A Comparative Analysis of the ADOS-G and ADOS-2 Algorithms: Preliminary Findings

Replication study for ADOS-2 cut-offs to assist evaluation of autism spectrum disorder

The Autism Diagnostic Observation Schedule, Second Edition (ADOS-2) has been widely used for ASD assessment. While prior studies investigated sensitivity and specificity of ADOS-2 Modules 1-3, there has been limited research addressing algorithm cut-off scores to optimize ADOS-2 classification. The goal of this study was to assess algorithm cut-off scores for diagnosing ASD with Modules 1-3, and to evaluate alignment of the ADOS-2 classification with the best estimate clinical diagnosis. Participants included 3144 children aged 31 months or older who received ADOS-2 Modules 1-3, as well as the best estimate clinical diagnosis. Five classification statistics were reported for each module: sensitivity, specificity, positive predictive value, negative predictive value, and accuracy (i.e., Receiver Operator Classification Statistic), and these statistics were calculated for the optimal cut-off score. Frequency tables were used to compare ADOS-2 classification and the best estimate clinical diagnosis. Half of the sample received Module 3, 21% received Module 2, and 29% received Module 1. The overall prevalence of ASD was 60%; the male-to-female ratio was 4:1, and half of the sample was non-White. Across all modules, the autism spectrum cut-off score from the ADOS-2 manual resulted in high sensitivity (95%+) and low specificity (63%-73%). The autism cut-off score resulted in better specificity (76%-86%) with favorable sensitivity (81%-94%). The optimal cut-off scores for all modules based on the current sample were within the autism spectrum classification range except Module 2 Algorithm 2. In the No ASD group, 29% had false positives (ADOS-2 autism spectrum classification or autism classification). The ADOS-2 autism spectrum classification did not indicate directionality for diagnostic outcome (ASD 56% vs. No ASD 44%). While cut-off scores of ADOS-2 Modules 1-3 in the manual yielded good clinical utility in ASD assessment, false positives and low predictability of the autism spectrum classification remain challenging for clinicians.

Different functional connectivity optimal frequency in autism compared with healthy controls and the relationship with social communication deficits: Evidence from gene expression and behavior symptom analyses

Studies have reported that different brain regions/connections possess distinct frequency properties, which are related to brain function. Previous studies have proposed altered brain activity frequency and frequency-specific functional connectivity (FC) patterns in autism spectrum disorder (ASD), implying the varied dominant frequency of FC in ASD. However, the difference of the dominant frequency of FC between ASD and healthy controls (HCs) remains unclear. In the present study, the dominant frequency of FC was measured by FC optimal frequency, which was defined as the intermediate of the frequency bin at which the FC strength could reach the maximum. A multivariate pattern analysis was conducted to determine whether the FC optimal frequency in ASD differs from that in HCs. Partial least squares regression (PLSR) and enrichment analyses were conducted to determine the relationship between the FC optimal frequency difference of ASD/HCs and cortical gene expression. PLSR analyses were also performed to explore the relationship between FC optimal frequency and the clinical symptoms of ASD. Results showed a significant difference of FC optimal frequency between ASD and HCs. Some genes whose cortical expression patterns are related to the FC optimal frequency difference of ASD/HCs were enriched for social communication problems. Meanwhile, the FC optimal frequency in ASD was significantly related to social communication symptoms. These results may help us understand the neuro-mechanism of the social communication deficits in ASD.

Diagnostic validity of Autism Diagnostic Observation Schedule, second edition (K-ADOS-2) in the Korean population

Background

Although the Korean version of the Autism Diagnostic Observation Schedule-2 (K-ADOS‐2) is widely being used to diagnose autism spectrum disorder (ASD) in South Korea, no previous study has examined the validity and reliability of all modules of K-ADOS-2 across a wide age range, particularly older children, adolescents, and adults.

Method

Data from 2,158 participants were included (mean age = 79.7 months; 73.6% male): 1473 participants with ASD and 685 participants without ASD (Toddler Module, n = 289; Module 1, n = 642; Module 2 n = 574; Module 3 n = 411; Module 4, n = 242). Participants completed a battery of tests, including the K-ADOS or K-ADOS-2 and other existing diagnostic instruments. Sensitivity, specificity, area under the receiver operating characteristic (ROC) curve, positive predictive value (PPV), negative predictive value (NPV), Cohen’s kappa (k), and agreement with existing diagnostic instruments were computed. Cronbach’s α values were also calculated.

Results

All developmental cells of the K-ADOS-2 showed sufficient ranges of sensitivity 85.4–100.0%; specificity, 80.4–96.8%; area under the ROC curve, .90-.97; PPV, 77.8–99.3%; NPV, 80.6–100.0%; and k values, .83–.92. The kappa agreements of developmental cells with existing diagnostic instruments ranged from .20 to .90. Cronbach’s α values ranged from .82 to .91 across all developmental cells.

Limitation

The best-estimate clinical diagnoses made in this study were not independent of the K-ADOS-2 scores. Some modules did not include balanced numbers of participants in terms of gender and diagnostic status.

Conclusion

The K-ADOS-2 is a valid and reliable instrument in diagnosing ASD in South Korea. Future studies exploring the effectiveness of the K-ADOS-2 in capturing restricted, repetitive behaviors and differentiating ASD from other developmental disabilities are needed.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13229-022-00506-5.

Deep learning for neuroimaging-based diagnosis and rehabilitation of Autism Spectrum Disorder: A review

Accurate diagnosis of Autism Spectrum Disorder (ASD) followed by effective rehabilitation is essential for the management of this disorder. Artificial intelligence (AI) techniques can aid physicians to apply automatic diagnosis and rehabilitation procedures. AI techniques comprise traditional machine learning (ML) approaches and deep learning (DL) techniques. Conventional ML methods employ various feature extraction and classification techniques, but in DL, the process of feature extraction and classification is accomplished intelligently and integrally. DL methods for diagnosis of ASD have been focused on neuroimaging-based approaches. Neuroimaging techniques are non-invasive disease markers potentially useful for ASD diagnosis. Structural and functional neuroimaging techniques provide physicians substantial information about the structure (anatomy and structural connectivity) and function (activity and functional connectivity) of the brain. Due to the intricate structure and function of the brain, proposing optimum procedures for ASD diagnosis with neuroimaging data without exploiting powerful AI techniques like DL may be challenging. In this paper, studies conducted with the aid of DL networks to distinguish ASD are investigated. Rehabilitation tools provided for supporting ASD patients utilizing DL networks are also assessed. Finally, we will present important challenges in the automated detection and rehabilitation of ASD and propose some future works.

Autism Diagnostic Observation Schedule (ADOS-2) elevations in a clinical sample of children and adolescents who do not have autism: Phenotypic profiles of false positives

OBJECTIVE

While the Autism Diagnostic Observation Schedule, Second Edition (ADOS-2) shows high sensitivity for detecting autism spectrum disorder (ASD) when present (i.e. true positives), scores on the ADOS-2 may be falsely elevated for individuals with cognitive impairments or psychological concerns other than ASD (i.e. false positives). This study examined whether demographic, psychological, cognitive, and/or adaptive factors predict ADOS-2 false positives and which psychiatric diagnoses most often result in false positives.

METHOD

Sensitivity, specificity, false positive, and false negative rates were calculated among 214 5- to 16-year-old patients who completed an ADOS-2 (module 3) as part of an ASD diagnostic evaluation. Additional analyses were conducted with the 101 patients who received clinically elevated ADOS-2 scores (i.e. 56 true positives and 45 false positives).

RESULTS

Results revealed a 34% false positive rate and a 1% false negative rate. False positives were slightly more likely to be male, have lower restricted and repetitive behavior (RRB) severity scores on the ADOS-2, and demonstrate elevated anxiety during the ADOS-2. Neither IQ, adaptive functioning, nor caregiver-reported emotional functioning was predictive of false positive status. Trauma-related psychiatric diagnoses were more common among false positives.

CONCLUSIONS

The ADOS-2 should not be used in isolation to assess for ASD, and, in psychiatrically-complex cases, RRB symptom severity may be particularly helpful in differentiating ASD from other psychiatric conditions. Additionally, heightened levels of anxiety, more so than overactivity or disruptive behavior, may lead to non-ASD specific elevations in ADOS-2 scores.

Linked Social-Communication Dimensions and Connectivity in Functional Brain Networks in Autism Spectrum Disorder

Much recent attention has been directed toward elucidating the structure of social interaction-communication dimensions and whether and how these symptom dimensions coalesce with each other in individuals with autism spectrum disorder (ASD). However, the underlying neurobiological basis of these symptom dimensions is unknown, especially the association of social interaction and communication dimensions with brain networks. Here, we proposed a method of whole-brain network-based regression to identify the functional networks linked to these symptom dimensions in a large sample of children with ASD. Connectome-based predictive modeling (CPM) was established to explore neurobiological evidence that supports the merging of communication and social interaction deficits into one symptom dimension (social/communication deficits). Results showed that the default mode network plays a core role in communication and social interaction dimensions. A primary sensory perceptual network mainly contributed to communication deficits, and high-level cognitive networks mainly contributed to social interaction deficits. CPM revealed that the functional networks associated with these symptom dimensions can predict the merged dimension of social/communication deficits. These findings delineate a link between brain functional networks and symptom dimensions for social interaction and communication and further provide neurobiological evidence supporting the merging of communication and social interaction deficits into one symptom dimension.

Systematic Review and Meta-Analysis of the Clinical Utility of the ADOS-2 and the ADI-R in Diagnosing Autism Spectrum Disorders in Children

The Autism Diagnostic Observation Schedule, Second Edition (ADOS-2) and the Autism Diagnostic Interview, Revised (ADI-R) have high accuracy as diagnostic instruments in research settings, while evidence of accuracy in clinical settings is less robust. This meta-analysis focused on efficacy of these measures in research versus clinical settings. Articles (n = 22) were analyzed using a hierarchical summary receiver operating characteristics (HSROC) model. ADOS-2 performance was stronger than the ADI-R. ADOS-2 sensitivity and specificity ranged from .89-.92 and .81-.85, respectively. ADOS-2 accuracy in research compared with clinical settings was mixed. ADI-R sensitivity and specificity were .75 and .82, respectively, with higher specificity in research samples (Research = .85, Clinical = .72). A small number of clinical studies were identified, indicating ongoing need for investigation outside research settings.

The Role of Demographics in the Age of Autism Diagnosis in Jerusalem

Early diagnosis of autism spectrum disorder (ASD) in children enables earlier access to services and better ability to predict subsequent development. A vast body of literature consistently shows discrepancies in the age of diagnosis between children from varying socio-economic levels, cultural and ethnic backgrounds. The present study examines the effect of sociodemographic factors on age of ASD diagnosis among the three primary ethnic sectors in Jerusalem region: secular and modern religious Jews, ultra-Orthodox Jews and Arabs. Findings indicate minimal differences in age of diagnosis prior to the age of six, although Arab children of this age were largely minimally verbal. After age six, no Arab children were referred for an evaluation.