Janet E. Lainhart - US grants

autism; mental health information system; biomedical facility; human data;

Results from both our laboratory and others indicate that macrocephaly is present in 14-26% of children and adults with idiopathic autism. MRl studies and post mortem studies of brain weight suggest that macrocephaly in autism is due to megalencephaly, abnormal enlargement of the brain. Results from our laboratory indicate that in autistic individuals unselected for head size, brain size is increased due to increased volume of the parietal, temporal and occipital (i.e., posterior) lobes. Relative to total brain volume the size of the corpus callosum is disproportionately decreased. Additional data suggest that cortical enlargement is predominantly right-sided. MR studies from other centers support the validity of these findings.- Neuropsychological and neurobiological evidence now suggest that autism is a disorder of a complex distributed neural network resulting in anomalous brain organization and connectivity. The finding of megalencephaly in autism suggests that abnormalities of cellular proliferation or programmed cell death are also involved. The overall goal of this application is to build on our previous work in structural imaging and genetics by exploring abnormalities in brain size and shape in autistic individuals with megalencephaly and by examining the significance of these abnormalities for the pathogenesis of this disorder. We propose to: 1) examine anomalous patterns of growth and organization in autistic individuals with megalencephaly as evidenced by abnormalities in the relationship between (a) anterior and posterior cortical brain volume, (b) cortical and corpus callosum size, (c) gray and white matter volume, and, (d) right- and left-sided cortical lobe volumes; 2) explore developmental mechanisms that may underlie brain enlargement in autism by examining the trajectory of changes in head circumference overtime; 3) examine heterogeneity in the syndrome of autism by exploring the relationship between brain size and shape abnormalities on MRI to clinical features associated with autism including behavioral and neuropsychological characteristics.

DESCRIPTION (provided by applicant): In addition to efforts to describe the earliest signs of autism, it is critical to continue efforts to understand the brain-basis of autism, how it changes over time, and how it is related to the changing manifestations of the disorder in children and adults. The population of already-affected individuals with autism is huge and growing. It has become clear that autism in these individuals is a dynamic disorder and that a sizeable proportion of affected individuals worsen rather than improve as they grow older. Individuals whose impairment does not worsen over time live with stable but significant lifelong disability. The objective of this research is to understand longitudinal brain mechanisms from childhood into adulthood in autism and how dynamic brain and clinical phenotype changes are related. This research will result in new information important for the development of new treatments and secondary and tertiary preventive interventions. The specific aims of this project are to collect time 3 and time 4 longitudinal structural and diffusion tensor neuroimaging at 3 Telsa along with clinical and neuropsychological data on a large cohort of children and adults with autism and typically developing individuals. The collection of time 1 and time 2 data was funded by the NIH Collaborative Program of Excellence in Autism. This is a case-control longitudinal study of the original cohort of 100 males with autism and 72 matched normal controls. Subjects in the cohort are equally divided into 4 age-bins. At time 1 they were 3-6, 7-11, 12-17, and 18-35 years of age. At time 2 they were 2 years older. At times 3 and 4 they will be 4 and 6 years older. 3 Telsa structural and diffusion tensor imaging data were collected at time 1 and time 2 and will be collected at time 3 and 4 on the single, same scanner used for time 1 and 2. The 3 major and minor eigenvalues and eigenvectors are determined and fractional anisotropy, mean diffusivity, and axial and radial diffusivity calculated for quantitative assessment of white matter integrity. Region of interests and whole-brain voxel-based analyses of both structural and diffusion tensor imaging data are performed, along with tractography. Detailed clinical and neuropsychological data are collected on all subjects and dynamic brain-behavior relationships studied. This project is a collaborative effort between the University of Utah, where all subjects are ascertained, assessed, and scanned, the University of Wisconsin, where all diffusion tensor imaging data are processed and analyzed, and Brigham Young University where all structural data are processed and analyzed. This collaborative group has been effectively and productively working together on this longitudinal study since 2002.

DESCRIPTION (provided by applicant): Autism spectrum disorders are now among the most prevalent medical conditions of childhood. Only a small fraction of the 486,000 individuals under 20 years of age with an autism-spectrum disorder (ASD) in the U.S are young enough to benefit from intensive early intervention. Overall prognosis for the older children with autism is not good. Despite improvements in treatment and education over the past 30 years, adult outcome even for non-mentally retarded individuals with autism has not significantly improved. The majority continue to need high levels of parental and community support throughout their lives. One reason for this huge public health problem is that the brain-basis of fundamental deficits in older children and adults with autism is not well understood. We propose collaboration between a longitudinal neuroimaging, clinical, and neuropsychological study of late neurodevelopment in autism and the National Alliance for Medical Imaging Computing (NA- MIC), one of the NIH Roadmap National Centers for Biomedical Computing. The collaboration will bring state-of-the-art brain imaging analysis tools developed by NA-MIC into autism clinical research and form a new highly interactive multidisciplinary research team. Working together, the computer scientists and clinical researchers will use critical biological questions in autism to drive the development of NA-MIC tools. The critical biological questions are 1) what is the microstructural basis of abnormal brain connectivity during late neurodevelopment in autism, and 2) how is brain microstructure related to deficits, developmental trajectory, and outcome. We will use Time 1 and Time 2 high-resolution MRI and diffusion tensor imaging data that have already been collected on a single 3Tesla scanner on a cohort of 100 males with high-functioning autism and 72 typically developing males. Time 3 and 4 data are being collected over the next 5 years (MH080826). We will use novel, non-tractography-based, diffusion tensor image analysis methods developed by NA-MIC to compare, at the level of both individuals and groups, microstructural features along entire white matter tracts in language, social, and repetitive behavior neural networks. We will integrate the white matter analysis with structural image analysis of cortical and subcortical gray matter. We will determine how microstructural white matter features, gray matter morphometric features, and clinical deficits are related to each other and change over time in autism.

DESCRIPTION (provided by applicant): The goal of this proposed research is to generate a genetic architecture of brain development. We aim to achieve this goal in two solid steps. We first utilize our existing brain MR imaging measurements (traditional MRI volumetry, brain connectivity, resting state activity) of 235 individuals, 125 with autism and 110 typically developing subjects, to build a data structure of brain regions of interest that have at least 50% estimated heritability for typical brains, sorted first by size and then by components of structura-functional variability. In our next step, we interrogate whole-exome and whole-genome sequences and network analysis of molecular pathways by state-of-the-art molecular genetics methods in reference to this data structure. We expect that this detailed analysis of variability and extremes in brain imaging phenotypes in autism will result in the discovery of new genetic associations that reveal new biological pathways and advance our understanding of autism neuropathology and its severely impairing clinical manifestations. Our results will thus change imaging genetics in autism research.

The goal of the proposed project is to determine how individual variation in brain development from childhood to adulthood is associated with variation in clinical course and outcome in autism. We will achieve this goal in three steps. We will first quantify individual whole and regional brain development from imaging data across six time points collected over 15 years using magnetic resonance imaging (volume, cortical thickness, functional connectivity, white matter microstructure) and concurrent clinical and neuropsychological evaluations. The six time points will include the four time points from our existing 10-year longitudinal study (140 male participants with autism and 75 age-matched typically developing participants) plus two more collected over the next five years by the same multisite interdisciplinary team. These extended cohort sequential longitudinal data will span 3-53 years of age. We will analyze age-period-cohort effects across our wide age range. More data will provide enough power to test existence of linear and curvilinear developmental arcs at individual, cohort, and group levels. We will investigate the specificity to autism by comparison to a reading disorder group. Second, we will identify mediating and modulating factors within brain development that help explain why some individuals continue to have severe autism while others improve. Third, we will analyze associations between longitudinal brain functional development and clinical outcome by evaluating the mechanism by which impairment in long- range connectivity and inhibition may ultimately impact adult prognosis. Finally, we explore how trajectories of late brain development may interact with the loss of secondary school services. Our findings will elucidate how brain changes modulate the severity of core autism symptoms and in the cognitive profile of individuals with the disorder. Understanding the paths of late brain development and the relation between brain maturation and cognitive/behavioral changes is essential to identify biological mechanisms involved and to understand how they interact with contextual factors.

DESCRIPTION (provided by applicant): The goal of this proposed research is to generate a genetic architecture of brain development. We aim to achieve this goal in two solid steps. We first utilize our existing brain MR imaging measurements (traditional MRI volumetry, brain connectivity, resting state activity) of 235 individuals, 125 with autism and 110 typically developing subjects, to build a data structure of brain regions of interest that have at least 50% estimated heritability for typical brains, sorted first by size and then by components of structura-functional variability. In our next step, we interrogate whole-exome and whole-genome sequences and network analysis of molecular pathways by state-of-the-art molecular genetics methods in reference to this data structure. We expect that this detailed analysis of variability and extremes in brain imaging phenotypes in autism will result in the discovery of new genetic associations that reveal new biological pathways and advance our understanding of autism neuropathology and its severely impairing clinical manifestations. Our results will thus change imaging genetics in autism research.