National Database For Autism Research Research Articles

Receptive language and receptive-expressive discrepancy in minimally verbal autistic children and adolescents.

Among the approximately one-third of autistic individuals who experience considerable challenges in acquiring spoken language and are minimally verbal (MV), relatively little is known about the range of their receptive language abilities. This study included 1579 MV autistic children and adolescents between 5 and 18 years of age drawn from the National Database for Autism Research and the SFARI Base data repository. MV autistic children and adolescents demonstrated significantly lower receptive language compared to the norms on standardized language assessment and parent report measures. Moreover, their receptive language gap widened with age. Overall, our sample demonstrated significantly better receptive than expressive language. However, at the individual level, only about 25% of MV autistic children and adolescents demonstrated significantly better receptive language relative to their minimal expressive levels. Social skills explained a significant proportion of the variance in parent-reported receptive language skills, while motor skills were the most significant predictor of greater receptive-expressive discrepancy. Findings from this study revealed the heterogeneous language profiles in MV autistic children and adolescents, underscoring the importance of individualizing interventions to match their different communication strengths and needs and integrating multiple interconnected areas to optimize their overall development of language comprehension, socialization, and general motor skills.

Autism spectrum disorder-specific changes in white matter connectome edge density based on functionally defined nodes.

Autism spectrum disorder (ASD) is associated with both functional and microstructural connectome disruptions. We deployed a novel methodology using functionally defined nodes to guide white matter (WM) tractography and identify ASD-related microstructural connectome changes across the lifespan. We used diffusion tensor imaging and clinical data from four studies in the national database for autism research (NDAR) including 155 infants, 102 toddlers, 230 adolescents, and 96 young adults - of whom 264 (45%) were diagnosed with ASD. We applied cortical nodes from a prior fMRI study identifying regions related to symptom severity scores and used these seeds to construct WM fiber tracts as connectome Edge Density (ED) maps. Resulting ED maps were assessed for between-group differences using voxel-wise and tract-based analysis. We then examined the association of ASD diagnosis with ED driven from functional nodes generated from different sensitivity thresholds. In ED derived from functionally guided tractography, we identified ASD-related changes in infants (pFDR ≤ 0.001-0.483). Overall, more wide-spread ASD-related differences were detectable in ED based on functional nodes with positive symptom correlation than negative correlation to ASD, and stricter thresholds for functional nodes resulted in stronger correlation with ASD among infants (z = -6.413 to 6.666, pFDR ≤ 0.001-0.968). Voxel-wise analysis revealed wide-spread ED reductions in central WM tracts of toddlers, adolescents, and adults. We detected early changes of aberrant WM development in infants developing ASD when generating microstructural connectome ED map with cortical nodes defined by functional imaging. These were not evident when applying structurally defined nodes, suggesting that functionally guided DTI-based tractography can help identify early ASD-related WM disruptions between cortical regions exhibiting abnormal connectivity patterns later in life. Furthermore, our results suggest a benefit of involving functionally informed nodes in diffusion imaging-based probabilistic tractography, and underline that different age cohorts can benefit from age- and brain development-adapted image processing protocols.

An end-to-end infant brain parcellation pipeline

Objective Accurate infant brain parcellation is crucial for understanding early brain development; however, it is challenging due to the inherent low tissue contrast, high noise, and severe partial volume effects in infant magnetic resonance images (MRIs). The aim of this study is to develop an end-to-end pipeline that enables accurate parcellation of infant brain MRIs. Methods We proposed an end-to-end pipeline that employs a two-stage global-to-local approach for accurate parcellation of infant brain MRIs. Specifically, in the global ROIs localization stage, a combination of transformer and convolution operations is employed to capture both global spatial features and fine texture features, enabling an approximate localization of the ROIs across the whole brain. In the local ROIs refinement stage, leveraging the position priors from the first stage along with the raw MRIs, the boundaries of the ROIs are refined for a more accurate parcellation. Results We utilize the Dice ratio to evaluate the accuracy of parcellation results. Results on 263 subjects from National Database for Autism Research (NDAR), Baby Connectome Project (BCP) and Cross-site datasets demonstrate the superior accuracy and robustness of our method than other competing methods. Conclusion Our end-to-end pipeline may be capable of accurately parcellating 6-month-old infant brain MRIs.

Autism spectrum disorder symptom expression in individuals with 3q29 deletion syndrome

BackgroundThe 1.6 Mb 3q29 deletion is associated with neurodevelopmental and neuropsychiatric phenotypes, including a 19-fold increased risk for autism spectrum disorder (ASD). Previous work by our team identified elevated social disability in this population via parent-report questionnaires. However, clinical features of ASD in this population have not been explored in detail.MethodsThirty-one individuals with 3q29 deletion syndrome (3q29del, 61.3% male) were evaluated using two gold-standard clinical ASD evaluations: the Autism Diagnostic Observation Schedule, Second Edition (ADOS-2), and the Autism Diagnostic Interview, Revised (ADI-R). Four matched comparators for each subject were ascertained from the National Database for Autism Research. Item-level scores on the ADOS-2 and ADI-R were compared between subjects with 3q29del and matched comparators.ResultsSubjects with 3q29del and no ASD (3q29del-ASD) had greater evidence of social disability compared to typically developing (TD) comparison subjects across the ADOS-2. Subjects with 3q29del and ASD (3q29del + ASD) were largely indistinguishable from non-syndromic ASD (nsASD) subjects on the ADOS-2. 3q29del + ASD performed significantly better on social communication on the ADI-R than nsASD (3q29 + ASD mean = 11.36; nsASD mean = 15.70; p = 0.01), and this was driven by reduced deficits in nonverbal communication (3q29 + ASD mean = 1.73; nsASD mean = 3.63; p = 0.03). 3q29del + ASD reported significantly later age at the first two-word phrase compared to nsASD (3q29del + ASD mean = 43.89 months; nsASD mean = 37.86 months; p = 0.01). However, speech delay was not related to improved nonverbal communication in 3q29del + ASD.LimitationsThere were not enough TD comparators with ADI-R data in NDAR to include in the present analysis. Additionally, our relatively small sample size made it difficult to assess race and ethnicity effects.Conclusions3q29del is associated with significant social disability, irrespective of ASD diagnosis. 3q29del + ASD have similar levels of social disability to nsASD, while 3q29del-ASD have significantly increased social disability compared to TD individuals. However, social communication is reasonably well preserved in 3q29del + ASD relative to nsASD. It is critical that verbal ability and social disability be examined separately in this population to ensure equal access to ASD and social skills evaluations and services.

A-269 Sex Differences in the Behavioral Profile of Adolescents with Autism Spectrum Disorder

Abstract Objective: Literature suggests discrepant prevalence rates between males and females with autism spectrum disorder (ASD) may be due to sex-specific ASD phenotypes. This study sought to identify sex-specific behavioral profiles for adolescents with ASD, as measured by the Autism Diagnostic Observation Schedule (ADOS-2). Sex differences in intellectual ability for individuals with ASD were also examined. Method: A sample of adolescents with ASD across the United States was retrieved from the National Database for Autism Research (NDAR) (N=80, 65 males, 15 females; M age=12.17 years, SD=.97). Participants were included within the age parameters (11 years, zero months to 16 years, 11 months) with data on Wechsler Intelligence Scale for Children (WISC-V) and ADOS-2. The study was a cross-sectional design analyzing the relationship between biological sex and intellectual abilities, overall autism severity, and deficits within social affect and repetitive and restricted behaviors and interests (RRBIs). Results: Significant sex differences were found in total severity of ASD after controlling for IQ, F(1,77)=4.59, p=.04. Adjusted marginal means demonstrated that males have higher ADOS total scores [M =13.37; 95% CI (12.47,14.34)] than females [M=11.13; 95% CI (9.33,12.98)]. No sex differences were found within the domain of intellectual ability, t(78)=.34, p= .738, or across the linear combination of the dependent variables of social affect and RRBIs, F(2,76)=2.95, p=.058. Conclusions: This study provides further evidence that males are diagnosed with ASD at higher rates than females and suggests males present with more severe ASD pathology than females on the ADOS-2. Additional analyses are required to understand the full extent of clinical implications.

Advancing methodologies to improve RRB outcome measures in autism research: Evaluation of the RBS-R.

This study evaluates the psychometric properties (dimensionality, item bias, reliability) of the Repetitive Behavior Scale-Revised (RBS-R), provides scoring guidelines for the dimensional measure, and makes recommendations for future RRB measure development. Participants included individuals from three large autism data repositories; Simon Foundation Powering Autism Research for Knowledge (SPARK), Simons Simplex Collection (SSC), and National Database for Autism Research (NDAR). The total sample included N = 15,318 autistic individuals ages 3-18. Confirmatory factor analysis was used to evaluate competing theoretical factor structures. Item response theory (IRT) was used to evaluate differential item functioning, estimate the reliability of each RBS-R subdomain, and score the subdomains. A unidimensional factor structure demonstrated clearly inadequate model fit, calling into question the practice of reporting a total score on the RBS-R. A five-dimensional factor structure was supported by the theoretical and empirical evidence, though the fifth factor (restricted interests) was not sufficiently reliable for use. IRT-based scoring tools were generated for use in research. The present study illustrates the promise in the future development of measures for RRBs, particularly in the development of measures to separately and specifically assess RRB constructs using rigorous methodological guidelines. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Embryonic lethal genetic variants and chromosomally normal pregnancy loss

To examine whether rare damaging genetic variants are associated with chromosomally normal pregnancy loss and estimate the magnitude of the association. Case-control. Cases were derived from a consecutive series of karyotyped losses at one New Jersey hospital. Controls were derived from the National Database for Autism Research. Cases comprised 19 chromosomally normal loss conceptus-parent trios. Controls comprised 547 unaffected siblings of autism case-parent trios. None. The rate of damaging variants in the exome (loss of function and missense-damaging) and the proportions of probands with at least one such variant among cases vs. controls. The proportions of probands with at least one rare damaging variant were 36.8% among cases and 22.9% among controls (odds ratio, 2.0; 99% confidence interval, 0.5-7.3). No case had a variant in a known fetal anomaly gene. The proportion with variants in possibly embryonic lethal genes increased in case probands (odds ratio, 14.5; 99% confidence interval, 1.5-89.7); variants occurred in BAZ1A, FBN2, and TIMP2. Rare genetic variants in the conceptus may be a cause of chromosomally normal pregnancy loss. A larger sample is needed to estimate the magnitude of the association with precision and identify relevant biologic pathways.

Neurodiversity and Intelligence: Evaluating the Flynn Effect in Children with Autism Spectrum Disorder.

The Flynn Effect (FE) among child and adolescent populations indicates that intelligence scores improve by about three points per decade. Using nine years of data from the National Database for Autism Research, this study examined whether general intelligence changed significantly for nine cohorts with autism spectrum disorder (ASD; N = 671). Analyses demonstrated a downward trend such that Cohen's d from 1998 to 2006 was -0.27. The mean IQ is 92.74 for years 1-3, 91.54 for years 4-6, and 87.34 for years 7-9, indicating a reverse FE of 5.4 points per decade. A linear regression revealed a significant negative FE comparable to the positive effect of age on IQ among those with ASD. Implications for research, practice, and law are discussed.

A Preliminary Psychometric Analysis of the Difficulties with Emotion Regulation Scale (DERS) Among Autistic Adolescents and Adults: Factor Structure, Reliability, and Validity.

Emotion dysregulation is common among autistic people, yet few measures have received psychometric evaluation in this population. We examined the factor structure, reliability, and validity of a commonly-used measure of emotion dysregulation, the Difficulties with Emotion Regulation Scale (DERS), in a sample of 156 autistic adolescents and adults. Data were drawn from the NIH National Database for Autism Research (NDAR) and an author's existing dataset. Results demonstrated that the factor structure generally conformed to the original 6-factor model, with modifications. Reliability analyses revealed good-to-excellent internal consistencies. Validity analyses indicated that the DERS was positively associated with measures of anxiety, depression, and alexithymia. Our findings provide preliminary evidence for the utility of the DERS in a small autistic sample, with minor modifications.

Differential Item Functioning Based on Autism Features, IQ, and Age on the Screen for Child Anxiety Related Disorders (SCARED) Among Youth on the Autism Spectrum.

Anxiety commonly occurs among youth on the autism spectrum, yet measurement of anxiety in this population is complicated by a number of factors, including potentially overlapping symptomatology, the child's intellectual functioning, and changes in anxiety across development. Moreover, few studies have examined the psychometric properties of anxiety measures in this population, and no study to date has tested whether there are systematic differences in the measurement of anxiety, or differential item functioning (DIF), across the high degree of heterogeneity and the developmental course of autism. To test this possibility, data were combined across multiple studies using the National Database for Autism Research, an NIH-funded data repository. Parent-report on the Screen for Child Anxiety Related Disorders (SCARED) and Social Communication Questionnaire (SCQ) were used as measures of anxiety and autism features, respectively. A confirmatory factor analysis indicated good fit of the literature standard five-factor structure. Moderated nonlinear factor analysis (MNLFA) revealed multiple items with intercept and loading DIF based on level of autism features, IQ, and age, especially for items related to social behavior. Therefore, although the measure's factor structure is consistent with that found in the general population, the SCARED may not capture differences in anxiety equivalently for all children on the spectrum and across their development. Clinicians and researchers need to be especially vigilant in measuring anxiety symptoms in children with autism by removing items flagged for DIF from the SCARED and/or by using multiple measures and informants. LAY ABSTRACT: Autistic youth often experience clinical levels of anxiety. Many tools used to measure anxiety were developed for the general population, but not for use with autistic youth. This study found that the Screen for Child Anxiety Related Disorders (SCARED) measures the same five dimensions of anxiety as in the general population. Parents, however, may respond differently to questions on the SCARED based on their child's autism features, intellectual functioning, and age, which impacts our ability to accurately measure anxiety among autistic youth.

Integrating genomic and resting State fMRI for efficient autism spectrum disorder classification

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with a complex clinical syndrome and difficult diagnosis. The fusion of multimodal data improves the accuracy of ASD diagnosis, benefiting from the complementary information contained in the multimodal data. The aim of this study was to diagnose ASD using predictors based on integrated resting state MRI (rs-fMRI) and genetic data. Our study used fMRI and gene expression data from 71 participants in the National Database for Autism Research (NDAR). T-test and SVM-RFE were used for feature reduction and optimal feature selection. To make the best use of the features extracted from each data source, we used the EasyMKL to establish a classification model for the prediction of ASD rather than simply concatenating feature matrices. The experimental results validate the effectiveness of our method. In addition, the imaging and genetic features we extracted have been shown to be significantly associated with ASD supported by previous studies.

Applying Item Response Theory Modeling to Identify Social (Pragmatic) Communication Disorder.

Purpose No diagnostic tools exist for identifying social (pragmatic) communication disorder (SPCD), a new Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition category for individuals with social communication deficits but not the repetitive, restricted behaviors and interests (RRBIs) that would qualify them for a diagnosis of autism spectrum disorder (ASD). We explored the value of items from a widely used screening measure of ASD for distinguishing SPCD from typical controls (TC; Aim 1) and from ASD (Aim 2). Method We applied item response theory (IRT) modeling to Social Communication Questionnaire-Lifetime (Rutter, Bailey, & Lord, 2003) records available in the National Database for Autism Research. We defined records from putative SPCD (n = 54), ASD (n = 278), and TC (n = 274) groups retrospectively, based on National Database for Autism Research classifications and Autism Diagnostic Interview-Revised responses. After assessing model assumptions, estimating model parameters, and measuring model fit, we identified items in the social communication and RRBI domains that were maximally informative in differentiating the groups. Results IRT modeling identified a set of seven social communication items that distinguished SPCD from TC with sensitivity and specificity > 80%. A set of five RRBI items was less successful in distinguishing SPCD from ASD (sensitivity and specificity < 70%). Conclusion The IRT modeling approach and the Social Communication Questionnaire-Lifetime item sets it identified may be useful in efforts to construct screening and diagnostic measures for SPCD.

Biomarker Acquisition and Quality Control for Multi-Site Studies: The Autism Biomarkers Consortium for Clinical Trials.

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

A PRELIMINARY VOLUMETRIC MRI STUDY OF AMYGDALA AND HIPPOCAMPAL SUBFIELDS IN AUTISM DURING INFANCY.

Currently, autism spectrum disorder (ASD) is mainly diagnosed by the observation of core behavioral symptoms. Consequently, the window of opportunity for effective intervention may have passed, when the disorder is detected until 3 years of age. Thus, it is of great importance to identify imaging-based biomarkers for early diagnosis of ASD. Previous findings indicate that an abnormal pattern of the amygdala and hippocampal development in autism persists through childhood and adolescence. However, due to the low tissue contrast and small structural size of amygdala and hippocampal subfields, our knowledge on their growth in autistics in early stage still remains very limited. In this paper, for the first time, we propose a volume-based analysis of the amygdala and hippocampal subfields of the infant subjects with risk of ASD at around 24 months of age. Specifically, to address the challenge of low tissue contrast, we propose a novel deep-learning approach, i.e., dilated-dense U-Net, to automatically segment the amygdala and hippocampal subfields. Experimental results on National Database for Autism Research (NDAR) show the advantages of our proposed method in terms of segmentation accuracy. Our volume-based analysis shows the overgrowths of amygdala and CA1-3 of hippocampus, which may link to the emergence of autism spectrum disorder.

How many girls are we missing in ASD? An examination from a clinic- and community-based sample

PurposeResearch has indicated that males diagnosed with autism spectrum disorder (ASD) outnumber females diagnosed with ASD, which has been attributed to a number of potential biological and genetic risk factors. The purpose of this paper is to estimate how many girls may be missing from ASD via a two-study format, comparing two distinct data sets to Centers for Disease Control and Prevention population estimates for sex distribution of males vs females in ASD.Design/methodology/approachIn Study 1, the authors utilized data from the National Database for Autism Research as a clinic-based sample. In Study 2, the authors utilized data from the National Survey of Children’s Health as a community-based sample.FindingsThe current study estimates that approximately 39 percent more girls should be diagnosed with ASD. The authors estimate that the sex distribution in ASD should be approximately 28 percent female and 72 percent male based upon current practices. Thus, it appears that more females are being identified as potentially having ASD but were not subsequently being diagnosed with ASD as compared to their male counterparts.Originality/valueThese results could suggest that a leaky pipeline in the assessment of girls with ASD may exist along one or more points in the ASD diagnostic process, with one potential point at the level of ASD-specific screening (i.e. the SCQ in Study 1) in the clinic setting and another in the community setting as a whole for universal screening (i.e. NSCH data in Study 2).

A Longitudinal MRI Study of Amygdala and Hippocampal Subfields for Infants with Risk of Autism.

Currently, there are still no early biomarkers to detect infants with risk of autism spectrum disorder (ASD), which is mainly diagnosed based on behavioral observations at three or four years of age. Since intervention efforts may miss a critical developmental window after 2 years old, it is clinically significant to identify imaging-based biomarkers at an early stage for better intervention, before behavioral diagnostic signs of ASD typically arising. Previous studies on older children and young adults with ASD demonstrate altered developmental trajectories of the amygdala and hippocampus. However, our knowledge on their developmental trajectories in early postnatal stages remains very limited. In this paper, for the first time, we propose a volume-based analysis of the amygdala and hippocampal subfields of the infant subjects with risk of ASD at 6, 12, and 24 months of age. To address the challenge of low tissue contrast and small structural size of infant amygdala and hippocampal subfields, we propose a novel deep-learning approach, dilated-dense U-Net, to digitally segment the amygdala and hippocampal subfields in a longitudinal dataset, the National Database for Autism Research (NDAR). A volume-based analysis is then performed based on the segmentation results. Our study shows that the overgrowth of amygdala and cornu ammonis sectors (CA) 1-3 May start from 6 months of age, which may be related to the emergence of autistic spectrum disorder.

Investigating autism etiology and heterogeneity by decision tree algorithm

Abstract Autism spectrum disorder (ASD) is a neurodevelopmental disorder that causes deficits in cognition, communication and social skills. ASD, however, is a highly heterogeneous disorder. This heterogeneity has made identifying the etiology of ASD a particularly difficult challenge, as patients exhibit a wide spectrum of symptoms without any unifying genetic or environmental factors to account for the disorder. For better understanding of ASD, it is paramount to identify potential genetic and environmental risk factors that are comorbid with it. Identifying such factors is of great importance to determine potential causes for the disorder, and understand its heterogeneity. Existing large-scale datasets offer an opportunity for computer scientists to undertake this task by utilizing machine learning to reliably and efficiently obtain insight about potential ASD risk factors, which would in turn assist in guiding research in the field. In this study, decision tree algorithms were utilized to analyze related factors in datasets obtained from the National Database for Autism Research (NDAR) consisting of nearly 3000 individuals. We were able to identify 15 medical conditions that were highly associated with ASD diagnoses in patients; furthermore, we extended our analysis to the family medical history of patients and we report six potentially hereditary medical conditions associated with ASD. Associations reported had a 90% accuracy. Meanwhile, gender comparisons highlighted conditions that were unique to each gender and others that overlapped. Those findings were validated by the academic literature, thus opening the way for new directions for the use of decision tree algorithms to further understand the etiology of autism.

FMEM: Functional Mixed Effects Models for Longitudinal Functional Responses.

The aim of this paper is to conduct a systematic and theoretical analysis of estimation and inference for a class of functional mixed effects models (FMEM). Such FMEMs consist of fixed effects that characterize the association between longitudinal functional responses and covariates of interest and random effects that capture the spatial-temporal correlations of longitudinal functional responses. We propose local linear estimates of refined fixed effect functions and establish their weak convergence along with a simultaneous confidence band for each fixed-effect function. We propose a global test for the linear hypotheses of varying coefficient functions and derive the associated asymptotic distribution under the null hypothesis and the asymptotic power under the alternative hypothesis are derived. We also establish the convergence rates of the estimated spatial-temporal covariance operators and their associated eigenvalues and eigenfunctions. We conduct extensive simulations and apply our method to a white-matter fiber data set from a national database for autism research to examine the finite-sample performance of the proposed estimation and inference procedures.

Gender differences in restricted and repetitive behaviors and interests in youth with autism.

Previous work has found gender differences in restricted and repetitive behaviors and interests (RRBI) for autism spectrum disorder (ASD). Compared to girls, affected boys have increased stereotyped and restricted behaviors; however much less is known about gender differences in other areas of RRBI. This study aims to identify whether specific RRBI (i.e., stereotyped, self-injurious, compulsive, insistence on sameness, ritualistic, and restricted), as measured by item-level data on the Repetitive Behavior Scale-Revised (RBS-R), can distinguish girls from boys with ASD. Participants included 615 individuals with ASD (507 boys; 82.4%), ages 3-18 years of age (M = 10.26, SD = 4.20), who agreed to share data with the National Database for Autism Research (NDAR). Multivariate analysis of variance and discriminant function analysis (DFA) were used to determine whether item-level RBS-R data could correctly classify cases by gender. DFA results suggest that RBS-R items significantly differentiate gender. Strongly differentiating RBS-R items had greater success in correctly classifying affected boys (67.90%) than girls (61.00%). Items that best-discriminated gender were heightened stereotyped behaviors and restricted interests items in boys and compulsive, sameness, restricted, and self-injurious behavior items in girls. This study is the first to find that girls with ASD may have increased compulsive, sameness, and restricted RRBI compared to boys. Additionally, findings support heightened self-injurious behaviors in affected girls. Future research should disentangle whether elevated rates of RRBI in girls are central to the presentation of ASD in girls or an epiphenomenon of the high rates of co-occurring disorders (e.g., anxiety) noted in girls. Autism Res 2019, 12: 274-283 © 2018 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: This study is the first to examine a comprehensive measure of repetitive behavior in children with autism, with findings of increased compulsive, insistence on sameness, and self-injurious behavior characterizing girls and increased stereotyped and restricted behavior characterizing boys. Future research should determine whether these elevated behaviors in girls are directly part of the autism presentation in girls or symptoms of co-occurring psychopathology. It is important for autism diagnostic measures to best capture the types of repetitive behavior girls may demonstrate.

Anatomy-guided joint tissue segmentation and topological correction for 6-month infant brain MRI with risk of autism.

Tissue segmentation of infant brain MRIs with risk of autism is critically important for characterizing early brain development and identifying biomarkers. However, it is challenging due to low tissue contrast caused by inherent ongoing myelination and maturation. In particular, at around 6 months of age, the voxel intensities in both gray matter and white matter are within similar ranges, thus leading to the lowest image contrast in the first postnatal year. Previous studies typically employed intensity images and tentatively estimated tissue probabilities to train a sequence of classifiers for tissue segmentation. However, the important prior knowledge of brain anatomy is largely ignored during the segmentation. Consequently, the segmentation accuracy is still limited and topological errors frequently exist, which will significantly degrade the performance of subsequent analyses. Although topological errors could be partially handled by retrospective topological correction methods, their results may still be anatomically incorrect. To address these challenges, in this article, we propose an anatomy-guided joint tissue segmentation and topological correction framework for isointense infant MRI. Particularly, we adopt a signed distance map with respect to the outer cortical surface as anatomical prior knowledge, and incorporate such prior information into the proposed framework to guide segmentation in ambiguous regions. Experimental results on the subjects acquired from National Database for Autism Research demonstrate the effectiveness to topological errors and also some levels of robustness to motion. Comparisons with the state-of-the-art methods further demonstrate the advantages of the proposed method in terms of both segmentation accuracy and topological correctness.