Atypical functional connectivity between the amygdala and visual, salience regions in infants with genetic liability for autism.

The amygdala undergoes a period of overgrowth in the first year of life, resulting in enlarged volume by 12 months in infants later diagnosed with ASD. The overgrowth of the amygdala may have functional consequences during infancy. We investigated whether amygdala connectivity differs in 12-month-olds at high likelihood (HL) for ASD (defined by having an older sibling with autism), compared to those at low likelihood (LL). We examined seed-based connectivity of left and right amygdalae, hypothesizing that the HL and LL groups would differ in amygdala connectivity, especially with the visual cortex, based on our prior reports demonstrating that components of visual circuitry develop atypically and are linked to genetic liability for autism. We found that HL infants exhibited weaker connectivity between the right amygdala and the left visual cortex, as well as between the left amygdala and the right anterior cingulate, with evidence that these patterns occur in distinct subgroups of the HL sample. Amygdala connectivity strength with the visual cortex was related to motor and communication abilities among HL infants. Findings indicate that aberrant functional connectivity between the amygdala and visual regions is apparent in infants with genetic liability for ASD and may have implications for early differences in adaptive behaviors.

Social impairment in survivors of pediatric brain tumors via reduced social attention and emotion-specific facial expression recognition.

BACKGROUND/OBJECTIVES: Survivors of pediatric brain tumors (SPBT) experience significant social challenges, including fewer friends and greater isolation than peers. Difficulties in face processing and visual social attention have been implicated in these outcomes. This study evaluated facial expression recognition (FER), social attention, and their associations with social impairments in SPBT. METHODS: SPBT (N = 54; ages 7-16) at least 2 years post treatment completed a measure of FER, while parents completed measures of social impairment. A subset (N = 30) completed a social attention assessment that recorded eye gaze patterns while watching videos depicting pairs of children engaged in joint play. Social Prioritization scores were calculated, with higher scores indicating more face looking. Correlations and regression analyses evaluated associations between variables, while a path analysis modeling tool (PROCESS) evaluated the indirect effects of Social Prioritization on social impairments through emotion-specific FER. RESULTS: Poorer recognition of angry and sad facial expressions was significantly correlated with greater social impairment. Social Prioritization was positively correlated with angry FER but no other emotions. Social Prioritization had significant indirect effects on social impairments through angry FER. CONCLUSION: Findings suggest interventions aimed at improving recognition of specific emotions may mitigate social impairments in SPBT. Further, reduced social attention (i.e., diminished face looking) could be a factor in reduced face processing ability, which may result in social impairments. Longitudinal research is needed to elucidate temporal associations between social attention, face processing, and social impairments.

Differential cognitive and behavioral development from 6 to 24 months in autism and fragile X syndrome.

BACKGROUND: Specifying early developmental differences among neurodevelopmental disorders with distinct etiologies is critical to improving early identification and tailored intervention during the first years of life. Recent studies have uncovered important differences between infants with fragile X syndrome (FXS) and infants with familial history of autism spectrum disorder who go on to develop autism themselves (FH-ASD), including differences in brain development and behavior. Thus far, there have been no studies longitudinally investigating differential developmental skill profiles in FXS and FH-ASD infants. METHODS: The current study contrasted longitudinal trajectories of verbal (expressive and receptive language) and nonverbal (gross and fine motor, visual reception) skills in FXS and FH-ASD infants, compared to FH infants who did not develop ASD (FH-nonASD) and typically developing controls. RESULTS: Infants with FXS showed delays on a nonverbal composite compared to FH-ASD (as well as FH-nonASD and control) infants as early as 6 months of age. By 12 months an ordinal pattern of scores was established between groups on all domains tested, such that controls > FH-nonASD > FH-ASD > FXS. This pattern persisted through 24 months. Cognitive level differentially influenced developmental trajectories for FXS and FH-ASD. CONCLUSIONS: Our results demonstrate detectable group differences by 6 months between FXS and FH-ASD as well as differential trajectories on each domain throughout infancy. This work further highlights an earlier onset of global cognitive delays in FXS and, conversely, a protracted period of more slowly emerging delays in FH-ASD. Divergent neural and cognitive development in infancy between FXS and FH-ASD contributes to our understanding of important distinctions in the development and behavioral phenotype of these two groups.

Inequality-Constrained 3D Morphable Face Model Fitting.

3D morphable model (3DMM) fitting on 2D data is traditionally done via unconstrained optimization with regularization terms to ensure that the result is a plausible face shape and is consistent with a set of 2D landmarks. This paper presents inequality-constrained 3DMM fitting as the first alternative to regularization in optimization-based 3DMM fitting. Inequality constraints on the 3DMM's shape coefficients ensure face-like shapes without modifying the objective function for smoothness, thus allowing for more flexibility to capture person-specific shape details. Moreover, inequality constraints on landmarks increase robustness in a way that does not require per-image tuning. We show that the proposed method stands out with its ability to estimate person-specific face shapes by jointly fitting a 3DMM to multiple frames of a person. Further, when used with a robust objective function, namely gradient correlation, the method can work "in-the-wild" even with a 3DMM constructed from controlled data. Lastly, we show how to use the log-barrier method to efficiently implement the method. To our knowledge, we present the first 3DMM fitting framework that requires no learning yet is accurate, robust, and efficient. The absence of learning enables a generic solution that allows flexibility in the input image size, interchangeable morphable models, and incorporation of camera matrix.

A global multicohort study to map subcortical brain development and cognition in infancy and early childhood.

The human brain grows quickly during infancy and early childhood, but factors influencing brain maturation in this period remain poorly understood. To address this gap, we harmonized data from eight diverse cohorts, creating one of the largest pediatric neuroimaging datasets to date focused on birth to 6 years of age. We mapped the developmental trajectory of intracranial and subcortical volumes in ∼2,000 children and studied how sociodemographic factors and adverse birth outcomes influence brain structure and cognition. The amygdala was the first subcortical volume to mature, whereas the thalamus exhibited protracted development. Males had larger brain volumes than females, and children born preterm or with low birthweight showed catch-up growth with age. Socioeconomic factors exerted region- and time-specific effects. Regarding cognition, males scored lower than females; preterm birth affected all developmental areas tested, and socioeconomic factors affected visual reception and receptive language. Brain-cognition correlations revealed region-specific associations.

Associations between early trajectories of amygdala development and later school-age anxiety in two longitudinal samples.

Amygdala function is implicated in the pathogenesis of autism spectrum disorder (ASD) and anxiety. We investigated associations between early trajectories of amygdala growth and anxiety and ASD outcomes at school age in two longitudinal studies: high- and low-familial likelihood for ASD, Infant Brain Imaging Study (IBIS, n = 257) and typically developing (TD) community sample, Early Brain Development Study (EBDS, n = 158). Infants underwent MRI scanning at up to 3 timepoints from neonate to 24 months. Anxiety was assessed at 6-12 years. Linear multilevel modeling tested whether amygdala volume growth was associated with anxiety symptoms at school age. In the IBIS sample, children with higher anxiety showed accelerated amygdala growth from 6 to 24 months. ASD diagnosis and ASD familial likelihood were not significant predictors. In the EBDS sample, amygdala growth from birth to 24 months was associated with anxiety. More anxious children had smaller amygdala volume and slower rates of amygdala growth. We explore reasons for the contrasting results between high-familial likelihood for ASD and TD samples, grounding results in the broader literature of variable associations between early amygdala volume and later anxiety. Results have the potential to identify mechanisms linking early amygdala growth to later anxiety in certain groups.

Comparison of Human Experts and AI in Predicting Autism from Facial Behavior.

Advances in computational behavior analysis via artificial intelligence (AI) promise to improve mental healthcare services by providing clinicians with tools to assist diagnosis or measurement of treatment outcomes. This potential has spurred an increasing number of studies in which automated pipelines predict diagnoses of mental health conditions. However, a fundamental question remains unanswered: How do the predictions of the AI algorithms correspond and compare with the predictions of humans? This is a critical question if AI technology is to be used as an assistive tool, because the utility of an AI algorithm would be negligible if it provides little information beyond what clinicians can readily infer. In this paper, we compare the performance of 19 human raters (8 autism experts and 11 non-experts) and that of an AI algorithm in terms of predicting autism diagnosis from short (3-minute) videos of N = 42 participants in a naturalistic conversation. Results show that the AI algorithm achieves an average accuracy of 80.5%, which is comparable to that of clinicians with expertise in autism (83.1%) and clinical research staff without specialized expertise (78.3%). Critically, diagnoses that were inaccurately predicted by most humans (experts and non-experts, alike) were typically correctly predicted by AI. Our results highlight the potential of AI as an assistive tool that can augment clinician diagnostic decision-making.

Enlarged Perivascular Spaces in Infancy and Autism Diagnosis, Cerebrospinal Fluid Volume, and Later Sleep Problems.

Importance: Perivascular spaces (PVS) and cerebrospinal fluid (CSF) are essential components of the glymphatic system, regulating brain homeostasis and clearing neural waste throughout the lifespan. Enlarged PVS have been implicated in neurological disorders and sleep problems in adults, and excessive CSF volume has been reported in infants who develop autism. Enlarged PVS have not been sufficiently studied longitudinally in infancy or in relation to autism outcomes or CSF volume. Objective: To examine whether enlarged PVS are more prevalent in infants who develop autism compared with controls and whether they are associated with trajectories of extra-axial CSF volume (EA-CSF) and sleep problems in later childhood. Design, Setting, and Participants: This prospective, longitudinal cohort study used data from the Infant Brain Imaging Study. Magnetic resonance images were acquired at ages 6, 12, and 24 months (2007-2017), with sleep questionnaires performed between ages 7 and 12 years (starting in 2018). Data were collected at 4 sites in North Carolina, Missouri, Pennsylvania, and Washington. Data were analyzed from March 2021 through August 2022. Exposure: PVS (ie, fluid-filled channels that surround blood vessels in the brain) that are enlarged (ie, visible on magnetic resonance imaging). Main Outcomes and Measures: Outcomes of interest were enlarged PVS and EA-CSF volume from 6 to 24 months, autism diagnosis at 24 months, sleep problems between ages 7 and 12 years. Results: A total of 311 infants (197 [63.3%] male) were included: 47 infants at high familial likelihood for autism (ie, having an older sibling with autism) who were diagnosed with autism at age 24 months, 180 high likelihood infants not diagnosed with autism, and 84 low likelihood control infants not diagnosed with autism. Sleep measures at school-age were available for 109 participants. Of infants who developed autism, 21 (44.7%) had enlarged PVS at 24 months compared with 48 infants (26.7%) in the high likelihood but no autism diagnosis group (P = .02) and 22 infants in the control group (26.2%) (P = .03). Across all groups, enlarged PVS at 24 months was associated with greater EA-CSF volume from ages 6 to 24 months (ß = 4.64; 95% CI, 0.58-8.72; P = .002) and more frequent night wakings at school-age (F = 7.76; η2 = 0.08; P = .006). Conclusions and Relevance: These findings suggest that enlarged PVS emerged between ages 12 and 24 months in infants who developed autism. These results add to a growing body of evidence that, along with excessive CSF volume and sleep dysfunction, the glymphatic system could be dysregulated in infants who develop autism.

Measuring Dyspraxia in Autism using a Five-Minute Praxis Exam.

Background: Difficulties with praxis, the ability to perform learned skilled movements, have been robustly demonstrated in autism spectrum disorder (autism). However, praxis assessment is not routinely included in autism characterization batteries, in part because it is traditionally time consuming to administer and score. We test whether dyspraxia in autism can be captured with a brief measure. Method: Youth with autism (n = 41) and matched typically developing controls (n = 32), aged 8 to 16 years, completed a 5-minute praxis battery. The 19-item battery included four subtests: gesture to command, tool use, familiar imitation, and meaningless imitation. Video recordings were coded for error types and compared to participant characterization variables. Results: Consistent with research using a lengthy battery, autistic youth made more errors overall, with a large effect size. Groups demonstrated similar distributions of error types, suggesting that dyspraxia in autism is not limited to a particular error form. In the autism group, praxis was associated with adaptive functioning, but not autism traits. Conclusions: A shortened battery is sufficiently sensitive to praxis differences between autistic and typically developing youth, increasing the feasibility of including praxis within clinical assessments or larger research batteries aimed at testing relationships with downstream skills.

Association of Sex With Neurobehavioral Markers of Executive Function in 2-Year-Olds at High and Low Likelihood of Autism.

Importance: Children with autism and their siblings exhibit executive function (EF) deficits early in development, but associations between EF and biological sex or early brain alterations in this population are largely unexplored. Objective: To investigate the interaction of sex, autism likelihood group, and structural magnetic resonance imaging alterations on EF in 2-year-old children at high familial likelihood (HL) and low familial likelihood (LL) of autism, based on having an older sibling with autism or no family history of autism in first-degree relatives. Design, Setting, and Participants: This prospective cohort study assessed 165 toddlers at HL (n = 110) and LL (n = 55) of autism at 4 university-based research centers. Data were collected from January 1, 2007, to December 31, 2013, and analyzed between August 2021 and June 2022 as part of the Infant Brain Imaging Study. Main Outcomes and Measures: Direct assessments of EF and acquired structural magnetic resonance imaging were performed to determine frontal lobe, parietal lobe, and total cerebral brain volume. Results: A total of 165 toddlers (mean [SD] age, 24.61 [0.95] months; 90 [54%] male, 137 [83%] White) at HL for autism (n = 110; 17 diagnosed with ASD) and LL for autism (n = 55) were studied. The toddlers at HL for autism scored lower than the toddlers at LL for autism on EF tests regardless of sex (mean [SE] B = -8.77 [4.21]; 95% CI, -17.09 to -0.45; η2p = 0.03). With the exclusion of toddlers with autism, no group (HL vs LL) difference in EF was found in boys (mean [SE] difference, -7.18 [4.26]; 95% CI, 1.24-15.59), but EF was lower in HL girls than LL girls (mean [SE] difference, -9.75 [4.34]; 95% CI, -18.32 to -1.18). Brain-behavior associations were examined, controlling for overall cerebral volume and developmental level. Sex differences in EF-frontal (B [SE] = 16.51 [7.43]; 95% CI, 1.36-31.67; η2p = 0.14) and EF-parietal (B [SE] = 17.68 [6.99]; 95% CI, 3.43-31.94; η2p = 0.17) associations were found in the LL group but not the HL group (EF-frontal: B [SE] = -1.36 [3.87]; 95% CI, -9.07 to 6.35; η2p = 0.00; EF-parietal: B [SE] = -2.81 [4.09]; 95% CI, -10.96 to 5.34; η2p = 0.01). Autism likelihood group differences in EF-frontal (B [SE] = -9.93 [4.88]; 95% CI, -19.73 to -0.12; η2p = 0.08) and EF-parietal (B [SE] = -15.44 [5.18]; 95% CI, -25.86 to -5.02; η2p = 0.16) associations were found in girls not boys (EF-frontal: B [SE] = 6.51 [5.88]; 95% CI, -5.26 to 18.27; η2p = 0.02; EF-parietal: B [SE] = 4.18 [5.48]; 95% CI, -6.78 to 15.15; η2p = 0.01). Conclusions and Relevance: This cohort study of toddlers at HL and LL of autism suggests that there is an association between sex and EF and that brain-behavior associations in EF may be altered in children at HL of autism. Furthermore, EF deficits may aggregate in families, particularly in girls.

Sensory Profiles in Relation to Later Adaptive Functioning Among Toddlers at High-Familial Likelihood for Autism.

This study investigated the extent to which sensory responsivity in infancy contributes to adaptive behavior development among toddlers at high-familial likelihood for autism. Prospective, longitudinal data were analyzed for 218 children, 58 of whom received an autism diagnosis. Results indicated that sensory profiles at age one year (hyperresponsivity, sensory seeking) were negatively associated with later adaptive behavior, particularly for socialization, at age 3 years regardless of diagnostic status. These results suggest that early differences in sensory responsivity may have downstream developmental consequences related to social development among young children with high-familial likelihood for autism.

Sex differences in the temporal dynamics of autistic children's natural conversations.

BACKGROUND: Autistic girls are underdiagnosed compared to autistic boys, even when they experience similar clinical impact. Research suggests that girls present with distinct symptom profiles across a variety of domains, such as language, which may contribute to their underdiagnosis. In this study, we examine sex differences in the temporal dynamics of natural conversations between naïve adult confederates and school-aged children with or without autism, with the goal of improving our understanding of conversational behavior in autistic girls and ultimately improving identification. METHODS: Forty-five school-aged children with autism (29 boys and 16 girls) and 47 non-autistic/neurotypical (NT) children (23 boys and 24 girls) engaged in a 5-min "get-to-know-you" conversation with a young adult confederate that was unaware of children's diagnostic status. Groups were matched on IQ estimates. Recordings were time-aligned and orthographically transcribed by trained annotators. Several speech and pause measures were calculated. Groups were compared using analysis of covariance models, controlling for age. RESULTS: Autistic girls used significantly more words than autistic boys, and produced longer speech segments than all other groups. Autistic boys spoke more slowly than NT children, whereas autistic girls did not differ from NT children in total word counts or speaking rate. Autistic boys interrupted confederates' speech less often and produced longer between-turn pauses (i.e., responded more slowly when it was their turn) compared to other children. Within-turn pause duration did not differ by group. LIMITATIONS: Our sample included verbally fluent children and adolescents aged 6-15 years, so our study results may not replicate in samples of younger children, adults, and individuals who are not verbally fluent. The results of this relatively small study, while compelling, should be interpreted with caution and replicated in a larger sample. CONCLUSION: This study investigated the temporal dynamics of everyday conversations and demonstrated that autistic girls and boys have distinct natural language profiles. Specifying differences in verbal communication lays the groundwork for the development of sensitive screening and diagnostic tools to more accurately identify autistic girls, and could inform future personalized interventions that improve short- and long-term social communication outcomes for all autistic children.

A Prospective Evaluation of Infant Cerebellar-Cerebral Functional Connectivity in Relation to Behavioral Development in Autism Spectrum Disorder.

Background: Autism spectrum disorder (ASD) is a neurodevelopmental disorder diagnosed based on social impairment, restricted interests, and repetitive behaviors. Contemporary theories posit that cerebellar pathology contributes causally to ASD by disrupting error-based learning (EBL) during infancy. The present study represents the first test of this theory in a prospective infant sample, with potential implications for ASD detection. Methods: Data from the Infant Brain Imaging Study (n = 94, 68 male) were used to examine 6-month cerebellar functional connectivity magnetic resonance imaging in relation to later (12/24-month) ASD-associated behaviors and outcomes. Hypothesis-driven univariate analyses and machine learning-based predictive tests examined cerebellar-frontoparietal network (FPN; subserves error signaling in support of EBL) and cerebellar-default mode network (DMN; broadly implicated in ASD) connections. Cerebellar-FPN functional connectivity was used as a proxy for EBL, and cerebellar-DMN functional connectivity provided a comparative foil. Data-driven functional connectivity magnetic resonance imaging enrichment examined brain-wide behavioral associations, with post hoc tests of cerebellar connections. Results: Cerebellar-FPN and cerebellar-DMN connections did not demonstrate associations with ASD. Functional connectivity magnetic resonance imaging enrichment identified 6-month correlates of later ASD-associated behaviors in networks of a priori interest (FPN, DMN), as well as in cingulo-opercular (also implicated in error signaling) and medial visual networks. Post hoc tests did not suggest a role for cerebellar connections. Conclusions: We failed to identify cerebellar functional connectivity-based contributions to ASD. However, we observed prospective correlates of ASD-associated behaviors in networks that support EBL. Future studies may replicate and extend network-level positive results, and tests of the cerebellum may investigate brain-behavior associations at different developmental stages and/or using different neuroimaging modalities.

Quantifying latent social motivation and its associations with joint attention and language in infants at high and low likelihood for autism spectrum disorder.

Social motivation-the psychobiological predisposition for social orienting, seeking social contact, and maintaining social interaction-manifests in early infancy and is hypothesized to be foundational for social communication development in typical and atypical populations. However, the lack of infant social-motivation measures has hindered delineation of associations between infant social motivation, other early-arising social abilities such as joint attention, and language outcomes. To investigate how infant social motivation contributes to joint attention and language, this study utilizes a mixed longitudinal sample of 741 infants at high (HL = 515) and low (LL = 226) likelihood for ASD. Using moderated nonlinear factor analysis (MNLFA), we incorporated items from parent-report measures to establish a novel latent factor model of infant social motivation that exhibits measurement invariance by age, sex, and familial ASD likelihood. We then examined developmental associations between 6- and 12-month social motivation, joint attention at 12-15 months, and language at 24 months of age. On average, greater social-motivation growth from 6-12 months was associated with greater initiating joint attention (IJA) and trend-level increases in sophistication of responding to joint attention (RJA). IJA and RJA were both positively associated with 24-month language abilities. There were no additional associations between social motivation and future language in our path model. These findings substantiate a novel, theoretically driven approach to modeling social motivation and suggest a developmental cascade through which social motivation impacts other foundational skills. These findings have implications for the timing and nature of intervention targets to support social communication development in infancy. HIGHLIGHTS: We describe a novel, theoretically based model of infant social motivation wherein multiple parent-reported indicators contribute to a unitary latent social-motivation factor. Analyses revealed social-motivation factor scores exhibited measurement invariance for a longitudinal sample of infants at high and low familial ASD likelihood. Social-motivation growth from ages 6-12 months is associated with better 12-15-month joint attention abilities, which in turn are associated with greater 24-month language skills. Findings inform timing and targets of potential interventions to support healthy social communication in the first year of life.

Sleep problems in autism: Sex differences in the school-age population.

Clinically significant sleep problems affect up to 86% of the autistic population in school-age. Sleep problems can have negative impacts on child cognition, behavior, and health. However, sex differences in the prevalence and types of sleep problems are not well understood in autism. To evaluate sex differences in sleep problems in the school-age autistic population, we obtained parent-report of sleep problems on the Children's Sleep Habits Questionnaire and conducted direct assessments to establish diagnosis and intellectual ability in 6-12-year-old children (autism n = 250; typical development [TD] n = 114). Almost 85% of autistic females demonstrated sleep problems compared to 65.8% of autistic males, 44.8% of TD females, and 42.4% of TD males; a statistically significant increase for autistic females. Autistic females demonstrated increased bedtime resistance, sleep anxiety, and sleepiness, and decreased sleep duration, but did not differ in sleep onset delay, night wakings, parasomnias, or disordered breathing compared with autistic males. Intellectual ability was not related to increased sleep problems. Higher anxiety scores were associated with more sleep problems for males but not females. In one of the first studies to evaluate sex differences in sleep in the school-age, autistic population, autistic females demonstrated increased sleep problems compared to autistic males, TD females, and TD males. Current autism assessment and intervention practices may benefit from increased attention to sleep problems in autistic school-age females and to anxiety in autistic males to enhance well-being and behavioral and health outcomes.

Predicting Autism from Head Movement Patterns during Naturalistic Social Interactions.

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized in part by difficulties in verbal and nonverbal social communication. Evidence indicates that autistic people, compared to neurotypical peers, exhibit differences in head movements, a key form of nonverbal communication. Despite the crucial role of head movements in social communication, research on this nonverbal cue is relatively scarce compared to other forms of nonverbal communication, such as facial expressions and gestures. There is a need for scalable, reliable, and accurate instruments for measuring head movements directly within the context of social interactions. In this study, we used computer vision and machine learning to examine the head movement patterns of neurotypical and autistic individuals during naturalistic, face-to-face conversations, at both the individual (monadic) and interpersonal (dyadic) levels. Our model predicts diagnostic status using dyadic head movement data with an accuracy of 80%, highlighting the value of head movement as a marker of social communication. The monadic data pipeline had lower accuracy (69.2%) compared to the dyadic approach, emphasizing the importance of studying back-and-forth social communication cues within a true social context. The proposed classifier is not intended for diagnostic purposes, and future research should replicate our findings in larger, more representative samples.

Meta-evaluation for 3D Face Reconstruction Via Synthetic Data.

The standard benchmark metric for 3D face reconstruction is the geometric error between reconstructed meshes and the ground truth. Nearly all recent reconstruction methods are validated on real ground truth scans, in which case one needs to establish point correspondence prior to error computation, which is typically done with the Chamfer (i.e., nearest neighbor) criterion. However, a simple yet fundamental question have not been asked: Is the Chamfer error an appropriate and fair benchmark metric for 3D face reconstruction? More generally, how can we determine which error estimator is a better benchmark metric? We present a meta-evaluation framework that uses synthetic data to evaluate the quality of a geometric error estimator as a benchmark metric for face reconstruction. Further, we use this framework to experimentally compare four geometric error estimators. Results show that the standard approach not only severely underestimates the error, but also does so inconsistently across reconstruction methods, to the point of even altering the ranking of the compared methods. Moreover, although non-rigid ICP leads to a metric with smaller estimation bias, it could still not correctly rank all compared reconstruction methods, and is significantly more time consuming than Chamfer. In sum, we show several issues present in the current benchmarking and propose a procedure using synthetic data to address these issues.

Social motivation in infancy is associated with familial recurrence of ASD.

Pre-diagnostic deficits in social motivation are hypothesized to contribute to autism spectrum disorder (ASD), a heritable neurodevelopmental condition. We evaluated psychometric properties of a social motivation index (SMI) using parent-report item-level data from 597 participants in a prospective cohort of infant siblings at high and low familial risk for ASD. We tested whether lower SMI scores at 6, 12, and 24 months were associated with a 24-month ASD diagnosis and whether social motivation's course differed relative to familial ASD liability. The SMI displayed good internal consistency and temporal stability. Children diagnosed with ASD displayed lower mean SMI T-scores at all ages and a decrease in mean T-scores across age. Lower group-level 6-month scores corresponded with higher familial ASD liability. Among high-risk infants, strong decline in SMI T-scores was associated with 10-fold odds of diagnosis. Infant social motivation is quantifiable by parental report, differentiates children with versus without later ASD by age 6 months, and tracks with familial ASD liability, consistent with a diagnostic and susceptibility marker of ASD. Early decrements and decline in social motivation indicate increased likelihood of ASD, highlighting social motivation's importance to risk assessment and clarification of the ontogeny of ASD.

A Data-Driven Approach in an Unbiased Sample Reveals Equivalent Sex Ratio of Autism Spectrum Disorder-Associated Impairment in Early Childhood.

BACKGROUND: Sex differences in the prevalence of neurodevelopmental disorders are particularly evident in autism spectrum disorder (ASD). Heterogeneous symptom presentation and the potential of measurement bias hinder early ASD detection in females and may contribute to discrepant prevalence estimates. We examined trajectories of social communication (SC) and restricted and repetitive behaviors (RRBs) in a sample of infant siblings of children with ASD, adjusting for age- and sex-based measurement bias. We hypothesized that leveraging a prospective elevated familial likelihood sample, deriving data-driven behavioral constructs, and accounting for measurement bias would reveal less discrepant sex ratios than are typically seen in ASD. METHODS: We conducted direct assessments of ASD symptoms at 6 to 9, 12 to 15, 24, and 36 to 60 months of age (total nobservations = 1254) with infant siblings of children with ASD (n = 377) and a lower ASD-familial-likelihood comparison group (n = 168; nobservations = 527). We established measurement invariance across age and sex for separate models of SC and RRB. We then conducted latent class growth mixture modeling with the longitudinal data and evaluated for sex differences in trajectory membership. RESULTS: We identified 2 latent classes in the SC and RRB models with equal sex ratios in the high-concern cluster for both SC and RRB. Sex differences were also observed in the SC high-concern cluster, indicating that girls classified as having elevated social concerns demonstrated milder symptoms than boys in this group. CONCLUSIONS: This novel approach for characterizing ASD symptom progression highlights the utility of assessing and adjusting for sex-related measurement bias and identifying sex-specific patterns of symptom emergence.

Calculating genetic risk for dysfunction in pleiotropic biological processes using whole exome sequencing data.

BACKGROUND: Numerous genes are implicated in autism spectrum disorder (ASD). ASD encompasses a wide-range and severity of symptoms and co-occurring conditions; however, the details of how genetic variation contributes to phenotypic differences are unclear. This creates a challenge for translating genetic evidence into clinically useful knowledge. Sleep disturbances are particularly prevalent co-occurring conditions in ASD, and genetics may inform treatment. Identifying convergent mechanisms with evidence for dysfunction that connect ASD and sleep biology could help identify better treatments for sleep disturbances in these individuals. METHODS: To identify mechanisms that influence risk for ASD and co-occurring sleep disturbances, we analyzed whole exome sequence data from individuals in the Simons Simplex Collection (n = 2380). We predicted protein damaging variants (PDVs) in genes currently implicated in either ASD or sleep duration in typically developing children. We predicted a network of ASD-related proteins with direct evidence for interaction with sleep duration-related proteins encoded by genes with PDVs. Overrepresentation analyses of Gene Ontology-defined biological processes were conducted on the resulting gene set. We calculated the likelihood of dysfunction in the top overrepresented biological process. We then tested if scores reflecting genetic dysfunction in the process were associated with parent-reported sleep duration. RESULTS: There were 29 genes with PDVs in the ASD dataset where variation was reported in the literature to be associated with both ASD and sleep duration. A network of 108 proteins encoded by ASD and sleep duration candidate genes with PDVs was identified. The mechanism overrepresented in PDV-containing genes that encode proteins in the interaction network with the most evidence for dysfunction was cerebral cortex development (GO:0,021,987). Scores reflecting dysfunction in this process were associated with sleep durations; the largest effects were observed in adolescents (p = 4.65 × 10-3). CONCLUSIONS: Our bioinformatic-driven approach detected a biological process enriched for genes encoding a protein-protein interaction network linking ASD gene products with sleep duration gene products where accumulation of potentially damaging variants in individuals with ASD was associated with sleep duration as reported by the parents. Specifically, genetic dysfunction impacting development of the cerebral cortex may affect sleep by disrupting sleep homeostasis which is evidenced to be regulated by this brain region. Future functional assessments and objective measurements of sleep in adolescents with ASD could provide the basis for more informed treatment of sleep problems in these individuals.