Clinically derived 12-factor structure and confirmatory factor analysis of the neurodevelopmental parent report for outcome monitoring.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by social communication and social interaction impairments accompanied by restrictive and repetitive behaviors or interests. Co-occurring conditions may greatly impact overall functioning and intervention needs, and contribute to individual variability and etiologic subtypes. Clinical care of individuals with ASD requires gathering a breadth of information across multiple domains. The neurodevelopmental parent report for outcome monitoring (ND-PROM) was developed to assess symptoms across core features of ASD as well as frequent concerns and comorbidities. The current study expands upon the initially reported psychometric properties of the ND-PROM and evaluates a proposed a clinically derived 12-factor structure of the ND-PROM. Methods and procedures: The ND-PROM was completed for 246 children with ASD ands tested using confirmatory factor analysis (CFA) and measurement invariance based on sex. Outcomes and results: A 12-factor correlated structure was found (expressive language, receptive language, nonverbal communication, social emotional understanding, social interaction, independent play, adaptive/toileting skills, restrictive and repetitive behaviors and interests, sensory processes, challenging behaviors, impulse/ADHD, and mental health), which did not vary by sex. Conclusions and implications: The ND-PROM captures a range of distinct aspects of developmental and behavioral functioning in ASD that can be used to track independent functioning across domains.

A functional model for studying common trends across trial time in eye tracking experiments.

Eye tracking (ET) experiments commonly record the continuous trajectory of a subject's gaze on a two-dimensional screen throughout repeated presentations of stimuli (referred to as trials). Even though the continuous path of gaze is recorded during each trial, commonly derived outcomes for analysis collapse the data into simple summaries, such as looking times in regions of interest, latency to looking at stimuli, number of stimuli viewed, number of fixations or fixation length. In order to retain information in trial time, we utilize functional data analysis (FDA) for the first time in literature in the analysis of ET data. More specifically, novel functional outcomes for ET data, referred to as viewing profiles, are introduced that capture the common gazing trends across trial time which are lost in traditional data summaries. Mean and variation of the proposed functional outcomes across subjects are then modeled using functional principal components analysis. Applications to data from a visual exploration paradigm conducted by the Autism Biomarkers Consortium for Clinical Trials showcase the novel insights gained from the proposed FDA approach, including significant group differences between children diagnosed with autism and their typically developing peers in their consistency of looking at faces early on in trial time.

Evaluation of clinical assessments of social abilities for use in autism clinical trials by the autism biomarkers consortium for clinical trials.

Clinical trials in autism spectrum disorder (ASD) often rely on clinician rating scales and parent surveys to measure autism-related features and social behaviors. To aid in the selection of these assessments for future clinical trials, the Autism Biomarkers Consortium for Clinical Trials (ABC-CT) directly compared eight common instruments with respect to acquisition rates, sensitivity to group differences, equivalence across demographic sub-groups, convergent validity, and stability over a 6-week period. The sample included 280 children diagnosed with ASD (65 girls) and 119 neurotypical children (36 girls) aged from 6 to 11 years. Full scale IQ for ASD ranged from 60 to 150 and for neurotypical ranged from 86 to 150. Instruments measured clinician global assessment and autism-related behaviors, social communication abilities, adaptive function, and social withdrawal behavior. For each instrument, we examined only the scales that measured social or communication functioning. Data acquisition rates were at least 97.5% at T1 and 95.7% at T2. All scales distinguished diagnostic groups. Some scales significantly differed by participant and/or family demographic characteristics. Within the ASD group, most clinical instruments exhibited weak (≥ |0.1|) to moderate (≥ |0.4|) intercorrelations. Short-term stability was moderate (ICC: 0.5-0.75) to excellent (ICC: >0.9) within the ASD group. Variations in the degree of stability may inform viability for different contexts of use, such as identifying clinical subgroups for trials versus serving as a modifiable clinical outcome. All instruments were evaluated in terms of their advantages and potential concerns for use in clinical trials.

Associations Between Infant Screen Use, Electroencephalography Markers, and Cognitive Outcomes.

Importance: Research evidence is mounting for the association between infant screen use and negative cognitive outcomes related to attention and executive functions. The nature, timing, and persistence of screen time exposure on neural functions are currently unknown. Electroencephalography (EEG) permits elucidation of the neural correlates associated with cognitive impairments. Objective: To examine the associations between infant screen time, EEG markers, and school-age cognitive outcomes using mediation analysis with structural equation modeling. Design, Setting, and Participants: This prospective maternal-child dyad cohort study included participants from the population-based study Growing Up in Singapore Toward Healthy Outcomes (GUSTO). Pregnant mothers were enrolled in their first trimester from June 2009 through December 2010. A subset of children who completed neurodevelopmental visits at ages 12 months and 9 years had EEG performed at age 18 months. Data were reported from 3 time points at ages 12 months, 18 months, and 9 years. Mediation analyses were used to investigate how neural correlates were involved in the paths from infant screen time to the latent construct of attention and executive functioning. Data for this study were collected from November 2010 to March 2020 and were analyzed between October 2021 and May 2022. Exposures: Parent-reported screen time at age 12 months. Main Outcomes and Measures: Power spectral density from EEG was collected at age 18 months. Child attention and executive functions were measured with teacher-reported questionnaires and objective laboratory-based tasks at age 9 years. Results: In this sample of 437 children, the mean (SD) age at follow-up was 8.84 (0.07) years, and 227 children (51.9%) were male. The mean (SD) amount of daily screen time at age 12 months was 2.01 (1.86) hours. Screen time at age 12 months contributed to multiple 9-year attention and executive functioning measures (η2, 0.03-0.16; Cohen d, 0.35-0.87). A subset of 157 children had EEG performed at age 18 months; EEG relative theta power and theta/beta ratio at the frontocentral and parietal regions showed a graded correlation with 12-month screen use (r = 0.35-0.37). In the structural equation model accounting for household income, frontocentral and parietal theta/beta ratios partially mediated the association between infant screen time and executive functioning at school age (exposure-mediator ß, 0.41; 95% CI, 0.22 to 0.59; mediator-outcome ß, -0.38; 95% CI, -0.64 to -0.11), forming an indirect path that accounted for 39.4% of the association. Conclusions and Relevance: In this study, infant screen use was associated with altered cortical EEG activity before age 2 years; the identified EEG markers mediated the association between infant screen time and executive functions. Further efforts are urgently needed to distinguish the direct association of infant screen use compared with family factors that predispose early screen use on executive function impairments.

The Autism Biomarkers Consortium for Clinical Trials: Initial Evaluation of a Battery of Candidate EEG Biomarkers.

OBJECTIVE: Numerous candidate EEG biomarkers have been put forward for use in clinical research on autism spectrum disorder (ASD), but biomarker development has been hindered by limited attention to the psychometric properties of derived variables, inconsistent results across small studies, and variable methodology. The authors evaluated the basic psychometric properties of a battery of EEG assays for their potential suitability as biomarkers in clinical trials. METHODS: This was a large, multisite, naturalistic study in 6- to 11-year-old children who either had an ASD diagnosis (N=280) or were typically developing (N=119). The authors evaluated an EEG battery composed of well-studied assays of resting-state activity, face perception (faces task), biological motion perception, and visual evoked potentials (VEPs). Biomarker psychometrics were evaluated in terms of acquisition rates, construct performance, and 6-week stability. Preliminary evaluation of use was explored through group discrimination and phenotypic correlations. RESULTS: Three assays (resting state, faces task, and VEP) show promise in terms of acquisition rates and construct performance. Six-week stability values in the ASD group were moderate (intraclass correlations ≥0.66) for the faces task latency of the P1 and N170, the VEP amplitude of N1 and P1, and resting alpha power. Group discrimination and phenotype correlations were primarily observed for the faces task P1 and N170. CONCLUSIONS: In the context of a large-scale, rigorous evaluation of candidate EEG biomarkers for use in ASD clinical trials, neural response to faces emerged as a promising biomarker for continued evaluation. Resting-state activity and VEP yielded mixed results. The study's biological motion perception assay failed to display construct performance. The results provide information about EEG biomarker performance that is relevant for the next stage of biomarker development efforts focused on context of use.

Concomitant medication use in children with autism spectrum disorder: Data from the Autism Biomarkers Consortium for Clinical Trials.

LAY ABSTRACT: Children with autism spectrum disorder are prescribed a variety of medications that affect the central nervous system (psychotropic medications) to address behavior and mood. In clinical trials, individuals taking concomitant psychotropic medications often are excluded to maintain homogeneity of the sample and prevent contamination of biomarkers or clinical endpoints. However, this choice may significantly diminish the clinical representativeness of the sample. In a recent multisite study designed to identify biomarkers and behavioral endpoints for clinical trials (the Autism Biomarkers Consortium for Clinical Trials), school-age children with autism spectrum disorder were enrolled without excluding for medications, thus providing a unique opportunity to examine characteristics of psychotropic medication use in a research cohort and to guide future decisions on medication-related inclusion criteria. The aims of the current analysis were (1) to quantify the frequency and type of psychotropic medications reported in school-age children enrolled in the ABC-CT and (2) to examine behavioral features of children with autism spectrum disorder based on medication classes. Of the 280 children with autism spectrum disorder in the cohort, 42.5% were taking psychotropic medications, with polypharmacy in half of these children. The most commonly reported psychotropic medications included melatonin, stimulants, selective serotonin reuptake inhibitors, alpha agonists, and antipsychotics. Descriptive analysis showed that children taking antipsychotics displayed a trend toward greater overall impairment. Our findings suggest that exclusion of children taking concomitant psychotropic medications in trials could limit the clinical representativeness of the study population, perhaps even excluding children who may most benefit from new treatment options.

The role of aperiodic spectral slope in event-related potentials and cognition among children with and without attention deficit hyperactivity disorder.

Aperiodic spectral slope is a measure of spontaneous neural oscillatory activity that is believed to support regulation of brain responses to environmental stimuli. Compared to typically developing (TD) control participants, children with attention deficit hyperactivity disorder (ADHD) have been shown to have flatter aperiodic spectral slope at rest as well as attenuated event-related potential (ERP) amplitudes in response to environmental stimuli. A small body of research suggests that aperiodic slope may also explain differences in behavioral responses. In this study, we examine associations between prestimulus aperiodic slope, stimulus characteristics, environmental demands, and neural as well as behavioral responses to these stimuli. Furthermore, we evaluate whether ADHD diagnostic status moderates these associations. Seventy-nine children with ADHD and 27 TD school-age children completed two visual ERP experiments with predictable alternating presentations of task-relevant and task-irrelevant stimuli. Aperiodic slope was extracted from prestimulus time windows. Prestimulus aperiodic slope was steeper for the TD relative to ADHD group, driven by task-relevant rather than task-irrelevant stimuli. For both groups, the aperiodic slope was steeper during a task with lower cognitive demand and before trials in which they responded correctly. Aperiodic slope did not mediate the association between ADHD diagnosis and attenuated P300 amplitude. The aperiodic spectral slope is dynamic and changes in anticipation of varying stimulus categories to support performance. The aperiodic slope and P300 amplitude reflect distinct cognitive processes. Background neural oscillations, captured via aperiodic slope, support cognitive behavioral control and should be included in etiological models of ADHD.NEW & NOTEWORTHY This study constitutes the first investigation of associations between aperiodic spectral slope and three aspects of neurocognition: event-related potential (ERP) amplitudes, cognitive load, and task performance. We find that background oscillatory activity is dynamic, shifting in anticipation of varying levels of task relevance and in response to increasing cognitive load. Moreover, we report that aperiodic activity and ERPs constitute distinct neurophysiological processes. Children with attention deficit hyperactivity disorder (ADHD) show reduced aperiodic dynamics in addition to attenuated ERP amplitudes.

Absence of dynamic neural oscillatory response to environmental conditions marks childhood attention deficit hyperactivity disorder.

BACKGROUND: Prior research suggests that symptoms of attention deficit hyperactivity disorder (ADHD) and related neurodevelopmental disorders may derive from alterations in the brain's ability to flexibly tune the balance between information integration and segregation and global versus local processing. This balance allows the brain to optimally filter salient stimuli in the environment and can be measured with electroencephalography (EEG) via calculation of the aperiodic spectral slope. A steeper aperiodic slope increases the capacity of global neural networks to process low-salience stimuli, while a flatter aperiodic slope reflects an emphasis on local neural networks that respond preferentially to high-salience input. Although aperiodic slope differences have been reported in ADHD, prior studies have not accounted for differing levels of stimulus input in experimental paradigms. There is evidence to suggest that dynamic shifts in neural oscillation patterns in response to changing environmental conditions could be critical for attention regulation. METHODS: Using high-density resting EEG, we measured aperiodic spectral slope during low contrast (lights off) and high contrast (lights on) environmental conditions in a sample of 88 7-11-year-old children diagnosed with ADHD and 29 controls (30% female). RESULTS: While controls showed a flatter aperiodic slope during the high contrast (lights on) as compared to low contrast (lights off) environmental condition, children with ADHD did not show any change in aperiodic slope across conditions. CONCLUSIONS: This study presents a novel etiological model of biological mechanisms associated with ADHD. Children with ADHD show suboptimal modulation of intrinsic neural activity in response to changing environmental input. The dynamic spectral slope is a promising candidate biomarker for ADHD. The possibility that dynamic spectral slope is associated with cognitive-behavioral regulation more broadly merits further investigation.

Identifying Age Based Maturation in the ERP Response to Faces in Children With Autism: Implications for Developing 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.

Multilevel hybrid principal components analysis for region-referenced functional electroencephalography data.

Electroencephalography experiments produce region-referenced functional data representing brain signals in the time or the frequency domain collected across the scalp. The data typically also have a multilevel structure with high-dimensional observations collected across multiple experimental conditions or visits. Common analysis approaches reduce the data complexity by collapsing the functional and regional dimensions, where event-related potential (ERP) features or band power are targeted in a pre-specified scalp region. This practice can fail to portray more comprehensive differences in the entire ERP signal or the power spectral density (PSD) across the scalp. Building on the weak separability of the high-dimensional covariance process, the proposed multilevel hybrid principal components analysis (M-HPCA) utilizes dimension reduction tools from both vector and functional principal components analysis to decompose the total variation into between- and within-subject variance. The resulting model components are estimated in a mixed effects modeling framework via a computationally efficient minorization-maximization algorithm coupled with bootstrap. The diverse array of applications of M-HPCA is showcased with two studies of individuals with autism. While ERP responses to match vs mismatch conditions are compared in an audio odd-ball paradigm in the first study, short-term reliability of the PSD across visits is compared in the second. Finite sample properties of the proposed methodology are studied in extensive simulations.

A randomized controlled trial of everolimus for neurocognitive symptoms in PTEN hamartoma tumor syndrome.

PTEN hamartoma tumor syndrome (PHTS) is a complex neurodevelopmental disorder characterized by mechanistic target of rapamycin (mTOR) overactivity. Limited data suggest that mTOR inhibitors may be therapeutic. No placebo-controlled studies have examined mTOR inhibition on cognition and behavior in humans with PHTS with/without autism. We conducted a 6-month phase II, randomized, double-blinded, placebo-controlled trial to examine the safety profile and efficacy of everolimus (4.5 mg/m2) in individuals (5-45 years) with PHTS. We measured several cognitive and behavioral outcomes, and electroencephalography (EEG) biomarkers. The primary endpoint was a neurocognitive composite derived from Stanford Binet-5 (SB-5) nonverbal working memory score, SB-5 verbal working memory, Conners' Continuous Performance Test hit reaction time and Purdue Pegboard Test score. Forty-six participants underwent 1:1 randomization: n = 24 (everolimus) and n = 22 (placebo). Gastrointestinal adverse events were more common in the everolimus group (P < 0.001). Changes in the primary endpoint between groups from baseline to Month 6 were not apparent (Cohen's d = -0.10, P = 0.518). However, several measures were associated with modest effect sizes (≥0.2) in the direction of improvement, including measures of nonverbal IQ, verbal learning, autism symptoms, motor skills, adaptive behavior and global improvement. There was a significant difference in EEG central alpha power (P = 0.049) and central beta power (P = 0.039) 6 months after everolimus treatment. Everolimus is well tolerated in PHTS; adverse events were similar to previous reports. The primary efficacy endpoint did not reveal improvement. Several secondary efficacy endpoints moved in the direction of improvement. EEG measurements indicate target engagement following 6 months of daily oral everolimus. Trial Registration Information: ClinicalTrials.gov NCT02991807 Classification of Evidence: I.

The autism biomarkers consortium for clinical trials: evaluation of a battery of candidate eye-tracking biomarkers for use in autism clinical trials.

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.

Neurobehavioral Biomarkers: An EEG Family Reunion.

SUMMARY: The field of clinical EEG has had an uneasy relationship with the use of this technology for clinical cognitive applications and often for good reason. However, apart from its clinical use, EEG has had a tradition as a major tool in cognitive psychology and cognitive neuroscience dating back at least to the 1960s. Based on accumulated knowledge from its research application, EEG-based biomarkers are beginning to see applications in clinical trials and may eventually enter clinical care. We address concerns surrounding quality control, the treatment of artifact, and normal variants and how developments in engineering, biomarker validation, and implementation science rigorously applied to these tools can lead to well-justified approaches.

Parental Language Input Predicts Neuroscillatory Patterns Associated with Language Development in Toddlers at Risk of Autism.

In this study we investigated the impact of parental language input on language development and associated neuroscillatory patterns in toddlers at risk of Autism Spectrum Disorder (ASD). Forty-six mother-toddler dyads at either high (n = 22) or low (n = 24) familial risk of ASD completed a longitudinal, prospective study including free-play, resting electroencephalography, and standardized language assessments. Input quantity/quality at 18 months positively predicted expressive language at 24 months, and relationships were stronger for high-risk toddlers. Moderated mediations revealed that input-language relationships were explained by 24-month frontal and temporal gamma power (30-50 Hz) for high-risk toddlers who would later develop ASD. Results suggest that high-risk toddlers may be cognitively and neurally more sensitive to their language environments, which has implications for early intervention.

EEG Phase-Amplitude Coupling Strength and Phase Preference: Association with Age over the First Three Years after Birth.

Phase-amplitude coupling (PAC), the coupling of the phase of slower electrophysiological oscillations with the amplitude of faster oscillations, is thought to facilitate dynamic integration of neural activity in the brain. Although the brain undergoes dramatic change and development during the first few years of life, how PAC changes through this developmental period has not been extensively studied. Here, we examined PAC through electroencephalography (EEG) data collected during an awake, eyes-open EEG collection paradigm in 98 children between the ages of three months and three years. We employed non-parametric clustering methods to identify areas of significant PAC across a range of frequency pairs and electrode locations, and examined how PAC strength and phase preference develops in these areas. We found that PAC, primarily between the α-ß and γ frequencies, was positively correlated with age from early infancy to early childhood (p = 2.035 × 10-6). Additionally, we found γ over anterior electrodes coupled with the rising phase of the α-ß waveform, while γ over posterior electrodes coupled with the falling phase of the α-ß waveform; this regionalized phase preference became more prominent with age. This opposing trend may reflect each region's specialization toward feedback or feedforward processing, respectively, suggesting opportunities for back translation in future studies.

Shifted phase of EEG cross-frequency coupling in individuals with Phelan-McDermid syndrome.

BACKGROUND: Phelan-McDermid Syndrome (PMS) is a rare condition caused by deletion or mutation of the SHANK3 gene. Individuals with PMS frequently present with intellectual disability, autism spectrum disorder, and other neurodevelopmental challenges. Electroencephalography (EEG) can provide a window into network-level function in PMS. METHODS: Here, we analyze EEG data collected across multiple sites in individuals with PMS (n = 26) and typically developing individuals (n = 15). We quantify oscillatory power, alpha-gamma phase-amplitude coupling strength, and phase bias, a measure of the phase of cross frequency coupling thought to reflect the balance of feedforward (bottom-up) and feedback (top-down) activity. RESULTS: We find individuals with PMS display increased alpha-gamma phase bias (U = 3.841, p < 0.0005), predominantly over posterior electrodes. Most individuals with PMS demonstrate positive overall phase bias while most typically developing individuals demonstrate negative overall phase bias. Among individuals with PMS, strength of alpha-gamma phase-amplitude coupling was associated with Sameness, Ritualistic, and Compulsive behaviors as measured by the Repetitive Behavior Scales-Revised (Beta = 0.545, p = 0.011). CONCLUSIONS: Increased phase bias suggests potential circuit-level mechanisms underlying phenotype in PMS, offering opportunities for back-translation of findings into animal models and targeting in clinical trials.

Autism Spectrum Disorder Parent Report for Outcome Monitoring: A Preliminary Report of Development and Clinical Utility.

OBJECTIVE: Children with autism spectrum disorder (ASD) face challenges across many functional domains. A tool that gathers relevant clinical information before visits, emphasizing symptoms that are likely to change over development and inform clinical interventions, could improve health care quality, allowing for more patient-centered and efficient care. This study evaluated the clinical utility and preliminary psychometrics of the ASD Parent Report for Outcome Monitoring (ASD-PROM), a web-based measure assessing competence in core features of ASD, along with the breadth of concerns and comorbidities that frequently co-occur with ASD. METHODS: An interdisciplinary team drafted the ASD-PROM and made iterative revisions based on parent feedback. Parents of 62 children completed the ASD-PROM before their autism-specialty clinical visit, 53 completed the ASD-PROM twice, and 48 completed the Vineland Adaptive Behavior Scales, Second Edition (Vineland-II) concurrently. Parents (n = 25) and clinicians (n = 13) completed postvisit surveys to assess utility (phase 1). The ASD-PROM was then released for general clinical use (phase 2). RESULTS: On a Likert scale (1 = very poorly, 10 = very well), parents found that ASD-PROM items described their child's abilities well (median = 8.0; interquartile range [IQR]: 7.0-9.5) and had a positive effect on care (median = 8.0; IQR: 7.0-10.0). Clinicians found the ASD-PROM effective in assessing parent-reported patient abilities (median = 9.0, IQR: 7.0-9.0) and felt the ASD-PROM helped make their care more patient-centered and efficient (both median = 8.0, IQR: 6.0-9.0). Two-week test-retest reliability was acceptable (0.95). ASD-PROM scores correlated positively with scores from similar domains on the Vineland-II (Pearson r 0.30-0.50, medium to large effects). CONCLUSION: The ASD-PROM is a freely available tool to gather information on developmental and behavioral functioning in children with ASD before autism-specialty clinical visits. Clinical utility and preliminary psychometrics are promising, although limitations (including a low response rate during clinical use and a need for additional in-depth assessments and potential resulting modifications to the tool) remain to be addressed. Ultimately, the ASD-PROM may help promote patient-centered and efficient care for children across a wide range of ages and developmental levels.

Use of longitudinal EEG measures in estimating language development in infants with and without familial risk for autism spectrum disorder.

Language development in children with autism spectrum disorder (ASD) varies greatly among affected individuals and is a strong predictor of later outcomes. Younger siblings of children with ASD have increased risk of ASD, but also language delay. Identifying neural markers of language outcomes in infant siblings could facilitate earlier intervention and improved outcomes. This study aimed to determine whether EEG measures from the first 2-years of life can explain heterogeneity in language development in children at low- and high-risk for ASD, and to determine whether associations between EEG measures and language development are different depending on ASD risk status or later ASD diagnosis. In this prospective longitudinal study EEG measures collected between 3-24 months were used in a multivariate linear regression model to estimate participants' 24-month language development. Individual baseline longitudinal EEG measures included (1) the slope of EEG power across 3-12 months or 3-24 months of life for 6 canonical frequency bands, (2) estimated EEG power at age 6-months for the same frequency bands, and (3) terms representing the interaction between ASD risk status and EEG power measures. Modeled 24-month language scores using EEG data from either the first 2-years (Pearson R = 0.70, 95% CI 0.595-0.783, P=1x10-18) or the first year of life (Pearson R=0.66, 95% CI 0.540-0.761, P=2.5x10-14) were highly correlated with observed scores. All models included significant interaction effects of risk on EEG measures, suggesting that EEG-language associations are different depending on risk status, and that different brain mechanisms effect language development in low-versus high-risk infants.

Day-to-Day Test-Retest Reliability of EEG Profiles in Children With Autism Spectrum Disorder and Typical Development.

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.