Altered right frontal cortical connectivity during facial emotion recognition in children with autism spectrum disorders

Research in Autism Spectrum Disorders 8 (2014) 1567–1577

Contents lists available at ScienceDirect

Research in Autism Spectrum Disorders Journal homepage: http://ees.elsevier.com/RASD/default.asp

Altered right frontal cortical connectivity during facial emotion recognition in children with autism spectrum disorders Michael K. Yeung a, Yvonne M.Y. Han b, Sophia L. Sze a, Agnes S. Chan a,* a Neuropsychology Laboratory, Department of Psychology, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region b Department of Special Education and Counselling, The Hong Kong Institute of Education, Tai Po, Hong Kong Special Administrative Region

A R T I C L E I N F O

A B S T R A C T

Article history: Received 4 July 2014 Received in revised form 25 August 2014 Accepted 26 August 2014 Available online 14 September 2014

A growing body of evidence suggests that autism spectrum disorders (ASD) is associated with altered functional connectivity of the brain and with impairment in recognizing others’ emotions. To better understand the relationships among these neural and behavioral abnormalities, we examined cortical connectivity which was indicated by theta coherence during tasks of facial emotion recognition in 18 children with ASD and 18 typically developing (TD) children who were between 6 and 18 years of age. We found that the children with ASD had general impairment in recognizing facial emotions, after controlling for response bias. Additionally, we found that the TD children demonstrated significant modulation of right frontal theta coherence in response to emotional faces compared to neutral faces, whereas children with ASD did not exhibit any modulation of theta coherence. The extent of modulation of theta coherence to emotions was further found to be related to the severity of social impairments in ASD. Our findings of a general impairment in facial emotion recognition and the involvement of disordered cortical connectivity in social deficits in children with ASD have shed light for future exploration of interventions regarding emotional processing and social functioning in ASD. ß 2014 Published by Elsevier Ltd.

Keywords: Autism spectrum disorders Facial emotion Social Connectivity Theta Coherence

1. Introduction Facial expressions convey important, specific social information, such as internal emotional states, to the observer (Blair, 2003). The interpretation and understanding of facial expressions of other people could guide appropriate actions, which are necessary for reciprocal social interaction (Baron-Cohen, Wheelwright, & Jolliffe, 1997). Thus, confusion of emotions and especially the misinterpretation of thinking that negative emotions are actually other kinds of emotions, for instance, positive emotions such as surprise (Kuusikko et al., 2009), might lead to inappropriate behavior and undesirable social outcomes. In school-aged children, the ability to recognize facial expressions of emotions was found to contribute to social competence (Goodfellow & Nowicki, 2009; Izard et al., 2001; Mostow, Izard, Fine, & Trentacosta, 2002). A longitudinal study

Abbreviations: ASD, autism spectrum disorders; CVT, Chinese vocabulary test; EEG, electroencephalography; TD, typically developing. * Corresponding author at: Department of Psychology, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong Special Administrative Region. Tel.: +852 3943 6654; fax: +852 2603 5019. E-mail addresses: [email protected] (M.K. Yeung), [email protected] (Yvonne M.Y. Han), [email protected] (S.L. Sze), [email protected] (A.S. Chan). http://dx.doi.org/10.1016/j.rasd.2014.08.013 1750-9467/ß 2014 Published by Elsevier Ltd.

1568

M.K. Yeung et al. / Research in Autism Spectrum Disorders 8 (2014) 1567–1577

by Izard et al. (2001) reported that the ability to recognize facial emotions at age 5 is a significant predictor of better social skills and fewer behavior problems at age 9, suggesting that the emotion recognition ability might have long-term effects on social competence. Although the exact neural basis of emotion processing is not known, recent meta-analytic studies have suggested that it involves the functional coupling of various brain regions, including the amygdala, insula, visual cortex, temporal lobe and various sectors of the prefrontal cortex (Fusar-Poli et al., 2009; Kober et al., 2008; Lindquist, Wager, Kober, Bliss-Moreau, & Barrett, 2012). Kober et al. (2008) identified six functional groups of co-activated brain regions that make up the large-scale neural network in emotion processing. These regions include two frontal cortical groups that are responsible for executive function and the conceptualization of emotion, two posterior cortical groups that are responsible for visual processing and two sub-cortical groups that are responsible for the regulation of autonomic physiological activities. These functional groups also interact with each other during emotion processing. For example, the lateral prefrontal cortex might be co-activated with the medial prefrontal cortex and sub-cortical groups during the appraisal of emotions. Autism spectrum disorders (ASD) is a group of lifelong neurodevelopmental disorders that are characterized by core impairments in reciprocal social interaction (American Psychiatric Association, 2000). Individuals with ASD tend to display inappropriate facial expressions and behavior in social situations (Lord, Rutter, & Le Couteur, 1994). One reason for such aberrant behavior might be their deficient skills in understanding others’ emotions. However, despite decades of research, controversy remains regarding whether the ability to recognize facial emotions is impaired in individuals with ASD (See Harms, Martin, & Wallace, 2010; Uljarevic & Hamilton, 2013, for review). Some studies found that these individuals have general deficits in recognizing facial emotions (e.g., Kuusikko et al., 2009; Sucksmith, Allison, Baron-Cohen, Chakrabarti, & Hoekstra, 2013), while other studies showed that these deficits were limited to recognize negative emotions (e.g., Ashwin, Chapman, Colle, & Baron-Cohen, 2006; Philip et al., 2010) or complex emotions, such as surprise (Baron-Cohen, Spitz, & Cross, 1993). In contrast, some studies did not find any deficits (e.g., Castelli, 2005; Tracy, Robins, Schriber, & Solomon, 2011). Although these inconsistent findings might be due to the differences in methodology across these studies, a recent metaanalytic study (Uljarevic & Hamilton, 2013) reported that there is a general emotion recognition difficulty in autism, which had a large mean effect size (Cohen’s d = 0.80). The mean effect is reduced to a medium (d = 0.41) effect that is still significant after correcting for publication bias. A growing body of evidence suggests that individuals with ASD are characterized by disordered functional connectivity of the brain (see Kana, Libero, & Moore, 2011, for review). In the resting state of the brain in these individuals, abnormal shortrange connectivity and abnormal long-range connectivity have been frequently reported (Cherkassky, Kana, Keller, & Just, 2006; Murias, Webb, Greenson, & Dawson, 2007; Weng et al., 2010). Murias et al. (2007) observed patterns of over- and under-connectivity at distinct temporal and spatial scales. Cherkassky et al. (2006) observed reduced frontal-posterior functional connectivity within the autistic brain. Additionally, disordered connectivity of the autistic brain was also extensively found in task-related states, such as during tasks of executive functioning (Just, Cherkassky, Keller, Kana, & Minshew, 2007), language processing (Just, Cherkassky, Keller, & Minshew, 2004), mental state attribution (Kana, Keller, Cherkassky, Minshew, & Just, 2009) and interpretations of the affective meaning of actions (Gre`zes, Wicker, Berthoz, & de Gelder, 2009). Because emotion processing involves short-range and long-range functional coupling of brain regions that have been consistently reported to be disordered in ASD, it is conceivable that individuals with ASD would exhibit abnormal functional connectivity of these brain regions during tasks of facial emotion recognition. Additionally, this altered connectivity might be related to emotion recognition performance and the severity of the social impairments that are observed in ASD. Electroencephalography (EEG) coherence measures have been used to study cognitive and affective processes. EEG coherence measures the level of synchronization between two cortical areas in terms of the EEG signals that are recorded at different channel sites of the scalp (Nunez & Srinivasan, 2006; Srinivasan, Nunez, & Silberstein, 1998) and could be influenced by the sub-cortical regions that mediate the two cortical regions (Locatelli, Cursi, Liberati, Franceschi, & Comi, 1998; Petsche, Kaplan, Von Stein, & Filz, 1997). High coherence indicates a high level of synchronization between the two brain areas, whereas low coherence indicates a low level of synchronization (Murias et al., 2007). It is believed that different EEG frequency bands correlate with different cognitive and emotional processes. The theta band (4–7.5 Hz) has been shown to be associated with affective processes (Aftanas, Varlamov, Pavlov, Makhnev, & Reva, 2001; Knyazev, Slobodskoj-Plusnin, & Bocharov, 2009). Previous studies that used typically developing (TD) individuals reported a robust increase of theta coherence that was mostly evident in the right frontal region in the initial stage of viewing affective pictures (Balconi, Brambilla, & Falbo, 2009) and static facial expressions of emotion (Balconi & Pozzoli, 2009). These studies found significant differences in theta coherence between viewing emotional and neutral pictures, regardless of valence. In the present study, we hypothesized that TD individuals would show modulation of theta coherence while viewing emotional faces, whereas individuals with ASD would exhibit an abnormal theta coherence pattern. The objective of the present study was to understand the relationships between cortical connectivity, facial emotion recognition ability and social impairment in children with ASD. We anticipated that these children would have a general impairment in recognizing facial emotions. Additionally, we anticipated that these children would show an abnormal theta coherence pattern in short-range and long-range connectivity during facial emotion recognition and that this pattern would differ from the pattern that is observed in TD children. Lastly, we hypothesized that the abnormality in the theta coherence pattern in children with ASD would be associated with their facial emotion recognition ability and the severity of their social deficits.

M.K. Yeung et al. / Research in Autism Spectrum Disorders 8 (2014) 1567–1577

1569

2. Methods 2.1. Participants Eighteen high-functioning children with ASD and 18 TD children were recruited from the community. For the ASD group, all of the children met the DSM-IV-TR diagnostic criteria for ASD (American Psychiatric Association, 2000), which was confirmed by the Autism Diagnostic Interview-Revised (ADI-R; Lord et al., 1994) through clinical interviews with their parents. Children with a history of seizures or concussions were excluded from the study. For the TD group, none of the children met the DSM-IV-TR diagnostic criteria of ASD or had a history of developmental, neurological, or psychiatric disorders. The intelligence quotient (IQ) was estimated for every participant by using the short form of the Wechsler Intelligence Scale for Children-Fourth Edition (WISC-IV) (Hong Kong) (Wechsler, 2010). All of the children had a full-scale IQ  85. Because verbal ability has been shown to be related to emotion recognition skills in TD (Schultz, Izard, Ackerman, & Youngstrom, 2001) and ASD (Jones, Pickles, et al., 2011; Jones, Swettenham, et al., 2011) individuals, we included the Chinese vocabulary test (CVT) (Chan, Cheung, Sze, Leung, & Cheung, 2008) to control for the verbal ability of the groups. All of the children had normal or corrected vision. The study was approved by the Joint Chinese University of Hong Kong-New Territories East Cluster (CUHK-NTEC) Clinical Research Ethics Review Committee. Table 1 presents the demographic and clinical characteristics of the ASD and TD groups. There were no significant differences between the two groups in terms of age (t = 0.99, p = .33), gender (x2 = 2.22, p = .14), full-scale IQ (t = 1.70, p = .10) or verbal ability, which was indicated by vocabulary recognition scores (t = 1.20, p = .24). The ASD group scored significantly higher than the TD group on the ADI-R social (t = 12.30, p < .001), verbal (t = 12.08, p < .001), and nonverbal communication (t = 7.39, p < .001) subscales, as well as on the repetitive/stereotyped behavior subscales (t = 7.34, p < .001). 2.2. Procedures All of the children and parents provided written informed consent prior to the study. The children had to attend both the cognitive assessment and EEG recording sessions individually, and the parents were interviewed about the developmental and medical history of their children. All of the assessments and interviews were administered by trained research assistants and by a clinical psychologist. During the cognitive assessment session, children were individually administered the short form of the WISC-IV (Hong Kong) (Wechsler, 2010) to assess their IQ and the CVT (Chan et al., 2008) to assess their verbal ability. During the EEG recording session, EEG data were recorded while the child took the facial emotion recognition test. Before the start of the test, each child was given a laminated paper that showed the seven emotion labels (happiness, sadness, anger, disgust, fear, surprise, and neutral). The child was asked to read the labels aloud, one by one, so that the researchers could verify the child’s understanding of each emotion. If the child did not understand a particular emotion, verbal descriptions and examples were given. Test instructions were given after the child had mastered the meaning of each emotion. The child was then asked to select an emotion label that matched the target photograph, which was presented on a computer screen and depicted one of the seven different emotions. There was no time constraint for the child to respond, and the target photograph would stay on screen until the child responded. No feedback regarding the accuracy of the response was given to the child. The next photograph was presented immediately via a key press, which was coded for a corresponding emotion by the experimenter, based on the responses of the participants. The test took around 8–12 min, depending on the speed of the participants’ responses. The sequence of the cognitive assessment and EEG recording sessions was counter-balanced to avoid order effects. 2.3. Materials 2.3.1. Facial emotion recognition test ¨ hman, 1998), The photographs of faces were taken from Karolinska Directed Emotional Faces (KDEF; Lundqvist, Flykt, & O a database of emotional faces that has been validated and shown to have good test–retest reliability (Goeleven, De Raedt, Table 1 Demographic and clinical characteristics of the autism spectrum disorders (ASD) and typically developing (TD) groups. Group

Age (years) Gender (male/female) IQ CVT-form A: MC version ADI-R social ADI-R verbal communication ADI-R nonverbal communication ADI-R stereotyped behavior

ASD

TD

(n = 18)

(n = 18)

9.61 (3.13) 15/3 101.33 (10.85) 15.44 (5.51) 21.61 (4.41) 13.89 (2.95) 7.56 (2.59) 5.11 (2.63)

10.72 (3.61) 11/7 107.06 (9.35) 17.75 (2.72) 4.78 (3.78) 2.17 (2.87) 1.67 (2.17) .33 (.84)

t/x2

p value

.99 2.22 1.70 1.20 12.30 12.08 7.39 7.34

.33 .14 .10 .24 <.001 <.001 <.001 <.001

Note. ADI-R, Autism Diagnostic Interview-Revised; CVT, Chinese vocabulary test; IQ, intelligence quotient as assessed by the Chinese version of the Wechsler Intelligence Scale for Children-Fourth Edition (WISC-IV) (Hong Kong). Standard deviations are in parenthesis.

1570

M.K. Yeung et al. / Research in Autism Spectrum Disorders 8 (2014) 1567–1577

Leyman, & Verschuere, 2008). Six basic emotions (happiness, sadness, anger, disgust, fear and surprise), as well as neutral expressions, were included. Twenty stimuli were taken from 10 male and 10 female actors for each emotion category, which results in a total of 140 stimuli for each participant. The same set of actors was selected for each emotion category, across which photographs were matched on facial identity. All of the images were forward facing. The stimuli were presented in a pre-set randomized order with the same sequence for all of the participants. 2.3.2. Chinese vocabulary test The CVT was developed by Chan et al. (2008) to assess verbal ability in the Hong Kong population. We used the shortened 18-item MC version of Form A to factor out verbal ability in explaining the differences in performance on the facial emotion recognition test, which required conceptual understanding of emotion labels. During the CVT, the participants had to choose between five answer options. Each item and its options were printed on a stimulus card that was shown to the participant. The participants were asked to select the option that best explained each vocabulary term. The scores for each item ranged from 0 to 2. The total score was 36, with higher scores indicating better vocabulary recognition abilities. 2.4. EEG recording and data reduction Each participant was tested individually using the DEYMED Diagnostic TruScan 32 Biofeedback Device. An electrode cap with 22 electrode sites (FP1, FP2, FPz, APz, F3, F4, F7, F8, Fz, T3, T4, T7, T8, C3, C4, Cz, P3, P4, Pz, O1, O2 and Oz), based on the International 10–20 System (Jasper, 1958) referenced to linked ears, was used to collect EEG data at a sampling rate of 256 Hz, with a low pass filter of 30 Hz and a high pass filter of 1 Hz. The impedance at each electrode site was kept at 10 kV or below. One-second epochs from 0 to 1000 ms after the stimulus onset were extracted and combined for each emotion condition. Artifacts in the epoched data were identified by visual inspection and then removed. The artifact-free data were spectrally processed through fast Fourier Transformation (FFT) using the Neuroguide (version 2.1.8) software to compute coherence values. In the present study, theta coherence measures (4–7.5 Hz) were used. EEG coherence is an index that measures the temporal synchronization of the EEG activity of two brain regions underneath the electrodes and reflects the functional connectivity between these two regions. The EEG channels were clustered into four regions: left frontal (FP1, F3, F7), right frontal (FP2, F4, F8), left posterior (T5, P3, O1) and right posterior (T6, P4, O2). A number of EEG coherence indices were computed and averaged from all of the possible combinations of the two channels within each of the four regions (i.e., four short-range intra-hemispheric indices) and within pairs of the regions (i.e., six long-range indices, which included inter-frontal, inter-posterior, two intra-hemispheric frontoposterior, and two inter-hemispheric frontoposterior indices) for each emotion type and for each group separately. The square root values were then normalized using Fisher’s Z transformation. 2.5. Data analysis The dependent variable was the unbiased hit rate, which was proposed by Wagner (1993) for each emotion condition. The unbiased hit rate, formulated as [hit rate  (correct classification for an emotion category/marginal frequency of using the respective emotion label)], is the joint probability that a stimulus category is correctly identified, given that it is presented at all, and that a response is correctly used, given that it is used at all. It is a more appropriate and sensitive measure than accuracy scores for assessing performance on tasks that require categorical judgment of nonverbal behavior and has been previously used to study facial emotion recognition in TD individuals (Elfenbein & Ambady, 2003; Leppa¨nen & Hietanen, 2004) and individuals with ASD (Doi et al., 2013). The maximum score for each emotion condition is 100%. The score was normalized with an arcsine transformation prior to the statistical analyses. A repeated measures ANOVA with emotion type as the within-subject factor and group as the between-subject factor was conducted for unbiased hit rates and was followed by post hoc t tests for each emotion type. Repeated measures ANOVAs with emotion type as the within-subject factor and group as the between-subject factor were conducted for each theta coherence index and were followed by independent t-tests, which calculated the main effect of group for each emotion type, if any group differences were found. To examine emotion-specific modulation effects, a series of planned paired t-tests (i.e., emotional-neutral contrast) were conducted for each theta coherence index. For theta coherence indices in which the modulation of theta coherence to facial emotions was evident, difference scores, which represented each emotional-neutral contrast value, were then correlated (Pearson’s r) with the unbiased hit rates for each emotion and with the ADI-R social scores for the ASD group. Given that the majority of the participants in the TD group scored within a limited range of ADI-R social scores (i.e., between 0 and 5 points), it may therefore restrain the reliability of linear correlation, and hence correlation analyses were only performed for the ASD group. Because specific hypotheses were tested and the sample size was relatively small, the Bonferroni correction was not applied to maintain a balance between committing Type I and Type II errors. The alpha level was set at .05. 3. Results 3.1. Deficient facial emotion recognition ability in children with ASD The means and standard deviations of the unbiased hit rates are presented in Fig. 1. A Group (ASD, TD)  Emotion (happiness, sadness, anger, disgust, fear, surprise, neutral) repeated measures ANOVA was conducted to examine group

M.K. Yeung et al. / Research in Autism Spectrum Disorders 8 (2014) 1567–1577

Arcsine-transformed unbiased hit rate

TD

1571

ASD

1.4 1.2

* p = .06

1 0.8

*

*

0.6

p = .07

0.4

*

p = .10

disgust

fear

0.2 0 happiness

sadness

anger

surprise

neutral

Fig. 1. Performance in regard to facial emotion recognition. The means and error bars (+/ 1 standard error) of arcsine-transformed unbiased hit rates (rad) of the autism spectrum disorders (ASD) and typically developing (TD) groups are shown. The maximum score is 1.57. Higher scores indicate better performance. The ASD group performed significantly worse in regard to recognizing happiness, sadness, and disgust (p < .05) and marginally worse in regard to recognizing anger and neutral faces (p = .07 and p = .06, respectively) compared with the TD group. Fear recognition, in spite of having a medium effect size of .58, was not significantly impaired in the ASD group. *p < .05.

performance, with arcsine-transformed unbiased hit rates as the dependent variables. Given that the Mauchly’s sphericity test was significant (W = .10, p < .001), the degrees of freedom on all of the significant results were adjusted with a Greenhouse–Geisser epsilon of .51. There was a main effect of emotion [F(3.04, 103.35) = 150.95, p < .001, h2 = .82]. There was also a main effect of group [F(1, 34) = 8.08, p = .008, h2 = .19], which suggests that, in general, children with ASD were significantly less accurate in recognizing facial emotions than were the TD children. The Group  Emotion interaction was not significant [F(3.04, 103.35) = 0.88, p = .46, h2 = .03]. The main effect analyses showed that children with ASD were significantly less accurate in recognizing happiness, sadness, disgust and surprise (p’s range from .020 to .035) than TD children (Cohen’s d’s range from 0.75 to 0.84). The ability to recognize angry and neutral faces was found to be marginally impaired in those with ASD (p = .065, Cohen’s d = 0.67 and p = .060, Cohen’s d = 0.66, respectively). Recognition of fear was not significantly different between the two groups (p = .098). However, because the effect size is medium (Cohen’s d = 0.58), this insignificance could be due to a lack of statistical power. Therefore, the results suggest that children with ASD have a general deficit in recognizing facial emotions, with disgust and fear as the worst and best recognized emotions, respectively. To compare the relative number of confusions among different emotions between the ASD and TD groups, confusability matrices are presented in Table 2. The matrices demonstrate that the confusion pattern was similar between the two groups. In both of the groups, anger and disgust were frequently confused, and fear was often misinterpreted as surprise. Nevertheless, there was a larger tendency for children with ASD to misinterpret disgust as anger (i.e., 42% of the time) than for them to interpret disgust correctly (i.e., 22% of the time). Overall, the results suggest that although children with ASD

Table 2 Confusability matrices that show the response patterns of the autism spectrum disorders (ASD) and typically developing (TD) groups. Actual emotion

Response (%) Happiness

Sadness

Anger

Disgust

Fear

Surprise

Neutral

ASD (n = 18) Happiness Sadness Anger Disgust Fear Surprise Neutral

90.56 1.39 1.39 0.56 3.61 6.39 3.89

0.83 70.56 9.17 25.00 18.33 2.50 4.17

1.11 3.89 53.33 42.22 8.33 1.39 3.89

1.94 5.00 20.28 22.22 5.28 1.67 2.50

1.11 9.44 4.72 2.22 22.22 6.94 5.56

2.78 2.22 3.61 3.61 35.00 75.56 1.39

1.67 7.50 7.50 4.17 7.22 5.56 78.61

TD (n = 18) Happiness Sadness Anger Disgust Fear Surprise Neutral

97.22 0.00 0.00 0.27 1.11 5.00 3.33

0.28 78.06 3.89 18.21 15.04 0.00 1.67

0.00 1.39 60.28 34.78 4.18 0.83 0.56

0.28 4.72 27.22 39.67 6.69 0.00 0.83

1.67 10.56 2.22 3.80 29.25 2.78 0.28

0.28 0.00 0.28 1.90 39.83 90.00 1.39

0.28 5.28 6.11 1.36 3.90 1.39 91.94

Note. Bold values indicate percentage of correct identification of facial emotions.

1572

M.K. Yeung et al. / Research in Autism Spectrum Disorders 8 (2014) 1567–1577

Table 3 The means and standard deviations of theta coherence for each theta coherence index in the (a) autism spectrum disorders (ASD) and (b) typically developing (TD) groups. Theta coherence index

Emotion Sad

Hap (a) ASD (n = 18) Short range Left frontal Right frontal Left posterior Right posterior Long range Intra-hemisphere Left frontoposterior Right frontoposterior Inter-hemisphere Inter-frontal Inter-posterior Left frontal-right posterior Right frontal-left posterior (b) TD (n = 18) Short-range Left frontal Right frontal Left posterior Right posterior Long-range Intra-hemisphere Left frontoposterior Right frontoposterior Inter-hemisphere Inter-frontal Inter-posterior Left frontal-right posterior Right frontal-left posterior

1.05 1.09 .99 1.04

(.18) (.20) (.25) (.18)

1.11 1.04 .99 1.00

Ang

(.16) (.21) (.19) (.19)

1.08 1.03 .97 1.00

Dis

(.16) (.20) (.26) (.22)*

1.09 .99 .99 1.04

Fea

(.13) (.18) (.25) (.19)

(.14) (.16) (.22) (.19)

(.13) (.12) (.24) (.21)

(.18) (.23) (.23) (.22)

.41 (.17)* .44 (.15)

.88 .80 .34 .36

(.23) (.17)* (.11) (.15)

.86 .99 .28 .35

.86 .98 .34 .37

(.16) (.22) (.17) (.14)

.81 1.01 .29 .33

(.16) (.21) (.12) (.12)

.84 1.00 .29 .32

(.16) (.20) (.10) (.12)

.82 1.02 .31 .33

(.16) (.21) (.11) (.16)

.84 1.02 .31 .34

(.22) (.21) (.16) (.14)

1.02 1.19 1.11 1.20

(.20) (.24)* (.21) (.25)*

1.03 1.15 1.05 1.15

.98 1.10 1.02 1.12

(.23) (.18) (.19) (.20)

1.04 1.16 1.07 1.10

(.17) (.17)* (.22) (.21)

1.02 1.16 1.03 1.13

(.15) (.17)* (.19) (.18)

1.05 1.17 1.05 1.12

(.21) (.16)* (.22) (.21)

1.00 1.09 1.06 1.13

(.20) (.16) (.21) (.19)

.43 (.18) .55 (.24) .88 .90 .36 .45

(.17) (.23)* (.17) (.22)

.36 (.13) .46 (.16) .87 1.10 .31 .36

(.17) (.20) (.13) (.15)

.36 (.14) .47 (.17) .82 1.07 .32 .37

(.18) (.18) (.14) (.15)

.41 (.16) .51 (.18) .89 1.09 .36 .43

(.17) (.21) (.15) (.17)

.34 (.13) .47 (.15) .86 1.08 .32 .35

(.16) (.17) (.11) (.15)

.35 (.12) .43 (.14)

1.09 1.03 .99 1.06

.35 (.11) .42 (.11)

(.23) (.13)* (.19) (.21)

.35 (.09) .41 (.09)

1.03 1.00 1.01 1.04

Neu

.36 (.13) .47 (.13)

(.20) (.18) (.10) (.13)

.36 (.13) .42 (.13)

1.05 1.05 .97 1.03

Sur

.40 (.16) .48 (.18) .89 1.08 .33 .40

(.16)* (.20) (.17) (.17)

.36 (.16) .43 (.19)

.39 (.12) .48 (.18) .83 1.10 .32 .39

(.19) (.18) (.13) (.15)

Note. Hap, happiness; Sad, sadness; Ang, anger; Dis, disgust; Fea, fear; Sur, surprise; Neu, neutral. Asterisks (*) indicate significant paired t-test results (p < .05) with neutral faces. Standard deviations are in parenthesis.

were less accurate in recognizing facial emotions, their error types were largely comparable to those made by TD children, with the exception that children with ASD misinterpreted disgust as anger more often than did TD children. 3.2. Abnormal emotion-induced modulation of right frontal theta coherence in children with ASD The descriptive statistics for all of the theta coherence indices for the ASD and TD groups are presented in Table 3. Group  Emotion repeated measures ANOVAs were conducted for each theta coherence index. There was a main effect of group for right frontal theta coherence [F(1, 34) = 4.75, p = .036, h2 = .12]; however, there were no main effects for the other indices (F’s range from 0.16 to 3.01, p’s range from .09 to .69). In regard to the right frontal theta coherence index, although the Group  Emotion interaction was not significant [F(6, 204) = 1.67, p = .13, h2 = .05], the analyses of the main effect of group for each emotion type demonstrated that children with ASD have significantly lower right frontal theta coherence for sadness (t = 2.08, p = .045), disgust (t = 2.89, p = .007) and surprise (t = 3.50, p = .001). Marginal significance was also obtained for fear (t = 1.98, p = .06). No significant differences were found for the other emotion types, including neutral expressions (t’s range from .98 to 1.36, p’s range from .18 to .33). These results suggest that theta coherence in the right frontal region for sadness, disgust, surprise and fear is lower for children with ASD than it is for TD children. To evaluate whether theta coherence was higher when viewing each of the emotional faces than it was when viewing neutral faces (i.e., emotion-specific modulation effect of theta coherence), paired t-tests were conducted on each pair of emotional–neutral contrasts for each theta coherence index. In the right frontal region (see Fig. 2), theta coherence for the TD group was significantly higher when viewing all of the emotional (except anger) faces than it was when viewing the neutral faces (t’s range from 2.19 to 3.28, p’s range from .004 to .043). In contrast, for the ASD group, theta coherence was not significantly higher when viewing any of the emotional faces than it was when viewing neutral faces (t’s range from 0.94 to 2.00, p’s range from .062 to .36). Emotion-specific modulation of theta coherence was not observed in the other short-range and long-range coherence pairs, which is demonstrated by significant results being obtained in a maximum of one out of six pairs of emotional–neutral contrast for each group [ASD: right posterior (anger), inter-posterior (happiness) and left frontoposterior (anger): t’s range from 2.17 to 7.49, p’s < .044; others: t’s range from .09 to 2.00, p’s range from .062 to .93; TD: right posterior (happiness), inter-frontal (surprise), inter-posterior (happiness): t’s range from 2.17 to 5.52, p’s < .044;

M.K. Yeung et al. / Research in Autism Spectrum Disorders 8 (2014) 1567–1577

Changes of Right Frontal Theta Coherence (Emotional - Neutral Faces)

0.15

*

TD * *

0.1

*

1573

ASD

**

0.05

0

-0.05

-0.1 happiness

sadness

anger

disgust

fear

surprise

Fig. 2. Emotion-specific modulation of right frontal theta coherence. Changes of the theta coherence (Fisher-transformed) in the right frontal regions for each emotional-neutral contrast pair in the autism spectrum disorders (ASD) and typically developing (TD) groups are shown. Positive values indicate increased theta coherence in response to emotional faces. The emotion-specific modulation effect of theta coherence was only present in this region when viewing facial emotions (except anger) and was not present when viewing neutral faces (paired t-tests, p < .05), which led to significant increases in the theta coherence in the TD group. The ASD group did not exhibit this modulation effect. *p < .05, **p < .01.

others: t’s range from .02 to 2.05, p’s range from .056 to .98]. Therefore, the effect of the modulation of theta coherence for emotional faces was only found in the right frontal region. Overall, the results support the hypothesis that children with ASD showed abnormal theta coherence patterns when viewing emotional faces, which is indicated by an absence of modulation of right frontal theta coherence in response to emotional faces in these children. 3.3. Association among right frontal theta coherence, facial emotion recognition ability and social impairment in children with ASD To test the possible associations among facial emotion recognition ability, right frontal theta coherence and the severity of social impairment, Pearson correlation analyses were performed for the group of children with ASD. It was hypothesized that the severity of social impairment would be negatively correlated with facial emotion recognition ability and with the magnitude of theta coherence changes in response to facial emotions. The results for the correlation between the changes in theta coherence when viewing emotional faces and the severity of social impairment in the ASD group are presented in Table 4. The results show that four out of six theta coherence difference scores (sadness, anger, disgust and surprise) had a significant negative correlation with the ADI-R social score, with large effect sizes (r’s range from .48 to .61, p’s range from .007 to .044). Marginally significant negative correlations, which were also of large effect sizes, with the ADI-R social score were obtained for the difference scores for happiness and fear (r = .44, p = .069 and r = .47, p = .050, respectively). These results suggest that greater increases in right frontal theta coherence in emotion modulation are associated with lower ADI-R social scores for children with ASD.

Table 4 Pearson correlations between the ADI-R social scores and changes in right frontal theta coherence when viewing emotional faces in the autism spectrum disorders (ASD) group. Higher coefficients indicate larger associations. Difference score

Group ASD (n = 18) ADI-R social

Happiness–neutral Sadness–neutral Anger–neutral Disgust–neutral Fear–neutral Surprise–neutral

.44 .49* .48* .55* .47 .61**

Note. ADI-R: Autism Diagnostic Interview-Revised. * p < .05. ** p < .01.

1574

M.K. Yeung et al. / Research in Autism Spectrum Disorders 8 (2014) 1567–1577

Finally, the unbiased hit rates for each emotion were not shown to be significantly correlated with changes in right frontal theta coherence when viewing emotional faces (r’s ranged from .26 to .37, p’s range from .13 to .30) or with the ADI-R social scores (r’s ranged from .24 to .19, p’s range from .34 to .45). Therefore, these results did not show that the ability to recognize facial emotions was associated with the magnitude of changes in right frontal theta coherence in response to emotional faces, as contrasted with neutral faces, or with the ADI-R social score for children with ASD. 4. Discussion The present study investigated the relationships between facial emotion recognition abilities, cortical connectivity, as indicated by theta coherence, and social impairment in children with ASD. We compared a group of children with ASD and a group of TD children who were matched by age, sex, and full-scale IQ. The two groups had similar verbal ability, as indicated by the vocabulary recognition scores. Our behavioral data suggest that children with ASD had a general impairment in recognizing facial emotions. Disgust was the worst recognized emotion and fear was the best recognized emotion by these children. Nevertheless, the error analyses suggested that the response pattern of children with ASD was similar to that of TD children, with disgust being easily confused with anger and fear being easily confused with surprise. Additionally, our EEG data suggest that TD children exhibited theta coherence elevations in the right frontal region while viewing emotional faces, whereas children with ASD did not exhibit these elevations. We further found that theta coherence patterns were associated with the ADI-R social scores in children with ASD, suggesting that abnormal cortical connectivity is related to the severity of social impairment in these children. Overall, our results are generally consistent with the following hypotheses: children with ASD were impaired in recognizing facial emotions and exhibited abnormal theta coherence patterns when viewing emotional faces, which were related to their severity of social impairment. Our behavioral data are consistent with previous studies, which found general deficits in facial emotion recognition in individuals with ASD (Celani, Battacchi, & Arcidiacono, 1999; Dalton et al., 2005; Hall, Szechtman, & Nahmias, 2003; Kennedy & Adolphs, 2012; Kuusikko et al., 2009; Sucksmith et al., 2013; Wallace et al., 2011). We also found impairment in the recognition of positive emotions (e.g., happiness) and simple emotions (e.g., sadness) in individuals with ASD, unlike previous studies that only found impairment in the recognition of negative emotions (Ashwin et al., 2006; Corden, Chilvers, & Skuse, 2008; Humphreys, Minshew, Leonard, & Behrmann, 2007; Philip et al., 2010) or complex emotions, such as surprise (Baron-Cohen et al., 1993), for these individuals. Our data contradict the results of studies that did not find any impairment in these individuals (Adolphs, Sears, & Piven, 2001; Baron-Cohen, Jolliffe, Mortimore, & Robertson, 1997; Baron-Cohen, Wheelwright, et al., 1997; Castelli, 2005; Grossman, Klin, Carter, & Volkmar, 2000; Leung, Ordqvist, Falkmer, Parsons, & Falkmer, 2013; Loveland et al., 1997; Rutherford & Towns, 2008; Tracy et al., 2011). These inconsistent results might be due to the large variety of differences in demographic factors and task demands that are featured across studies (Harms et al., 2010) and due to the use of a sensitive measure of performance (i.e., unbiased hit rates instead of accuracy scores) in the present study. A previous study (Doi et al., 2013) used the same unbiased hit rate measure to study facial emotion recognition in Asperger adults and found that these adults were impaired in recognizing simple emotions, such as happiness and sadness. In the present study, we extended the previous knowledge by using unbiased hit rates to demonstrate that impairment in facial emotion recognition in ASD is general and does not appear to be limited to specific emotions. Additionally, our findings are consistent with the results of studies that found deficits in facial emotion recognition in ASD after controlling for verbal and full-scale IQ (Bal et al., 2010; Humphreys et al., 2007; Law Smith, Montagne, Perrett, Gill, & Gallagher, 2010). Although our data demonstrate that children with ASD had impairments in recognizing facial emotions, the response pattern and the types of errors that the children made were largely similar to the errors made by TD children. These results are generally consistent with previous findings (Humphreys et al., 2007; Kennedy & Adolphs, 2012; Philip et al., 2010; Wallace et al., 2011). Nevertheless, we found that there was a greater tendency for children with ASD to misinterpret disgusted faces as being angry faces (i.e., 42% of the time). This frequent misinterpretation, for example, might partially explain the high occurrence of aggressive behavior in individuals with ASD (Farmer & Aman, 2011; Matson & NebelSchwalm, 2007). For example, these individuals might respond aggressively to others who display disgust due to their aberrant behavior. However, further studies that examine the social consequences of the misinterpretation of facial emotions in ASD are needed to verify this claim. In regard to the EEG measurements, the results of TD children were consistent with the results of previous studies, which indicated that theta coherence was higher in the initial stage of viewing emotional stimuli, particularly in the right frontal region (Balconi et al., 2009; Balconi & Pozzoli, 2009). It is postulated that the localization of this effect, which is in the frontal region, might be due to the probable generators of scalp-measurable theta being located in different frontal regions, such as the medial prefrontal cortex, anterior cingulate cortex (Ishii et al., 1999) and lateral prefrontal cortex (Anderson, Rajagovindan, Ghacibeh, Meador, & Ding, 2010; Ertl, Hildebrandt, Ourina, Leicht, & Mulert, 2013). These regions have been shown to be critically involved during tasks of facial emotion recognition (Sprengelmeyer, Rausch, Eysel, & Przuntek, 1998; Tsuchida & Fellows, 2012). The use of magnetoencephalography (MEG), which has a better spatial resolution, in future studies might be promising for locating the interconnected brain regions that are involved in facial emotion recognition on a finer spatial scale. On the other hand, the modulation of theta coherence to different types of emotions, regardless of valence, suggests that the change in the right frontal theta coherence might reflect the presence of a general process that underlies the

M.K. Yeung et al. / Research in Autism Spectrum Disorders 8 (2014) 1567–1577

1575

viewing of facial emotions. There is evidence showing that theta coherence might be linked to sustained and selective attention processes (Sauseng, Hoppe, Klimesch, Gerloff, & Hummel, 2007; Womelsdorf & Fries, 2007). Some studies have also shown that the right frontal cortex might be more involved than the left frontal cortex in regard to sustained attention (Rueckert & Grafman, 1996; Wilkins, Shallice, & McCarthy, 1987) and stimulus-driven reorienting processes (Corbetta & Shulman, 2002). Therefore, it is possible that the lateralization of this effect, which is to the right side of the frontal lobe, might be due to attention modulation to emotional stimuli. The modulation of right frontal theta coherence in response to emotional faces was found to be absent in children with ASD, which suggests the presence of altered cortical connectivity during facial emotion recognition. The present study, to our knowledge, is the first EEG study to examine and find abnormal functional connectivity during facial emotion recognition in individuals with ASD. Our findings are similar to the findings of previous functional neuroimaging studies that reported abnormal functional and effective connectivity of the brain in individuals with ASD during facial emotion recognition (Monk et al., 2010; Rudie et al., 2012; Wicker et al., 2008). In particular, the frontal region has been consistently reported to be a part of the abnormal circuitry in facial emotion processing in individuals with ASD. Because theta coherence changes might reflect the modulation of attention to salient emotional stimuli, we postulated the presence of abnormalities in the modulation of attention to emotional stimuli in individuals with ASD, such as reduced attention to eye regions of emotional faces (Corden et al., 2008; Pelphrey et al., 2002) and increased eye contact to regions outside of the core facial features that are engaged in emotion processing (Bal et al., 2010). However, such interpretations should be taken with caution. Future studies that use a combination of eye tracking and EEG coherence measures might shed insight into the relationship between attention to emotions and neural processing in individuals with ASD. Our findings suggest that the extent of modulation of right frontal theta coherence to all of the facial emotion types is negatively correlated with ADI-R social scale scores, which suggests that disordered cortical connectivity is associated with social impairment in individuals with ASD. The finding of this brain–behavior relationship in individuals with ASD is consistent with the findings of recent studies, which also reported a link between altered brain connectivity and social malfunctioning in individuals with ASD (Monk et al., 2009; Weng et al., 2010). For example, Weng et al. (2010) studied resting-state functional connectivity in adolescents with ASD and found a negative association between social functioning, indicated by ADI-R social scores, and functional connectivity in the superior frontal gyrus. Therefore, converging evidence suggests that altered brain connectivity, such as activity in the frontal region, might form the neuropathological basis of the social impairment in individuals with ASD. There are some limitations to the present study. First, we found that the recognition of neutral expressions was also impaired in children with ASD. Therefore, we do not know if these individuals were also impaired in face processing, which would have confounded their impairment in recognizing all of the facial emotion types. Second, the ASD group was overrepresented by high-functioning children. All of the participants have at least 85 full-scale IQ points. The present findings might, thus, not be generalizable to low-functioning individuals. Third, the ASD group in the present study was heterogeneous. We do not know if the findings are representative of the population of the entire disorder spectrum. However, because the sample size of the present study was relatively small, subgroup comparisons were not feasible. Finally, we used static photographs of facial emotions. We do not know if the current results would also be obtained by using dynamic stimuli (e.g., emotional films) or morphed expressions, which are closer to facial expressions of emotions that are encountered in daily life. Because the processing of dynamic and static emotional stimuli might be different (Kilts, Egan, Gideon, Ely, & Hoffman, 2003), the EEG coherence patterns that were obtained might be different when dynamic stimuli are employed. Based on these limitations, future research that employs a larger sample with different demographic characteristics and different types of emotional stimuli is, therefore, warranted.

5. Conclusion We observed a general impairment in facial emotion recognition that was not confounded by intellectual level or verbal ability in children with ASD. Additionally, the current study extended the previous knowledge by revealing altered cortical connectivity in the context of emotion processing in these children. Our findings support the model of disrupted functional connectivity as a neuropathological explanation of ASD. The link between altered cortical connectivity and the severity of social impairment observed in individuals with ASD suggests that further studies on the nature of functional connectivity of the brain of these individuals are necessary to better understand the symptomatology of ASD. Furthermore, this link also suggests that the consideration of the functional connectivity of the brain might be central to the future development of interventions that aim to improve emotion processing and social functioning in the population with ASD. Acknowledgments We would especially like to thank all of the parents and children who participated in this study. We would also like to thank Carmen Chu, Debbie Yan, Hannah Lee, Lan He, Man-ying Mo, Rex Wong, Thomas Lee, and Winnie Cheung for their efforts in data collection and data management.

1576

M.K. Yeung et al. / Research in Autism Spectrum Disorders 8 (2014) 1567–1577

References Adolphs, R., Sears, L., & Piven, J. (2001). Abnormal processing of social information from faces in autism. Journal of Cognitive Neuroscience, 13(2), 232–240. Aftanas, L., Varlamov, A., Pavlov, S., Makhnev, V., & Reva, N. (2001). Affective picture processing: Event-related synchronization within individually defined human theta band is modulated by valence dimension. Neuroscience Letters, 303(2), 115–118. American Psychiatric Association (2000). Diagnostic and statistical manual of mental disorders (4th text rev. ed.). Washington, DC: American Psychiatric Association. Anderson, K. L., Rajagovindan, R., Ghacibeh, G. A., Meador, K. J., & Ding, M. (2010). Theta oscillations mediate interaction between prefrontal cortex and medial temporal lobe in human memory. Cerebral Cortex, 20(7), 1604–1612. Ashwin, C., Chapman, E., Colle, L., & Baron-Cohen, S. (2006). Impaired recognition of negative basic emotions in autism: A test of the amygdala theory. Social Neuroscience, 1(3–4), 349–363. Bal, E., Harden, E., Lamb, D., Van Hecke, A. V., Denver, J. W., & Porges, S. W. (2010). Emotion recognition in children with autism spectrum disorders: Relations to eye gaze and autonomic state. Journal of Autism and Developmental Disorders, 40(3), 358–370. Balconi, M., Brambilla, E., & Falbo, L. (2009). BIS/BAS, cortical oscillations and coherence in response to emotional cues. Brain Research Bulletin, 80(3), 151–157. Balconi, M., & Pozzoli, U. (2009). Arousal effect on emotional face comprehension: Frequency band changes in different time intervals. Physiology & Behavior, 97(3), 455–462. Baron-Cohen, S., Jolliffe, T., Mortimore, C., & Robertson, M. (1997). Another advanced test of theory of mind: Evidence from very high functioning adults with autism or Asperger syndrome. Journal of Child Psychology and Psychiatry, 38(7), 813–822. Baron-Cohen, S., Spitz, A., & Cross, P. (1993). Do children with autism recognise surprise? A research note. Cognition & Emotion, 7(6), 507–516. Baron-Cohen, S., Wheelwright, S., & Jolliffe, A. T. (1997). Is there a language of the eyes? Evidence from normal adults, and adults with autism or Asperger syndrome. Visual Cognition, 4(3), 311–331. Blair, R. J. (2003). Facial expressions, their communicatory functions and neuro-cognitive substrates. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 358(1431), 561–572. Castelli, F. (2005). Understanding emotions from standardized facial expressions in autism and normal development. Autism: The International Journal of Research and Practice, 9(4), 428–449. Celani, G., Battacchi, M. W., & Arcidiacono, L. (1999). The understanding of the emotional meaning of facial expressions in people with autism. Journal of Autism and Developmental Disorders, 29(1), 57–66. Chan, A. S., Cheung, M., Sze, S. L., Leung, W. W., & Cheung, R. W. (2008). Measuring vocabulary by free expression and recognition tasks: Implications for assessing children, adolescents, and young adults. Journal of Clinical and Experimental Neuropsychology, 30(8), 892–902. Cherkassky, V. L., Kana, R. K., Keller, T. A., & Just, M. A. (2006). Functional connectivity in a baseline resting-state network in autism. Neuroreport, 17, 1687–1690. Corbetta, M., & Shulman, G. L. (2002). Control of goal-directed and stimulus-driven attention in the brain. Nature Reviews Neuroscience, 3(3), 201–215. Corden, B., Chilvers, R., & Skuse, D. (2008). Avoidance of emotionally arousing stimuli predicts social–perceptual impairment in Asperger’s syndrome. Neuropsychologia, 46(1), 137–147. Dalton, K. M., Nacewicz, B. M., Johnstone, T., Schaefer, H. S., Gernsbacher, M. A., Goldsmith, H., et al. (2005). Gaze fixation and the neural circuitry of face processing in autism. Nature Neuroscience, 8(4), 519–526. Doi, H., Fujisawa, T. X., Kanai, C., Ohta, H., Yokoi, H., Iwanami, A., et al. (2013). Recognition of facial expressions and prosodic cues with graded emotional intensities in adults with Asperger syndrome. Journal of Autism and Developmental Disorders, 43(9), 2099–2113. Elfenbein, H. A., & Ambady, N. (2003). When familiarity breeds accuracy: Cultural exposure and facial emotion recognition. Journal of Personality and Social Psychology, 85(2), 276. Ertl, M., Hildebrandt, M., Ourina, K., Leicht, G., & Mulert, C. (2013). Emotion regulation by cognitive reappraisal – The role of frontal theta oscillations. NeuroImage, 81, 412–421. Farmer, C. A., & Aman, M. G. (2011). Aggressive behavior in a sample of children with autism spectrum disorders. Research in Autism Spectrum Disorders, 5(1), 317– 323. Fusar-Poli, P., Placentino, A., Carletti, F., Landi, P., Allen, P., Surguladze, S., et al. (2009). Functional atlas of emotional faces processing: A voxel-based meta-analysis of 105 functional magnetic resonance imaging studies. Journal of Psychiatry & Neuroscience: JPN, 34(6), 418–432. Goeleven, E., De Raedt, R., Leyman, L., & Verschuere, B. (2008). The karolinska directed emotional faces: A validation study. Cognition and Emotion, 22(6), 1094– 1118. Goodfellow, S., & Nowicki, S., Jr. (2009). Social adjustment, academic adjustment, and the ability to identify emotion in facial expressions of 7-year-old children. Journal of Genetic Psychology, 170(3), 234–243. Gre`zes, J., Wicker, B., Berthoz, S., & De Gelder, B. (2009). A failure to grasp the affective meaning of actions in autism spectrum disorder subjects. Neuropsychologia, 47(8), 1816–1825. Grossman, J. B., Klin, A., Carter, A. S., & Volkmar, F. R. (2000). Verbal bias in recognition of facial emotions in children with Asperger syndrome. Journal of Child Psychology and Psychiatry, 41(3), 369–379. Hall, G. B., Szechtman, H., & Nahmias, C. (2003). Enhanced salience and emotion recognition in autism: A PET study. American Journal of Psychiatry, 160(8), 1439– 1441. Harms, M. B., Martin, A., & Wallace, G. L. (2010). Facial emotion recognition in autism spectrum disorders: A review of behavioral and neuroimaging studies. Neuropsychology Review, 20(3), 290–322. Humphreys, K., Minshew, N., Leonard, G. L., & Behrmann, M. (2007). A fine-grained analysis of facial expression processing in high-functioning adults with autism. Neuropsychologia, 45(4), 685–695. Ishii, R., Shinosaki, K., Ukai, S., Inouye, T., Ishihara, T., Yoshimine, T., et al. (1999). Medial prefrontal cortex generates frontal midline theta rhythm. Neuroreport, 10(4), 675–679. Izard, C., Fine, S., Schultz, D., Mostow, A., Ackerman, B., & Youngstrom, E. (2001). Emotion knowledge as a predictor of social behavior and academic competence in children at risk. Psychological Science, 12(1), 18–23. Jasper, H. (1958). The 10/20 international electrode system. EEG and Clinical Neurophysiology, 10, 371–375. Jones, C. R., Pickles, A., Falcaro, M., Marsden, A. J., Happe´, F., Scott, S. K., et al. (2011). A multimodal approach to emotion recognition ability in autism spectrum disorders. Journal of Child Psychology and Psychiatry, 52(3), 275–285. Jones, C. R., Swettenham, J., Charman, T., Marsden, A. J., Tregay, J., Baird, G., et al. (2011). No evidence for a fundamental visual motion processing deficit in adolescents with autism spectrum disorders. Autism Research, 4(5), 347–357. Just, M. A., Cherkassky, V. L., Keller, T. A., Kana, R. K., & Minshew, N. J. (2007). Functional and anatomical cortical underconnectivity in autism: Evidence from an FMRI study of an executive function task and corpus callosum morphometry. Cerebral Cortex, 17(4), 951–961. Just, M. A., Cherkassky, V. L., Keller, T. A., & Minshew, N. J. (2004). Cortical activation and synchronization during sentence comprehension in high-functioning autism: Evidence of underconnectivity. Brain, 127(8), 1811–1821. Kana, R. K., Keller, T. A., Cherkassky, V. L., Minshew, N. J., & Just, M. A. (2009). Atypical frontal-posterior synchronization of theory of mind regions in autism during mental state attribution. Social Neuroscience, 4(2), 135–152. Kana, R. K., Libero, L. E., & Moore, M. S. (2011). Disrupted cortical connectivity theory as an explanatory model for autism spectrum disorders. Physics of Life Reviews, 8(4), 410–437. Kennedy, D. P., & Adolphs, R. (2012). Perception of emotions from facial expressions in high-functioning adults with autism. Neuropsychologia, 50(14), 3313–3319. Kilts, C. D., Egan, G., Gideon, D. A., Ely, T. D., & Hoffman, J. M. (2003). Dissociable neural pathways are involved in the recognition of emotion in static and dynamic facial expressions. Neuroimage, 18(1), 156–168.

M.K. Yeung et al. / Research in Autism Spectrum Disorders 8 (2014) 1567–1577

1577

Knyazev, G., Slobodskoj-Plusnin, J. Y., & Bocharov, A. (2009). Event-related delta and theta synchronization during explicit and implicit emotion processing. Neuroscience, 164(4), 1588–1600. Kober, H., Barrett, L. F., Joseph, J., Bliss-Moreau, E., Lindquist, K., & Wager, T. D. (2008). Functional grouping and cortical–subcortical interactions in emotion: A meta-analysis of neuroimaging studies. NeuroImage, 42(2), 998–1031. Kuusikko, S., Haapsamo, H., Jansson-Verkasalo, E., Hurtig, T., Mattila, M., Ebeling, H., et al. (2009). Emotion recognition in children and adolescents with autism spectrum disorders. Journal of Autism and Developmental Disorders, 39(6), 938–945. Law Smith, M. J., Montagne, B., Perrett, D. I., Gill, M., & Gallagher, L. (2010). Detecting subtle facial emotion recognition deficits in high-functioning autism using dynamic stimuli of varying intensities. Neuropsychologia, 48(9), 2777–2781. Leppa¨nen, J. M., & Hietanen, J. K. (2004). Positive facial expressions are recognized faster than negative facial expressions but why? Psychological Research, 69(1–2), 22–29. Leung, D., Ordqvist, A., Falkmer, T., Parsons, R., & Falkmer, M. (2013). Facial emotion recognition and visual search strategies of children with high functioning autism and Asperger syndrome. Research in Autism Spectrum Disorders, 7(7), 833–844. Lindquist, K. A., Wager, T. D., Kober, H., Bliss-Moreau, E., & Barrett, L. F. (2012). The brain basis of emotion: A meta-analytic review. Behavioral and Brain Sciences, 35(3), 121–143. Locatelli, T., Cursi, M., Liberati, D., Franceschi, M., & Comi, G. (1998). EEG coherence in Alzheimer’s disease. Electroencephalography and Clinical Neurophysiology, 106(3), 229–237. Lord, C., Rutter, M., & Le Couteur, A. (1994). Autism diagnostic interview-revised: A revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders. Journal of Autism and Developmental Disorders, 24(5), 659–685. Loveland, K. A., Tunali-Kotoski, B., Chen, Y. R., Ortegon, J., Pearson, D. A., Brelsford, K. A., et al. (1997). Emotion recognition in autism: Verbal and nonverbal information. Development and Psychopathology, 9(3), 579–593. ¨ hman, A. (1998). The Karolinska directed emotional faces – KDEF. Stockholm, Sweden: CD-ROM from Department of Clinical Lundqvist, D., Flykt, A., & O Neuroscience, Psychology Section, Karolinska Institutet. Matson, J. L., & Nebel-Schwalm, M. (2007). Assessing challenging behaviors in children with autism spectrum disorders: A review. Research in Developmental Disabilities, 28(6), 567–579. Monk, C. S., Peltier, S. J., Wiggins, J. L., Weng, S. J., Carrasco, M., Risi, S., et al. (2009). Abnormalities of intrinsic functional connectivity in autism spectrum disorders. Neuroimage, 47(2), 764–772. Monk, C. S., Weng, S. J., Wiggins, J. L., Kurapati, N., Louro, H. M., Carrasco, M., et al. (2010). Neural circuitry of emotional face processing in autism spectrum disorders. Journal of Psychiatry & Neuroscience: JPN, 35(2), 105–114. Mostow, A. J., Izard, C. E., Fine, S., & Trentacosta, C. J. (2002). Modeling emotional, cognitive, and behavioral predictors of peer acceptance. Child Development, 73(6), 1775–1787. Murias, M., Webb, S. J., Greenson, J., & Dawson, G. (2007). Resting state cortical connectivity reflected in EEG coherence in individuals with autism. Biological Psychiatry, 62(3), 270–273. Nunez, P. L., & Srinivasan, R. (2006). Electric fields of the brain: The neurophysics of EEG. Oxford University Press. Pelphrey, K. A., Sasson, N. J., Reznick, J. S., Paul, G., Goldman, B. D., & Piven, J. (2002). Visual scanning of faces in autism. Journal of Autism and Developmental Disorders, 32(4), 249–261. Petsche, H., Kaplan, S., Von Stein, A., & Filz, O. (1997). The possible meaning of the upper and lower alpha frequency ranges for cognitive and creative tasks. International Journal of Psychophysiology, 26(1–3), 77–97. Philip, R., Whalley, H., Stanfield, A., Sprengelmeyer, R., Santos, I., Young, A., et al. (2010). Deficits in facial, body movement and vocal emotional processing in autism spectrum disorders. Psychological Medicine, 40(11), 1919. Rudie, J. D., Shehzad, Z., Hernandez, L. M., Colich, N. L., Bookheimer, S. Y., Iacoboni, M., et al. (2012). Reduced functional integration and segregation of distributed neural systems underlying social and emotional information processing in autism spectrum disorders. Cerebral Cortex, 22(5), 1025–1037. Rueckert, L., & Grafman, J. (1996). Sustained attention deficits in patients with right frontal lesions. Neuropsychologia, 34(10), 953–963. Rutherford, M., & Towns, A. M. (2008). Scan path differences and similarities during emotion perception in those with and without autism spectrum disorders. Journal of Autism and Developmental Disorders, 38(7), 1371–1381. Sauseng, P., Hoppe, J., Klimesch, W., Gerloff, C., & Hummel, F. (2007). Dissociation of sustained attention from central executive functions: Local activity and interregional connectivity in the theta range. European Journal of Neuroscience, 25(2), 587–593. Schultz, D., Izard, C. E., Ackerman, B. P., & Youngstrom, E. A. (2001). Emotion knowledge in economically disadvantaged children: Self-regulatory antecedents and relations to social difficulties and withdrawal. Development and Psychopathology, 13(1), 53–67. Sprengelmeyer, R., Rausch, M., Eysel, U. T., & Przuntek, H. (1998). Neural structures associated with recognition of facial expressions of basic emotions. Proceedings of the Royal Society B: Biological Sciences, 265(1409), 1927–1931. Srinivasan, R., Nunez, P. L., & Silberstein, R. B. (1998). Spatial filtering and neocortical dynamics: Estimates of EEG coherence. Biomedical Engineering, IEEE Transactions on, 45(7), 814–826. Sucksmith, E., Allison, C., Baron-Cohen, S., Chakrabarti, B., & Hoekstra, R. (2013). Empathy and emotion recognition in people with autism, first-degree relatives, and controls. Neuropsychologia, 51(1), 98–105. Tracy, J. L., Robins, R. W., Schriber, R. A., & Solomon, M. (2011). Is emotion recognition impaired in individuals with autism spectrum disorders? Journal of Autism and Developmental Disorders, 41(1), 102–109. Tsuchida, A., & Fellows, L. K. (2012). Are you upset? Distinct roles for orbitofrontal and lateral prefrontal cortex in detecting and distinguishing facial expressions of emotion. Cerebral Cortex, 22(12), 2904–2912. Uljarevic, M., & Hamilton, A. (2013). Recognition of emotions in autism: A formal meta-analysis. Journal of Autism and Developmental Disorders, 43(7), 1517–1526. Wagner, H. L. (1993). On measuring performance in category judgment studies of nonverbal behavior. Journal of Nonverbal Behavior, 17(1), 3–28. Wallace, G. L., Case, L. K., Harms, M. B., Silvers, J. A., Kenworthy, L., & Martin, A. (2011). Diminished sensitivity to sad facial expressions in high functioning autism spectrum disorders is associated with symptomatology and adaptive functioning. Journal of Autism and Developmental Disorders, 41(11), 1475–1486. Wechsler, D. (2010). Wechsler intelligence scale for children (4th ed.). Hong Kong, China: King-May Psychological Assessment. Weng, S., Wiggins, J. L., Peltier, S. J., Carrasco, M., Risi, S., Lord, C., et al. (2010). Alterations of resting state functional connectivity in the default network in adolescents with autism spectrum disorders. Brain Research, 1313, 202–214. Wicker, B., Fonlupt, P., Hubert, B., Tardif, C., Gepner, B., & Deruelle, C. (2008). Abnormal cerebral effective connectivity during explicit emotional processing in adults with autism spectrum disorder. Social Cognitive and Affective Neuroscience, 3(2), 135–143. Wilkins, A., Shallice, T., & McCarthy, R. (1987). Frontal lesions and sustained attention. Neuropsychologia, 25(2), 359–365. Womelsdorf, T., & Fries, P. (2007). The role of neuronal synchronization in selective attention. Current Opinion in Neurobiology, 17(2), 154–160.