IntroductionThis study evaluated a machine learning tool designed to non-intrusively quantify and analyze biometric data of gaze, facial expressions, and paralinguistic social communication features during standardized autism observational assessments. The primary aim was to assess the diagnostic accuracy of this multimodal tool in capturing key social communication features of autism in a diverse neurodevelopmental disabilities cohort and neurotypical (NT) cohort, ages 2-12.MethodsThe study enrolled 546 participants across four sites in the USA (n=246) and Qatar (n=300). Of these, 458 (83.6%) met quality indicators for both video and audio recordings and comprised the analysis set. Primary outcome measures were diagnostic accuracy, sensitivity and specificity relative to reference diagnoses. Random Forest classifiers were trained using a developmentally-adaptive approach: separate models for three developmental groups (few-to-no words, phrase speech, fluent speech) using 97 biometric features from video, audio and gaze data. Performance was assessed via leave-one-out cross-validation on the inner set (n=338) and validated on an independent hold-out test set (n=120).ResultsClassification between ASD (idiopathic and syndromic) and non-ASD (clinical and NT) participants achieved 77.8% sensitivity and specificity in the inner set. When distinguishing ASD from NT participants alone, the sensitivity and specificity both increased to 82.0%. In the hold-out test set, the model demonstrated 62.3% sensitivity and 81.4% specificity for ASD versus non-ASD, and 72.1% sensitivity and 88.6% specificity for ASD versus NT. Performance varied by demographics in the case of sex, with males showing higher sensitivity (79% to 75%) and females showing higher specificity (84% to 70%).DiscussionThis study demonstrates the feasibility of using semi-automated multimodal computational analysis to quantify multi-modal autism social communication behaviors and distinguish ASD from NT in clinically and ethnically diverse samples. Known difficulties with differential diagnosis in non-ASD neurodevelopmental conditions with autism-like features remain a limitation requiring further development. Data suggest promise for such tools to support task-sharing models within existing clinical approaches.

The rising prevalence of autism spectrum disorder, coupled with limited professional resources, highlights the urgency of developing efficient diagnostic tools. While standardized assessments exist, identifying subtle communication deficits, especially during multimodal interactions, remains time-consuming and prone to human error. To address this, we propose an automated behavior analysis framework that aims to support clinicians by accurately detecting both verbal and non-verbal communication markers. Specifically, we put forth a composite artificial intelligence framework that integrates various deep learning algorithms to analyze information from body and hand poses, object detection, tracking and manipulation, and speech. By combining these features with a rule-based system, we can identify events within the Autism Diagnostic Observation Schedule second edition, Construction Task, where participants initiate requests. These requests can be verbal, non-verbal or a combination of both resulting in multimodal interactions. Building on our prior work, this paper introduces a smart glass technology component, integrating gaze and blinking analysis, which are challenging for clinicians to monitor, given the multi-task nature of their role. These additions enable the detection of eye contact, a crucial social cue. Our approach allows us to recognize gestures, identify hand object manipulations, detect eye contact, and understand the natural language in clinician-participant interactions. We achieve 94% and 73% F-1 score, on verbal and non-verbal request detection, respectively, which may improve, as deep learning advances.

BACKGROUND:Pediatric asthma exacerbations remain a critical public health concern, particularly in historically underserved urban settings. OBJECTIVE:This study investigates sociome factors-the social context of disease-associated with asthma exacerbations among children living in Chicago's South Side, leveraging clinical and publicly available generalizable census tract-level datasets from agencies including ChiVes, the City of Chicago Data Portal, EPA, Census Bureau, HUD, NOAA, and more. The aim is to uncover novel hypotheses for potential new interventions. METHODS:A generalized linear model assessed associations with the outcome of asthma exacerbations while accounting for clustering at the patient level. Predictors included all variables from the Sociome Data Commons, including social, environmental, behavioral, economic, housing, and school variables. RESULTS:Predictors of decreased risk included patient age (+4.8 years, -22%), tree crown density (+6% coverage, -17%), parks per acre (+0.41, -8%), and labor market engagement (+0.8 points, -9%). Conversely, predictors of increased risk included increased distance to the nearest pharmacy (+0.28 miles, +12%), limited English skills (+2.3%, +10%), higher inequality (+0.08 points, +8%), and visits in the Spring (+11%) and Fall (+20%). CONCLUSION:The results suggest that tree crown density, a novel finding in the context of asthma exacerbations, may play a protective role. Limited access to health care facilities such as pharmacies continues to complicate care. CLINICAL IMPLICATIONS:These findings provide hypotheses for future interventions for long-standing asthma disparities.

Examine how milestone development, demographics, and emotional/behavioral functioning predict autistic females meeting the cutoff on a commonly used Autism screening tool (Social Communication Questionnaire: SCQ). We hypothesized that autistic girls with fewer developmental delays, whose parents have lower education, or are Black or Multiracial would be less likely to meet the SCQ cutoff. Further, those with more symptoms of Withdrawal/Depression, Social Problems, Thought Problems, and Attention Problems on the (Child Behavioral Checklist: CBCL) would be more likely to screen positive. A subset of participants enrolled in a large national cohort (SPARK) were included (5,946 autistic females). A cutoff score on the SCQ of 11 was used to form groups: Meet (M: N = 5,186) and Not Meeting (NM: N = 760). Autistic girls who had delayed toileting and motor milestones and whose parents attained higher education were more likely to screen positive. Girls who scored within the clinical range on the CBCL Thought Problems and Attention Problems syndrome scales were more likely to screen positive. Race and reported symptoms on the Withdrawn/Depressed and Social Problems syndrome scales did not relate to screening status. Results further support the existing literature suggesting that autistic girls must present with more significant delays/symptoms to be screened and diagnosed with autism, which can could impact their access to early intervention services and future skill development. Future research should examine additional factors that specifically put females at a disadvantage for being accurately identified, particularly for those who are speaking and/or of average cognitive ability.

Background/Objective: Non-clinical aspects of life, such as social, environmental, behavioral, psychological, and economic factors, what we call the sociome, play significant roles in shaping patient health and health outcomes. This paper introduces the Sociome Data Commons (SDC), a new research platform that enables large-scale data analysis for investigating such factors. Methods: This platform focuses on “hyper-local” data, i.e., at the neighborhood or point level, a geospatial scale of data not adequately considered in existing tools and projects. We enumerate key insights gained regarding data quality standards, data governance, and organizational structure for long-term project sustainability. A pilot use case investigating sociome factors associated with asthma exacerbations in children residing on the South Side of Chicago used machine learning and six SDC datasets. Results: The pilot use case reveals one dominant spatial cluster for asthma exacerbations and important roles of housing conditions and cost, proximity to Superfund pollution sites, urban flooding, violent crime, lack of insurance, and a poverty index. Conclusion: The SDC has been purposefully designed to support and encourage extension of the platform into new data sets as well as the continued development, refinement, and adoption of standards for dataset quality, dataset inclusion, metadata annotation, and data access/governance. The asthma pilot has served as the first driver use case and demonstrates promise for future investigation into the sociome and clinical outcomes. Additional projects will be selected, in part for their ability to exercise and grow the capacity of the SDC to meet its ambitious goals.

BACKGROUND:Pragmatic communication difficulties encompass many distinct behaviours, including the use of vague and/or insufficient language, a common characteristic following traumatic brain injury (TBI) that negatively impacts psychosocial outcomes. Existing assessments evaluate pragmatic communication broadly, often with only one or two items capturing each behaviour, thus limiting sensitivity and precision to variations within each behaviour. Given that greater nuance is needed to detect subtle pragmatic communication differences and investigate underlying cognitive mechanisms, a more refined measure is critical to improve psychosocial outcomes. The Vague scale was developed to address those needs. AIM:To provide preliminary evidence supporting the novel Vague language use (Vague) scale's reliability, validity and clinical utility. METHODS AND PROCEDURES:The Vague scale rates each discourse sample utterance for vague language use on a 3-point scale; the measure's Vague score represents the mean of utterance-level ratings. Using the Vague scale, two raters naïve to diagnosis evaluated Cinderella narratives of 46 adults with severe TBI and 46 controls with no brain injury, providing reliability, construct validity and classification accuracy evidence. Vague scores were also compared to other clinical measures to gather criterion-related validity evidence. OUTCOMES AND RESULTS:Interrater agreement across all transcripts was moderate. Construct validity was supported by expected group differences and criterion validation, including significant relationships with increased violations of Grice's maxim of quantity and measures of lexical variation; significant relationships with psychosocial outcomes, supporting clinical utility; and nonsignificant relationships with measures of syntax and overall pragmatic communication. Classification accuracy expectedly did not support using Vague scores in isolation for diagnosis, due to unacceptable sensitivity (0.696). CONCLUSIONS AND IMPLICATIONS:Evidence supported the Vague scores' psychometric properties. Thus, the Vague scale shows promise as a measure of one distinct pragmatic communication behaviour: vague language use. Future research should apply the Vague scale to determine its sensitivity in individuals with subtle social communication challenges (e.g., mild TBI), explore its utility with more naturalistic discourse samples as part of a pragmatic communication battery, longitudinally examine changes in Vague scores, and investigate cognitive mechanisms underlying this specific pragmatic communication behaviour. WHAT THIS PAPER ADDS:What is already known on this subject Use of vague language is common following traumatic brain injury (TBI) and may contribute to negative psychosocial outcomes related to employment and relationships. However, existing measures of vague language lack sensitivity and precision, limiting their utility for identifying subtle performance variations or determining the cognitive mechanisms underlying this specific pragmatic communication behaviour. What this study adds to existing knowledge The current study gathered promising reliability, validity and clinical utility evidence to support using the novel Vague language use (Vague) scale, which was developed to address these limitations. Based on complex (Cinderella) stories of adults with and without TBI, Vague scores demonstrated moderate interrater agreement and promising construct validity based on expected group differences and criterion validation, including moderate associations with related constructs and weak associations with unrelated constructs. Because pragmatic communication profiles post-TBI can vary, classification accuracy expectedly indicated that Vague scores should not be used alone to identify communication differences post-TBI. What are the clinical implications of this work? Vague scores show promise for documenting clients' use of vague language and planning intervention to address associated discourse-level challenges. Future research should investigate applying the Vague scale to naturalistic discourse samples (1) as part of a battery that sensitively and precisely identifies pragmatic communication differences and (2) as a tool for investigating cognitive mechanisms underlying vague language use, with the goal of improving interventions that support pragmatic communication and enhance psychosocial outcomes.

Relative to males, women with autism spectrum disorder (ASD) have neurobiological and clinical presentation differences. Recent research suggests that the male/female ASD prevalence gap is smaller than previously reported. Sex differences in symptom presentation as well as the male bias of ASD account for delayed/missed diagnosis among women. Investigating ASD and providing psychological evaluation referrals for women who are struggling socially and present with complex mental health conditions (e.g., ADHD, depression), even when they do not show typical autistic characteristics, is important. Accurate diagnosis facilitates understanding of challenges, increases access to treatments, and alleviates the burden of ASD.

OBJECTIVES/GOALS: 1) Investigate the utility of pragmatic communication profiles in a sample of children with autism at baseline to predict response to treatment in a randomized clinical trial (RCT) of oxytocin augmentation and social cognitive skills training at week 12. 2) Determine if levels of anxiety or hyperactivity moderate child outcome performance. METHODS/STUDY POPULATION: 40 children (37M, 3F), aged 8-11(M=9.25, SD=1.10), with confirmed autism spectrum disorder (ASD), enrolled in an RCT(NCT02918864) were evaluated at baseline on: an assessment of ASD (Autism Diagnosis Observation Schedule, ADOS-2), a task of perspective taking, Theory of Mind ToM, (Reading the Mind from the Eyes Task), pragmatic communication (Pragmatic Rating Scale-School Aged; PRS-SA), IQ (WAIS_I, WISC-V) and anxiety and hyperactivity (Behavior Assessment Scales for Children-3; BASC-3). A Tobii T60 XL was used for eye-tracking visual patterns and attention during the RMET. The PRS-SA was coded by trained, reliable clinicians. Parent ratings indicated over half of the participants’ had At Risk levels or higher on anxiety and hyperactivity on the BASC-3. Week 12 measures included all but the PRS-SA and ADOS-2. RESULTS/ANTICIPATED RESULTS: Baseline preliminary analysis indicated the participants spent more time looking at words (.41ms) than eye images on the RMET(.15ms, p DISCUSSION/SIGNIFICANCE: Findings at baseline suggest pragmatic communication skills are more related to ToM than gaze and attention on the RMET. This relationship will be further investigated over the time of the trial. Mental health indicators need to be considered further in this population. Child profiles at baseline may inform appropriate triage and treatment targets.

Social interactions are fundamental to human life. Accurately identifying and interpreting verbal and non-verbal cues is essential for analyzing human behavior and human-machine interactions. The complexity of these interactions, along with the different communication signals, and their varying frequencies is a challenge that Deep Neural Networks cannot yet address. Composite AI, which combines Deep Learning (DL) and traditional AI methods, emerges as a potential approach. We propose a framework comprising three main modules: feature extraction, episode detection, and activity interpretation. The feature extraction module uses DL methods to extract relevant information about the environment, interacting partners, and context of the interaction. By combining these features with rule-based systems, we restrict the analysis to key episodic components, effectively reducing the false positive detection rate. Classifiers are then used to identify and recognize specific sub-events. We show our system’s effectiveness in detecting communication cues on recorded sessions of a standardized autism diagnostic test (ADOS-2) involving autistic children, where detecting such cues can be highly challenging. In this scenario, we achieve 87% and 89% accuracy for verbal and non-verbal requests, respectively, and zero false positives. This comprehensive framework can significantly enhance social interaction analysis, enabling more effective automated event analysis. We also highlight the importance of sub-event segmentation and the components that could be improved with additional algorithmic combinations or data-driven improvements of the detection systems.

Objective To examine overlap and divergence of symptomatology in Autism Spectrum Disorder (ASD) with and without co-occurring Attention/Deficit Hyperactivity Disorder (ADHD) and/or Anxiety Disorder by age and sex. Method Participants included 25,078 individuals registered in the SPARK cohort, age 6-18 years. SPARK participation includes online consent and registration, as well as parent-reported ASD, ADHD, and Anxiety Disorder diagnoses, developmental, medical, and intervention history, and standardized rating scales. Individuals with ASD, ASD + ADHD, ASD + Anxiety, or ASD + ADHD + Anxiety were compared on measures assessing social communication, restricted and repetitive behaviors (RRBs), and motor functioning, and differences between male and female profiles were examined. Results Significant differences in symptom presentation between females/males, school-age/adolescent individuals, and by co-occurring conditions (ASD/ADHD/Anxiety) are apparent, and the impact of co-occurring conditions differed by age and sex. Most notably, school-age femaleswith ASD without co-occurring conditions present with significantly fewer concerns about social communication skills and have better motor skills, but have more prominent RRBs as compared to same-aged males with ASD alone; co-occurring conditions were associated with increased social communication problems and motor concerns, most consistently for school-age females. Conclusions School-age females with ASD are at highest risk for underestimation of autism-related symptoms, including underestimation of symptoms beyond core ASD features (motor skills). Further, across ages, particular consideration should be given when probing for social communication symptoms, RRBs, and motor skills in females with ASD alone, as well as with co-occurring ADHD and/or Anxiety. For females with co-occurring symptoms and conditions, use of symptom-specific measures in lieu of omnibus measures should be considered.

The purpose of this mixed-method study was to examine racial differences in parental beliefs and concern about autism spectrum disorder (ASD) versus clinical judgment. The sample included 489 children with ASD undergoing their first ASD evaluation. Parent belief that their child had ASD was highest among parents of White children. White children whose parents believed the child had ASD had lower ASD severity. Parents of Black/African American and Hispanic children were more likely to report communication concerns than parents of White children. Parental concern about social communication was related to higher ASD severity for Hispanic children. Implications for diagnostic processes are discussed. Lay abstract The goal of this study was to examine if there were differences between races in parental concern and belief about autism spectrum disorder (ASD) and the perspectives of clinicians. We studied 489 children with ASD who were having their first evaluation at an ASD clinic. Parents of White children most often believed that their child had ASD. However, White children whose parents believed the child had ASD were less severe in their symptoms. Parents of Black/African American or Hispanic children were more likely to have concerns about communication than parents of White children. In Hispanic families, parental concern about social communication was related to more severe symptoms in children. We discuss the implications of our findings for diagnosis.

Parent involvement in early intervention has shown promising impacts on developmental outcomes and is considered a fundamental component of comprehensive intervention for children with autism spectrum disorder (ASD). This chapter provides an overview of evidence-based approaches that include parents as active participants in interventions targeting core symptoms of ASD as well as interventions focused on reducing co-occurring maladaptive behaviors. Exemplar programs, including their individual research bases, are reviewed and summarized. In addition, telehealth adaptations of these parent implementation interventions are discussed. The chapter closes with a review of the clinical implications of this important literature and recommendations for advancing future research in this field.

Discernment of possible sex-based variations in presentations of autism spectrum disorder (ASD) symptoms is limited by smaller female samples with ASD and confounds with ASD ascertainment. A large national cohort of individuals with autism, SPARK, allowed parent report data to be leveraged to examine whether intrinsic child characteristics and extrinsic factors differentially impact males and females with ASD. Small but consistent sex differences in individuals with ASD emerged related to both intrinsic and extrinsic factors, with different markers for males and females. Language concerns in males may make discernment of ASD more straightforward, while early motor concerns in females may hamper diagnosis as such delays are not identified within traditional ASD diagnostic criteria.

Purpose Social communication or pragmatic skills are continuously distributed in the general population. Impairment in these skills is associated with two clinical disorders, autism spectrum disorder (ASD) and social (pragmatic) communication disorder. Such impairment can impact a child's peer acceptance, school performance, and current and later mental health. Valid, reliable, examiner-rated observational measures of social communication from a semistructured language sample are needed to detect social communication impairment. We evaluated the psychometrics of an examiner-rated measure of social (pragmatic) communication, the Pragmatic Rating Scale-School Age (PRS-SA). Method The analytic sample consisted of 130 children, ages 7-12 years, from five mutually exclusive groups: ASD (n = 25), language concern (LC; n = 5), ASD + LC (n = 10), social communication impairment only (n = 22), and typically developing (TD; n = 68). All children received language and autism assessments. The PRS-SA was rated separately using video-recorded communication samples from the Autism Diagnostic Observation Schedule. Assessment data were employed to evaluate the psychometrics of the PRS-SA. Analysis of covariance models were used to assess whether the PRS-SA would detect differences in social communication functioning across the five groups. Results The PRS-SA demonstrated strong internal reliability, concurrent validity, and interrater reliability. PRS-SA scores were significantly higher in all groups compared to the TD group and differed significantly in most pairwise comparisons; the ASD + LC group had the highest (more atypical) scores. Conclusions The PRS-SA shows promise as a measure of social communication skills in school-age verbally fluent children with a range of social and language abilities. More research is needed with a larger sample, including a wider age range and geographical diversity, to replicate findings. Supplemental Material https://doi.org/10.23641/asha.15138240.

Background: Eliciting parents' concerns about their children is an important initial step in the ASD diagnostic process. This information is often collected through forced-choice questionnaires utilizing professional terminology and may limit the potential concerns that can be reported. Parent concern studies to date have largely used deductive qualitative methods with only one age group of children. Inductive qualitative studies are needed to examine parental concerns of children with ASD across age groups with one coding scheme. Method: We used an inductive qualitative analysis process to analyze concerns reported by parents of children ages 1-11 years on intake forms (n = 455) at an urban outpatient ASD specialty clinic. Analyses were based on three age groups (toddler, preschooler, middle childhood). Results: Using conventional content analysis, 12 categories of concerns emerged from parents' responses: communication, social, behavioral/emotional, cognition, life skills, atypical behaviors, sensory, academic, health, seeking diagnostic clarity or resources, developmental, and motor skills. We found that parents reported the same concerns about their children across age groups in six of the 12 categories. The biggest difference in reported concerns across age groups was that parents of children in the preschooler and middle childhood groups reported a greater number of concerns related to mental health than parents of toddlers. Conclusion: Our analysis yields specific information about similarities and differences in parents' concerns depending on their child's age. Ensuring that ASD evaluations are tailored to children's unique needs has implications for timely diagnosis and access to care.

A reliable autism spectrum disorder (ASD) diagnosis can occur as early as 18 months; however, the average age at diagnosis in the United States is over 2 years later. While there are numerous well-known barriers to seeking an ASD diagnosis, no research has examined if separation between a child’s biological parents affects timing of ASD diagnosis for their child. Data for this study were obtained from 561 children ( M age = 5.4 years, SD = 3.9 years) referred to an urban, outpatient ASD specialty clinic for their first ASD evaluation. Biological parents self-reported their relationship status during the evaluation, which was then categorized as either “together” (married or living together but not married) or “not together” (separated, divorced, or never married). An inverse-probability of exposure weighted linear regression model, which adjusted for 16 different child, family, and sociodemographic variables, was utilized to assess differences in child age of ASD diagnosis between groups. At the time of diagnosis, most children’s biological parents were together (69%) versus not together (31%). In the fully adjusted model, children of parents who were together were diagnosed 1.4 years earlier than those who were not together ( p < 0.001). Strategies for supporting these families and reducing age disparities are indicated. Lay abstract Autism spectrum disorder (ASD) can be diagnosed as early as 18 months of age. However, the average age at diagnosis in the United States is over 2 years later. A lot has been written about the many barriers families face when seeking a diagnosis for their child. One area of research that has received no attention is whether separation between a child’s biological parents affects the age at which a child is diagnosed with ASD. This study was conducted among 561 children who were receiving an ASD diagnosis for the first time. On average, these children were 5 years of age. The study took place in an urban, outpatient specialty autism clinic in the United States. Biological parents self-reported their relationship status during the evaluation. This was categorized as either “together” (married or living together but not married) or “not together” (separated, divorced, or never married). At the time of diagnosis, most children’s biological parents were together (69%). We found children of parents who were together were diagnosed 1.4 years earlier than those who were not together. These findings have important implications for providing support to families that separate early in a child’s life, with the goal of reducing the age at ASD evaluation among single parents and those who have been separated from their child’s other biological parent. Providing support to these families is important since earlier age at diagnosis leads to earlier intervention, which can improve long-term outcomes for the child, family, and community as a whole.

We examined whether different profiles of quality of life (QoL) existed among youth referred to an autism spectrum disorder (ASD) specialty clinic and, if present, determined if these groups were associated with different characteristics. Data were from parental report of 5–17 year-old youth (N = 476) who were scheduled to receive an evaluation at an ASD clinic. Parents completed questionnaires, including the Pediatric Quality of Life Inventory, assessing child and family functioning; providers reported diagnostic impressions. A latent profile analysis found five distinct groups: Low Risk, School Problems, Only Social Emotional Problems, and two Physical/Social Emotional Problems. The groups differed on clinical characteristics and family functioning. These findings have implications for more efficient and effective evaluations in service delivery systems serving complex patients.

1552 Background: Fragile X syndrome (FXS), a subtype of autism spectrum disorder (ASD), is caused by (A) epigenetic silencing of the Fragile X Mental Retardation 1 (FMR1) gene leading to the loss or deficit of Fragile X Mental Retardation Protein (FMRP) or (B) mutation in the RNA binding of specific mRNA targets. The lack of FMRP leading to upregulated metabotropic glutamate receptor subtype 5 (mGluR5)signaling in an animal model of FXS has not been confirmed in humans with FXS. However, modification of mGluR5 signaling, a promising new, targeted pharmacologic treatment to restore excitatory/inhibitory balance in the FXS brain, has not been confirmed in humans with FXS. Many clinical trials of pharmacological agents for mGluR5 have been unsuccessful due to the lack of a tool to confirm that the study drug actually engages the desired target (Budimirovic, et al., 2017). We have developed a biomarker to demonstrate engagement of mGluR5 (Wong, et al., 2013) for use in clinical trials of FXS (Brasic, et al., 2019). Objectives: To measure mGluR5 density in relevant brain regions of men with and without fragile X syndrome (FXS) and autism spectrum disorder (ASD) (Fatemi, et al., 2018). Methods: A 24-year-old man with mutation-confirmed FXS, six men with ASD aged 20 + 2.10 years, and three healthy control (HC) men without FXS or ASD aged 27 + 3.61 years underwent structural magnetic resonance imaging (MRI) and positron emission tomography (PET) in a high resolution research tomograph (HRRT) with resolution approaching 2 mm (Rahmim, et al., 2005; Sossi, et al., 2005) for 90 min after the intravenous bolus injection of 185 megabecquerels (MBq) [5 mCi] 3-[18F]fluoro-5-(2-pyridinylethynyl)benzonitrile ([18F]FPEB), a potent, selective mGluR5 inhibitor (Wong, et al., 2013). The images of the MRI and PET were coregistered for analysis (Wong, et al., 2013). The nondisplaceable binding potential (BPND) (Innis, et al., 2007) of [18F]FPEB, a measurement proportional to the density of unoccupied receptors (Bmax9), was estimated in relevant volumes of interest in the brain utilizing the cerebellum as the reference tissue for graphical analysis (RTGA) (Logan, et al., 1996, 2011). Results: BPNDs for the man with FXS were uniformly lower than the means of the ASD and HC groups (Table). The observation that the sample of men with ASD had significantly higher BPNDs in the cerebellum and postcentral gyrus than the HC men with the multilinear reference tissue, two parameter model (Ichise, et al., 2002) was not confirmed with the RTGA (Logan, et al., 1996, 2011). Conclusions: Although one cannot generalize results from a single man with FXS to the population of individuals with FXS, the current investigation demonstrates that performing sophisticated imaging of mGluR5 in men with FXS is feasible by a team of experts who specialize in FXS. Investigations with an adequate sample size will likely demonstrate that the proposed protocol will provide the tool to establish target engagement for clinical trials of novel agents for mGluR5 in FXS (Brasic, et al., 2019). The proposed protocol will then be key to establish drug occupancy for future clinical trials of novel agents for mGluR5 in FXS.