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1.  Imaging-Genetics in Autism Spectrum Disorder: Advances, Translational Impact, and Future Directions 
Autism Spectrum Disorder (ASD) refers to a group of heterogeneous neurodevelopmental disorders that are unified by impairments in reciprocal social communication and a pattern of inflexible behaviors. Recent genetic advances have resolved some of the complexity of the genetic architecture underlying ASD by identifying several genetic variants that contribute to the disorder. Different etiological pathways associated with ASD may converge through effects on common molecular mechanisms, such as synaptogenesis, neuronal motility, and axonal guidance. Recently, with more sophisticated techniques, neuroimaging, and neuropathological studies have provided some consistency of evidence that altered structure, activity, and connectivity within complex neural networks is present in ASD, compared to typically developing children. The imaging-genetics approach promises to help bridge the gap between genetic variation, resultant biological effects on the brain, and production of complex neuropsychiatric symptoms. Here, we review recent findings from the developing field of imaging-genetics applied to ASD. Studies to date have indicated that relevant risk genes are associated with alterations in circuits that mediate socio-emotional, visuo-spatial, and language processing. Longitudinal studies ideally focused on early development, in conjunction with investigation for gene–gene, and gene–environment interactions may move the promise of imaging-genetics in ASD closer to the clinical domain.
PMCID: PMC3351673  PMID: 22615702
autism spectrum disorder; neuroimaging; genetics; imaging-genetics; neurodevelopment
2.  Impaired Structural Connectivity of Socio-Emotional Circuits in Autism Spectrum Disorders: A Diffusion Tensor Imaging Study 
PLoS ONE  2011;6(11):e28044.
Abnormal white matter development may disrupt integration within neural circuits, causing particular impairments in higher-order behaviours. In autism spectrum disorders (ASDs), white matter alterations may contribute to characteristic deficits in complex socio-emotional and communication domains. Here, we used diffusion tensor imaging (DTI) and tract based spatial statistics (TBSS) to evaluate white matter microstructure in ASD.
Methods/Principal Findings
DTI scans were acquired for 19 children and adolescents with ASD (∼8–18 years; mean 12.4±3.1) and 16 age and IQ matched controls (∼8–18 years; mean 12.3±3.6) on a 3T MRI system. DTI values for fractional anisotropy, mean diffusivity, radial diffusivity and axial diffusivity, were measured. Age by group interactions for global and voxel-wise white matter indices were examined. Voxel-wise analyses comparing ASD with controls in: (i) the full cohort (ii), children only (≤12 yrs.), and (iii) adolescents only (>12 yrs.) were performed, followed by tract-specific comparisons. Significant age-by-group interactions on global DTI indices were found for all three diffusivity measures, but not for fractional anisotropy. Voxel-wise analyses revealed prominent diffusion measure differences in ASD children but not adolescents, when compared to healthy controls. Widespread increases in mean and radial diffusivity in ASD children were prominent in frontal white matter voxels. Follow-up tract-specific analyses highlighted disruption to pathways integrating frontal, temporal, and occipital structures involved in socio-emotional processing.
Our findings highlight disruption of neural circuitry in ASD, particularly in those white matter tracts that integrate the complex socio-emotional processing that is impaired in this disorder.
PMCID: PMC3223195  PMID: 22132206
3.  Neurexin-1 and Frontal Lobe White Matter: An Overlapping Intermediate Phenotype for Schizophrenia and Autism Spectrum Disorders 
PLoS ONE  2011;6(6):e20982.
Structural variation in the neurexin-1 (NRXN1) gene increases risk for both autism spectrum disorders (ASD) and schizophrenia. However, the manner in which NRXN1 gene variation may be related to brain morphology to confer risk for ASD or schizophrenia is unknown.
Method/Principal Findings
53 healthy individuals between 18–59 years of age were genotyped at 11 single nucleotide polymorphisms of the NRXN1 gene. All subjects received structural MRI scans, which were processed to determine cortical gray and white matter lobar volumes, and volumes of striatal and thalamic structures. Each subject's sensorimotor function was also assessed. The general linear model was used to calculate the influence of genetic variation on neural and cognitive phenotypes. Finally, in silico analysis was conducted to assess potential functional relevance of any polymorphisms associated with brain measures. A polymorphism located in the 3′ untranslated region of NRXN1 significantly influenced white matter volumes in whole brain and frontal lobes after correcting for total brain volume, age and multiple comparisons. Follow-up in silico analysis revealed that this SNP is a putative microRNA binding site that may be of functional significance in regulating NRXN1 expression. This variant also influenced sensorimotor performance, a neurocognitive function impaired in both ASD and schizophrenia.
Our findings demonstrate that the NRXN1 gene, a vulnerability gene for SCZ and ASD, influences brain structure and cognitive function susceptible in both disorders. In conjunction with our in silico results, our findings provide evidence for a neural and cognitive susceptibility mechanism by which the NRXN1 gene confers risk for both schizophrenia and ASD.
PMCID: PMC3110800  PMID: 21687627
4.  Quantitative Examination of a Novel Clustering Method using Magnetic Resonance Diffusion Tensor Tractography 
NeuroImage  2008;45(2):370-376.
MR diffusion tensor imaging (DTI) can measure and visualize organization of white matter fibre tracts in vivo. DTI is a relatively new imaging technique, and new tools developed for quantifying fibre tracts require evaluation. The purpose of this study was to compare the reliability of a novel clustering approach with a multiple region of interest (MROI) approach in both healthy and disease (schizophrenia) populations. DTI images were acquired in 20 participants (n=10 patients with schizophrenia: 56 ± 15 years; n=10 controls: 51 ± 20 years) (1.5 Tesla GE system) with diffusion gradients applied in 23 non-collinear directions, repeated three times. Whole brain seeding and creation of fibre tracts were then performed. Interrater reliability of the clustering approach, and the MROI approach, were each evaluated and the methods compared. There was high spatial (voxel-based) agreement within and between the clustering and MROI methods. Fractional anisotropy, trace, and radial and axial diffusivity values showed high intraclass correlation (p<0.001 for all tracts) for each approach. Differences in scalar indices of diffusion between the clustering and MROI approach were minimal. The excellent interrater reliability of the clustering method and high agreement with the MROI method, quantitatively and spatially, indicates that the clustering method can be used with confidence. The clustering method avoids biases of ROI drawing and placement, and, not limited by a priori predictions, may be a more robust and efficient way to identify and measure white matter tracts of interest.
PMCID: PMC2646811  PMID: 19159690
diffusion tensor imaging; tractography; streamline; clustering; region of interest; schizophrenia

Results 1-4 (4)