John R. Pruett, Ph.D. - US grants

[unreadable] DESCRIPTION (provided by applicant): This project represents a first step toward our long-term goal of understanding the psychological and brain bases of social attention deficits in autism. Findings would inform many areas of autism research, including: phenomenology, genetic epidemiology, screening and diagnostics, and treatment response. Disrupted attention to eye gaze and abnormal visual processing of faces are two attractive candidate hypotheses about core autistic deficits. The experimental literature on these topics, however, describes conflicting findings. This study will test hypotheses about disrupted attention to eye gaze in autism by adapting a classical visual attention task (comparing gaze cues to simple box and arrow cues). Discrepant findings in the field are likely due to the complexities of controlling key variables in such tasks in an extremely challenging study population. Specific Aim 1: The design in this proposal addresses these issues in an incisive way, controlling for confounding effects of eye movement and adding within-subject control conditions to the spatial attention task, necessary to disambiguate results seen with prior studies using gaze cues. Specific Aim 2: A converging task, using variants of classical visual search methods, will explore the potential failure of the eye region of the face to capture attention in autistic individuals. These experiments will involve 60 children, ages 9-12: 30 each from two contrasting groups: high functioning autistic disorder (mental retardation excluded) and typically developing (no diagnosis). The between-group design tests for social but not basic attention deficits in autism and normal attention in the typical children. These pilot behavioral experiments will set the stage for a future neuroimaging proposal (functional magnetic resonance imaging: fMRI). Should the efforts proposed here be successful, future proposals for behavioral and imaging studies would include developmental contrasts (children of different ages and adults), clinical contrasts (e.g., attention-deficit/hyperactivity disorder sub types), and genetically informed designs (e.g., sibling designs for autistic spectrum individuals; and affected and unaffected siblings within population-defined, heritable ADHD subtypes) to explore the heritability of psychological and neural factors associated with autism (and ADHD). PUBLIC HEALTH RELEVANCE: This study of the psychological and brain bases of autism is relevant to public health because it will help characterize aspects of the most serious psychiatric disorder of childhood in ways that will make autism more amenable to genetic, screening, and interventional studies. As such it will improve early identification, which is crucial for improving outcome in affected individuals. [unreadable] [unreadable] [unreadable]

DESCRIPTION (provided by applicant): This ESI / New Investigator clinician-scientist brings an expert team to test the altered brain functional connectivity hypothesis of autism during infancy. We will conduct graph-theory-based network analyses on functional connectivity magnetic resonance imaging (fcMRI) data acquired in high-risk infants who are currently being studied in the multi-site, NIH-funded Autism Center of Excellence (ACE) study R01 HD055741: A Longitudinal MRI Study of Infants at Risk for Autism - Joseph Piven: PI. We will prospectively study diverging developmental trajectories for functional networks of brain regions in infants who do and do not develop an Autistic Spectrum Disorder (ASD), during a suspected period of altered brain growth in autism, and prior to symptom expression. All four ACE data collection sites and their existing Data Coordination Center will participate. These efforts will provide longitudinal fcMRI data (scans at 6, 12, and 24 months old) in up to 664 high-risk and control infants (~15% of the 544 high-risk infants will develop an ASD). All four ACE data collection sites are already (as of May, 2010) - in advance of any dedicated funding, to ensure enough fcMRI data - acquiring fcMRI data on their subjects. Interfacing with the high-risk infant ACE structural imaging study and its supplements will allow us to build on existing infrastructure, save effort and cost, standardize collections, and merge databases. It also provides the potential for future genetic-functional imaging associations. Our fcMRI approach considers the problem of autism from the perspective of alterations in the behavior of brain-wide networks and changes in strengths of functional connectivity between and within rigorously defined sub-networks of brain regions. We believe this approach is more fruitful than those which consider smaller numbers of brain regions. We will additionally use a novel cortical functional areal parcellation routine (that Co-I Steve Petersen et al. have developed) to enhance the sensitivity of the infant fcMRI data analyses. Unique insights may come from comparing our network-based fcMRI analyses to the structural MRI, diffusion tensor imaging, and extensive phenotypic data generated by the existing ACE study (fcMRI acquired in the same infants). Once diagnoses are assigned in the ACE study, we will use a machine learning, multivariate pattern classification approach (Support Vector Machine: SVM) to explore the possibility of developing a diagnostic classifier for ASD. The eventual goal would be to train an SVM to predict which infants will and will not develop ASD, based on network properties of their fcMRI data, prior to the expression of symptoms. If successful, our proposed program of research could inform the future development of pre- symptomatic diagnostic tests for ASD, increase our knowledge about the neurobiology of autism, and provide methods for studying functional brain changes in response to interventions. These approaches could be adapted for the study of other neurodevelopmental and early-onset psychiatric disorders. PUBLIC HEALTH RELEVANCE: The proposed work is relevant to public health because autism is one of the most devastating psychiatric disorders of childhood, and disorders spanning the broader spectrum affect approximately 1/100 individuals. We propose to test the brain functional connectivity hypothesis of autism with functional connectivity magnetic resonance imaging (fcMRI) in infants who are at high risk for autism. If successful, we may contribute to the eventual development of pre-symptomatic diagnostic tests for autism that will improve early identification, which is critical for enhancing outcome in affected individuals. More broadly, our proposed studies will also increase our knowledge about autism in ways that will complement future genetic, screening, and intervention studies.

ABSTRACT The overarching goal of this proposal is to lower the age of detection in autism to early infancy, making presymptomatic (i.e., before the emergence of ASD-specific behavioral features) intervention feasible. Infants with an older autistic sibling have up to a 20% risk of developing autism spectrum disorder (ASD). Prospective high familial risk (HR) infant sibling studies have shown that the defining behaviors of ASD do not emerge until the latter part of the first year and into the second year of life. Therefore, the vast majority of affected children are diagnosed after age 2. No behavioral markers in the first year of life have yet been identified that can predict later ASD diagnosis with sufficient accuracy (i.e., positive predictive value: PPV ? 80%) to justify presymptomatic intervention. We recently published two independent approaches that use brain imaging in the first year of life to predict which HR infants will be diagnosed with ASD at 2 years of age. Specifically, structural MRI (sMRI) at 6 and 12 months of age, and resting state functional connectivity MRI (fcMRI) at 6 months of age independently predicted later ASD diagnosis in HR infants with over 80% PPV. Our preliminary data show that a third MRI approach, using regions of CSF volume and cortical shape at 6 months of age can also accurately predict later ASD diagnosis. If we replicate and extend these findings, we will be able to identify individual infants at ?ultra-high risk? (80% chance) of developing ASD, rather than being limited to group-level risk (20% chance), where we do not know who will later be affected. This R01 application aims to move our initial findings toward a clinical test for ASD in HR infants in the first year of life. Aim 1 will validate our previous findings in a new, independent sample of HR infants, extend our methods to a new MRI platform, and examine whether fcMRI and/or sMRI, with and without behavioral information, during the presymptomatic period in infancy, accurately predict ASD diagnosis at 24 months of age. Aim 2 will move beyond predicting categorical diagnosis to predicting dimensional, clinically-relevant characteristics for individual infants. Specific dimensional targets include expressive language level, social responsiveness, initiation of joint attention, and repetitive behavior. Validating and extending our findings on presymptomatic prediction of ASD in a new sample, on a different MRI scanner, and with dimensional developmental characteristics are critical next steps for moving the field forward toward (a) the development of a clinically-useful, presymptomatic test for identifying ultra-high risk infants who would benefit from very early intervention in infancy, (b) efficient studies of presymptomatic intervention strategies in individuals at ultra-high risk, and (c) the development of future presymptomatic tests for use in the general (not just HR) population.