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IntroductionMuch of our understanding of infant psychological development relies on an in-person, laboratory-based assessment. This limits research generalizability, scalability, and equity in access. One solution is the development of new, remotely deployed assessment tools that do not require real-time experimenter supervision.MethodsThe current nationwide (Sweden) infant twin study assessed participants remotely via their caregiver's tablets (N = 104, ages 3 to 17 months). To anchor our findings in previous research, we used a gaze-following task where experimental and age effects are well established.ResultsClosely mimicking results from conventional eye tracking, we found that a full head movement elicited more gaze following than isolated eye movements. Furthermore, predictably, we found that older infants followed gaze more frequently than younger infants. Finally, while we found no indication of genetic contributions to gaze-following accuracy, the latency to disengage from the gaze cue and orient toward a target was significantly more similar in monozygotic twins than in dizygotic twins, an indicative of heritability.DiscussionTogether, these results highlight the potential of remote assessment of infants' psychological development, which can improve generalizability, inclusion, and scalability in developmental research.

Abstract Background Social attention differences, expressed through gaze patterns, have been documented in autism spectrum disorder (ASD), with subtle differences also reported among first-degree relatives, suggesting a shared genetic link. Findings have mostly been derived from standard eye-tracking methods (total fixation count or total fixation duration). Given the dynamics of visual attention, these standard methods may obscure subtle, yet core, differences in visual attention mechanisms, particularly those presenting sub-clinically. This study applied a constellation of eye-tracking analyses to gaze data from individuals with ASD and their parents. Methods This study included n = 156 participants across groups, including ASD (n = 24) and control (n = 32) groups, and parents of individuals with ASD (n = 61) and control parents (n = 39). A complex scene with social/non-social elements was displayed and gaze tracked via an eye tracker. Eleven analytic methods from the following categories were analyzed: (1) standard variables, (2) temporal dynamics (e.g., gaze over time), (3) fixation patterns (e.g., perseverative or regressive fixations), (4) first fixations, and (5) distribution patterns. MANOVAs, growth curve analyses, and Chi-squared tests were applied to examine group differences. Finally, group differences were examined on component scores derived from a principal component analysis (PCA) that reduced variables to distinct dimensions. Results No group differences emerged among standard, first fixation, and distribution pattern variables. Both the ASD and ASD parent groups demonstrated on average reduced social attention over time and atypical perseverative fixations. Lower social attention factor scores derived from PCA strongly differentiated the ASD and ASD parent groups from controls, with parent findings driven by the subset of parents demonstrating the broad autism phenotype. Limitations To generalize these findings, larger sample sizes, extended viewing contexts (e.g., dynamic stimuli), and even more eye-tracking analytical methods are needed. Conclusions Fixations over time and perseverative fixations differentiated ASD and the ASD parent groups from controls, with the PCA most robustly capturing social attention differences. Findings highlight their methodological utility in studies of the (broad) autism spectrum to capture nuanced visual attention differences that may relate to clinical symptoms in ASD, and reflect genetic liability in clinically unaffected relatives. This proof-of-concept study may inform future studies using eye tracking across populations where social attention is impacted.

Abstract Background Eye tracking (ET) is a powerful methodology for studying attentional processes through quantification of eye movements. The precision, usability, and cost-effectiveness of ET render it a promising platform for developing biomarkers for use in clinical trials for autism spectrum disorder (ASD). Methods The Autism Biomarkers Consortium for Clinical Trials conducted a multisite, observational study of 6–11-year-old children with ASD (n = 280) and typical development (TD, n = 119). The ET battery included: Activity Monitoring, Social Interactive, Static Social Scenes, Biological Motion Preference, and Pupillary Light Reflex tasks. A priori, gaze to faces in Activity Monitoring, Social Interactive, and Static Social Scenes tasks were aggregated into an Oculomotor Index of Gaze to Human Faces (OMI) as the primary outcome measure. This work reports on fundamental biomarker properties (data acquisition rates, construct validity, six-week stability, group discrimination, and clinical relationships) derived from these assays that serve as a base for subsequent development of clinical trial biomarker applications. Results All tasks exhibited excellent acquisition rates, met expectations for construct validity, had moderate or high six-week stabilities, and highlighted subsets of the ASD group with distinct biomarker performance. Within ASD, higher OMI was associated with increased memory for faces, decreased autism symptom severity, and higher verbal IQ and pragmatic communication skills. Limitations No specific interventions were administered in this study, limiting information about how ET biomarkers track or predict outcomes in response to treatment. This study did not consider co-occurrence of psychiatric conditions nor specificity in comparison with non-ASD special populations, therefore limiting our understanding of the applicability of outcomes to specific clinical contexts-of-use. Research-grade protocols and equipment were used; further studies are needed to explore deployment in less standardized contexts. Conclusions All ET tasks met expectations regarding biomarker properties, with strongest performance for tasks associated with attention to human faces and weakest performance associated with biological motion preference. Based on these data, the OMI has been accepted to the FDA’s Biomarker Qualification program, providing a path for advancing efforts to develop biomarkers for use in clinical trials.

Recent proposals have suggested the potential for neural biomarkers to improve clinical trial processes in neurodevelopmental conditions; however, few efforts have identified whether chronological age-based adjustments will be necessary (as used in standardized behavioral assessments). Event-related potentials (ERPs) demonstrate early differences in the processing of faces vs. objects in the visual processing system by 4 years of age and age-based improvement (decreases in latency) through adolescence. Additionally, face processing has been proposed to be related to social skills as well as autistic social-communication traits. While previous reports suggest delayed latency in individuals with autism spectrum disorder (ASD), extensive individual and age based heterogeneity exists. In this report, we utilize a sample of 252 children with ASD and 118 children with typical development (TD), to assess the N170 and P100 ERP component latencies (N170L and P100L, respectively), to upright faces, the face specificity effect (difference between face and object processing), and the inversion effect (difference between face upright and inverted processing) in relation to age. First, linear mixed models (LMMs) were fitted with fixed effect of age at testing and random effect of participant, using all available data points to characterize general age-based development in the TD and ASD groups. Second, LMM models using only the TD group were used to calculate age-based residuals in both groups. The purpose of residualization was to assess how much variation in ASD participants could be accounted for by chronological age-related changes. Our data demonstrate that the N170L and P100L responses to upright faces appeared to follow a roughly linear relationship with age. In the ASD group, the distribution of the age-adjusted residual values suggest that ASD participants were more likely to demonstrate slower latencies than would be expected for a TD child of the same age, similar to what has been identified using unadjusted values. Lastly, using age-adjusted values for stratification, we found that children who demonstrated slowed age-adjusted N170L had lower verbal and non-verbal IQ and worse face memory. These data suggest that age must be considered in assessing the N170L and P100L response to upright faces as well, and these adjusted values may be used to stratify children within the autism spectrum.

Abstract Background Diminished visual monitoring of faces and activities of others is an early feature of autism spectrum disorder (ASD). It is uncertain whether deficits in activity monitoring, identified using a homogeneous set of stimuli, persist throughout the lifespan in ASD, and thus, whether they could serve as a biological indicator (“biomarker”) of ASD. We investigated differences in visual attention during activity monitoring in children and adult participants with autism compared to a control group of participants without autism. Methods Eye movements of participants with autism (n = 122; mean age [SD] = 14.5 [8.0] years) and typically developing (TD) controls (n = 40, age = 16.4 [13.3] years) were recorded while they viewed a series of videos depicting two female actors conversing while interacting with their hands over a shared task. Actors either continuously focused their gaze on each other’s face (mutual gaze) or on the shared activity area (shared focus). Mean percentage looking time was computed for the activity area, actors’ heads, and their bodies. Results Compared to TD participants, participants with ASD looked longer at the activity area (mean % looking time: 58.5% vs. 53.8%, p 60] limited the ability to include individuals with substantial intellectual disability. Conclusions Differences in attention to faces could constitute a feature discriminative between individuals with and without ASD across the lifespan, whereas between-group differences in looking at activities may shift with development. These findings may have applications in the search for underlying biological indicators specific to ASD. Trial registration ClinicalTrials.gov identifier NCT02668991.

Abstract Background Reduction or differences in facial expression are a core diagnostic feature of autism spectrum disorder (ASD), yet evidence regarding the extent of this discrepancy is limited and inconsistent. Use of automated facial expression detection technology enables accurate and efficient tracking of facial expressions that has potential to identify individual response differences. Methods Children and adults with ASD (N = 124) and typically developing (TD, N = 41) were shown short clips of “funny videos.” Using automated facial analysis software, we investigated differences between ASD and TD groups and within the ASD group in evidence of facial action unit (AU) activation related to the expression of positive facial expression, in particular, a smile. Results Individuals with ASD on average showed less evidence of facial AUs (AU12, AU6) relating to positive facial expression, compared to the TD group (p < .05, r = − 0.17). Using Gaussian mixture model for clustering, we identified two distinct distributions within the ASD group, which were then compared to the TD group. One subgroup (n = 35), termed “over-responsive,” expressed more intense positive facial expressions in response to the videos than the TD group (p < .001, r = 0.31). The second subgroup (n = 89), (“under-responsive”), displayed fewer, less intense positive facial expressions in response to videos than the TD group (p < .001; r = − 0.36). The over-responsive subgroup differed from the under-responsive subgroup in age and caregiver-reported impulsivity (p < .05, r = 0.21). Reduced expression in the under-responsive, but not the over-responsive group, was related to caregiver-reported social withdrawal (p < .01, r = − 0.3). Limitations This exploratory study does not account for multiple comparisons, and future work will have to ascertain the strength and reproducibility of all results. Reduced displays of positive facial expressions do not mean individuals with ASD do not experience positive emotions. Conclusions Individuals with ASD differed from the TD group in their facial expressions of positive emotion in response to “funny videos.” Identification of subgroups based on response may help in parsing heterogeneity in ASD and enable targeting of treatment based on subtypes. Trial registration ClinicalTrials.gov , NCT02299700 . Registration date: November 24, 2014

Biomarker development is currently a high priority in neurodevelopmental disorder research. For many types of biomarkers (particularly biomarkers of diagnosis), reliability over short periods is critically important. In the field of autism spectrum disorder (ASD), resting electroencephalography (EEG) power spectral densities (PSD) are well-studied for their potential as biomarkers. Classically, such data have been decomposed into pre-specified frequency bands (e.g., delta, theta, alpha, beta, and gamma). Recent technical advances, such as the Fitting Oscillations and One-Over-F (FOOOF) algorithm, allow for targeted characterization of the features that naturally emerge within an EEG PSD, permitting a more detailed characterization of the frequency band-agnostic shape of each individual’s EEG PSD. Here, using two resting EEGs collected a median of 6 days apart from 22 children with ASD and 25 typically developing (TD) controls during the Feasibility Visit of the Autism Biomarkers Consortium for Clinical Trials, we estimate test-retest reliability based on the characterization of the PSD shape in two ways: (1) Using the FOOOF algorithm we estimate six parameters (offset, slope, number of peaks, and amplitude, center frequency and bandwidth of the largest alpha peak) that characterize the shape of the EEG PSD; and (2) using nonparametric functional data analyses, we decompose the shape of the EEG PSD into a reduced set of basis functions that characterize individual power spectrum shapes. We show that individuals exhibit idiosyncratic PSD signatures that are stable over recording sessions using both characterizations. Our data show that EEG activity from a brief 2-min recording provides an efficient window into characterizing brain activity at the single-subject level with desirable psychometric characteristics that persist across different analytical decomposition methods. This is a necessary step towards analytical validation of biomarkers based on the EEG PSD and provides insights into parameters of the PSD that offer short-term reliability (and thus promise as potential biomarkers of trait or diagnosis) vs. those that are more variable over the short term (and thus may index state or other rapidly dynamic measures of brain function). Future research should address the longer-term stability of the PSD, for purposes such as monitoring development or response to treatment.

Clinical research in neurodevelopmental disorders remains reliant upon clinician and caregiver measures. Limitations of these approaches indicate a need for objective, quantitative, and reliable biomarkers to advance clinical research. Extant research suggests the potential utility of multiple candidate biomarkers; however, effective application of these markers in trials requires additional understanding of replicability, individual differences, and intra-individual stability over time. The Autism Biomarkers Consortium for Clinical Trials (ABC-CT) is a multi-site study designed to investigate a battery of electrophysiological (EEG) and eye-tracking (ET) indices as candidate biomarkers for autism spectrum disorder (ASD). The study complements published biomarker research through: inclusion of large, deeply phenotyped cohorts of children with ASD and typical development; a longitudinal design; a focus on well-evidenced candidate biomarkers harmonized with an independent sample; high levels of clinical, regulatory, technical, and statistical rigor; adoption of a governance structure incorporating diverse expertise in the ASD biomarker discovery and qualification process; prioritization of open science, including creation of a repository containing biomarker, clinical, and genetic data; and use of economical and scalable technologies that are applicable in developmental populations and those with special needs. The ABC-CT approach has yielded encouraging results, with one measure accepted into the FDA’s Biomarker Qualification Program to date. Through these advances, the ABC-CT and other biomarker studies in progress hold promise to deliver novel tools to improve clinical trials research in ASD.

ObjectiveThe relationship between sleep (caregiver-reported and actigraphy-measured) and other caregiver-reported behaviors in children and adults with autism spectrum disorder (ASD) was examined, including the use of machine learning to identify sleep variables important in predicting anxiety in ASD.MethodsCaregivers of ASD (n = 144) and typically developing (TD) (n = 41) participants reported on sleep and other behaviors. ASD participants wore an actigraphy device at nighttime during an 8 or 10-week non-interventional study. Mean and variability of actigraphy measures for ASD participants in the week preceding midpoint and endpoint were calculated and compared with caregiver-reported and clinician-reported symptoms using a mixed effects model. An elastic-net model was developed to examine which sleep measures may drive prediction of anxiety.ResultsPrevalence of caregiver-reported sleep difficulties in ASD was approximately 70% and correlated significantly (p < 0.05) with sleep efficiency measured by actigraphy. Mean and variability of actigraphy measures like sleep efficiency and number of awakenings were related significantly (p < 0.05) to ASD symptom severity, hyperactivity and anxiety. In the elastic net model, caregiver-reported sleep, and variability of sleep efficiency and awakenings were amongst the important predictors of anxiety.ConclusionCaregivers report problems with sleep in the majority of children and adults with ASD. Reported problems and actigraphy measures of sleep, particularly variability, are related to parent reported behaviors. Measuring variability in sleep may prove useful in understanding the relationship between sleep problems and behavior in individuals with ASD. These findings may have implications for both intervention and monitoring outcomes in ASD.

The objective of the Autism Biomarkers Consortium for Clinical Trials (ABC-CT) is to evaluate a set of lab-based behavioral video tracking (VT), electroencephalography (EEG), and eye tracking (ET) measures for use in clinical trials with children with autism spectrum disorder (ASD). Within the larger organizational structure of the ABC-CT, the Data Acquisition and Analytic Core (DAAC) oversees the standardization of VT, EEG, and ET data acquisition, data processing, and data analysis. This includes designing and documenting data acquisition and analytic protocols and manuals; facilitating site training in acquisition; data acquisition quality control (QC); derivation and validation of dependent variables (DVs); and analytic deliverables including preparation of data for submission to the National Database for Autism Research (NDAR). To oversee consistent application of scientific standards and methodological rigor for data acquisition, processing, and analytics, we developed standard operating procedures that reflect the logistical needs of multi-site research, and the need for well-articulated, transparent processes that can be implemented in future clinical trials. This report details the methodology of the ABC-CT related to acquisition and QC in our Feasibility and Main Study phases. Based on our acquisition metrics from a preplanned interim analysis, we report high levels of acquisition success utilizing VT, EEG, and ET experiments in a relatively large sample of children with ASD and typical development (TD), with data acquired across multiple sites and use of a manualized training and acquisition protocol.

Abstract Background Multiple eye-tracking studies have highlighted the “atypical” nature of social attention in autism. However, it is unclear how “atypical” or “typical” should be quantified. Methods We developed a method for identifying moments when members of a group looked at similar places (High-Cohesion Time Frames; HCTFs). We defined typicality as the proximity of gaze points to typically developing (TD) gaze points during TD HCTFs. Comparing toddlers with ASD (n = 112) to developmentally delayed (DD, n = 36) and TD (n = 163) toddlers during a video with Dyadic Bid, Sandwich-Making, Joint Attention, and Animated Toys conditions, we examined (a) individual typicality scores, (b) the relationship between typicality and symptom severity, and (c) HCTF distributions associated with each diagnostic group. Results The ASD group had lower gaze typicality scores compared to the TD and DD groups in the Dyadic Bid and Sandwich-Making conditions but not during Animated Toys. The DD and TD groups did not differ in any condition. Correlational analyses indicated that higher typicality scores were associated with increased looking at pre-planned locations of the scene indexed by each experimental condition. In the ASD group, lower gaze typicality was associated with more severe autism symptoms. Examining ASD HCTFs, the gaze of toddlers with ASD was least cohesive during Dyadic Bid and most cohesive during Animated Toys. Conclusion In contrast to non-ASD groups, toddlers with ASD show high cohesion during salient nonsocial events, suggesting that consistency in looking strategies may depend more on perceptual features. These findings are consequential for understanding individual differences in visual attention in ASD and for the design of more sensitive biomarker tasks for stratification, between-group differentiation, and measuring response to treatment.

Abstract Background Childhood disintegrative disorder (CDD) is a rare form of autism spectrum disorder (ASD) of unknown etiology. It is characterized by late-onset regression leading to significant intellectual disability (ID) and severe autism. Although there are phenotypic differences between CDD and other forms of ASD, it is unclear if there are neurobiological differences. Methods We pursued a multidisciplinary study of CDD (n = 17) and three comparison groups: low-functioning ASD (n = 12), high-functioning ASD (n = 50), and typically developing (n = 26) individuals. We performed whole-exome sequencing (WES), copy number variant (CNV), and gene expression analyses of CDD and, on subsets of each cohort, non-sedated functional magnetic resonance imaging (fMRI) while viewing socioemotional (faces) and non-socioemotional (houses) stimuli and eye tracking while viewing emotional faces. Results We observed potential differences between CDD and other forms of ASD. WES and CNV analyses identified one or more rare de novo, homozygous, and/or hemizygous (mother-to-son transmission on chrX) variants for most probands that were not shared by unaffected sibling controls. There were no clearly deleterious variants or highly recurrent candidate genes. Candidate genes that were found to be most conserved at variant position and most intolerant of variation, such as TRRAP, ZNF236, and KIAA2018, play a role or may be involved in transcription. Using the human BrainSpan transcriptome dataset, CDD candidate genes were found to be more highly expressed in non-neocortical regions than neocortical regions. This expression profile was similar to that of an independent cohort of ASD probands with regression. The non-neocortical regions overlapped with those identified by fMRI as abnormally hyperactive in response to viewing faces, such as the thalamus, cerebellum, caudate, and hippocampus. Eye-tracking analysis showed that, among individuals with ASD, subjects with CDD focused on eyes the most when shown pictures of faces. Conclusions Given that cohort sizes were limited by the rarity of CDD, and the challenges of conducting non-sedated fMRI and eye tracking in subjects with ASD and significant ID, this is an exploratory study designed to investigate the neurobiological features of CDD. In addition to reporting the first multimodal analysis of CDD, a combination of fMRI and eye-tracking analyses are being presented for the first time for low-functioning individuals with ASD. Our results suggest differences between CDD and other forms of ASD on the neurobiological as well as clinical level.

Infants are responsive to and show a preference for human vocalizations from very early in development. While previous studies have provided a strong foundation of understanding regarding areas of the infant brain that respond preferentially to social vs. non-social sounds, how the infant brain responds to sounds of varying social significance over time, and how this relates to behavior, is less well understood. The current study uniquely examined longitudinal brain responses to social sounds of differing social-communicative value in infants at 3 and 6 months of age using functional near-infrared spectroscopy (fNIRS). At 3 months, infants showed similar patterns of widespread activation in bilateral temporal cortices to communicative and non-communicative human non-speech vocalizations, while by 6 months infants showed more similar, and focal, responses to social sounds that carried increased social value (infant-directed speech and human non-speech communicative sounds). In addition, we found that brain activity at 3 months of age related to later brain activity and receptive language abilities as measured at 6 months. These findings suggest areas of consistency and change in auditory social perception between 3 and 6 months of age. Keywords: fNIRS, Infancy, Social perception, Brain development, Auditory stimuli

Objective: The Janssen Autism Knowledge Engine (JAKE®) is a clinical research outcomes assessment system developed to more sensitively measure treatment outcomes and identify subpopulations in autism spectrum disorder (ASD). Here we describe JAKE and present results from its digital phenotyping (My JAKE) and biosensor (JAKE Sense) components.Methods: An observational, non-interventional, prospective study of JAKE in children and adults with ASD was conducted at nine sites in the United States. Feedback on JAKE usability was obtained from caregivers. JAKE Sense included electroencephalography, eye tracking, electrocardiography, electrodermal activity, facial affect analysis, and actigraphy. Caregivers of individuals with ASD reported behaviors using My JAKE. Results from My JAKE and JAKE Sense were compared to traditional ASD symptom measures.Results: Individuals with ASD (N = 144) and a cohort of typically developing (TD) individuals (N = 41) participated in JAKE Sense. Most caregivers reported that overall use and utility of My JAKE was “easy” (69%, 74/108) or “very easy” (74%, 80/108). My JAKE could detect differences in ASD symptoms as measured by traditional methods. The majority of biosensors included in JAKE Sense captured sizable amounts of quality data (i.e., 93–100% of eye tracker, facial affect analysis, and electrocardiogram data was of good quality), demonstrated differences between TD and ASD individuals, and correlated with ASD symptom scales. No significant safety events were reported.Conclusions: My JAKE was viewed as easy or very easy to use by caregivers participating in research outside of a clinical study. My JAKE sensitively measured a broad range of ASD symptoms. JAKE Sense biosensors were well-tolerated. JAKE functioned well when used at clinical sites previously inexperienced with some of the technologies. Lessons from the study will optimize JAKE for use in clinical trials to assess ASD interventions. Additionally, because biosensors were able to detect features differentiating TD and ASD individuals, and also were correlated with standardized symptom scales, these measures could be explored as potential biomarkers for ASD and as endpoints in future clinical studies.Clinical Trial Registration:https://clinicaltrials.gov/ct2/show/NCT02668991 identifier: NCT02668991

Objective: To test usability and optimize the Janssen Autism Knowledge Engine (JAKE®) system's components, biosensors, and procedures used for objective measurement of core and associated symptoms of autism spectrum disorder (ASD) in clinical trials.Methods: A prospective, observational study of 29 children and adolescents with ASD using the JAKE system was conducted at three sites in the United States. This study was designed to establish the feasibility of the JAKE system and to learn practical aspects of its implementation. In addition to information collected by web and mobile components, wearable biosensor data were collected both continuously in natural settings and periodically during a battery of experimental tasks administered in laboratory settings. This study is registered at clinicaltrials.gov, NCT02299700.Results: Feedback collected throughout the study allowed future refinements to be planned for all components of the system. The Autism Behavior Inventory (ABI), a parent-reported measure of ASD core and associated symptoms, performed well. Among biosensors studied, the eye-tracker, sleep monitor, and electrocardiogram were shown to capture high quality data, whereas wireless electroencephalography was difficult to use due to its form factor. On an exit survey, the majority of parents rated their overall reaction to JAKE as positive/very positive. No significant device-related events were reported in the study.Conclusion: The results of this study, with the described changes, demonstrate that the JAKE system is a viable, useful, and safe platform for use in clinical trials of ASD, justifying larger validation and deployment studies of the optimized system.