1. The Microstructural Basis of Abnormal Connectivity in Autism Janet Lainhart, MD Associate Professor of Psychiatry, Pediatrics, Psychology, and Faculty Member Interdepartmental Neuroscience Program and The Brain Institute University of Utah

2. The Problem: Striking Clinical Manifestations, Lifelong Impairment, Cause Unknown

3.  Autism is the  fastest growing  developmental  disability.  There are no biological markers of autism.  There is no way to prevent the disorder from developing.  There are no specific treatments based on the underlying neurobiology of the disorder because the neural basis of autism in unknown. Challenge to Neuroscience

4. Science, June 2005 Identification of infants at risk for autism and young children early in the course of the disorder is important. *It is also important to understand the neural basis of autism in older children, adolescents, and adults with the disorder. --the majority are currently destined to live with significant lifelong impairment. --1/3rd worsen over time rather than remain stable or improve. *This collaboration with NA-MIC focuses on these individuals. The Clinical Problem

5. Science, June 2005 The Clinical problem. There is substantial evidence that autism is a disorder of brain connectivity.

6. The Clinical Problem  We and others have found widespread but not uniformly diffuse microstructural abnormalities in white matter.  Abnormalities of cortical and subcortical gray matter are also reported.  How abnormalities of gray matter are related to abnormalities of white matter microstructure, and how GM-WM interactions are related to clinical features of autism are not known.

7. Clinical Aims  Determine, at both the group and individual levels, the morphometric features of gray matter and microstructural features of white matter in the neural networks involved in language and social impairment, stereotyped and repetitive behaviors, and associated psychiatric comorbidity in autism. At the group level, determine how gray matter morphometry and white matter microstucture in these networks differ in individuals with autism and typically developing controls Within the autism group, determine how gray matter morphometry and white matter microstructure in the neural networks are related to age and variations in the clinical phenotypes.

8. Technology Aims  Develop volumetric tools for analysis of diffusion tensor images in order to quantitatively assess the microintegrity of the white matter connections along the entire length of the tracts which evidence strongly suggests are involved in autism.  Develop new image analysis tools to integrate volumetric DTI analyses with structural MRI morphometric measures of the integrity of the main cortical and subcortical areas for which there is strong evidence of involvement in autism.

9.  Develop new “clinician-friendly” computationally- based integrated GM-WM image analysis and statistical analysis tools for the clinical evaluation, structural neural network characterization of individuals with autism.  Scan (3T MRI+DTI) -- > Neural network phenotyping (GM morphometry/WM microstructure) Diagnosis Specific subtypes of autism Clinically useful information Phenotype:genotype studies Translational Goal

10. Year 1 Progress: DTI analysis pipeline  We have built an automatic pipeline for analyzing diffusion tensor images (DTI) using a novel volumetric method, consisting of steps for DTI preprocessing, tract segmentation, and co-registering diffusion MRI with structural T1/T2 images.

11. Year 1 Progress: DBAC  We are constructing a semi-automated computerized Database and Analysis Center (DABC) that stores and combines imaging data output from the pipeline with clinical data, and semi-automatically performs group- and individual-level analyses.

12. Year 1 Progress: Language network analysis – arcuate fasciculus  We have analyzed the left arcuate fasciculus and right arcuate fasciculus in 28 typically developing individuals and 29 individuals with autism using the pipeline

13. Year 1 Results: Arcuate Fasciculus Preliminary Analysis: autism males and matched control males, 11-17yrs.  Mean diffusivity and radial diffusivity are increased in the left arcuate fasciculus in autism group compared to normal controls.  Asymmetry of MD and Dr are also atypical: the Rt > Lt asymmetry in MD and Dr present in controls is absent in the autism group. Linear mixed effects model analysis of MD, adjusted for total brain WM MD. Units for the estimated effect size and standard error are 10 -3 mm 2 /s. Covariate Est. Effect SE t-value p-value Autism 0.006 0.005 1.10 n.s. LH -0.015 0.005 -6.91 <10 -4 Autism x LH 0.013 0.003 4.15 0.002 age (years) -0.002 0.001 -1.04 n.s.  No case-control differences in arcuate fasciculus FA, Da, or volume. Fletcher et al., submitted

14. Arcuate Analysis in Autism

15. Year 1 Results: Classification  A novel classification method, using only DTI data, that discriminated between 30 males with high-functioning autism and 30 matched typically developing males. 91.6% accuracy, 93.6% sensitivity, and 89.6% specificity. When replicated in an independent sample of 12 males with autism and 7 matched controls, discrimination accuracy increased to 94.7%.  Highest sensitivity, specificity, and accuracy of any classification method published to date in autism that has been replicated in an independent sample. Lange et al., submitted

16. The Microstructural Basis of Abnormal Connectivity in Autism Janet Lainhart MD (UU Autism) Tom Fletcher PhD** (SCI) Nicholas Lange ScD (Harvard/McLean) Ross Whitaker PhD**(SCI) Guido Gerig PhD**(SCI) Erin Bigler PhD (BYU) Andrew Alexander PhD (U Wisc) **NA-MIC Kristen Zygmunt MS (SCI) Xiang Hao (SCI) Anna Cariello (UU) Molly DuBray MS (UU Neuroscience) Jason Cooperrider (UU Neuroscience) Alyson Froehlich PhD (UU Autism Lab) Caitlin Ravichandran ScD (Harvard/McLean)