EEG study of the mirror neuron system in children with high functioning autism

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EEG study of the mirror neuron system in children with high functioning autism Ruth Raymaekers⁎, Jan Roelf Wiersema, Herbert Roeyers Ghent University, Belgium

A R T I C LE I N FO

AB S T R A C T

Article history:

Individuals with Autism Spectrum Disorder (ASD) are characterised by an impaired

Accepted 17 September 2009

imitation, thought to be critical for early affective, social and communicative

Available online 25 September 2009

development. One neurological system proposed to underlie this function is the mirror neuron system (MNS) and previous research has suggested a dysfunctional MNS in ASD. The

Keywords:

EEG mu frequency, more precisely the reduction of the mu power, is considered to be an

HFA

index for mirror neuron functioning. In this work, EEG registrations are used to evaluate the

Mirror neurons

mirror neuron functioning of twenty children with high functioning autism (HFA) between

EEG

8 and 13 years. Their mu suppression to self-executed and observed movement is compared

Mu frequency

to typically developing peers and related to age, intelligence and symptom severity. Both groups show significant mu suppression to both self and observed hand movements. No group differences are found in either condition. These results do not support the hypothesis that HFA is associated with a dysfunctional MNS. The discrepancy with previous research is discussed in light of the heterogeneity of the ASD population. © 2009 Elsevier B.V. All rights reserved.

1.

Introduction

Several studies have suggested that individuals with autism spectrum disorder (ASD) suffer from impairments in imitation, which is thought to be critical for early affective, social and communicative development (for a review see Williams et al., 2004). In turn, a deficit in self-other mapping has been suggested as a possible cause for these imitation impairments (Rogers & Pennington, 1991; Uddin et al., 2007; Williams et al., 2004). Rogers and Pennington (1991) suggested that this deficit in self-other mapping leads also to other social-communicative deficits: impairments in pragmatic language, difficulties with theory of mind abilities, and failure to show common empathic reactions in social interactions. One neurological system proposed to underlie these functions is the mirror

neuron system (MNS), i.e. an observation–execution matching system (Rizzolatti et al., 2001). Mirror neurons were initially discovered in the ventral premotor cortex of macaque monkeys. These neurons fire when a monkey either performs an action or observes the same action performed by another monkey or an experimenter (Gallese et al., 1996; Rizzolatti et al., 1996), but they are not activated when a monkey merely observes the object or the movement alone. This supports the notion that mirror neurons form the basis of an observation–execution matching system, a possible mechanism by which action recognition, action understanding and imitation can be achieved (Gallese et al., 1996; Rizzolatti et al., 2001; Umiltà et al., 2001). Various studies using diverse neuroimaging techniques suggest that a comparable system exists in the human brain.

⁎ Corresponding author. Department of Experimental Clinical and Health Psychology, Research Group Developmental Disorders, Henri Dunantlaan 2, B-9000 Ghent, Belgium. Fax: +32 9 2646489. E-mail address: [email protected] (R. Raymaekers). 0006-8993/$ – see front matter © 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.brainres.2009.09.068

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Functional neuroimaging studies using functional magnetic resonance imaging (fMRI) or positron emission tomography (PET) have shown that the caudal part of the inferior frontal gyrus with the adjacent premotor cortex and the rostral part of the inferior parietal lobule are active during the observation of goal-directed actions (Buccino et al., 2004; Grézes et al., 2003; Iacoboni et al., 1999; Rizzolatti et al., 1996). Several transcranial magnetic stimulation (TMS) studies have shown enhanced motor-evoked potentials (MEPs) when subjects observed an individual manipulating objects (Fadiga et al., 1995; Gangitano et al., 2001; Maeda et al., 2002; Strafella and Paus, 2000). Magnetoencephalographic (MEG) measurements have shown that left BA 44 and bilateral M1 are activated during the observation, execution, and imitation of a precision grip as compared to the observation of a simple hand extension (Nishitani and Hari, 2000). As suggested by Muthukumaraswamy and others (Muthukumaraswamy and Johnson, 2004; Muthukumaraswamy et al., 2004), analysis of electroencephalographic (EEG) mu frequency band oscillations can provide an inexpensive, non-invasive method to study human mirror neuron functioning. At rest, the synchronous action of the neurons in the sensorimotor cortex generates large EEG oscillations in the 8 through 13 Hz frequency band, referred to as the mu band (Pfurtscheller et al., 1997). When individuals execute or observe a movement the power of these EEG oscillations is attenuated, this is called mu wave suppression (Babiloni et al., 2002; Cochin et al., 1999; Lepage and Theoret, 2006; Muthukumaraswamy et al., 2004; Pineda et al., 2000). Decreases in amplitude EEG oscillations in the mu band indicate desynchronization of the underlying neurons, reflecting greater levels of active processing during motor movement and observation (Cochin et al., 1999; Pineda et al., 2000). EEG topography suggests that mu wave suppression is likely to be a result of activation of several neuronal systems in the premotor and sensorimotor cortices (Babiloni et al., 1999; Muthukumaraswamy and Johnson, 2004, Pfurtscheller and Neuper, 1997). During observation of actions it has been hypothesized that the MNS is the only network in this area to be active (Muthukumaraswamy et al., 2004). In addition, bodily movement does not account for the mu wave suppression while observing other's actions (Muthukumaraswamy and Johnson, 2004; Muthukumaraswamy et al., 2004). This suggests that mu wave suppression during action observation could be used as a selective measure of MNS functioning (Muthukumaraswamy and Johnson, 2004). Various properties of the mu frequency band provide further support to the use of the mu wave suppression as an index for the MNS functioning. Similar to the mirror neurons, mu frequency band oscillations react to self-executed, observed and imagined actions (Cochin et al., 1998; Pineda et al., 2000). In addition, both respond to animate stimuli (Rizzolatti and Fadiga, 1998) and react more to target-directed actions than to non-goal-directed actions (Muthukumaraswamy et al., 2004). Their overlapping neural sources in sensorimotor networks further support the argument that they are related and involved in linking perception to action. Furthermore, the correspondence between EEG and fMRI manifestations of MNS to similar experimental manipulations strengthens the notion that mu suppression is a valid index for MNS functioning (Buccino et al., 2001; Perry and Bentin, 2009). The mu frequency band has often been

misidentified and confused with the posterior alpha frequency band, since both are two kinds of alpha rhythms (Niedermeyer, 1997). However, research indicates that mu and alpha rhythms have a distinct spatial distribution, differences in source generation, in sensitivity to sensory events and in power (Hari et al., 1997; Makeig et al., 2002; Manshanden et al., 2002, Niedermeyer, 1997). In addition to its involvement in low-level, motor-related imagery, the human MNS has been implicated in high-level processes: imitative learning (Rizzolatti and Craighero, 2004; Umiltà et al., 2001), language (Arbib, 2005), theory of mind abilities (Gallese and Goldman, 1998; Williams et al., 2001), empathic reactions (Gallese, 2003; Leslie et al., 2004) and social cognition (Gallese et al., 2004). These high-level functions associated with the MNS are–as mentioned above–impaired in ASD. So, in this respect the MNS seems to be one of the more promising theories to elucidate the neural origin of ASD. So far, the majority of research findings have supported the link between impairments in the MNS and ASD. Nishitani et al. (2004) used MEG and recorded cortical activations from 8 adults with Asperger's syndrome. Similarly, Villalobos et al. (2005) found that the prefrontal mirror neuron area had reduced functional connectivity with the primary visual cortex in individuals with autism. Theoret and colleagues (2005) recorded TMS-induced MEPs while subjects watched videos of finger movements. The individuals with ASD showed increased MEPs only to actions facing toward the subject (allocentric view) and no significant change from baseline during the actions facing away from the subject (egocentric view). The researchers explained this in terms of a mirror neuron deficit leading to a general self-other representation deficit. In a recent fMRI experiment conducted by Dapretto and colleagues (2006), children with ASD were asked to both imitate and observe emotional facial expressions. The clinical group did not show significant activation of the inferior frontal gyrus (MNS region). Furthermore the activity within the MNS region was inversely correlated with severity of social dysfunction. Similarly, Williams et al. (2006) found less activity attributable to mirror neurons in areas of the right parietal lobe in adolescents and young adults with ASD. Hadjikhani et al. (2006) have reported that adults with ASD displayed significantly reduced cortical thickness in the main mirror neuron areas. Using the EEG methodology, Altschuler and colleagues (2000) found a lack of mu wave suppression in one child with autism in response to observation of actions by others. Oberman and colleagues (2005) corroborated this finding by demonstrating an absence of mu wave suppression in a sample of 10 individuals with ASD while they watched videos of another person's actions. Furthermore, Bernier et al. (2007) found 14 adults with ASD to show significantly reduced attenuation of the mu wave when observing movement, and they related this to the degree of imitation impairment. The above-mentioned studies embody the current literature supporting the role of an impaired MNS in individuals with ASD. However, some authors suggest that some MNS components still preserve a certain degree of function in both children and adults with ASD, as indicated by findings of their intact abilities to imitate and represent others' actions (Bird et al., 2007; Hamilton et al., 2007; Hobson and Lee, 1999; McIntosh et al., 2006; Sebanz et al., 2005; Southgate and

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Hamilton, 2008; Williams et al., 2004) and of an equivalent movement interference effect on an interpersonal interference task (Gowen et al., 2008). Furthermore some neuroimaging evidence exists that is inconsistent with the hypothesis of a global mirror neuron dysfunction in ASD. Avikainen et al. (1999) suggested a normal mirror neuron functioning in five individuals with ASD using MEG. Both groups showed significant rebound activity during self-initiated and observed hand movements. Although the magnitude of the rebound seen in the ASD group was smaller than that in the control group, this group difference was not significant, so normal MNS functioning was suggested. The disparate conclusions in literature call for further examination of the mirror neuron hypothesis in ASD. Furthermore, EEG research of the MNS in ASD is mainly performed in small samples or in samples with a large age range, which is difficult to reconcile with the heterogeneity of ASD. This is to some extent accounted for in the current study, which is modeled on the paradigm of Oberman and colleagues (2005). Mu wave suppression is compared between children with high functioning autism (HFA) and typically developing children during observation and execution of a motor act. Compared to the aforementioned studies the composition of the sample is improved: the number of participants is augmented to 20 subjects in each group and the age range is limited from 8 to 13 years.

2.

Results

All subjects performed the counting task with 100% accuracy. Thus, differences in attending to the stimuli are not responsible for any differences found in mu wave suppression. Fig. 1 presents the log ratio of mu wave suppression for each condition and each electrode in the control and the HFA group. This figure shows that mu wave suppression in both groups was maximal for the self condition and decreased during the hand condition. During the observation of the moving balls no mu wave suppression was apparent in either

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group. The means and standard deviations of mu wave power for each electrode and condition in both groups are presented in Table 1. Significant mu wave suppression from baseline was found for both groups for the self and hand condition. For the control group: self t(18)= −3.85, p < 0.01 and hand t(18)= −2.90, p < 0.05. For the HFA group: self t(19)= −4.24, p < 0.001 and hand t(19)= −2.59, p < 0.05. For the balls condition no significant suppression from baseline was found in control and HFA group, respectively t(18)= −0.37, n.s. and t(19)= −0.11, n.s. A 2 × 3 × 3 repeated-measures MANOVA with group (control and HFA) as within factor and condition (self, hand, and balls) and electrode (C3, Cz, and C4) as between factors showed a main effect of condition (F(2,74) = 28.59, p = 0.00) and of electrode (F(2,74) = 8.90 p = 0.00) for mu wave suppression. The two-way interaction between condition and electrode was also significant, F(4,148) = 9.42, p = 0.00. There was no significant group main effect (F(1,37) = 0.10, n.s.), nor significant two-way or three-way interactions between group, electrode, and condition (G × C: F(2,74) = 0.04; G × E: F(2,74) = 0.32 and G × C × E: F(4,148) = 0.99). To further test the condition effect separate 2 × 2 ANOVAs with repeated measures were performed for the different electrodes, with Group as the between-subject factor and Condition as the within-subject factor. The results of these analyses are presented in Table 2. Only the main effects of condition for all electrodes were significant. Analyses showed greater suppression for the self condition relative to the hand condition, and a higher degree of mu wave suppression in the self condition and in the hand condition compared to the balls condition. Further testing of the condition effect showed for both groups greater suppression for the self condition relative to the hand condition (control: t(18) = −3.76, p < 0.01; HFA: t(19) = −3.26, p < 0.01) and the balls condition (control: t(18) = −4.52, p = 0.00; HFA: t(19) = −4.15, p < 0.01). Furthermore there was a higher degree of suppression in the hand condition than in the balls condition (control: t(18) = −2.83, p < 0.05; HFA: t(19) = −2.32, p < 0.05).

Fig. 1 – Log ratio of mu wave suppression for each condition and each electrode for control and HFA group (larger bars indicate more suppression).

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Table 1 – Mean and standard deviation of mu wave power values for both groups for each electrode and condition (in μV2). HFA (n = 20)

C3

Cz

C4

Baseline Hand Self Ball Baseline Hand Self Ball Baseline Hand Self Ball

Control (n = 19)

M

Sd

M

Sd

1.32288 0.98992 0.67505 1.28247 0.70633 0.66435 0.61915 0.69431 1.08997 0.95669 0.68273 1.12162

1.34576 0.76242 0.37103 0.55828 0.47994 0.42592 0.41525 0.46037 0.74586 0.50698 0.37775 0.41819

1.04781 0.69142 0.46625 0.81199 0.47768 0.46736 0.42296 0.49684 0.73444 0.58930 0.48133 0.73918

1.04471 0.42887 0.18324 0.50469 0.32211 0.32716 0.32619 0.35078 0.34013 0.22001 0.17572 0.33632

2.1.

Relation between age and mu wave suppression

Correlation analyses revealed no significant relations between mu wave suppression and age. Mu wave suppression for the self condition was not significantly correlated with age for both groups combined r(39) = −0.25, p = 0.12. The correlation coefficients for both groups separately were: control group: r(19) = − 0.14, p = 0.56 and HFA group: r(20) = − 0.39, p = 0.09. In addition, mu wave suppression for the hand condition was not significantly correlated with age for both groups combined r(39) = −0.18, p = 0.27. However, as Fig. 2 depicts, analyses with both groups separately revealed no correlation in the control group (r(19) = 0.02, p = 0.94), but a strong, nearly significant correlation in the HFA group (r(20) = −0.44, p = 0.05).

2.2. Relation between intelligence and mu wave suppression

HFA = High functioning autism.

To further test the electrode effect separate 2 × 2 ANOVAs with repeated measures were performed for the different conditions, with Group as the between-subject factor and Electrode as the within-subject factor. Analyses showed significant less suppression for Cz compared to C3 and C4 in the hand condition (respectively F(1,37) = 10.75, p = 0.00 and F(1,37) = 4.94, p < 0.05) and self condition (respectively F(1,37) = 17.24, p = 0.00 and F(1,37) = 14.68, p = 0.00), and significant more suppression for C3 than for C4 in the hand condition (F(1,37) = 4.72, p < 0.05). Other effects were not significant. To assure that the effects of suppression were specific to the mu frequency and not the result of other activity, electrodes selected from the anterior and posterior regions were looked at. Across the conditions no consistent pattern of suppression was observed in the frequency band at these electrodes. This indicates that the observed suppression was specific to the central electrodes and not the result of other activity. Furthermore informal analyses showed no systematic topographic or amplitude difference in higher frequency bands (e.g. beta).

Correlation analyses revealed significant relations between mu wave suppression and intelligence. Mu wave suppression for the self condition was significantly correlated with the FSIQ score for both groups combined r(39) = −0.40, p = 0.01. A similar correlation coefficient was found for both groups separately: control group: r(19) = −0.44, p = 0.06. and HFA group: r(20) = − 0.38, p = 0.10. Mu wave suppression for the hand condition was significantly correlated with the total IQ score for both groups combined r(39) = − 0.36, p = 0.03. A similar correlation coefficient was found for both groups separately: control group: r(19) = −0.30, p = 0.22 and HFA group: r(20) = −0.40, p = 0.08.

2.3. Relation between symptom severity and mu wave suppression Correlation analyses revealed no relations between mu wave suppression and symptom severity, expressed by the SRS total score and the SRS domain scores. For both groups combined no significant correlations were found (ranging from 0.06 to 0.17), neither for the control group (ranging from 0.03 to 0.21) nor for the HFA group (ranging from 0.01 to 0.23) separately.

3. Table 2 – Separate ANOVA's with repeated measures for the different electrodes, with Group as between-subject factor and Condition as within subject factor. Condition

C3

Cz

C4

S-H S-B H-B S-H S-B H-B S-H S-B H-B

Group

Condition × Group

F(1,37)

p

F(1,37)

p

F(1,37)

p

16.52 32.91 9.72 6.60 11.24 3.94 29.31 34.58 10.10

0.00 0.00 0.00 0.01 0.00 0.05 0.00 0.00 0.00

0.15 0.23 0.31 0.00 0.04 0.87 0.11 0.02 0.77

0.71 0.63 0.58 0.96 0.84 0.36 0.74 0.89 0.39

0.01 0.00 0.09 0.70 0.72 0.07 1.87 0.09 0.96

0.82 0.98 0.77 0.41 0.40 0.79 0.18 0.77 0.33

HFA = High functioning autism; S = Self condition; H = Hand condition; B = Balls condition.

Discussion

The aim of the current study was to investigate whether the MNS is dysfunctional in children with HFA using the EEG methodology. Therefore one of the first proposed and most strongly supported functions of the MNS, namely simulation of motor actions was investigated. Both children with HFA and typically developing children showed significant mu wave suppression during the execution of hand movement as compared to the baseline condition (observation of visual white noise). In addition, both groups showed significant mu wave suppression during the observation of a video of hand movement as compared to the baseline condition. Both groups showed more suppression of the mu frequency during execution of the action than during observation of the action. On the other hand, no significant difference in mu wave suppression was found between the observation of a video of bouncing balls and the baseline condition in both groups.

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Fig. 2 – Mu suppression in the hand condition and age. Negative correlation was found in the HFA group (r = −0.44, p = 0.05).

The occurrence of comparable mu wave suppression in the execution condition for both groups suggests that similar sensorimotor systems underlie self-performed actions, as was expected. As far as mu wave suppression during action observation is a sensitive index of the MNS functioning, the results suggest that this system functions also similarly in children with HFA as in typically developing children. This is contrary to what was expected, as evidence is accumulating for an impaired MNS in ASD (Bernier et al., 2007; Dapretto et al., 2006; Hadjikhani et al., 2006; Oberman et al., 2005; Williams et al., 2006). Yet, most of these studies were conducted in adult samples or in samples with a large age range. In the Oberman et al. study (2005) no relation was found between the MNS functioning and age (control group: r = −0.08 and autism group: r = −0.05). However, in our study another picture seemed to emerge when we looked at both groups separately. The correlation between mu wave suppression and age in the HFA group was −0.44 (p = 0.05) compared to a correlation of 0.02 in the control group. So age seems to have an influence on the MNS functioning in the autism group, with more suppression being linked to older age. This is however difficult to reconcile with the findings of the Oberman et al. (2005) study, which included subjects between 6 and 47 years, with a far higher mean age (16.6) compared to this sample. Research into the amplitude development of mu frequency is sparse (Marschall et al., 2002) but research that addresses this topic suggests that mu wave power at central sites increases over childhood to early adolescence (Benninger et al., 1984; Niedermeyer, 1997, Somsen et al., 1997). The intelligence level of the participants could also be a factor that differed between our study and the others. In the current study, more suppression is linked to a greater intelligence for both groups (r = − 0.36). The more intelligent the children are the more they seem to activate the MNS. One could argue that the task was too simple and therefore not sensitive enough to find group differences. However, this seems unlikely since Oberman and colleagues,

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using the same paradigm with an older sample with the same mean intelligence, found a dysfunctional MNS in the autism group. Another factor that could be different between studies and that already proved to be important for MNS functioning (Dapretto et al., 2006) is symptom severity. In the current study, the SRS was used to measure this aspect but no link was noted between MNS activity and symptom severity. One could argue that no group differences were found because only children who were mildly affected with ASD were included in the HFA sample. Yet, of the 20 subjects only seven had scores on the SRS that are typical for children with mild or high functioning ASD (60T to 75T). All other scores exceeded 76T, indicating more severe cases of ASD (Constantino, 2002). The comparison of ASD samples across MNS studies is also often problematic because symptom severity is not always reported in detail (Bernier et al., 2007; Oberman et al., 2005) or other measurements are used e.g. ADOS-G (Dapretto et al., 2006). Since individuals with ASD constitute a heterogeneous group e.g. much variability in theory of mind abilities, other aspects of social cognition, intelligence, it will be necessary that future research investigates to what extent the functioning of the MNS is related to these factors. Although we were not able to reveal any group difference in MNS functioning, the findings support the notion that the mu frequency indexes an execution/observation matching system in a human brain system that is functionally comparable to the monkey mirror neuron system. The system is only activated by biological motion and not by non-biological directional motion such as bouncing balls. The absence of mu wave suppression in the balls condition suggests that it is unlikely that generalized attentional differences between baseline and experimental conditions bring about the observed mu wave suppression (Rossi et al., 2002). The baseline condition did not control for attention, whereas in the other conditions an attention task (counting) was included. It seems unlikely that this additional task would draw attention away from the processing of the stimuli since there was no difference in mu suppression between the baseline condition and the balls condition. It is also unlikely that the recordings from C3, Cz and C4 are affected by anterior and posterior alpha activity. First, all conditions involved visual stimuli and eyes had to be open during the whole experiment, so a systematic difference in this activity between conditions was not expected. Second, the first and last 10 s of each block were removed, so the effects of alpha modulations due to attention on the mu wave power were reduced. Third, Oberman and colleagues (2005) used the same paradigm and reported no mediation of the mu wave power by posterior alpha activity. Finally, no other electrodes showed a similar pattern of suppression in the mu frequency band. One could argue that the findings could be confounded by subtle movements of the subjects in the observation task. Although we continuously monitored the children's behavior and did not observe any overt movements, no electromyography was recorded and so we cannot be sure no covert movements occurred. However, other research (Muthukumaraswamy and Johnson, 2004; Muthukumaraswamy et al., 2004) found that mu wave suppression during action observation is strictly a central phenomenon. So, movement in the body would not account for mu suppression during observation. As

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it is improbable that subjects moved consistently different during the hand and the balls condition, the differences in mu suppression cannot be attributed to movement. This is the first study to demonstrate that mirror neuron functioning, as measured with EEG, is similar in children with HFA as in typically developing peers. This is in line with behavioral research pointing to several weaknesses in the evidence of the role of the MNS in autism (e.g. Hamilton et al., 2007). Furthermore, since mirroring phenomena are apparent only for viewing actions attributed to another person (SchutzBosbach et al., 2006), it would seem that Theoret and colleagues (2005) incorrectly attributed their findings to an impaired MNS functioning in ASD. A different research group (Avikainen et al., 1999) also suggested a normal mirror neuron functioning in ASD using magnetoencephalography (MEG). However, it has been claimed that this study lacked power in both the limited subject size (n = 5) and the fact that the patients tested were adult individuals with only a mild form of the disorder (Williams et al., 2001). A further extensive and thorough exploration of the MNS functioning in ASD is warranted. Since mirror neurons are part of a broader network, which may be modulated by multiple systems throughout the brain, future research should use higher resolution EEG to investigate whether the MNS is indeed dysfunctional, whether dysfunctional input from other brain regions is transmitted to a functional MNS or whether a more general dysfunction of neural connectivity (Murias et al., 2007; Mostofsky et al., 2009) is evident in ASD. The combination of EEG and fMRI recordings while manipulating different properties of the MNS can also help to increase insight in the link between mu suppression and MNS and between MNS and ASD. Future research should also focus on the contribution of mirror neuron activity to behavioral characteristics in ASD taking into account e.g. the contributions of age, intelligence and symptom severity.

the clinical group was obtained by means of the Child Behavior CheckList (CBCL; Achenbach, 1991; Dutch translation: Verhulst et al., 1996), the Disruptive Behavior Disorder Rating Scale (DBD; Pelham et al., 1992; Dutch translation: Oosterlaan et al., 2000), the Social Communication Questionnaire (SCQ; Rutter et al., 2003; Dutch translation: Warreyn et al., 2004) and the Social Responsiveness Scale (SRS; Constantino, 2002). The CBCL gives standardized descriptions, reported by parents, of emotional, social and behavioral problems in 4 to 18 year-old children. The DBD is divided into four scales composed of the DSM-IV-TR items for Attention Deficit Hyperactivity Disorder (ADHD) inattentive subtype, ADHD hyperactive/impulsive subtype, Oppositional Defiant Disorder (ODD), and Conduct Disorder (CD). The SCQ “current” version is a screening instrument for ASD, derived from the ADI-R algorithm, in which the present behavior is assessed by the parents. The SRS ascertains the severity of social impairment associated with ASD over the past 6 months. CBCL, DBD and SCQ, were used as selection instruments in the non-clinical control group, which was recruited from several regular schools. The SRS was used to assess the social behavior of these children in detail. Children were excluded from the study if either: (1) the parent or the teacher stated that the child had ever had a clinical diagnosis or was using medication; (2) the scores of the CBCL reached the 85th percentile; (3) a score exceeded the normal range on one of the four subscales of the DBD; or (4) the total score of the SCQ was above or as high as the cut-off. Children on stimulant medication were asked to discontinue their medication at least 20 h prior to testing, ensuring a

Table 3 – Descriptive characteristics of the sample. HFA (n = 20)

4.

Experimental procedures

4.1.

Participants

20 children with HFA (2 girls) and a control group of 19 typically developing children (5 girls) participated in the study. All children were between 8 and 13 years old and had a FSIQ above 85, estimated using the subtests arithmetic, vocabulary, block design and picture arrangement of the Wechsler Intelligence Scale for Children III (WISC-III; Wechsler, 1991). Both groups were matched on age and intelligence. The participants with HFA were recruited for participation through rehabilitation centers, special school services, and other agencies specialized in the care of children with HFA. All had previously been diagnosed as having ASD by a multidisciplinary team according to established criteria, as specified in DSMIV-TR (APA, 2000). The diagnosis of each child was confirmed with the use of a parent interview, namely the Autism Diagnostic Interview-Revised (ADI-R; Lord et al., 1994). The ADI-R is a semi-structured interview for parents that explores symptoms of an ASD, and is currently considered as the “gold standard” diagnostic parental interview for ASD (Filipek et al., 1999). A further description of the behavior of the children in

Chronological age (months) Total IQ Total SRS Aware Cognition Communication Motivation Mannerism Total SCQ Total CBCL Internalizing Externalizing DBD Attention Hyperactivity ODD CD

Control (n = 19)

t(37)

M

SE

M

SE

133.90

3.90

128.63

3.46

1.01

103.20 90.38 11.75 16.88 29.56 15.94 16.25 15.38 70.06 67.67 64.33

2.74 5.95 0.66 1.13 2.20 1.57 1.71 1.29 1.83 2.17 2.29

112.73 36.72 7.78 6.83 11.72 7.39 3.00 5.00 50.69 51.25 48.75

4.40 4.43 0.80 0.90 1.62 1.01 0.77 1.00 2.73 2.87 2.42

−1.87 7.33⁎ 3.78⁎⁎ 7.04⁎ 6.63⁎ 4.69⁎ 7.34⁎ 6.39⁎ 6.01⁎ 4.63⁎ 4.67⁎

14.61 13.50 9.17 2.73

1.40 1.25 1.30 0.81

5.84 4.37 2.95 0.47

1.14 1.05 0.69 0.19

4.88⁎ 5.63⁎ 4.29⁎ 2.78⁎⁎⁎

HFA = High Functioning Autism; SRS = Social Responsiveness Scale ; SCQ = Social Communication Questionnaire; CBCL = Child Behavior CheckList; DBD = Disruptive Behavior Disorder Rating Scale; ODD = Oppositional Defiant Disorder; CD = Conduct Disorder. ⁎p = 0.00, ⁎⁎p = 0.001, ⁎⁎⁎p < 0.01.

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complete wash-out. Independent t-tests conducted on chronological age and IQ showed that the groups did not differ significantly. The means and standard deviations for chronological age, IQ and scores on the parental questionnaires are presented in Table 3.

4.2.

Procedure

Parents and child were invited to the university to partake in the experiment. First, the procedure was explained to the child. Subsequently, an electrode-cap was placed on the child's head. The recording session contained the actual experiment, explained below, and always ended with an additional task in which children had to watch and imitate facial expressions portrayed in photographs. The results of that task are outside the scope of this work and are not discussed here. Intelligence was tested after the recording session. The actual experiment, based on the paradigm of Oberman and colleagues (2005), consisted of four conditions: (1) observing a video of a moving hand (hand), (2) moving own hand (self), (3) watching a video of two bouncing balls (balls), and (4) watching visual white noise (baseline). Each of the four conditions was run two times and the order was counterbalanced across subjects, with the restriction that the self condition always came after the hand condition. A synchronization pulse was sent to the EEG recording equipment at the moment the conditions started. In the hand condition, children viewed a video of a right hand opening and closing with fingers and thumb held straight at a rate of approximately 1 Hz. In the self condition, children moved their right hand as seen on the video, at eye level and with a similar rate as in the video. In the balls condition, children viewed a video of two balls moving vertically towards each other, touching in the centre of the screen and moving back apart to their initial starting position. The trajectory of the balls was visually identical to that of the fingertips in the hand video. White noise was presented as a baseline condition. Videos were presented at a viewing distance of 80 cm. The stimuli (hand and balls) were grey on a black background, moved at a rate of 1 Hz, and subtended 5° of visual angle when open/maximal point of separation and 2° when close/touching. All conditions lasted for 80 s. During the videos both the child and experimenter sat still, hands resting on the lap. Between four and six times during the hand and balls videos, the movement of the stimuli was paused for 1 s. To ensure that subjects attended to the videos, they were asked at the end of each video to inform the experimenter on the number of pauses (counting task).

4.3.

EEG recording and analysis

EEG recordings were obtained from 10 mm Ag/AgCl electrodes (inter-electrode impedance below 10 kΩ) at F3, Fz, F4, C3, Cz, C4 and Pz (10/20 International System) referenced to the left mastoid using a Twente Medical Systems International (TMSi) recording system. The electrooculogram (EOG) was monitored via electrodes positioned above and below the left eye (in line with the pupil) (VEOG) and electrodes

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placed at the outer canthus (HEOG). A ground electrode was affixed to the forehead. The EEG was recorded with a sample rate of 500 Hz, a low pass filter of 30 Hz and a time constant of 1 s. Waveforms were digitally filtered with a 50Hz notch filter. EEG data were collected for 160 s per condition. The first and last 10 s of each segment of data were removed to eliminate the possibility of attentional transients due to initiation and termination of the stimulus and the two 60-s segments of data were combined, resulting in a 120-s segment per condition. Data were epoched offline in 2-s segments. The EEG from individual trials was visually inspected and corrected for horizontal and vertical eye movements using the Gratton & Coles algorithm (Gratton et al., 1983), as implemented in Vision analyzer (version 1.05). Segments with remaining EEG artifacts exceeding ± 100 μV in any EEG channel were rejected. Data were only further analyzed if there was sufficient clean data with no artefacts. The mean rejection rate was 1.78% across both groups, with a range of 0– 21.67%. There were no differences between groups in rate of data rejection (control M = 1.32%; HFA M = 2.15%). Fast Fourier Transforms (FFTs) were performed on the 2-s clean EEG segments. A cosine window was used to control for artefacts resulting from data splicing. The resulting power spectra (magnitudes) were averaged across segments for each condition. As the mu frequency has been proposed to reflect MNS functioning, the study focused on mu band activity only, defined as the frequency band ranging from 8 through 13 Hz that is topographically centered on the standard C3, Cz and C4 positions (Muthukumaraswamy et al., 2004). Mu wave suppression was calculated as a ratio of the 8– 13 Hz power during each of the self, hand and balls conditions relative to the 8–13 Hz power in the baseline condition. A ratio was used to control for individual variability in overall absolute EEG power. Due to the inherent non-normality of ratio data, a log transform was calculated for each ratio. A log transform result with a negative value represents mu wave suppression and with a positive value mu enhancement.

4.4.

Statistical analysis

Firstly, C3, C4 and Cz positions were selected where sensorimotor activity would most likely be represented (Babilioni et al., 2002). In the one-sample t-tests and the correlation analyses, the mean suppression over the three electrode positions was used as a measure of mu wave suppression. Secondly additional analyses were done for the other positions to exclude anterior or posterior alpha effects on the data. t-Tests were performed comparing suppression, as indicated by the log ratio between the mu wave power in each of the experimental conditions and the mu wave power in the baseline condition, to zero. A three-way MANOVA with repeated measures was used with Group (HFA and Control) as the between-subject factor, Electrode (C3, Cz, and C4) and Condition (self, hand, and balls) as within-subject factors. Separate ANOVAs with repeated measures were performed for the different electrodes, with Group as the between-subject factor and Condition as the within-subject factor. In addition, separate ANOVAs with repeated measures were performed for the different

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conditions, with Group as the between-subject factor and Electrode as the within-subject factor. Correlation analyses were implemented to examine the relationship between mu wave suppression during the self and hand condition and age, FSIQ and symptom severity (expressed by the SRS total score and the SRS domain scores). An alpha level of 0.05 (two-tailed) was used for all statistical tests. For each group and for each dependent measure, values more than three standard deviations from the mean were considered as outliers and were removed per subject from the analyses. This was the case for 2 subjects in the HFA group on 2 different dependent measures and for 1 subject in the Control group for 3 dependent measures.

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