Summary of Advances
In Autism Spectrum Disorder Research
EEG Analytics for Early Detection of Autism Spectrum Disorder: A data-driven approach.
Bosl WJ, Tager-Flusberg H, Nelson CA. Sci Rep. 2018 May 1;8(1):6828. [PMID: 29717196]
ASD is usually diagnosed by observing behavioral symptoms in the first few years of a child’s life. Because symptoms are highly variable among children, this method of diagnosis can be challenging and may result in a late diagnosis. Consequently, many researchers are interested in studying biomarkers — unique biological indicators of a condition or disorder – to help clinicians diagnose ASD earlier without relying on diverse behavioral symptoms that may present later in life.
In this study, researchers used EEG (electroencephalogram), a cost-effective tool used to record electrical activity in different areas of the brain. This technology can monitor atypical brain development and be used to identify potential biomarkers in children at risk for ASD. To test the effectiveness of EEG as a tool for early ASD detection, the researchers obtained EEG recordings from three groups of infants: those at high risk for ASD (determined by an older sibling diagnosed with ASD) who were subsequently determined to not have ASD, those with low risk, and those at high or low risk who ultimately received an ASD diagnosis.
The researchers gathered EEG recordings from the infants every three to six months, beginning when the children were 3 months old and ending at 36 months old. At multiple time points during the study, the children were assessed using the Autism Diagnostic Observation Schedule (ADOS), a standard ASD assessment tool. Children who met ADOS criteria at any assessment point were evaluated by a licensed clinical psychologist for ASD diagnosis.
The researchers found that EEG recordings were predictive of a clinical diagnostic outcome of ASD and estimated the severity of symptoms as early as three months of age. They noted that children who did develop ASD showed different activity patterns in certain areas of the brain compared to those in the low-risk and high-risk groups. Children who later developed ASD and children who were at high risk but did not develop ASD started with similar EEG patterns, however their patterns deviated at the 12-month interval and the high-risk group began following patterns similar to the low-risk group, although at a higher electrical frequency.
This study suggests that electrical activity in these brain regions may be used as biomarkers to diagnose ASD and predict symptom severity. EEG recordings present a promising method for early detection of ASD. Future research should focus on determining whether this technology can be easily administered during routine well-baby checkups to increase the likelihood of detecting ASD in infancy.
Automatic Emotion and Attention Analysis of Young Children at Home: A ResearchKit Autism Feasibility Study.
Egger HL, Dawson G, Hashemi J, Carpenter KLH, Espinosa S, Campbell K, Brotkin S, Schaich-Borg J, Qiu Q, Tepper M, Baker JP, Bloomfield RA, and Sapiro G. npj Digital Medicine. 2018 Jun 1;1(20). http://www.nature.com/articles/s41746-018-0024-6
Children with ASD are typically diagnosed through behavioral observation by trained professionals in a clinical setting. Professional evaluation using behavioral assessment tools is often costly, time-consuming, and depends on the availability and accessibility of qualified evaluators; these factors can delay diagnosis and intervention. Health care providers also rely heavily on caregiver reports. Although these reports are important components of the diagnostic process, they can be subjective and are influenced by the caregiver’s educational background and familiarity with the questions being asked. To alleviate these problems, researchers are working to develop tools that they can use to objectively observe and measure ASD-related behavior in children in non-clinical settings.
In this study, researchers developed a mobile app for assessing children’s behavior in their usual settings, such as at home or in school. They hoped that this naturalistic method of observation would provide them with firsthand evidence of children’s behavior outside of a formal office or clinic setting. They also aimed to use the data from these recordings to objectively identify behavior patterns, potentially eliminating the need to rely on personal caregiver reports to screen for ASD.
The study included 1,756 caregivers of children ages 12-72 months with and without ASD. Caregivers downloaded the app to an iPhone and completed questionnaires related to their child’s behavior. Caregivers also completed the Modified Checklist for Autism and Toddlers (M-CHAT), a screening tool that evaluates ASD risk. After activating the app, each caregiver’s iPhone displayed short movies that were designed to elicit different patterns of attention and facial expressions while the camera in the iPhone recorded the child’s responses. This allowed the researchers to quantify the child’s behavioral reactions as they watched the movies. Computer vision analysis was then used to automatically code the child’s facial movements and determine when the child’s facial expression was positive, negative, or neutral, and whether or not the child was paying attention to the video.
The researchers found that children who were at a high risk for ASD (based on the child’s M-CHAT score and the information provided by the caregiver) were more likely to react neutrally to the videos and less likely to show positive emotions, compared to low-risk children. They also found that girls at high risk for ASD were less attentive to the videos than girls at low risk for ASD. Lastly, the researchers found that the attention responses for girls at high risk for ASD were significantly lower than boys at high risk for ASD, which suggests a need for further investigation into possible sex differences in attention response that could be useful in improving screening and diagnostic tools.
This study suggests that this is a promising approach that may facilitate screening of ASD at an early age. The researchers suggest that digital phenotyping may be a valuable supplement to traditional diagnostic techniques, potentially providing clinicians with a more objective and complete picture of a child’s behavior.
A longitudinal study of parent-reported sensory responsiveness in toddlers at-risk for autism.
Wolff JJ, Dimian AF, Botteron KN, Dager SR, Elison JT, Estes AM, Hazlett HC, Schultz RT, Zwaigenbaum L, Piven J; IBIS Network. Child Psychol Psychiatry. 2018 Oct 23. [PMID: 30350375]
Children with ASD often respond differently to sensory stimuli than do typically developing children. Children with ASD may exhibit hyperresponsivity (overreaction to sensory stimuli), hyporesponsivity (underreaction to sensory stimuli), or sensory-seeking behavior (actively seeking out a particular stimulus). Currently, the Diagnostic and Statistical Manual of Mental Disorders, 5th edition (DSM-5) broadly categorizes atypical sensory responsivity as a type of restricted and repetitive behavior (RRB). However, not much is known about the association between RRBs and the emergence of sensory responsivity symptoms in early childhood. Furthermore, there is uncertainty about the age at which atypical sensory responses can be used to determine ASD risk.
In this study, researchers sought to assess the relationship between responses to sensory stimuli and the risk for developing ASD. They administered the Sensory Experiences Questionnaire (SEQ) and the Repetitive Behavior Scale-Revised (RBS-R) to parents of infants who were at high and low risk for developing ASD at 12 months and 24 months of age. Infants were considered high risk if they had at least one older sibling with ASD. When the children reached 2 years old, they were evaluated for ASD using the Autism Diagnostic Observation Scale (ADOS), the Autism Diagnostic Interview-Revised (ADI-R), the Mullen Scales of Early Learning (MSEL), and the Vineland Adaptive Behavior Scales-II (Vineland-II). Based on their risk level and diagnosis, infants were classified as LR controls (low-risk infants who were not diagnosed with ASD), HR-neg (high-risk infants who were not subsequently diagnosed with ASD), and HR-ASD (high-risk infants who were subsequently diagnosed with ASD).
Researchers compared the children’s total SEQ scores, as well as subscores for hyperresponsiveness, hyporesponsiveness, and sensory seeking behaviors. In addition, the researchers analyzed subscale scores for sensory experiences, including visual, auditory, and tactile (touch) modalities. They found that, at 12 months of age, the HR-ASD group scored higher in the hyperresponsivity and tactile modalities relative to both HR-neg and LR controls. Between 12 and 24 months, the likelihood of atypical total SEQ scores and subscores increased in the HR-ASD group and decreased in the HR-neg and LR-control groups.
The researchers also wanted to better understand the relationship between sensory responses and RRBs, so they compared the scores from the SEQ with the RBS-R and the Vineland II. They found that, at 12 and 24 months of age, HR-ASD children had SEQ scores that correlated with their scores on the RBS-R, and that the association between the two was strongest at 24 months of age. The SEQ scores at 24 months also had a negative association to the Vineland II socialization and communication scores, supporting previous research that RRBs may interfere with social-communication skills.
The results from this study support the hypothesis that these behaviors can be observed in infants who develop ASD as early as 12 months of age. Furthermore, the finding that sensory responsivity is significantly associated with RRBs supports categorizing atypical sensory responses as RRBs in the DSM-5.