The current study contrasted neural specialization for social and non-social information in individuals with ASD and a cohort of typically developing individuals of comparable age, ethnicity, sex, handedness, and cognitive ability. A critical social stimulus with which most adults possess great experience, the human face, was contrasted with a comparably complex visual stimulus without interpersonal relevance, houses. Consistent with predictions and with prior research (
McPartland et al., 2004), individuals with ASD displayed a selective processing delay for human faces in the right hemisphere relative to typical counterparts. Individuals with ASD also showed reduced hemispheric specialization compared to typical counterparts, who showed a marked right lateralization effect for faces. The inversion effect, a marker of neural specialization and processing experience for faces, was evident in typically developing individuals but not those with ASD. On a behavioral measure of face recognition, individuals with ASD, despite comparable intellectual ability, performed significantly worse than typically developing counterparts. Face recognition performance was associated with processing efficiency for faces; in both groups, individuals with better face recognition abilities displayed faster N170 response. Among individuals with ASD, increased inversion effects, as reflected by a stronger response to inverted faces, were associated with better face recognition performance. This pattern of anomalies, i.e., decreased efficiency of processing, insensitivity to inversion, and impaired face recognition, is hypothesized to reflect underdeveloped specialization for faces, a downstream effect of decreased attention to faces during childhood secondary to reduced social drive from infancy (
Dawson, Webb & McPartland, 2005). Indeed, the observed correlation between neural response to face inversion and recognition performance suggests that, in this case, development of expertise and processing proficiency are related.
Though differences related to condition and lateralization were observed in an earlier component, the P1, no between-group differences were detected. Given the P1’s role in basic visual attention and low-level sensory perception (
Key, Dove, & Maguire, 2005), this pattern of results indicates that children with ASD did not differ from typically developing peers in terms of fundamental sensory perception. Indeed, despite observed differences in the basic sensory response to different classes of stimuli, groups responded similarly. These findings suggest normative sensory perception for visual information in ASD, with differences emerging at subsequent processing stages related to social perception.
Current findings concord with prior work describing deviant social development in ASD. However, scant evidence to-date has informed the specificity of the observed neural processing anomalies to social information. By measuring responses to non-social expert stimuli, the current study demonstrated the selectivity of perceptual deficits in ASD. Following up on work showing N170-related expertise effects for letters, ERP response to letters of the Roman alphabet were compared to a confabulated alphabet of pseudoletters (
Wong et al., 2005). In contrast to the discrepancies observed during perception of social stimuli, individuals with ASD displayed neural responses comparable to typical counterparts; both groups showed enhanced N170 to familiar letters. Similar results were obtained on behavioral measures, with individuals with ASD obtaining word reading and decoding scores that were comparable to the typical participants in this study and within the average range. These results suggest intact behavioral and brain specialization for non-social information in individuals with ASD. These results are consistent with prior behavioral work demonstrating preserved word-reading and word-decoding ability in cognitively able individuals with ASD (
Huemer & Mann, 2009;
Nation et al., 2006;
Newman et al., 2006) despite complications with reading comprehension and more sophisticated aspects of language (
O’Connor & Klein, 2004). They are consistent with the notion that the social element of communication rather than language, more generally, may be most directly impacted in ASD (
Paul, 2003).
The current findings have significant implications for understanding the neuropathology of autism spectrum disorders. Two prevailing classes of theories attribute autistic impairments to dysfunctional brain structures supporting social information processing (
Dawson, Webb & McPartland, 2005) or altered connectivity among distributed brain regions (
Minshew & Williams, 2007). The former emphasizes the import of the content that is processed, and the latter accentuates the nature of processing itself. Social brain theories posit that social information is qualitatively unique and that specific brain systems have evolved to support this type of information processing. Connectivity theories, in contrast, have traditionally argued that social information is relevant only insofar as it relies on complex or cortically distributed processing mechanisms. The current work demonstrates, for the first time in a substantial sample of children with ASD, preserved specialization for a cognitive process subserved by distributed cortical regions. Development of specialized letter processing is a process that develops over time and requires elaborate communication of anterior and posterior cortical regions (
Krigolson, Pierce, Holroyd & Tanaka, 2009). The demonstration of preserved neural specialization for this type of “expert” processing in ASD is not consistent with models of non-specific, brain-wide dysfunction. Taking into account consideration considerable evidence for atypical patterns of connectivity in ASD (
Minshew & Williams, 2007), current findings emphasize the potential value of studying connectivity
within specific brain systems in developmental context. The observed latency delays may reflect reduced connectivity, and their specificity to social information compared to non-social information may indicate system-specificity in terms of atypical connectivity. By studying connectivity within specific neural circuits, scientists may also extricate atypical connectivity as potential cause or consequence of autistic dysfunction; it is likely that origins of dysfunction in functionally specific brain systems would, through developmental maturation, lead to broader connectivity problems. Such research may also clarify to what degree problems with connectivity uniquely differentiate autism from the diversity of developmental and psychiatric disorders also manifesting atypical connectivity, e.g. obsessive-compulsive disorder, (
Garibotto et al., 2010), schizophrenia (
Friston, 2002), ADHD (
Murias, Swanson & Srinivasan, 2007), and intellectual impairment (
Zhou et al., 2008).
This work yields clinically relevant implications for the detection and treatment of ASD. Results are supportive of the broad class of interventions designed to direct the attention of children with ASD to relevant social information. When children are appropriately engaged and attuned to information, in this case, letters, typical patterns of neural specialization develop; given the right input, the brain of a person with autism can function like that a of a typical peer, without ostensible reliance on compensatory mechanisms or alternative processing strategies. Findings add to a body of evidence that electrophysiological brain activity to faces represents a viable bio-behavioral risk marker for ASD, as temporal anomalies in neural correlates of face perception have been observed in children with ASD (
Dawson, Webb, Wijsman et al., 2005) and infants at-risk for ASD (
McCleery et al., 2009).
Though the current work replicates initial findings of temporal anomalies to faces (
McPartland et al., 2004), these findings have not fully replicated in all samples (
Kemner, Schuller & van Engeland, 2006;
Senju, Tojo, Yaguchi & Hasegawa, 2005;
Webb et al., 2009). Some of this variability may reflect methodological inconsistencies in terms of electrode selection (e.g.,
Webb et al., 2009) or employment of gold-standard diagnostic procedures (e.g.,
Grice, Halit, Farroni, Baron-Cohen, Bolton & Johnson, 2005); however, varied results may accurately reflect the phenotypic heterogeneity evident in ASD. Despite the unifying characteristic of social impairment, ASD presents in a remarkable diversity of manifestations, likely representing multiple etiologic pathways and developmental experiences (
Jones & Klin, 2009). Considering the manner in which face processing (especially in older children and adults) has been actively shaped by experience, it is intuitive that anomalies might emerge in different ways or might not emerge universally (
Jemel, Mottron & Dawson, 2006). In this regard, like any of the symptoms characterizing autism, anomalous face perception is neither necessary nor specific. It is one potential manifestation of atypical social development that, by virtue of a deep understanding of behavioral and brain bases in typical social development, is a viable avenue for investigating social disability. Variability in electrophysiological studies of face perception may also relate to differences in visual attention (
Webb et al., 2009), a trend observed in hemodynamic studies (
Dalton, Nacewicz, Alexander & Davidson, 2007;
Dalton et al., 2005). Our employment of a pre-stimulus fixation crosshair reduces the likelihood that between-group differences are attributable to differences in visual attention. Furthermore, given comparable N170 latencies to visual fixations to eyes and mouths in typical development (
McPartland, Cheung, Perszyk & Mayes, 2010) and delayed processing in ASD irrespective of point of gaze on the face (
McPartland, Perszyk, Crowley, Naples & Mayes, 2011), it is unlikely that variation in visual attention alone could account for observed latency differences; resolution of this matter will ultimately require co-registration of eye-tracking and EEG.
There are several aspects of the current work that are being revisited and improved upon in ongoing research. Limiting the sample to high-functioning individuals was a necessary first step towards addressing the research questions posed in this study, but it limits generalizability to the broader range of individuals with ASD. Given that even many nonverbal children with ASD are capable of reading, these types of experiments offer a window into domains of strength and preserved neural functions of children on the autism spectrum, important goals for tailoring interventions and proscribing specific treatments. The sample in the current study focused on pre-adolescence, a time of rapid maturation of brain systems subserving face perception. Additional research in younger and older children and adults will elucidate the protracted maturational course of specialization for face perception in ASD and of letter expertise in both typical and atypical development. Of note, many participants in the current study displayed the bifid waveform morphology characteristic of pre-adult face responses (
Taylor, Batty & Itier, 2004); however, this was not evident for letter N170s. Exploiting the dense spatial sampling afforded by the 256 electrode sensor net, analyses in progress are using individual-specific three-dimensional head models (computed with sensor registration images acquired with the Geodesic Photogrammetry System) to localize potentially distinct neural sources for these facets of the developing N170 (
Perszyk et al., 2010).
Our current results reveal a different relationship between performance on the behavioral face processing task and brain responses in individuals with ASD than observed in a prior study (
McPartland et al., 2004). Previously, results indicated slowed processing to be associated with improved face recognition. In the current study, however, the group of people with ASD, like the typically developing counterparts in the current and prior study, displayed an association between faster processing and better face recognition. We hypothesize that this reflects age-related differences in the application of compensatory strategies over time. In this younger sample, more normative brain responses correlated with more normative face recognition ability in ASD. In the older sample studied previously, the opposite trend was observed in the hemisphere contralateral to that typically associated with face perception; we interpret this as reflective of effective compensatory processing strategies. In the approximately 10 years between age 11 (current study) and age 21 (prior study), increased reliance on compensatory strategies may “overtake” weakened default processing mechanisms, ultimately resulting in better performance associated with these compensatory strategies. Though we see the pattern of results as supportive of this interpretation, it is also possible that the differences observed between studies simply reflect task effects, as the prior work relied on a visual recall task and the current study utilized a visual discrimination task with reduced memory demands. Face recognition performance in ASD is demonstrated to vary with task characteristics (
McPartland, Webb, Keehn & Dawson, 2011).
Understanding developmental factors is particularly important in the current context in that neural specialization for letters is clearly a distinct phenomenon from face expertise, occurring over a relatively compressed period of time rather than from birth. Moreover, it is likely that qualitatively different types of experience are associated with the accrual of proficiency in letter versus face processing. It will thus be essential to examine development of specialized processing mechanisms for a greater variety of stimuli. Though it has been proposed that, like faces, letters are encoded using a holistic processing strategy (
Martelli, Majaj & Pelli, 2005), unlike faces, letters are processed at a basic rather than subordinate level of identification (
James et al., 2005). The N170 has been posited to denote specialization at this basic level of identification, while later components, such as the N250, index expertise at the subordinate level of identification (
Scott, Tanaka, Sheinberg & Curran, 2006). Similar mechanisms underlying neural specialization for both faces and letters exist at early processing stages as indexed by the N170, but study of a broader range of electrophysiological components and expert stimuli will paint a clearer picture of the development and potential limitations of neural specialization in ASD.
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social information in autism
