| Examining Diagnostic Trends and Gender Differences in the ADOS-II | 10.15154/jxd2-vw05 | Approximately 3–4 boys for every girl meet the clinical criteria for autism in studies of community diagnostic patterns and studies of autism using samples of convenience. However, girls with autism have been hypothesized to be underdiagnosed, possibly because they may present with differing symptom profiles as compared to boys. This secondary data analysis used the National Database of Autism Research (NDAR) to examine how gender and symptom profiles are associated with one another in a gold standard assessment of autism symptoms, the Autism Diagnostic Observation Schedule II (ADOS-II; Lord, C., Luyster, R., Guthrie, W., & Pickles A. (2012a). Patterns of developmental trajectories in toddlers with autism spectrum disorder. Journal of Consulting and Clinical Psychology, 80(3):477–489. https://doi.org/10.1037/a0027214. Epub 2012 Apr 16. PMID: 22506796, PMCID: PMC3365612). ADOS-II scores from 6183 children ages 6–14 years from 78 different studies in the NDAR indicated that gender was a significant predictor of total algorithm, restrictive and repetitive behavioral, and social communicative difficulties composite severity scores. These findings suggest that gender differences in ADOS scores are common in many samples and may reflect on current diagnostic practices. | 143/5615 | Secondary Analysis | Shared |
| Gender Differences: Confirmatory Factor Analysis of the ADOS-II | 10.15154/4sxe-qh09 | Purpose
Recent research has suggested that autism may present differently in girls compared to boys, encouraging the exploration of a sex-differential diagnostic criteria. Gender differences in diagnostic assessments have been shown on the ADOS-II, such that, on average, females score significantly lower than males on all scales and are less likely to show atypicality on most items related to social communicative difficulties. Yet, gender differences in the latent structure of instruments like the ADOS-II have not been examined systematically.
Methods
As such, this secondary data analysis examined 4,100 youth diagnosed with autism (Mage = 9.9, 813 female & 3287 male) examined item response trends by gender on the ADOS-II Module 3.
Results
Multi-Group Confirmatory Factor Analysis results show that the factor loadings of four ADOS-II items differ across the genders. One SCD item and one RRB item are strongly related to the latent factor in the female group, while two RRB items have larger factor loadings in the male group.
Conclusion
The assumption of an identical latent factor structure for the ADOS-II Module 3 for males and females might not be justifiable. Possible diagnostic implications are discussed. | 143/5615 | Secondary Analysis | Shared |
| Prognostic early snapshot stratification of autism based on adaptive functioning | 10.15154/0z1c-1d37 | A major goal of precision medicine is to predict prognosis based on individualized information at the earliest possible points in development. Using early snapshots of adaptive functioning and unsupervised data- driven discovery methods, we uncover highly stable early autism subtypes that yield information relevant to later prognosis. Data from the National Institute of Mental Health Data Archive (NDA) (n = 1,098) was used to uncover three early subtypes (<72 months) that generalize with 96% accuracy. Outcome data from NDA (n = 2,561; mean age, 13 years) also reproducibly clusters into three subtypes with 99% generalization accuracy. Early snapshot subtypes predict developmental trajectories in non-verbal cognitive, language and motor domains and are predictive of membership in different adaptive functioning outcome subtypes. Robust and prognosis- relevant subtyping of autism based on early snapshots of adaptive functioning may aid future research work via prediction of these subtypes with our reproducible stratification model. | 37/3517 | Secondary Analysis | Shared |
| Investigating autism etiology and heterogeneity by decision tree algorithm | 10.15154/1518655 | 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.
| 47/3382 | Secondary Analysis | Shared |
| The striatal matrix compartment is expanded in autism spectrum disorder. | 10.15154/khn8-jf08 | Background: Autism spectrum disorder (ASD) is the second-most common neurodevelopmental disorder in childhood. This complex developmental disorder that manifests with restricted interests, repetitive behaviors, and difficulties in communication and social awareness. The inherited and acquired causes of ASD impact many and diverse brain regions, challenging efforts to identify a shared neuroanatomical substrate for this range of symptoms. The striatum and its connections are among the most implicated sites of abnormal structure and/or function in ASD. Striatal projection neurons develop in segregated tissue compartments, the matrix and striosome, that are histochemically, pharmacologically, and functionally distinct. Immunohistochemical assessment of ASD and animal models of autism described abnormal matrix:striosome volume ratios, with an possible shift from striosome to matrix volume. Shifting the matrix:striosome ratio could result from expansion in matrix, reduction in striosome, spatial redistribution of the compartments, or a combination of these changes. Each type of ratio-shifting abnormality may predispose to ASD but yield different combinations of ASD features.
Methods: We developed a cohort of 426 children and adults (213 matched ASD-control pairs) and
performed connectivity-based parcellation (diffusion tractography) of the striatum. This identified voxels with matrix-like and striosome-like patterns of structural connectivity.
Results: Matrix-like volume was increased in ASD, with no evident change in the volume or organization of the striosome-like compartment. The inter-compartment volume difference (matrix minus striosome) within each individual was 31% larger in ASD. Matrix-like volume was increased in both caudate and putamen, and in somatotopic zones throughout the rostral-caudal extent of the striatum. Subjects with moderate elevations in ADOS (Autism Diagnostic Observation Schedule) scores had increased matrix-like volume, but those with highly elevated ADOS scores had 3.7-fold larger increases in matrix-like volume.
Conclusions: Matrix and striosome are embedded in distinct structural and functional networks, suggesting that compartment-selective injury or maldevelopment may mediate specific and distinct clinical features. Previously, assessing the striatal compartments in humans required post mortem tissue. Striatal parcellation provides a means to assess neuropsychiatric diseases for compartment-specific abnormalities in vivo. While this ASD cohort had increased matrix-like volume, other mechanisms that shift the matrix:striosome ratio may also increase the chance of developing the diverse social, sensory, and motor phenotypes of ASD.
| 2/2166 | Secondary Analysis | Shared |
| Imbalanced social-communicative and restricted repetitive behavior subtypes in autism spectrum disorder exhibit different neural circuitry | 10.15154/1524418 | Social-communication (SC) and restricted repetitive behaviors (RRB) are autism diagnostic symptom domains. SC and RRB severity can markedly differ within and between individuals and may be underpinned by different neural circuitry and genetic mechanisms. Modeling SC-RRB balance could help identify how neural circuitry and genetic mechanisms map onto such phenotypic heterogeneity. Here, we developed a phenotypic stratification model that makes highly accurate (97–99%) out-of-sample SC = RRB, SC > RRB, and RRB > SC subtype predictions. Applying this model to resting state fMRI data from the EU-AIMS LEAP dataset (n = 509), we find that while the phenotypic subtypes share many commonalities in terms of intrinsic functional connectivity, they also show replicable differences within some networks compared to a typically-developing group (TD). Specifically, the somatomotor network is hypoconnected with perisylvian circuitry in SC > RRB and visual association circuitry in SC = RRB. The SC = RRB subtype show hyperconnectivity between medial motor and anterior salience circuitry. Genes that are highly expressed within these networks show a differential enrichment pattern with known autism-associated genes, indicating that such circuits are affected by differing autism-associated genomic mechanisms. These results suggest that SC-RRB imbalance subtypes share many commonalities, but also express subtle differences in functional neural circuitry and the genomic underpinnings behind such circuitry. | 8/1708 | Secondary Analysis | Shared |
| Sensorimotor variability distinguishes early features of cognition in toddlers with autism | 10.15154/qfej-cf04 | The potential role of early sensorimotor features to atypical human cognition in autistic children has received surprisingly little attention given that appropriate movements are a crucial element that connects us to other people. Crucially it is sensorimotor function prior to the development of autism which would be of most interest in terms of establishing causal links. We examined movements acquired during natural sleep in over 200 toddlers recruited for neuroimaging studies, months before they were diagnosed with Autism Spectrum Disorder (ASD) or were ascertained as typically developing. We show there are significant correlations between objective, quantitative features of the distribution of head speed in the scanner (coefficient of variance and skewness) and subsequent cognitive abilities (intelligence quotient, IQ) in these children. Relative to higher-IQ ASD toddlers, those with lower-IQ had significantly altered sensorimotor features. Remarkably, we found that higher cognitive abilities in autistic toddlers confer resilience to atypical movement, as sensorimotor features in higher-IQ ASD toddlers were indistinguishable from those of typically developing healthy control toddlers. In a second set of analyses, we examined general motor skills assessed using observational instruments during wakefulness in an independent sample of over 1,000 infants and toddlers who received ASD diagnoses by 3-4 years. We detected significantly lower motor scores for lower-IQ vs. higher IQ autistic children, at 6, 12, 18, 24, and 30 months. We suggest that the altered movement patterns may affect key autistic behaviors in those with lower intelligence by affecting sensorimotor learning mechanisms. Atypical sensorimotor functioning is a novel, key feature in lower-IQ early childhood autism. | 2/1078 | Secondary Analysis | Shared |
| comparing EEG metrics during eyes closed versus eyes open rest in autism | 10.15154/1528590 | Understanding the complex relationship between brain dynamics and mental disorders has proved difficult. Sample sizes have often been small, and brain dynamics have often been evaluated in only one state. Here, data obtained from the NIMH data archive were used to create a sample of 395 individuals with both eyes open and eyes closed resting state EEG data. All data were submitted to a standard pipeline to extract power spectra, peak alpha frequency, the slope of the 1/f curve, multi scale sample entropy, phase amplitude coupling, and intersite phase clustering. These data along with the survey data collected at the time of data collection form a valuable resource for interogating the relationship between brain state changes and autism diagnosis. | 4/336 | Secondary Analysis | Shared |
| Mapping Along-Tract Commissural and Association White Matter Microstructural Differences in Autistic Children and Young Adults | 10.15154/91w9-gc71 | Previous diffusion magnetic resonance imaging (dMRI) research has indicated altered white matter microstructure in autism, but the implicated regions are inconsistent across studies. Such prior work has largely used conventional dMRI analysis methods, including the traditional microstructure model, diffusion tensor imaging (DTI). However, these methods are limited in their ability to precisely map microstructural differences and accurately resolve complex fiber configurations. In our study, we investigated white matter microstructure alterations in autism using the refined along-tract analytic approach, BUndle ANalytics (BUAN), with both an advanced microstructure model, the tensor distribution function (TDF) and DTI. We analyzed dMRI data from 365 autistic and neurotypical participants (5-24 years; 34% female) from 10 cohorts to examine commissural and association tracts. Autism was associated with lower fractional anisotropy and higher diffusivity in localized portions of nearly every commissural and association tract examined; these tracts inter-connected a wide range of brain regions, including frontal, temporal, parietal, and occipital regions. Taken together, BUAN and TDF allow robust and spatially precise mapping of microstructural properties in autism. Our findings rigorously demonstrate that white matter microstructure alterations in autism may be greater within specific regions of individual tracts, and that the implicated tracts are distributed across the brain. | 3/259 | Secondary Analysis | Shared |
| Cortico-Basal Ganglia Brain Structure and Links to Restricted, Repetitive Behavior in Autism Spectrum Disorder | 10.15154/1528130 | Restricted repetitive behavior (RRB) is one of two criteria domains required for the diagnosis of autism spectrum disorder (ASD). Neuroimaging is widely used to study brain alterations associated with ASD and the domain of social and communication deficits, but there has been less work regarding alterations associated with RRB. In this study we utilized neuroimaging data available from the National Database for Autism Research to assess volume in the basal ganglia and cerebellum, as well as microstructure in basal ganglia and cerebellar white matter tracts in ASD. We also investigated whether these measures differed between males and females with ASD, and how these factors correlated with clinical measures of RRB from the same individuals. We found that individuals with ASD had significant differences in free-water corrected fractional anisotropy (FAT) and free-water in cortico-basal ganglia white matter tracts, but that these measures did not differ between males versus females with ASD. Moreover, both FAT and free-water in these tracts were significantly correlated with measures of RRB. Despite no differences in volumetric measures in basal ganglia and cerebellum, these findings suggest the links between RRB and brain structure are within specific cortico-basal ganglia white matter tracts. | 3/192 | Secondary Analysis | Shared |
