Designed especially for neurobiologists, FluoRender is an interactive tool for multi-channel fluorescence microscopy data visualization and analysis.
Large scale visualization on the Powerwall.
BrainStimulator is a set of networks that are used in SCIRun to perform simulations of brain stimulation such as transcranial direct current stimulation (tDCS) and magnetic transcranial stimulation (TMS).
Developing software tools for science has always been a central vision of the SCI Institute.

Image-Based, Intermediate Biological Phenotypes Associated with Autism Spectrum Disorder

Janet Lainhart

University of Utah: Department of Psychiatry

Mark Leppert

Department of Human Genetics

The purpose of this collaboration is to develop image-based, intermediate biological phenotypes for diagnosing and characterizing autism. This collobration combines SCI-Institute expertise in image analysis (Whitaker, Gerig, Fletcher), with genetics (Leppert), and autism (Lainhart). The hypothesis is that intermediate biological phenotypes or image-based biomarkers for autism will correlate with genetic clinical parameters associated with specific subgroups of the autism spectrum. These biomarkers would potentially enable more rapid and definitive diagnosis of autism and help to better align therapies to specific subgroups within this spectrum disorder. The goals for this project are:
  1. An image analysis pipeline for comprehensive morphometric analysis;
  2. A morphometric analysis database of approximately 100 individuals diagnosed with autism;
  3. Exome-based genetic analysis of individuals with common morphometric image analysis findings;
  4. Prototype image-based biomarkers that align with subgroups within the 100 patient database; and
  5. New risk genes in autism with associated image-based, intermediate phenotypes.

Most recent reviews1 estimate a prevalence of one to two cases per 1,000 people for autism, and about six per 1,000 for Autism Spectrum Disorder (ASD).1,2 The number of children known to have autism has increased dramatically since the 1980s, at least partly due to changes in diagnostic practice; the question of whether actual prevalence has increased is unresolved.2 ASD is associated with several genetic disorders,3 other pervasive development delay (PDD) disorders,4 and with epilepsy.5 Evidence from genetic studies of autism suggests that it is highly heritable.6 The first studies of twins estimated heritability to be more than 90%.6,7 When only one identical twin is autistic, the other often has learning or social disabilities. For adult siblings, the risk of having one or more features of the broader autism phenotype might be as high as 30%,8 much higher than the risk in controls.9 In genetic studies more than 100 candidate genes have been examined with the expectation that autism and ASD have high heterogeneity with regard to participating genes.2

This study will examine three classes of image phenotypes: volumetric measurement of brain structures, anatomical shape features, and white matter integrity from diffusion tensor imaging (DTI). We will focus on brain structures that have been indicated in the literature to be affected in autism, specifically, the total intracrantial and brain volumes, the corpus callosum10 and the amygdala.11 We hypothesize that the trajectory of neuroanatomical changes, rather than the structure of the brain at a snapshot in time, will be a more sensitive descriptor of the autism phenotype, i.e., it will correlate better with genetic variants. Therefore, in addition to computing each image measurement at baseline, we will also compute its rate of change over time as an additional image phenotype.

In recent years genome-wide association studies (GWAS) have had a significant impact on genetic research. Genetics researchers have worked together, sharing data in a collaborative effort advancing GWAS to identify common genetic factors that influence health and disease. The National Institutes of Health (NIH) defines GWAS as any study of genetic variation across the entire human genome that is designed to identify genetic associations with observable traits (such as blood pressure or weight), or the presence or absence of a disease or condition. Although these GWAS results provided new biological insights, they explain only a modest fraction of heritable risk in each specific disease, casting doubt over the validity of the common-disease-common-variant hypothesis.

These collaborators will pursue a new, promising method, which is to carry out a recently developed genetic analysis on autistic individuals defined by intermediate brain imaging phenotypes. This new approach combines the advantages of a population of patients who are risk for autism-related genotypes with more precise phenotyping, and provides us with the oppurtunity to form closer associations between genes and gene products. Program Director/Principal Investigator: Johnson, C.R. Collaborations and Service Macrocephaly, brain size, and DTI findings are excellent first phenotypes to carry out this experimental genetic analysis. These findings are robust and, in the case of DTI, are observed in young children, adolescents, and adults, indicating stability of this intermediate phenotype. We plan first to collect blood from all subjects in the ongoing, longitudinal, autism study, including controls and isolate DNA from those blood samples. We will then examine DNA samples from those individuals with significant morphometric findings. The Leppert laboratory will apply techniques in human exome sequencing to sequence the entire human genome (about 180,000 exons). This approach utilizes microarray-based DNA sequence capture and next-generation, high-throughput DNA sequencing. 12,13 These data will be evaluated relative to the brain imaging findings for statistically significant correlations.

The contribution of the Center to this project is in the statistical analysis of subcortical shapes and cortical morphometry (e.g. using ShapeWorks) and the visualization of these results. This work will combine technologies in shape analysis, visualization, and statistical analysis from the Image-Based Modeling, Simulation, and Estimation TRDs.

Cited References

  1. Wikipedia. "Epidemiology of autism", 2009. [Online; accessed Sept-2009].
  2. C. Newschaffer, L.A. Croen, J. Daniels, E. Giarelli, J.K. Grether, S.E. Levy, D.S. Mandell, L.A. Miller, J. Pinto-Martin, J. Reaven, A.M. Reynolds, C.E. Rice, D. Schendel, and G.C. Windham. "The epidemiology of autism spectrum disorders". Annu Rev Public Health, 28:235–258, 2007.
  3. S. Chakrabarti and E. Fombonne. "Pervasive developmental disorders in preschool children". JAMA, 285(24):3093, 2001.
  4. P. Levisohn. "The autism-epilepsy connection." Epilepsia, 48:33–35, 2007.
  5. P. Szatmari and M. Jones. "Genetic epidemiology of autism spectrum disorders". In F.R. Volkmar, editor, Autism and Pervasive Developmental Disorders (2nd ed.), pages 157–178. Cambridge University Press, 2007.
  6. A. Bailey, A. Le Couteur, I. Gottesman, P. Bolton, E. Simonoff, E. Yuzda, and M. Rutter. "Autism as a strongly genetic disorder: evidence from a british twin study". Psychol Med, 25(1):63–77, 1995.
  7. C. Freitag. "The genetics of autistic disorders and its clinical relevance: a review of the literature". Mol. Psychiatry, 12(1):2–22, 2007.
  8. S. Folstein and B. Rosen-Sheidley. "Genetics of autism: complex aetiology for a heterogeneous disorder". Nat. Rev. Genet., 2(12), 2001.
  9. et al. Bolton, P. "A case-control family history study of autism". J Child Psychol Psychiatry, 35(5):877–900, 1994.
  10. A.L. Alexander, J.E. Lee, M. Lazar, R. Boudos, M.B. DuBray, T.R. Oakes, J.N. Miller, J. Lu, E.K. Jeong, W.M. McMahon, E.D. Bigler, and J.E. Lainhart. "Diffusion tensor imaging of the corpus callosum in autism". NeuroImage, 34:61–73, 2007.
  11. H.C. Hazlett, M. Poe, G. Gerig, R.S. Gimpel, and J. Piven. "A longitudinal study of amygdala volume and joint attention in 2-4 year old children with autism". Arch Gen Psychiatry, 66(5):509–516, 2009.
  12. et al. Okou DT. "Microarray-based genomic selection for high-throughput resequencing". Nature Methods, 4(11):891–892, 2007.
  13. et al. Albert TJ. "Direct selection of human genomic loci by microarray hybridization". Nature Methods, 4(11):891–892, 2007.