Understanding Friendship Sex Heterophily and Relational Characteristics to Optimize the Selection of Peer Models for Children with Autism Spectrum Disorder

This study used social network analysis to evaluate whether sex heterophily, the degree to which peers are different in sex, between 126 children with autism (ages 5-12 years) and their peers affected social network connectivity. Results indicate that: (1) the quantity and sex of friends were more important in predicting social network connectivity than the relational characteristics of the friends (friendship nominations and social network salience/popularity); and (2) sex heterophily is an important factor in predicting social network connectivity. For males with autism, having friends of the same sex was associated with better social network connectivity; this was not true for females with autism. These findings have important implications for the selection of peer models for elementary-aged children with autism.

Keywords: Autism; Peer models; Peer relationships; Schools; Sex; Social networks.

Autism spectrum disorders (ASDs) are increasingly prevalent neurodevelopmental disorders characterized by sociocommunicative impairments. Growing consensus indicates that neurobehavioral abnormalities require explanation in terms of interconnected networks. Despite theoretical speculations about increased local and reduced distal connectivity, links between local and distal functional connectivity have not been systematically investigated in ASDs. Specifically, it remains open whether hypothesized local overconnectivity may reflect isolated versus overly integrative processing. Resting state functional MRI data from 57 children and adolescents with ASDs and 51 typically developing (TD) participants were included. In regional homogeneity (ReHo) analyses, pericalcarine visual cortex was found be locally overconnected (ASD > TD). Using this region as seed in whole-brain analyses, we observed overconnectivity in distal regions, specifically middle frontal gyri, for an ASD subgroup identified through k-means clustering. While in this subgroup local occipital to distal frontal overconnectivity was associated with greater symptom severity, a second subgroup showed the opposite pattern of connectivity and symptom severity correlations. Our findings suggest that increased local connectivity in ASDs is region-specific and may be partially associated with more integrative long-distance connectivity. Results also highlight the need to test for subtypes, as differential patterns of brain-behavior links were observed in two distinct subgroups of our ASD cohort.

Keywords: autism; frontal cortex; functional connectivity MRI; local connectivity; visual cortex.

Background: The objective of this study was to examine intrinsic whole-brain functional connectivity in autism spectrum disorder (ASD) using the framework of functional segregation and integration. Emphasis was given to potential gender and developmental effects as well as identification of specific networks that may contribute to the global results.

Methods: We leveraged an open data resource (N = 1587) of resting-state functional magnetic resonance imaging data in the Autism Brain Imaging Data Exchange (ABIDE) initiative, combining data from more than 2100 unique cross-sectional datasets in ABIDE I and ABIDE II collected at different sites. Modularity and global efficiency were utilized to assess functional segregation and integration, respectively. A meta-analytic approach for handling site differences was used. The effects of age, gender, and diagnostic category on segregation and integration were assessed using linear regression.

Results: Modularity decreased nonlinearly in the ASD group with age, as evidenced by an increase and then decrease over development. Global efficiency had an opposite relationship with age by first decreasing and then increasing in the ASD group. Both modularity and global efficiency remained largely stable in the typically developing control group during development, representing a significantly different effect than seen in the ASD group. Age effects on modularity were localized to the somatosensory network. Finally, a marginally significant interaction between age, gender, and diagnostic category was found for modularity.

Conclusions: Our results support prior work that suggested a quadratic effect of age on brain development in ASD, while providing new insights about the specific characteristics of developmental and gender effects on intrinsic connectivity in ASD.

Keywords: ABIDE; Age; Autism; Functional connectivity; Functional integration; Gender.

Background: Autism spectrum disorder (ASD) is associated with impaired face processing. The N170 event-related potential (ERP) has been considered a promising neural marker of this impairment. However, no quantitative review to date has integrated the literature to assess whether the N170 response to faces in individuals with ASD differs from that of typically developing (TD) individuals.

Methods: This meta-analysis examined the corpus of literature investigating difference in N170 response to faces in individuals with ASD and without ASD. Data from 23 studies (NASD = 374, NTD = 359) were reviewed. Meta-analysis was used to examine the effect size of the difference in N170 latency and amplitude among individuals with ASD and without ASD. Analyses were also conducted examining hemispheric differences, potential moderators, and publication bias.

Results: On average, N170 latencies to faces were delayed in individuals with ASD, but amplitudes did not differ for individuals with ASD and TD individuals. Moderator analyses revealed that N170 amplitudes were smaller in magnitude in the ASD participants relative to the TD participants in adult samples and in those with higher cognitive ability. However, effects differed as a function of hemisphere of recording. No evidence of publication bias was found.

Conclusions: Atypicality of N170-particularly latency-to faces appears to be a specific biomarker of social-communicative dysfunction in ASD and may relate to differential developmental experiences and use of compensatory cognitive mechanisms. Future research should examine phenotypic differences that contribute to N170 heterogeneity, as well as specificity of N170 differences in ASD versus non-ASD clinical populations, and N170 malleability with treatment.

Keywords: Autism spectrum disorder (ASD); Electroencephalography (EEG); Event-related potential (ERP); Face processing; Meta-analysis; N170.

The parts of the body that are used to produce and perceive signed languages (the hands, face, and visual system) differ from those used to produce and perceive spoken languages (the vocal tract and auditory system). In this paper we address two factors that have important consequences for sign language acquisition. First, there are three types of lexical signs: one-handed, two-handed symmetrical, and two-handed asymmetrical. Natural variation in hand dominance in the population leads to varied input to children learning sign. Children must learn that signs are not specified for the right or left hand but for dominant and non-dominant. Second, we posit that children have at least four imitation strategies available for imitating signs: anatomical (Activate the same muscles as the sign model), which could lead learners to inappropriately use their non-dominant hand; mirroring (Produce a mirror image of the modeled sign), which could lead learners to produce lateral movement reversal errors or to use the non-dominant hand; visual matching (Reproduce what you see from your perspective), which could lead learners to produce inward-outward movement and palm orientation reversals; and reversing (Reproduce what the sign model would see from his/her perspective). This last strategy is the only one that always yields correct phonological forms in signed languages. To test our hypotheses, we turn to evidence from typical and atypical hearing and deaf children as well as from typical adults; the data come from studies of both sign acquisition and gesture imitation. Specifically, we posit that all children initially use a visual matching strategy but typical children switch to a mirroring strategy sometime in the second year of life; typical adults tend to use a mirroring strategy in learning signs and imitating gestures. By contrast, children and adults with autism spectrum disorder (ASD) appear to use the visual matching strategy well into childhood or even adulthood. Finally, we present evidence that sign language exposure changes how adults imitate gestures, switching from a mirroring strategy to the correct reversal strategy. These four strategies for imitation do not exist in speech and as such constitute a unique problem for research in language acquisition.

Keywords: American Sign Language (ASL); Autism Spectrum Disorders (ASD); imitation; language acquisition; sign language; visual perspective-taking.

Electroenchephalography (EEG) recordings collected with developmental populations present particular challenges from a data processing perspective. These EEGs have a high degree of artifact contamination and often short recording lengths. As both sample sizes and EEG channel densities increase, traditional processing approaches like manual data rejection are becoming unsustainable. Moreover, such subjective approaches preclude standardized metrics of data quality, despite the heightened importance of such measures for EEGs with high rates of initial artifact contamination. There is presently a paucity of automated resources for processing these EEG data and no consistent reporting of data quality measures. To address these challenges, we propose the Harvard Automated Processing Pipeline for EEG (HAPPE) as a standardized, automated pipeline compatible with EEG recordings of variable lengths and artifact contamination levels, including high-artifact and short EEG recordings from young children or those with neurodevelopmental disorders. HAPPE processes event-related and resting-state EEG data from raw files through a series of filtering, artifact rejection, and re-referencing steps to processed EEG suitable for time-frequency-domain analyses. HAPPE also includes a post-processing report of data quality metrics to facilitate the evaluation and reporting of data quality in a standardized manner. Here, we describe each processing step in HAPPE, perform an example analysis with EEG files we have made freely available, and show that HAPPE outperforms seven alternative, widely-used processing approaches. HAPPE removes more artifact than all alternative approaches while simultaneously preserving greater or equivalent amounts of EEG signal in almost all instances. We also provide distributions of HAPPE’s data quality metrics in an 867 file dataset as a reference distribution and in support of HAPPE’s performance across EEG data with variable artifact contamination and recording lengths. HAPPE software is freely available under the terms of the GNU General Public License at https://github.com/lcnhappe/happe.

Keywords: EEG; EEG processing; artifact removal; automated; data quality; development; electroencephalography; pipeline.

2018Alycia Halladay