timing, on a resolution that is not matched by other methodologies, providing the ability to scrutinize physiological changes at a unique level of analysis. Benefits also include the ability to develop and utilize passive paradigms that are not reliant on the insight of participants, not under explicit control (i.e., most participants can not purposely alter brain activity), and may be less subjective to experimenter bias during data collection. Given the variability in the autism phenotype and the necessary focus on development, data can be collected under (relatively) similar paradigms across functioning levels and across the lifespan. Similarly, EEG can be utilized in other species, increasing our ability to model and evaluate similar neural processes under more tightly constrained circumstances.

With any methodology, risks are also present in the development, analysis, and interpretation of research findings. Primarily, behavioral compliance and resulting noise/error within EEG recordings is a significant issue. Second, known (and unknown) differences in anatomical architecture will impact and contribute to signal propagation and the scalp recording of EEG activity. These differences may reflect within and between group variability, as well as natural variability in the developmental trajectory of neural architecture. The spatial resolution of EEG may be improved with source localization methods, specifically when used in combination with MRI images. Third, similar to behavioral paradigms, researchers often assume that two individuals or two groups are “participating” in the same manner; that is, similar external behaviors reflect a similar internal strategy. Even under directed states, such as counting or resting with eyes closed, participants may have different strategies for completing the same task. Lastly, facility with EEG analysis can be varied and often the detection of noise, eye blinks or eye movements, and other noise signals is subjective or poorly defined across reports.


In the study of autism, EEG/ERP findings have demonstrated slowed neural speed, lack of stimulus or condition differentiation, both reduced and increased activation, and altered topographical distribution. Each of these results has independently contributed to our understanding of autism and has been used to redefine our understanding of the processes that contribute to autism behaviors. For example, a failure or delay in activating neural structures that are normally specialized for a given process such as the fusiform gyrus for faces, could result in a failure to interconnect information such as linking a facial movement to vocalization. Temporal asynchrony in processing has consequences for both the formation of neural circuits and the behaviors that directly result from the utilization of that circuitry (Dawson, Webb, & McPartland, 2005; Brock, Brown, Boucher, & Rippon, 2002). As well, alterations in coherence across the frequency spectrum may signal distinct patterns of over- and underconnectivity supporting neuroanatomical models of autism (Horwitz et al., 1988; Just et al., 2004) that may be directly assessed as risk factors, predictors, and measures of response to treatment.

Our current understanding of autism has benefited greatly from the integration of information across multiple levels of analysis. The EEG methodology provides information that allows us to get closer to neural functioning, to divide the behaviors of autism into meaningful endophenotypes that can be investigated across the spectrum of the disorder, and to characterize stages of cognitive and affective processing. This methodology is also well suited for developmental studies as it can be utilized with individuals from infancy through late adulthood. EEG has promise as an endophenotype or biological marker of variability, which will allow for more refined measurements and may yield greater precision in investigating the systems that contribute to risk and outcome in individuals affected by autism.

Challenges and Future Directions

  • The risks of utilizing EEG/ERP methodologies to elucidate autism profiles are similar to other methodological domains and include within- and between-group variability, verbal understanding, tactile hypersensitivity, hyperactivity, and inattention.


Writing of this chapter was supported by the NIH University of Washington Autism Center of Excellence (Webb; P50 HD 055782), NIH Shared Neurobiology of Autism and Fragile X (Webb; R03 HD 057321), Autism Speaks and Cure Autism Now (Murias), the Simons Foundation (Bernier) and the support of the University of Washington Psychophysiology and Behavioral Systems lab. Most importantly, we thank all of the individuals with autism and their families who participated in the research reported in this chapter.

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