The Performance of the First Year Inventory (FYI) Screening on a Sample of High-Risk 12-Month-Olds Diagnosed with Autism Spectrum Disorder (ASD) at 36 Months

Prediction of autism in infants: progress and challenges

Autism spectrum disorder (henceforth autism) is a neurodevelopmental condition that can be reliably diagnosed in children by age 18-24 months. Prospective longitudinal studies of infants aged 1 year and younger who are later diagnosed with autism are elucidating the early developmental course of autism and identifying ways of predicting autism before diagnosis is possible. Studies that use MRI, EEG, and near-infrared spectroscopy have identified differences in brain development in infants later diagnosed with autism compared with infants without autism. Retrospective studies of infants younger than 1 year who received a later diagnosis of autism have also showed an increased prevalence of health conditions, such as sleep disorders, gastrointestinal disorders, and vision problems. Behavioural features of infants later diagnosed with autism include differences in attention, vocalisations, gestures, affect, temperament, social engagement, sensory processing, and motor abilities. Although research findings offer insight on promising screening approaches for predicting autism in infants, individual-level predictions remain a future goal. Multiple scientific challenges and ethical questions remain to be addressed to translate research on early brain-based and behavioural predictors of autism into feasible and reliable screening tools for clinical practice.

Tools for early screening of autism spectrum disorders in primary health care – a scoping review

Background

Autism spectrum disorder (ASD) is a neurodevelopmental disorder that manifests itself in early childhood. Early diagnosis of these disorders allows for the initiation of early therapy, which is crucial for the child's further functioning in society.

Objectives

This review aims to gather and present the existing ASD screening tools that can be used in primary care and adapted to different countries conditions linguistically and culturally.

Eligibility criteria

We searched for English-language publications on ASD screening tools for children aged 0–3 years suitable for use in primary care (i.e. free, requiring no additional training or qualifications).

Sources of evidence

Four databases were explored to find English studies on ASD screening tools intended for the rapid assessment of children aged 0–3.

Charting methods

The information sought (specific features of the questionnaires relevant to primary health care workers, psychometric and diagnostic values of a given cultural adaptation of screening tools, and the linguistic and cultural changes made) were extracted and collected to create profiles of these tools.

Results

We found 81 studies which met inclusion criteria and underwent full data extraction. Three additional data sources were included. These allowed to create 75 profiles of adaptations for 26 different screening tools and collect data on their psychometric values and characteristic features.

Conclusions

The results of our study indicate the availability of several diagnostic tools for early ASD screening in primary care setting concordant culturally and linguistically with a given population. They could be an effective method of accelerating the diagnostic process and starting personalized therapy faster. However, most tools have significant limitations – some are only available for research purposes, while others do not have scientific evidence to prove their effectiveness.

Hypoconnectivity between anterior insula and amygdala associates with future vulnerabilities in social development in a neurodiverse sample of neonates

Altered resting state functional connectivity (FC) involving the anterior insula (aINS), a key node in the salience network, has been reported consistently in autism. Here we examined, for the first time, FC between the aINS and the whole brain in a sample of full-term, postmenstrual age (PMA) matched neonates (mean 44.0 weeks, SD = 1.5) who due to family history have high likelihood (HL) for developing autism (n = 12) and in controls (n = 41) without family history of autism (low likelihood, LL). Behaviors associated with autism were evaluated between 12 and 18 months (M = 17.3 months, SD = 2.5) in a subsample (25/53) of participants using the First Year Inventory (FYI). Compared to LL controls, HL neonates showed hypoconnectivity between left aINS and left amygdala. Lower connectivity between the two nodes was associated with higher FYI risk scores in the social domain (r(25) = -0.561, p = .003) and this association remained robust when maternal mental health factors were considered. Considering that a subsample of LL participants (n = 14/41) underwent brain imaging during the fetal period at PMA 31 and 34 weeks, in an exploratory analysis, we evaluated prospectively development of the LaINS-Lamy connectivity and found that the two areas strongly coactivate throughout the third trimester of pregnancy. The study identifies left lateralized anterior insula-amygdala connectivity as a potential target of further investigation into neural circuitry that enhances likelihood of future onset of social behaviors associated with autism during neonatal and potentially prenatal periods.

Pre-symptomatic intervention for autism spectrum disorder (ASD): defining a research agenda

Autism spectrum disorder (ASD) impacts an individual's ability to socialize, communicate, and interact with, and adapt to, the environment. Over the last two decades, research has focused on early identification of ASD with significant progress being made in understanding the early behavioral and biological markers that precede a diagnosis, providing a catalyst for pre-symptomatic identification and intervention. Evidence from preclinical trials suggest that intervention prior to the onset of ASD symptoms may yield more improved developmental outcomes, and clinical studies suggest that the earlier intervention is administered, the better the outcomes. This article brings together a multidisciplinary group of experts to develop a conceptual framework for behavioral intervention, during the pre-symptomatic period prior to the consolidation of symptoms into diagnosis, in infants at very-high-likelihood for developing ASD (VHL-ASD). The overarching goals of this paper are to promote the development of new intervention approaches, empirical research, and policy efforts aimed at VHL-ASD infants during the pre-symptomatic period (i.e., prior to the consolidation of the defining features of ASD).

Towards a Data-Driven Approach to Screen for Autism Risk at 12 Months of Age

OBJECTIVE

This study aimed to develop a classifier for infants at 12 months of age based on a parent-report measure (the First Year Inventory 2.0 [FYI]), for the following reasons: (1) to classify infants at elevated risk, above and beyond that attributable to familial risk status for ASD; and (2) to serve as a starting point to refine an approach for risk estimation in population samples.

METHOD

A total of 54 high-familial risk (HR) infants later diagnosed with ASD (HR-ASD), 183 HR infants not diagnosed with ASD at 24 months of age (HR-Neg), and 72 low-risk controls participated in the study. All infants contributed FYI data at 12 months of age and had a diagnostic assessment for ASD at age 24 months. A data-driven, cross-validated analytic approach was used to develop a classifier to determine screening accuracy (eg, sensitivity) of the FYI to classify HR-ASD and HR-Neg.

RESULTS

The newly developed FYI classifier had an estimated sensitivity of 0.71 (95% CI: 0.50, 0.91) and specificity of 0.72 (95% CI: 0.49, 0.91).

CONCLUSION

This classifier demonstrates the potential to improve current screening for ASD risk at 12 months of age in infants already at elevated familial risk for ASD, increasing opportunities for detection of autism risk in infancy. Findings from this study highlight the utility of combining parent-report measures with machine learning approaches.

Screening for Autism Spectrum Disorder in Premature Subjects Hospitalized in a Neonatal Intensive Care Unit

Considering that the average age for diagnosis of autism spectrum disorder (ASD) is 4–5 years, testing screening methods for ASD risk in early infancy is a public health priority. This study aims to identify the risks for development of ASD in children born prematurely and hospitalized in a neonatal intensive care unit (NICU) and explore the association with pre-, peri- and postnatal factors. Methods: The children’s families were contacted by telephone when their child was between 18 and 24 months of age, to apply the Modified Checklist for Autism in Toddlers (M-CHAT). The sample consisted of 40 children (57.5% boys). M-CHAT screening revealed that 50% of the sample showed early signs of ASD. Although the frequency of delayed development was higher in boys, this difference was not statistically significant between the sexes (p = 0.11). Assessment of the association between perinatal conditions and early signs of autism in children hospitalized in an NICU exhibited no correlation between the factors analyzed (birth weight and type of delivery). The findings indicate a high risk of ASD in premature children, demonstrating no associations with gestational and neonatal variables or the hospitalization conditions of the NICUs investigated.

The Criterion Validity of the First Year Inventory and the Quantitative-CHecklist for Autism in Toddlers: A Longitudinal Study

Pediatric surveillance through screening procedures is needed to detect warning signs of risk for Autism Spectrum Disorder under 24 months of age and to promote early diagnosis and treatment. The main purpose of this study is to extend the literature regarding the psychometric properties of two screening tools, the First Year Inventory (FYI) and the Quantitative-CHecklist for Autism in Toddler (Q-CHAT), testing their criterion validity. They were administered during a three-wave approach involving the general population. At T1, 657 children were tested with the FYI and 36 of them were found to be at risk. At T2, 545 were tested with the Q-CHAT and 29 of them were found to be at risk. At T3, 12 out of the 36 children with a high score on the FYI and 11 out of the 29 children with a high score on the Q-CHAT were compared to 15 typically developing children. The criterion validity was tested considering the severity of the autistic symptoms, emotional/behavioral problems, and limited global functioning as criteria. Accuracy parameters were also calculated. Furthermore, we investigated which dimension of each questionnaire better predicted the aforementioned criterion. The results corroborated the hypotheses and confirmed the criterion validity of FYI and Q-CHAT.

Construct validity of the First-Year Inventory (FYI Version 2.0) in 12-month-olds at high-risk for Autism Spectrum Disorder

LAY ABSTRACT

The First-Year Inventory 2.0 is a parent-report screening instrument designed to identify 12-month-old infants at risk for an eventual diagnosis of Autism Spectrum Disorder. This instrument focuses on Social-Communication and Sensory-Regulatory areas of infant behavior. Although the First-Year Inventory 2.0 screening performance has been previously studied, its validity has not been examined. Establishing validity of an instrument is important because it supports the effectiveness and the reliability of the instrument. In this study, we examined relationship between the First-Year Inventory 2.0 (Social-Communication and Sensory-Regulatory areas) and other instruments that measure similar areas of infant behavior in a sample of high-risk infant siblings of children with Autism Spectrum Disorder. These other instruments share some common aims and theoretical areas with the First-Year Inventory 2.0: the Autism Observation Scale for Infants, the Mullen Scales of Early Learning, the Vineland Adaptive Behavior Scales-II, and the Infant Behavior Questionnaire. Findings generally supported the validity of the First-Year Inventory 2.0 with other instruments. In particular, the Social-Communication area of the First-Year Inventory 2.0 showed greater commonality with other instruments than in the Sensory-Regulatory area. The Sensory-Regulatory area seemed to be a unique feature of the First-Year Inventory 2.0 instrument. Considering different aims and strengths of assessments, researchers and clinicians are encouraged to utilize a variety of instruments in a comprehensive evaluation of a child.

Language Growth in Young Children with Autism: Interactions Between Language Production and Social Communication

Young children with autism spectrum disorder (ASD) present with a broad range of spoken language abilities, as well as delays in precursor skills such as gesture production and joint attention skills. While standardized assessments describe language strengths, the Communication and Symbolic Behavior Scales (CSBS-DP) is a particularly robust measure as it additionally characterizes precise aspects of social communication. This study provides a unique contribution by assessing the interactional effects of CSBS-DP Social Composite performance with early language samples on later language outcomes. Our results indicate that multiple social communication elements significantly interact with early spoken language to predict later language. Our findings also highlight the transactional relationship between early spoken vocabulary and social communication skills that bolster language development growth.

Autism spectrum disorder risk prediction: A systematic review of behavioral and neural investigations

A reliable diagnosis of autism spectrum disorder (ASD) is difficult to make until after toddlerhood. Detection in an earlier age enables early intervention, which is typically more effective. Recent studies of the development of brain and behavior in infants and toddlers have provided important insights in the diagnosis of autism. This extensive review focuses on published studies of predicting the diagnosis of autism during infancy and toddlerhood younger than 3 years using behavioral and neuroimaging approaches. After screening a total of 782 papers, 17 neuroimaging and 43 behavioral studies were reviewed. The features for prediction consist of behavioral measures using screening tools, observational and experimental methods, brain volumetric measures, and neural functional activation and connectivity patterns. The classification approaches include logistic regression, linear discriminant function, decision trees, support vector machine, and deep learning based methods. Prediction performance has large variance across different studies. For behavioral studies, the sensitivity varies from 20% to 100%, and specificity ranges from 48% to 100%. The accuracy rates range from 61% to 94% in neuroimaging studies. Possible factors contributing to this inconsistency may be partially due to the heterogeneity of ASD, different targeted populations (i.e., high-risk group for ASD and general population), age when the features were collected, and validation procedures. The translation to clinical practice requires extensive further research including external validation with large sample size and optimized feature selection. The use of multi-modal features, e.g., combination of neuroimaging and behavior, is worth further investigation to improve the prediction accuracy.