MRI Neuroanatomy in Young Girls With Autism

San Diego State University and University of California, San Diego Joint Doctoral Program in Clinical Psychology, USA.
Journal of the American Academy of Child & Adolescent Psychiatry (Impact Factor: 7.26). 05/2007; 46(4):515-23. DOI: 10.1097/chi.0b013e318030e28b
Source: PubMed


To test the hypothesis that young girls and boys with autism exhibit different profiles of neuroanatomical abnormality relative to each other and relative to typically developing children.
Structural magnetic resonance imaging was used to measure gray and white matter volumes (whole cerebrum, cerebral lobes, and cerebellum) and total brain volume in nine girls (ages 2.29-5.16) and 27 boys (ages 1.96-5.33) with autism and 14 girls (ages 2.17-5.71) and 13 boys (ages 1.72-5.50) with typical development. Structure size and the relationship between size and age were examined. Diagnostic and cognitive outcome data were obtained after the children reached 4 to 5 years of age.
Girls with autism exhibited nearly every size-related abnormality exhibited by boys with autism. Furthermore, additional sites of abnormality were observed in girls, including enlargement in temporal white and gray matter volumes and reduction in cerebellar gray matter volume. Significant correlations were observed between age and white matter volumes (e.g., cerebral white matter rs = 0.950) for the girls with autism, whereas no significant age-structure size relationships were observed for the boys with autism.
Results suggest sex differences in etiological factors and the biological time course of the disorder.

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    • "Most studies thus far that have reported on gender effects have not found significant differences in which brain areas are affected, although the magnitude of effects in areas has tended to be larger in females (Bloss and Courchesne, 2007; Schumann et al., 2009, 2010; Calderoni et al., 2012). One study however found reduced cerebral GM and WM volumes and reduced temporal GM volumes in females versus males with ASD; in addition, cerebral, frontal, parietal, and occipital WM volumes were only correlated with age in girls but not in boys (Bloss and Courchesne, 2007). This was consistent with the gender effects observed in a meta-analysis of brain structural differences in ASD, which found that differences in the cerebellum were more likely to be observed when there were fewer males included in the study, suggesting that females may be contributing greater differences (Stanfield et al., 2008). "
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    ABSTRACT: Autism spectrum disorders (ASD) display significant heterogeneity. Although most neuroimaging studies in ASD have been designed to identify commonalities among affected individuals, rather than differences, some studies have explored variation within ASD. There have been two general types of approaches used for this in the neuroimaging literature to date: comparison of subgroups within ASD, and analyses using dimensional measures to link clinical variation to brain differences. This review focuses on structural and functional magnetic resonance imaging studies that have used these approaches to begin to explore heterogeneity between individuals with ASD. Although this type of data is yet sparse, recognition is growing of the limitations of behaviorally defined categorical diagnoses for understanding neurobiology. Study designs that are more informative regarding the sources of heterogeneity in ASD have the potential to improve our understanding of the neurobiological processes underlying ASD.
    Frontiers in Human Neuroscience 10/2013; 7:733. DOI:10.3389/fnhum.2013.00733 · 2.99 Impact Factor
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    • "Previous studies have observed reduction in cerebellar grey matter volume in girls with ASD aged between two and six years [105] and increased cerebellar white matter volume (increased by 39%), no enlargement of cerebellar gray matter, and reduced vermis lobules VI-VII in two- and three-year-old ASD children [106]. Sparks et al. [95] found an increase of 7% in the volume of the whole cerebellum in three- and four-year-old ASD children. "
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    Behavioural neurology 08/2013; 2014(3). DOI:10.3233/BEN-130350 · 1.45 Impact Factor
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    • "Only eight female autistic and control cases passed the quality control measures in steps 1–4. Since this was an extremely small sample, not well age-matched with control samples, and it is hypothesized that pathogenic mechanisms differ between male and female autistic cases (Bloss and Courchesne, 2007), we decided to remove these cases from the differential expression analysis between autistic male and control male cases. These cases included 7A, 7B, 14A, 17A, 24A, 47A, 54B, 56A, and 60A. "
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    ABSTRACT: Available statistical preprocessing or quality control analysis tools for gene expression microarray datasets are known to greatly affect downstream data analysis, especially when degraded samples, unique tissue samples, or novel expression assays are used. It is therefore important to assess the validity and impact of the assumptions built in to preprocessing schemes for a dataset. We developed and assessed a data preprocessing strategy for use with the Illumina DASL-based gene expression assay with partially degraded postmortem prefrontal cortex samples. The samples were obtained from individuals with autism as part of an investigation of the pathogenic factors contributing to autism. Using statistical analysis methods and metrics such as those associated with multivariate distance matrix regression and mean inter-array correlation, we developed a DASL-based assay gene expression preprocessing pipeline to accommodate and detect problems with microarray-based gene expression values obtained with degraded brain samples. Key steps in the pipeline included outlier exclusion, data transformation and normalization, and batch effect and covariate corrections. Our goal was to produce a clean dataset for subsequent downstream differential expression analysis. We ultimately settled on available transformation and normalization algorithms in the R/Bioconductor package lumi based on an assessment of their use in various combinations. A log2-transformed, quantile-normalized, and batch and seizure-corrected procedure was likely the most appropriate for our data. We empirically tested different components of our proposed preprocessing strategy and believe that our results suggest that a preprocessing strategy that effectively identifies outliers, normalizes the data, and corrects for batch effects can be applied to all studies, even those pursued with degraded samples.
    Frontiers in Genetics 02/2012; 3:11. DOI:10.3389/fgene.2012.00011
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