The Autism Biomarkers Consortium for Clinical Trials: evaluation of a battery of candidate eye-tracking biomarkers for use in autism clinical trials
(Full-text capture 2026-09-21; web artifacts lightly stripped; truncated.)
Frederick Shic
1Center for Child Health, Behavior, and Development, Seattle Children’s Research Institute, 1920 Terry Ave, Seattle, WA 98101 USA
2Department of General Pediatrics, University of Washington School of Medicine, Seattle, WA USA
1,2,✉, Adam J Naples
Adam J Naples
3Yale Child Study Center, Yale University School of Medicine, 230 South Frontage Road, New Haven, CT 06520 USA
Erin C Barney
1Center for Child Health, Behavior, and Development, Seattle Children’s Research Institute, 1920 Terry Ave, Seattle, WA 98101 USA
3Yale Child Study Center, Yale University School of Medicine, 230 South Frontage Road, New Haven, CT 06520 USA
1,3, Shou An Chang
Shou An Chang
17Department of Psychology, Yale University, 2 Hillhouse Ave, New Haven, CT 06520 USA
17, Beibin Li
Beibin Li
1Center for Child Health, Behavior, and Development, Seattle Children’s Research Institute, 1920 Terry Ave, Seattle, WA 98101 USA
4Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA USA
1,4, Takumi McAllister
Takumi McAllister
3Yale Child Study Center, Yale University School of Medicine, 230 South Frontage Road, New Haven, CT 06520 USA
3, Minah Kim
Minah Kim
18Department of Psychology, University of Virginia, 102 Gilmer Hall, P.O. Box 400400, Charlottesville, VA 22904 USA
18, Kelsey J Dommer
Kelsey J Dommer
1Center for Child Health, Behavior, and Development, Seattle Children’s Research Institute, 1920 Terry Ave, Seattle, WA 98101 USA
Simone Hasselmo
3Yale Child Study Center, Yale University School of Medicine, 230 South Frontage Road, New Haven, CT 06520 USA
3, Adham Atyabi
Adham Atyabi
1Center for Child Health, Behavior, and Development, Seattle Children’s Research Institute, 1920 Terry Ave, Seattle, WA 98101 USA
2Department of General Pediatrics, University of Washington School of Medicine, Seattle, WA USA
5Department of Computer Science, University of Colorado - Colorado Springs, Colorado Springs, CO USA
1,2,5, Quan Wang
Quan Wang
3Yale Child Study Center, Yale University School of Medicine, 230 South Frontage Road, New Haven, CT 06520 USA
Gerhard Helleman
19Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, AL USA
19, April R Levin
April R Levin
7Department of Neurology, Boston Children’s Hospital, Boston, MA USA
8Harvard Medical School, Boston, MA USA
7,8, Helen Seow
Helen Seow
3Yale Child Study Center, Yale University School of Medicine, 230 South Frontage Road, New Haven, CT 06520 USA
Raphael Bernier
9Department of Psychiatry and Behavioral Science, University of Washington School of Medicine, Seattle, WA USA
Katarzyna Charwaska
3Yale Child Study Center, Yale University School of Medicine, 230 South Frontage Road, New Haven, CT 06520 USA
Geraldine Dawson
10Duke Center for Autism and Brain Development, Duke University, Durham, NC USA
10, James Dziura
James Dziura
11Emergency Medicine, Yale University School of Medicine, New Haven, CT USA
11, Susan Faja
Susan Faja
8Harvard Medical School, Boston, MA USA
12Department of Pediatrics, Boston Children’s Hospital, Boston, MA USA
8,12, Shafali Spurling Jeste
Shafali Spurling Jeste
13Present Address: Division of Neurology, Department of Pediatrics, Children’s Hospital Los Angeles, Los Angeles, CA USA
13, Scott P Johnson
Scott P Johnson
14Department of Psychology, University of California Los Angeles, Los Angeles, CA USA
14, Michael Murias
Michael Murias
15Institute for Innovations in Developmental Sciences, Northwestern University, Chicago, IL USA
15, Charles A Nelson
Charles A Nelson
8Harvard Medical School, Boston, MA USA
12Department of Pediatrics, Boston Children’s Hospital, Boston, MA USA
16Graduate School of Education, Harvard University, Boston, MA USA
8,12,16, Maura Sabatos-DeVito
Maura Sabatos-DeVito
10Duke Center for Autism and Brain Development, Duke University, Durham, NC USA
10, Damla Senturk
Damla Senturk
6Department of Biostatistics, University of California Los Angeles, Los Angeles, CA USA
Catherine A Sugar
6Department of Biostatistics, University of California Los Angeles, Los Angeles, CA USA
13Present Address: Division of Neurology, Department of Pediatrics, Children’s Hospital Los Angeles, Los Angeles, CA USA
6,13, Sara J Webb
Sara J Webb
1Center for Child Health, Behavior, and Development, Seattle Children’s Research Institute, 1920 Terry Ave, Seattle, WA 98101 USA
9Department of Psychiatry and Behavioral Science, University of Washington School of Medicine, Seattle, WA USA
1,9, James C McPartland
James C McPartland
3Yale Child Study Center, Yale University School of Medicine, 230 South Frontage Road, New Haven, CT 06520 USA
3,✉
- Author information
- Article notes
- Copyright and License information
1Center for Child Health, Behavior, and Development, Seattle Children’s Research Institute, 1920 Terry Ave, Seattle, WA 98101 USA
2Department of General Pediatrics, University of Washington School of Medicine, Seattle, WA USA
3Yale Child Study Center, Yale University School of Medicine, 230 South Frontage Road, New Haven, CT 06520 USA
4Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA USA
5Department of Computer Science, University of Colorado - Colorado Springs, Colorado Springs, CO USA
6Department of Biostatistics, University of California Los Angeles, Los Angeles, CA USA
7Department of Neurology, Boston Children’s Hospital, Boston, MA USA
8Harvard Medical School, Boston, MA USA
9Department of Psychiatry and Behavioral Science, University of Washington School of Medicine, Seattle, WA USA
10Duke Center for Autism and Brain Development, Duke University, Durham, NC USA
11Emergency Medicine, Yale University School of Medicine, New Haven, CT USA
12Department of Pediatrics, Boston Children’s Hospital, Boston, MA USA
13Present Address: Division of Neurology, Department of Pediatrics, Children’s Hospital Los Angeles, Los Angeles, CA USA
14Department of Psychology, University of California Los Angeles, Los Angeles, CA USA
15Institute for Innovations in Developmental Sciences, Northwestern University, Chicago, IL USA
16Graduate School of Education, Harvard University, Boston, MA USA
17Department of Psychology, Yale University, 2 Hillhouse Ave, New Haven, CT 06520 USA
18Department of Psychology, University of Virginia, 102 Gilmer Hall, P.O. Box 400400, Charlottesville, VA 22904 USA
19Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, AL USA
✉
Corresponding author.
Received 2021 Jul 5; Accepted 2021 Dec 20; Collection date 2022.
© The Author(s) 2022, corrected publication 2022
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
PMCID: PMC10124777 PMID: 35313957
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.
Supplementary Information
The online version contains supplementary material available at 10.1186/s13229-021-00482-2.
Keywords: Autism spectrum disorder, Biomarkers, Eye tracking, Visual attention, Face processing, Biological motion, Gaze pattern
Introduction
Autism spectrum disorder (ASD) is associated with social communication difficulties, the presence of restricted patterns of behaviors, and atypical response to sensory information [ 1]. ASD is extremely heterogeneous, with extensive variation across individuals in social, cognitive, regulatory, and attentional phenotypes. Progress in developing interventions for ASD has been hindered by a lack of measures that can, within this heterogeneity, provide objective quantification of intrinsic features of ASD with sensitivity, reliability, and mechanistic relationship to core symptoms or intervention response. Biomarkers offer promise to address this need in ASD.
A biomarker is “a defined characteristic that is measured as an indicator of normal biological processes, pathogenic processes, or biological responses to an exposure or intervention” [ 2]. Biomarkers may quantify performance relevant to specific functional processes [ 3] and differ from clinical outcome assessments by virtue of focus on objective quantifiability and underlying mechanism. However, currently there exists no widely-accepted biomarkers established with sufficient rigor for guiding clinical practice or for broad use in clinical trials for ASD [for recent discussions, see 4, 5]. One challenge is the extensive infrastructure, spanning methodological, clinical, and trial management expertise, that is often required in order to establish a biomarker’s analytical validity. Acceleration of the clinical trial pipeline through biomarker development and qualification, an area of concerted focus for over 15 years [ 6, 7], may benefit from the design and evaluation of biomarker primitives with applications to multiple downstream clinical applications.
Social attention is a key functional process relevant to biomarker research in ASD [ 8]. Across a variety of studies, experimental modalities, and tasks, individuals with ASD exhibit altered attention to social information compared to non-ASD controls [e.g. 9– 11; review 12, 13]. ET offers insight into social attention by allowing for the precise moment-by-moment quantification of the gaze patterns of individuals as they visually process social information. Because ET is safe, noninvasive, scalable, and easily tolerated by participants from infancy through adulthood and across a wide range of function including significant cognitive impairment [ 14], it offers a powerful approach for the identification and development of social attentional biomarkers in heterogeneous conditions such as ASD.
Like many biomarker technologies, ET-based biomarkers for ASD could potentially advance various contexts of use, e.g., as diagnostic, predictive, prognostic, or response biomarkers [ 15, 16]. Recent work has suggested that ET biomarkers may associate with clinical assessments [ 17, 18], response to behavioral intervention [ 19], and administration of novel pharmacological compounds [ 20, 21]. ET biomarkers additionally may serve as diagnostic enrichment biomarkers [ 22] to decrease variability in a study population, permitting more efficient evaluation of intervention in smaller homogeneous samples.
Across contexts of use, biomarkers must exhibit specific properties as a requirement for practical utility. For biomarker deployment in clinical trials, minimum requirements are that the biomarkers evidence construct validity, feasibility in data acquisition, and reliability. The Autism Biomarkers Consortium for Clinical Trials (ABC-CT) [ 23] was designed to develop and validate these aspects of biomarker performance in children with ASD, addressing limitations in currently available studies, specifically small sample sizes and heterogenous acquisition and analytic methodologies [ 24]. From a candidate set of nine ET biomarkers (originally selected based on a review of extant eye-tracking paradigms demonstrating robust findings across multiple studies or in large samples of children with ASD prior to the inception of project funding), five ET tasks were selected for inclusion based on construct validity, evidence of ASD-control differences, and relation to ASD symptoms in an initial Feasibility Study prior to the Main Study reported here (see [ 25] for additional details regarding ET biomarker selection). Four of these tasks focused on social-attentional constructs and included: (1) Activity Monitoring (ActivityMonitoring), depicting videos of two adults playing with toys; (2) the Social Interactive (SocialInteractive) task, videos of two children engaged in parallel and joint play; (3) Static Social Scenes (StaticScenes), images depicting varied naturalistic scenes involving people; and (4) Biological Motion Preference (Biomotion), point-light display videos of biological motion versus non-biological control stimuli shown side-by-side. A fifth task, (5) Pupillary Light Reflex (PLR), was included in the ET battery as a measure associated with autonomic nervous system function and measured pupillary constriction in response to a light flash.
A composite variable representing gaze to faces across three tasks (ActivityMonitoring, SocialInteractive, StaticScenes), the Oculomotor Index of Gaze to Human Faces (OMI), was developed a priori (see Supplemental Information) based on preliminary data and served as the overall main outcome measure for the ET battery. Additional primary and secondary variables for each individual task were also pre-specified. Data were acquired and evaluated using stringent and rigorous manualized protocols with evaluation focused on metrics of biomarker viability in terms of (1) feasibility of acquisition, as measured by acquisition rates; (2) construct validity, as demonstrated by expected within-subject task performance in typically developing children; (3) stability across two timepoints separated by six weeks; (4) discrimination between ASD and TD groups as a means of illuminating regimes of atypical performance in ASD; and (5) association with clinical and behavioral phenotypic characteristics. These specific properties were selected for evaluation in order to assess fundamental psychometric properties of examined biomarkers that would be necessary for understanding their applicability and general usability for clinical trials in ASD. See [ 25] for further details regarding the protocol and analytical design considerations.
The objective of this work was to pair rigorous methodology and a large, well-characterized sample for the purpose of assessing early-stage viability of these markers for use in future biomarker applications for clinical trials. Toward this goal, this work seeks to characterize performance of ET biomarkers across fundamental evaluative dimensions so as to provide a template for ongoing biomarker development and deployment as well as to speak to their applicability for future, specific contexts-of-use.
Methods and materials
Autism Biomarkers Consortium for Clinical Trials (ABC-CT) protocol
The first ABC-CT study was a five-site observational study involving clinician, caregiver, and lab-based measures as well as a battery of electroencephalography (EEG) and ET tasks. Participants were school-age children with ASD or typical development (TD) assessed across three timepoints: Time 1 (T1), Time 2 (T2: T1 + 6 weeks), and Time 3 (T3: T1 + 24 weeks), with each timepoint conducted over two days. ET tasks were administered on both days at each timepoint. This report focused on data from T1 and T2, as the six-week span between the two timepoints approximates the duration of many clinical trials and is relevant to understanding short term stability. T3 data are being analyzed elsewhere in the context of longer-term developmental change and change in clinical status.
Informed consent/assent was obtained from all guardians and participants after procedures were fully explained and the opportunity to ask questions offered. The protocol was approved and overseen by a central IRB at Yale University.
An overview of the ABC-CT history and protocol is available in [ 23], with data acquisition and quality control details in [ 25]. More extensive protocol, participant, and ET methodological details are provided in Supplemental Information. Study data are available in [ 26].
Participant characteristics
Participants were children 6;0 to 11;6 years old at T1, an age range selected to constrain age-related developmental heterogeneity and increase likelihood of successful biomarker data acquisition [ 23]. Children in the ASD group ( n = 280) met DSM-5 diagnostic criteria for ASD [ 1] based on gold-standard research diagnostic criteria with the ADOS-2 and the ADI-R and had full scale IQ between 60 and 150. TD children ( n = 119) were screened for the presence of ASD, emotional and behavioral disorders (based on [ 27] and medical history), and had full scale IQs between 80 and 150. Exclusions for both groups included genetic or neurological conditions, or sensory challenges that would impact protocol completion. In the ASD group, medications were stable for 8 weeks prior to enrollment. See Supplemental Information for additional inclusion, exclusion, and assessment details. Groups did not differ by age ( t = 0.199, p = 0.843) nor sex 1 (Χ2 = 2.19, p = 0.139) but differed in diagnostic and clinical characterization (Table 1). Patterns of results were unchanged when considering subsets of participants with valid data for each ET biomarker (Additional file 1: Tables S1ab).
Table 1.
Participant characteristics. Mean and standard deviation are presented for clinical assessments for the full sample at T1. For characterization associated with subsets completing ET tasks, see Additional file 1: Tables S1ab. For clinical variable descriptions, see Additional file 1: Table S2
| Time 1 Demographics | ASD | TD | Assessment [ M (SD)] | ASD | TD |
|---|---|---|---|---|---|
| Participant | Full Scale IQ | 96.58 (18.11) | 115.12 (12.55) | ||
| N | 280 | 119 | Verbal IQ | 95.95 (20.69) | 116.27 (11.22) |
| Sex [ N Males: N Females] | 215: 65 | 83: 36 | Nonverbal IQ | 97.52 (16.91) | 112.18 (14.05) |
| %Male | 76.8% | 69.7% | ADOS CSS | 7.65 (1.77) | 1.58 (0.87) |
| Age in years [ M (SD)] | 8.55 (1.64) | 8.51 (1.61) | ADOS SA | 7.34 (1.79) | 1.91 (1.34) |
| Participant Race [N (%)] | ADOS RRB | 8.05 (1.73) | 3.04 (2.48) | ||
| White | 190 (67.9%) | 98 (82.4%) | VABS3 ABC | 73.37 (11.14) | 102.74 (9.84) |
| American Indian/Alaskan Native | 2 (0.7%) | 0 (0%) | VABS3 Soc SS | 69.89 (16.15) | 104.55 (9.23) |
| Black/African American | 22 (7.9%) | 4 (3.4%) | VABS3 Com SS | 76.44 (15.07) | 103.44 (9.16) |
| Asian | 15 (5.4%) | 2 (1.7%) | SRS-2 Total | 73.54 (10.92) | 42.57 (4.66) |
| Mixed race | 45 (16.1%) | 14 (11.8%) | SRS-2 SCI T | 72.65 (10.83) | 42.47 (5.05) |
| Other | 6 (2.1%) | 1 (0.8%) | SRS-2 RIRB T | 73.76 (12.18) | 43.97 (3.72) |
| Participant Ethnicity [N (%)] | PDDBI Soc App T | 54.21 (9.3) | 69.83 (3.04) | ||
| Hispanic | 52 (18.6%) | 8 (6.7%) | PDDBI REPRIT T | 49.6 (11.52) | 28.03 (2.61) |
| Non-Hispanic | 228 (81.4%) | 111 (93.3%) | Face Memory SS | 7.86 (3.67) | 10.53 (3.49) |
ADOS CSS — Autism Diagnostic Observation Schedule calibrated severity score (comparison score); ADOS SA — social affect comparison score; ADOS RRB — restricted interests and repetitive behavior comparison score; VABS3 ABC — Vineland Adaptive Behavior Scales adaptive behavior composite standard score; VABS3 Soc SS — socialization standard score; VABS3 Com SS — communication standard score; SRS-2 Total —Social Responsiveness Scale total T-score; SRS-2 SCI T —social communication and interaction T-score; SRS-2 RIRB T — restricted interest and repetitive behavior T-score; PDDBI Soc App T — Pervasive Developmental Disorders Behavior Inventory Social Approach Behaviors T-score; PDDBI REPRIT T — Repetitive, Ritualistic, and Pragmatic Problems Composite T-score; Face Memory SS — NEPSY memory for faces subtask score
Data acquisition
ET data acquisition was stringently standardized [ 25], with all sites achieving and maintaining protocol fidelity through rigorous training, manualization, and quality control procedures overseen by the Data Acquisition and Analysis Core (DAAC) of the ABC-CT. Manuals (see Supplemental Information) are available upon request.
Equipment
Sites used SR Research Eyelink 1000 Plus binocular remote eye trackers operating at 500 Hz. Stimuli were presented on 24″ 1920 × 1200 pixel 60 Hz monitors and controlled via identically configured presentation computers using Neurobehavioral Systems Presentation v18.1. Video cameras recorded the face and upper torso of the child and were multiplexed with video feeds from the ET control (host) computer and the presentation screen for subsequent behavioral review and quality assurance. See [ 25] for additional equipment details.
Protocol
ET sessions began with children seated (eye-to-monitor distance: 65 cm) in front of the stimulus presentation monitor. No head supports/restraints were used. A child-appropriate movie was played to capture the child’s attention, followed by a 5-point ET calibration procedure, and then administration of ET tasks.
Site behavioral assistants added supplemental verbal directions (e.g., “Sit back”, “Talk later”, “Watch TV”) and behavioral supports appropriate to the cognitive level and behavioral needs of children.
ET sessions were conducted on both days of each timepoint, with each session lasting approximately 14.5 min (involving 9.7 min/54 trials of experiments; see Additional file 1: Table S3 for experimental task administration details). Trials from ET tasks were interleaved in blocks to reduce fatigue and optimize child engagement. Validation targets were periodically administered to facilitate error estimation and scanpath recalibration. Task order was counterbalanced across participants.
Acquisition metrics, quality control, and derived variables
Subsequent to transfer of data from sites to the ABC-CT Data Coordinating Core, acquired ET data were processed centrally by the DAAC to extract acquisition metrics and derived variables.
Trial validity criteria for ActivityMonitoring, SocialInteractive, StaticScenes, and Biomotion tasks were percent of acquired ET data relative to stimulus presentation time (%Valid Data) ≥ 50% and calibration error (Cal Error) ≤ 2.5° (visual degrees, 1° = 42 pixels). For PLR, additional criteria were imposed to ensure rigor of latency and constriction size estimates.
Data from an ET session (single day) were invalidated if experimental counterbalancing errors, technical malfunctions, or non-standardized verbal cues (e.g., specific direction of attention to the stimuli) occurred. Data from an ET timepoint (both days) were invalidated if fewer than 25% of trials were valid (%Valid Trials). The OMI biomarker (made up of ActivityMonitoring, SocialInteractive, and StaticScenes tasks) was considered valid only if all constituent sub-tasks (ActivityMonitoring, SocialInteractive, and StaticScenes) were valid. Aggregated acquisition metrics at the task-level were: %Valid Data, Cal Error, and %Valid Trials.
Derived measures for each individual at each timepoint were averaged over all valid trials for that task. OMI, ActivityMonitoring, SocialInteractive, StaticScenes, and Biomotion involved region-of-interest (ROI) analysis (Additional file 1: Figure S1), where presented scenes were divided into zones associated with semantic labels and the proportion of valid gaze data within those zones calculated (e.g., %Face for percentage of time spent looking at faces). For PLR, latency and relative pupil constriction were computed as in [ 28].
All quality control (QC) criteria and derived variable definitions were formulated before ABC-CT main study enrollment and maintained throughout the entirety of the study. See Supplementary Information for additional details regarding QC, acquisition metrics, derived variables, and pre-hypothesized effects.
Experimental tasks
Five experimental ET tasks were administered (Fig. 1). Based on preliminary findings from the ABC-CT Feasibility Study [ 25], conducted prior to the main study reported here, an additional biomarker, the Oculomotor Index of Gaze to Human Faces (OMI), was constructed as the average of %Face from ActivityMonitoring, SocialInteractive, and StaticScenes tasks. See Additional file 1: Table S3 and Supplementary Information for details regarding experimental tasks including OMI derivation (Additional file 1: Tables S4-5).
Fig. 1.
Experimental Tasks. ( Top row) Tasks comprising the Oculomotor Index of Gaze to Human Faces (OMI): ActivityMonitoring (AM, videos depicting two actors engaged in a shared activity), SocialInteractive Scenes (SI, videos depicting two children involved in interactive and parallel play activities), and StaticScenes (SS, Social Static Scene images showing everyday scenes involving social interactions). ( Bottom row) Biomotion (BM, Biological Motion preferential looking videos with point-light displays of human actions paired with non-human control conditions. Lines in human figure added for illustrative purposes only), and Pupillary Light Reflex task (PLR, images depict frames in the video sequence including the bright screen flash)
Activity monitoring (activitymonitoring)
This task [ 29, 30] showed interleaved eight trials of static images (10 s each) and eight trials of dynamic videos (20 s each) of two actresses playing with children’s toys. During static image trials, a wordless soundtrack was played. During video trials, the actresses spoke in child-friendly language and directed their eyes to each other (mutual gaze) or the joint activity (activity gaze). The primary dependent variable was percentage of time spent looking at the heads and faces of the actresses (%Face), relative to the amount of validly acquired ET data during a trial. Secondary variables included percentage of valid time spent looking at actress activities (%Activity).
Social interactive task (socialinteractive)
This task [ 31] showed silent 15-s videos of two school-aged children engaged in parallel (11 trials) or cooperative play (11 trials) with toys. The primary dependent variable was percentage of valid time spent looking at heads and faces of actors (%Face). Secondary variables included percentage of valid time spent looking at any part of the actors (%Social: sum of face, body, and activity regions).
Static social scenes task (staticscenes)
This task showed, for 20-s each, six photographs of solitary and social interactions of children or of children and adults [ 32]. It was repeated on each day of each timepoint, with images flipped horizontally on the second day. Like the SocialInteractive task, the primary variable was %Face, and secondary %Social.
Oculomotor index of gaze to human faces (OMI)
A principal component analysis of ET derived variable data from the Feasibility stage of the ABC-CT study (see 23) revealed a primary component dominated by %Face variables from ActivityMonitoring, SocialInteractive, and StaticScenes tasks. As the weights for all of these variables were comparable, we created the OMI biomarker as a composite score averaging ActivityMonitoring, SocialInteractive, and StaticScenes %Face with equal weights.
Biological motion preference task (biomotion)
The Biological Motion Preference task involved 40 trials of soundless point light displays of human biological motion side-by-side with a non-biological motion control based on [ 33]. Human biological motion included primitive motor, affective, communicative, tool-oriented, or goal-oriented movements from [ 34]. Control conditions were either rotating or scrambled point light displays. The primary variable was biological motion preference percentage (%Bio, time looking at biological motion divided by time looking at biological motion or control). Secondary variables included biological motion preference from affective stimuli (%BioAffect).
Pupillary light reflex task (PLR)
The Pupillary Light Reflex task included 18 trials of a dark screen with a small, 0.7 degree animation at the center, then a flash of white for four frames, followed by the return of the dark screen and central animation [ 28]. A sound effect accompanied the animation throughout each trial. The primary variable was latency to minimum pupil size acceleration (Latency). Secondary variables included relative pupil constriction (Constrict) [ 28, 35].
Analytic plan
Analyses were pre-specified as highlighted in [ 23, 25]. Notably, examination of distributional characteristics of biomarker outputs [ 25] did not reveal statistical pathologies that would interfere with analytical interpretation. Nonetheless, ANOVA methods used heteroskedastic consistent covariance matrices to accommodate unequal group variances; correlations relied upon Spearman rank correlation coefficients for robustness against potential leverage effects due to outliers or severe non-normality. See Supplemental Information for additional details on correlation method rationale.
As a primarily descriptive study, no controls for multiple comparisons were enacted. However, we note that hypotheses for primary analytical aims were pre-specified; secondary analyses are presented primarily in Supplemental information.
Acquisition
For each ET biomarker, we examined rates of data acquisition (percentage of children generating any data) and data validity (percentage of children whose data passed all quality control criteria) (Tables 2, S6ab). We considered > 70% data validity in both ASD and TD groups to index suitability for clinical trials based on data acquisition rates reported in prior published experimental studies, consultation with statistical and biomarker-domain experts, and consensus across project stakeholders and external reviewers. Diagnostic group and potential site differences in acquisition rates were assessed with chi-square tests. Differences in acquisition metrics (%Valid Trials, %Valid Data, and Cal Error) were assessed with univariate ANOVA (Additional file 1: Table S7). Relationships among acquisition metrics and child characteristics were assessed using Spearman’s rank correlation (Additional file 1: Table S8). Analyses were conducted both unadjusted and adjusted for age, IQ, and site.
Table 2.
Biomarker properties. For extended data see Supplemental Tables.
| OMI | AM | SI | SS | BM | PLR | |
|---|---|---|---|---|---|---|
| Signal Acquisition and Validity at T1 | ||||||
| ASD | ||||||
| Acquisition valid | 279 (99%) | 280 (100%) | 280 (100%) | 279 (100%) | 280 (100%) | 278 (99%) |
| Signal valid | 272 (97%) | 280 (100%) | 276 (99%) | 273 (98%) | 277 (99%) | 266 (95%) |
| Site differences | X 2 = 6.6, p = .16 | – | X 2 = 1.0, p = .90 | X 2 = 7.6, p = .11 | X 2 = 5.2, p = .27 | X 2 = 16.1, p < .01 |
| TD | ||||||
| Acquisition valid | 119 (100%) | 119 (100%) | 119 (100%) | 119 (100%) | 119 (100%) | 119 (100%) |
| Signal valid | 119 (100%) | 119 (100%) | 119 (100%) | 119 (100%) | 119 (100%) | 117 (98%) |
| Site differences | – | – | – | – | – | X 2 = 3.0, p = .56 |
| Task | OMI | AM | SI | SS | BM | PLR |
|---|---|---|---|---|---|---|
| Construct validity for each ET task based on TD participants at T1 | ||||||
| Construct | Face preference | Face preference | Face preference | Face preference | Biomotion preference | Pupillary light reflex |
| Test | All sub-tasks valid | > Chance face gaze | > Chance face gaze | > Chance face gaze | > Chance biomotion gaze | Pupil constriction post flash |
| Null hypothesis | – | %Face = 3.2% | %Face = 8.3% | %Face = 3.9% | %Bio = 50% | Constrict > 0 |
| Sample values | AM✓ SI✓ SS✓ | M = 27.6%, SD = 8.5% | M = 30.4%, SD = 9.6% | M = 34.9%, SD = 8.0% | M = 54.8%, SD = 6.1% | M = .505, SD = .074 |
| Statistic | – | t(118) = 31.3 | t(118) = 25.0 | t(118) = 42.1 | t(118) = 8.5 | t(116) = 73.9 |
| p | – | < .001 | < .001 | < .001 | < .001 | < .001 |
| d | – | 2.87 | 2.30 | 3.88 | .79 | 6.83 |
| Validity | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
| Group | df range | OMI | AM %Face | SI %Face | SS %Face | BM %Bio | PLR latency |
|---|---|---|---|---|---|---|---|
| Six-week stability (ICC) from T1 to T2 for primary pre-specified biomarkers | |||||||
| TD | 106 ≤ df ≤ 112 | .828 | .848 | .842 | .569 | .441 | .866 |
| ASD | 223 ≤ df ≤ 274 | .836 | .834 | .813 | .680 | .505 | .749 |
| ASD < 8.5 years | 116 ≤ df ≤ 144 | .837 | .820 | .813 | .657 | .462 | .775 |
| ASD ≥ 8.5 years | 106 ≤ df ≤ 129 | .832 | .842 | .812 | .695 | .530 | .725 |
| ASD IQ < 75 | 24 ≤ df ≤ 30 | .754 | .801 | .794 | .476 | .238 | .741 |
| ASD IQ ≥ 75 | 198 ≤ df ≤ 243 | .849 | .836 | .819 | .701 | .526 | .749 |
| OMI | AM %Face | SI %Face | SS %Face | BM %Bio | PLR latency | |
|---|---|---|---|---|---|---|
| Group discrimination at T1 for primary biomarkers | ||||||
| M (SD) | ||||||
| ASD values | 24.4% (8.5%) | 18.7% (8.7%) | 24.2% (10.7%) | 29.9% (9.8%) | 53.4% (6.8%) | 285 ms (15 ms) |
| TD Values | 30.9% (7.6%) | 27.6% (8.5%) | 30.4% (9.6%) | 34.9% (8.0%) | 54.8% (6.1%) | 279 ms (15 ms) |
| No covariates | ||||||
| Statistic | F(1,389) = 55.6 | F(1,397) = 90.7 | F(1,393) = 31.5 | F(1,390) = 27.7 | F(1,394) = 4.0 | F(1,381) = 10.1 |
| p | < .001 | < .001 | < .001 | < .001 | .046 | .002 |
| d | −.788 | − 1.037 | −.593 | −.537 | −.211 | .350 |
| ηp 2 | .117 | .184 | .069 | .058 | .009 | .025 |
| Age + IQ + Acq + Site Control | ||||||
| Statistic | F(1,382) = 24.5 | F(1,390) = 55.3 | F(1,386) = 21.8 | F(1,383) = 3.9 | F(1,387) = 1.9 | F(1,374) = 6.7 |
| p | < .001 | < .001 | < .001 | .048 | .165 | .010 |
| ηp 2 | .057 | .120 | .050 | .009 | .006 | .018 |
| Phenotypic characteristic | OMI | AM %Face | SI %Face | SS %Face | BM %Bio | PLR Latency |
|---|---|---|---|---|---|---|
| Spearman’s Correlations between ET and child behaviors in the ASD group at T1 | ||||||
| Age | .115 | .147* | .077 | .087 | − .063 | .153* |
| Full IQ | .123* | .140* | .033 | .192** | − .063 | − .006 |
| Verbal IQ | .183** | .188** | .122* | .199*** | − .058 | .026 |
| NV IQ | .059 | .081 | − .029 | .147* | − .072 | − .030 |
| ADOS SA | −.165** | −.228*** | −.110 | −.168** | .041 | −.018 |
| ADOS RRB | −.035 | −.087 | .001 | .004 | .024 | −.100 |
| VABS3 Soc SS | .125* | .150* | .103 | .118 | .008 | −.032 |
| VABS3 Com SS | .182** | .205*** | .144* | .203*** | .029 | −.049 |
| SRS SCI T | −.070 | −.059 | −.090 | −.062 | −.117 | .096 |
| SRS RIRB T | −.108 | −.090 | −.119* | −.092 | −.041 | .086 |
| PDDBI SocApp T | .066 | .130* | .079 | .038 | .146* | −.058 |
| PDDBI REPRIT T | −.238*** | −.211*** | −.210*** | −.191** | −.001 | .106 |
| Face Memory SS | .316*** | .359*** | .256*** | .301*** | −.045 | .051 |
OMI —Oculomotor Index of Gaze to Human Faces; BM = Biological Motion Preference; PLR — Pupillary Light Reflex; AM = Activity Monitoring; SI—Social Interactive; SS = Static Scenes; [Construct Validity] Task —ET task; Construct —hypothesized construct under investigation; Test —how the construct is tested; Null Hypothesis —formal definition of the construct validity test; Sample Values —TD performance on null hypothesis variable at T1; [Signal Acquisition] Acquisition Valid —participant generated ET data for some portion of the assay; Signal Valid —valid signal for primary DV (meeting all quality control criteria for admission of data); Difference in Site Valid Signal Rates —Pearson’s Chi-Squared test for site differences in valid signal (consistent with Monte Carlo simulation and unable to be computed for 100% data validity); [Six-week Stability] ICC —Intraclass Correlation Coefficients (ICC3); DV — dependent variable; IQ —DAS Full Scale IQ; df — degrees of freedom across task DVs in calculation of ICCs; [Group Discrimination] DV —dependent variable; Age = participant age; IQ = Full Scale IQ; Acq = %Valid data collection rate; Site = data collection site; d = Cohen’s d; ηp2 = partial eta squared; [Phenotypic Characteristic Correlations] Full IQ = DAS Full Scale IQ; NV IQ = DAS Nonverbal IQ; ADOS SA — Autism Diagnostic Observation Schedule social affect comparison score; ADOS RRB —restricted interests and repetitive behavior comparison score; VABS3 Soc — Vineland Adaptive Behavior Scales adaptive behavior socialization standard score; VABS3 Com — communication standard score; SRS-2 SCI —Social Responsiveness Scale social communication and interaction T-score; SRS-2 RRB — restricted interest and repetitive behavior T-score; PDDBI SocApp T — Pervasive Developmental Disorders Behavior Inventory Social Approach Behaviors T-score; PDDBI REPRIT — Repetitive, Ritualistic, and Pragmatic Problems Composite T-score; Face Mem SS — NEPSY memory for faces subtask score. * p < .05; ** p < .01; \\\* p < .001. Underlined cells are significant even after controlling for Age, Full Scale IQ, and %Valid Data. Italicized cells cannot be controlled for these variables due to collinearity
Construct validity
To ascertain whether tasks successfully tapped constructs of interest, we examined pre-defined hypotheses for each task in the TD group (Tables 2, S11a). These hypotheses primarily served to verify that tasks were eliciting expected responses from TD children based on their intended design. ActivityMonitoring, SocialInteractive, and StaticScenes tasks were all designed wholly or in part to examine attentional predispositions for directing gaze toward social information as present in faces, motivated by studies indicating that faces are
(Truncated: full text at source URL.)