Behavioural Brain Research 263 (2014) 1–8

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Social cognition and neural substrates of face perception: Implications for neurodevelopmental and neuropsychiatric disorders Steven M. Lazar 1 , David W. Evans ∗,1 , Scott M. Myers, Andres Moreno-De Luca, Gregory J. Moore Geisinger-Bucknell Autism and Developmental Medicine Center, 120 Hamm Drive, Lewisburg, PA 17837, United States

h i g h l i g h t s • • • • •

Autism and schizophrenia are typically regarded as dichotomous clinical entities. These conditions are associated with social deficits with known neural correlates. Social behavior may be regarded as a continuous, normally distributed trait. Here, normal variation in social behavior predicted neural structure and function. Brain-behavior links observed in atypical populations are preserved in controls.

a r t i c l e

i n f o

Article history: Received 9 December 2013 Received in revised form 9 January 2014 Accepted 13 January 2014 Available online 22 January 2014 Keywords: FMRI Face processing Autism spectrum disorder Social cognition Quantitative traits

a b s t r a c t Background: Social cognition is an important aspect of social behavior in humans. Social cognitive deficits are associated with neurodevelopmental and neuropsychiatric disorders. In this study we examine the neural substrates of social cognition and face processing in a group of healthy young adults to examine the neural substrates of social cognition. Methods: Fifty-seven undergraduates completed a battery of social cognition tasks and were assessed with electroencephalography (EEG) during a face-perception task. A subset (N = 22) were administered a face-perception task during functional magnetic resonance imaging. Results: Variance in the N170 EEG was predicted by social attribution performance and by a quantitative measure of empathy. Neurally, face processing was more bilateral in females than in males. Variance in fMRI voxel count in the face-sensitive fusiform gyrus was predicted by quantitative measures of social behavior, including the Social Responsiveness Scale (SRS) and the Empathizing Quotient. Conclusions: When measured as a quantitative trait, social behaviors in typical and pathological populations share common neural pathways. The results highlight the importance of viewing neurodevelopmental and neuropsychiatric disorders as spectrum phenomena that may be informed by studies of the normal distribution of relevant traits in the general population. © 2014 Elsevier B.V. All rights reserved.

1. Introduction Social cognition comprises a set of skills that enable us to understand thoughts and intentions of others and respond appropriately to their social actions [1]. These skills develop under genetic and experiential influences, including environmental and cultural factors, and are vital for adaptive social behavior. Deficits in social cognition are key features in a variety of neurodevelopmental and psychiatric disorders including autism spectrum disorder (ASD) and schizophrenia [2].

∗ Corresponding author. E-mail address: [email protected] (D.W. Evans). 1 These authors contributed equally to the manuscript. 0166-4328/$ – see front matter © 2014 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.bbr.2014.01.010

Rather than existing as a dichotomy, behaviors associated with neurodevelopmental and neuropsychiatric syndromes often represent the severe end of continuous distributions of core competencies and/or deficiencies that occur in nature [3,4]. For example, autistic symptoms or traits (social/communication deficits and restricted interests and repetitive behaviors) aggregate in close relatives of children with ASD, including those who do not meet the threshold for clinical diagnosis of ASD – known as the “broader autism phenotype” [5–7]. Indeed, many behavioral traits that are symptomatic of neurodevelopmental and neuropsychiatric disorders are represented in the general population, with the normality of this distribution being highly dependent on the sensitivity of the measurement. Standard diagnostic measures of ASD (such as the Autism Diagnostic Observation Schedule, or ADOS) reflect the traditional

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categorical approach to symptom expression, and as such yield near floor-effects in non-clinical populations. These floor effects obscure important genes-brain-behavior links that may underlie the typical manifestations of the behaviors in question by attenuating statistical variability. Epidemiologic studies have shown that quantifiable traits that make up the core impairments of ASD (such as those measured with the Social Responsiveness Scale (SRS)), are continuously distributed in the general population [8,9] Other features associated with ASD that are present in the general population include repetitive behaviors and restricted interests [10,11] as well as deficits in empathy, and a systemizing cognitive style (the tendency to analyze, understand, predict, control, and construct rule-based systems) [12,13]. Sex-related differences have also been observed in social-cognitive traits [8,14], and these differences may be linked to the uneven sex distribution observed in ASD diagnoses which approaches a 4:1 male–female ratio, as well as to possible sex differences in the lateralization of certain functions (e.g., face perception) that is also prevalent in ASD [15]. Face processing and its neurophysiologic correlates are important markers of social cognition. Normal infants exhibit visual preference for faces in the first few days of life and within the first 6 months develop the ability to distinguish familiar from unfamiliar faces, differentially process inverted versus upright faces, and differentiate facial emotional expressions [16]. Eye-tracking, functional neuroimaging, and electrophysiological studies have demonstrated that children and adults with ASDs process faces and decode facial expressions differently than typically-developing individuals [17–20]. Converging evidence from ERP and fMRI studies indicates specialized activity in regions of the occipital and temporal cortex that are involved in face processing, including the inferior temporal cortex fusiform gyrus and the superior temporal sulcus [21–23]. The N170 event-related potential (ERP) component exhibits larger amplitudes and shorter latencies to faces relative to other stimuli. ERP face perception studies also report right hemisphere dominance and inversion effects, (larger amplitude and/or longer latency responses to inverted faces relative to upright faces) [24–26]. In high-functioning adolescents and adults with ASD, the N170 response to faces is delayed relative to controls, and the typical right hemisphere lateralized pattern is often absent. Furthermore, unlike controls, the ASD group did not exhibit the inversion effect [27,28], suggesting not only slower processing of faces, but also a qualitatively different processing strategy. Parents of individuals with autism also fail to show right hemisphere lateralization or a shorter latency N170 to faces compared to objects [29]. A study of adolescent twins provided evidence for substantial heritability of neurophysiologic indicators of face processing; 36–64% of individual variability in the ERP components elicited by changes in facial expression was accounted for by genetic factors [30]. Even in non-clinical populations, quantitative traits of symptoms associated with ASD and obsessive–compulsive disorder are linked to variations in ERPs during the processing of certain stimuli including faces [31,32]. These findings suggest that ERP components sensitive to face processing, including emotional expressions, can potentially serve as endophenotypes for disorders characterized by abnormalities in social cognition and behavior. Similarly, fMRI studies utilizing blood-oxygen-level-dependent (BOLD) contrasts reveal activation of a right lateralized inferior temporal area in the fusiform gyrus (FFG) when subjects look at human faces [33,34]. While right-hemisphere lateralization is relatively robust, the finding is not universally reported [34–36]. Relative expertise in face processing is believed to have developed through evolutionary pressures that place significant import on our ability to recognize and perceive faces [37] which is linked to better social cognition and social behavior, more generally. Individuals with quantitatively low levels of social

cognition – specifically those clinically diagnosed with ASD – do not develop face processing expertise to the extent that typicallydeveloping individuals do. Relative to controls, individuals with ASD exhibit hypoactivation in areas related to social cognition and face perception [38–40] such as the orbito-frontal cortex, superior temporal gyrus, amygdala [41] and fusiform gyrus [21,39,42–45]. Few published studies have examined the association between social cognition and fMRI and ERP markers of face processing in non-clinical populations. ASD traits in typically-developing individuals have been shown to predict neural responses to eye gaze [46] and the structure and function in the posterior superior temporal sulcus [47]. Among typically developing children, more negative N170 amplitude (larger ERP) to upright faces is associated with fewer atypical social behaviors [19]. Smaller (less negative) N170 amplitude is thought to reflect less face processing expertise, and this inefficiency in neural processing may result in less fluid and more effortful reciprocal social interactions and therefore more atypical social behavior [19]. The purpose of this study is to explore the associations among quantitative self-report and performance-based measures of social cognition and neurophysiologic correlates of face processing. We hypothesize that measures of social competence will predict variation in (1) face-related N170 amplitude and latency and (2) fusiform gyrus activation on fMRI in response to faces relative to a control condition (houses). Higher social cognitive competence (higher EQ scores, lower SRS scores, higher scores on a social attribution task) is expected to be associated with more activation of the fusiform gyrus in response to faces on fMRI, and with decreased latency and increased amplitude of the N170 on ERP. We also examine sex differences in the lateralization of face processing ability. 2. Materials and methods Ethics statement. The research protocol and consent procedures were approved by a university Institutional Review Board (IRB#1112-033, “Social Cognition and Face Perception”). All subjects involved in the study gave written informed consent. Statistical analysis. All analyses were performed using SPSS 20 (IBM) with a significance threshold of p < 0.05. Variable distributions were checked for normality, and non-violated assumptions for parametric tests. Participants. Subjects were undergraduate students at a liberal arts university in central Pennsylvania (N = 57; 20 males, 37 females). A subset of the participants completed the fMRI portion of the protocol (N = 24; 12 males, 12 females). Subjects were recruited through an introductory psychology course, satisfying participation in research. Subjects who completed the entire protocol (including fMRI) received additional monetary compensation. The average age of the participants was 18.87 years (SD = 0.93). Of the 57 total participants, 50 were self-described as Caucasian (84.7%), three as East Asian or Pacific Islander (5.3%), and two each as African American, South Asian, or of more than one race (3.5% each). Of the 57 participants, 24 participants (42%) volunteered to complete fMRI testing in addition to behavioral and EEG testing. Of these 24 one participant was excluded based on left handedness and another was excluded for taking medication for a psychiatric diagnosis. This left a total of 57 (20 males) participants and a subset of 22 (10 females) with fMRI scans. None of the participants was diagnosed with a psychiatric disorder or reported any known familial history of an ASD diagnosis. 2.1. Behavioral and demographic measures All self-report measures described below (Demographics, Empathizing and Systemizing, and the Social Responsiveness Scale)

S.M. Lazar et al. / Behavioural Brain Research 263 (2014) 1–8

Fig. 1. Exemplar house and face images presented in the ERP task.

were administered through an online battery of inventories using Qualtrics (http://www.qualtrics.com/) survey software. Permission was received for adapting these standardized measures into online format. Demographics. Subjects completed an online demographics form with information on gender, race, date of birth, ethnicity, religion, psychiatric history, familial psychiatric history, and parental behaviors. Empathizing quotient and systemizing quotient – revised. (EQ and SQ-R)[13,48]. The EQ is a 40-item self-report measure of empathy. The SQ-R is a 75-item self-report measure assessing systemizing tendencies. Both are rated on a 4-point scale, “definitely agree”, “slightly agree”, “slightly disagree”, and “definitely disagree.” Social responsiveness scale – adult self-report (SRS-A) [49,50]. The SRS-A is a 65-item self-report measure of ASD traits. Each item is measured on a 4-point (0–3) scale. The SRS has been studied extensively as a valid and reliable quantitative measure of ASD-related behaviors. The SRS norms are based on samples ages 19 and older. Because the majority of the participants were older than 18 years of age, and those who were 18 were approaching their 19th birthday within the ensuing six months, the decision was made to use the adult self-report form for all participants, rather than administer different versions of the measure across subjects. 2.2. Social attribution Social attribution task – multiple choice (SAT-MC) [51]. Subjects are shown a one-minute video of several geometric figures moving around the screen. This animation is an adaptation of a measure [52] that was created to represent a social drama. Subjects are shown the video twice in rapid succession. Following the video, subjects complete 19 multiple choice questions about the video. Responses that correctly anthropomorphize the shapes and their “intentions” are summed and range from 0 (inability to anthropomorphize) to 19 (high anthropomorphizing ability). 2.3. ERP data collection and analysis The ERP faces-houses task consisted of pictures of 100 houses and 100 cropped faces with a neutral expression (Fig. 1) presented in a pseudo-random order. Images were presented on the screen for 500 ms with a 1500 ms inter-stimulus interval (ISI). Event-related potentials were recorded using a 32-channel amplifier, Ag-AgCl electrode fabric cap arranged in the international 10–20 system, grounded at site AFz (ANT, Enschede, Netherlands). Signals were recorded at a sampling rate of 512 Hz, filtered continuously with a high pass of .3 Hz and a low pass of 30 Hz (as recommended by ANT). Averaging epochs were set to −0.1 s before, to 0.25 s after stimulus presentation. Impedances were maintained below 10 k. ERP data were analyzed with ASA (Advanced Source Analysis) version 4.7.3

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Fig. 2. Experimental design diagram for fMRI stimuli presentation.

and grand averages were calculated using the Experiment Manager Application version 9.1 for use with ASA. This is a passive task, in that no behavioral response is required. 2.4. MRI data collection For the fMRI faces-houses task, image presentation occurred in alternating 24 s long blocks of pairs of faces (side-by-side) and pairs of houses (also side by side) interspaced by a fixation crosshair for 12 s (Fig. 2). Each pair was displayed for 3500 ms with an ISI = 1000. Face stimuli consisted of cropped images of unfamiliar faces with external features such as hair and neck [53–55]. The faces exhibited either an anger expression or a neutral expression and were presented pseudorandomly within each face block. House blocks consisted of pairs of houses shown at various viewing angles. Subjects were asked to respond whether the face or house pairs were different images of the same face or house by pressing a button with their right index finger. Magnetic resonance imaging was performed with a GE Discovery MR750 3T scanner (GE Medical Systems) with a standard 16 channel RF head coil at Geisinger Medical Center, Danville, PA. Functional images were collected with an echo-planar T2*-weighted (EPI) imaging sequence sensitive to BOLD signal contrast (40 oblique slices, 4 mm slice thickness; TR = 3000 ms; TE = 35 ms; flip angle = 90o ; FOV = 240 mm; voxel size = 3.75 mm × 3.75 mm × 4 mm). High contrast threedimensional T1-weighted structural images were acquired at a resolution of 0.938 mm × 0.938 mm × 1.2 mm for anatomical co-registration of the functional images. The ERP, and fMRI testing were conducted at two different sites, and each testing session was separated by at least one week. 2.5. fMRI data preprocessing and analysis FMRI preprocessing and analysis was conducted using the Statistical Parametric Mapping 8 (SPM8) software (http://www. fil.ion.ucl.ac.uk/spm/). Functional images were realigned to the first image in the series and quality checked for movement. The realigned images were then co-registered to the original T1 structural image to verify matching coordinate spaces. Structural images were then segmented into standard grey and white matter images for normalization. Linear normalization of the realigned images and T1 structural image was then performed to fit the images into standard Montreal Neurological Institute (MNI) space using the MNI152 template. Residual anatomical differences were then reduced through special smoothing with a Gaussian kernel filter of 6 mm × 6 mm × 6 mm. Statistical analysis was performed on group basis according to implementation of the general linear model (GLM).

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Table 1 Behavioral measure comparisons by sex. Male

SRS Standards T-Score SAT-MC Total Score EQ Total Score SQ Total Score

Female

N

Mean ± SD

N

Mean ± SD

20 19 20 20

50.25 ± 6.00 16.32 ± 2.43 49.50 ± 11.52 60.50 ± 19.88

37 37 37 37

52.08 ± 8.29 15.70 ± 2.41 47.89 ± 11.89 51.00 ± 16.45

Conditions were modeled as box-car functions convolved with a canonical hemodynamic response function. Data were highpass filtered to remove low frequency drifts in signal. At a first level, within-subject analysis using GLM was conducted on the functional data. The resulting contrast images were then taken through second-level analysis, determining global activation across conditions in all subjects. Resulting images were viewed and anatomically analyzed using the xjview toolbox (http://www.alivelearn.net/xjview) and automated anatomical labeling (AAL, [56,57]). Suprathreshold voxel counts in bilateral fusiform gyri as defined by AAL were recorded as a proxy for a continuous variable of activation. Suprathreshold voxels were defined as voxels reaching p < 0.001 uncorrected for multiple comparisons. 3. Results No measure of social cognition differed between males and females, but there was a non-significant trend in which males had slightly higher SQ-R scores than females (p = 0.058, see Table 1). There were also no systematic differences on any of the relevant variables between the subset of participants who completed the fMRI portion of the study than those who did not, leading to the conclusion that the fMRI subset is representative of the whole sample (One-Way ANOVA tests of age (F(1,54) = 1.93, p = 0.17), SAT Total Score (F(1,55) = 0.11, p = 0.75), EQ Raw Total Score (F(1,56) = 0.45, p = 0.50), SQ Raw Total Score (F(1,56) = 0.17, p = 0.68), and SRS Standards T-Score (F(1,56) = 0.16, p = 0.69). A significant bilateral N170 response was observed on the ERP faces-houses task. Peak face amplitude for the N170 was significantly greater than peak house amplitude at electrode sites P7 (t(55) = −8.62, p < .001) and P8 (t(55) = −9.75, p < 0.001). Latency of the N170 peak was also significantly shorter for faces than houses at electrode sites P7 (t(55) = −4.13, p < 0.001) and P8 (t(55) = −5.44, p < 0.001). These results indicate that our paradigm produced a canonical N170 response within our sample based on decreased latency and increased absolute amplitude for faces as compared to non-face objects (houses) (Fig. 3).

F

Sig.

0.758 0.807 0.242 3.735

0.388 0.373 0.624 0.058

As expected, on the fMRI faces-houses task, significant activation of the bilateral FG emerged. Areas of significant activation based on second level-analysis are presented in Table 2 and significant group activations are shown in Fig. 4. Additionally there was a significant cluster of activation in the right inferior frontal gyrus. Significant clusters were determined at a statistical threshold of p = 0.05, corrected for multiple comparisons. As with measures of social cognition, no systematic differences emerged on neurophysiological and imaging results including the number of suprathreshold voxels in right and left fusiform gyrus (FG) and left and right N170 amplitude and latency (Table 3). There was a trend in the right hemisphere N170 at electrode site P8 for females to have a faster peak latency than males (p = 0.051). Based on the longstanding tradition of describing the right hemisphere dominance in face perception, the neuroimaging data were subjected to paired t-tests to compare left and right hemisphere activity. The magnitude of the neurophysiological response to faces versus houses was significantly greater in the right hemisphere than in the left hemisphere, which is consistent with the general model of right hemisphere dominance (Fig. 5). The latency of the N170 minimum peak did not differ between hemispheres. Correlations were conducted between the amplitude difference of the house and face conditions at electrodes P7 and P8. Consistent with Proverbio, et al. [58], females were more bilateral in their face processing than males (the correlations between P7 and P8 was r = 0.55, p = 0.001 for females; for males, p = 0.17). Stepwise multiple regressions using the measures of social cognition to predict N170 latencies and amplitudes were performed. The SAT-MC predicted significant variance (17%) in the N170 peak latency at electrode site P7, (F(1,54) = 12.13, p = 0.001, ˇ = −0.43). Empathizing ability significantly predicted peak amplitude at P8 (F(1,54) = 6.76, p = 0.012, ˇ = −0.33, R2 = 0.11). The positive ˇ (0.33) in the EQ and P8 amplitude comparison reveals an association in the opposite direction than expected; increasing empathy scores reveal decreased neural response to faces. In order to further understand this, the degree of lateralization was tested using the same predictors in a step-wise multiple regression. The difference between P8 and P7 peak N170 amplitudes was predicted by the EQ

Fig. 3. Areas of significant activation across the entire sample. Areas of significant activation are shown in yellow and red with a statistical threshold of p = 0.05, FWE corrected. Sagittal image is presented in the plane of the right FG.

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Table 2 Significant clusters of activation in houses and faces fMRI task. Area of activation

Hemisphere

x

y

z

T-score

Fusiform gyrus Fusiform gyrus Inferior frontal gyrus

R L R

38 −37 50

−56 −67 8

−18 −18 30

10.98 9.07 9.43

Cluster size 100 159 112

Fig. 4. N170 response of houses compared to faces. (A) Grand averaged head map of cortical electrical activity during the N170 faces and houses ERP task. Response to houses is on the right while responses to faces are on the left. Maps show absolute voltage at 170 ms post- stimuli. (B) Grand averaged N170 waveforms at electrode sites P7 (left) and P8 (right). Waveform is shown from 100 ms before image presentation to 250 ms after image presentation. Red line signifies 170 ms post stimuli presentation and neural responses to faces are represented in blue, while neural responses to houses are represented in black.

(F(1,54) = 6.94, p = 0.011, ˇ = 0.34, R2 = 0.11). This finding indicates that it is not an absolute decrease in N170 amplitude per se that is predicted by the EQ, but rather, a shift from right lateralization to bilaterality. Female subjects followed a similar pattern as when the sexes were analyzed together. In females, the SAT-MC significantly predicted P7 peak latency (F(1,35) = 8.36, p = 0.007, ˇ = −0.44, R2 = 0.19), and the EQ predicted P8 peak amplitude (F(1,35) = 7.45, p = 0.01, ˇ = −0.42, R2 = 0.18). Again, EQ scores predicted a shift from right lateralization to bilaterality (F(1,35) = 9.69, p = 0.004, ˇ = −0.47, R2 = 0.22). None of the social cognition variables

predicted N170 amplitude or latency for males. These findings further support the connection between social cognition and neurophysiological measures of face perception observed with fMRI. The next series of analysis extended the ERP-social cognitive findings to fMRI. Multiple regressions examined social cognition as predictors of FG activity. The EQ Total Score significantly predicted the number of suprathreshold voxels in the right FG during the faces versus houses fMRI task, accounting for 20% of the variance (F(1,20) = 4.81, p = 0.04, ˇ = −0.45, R2 = 0.20). For left hemisphere FG activation EQ Total Score also predicted the number of suprathreshold FG voxels accounting for 29% of the variance (F(1,20) = 7.70,

Table 3 Neurophysiological and imaging results by sex. Male

Right FG suprathreshold voxels Left FG suprathreshold voxels Left N170 amplitude (P7, ␮V) Left N170 latency (P7, ms) Right N170 amplitude (P8, ␮V) Right N170 latency (P8, ms)

Female

N

Mean ± SD

N

Mean ± SD

12 12 19 19 19 19

53.00 ± 39.50 49.10 ± 31.59 −0.60 ± 4.20 166.34 ± 9.96 −3.71 ± 3.60 169.94 ± 10.12

10 10 37 37 37 37

49.10 ± 31.59 30.20 ± 23.61 −1.94 ± 4.64 166.26 ± 9.85 −4.32 ± 4.98 164.15 ± 10.37

F

Sig.

0.063 1.149 1.109 0.001 0.225 3.984

0.804 0.297 0.297 0.975 0.637 0.051

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p = 0.01, ˇ = −0.54, R2 = 0.29). No other measures of social cognition – the SRS-A, SQ-R, or SAT – accounted for additional significant variance. In light of the literature on observed sex differences in face processing, ASD prevalence and social cognition [59] the same analyses were conducted separately for males and females. For females, the SRS-A predicted the number of suprathreshold voxels in the left FG (F(1,8) = 28.91, p = 0.001, ˇ = −0.72, R2 = 0.77). This finding persisted in the right FG as well (F(1,8) = 10.36, p = 0.012, ␤ = −0.75, R2 = 0.56). No other measures of social cognition predicted significant variance. For males, no social cognition variables predicted FG activation during the face processing task.

70 Number of Suprthreshold FG Voxels

6

50 40 30 20 10

4. Discussion

0

Le

Right

Hemisphere

Hemisphere

0

N170 Minimum Amplitude (µV)

-1 -2 -3 -4 -5

***

-6 Le

Right

Hemisphere

Hemisphere

167 166.5 N170 Minimum Latency (ms)

This study examined the neural substrates of face processing and their associations with the quantitative measures of social behavior. Consistent with our hypotheses, the face sensitive N170 marker was modulated by measures of social cognition. Specifically the ability to make social attributions predicted left hemispheric cortical activity (N170 peak latency); the more facile one is in attributing social meaning to moving shapes, the faster their neural response when exposed to faces. Empathy was linked to more bilateral processing of faces, highlighting the importance of examining laterality in males and females [59]. That the sexes did not differ significantly on the social cognition variables, suggests that the functional association between the brain and behavior measures of social cognition is more salient than any sex differences in social cognition and empathy. This hypothesis is consistent with previously described discrepancies in the lateralization of social stimuli between males and females [58], especially relating to neuropeptides such as oxytocin and vasopressin that are believed to play an important role in social cognition [60]. Empathy significantly predicted the area of bilateral FG activation (the number of suprathreshold voxels) during a face perception task. This illustrates the integrative nature of various systems associated with social behavior, linking self-perceived empathy with neural correlates of face processing ability. Such findings highlight the importance of using continuous, quantitative measures of behavioral and neural traits that are impaired in a variety of neurodevelopmental and neuropsychiatric disorders including autism and schizophrenia. When males and females were analyzed separately the SRSA score emerged as a significant predictor of face perception in females, but not males. Females demonstrated an increased area of bilateral FG activation with better social responsiveness (SRS-A). The EQ and SRS both represent social cognitive quantitative traits and share significant variance; therefore it is not surprising that both predict social cognitive abilities to some extent. However, it is also the case that the EQ and SRS assess somewhat different aspects of social cognition, with the EQ highlighting more affective components, whereas the SRS emphasizes a broader range of social cognitive abilities, including the cognitive and behavioral aspects of social behavior associated with ASD. Subsequent analyses revealed that the SRS-A and EQ correlation was driven by the female subjects with no correlation for males. These finding calls for greater scrutiny into the degree to which various measures of social-cognitive ability assess common or unique facets of social cognition and whether the link among these measures and constructs varies as a function of demographic variables such as clinical status, age, and sex. There are a number of intermediate factors that may influence these findings but do not necessarily contradict them. For instance, face-related ERPs are modulated by point of gaze. There is an increased N170 amplitude with fixation to the upper portion of

*

60

166 165.5 165 164.5

Le Hemisphere

Right Hemisphere

Fig. 5. Bar graphs represent mean ± S.E.M. of neurophysiological results. (A) The number of suprathreshold voxels in the Faces versus Houses fMRI task was significantly greater in the right hemisphere than in the left hemisphere (t(21) = −2.46, p = 0.02). (B) The minimum amplitude of the N170 in the faces versus houses ERP task was greater in the right hemisphere, at electrode site P8, than in the left hemisphere, at electrode site P7 (t(55) = 5.50, p < 0.001). (C) There was no significant difference between the minimum peak latency of the N170 between hemispheres (t(21) = 0.13, p = 0.90). p-value < 0.05*. p-value < 0.001***.

the face, including the eyes, as compared to other fixation points [61]. This has major implications for our findings as there are many links between eye and gaze processing and social cognition [62]. It is possible that increased attention to the eyes when viewing a face may be a mediating factor between quantitative

S.M. Lazar et al. / Behavioural Brain Research 263 (2014) 1–8

traits related to ASD and schizophrenia and neural substrates of face processing. This also merits further exploration in research combining eye-tracking with ERP and fMRI. The data presented here extend the findings of fMRI and ERP studies of face processing deficits in individuals with a diagnosed ASD [17,27,39,42] to the general population. The findings are consistent with the work noting decreased empathy in individuals with an ASD [12,13], and speak to the relative continuity between symptom expression in clinical populations and the more subtle aspects of behavior that represent normal variants of behaviors that are observed in neurodevelopmental and neuropsychiatric disorders. This work underscores the importance of quantitative measures of both behavioral and neural phenotypes that are sensitive to the distribution of behavioral traits in non-clinical populations. The neuroimaging correlates of face perception and socialcognitive behaviors give credence to the use of non-clinical individuals to understand the interrelations among processes in the context of clinical research. The implications of a quantitative methodological approach are widespread and may serve to better understand the brain-behavior links underlying social processes in clinical populations where social-cognitive deficits are prominent. Such work necessitates further development and refinement of sensitive quantitative measures of traits that subsume neurodevelopmental and neuropsychiatric disorders. We recommend continued examination of the broader range of the ASD features observed in individuals with ASD in future work using quantitative measures to explore genes-brain-behavior links of ASD traits in clinical and non-clinical samples. Financial disclosure None of the authors reports any biomedical financial interests or potential conflicts of interest. The authors acknowledge financial support from the Bucknell-Geisinger Research Initiative Grant (BGRI), awarded to DWE and GJM. Acknowledgements The authors gratefully acknowledge the assistance and support of several people: Mylissa Slane and other members of the Evans Laboratory of Bucknell University offered key support. Edward Stefanowicz of the Radiology Department at Geisinger Health System was instrumental in the completion of the fMRI portion of the project. The authors also acknowledge the support of the members of the Geisinger-Bucknell Autism and Developmental Medicine Center, Lewisburg, PA, most notably, Drs. David H. Ledbetter and Thomas Challman. Dr. Heidi Marsh, University of Western Ontario gave important editorial feedback. A version of this manuscript was submitted as an honors thesis to the Program in Neuroscience at Bucknell University by Steven M. Lazar (David W. Evans, PhD supervisor). References [1] Skuse DH, Gallagher L. Genetic influences on social cognition. Pediatr Res 2011;69:85R–91R. [2] Couture SM, Penn DL, Losh M, Adolphs R, Hurley R, Piven J. Comparison of social cognitive functioning in schizophrenia and high functioning autism: more convergence than divergence. Psychol Med 2010;40:569–79. [3] Constantino JN. The quantitative nature of autistic social impairment. Pediatr Res 2011;69:55R–62R. [4] Moreno-De-Luca A, Myers SM, Challman TD, Moreno-De-Luca D, Evans DW, Ledbetter DH. Developmental brain dysfunction: revival and expansion of old concepts based on new genetic evidence. Lancet Neurol 2013;12:406–14. [5] Losh M, Childress D, Lam K, Piven J. Defining key features of the broad autism phenotype: a comparison across parents of multiple- and single-incidence autism families. Am J Med Genet B Neuropsychiatr Genet 2008;147B:424–33.

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