title: Fractional anisotropy distributions in 2- to 6-year-old children with autism.
abstract: BACKGROUND
  Increasing evidence suggests that autism is a disorder of distributed neural networks that may exhibit abnormal developmental trajectories. Characterisation of white matter early in the developmental course of the disorder is critical to understanding these aberrant trajectories.
  
  
  METHODS
  A cross-sectional study of 2- to 6-year-old children with autism was conducted using diffusion tensor imaging combined with a novel statistical approach employing fractional anisotropy distributions. Fifty-eight children aged 18-79 months were imaged: 33 were diagnosed with autism, 8 with general developmental delay, and 17 were typically developing. Fractional anisotropy values within global white matter, cortical lobes and the cerebellum were measured and transformed to random F distributions for each subject. Each distribution of values for a region was summarised by estimating δ, the estimated mean and standard deviation of the approximating F for each distribution.
  
  
  RESULTS
  The estimated δ parameter, , was significantly decreased in individuals with autism compared to the combined control group. This was true in all cortical lobes, as well as in the cerebellum, but differences were most robust in the temporal lobe. Predicted developmental trajectories of across the age range in the sample showed patterns that partially distinguished the groups. Exploratory analyses suggested that the variability, rather than the central tendency, component of was the driving force behind these results.
  
  
  CONCLUSIONS
  While preliminary, our results suggest white matter in young children with autism may be abnormally homogeneous, which may reflect poorly organised or differentiated pathways, particularly in the temporal lobe, which is important for social and emotional cognition.
authors.affiliation: Psychiatry and Kennedy Center for Research on Human Development, Vanderbilt University, Nashville, Tennessee, USA Department of Biostatistics, Human Genome Sciences, Rockville, Maryland, USA Department of Computer Science, University of Utah, Salt Lake City, Utah, USA Psychiatry/Carolina Institute for Developmental Disabilities, University of North Carolina, Chapel Hill, North Carolina, USA Department of Computer Science, University of North Carolina, Chapel Hill, North Carolina, USA Frank Porter Graham Child Development Institute, Chapel Hill, North Carolina, USA Department of Epidemiology and Health Policy Research, University of Florida, Gainesville, Florida, USA.
authors.forename: C
authors.initials: C
authors.lastname: Cascio
authors.forename: M
authors.initials: M
authors.lastname: Gribbin
authors.forename: S
authors.initials: S
authors.lastname: Gouttard
authors.forename: R G
authors.initials: RG
authors.lastname: Smith
authors.forename: M
authors.initials: M
authors.lastname: Jomier
authors.forename: S
authors.initials: S
authors.lastname: Field
authors.forename: M
authors.initials: M
authors.lastname: Graves
authors.forename: H C
authors.initials: HC
authors.lastname: Hazlett
authors.forename: K
authors.initials: K
authors.lastname: Muller
authors.forename: G
authors.initials: G
authors.lastname: Gerig
authors.forename: J
authors.initials: J
authors.lastname: Piven
mesh_terms: D016880:Anisotropy; D001321:Autistic Disorder / Q000473:pathology*; D001921:Brain / Q000254:growth & development / Q000473:pathology*; D002531:Cerebellum / Q000254:growth & development / Q000473:pathology; D002540:Cerebral Cortex / Q000254:growth & development / Q000473:pathology; D002648:Child; D002675:Child, Preschool; D003430:Cross-Sectional Studies; D056324:Diffusion Tensor Imaging / Q000379:methods*; D005260:Female; D006801:Humans; D008137:Longitudinal Studies; D008297:Male; D009413:Nerve Fibers, Myelinated / Q000473:pathology*