Innovative approaches to the study of social phenotypes in neurodevelopmental disorders: an introduction to the research topic

R01 HD033470 - NICHD NIH HH

Can Lessons from Public Health Disease Surveillance Be Applied to Environmental Public Health Tracking?

Disease surveillance has a century-long tradition in public health, and environmental data have been collected at a national level by the U.S. Environmental Protection Agency for several decades. Recently, the Centers for Disease Control and Prevention announced an initiative to develop a national environmental public health tracking (EPHT) network with “linkage” of existing environmental and chronic disease data as a central goal. On the basis of experience with long-established disease surveillance systems, in this article we suggest how a system capable of linking routinely collected disease and exposure data should be developed, but caution that formal linkage of data is not the only approach required for an effective EPHT program. The primary operational goal of EPHT has to be the “treatment” of the environment to prevent and/or reduce exposures and minimize population risk for developing chronic diseases. Chronic, multifactorial diseases do not lend themselves to data-driven evaluations of intervention strategies, time trends, exposure patterns, or identification of at-risk populations based only on routinely collected surveillance data. Thus, EPHT should be synonymous with a dynamic process requiring regular system updates to a) incorporate new technologies to improve population-level exposure and disease assessment, b) allow public dissemination of new data that become available, c) allow the policy community to address new and emerging exposures and disease “threads,” and d) evaluate the effectiveness of EPHT over some appropriate time interval. It will be necessary to weigh the benefits of surveillance against its costs, but the major challenge will be to maintain support for this important new system

Maternal vocal feedback to 9-month-old infant siblings of children with ASD

Infant siblings of children with autism spectrum disorder display differences in early language and social communication skills beginning as early as the first year of life. While environmental influences on early language development are well documented in other infant populations, they have received relatively little attention inside of the infant sibling context. In this study, we analyzed home video diaries collected prospectively as part of a longitudinal study of infant siblings. Infant vowel and consonant-vowel vocalizations and maternal language-promoting and non-promoting verbal responses were scored for 30 infant siblings and 30 low risk control infants at 9 months of age. Analyses evaluated whether infant siblings or their mothers exhibited differences from low risk dyads in vocalization frequency or distribution, and whether mothers' responses were associated with other features of the high risk context. Analyses were conducted with respect to both initial risk group and preliminary outcome classification. Overall, we found no differences in infants' consonant-vowel vocalizations, the frequency of overall maternal utterances, or the distribution of mothers' response types. Both groups of infants produced more vowel than consonant-vowel vocalizations, and both groups of mothers responded to consonant-vowel vocalizations with more language-promoting than non-promoting responses. These results indicate that as a group, mothers of high risk infants provide equally high quality linguistic input to their infants in the first year of life and suggest that impoverished maternal linguistic input does not contribute to high risk infants' initial language difficulties. Implications for intervention strategies are also discussed.R21 DC 08637 - NIDCD NIH HHS; T32 MH073124 - NIMH NIH HHS; R01-DC010290 - NIDCD NIH HHS; R21 DC008637 - NIDCD NIH HHS; R01 DC010290 - NIDCD NIH HHS; U54 HD090255 - NICHD NIH HH

Diary reports of concerns in mothers of infant siblings of children with autism across the first year of life

We examined the home-based concerns reported by mothers of infant siblings of children with autism across the first year of life. At all three ages measured, mothers of high-risk infants were significantly more likely than mothers of low-risk infants to report language, social communication, and restricted and repetitive behavior concerns but were not more likely to report general, medically based concerns. At 6 and 9 months of age, maternal concerns were poorly related to infant or family variables. At 12 months of age, there were moderate correlations between maternal concerns and infant behavior, and concerns were associated with the proband's autism symptoms and mothers' concurrent depressive symptoms. These findings highlight the need to examine high-risk infants' development in the family context.R21 DC 08637 - NIDCD NIH HHS; R01-DC010290 - NIDCD NIH HHS; AS1323 - Autism Speaks; R21 DC008637 - NIDCD NIH HHS; R01 DC010290 - NIDCD NIH HH

EEG complexity as a biomarker for autism spectrum disorder risk

BACKGROUND: Complex neurodevelopmental disorders may be characterized by subtle brain function signatures early in life before behavioral symptoms are apparent. Such endophenotypes may be measurable biomarkers for later cognitive impairments. The nonlinear complexity of electroencephalography (EEG) signals is believed to contain information about the architecture of the neural networks in the brain on many scales. Early detection of abnormalities in EEG signals may be an early biomarker for developmental cognitive disorders. The goal of this paper is to demonstrate that the modified multiscale entropy (mMSE) computed on the basis of resting state EEG data can be used as a biomarker of normal brain development and distinguish typically developing children from a group of infants at high risk for autism spectrum disorder (ASD), defined on the basis of an older sibling with ASD. METHODS: Using mMSE as a feature vector, a multiclass support vector machine algorithm was used to classify typically developing and high-risk groups. Classification was computed separately within each age group from 6 to 24 months. RESULTS: Multiscale entropy appears to go through a different developmental trajectory in infants at high risk for autism (HRA) than it does in typically developing controls. Differences appear to be greatest at ages 9 to 12 months. Using several machine learning algorithms with mMSE as a feature vector, infants were classified with over 80% accuracy into control and HRA groups at age 9 months. Classification accuracy for boys was close to 100% at age 9 months and remains high (70% to 90%) at ages 12 and 18 months. For girls, classification accuracy was highest at age 6 months, but declines thereafter. CONCLUSIONS: This proof-of-principle study suggests that mMSE computed from resting state EEG signals may be a useful biomarker for early detection of risk for ASD and abnormalities in cognitive development in infants. To our knowledge, this is the first demonstration of an information theoretic analysis of EEG data for biomarkers in infants at risk for a complex neurodevelopmental disorder.This research was supported by a grant from Autism Speaks (to HTF), National Institute on Deafness and Other Communication Disorders (NIDCD) grant R21 DC08647 (to HTF), NIDCD grant R01 DC 10290 (to HTF and CAN) and a grant from the Simons Foundation (to CAN and WJB). We thank the following people for their help in data collection: Tara Augenstein, Leah Casner, Laura Kasparian, Nina Leezenbaum, Vanessa Vogel-Farley and Annemarie Zuluaga. We are especially grateful to the families who participated in this study. (Autism Speaks; R21 DC08647 - National Institute on Deafness and Other Communication Disorders (NIDCD); R01 DC 10290 - National Institute on Deafness and Other Communication Disorders (NIDCD); Simons Foundation

EEG analytics for early detection of autism spectrum disorder: a data-driven approach

Autism spectrum disorder (ASD) is a complex and heterogeneous disorder, diagnosed on the basis of behavioral symptoms during the second year of life or later. Finding scalable biomarkers for early detection is challenging because of the variability in presentation of the disorder and the need for simple measurements that could be implemented routinely during well-baby checkups. EEG is a relatively easy-to-use, low cost brain measurement tool that is being increasingly explored as a potential clinical tool for monitoring atypical brain development. EEG measurements were collected from 99 infants with an older sibling diagnosed with ASD, and 89 low risk controls, beginning at 3 months of age and continuing until 36 months of age. Nonlinear features were computed from EEG signals and used as input to statistical learning methods. Prediction of the clinical diagnostic outcome of ASD or not ASD was highly accurate when using EEG measurements from as early as 3 months of age. Specificity, sensitivity and PPV were high, exceeding 95% at some ages. Prediction of ADOS calibrated severity scores for all infants in the study using only EEG data taken as early as 3 months of age was strongly correlated with the actual measured scores. This suggests that useful digital biomarkers might be extracted from EEG measurements.This research was supported by National Institute of Mental Health (NIMH) grant R21 MH 093753 (to WJB), National Institute on Deafness and Other Communication Disorders (NIDCD) grant R21 DC08647 (to HTF), NIDCD grant R01 DC 10290 (to HTF and CAN) and a grant from the Simons Foundation (to CAN, HTF, and WJB). We are especially grateful to the staff and students who worked on the study and to the families who participated. (R21 MH 093753 - National Institute of Mental Health (NIMH); R21 DC08647 - National Institute on Deafness and Other Communication Disorders (NIDCD); R01 DC 10290 - NIDCD; Simons Foundation)Published versio