1. Bonneh YS, Levanon Y, Dean-Pardo O, Lossos L, Adini Y. {{Abnormal speech spectrum and increased pitch variability in young autistic children}}. {Front Hum Neurosci};2011;4:237.
Children with autism spectrum disorder (ASD) who can speak often exhibit abnormal voice quality and speech prosody, but the exact nature and underlying mechanisms of these abnormalities, as well as their diagnostic power are currently unknown. Here we quantified speech abnormalities in terms of the properties of the long-term average spectrum (LTAS) and pitch variability in speech samples of 83 children (41 with ASD, 42 controls) ages 4-6.5 years, recorded while they named a sequence of daily life pictures for 60 s. We found a significant difference in the group’s average spectra, with ASD spectra being shallower and exhibiting less harmonic structure. Contrary to the common impression of monotonic speech in autism, the ASD children had a significantly larger pitch range and variability across time. A measure of this variability, optimally tuned for the sample, yielded 86% success (90% specificity, 80% sensitivity) in classifying ASD in the sample. These results indicate that speech abnormalities in ASD are reflected in its spectral content and pitch variability. This variability could imply abnormal processing of auditory feedback or elevated noise and instability in the mechanisms that control pitch. The current results are a first step toward developing speech spectrum-based bio-markers for early diagnosis of ASD.
2. Jou RJ, Mateljevic N, Minshew NJ, Keshavan MS, Hardan AY. {{Reduced central white matter volume in autism: Implications for long-range connectivity}}. {Psychiatry Clin Neurosci};2011 (Feb);65(1):98-101.
Cortical and central white matter (WM) volumes were measured to assess short- and long-range connectivity in autism, respectively. Subjects included 23 boys with autism and 23 matched controls, all without intellectual disability. Magnetic resonance imaging data obtained at 1.5 T were analyzed using BRAINS2 software (University of Iowa, Iowa City, IA, USA). Central WM volume was quantified by subtracting cortical from supratentorial WM volumes. Reduced central WM volume was observed in the autism group. IQ was higher in controls with no observed correlations between WM volumes and IQ. This preliminary evidence of reduced central WM volume in autism suggests abnormal long-range connectivity.
3. Kumar A, Wadhawan R, Swanwick CC, Kollu R, Basu SN, Banerjee-Basu S. {{Animal model integration to AutDB, a genetic database for autism}}. {BMC Med Genomics};2011 (Jan 27);4(1):15.
ABSTRACT: BACKGROUND: : In the post-genomic era, multi-faceted research on complex disorders such as autism has generated diverse types of molecular information related to its pathogenesis. The rapid accumulation of putative candidate genes/loci for Autism Spectrum Disorders (ASD) and ASD-related animal models poses a major challenge for systematic analysis of their content. We previously created the Autism Database (AutDB) to provide a publicly available web portal for ongoing collection, manual annotation, and visualization of genes linked to ASD. Here, we describe the design, development, and integration of a new module within AutDB for ongoing collection and comprehensive cataloguing of ASD-related animal models. DESCRIPTION: As with the original AutDB, all data is extracted from published, peer-reviewed scientific literature. Animal models are annotated with a new standardized vocabulary of phenotypic terms developed by our researchers which is designed to reflect the diverse clinical manifestations of ASD. The new Animal Model module is seamlessly integrated to AutDB for dissemination of diverse information related to ASD. Animal model entries within the new module are linked to corresponding candidate genes in the original « Human Gene » module of the resource, thereby allowing for cross-modal navigation between gene models and human gene studies. Although the current release of the Animal Model module is restricted to mouse models, it was designed with an expandable framework which can easily incorporate additional species and non-genetic etiological models of autism in the future. CONCLUSIONS: : Importantly, this modular ASD database provides a platform from which data mining, bioinformatics, and/or computational biology strategies may be adopted to develop predictive disease models that may offer further insights into the molecular underpinnings of this disorder. It also serves as a general model for disease-driven databases curating phenotypic characteristics of corresponding animal models.
4. Spencer CM, Alekseyenko O, Hamilton SM, Thomas AM, Serysheva E, Yuva-Paylor LA, Paylor R. {{Modifying behavioral phenotypes in Fmr1KO mice: genetic background differences reveal autistic-like responses}}. {Autism Res};2011 (Jan 25)
Fragile X syndrome (FXS) is the most common inherited form of intellectual disability in humans. In addition to cognitive impairment, patients may exhibit hyperactivity, attention deficits, social difficulties and anxiety, and autistic-like behaviors. The degree to which patients display these behaviors varies considerably and is influenced by family history, suggesting that genetic modifiers play a role in the expression of behaviors in FXS. Several studies have examined behavior in a mouse model of FXS in which the Fmr1 gene has been ablated. Most of those studies were done in Fmr1 knockout mice on a pure C57BL/6 or FVB strain background. To gain a better understanding of the effects of genetic background on behaviors resulting from the loss of Fmr1 gene expression, we generated F1 hybrid lines from female Fmr1 heterozygous mice on a pure C57BL/6J background bred with male Fmr1 wild-type (WT) mice of various background strains (A/J, DBA/2J, FVB/NJ, 129S1/SvImJ and CD-1). Male Fmr1 knockout and WT littermates from each line were examined in an extensive behavioral test battery. Results clearly indicate that multiple behavioral responses are dependent on genetic background, including autistic-like traits that are present on limited genetic backgrounds. This approach has allowed us to identify improved models for different behavioral symptoms present in FXS including autistic-like traits.
