Jed T. Elison - US grants

DESCRIPTION (provided by applicant): Infant Brain and Behavioral Signatures of Later Emerging Risk for Psychopathology Little is known about the trajectories of brain and behavioral development during infancy that anticipate and predict clinically impairing patterns of functioning observed during the preschool years. The prevalence of psychiatric symptomatology in preschool-aged children, combined with the possibility of common initializing pathophysiological events shared across various disorders, converge to highlight the infant and toddler period as uniquely suited for identifying atypical trajectories of brain and behavioral development that anticipate later emerging psychopathology. Characterizing trajectories that deviate from normative patterns of development during this time period may elucidate causal mechanisms of mental illness, mechanisms that subsequently could be targeted for strategic intervention/prevention. The proposed research will combine state-of-the-art neuroimaging technologies developed by the Human Connectome Project with a developmental approach to the NIMH Research Domain Criteria initiative. Longitudinal brain imaging (sMRI/DWI/fcMRI) and behavioral assessment (selected for relevance to later emerging psychopathology and acquired via direct assessment, parent report, and state-of-the art eye tracking procedures) will be conducted on 4 occasions between 3 and 15 months with a 5th assessment at 24 months of age. Dimensional aspects of clinically relevant behaviors will be characterized during a final 6th assessment between 30 and 36 months of age. The contribution of the proposed research is expected to be a comprehensive characterization of longitudinal brain development in the first years of life, coupled with a longitudinal characterization of behavioral constructs selected for their relevance to later emerging psychopathology. This contribution will be significant because the empirical data acquired will anchor a new paradigm of inquiry into the developmental processes that temporally precede the emergence of clinically impairing patterns of functioning, augmenting future efforts focused on strategic prevention of mental illness. The interdisciplinary synthesis that approaches clinical neuroscience or biological psychiatry from a developmental perspective represents an innovative departure from efforts designed to characterize neural signatures of DSM defined disorders examined years after the incipient pathophysiological events. More specifically, mapping trajectories of neural circuit development to behavioral constructs specifically selected for their relevance to later emerging psychopathology (Elison et al., 2013a; Elison et al., 2013b) highlights a novel developmental approach to the RDoC initiative. Indeed, these mappings will be characterized prior to the manifestation of clinically impairing patterns of functioning, and will be used to predict later emerging clinically relevant behaviors. Characterizing developmental signatures of later emerging psychopathology has the potential to alter the landscape of early identification and early intervention/prevention of psychiatric and neurodevelopmental disorders.

Project Summary This application is in response to the RFA-MH-16-160, entitled ?Lifespan Human Connectome Project (HCP): Baby Connectome?. Investigators at The University of North Carolina at Chapel Hill (UNC) and The University of Minnesota (UMN) will join forces to accomplish the goals outlined by this RFA. The team at UNC has over 10 years of experience in recruiting and imaging typically developing and at-risk children, scanning over 1000 children from birth to five years1-40. Well established infrastructure at the Biomedical Research Imaging Center (BRIC) at UNC and Center for Magnetic Resonance Research (CMRR) at UMN are in place to recruit and retain pediatric subjects and facilitate the coordination of pediatric imaging studies. Our past and ongoing studies for imaging children (birth ? five years of age) without sedation have achieved an overall success rate of 81% and attrition rate of 29.3%. Our track record demonstrates that we possess the critical and essential components to successfully conduct longitudinal pediatric imaging studies focusing on early brain development, a critically-important aspect of this RFA. Our ability to recruit, retain, and image non-sedated, typically developing children is further strengthened by our image analysis team, which has developed novel image analysis tools specifically for early brain development. The expertise at UNC is complementary to and strengthened by the expertise of the team at UMN. The CMRR at UMN has been one of the leading groups in the HCP project and has developed novel MR imaging approaches to dramatically shorten data acquisition time. Furthermore, the team at UMN has extensive experience in behavioral and cognitive studies of early child development. Together, our combined team is well positioned to accomplish the goals of this RFA. To this end, a total of 500 typically developing children between birth and five years of age will be recruited across two data collection sites in a sequential cohort, accelerated longitudinal study design. The participants are divided into two main groups, longitudinal (n=285) and cross-sectional (n=215) groups, respectively. This hybrid longitudinal and cross-sectional design enables detailed characterization of early brain development from both brain structural/functional using MRI and behavioral aspects using behavioral assessments. All of the acquired images and behavioral assessments will undergo extensive quality assurance and control processes to ensure that high quality data is obtained and transferred to the Central Connectome Facility at Washington University. In addition, we will integrate novel image analysis tools, developed by our team onto the existing HCP pipelines.

A major impediment to early identification and intervention for autism spectrum disorder (ASD) is our limited understanding of how different children present signs as toddlers, including what risk symptoms coincide across multiple dimensions to predict outcome. Our objectives are to quantify behavioral and brain connectivity based subtypes of risk that model the variability of ASD symptom expression in a community sample of toddlers. We will then test the predictive validity of this approach in the same cohort of children at three years of age in order to identify risk profiles that differentially predict later cognitive, behavioral, and clinical features. First, we will implement two unsupervised data-driven computational approaches in a community sample of 3000 children between 18-24 months old in order to characterize clusters of risk profiles. We hypothesize that each approach will identify a proportion of high-risk individuals consistent with epidemiological estimates of ASD and associated developmental disabilities (e.g., language or global DD). Based on our preliminary data, we anticipate that ~300 children will be identified by these data-driven risk-profiling methods. We also hypothesize that distinct patterns of structural and functional connectivity will distinguish groups of at-risk children and that these groups will differ from low-risk children. All children will be scanned with the same brain imaging sequences and procedures implemented in the Baby Connectome Project and will be compared to data from 100 low-risk children from that project. Our neuroimaging sample of 300 children will be reassessed at age three with direct clinical assessment using gold-standard diagnostic instruments as well as parent report. This will allow us to validate the risk profiling approach implemented at 18-24 months, to compare with a current screening approach, and to refine the risk profiling approach with supervised training of prediction algorithms that incorporates behavioral/clinical outcome data. We expect this method for risk stratification/subtyping to better model the heterogeneity inherent to the early at-risk and resilient phenotypes, which will subsequently improve early identification/diagnosis efforts. These outcomes will have translational impact because improved methods for early identification in ASD are necessary for the successful development of efficacious, personalized early interventions.