Diffusion tensor imaging of the corpus callosum in Autism

Alexander, Andrew L.; Lee, Jee Eun; Lazar, Mariana; Boudos, Rebecca; DuBray, Molly B.; Oakes, Terrence R.; Miller, Judith; Lu, Jeff Zhiqiang; Jeong, Eun Kee; McMahon, William M.; Bigler, Erin D.; Lainhart, Janet E.

doi:10.1016/j.neuroimage.2006.08.032

Cited by 531 publications

(545 citation statements)

References 68 publications

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“…For example, the trajectory of head growth, which corresponds to brain size, seems to be reproducibly abnormal in children with ASD, who have smaller head circumferences at birth followed by a burst in head circumferences postnatally, eventually reaching normal size around adolescence (Figure 2a) [144-146]. Studies have also repeatedly shown decreases in white matter tracts in autism (Figure 2c) [147,148]. Specifically, long-range connections seem to be weakened, whereas local connections are strengthened [149,150].…”

Section: Emerging Biological Themesmentioning

confidence: 99%

Autism genetics: searching for specificity and convergence

2012

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Section: Emerging Biological Themesmentioning

confidence: 99%

Autism genetics: searching for specificity and convergence

2012

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“…In a recent meta-analysis, for instance, Patel et al (2011) concluded that the splenium of the corpus callosum has significantly lower FA in patients with schizophrenia vs controls. Recent DTI studies have also identified callosal abnormalities in patients with other brain disorders such as bipolar disorder (Benedetti et al, 2011), post-traumatic stress disorder (Jackowski et al, 2008), and autism (Alexander et al, 2007). It would therefore be beneficial, clinically, to know an individual's personalized genetic risk for a corpus callosum structural abnormality.…”

Section: Discussionmentioning

confidence: 99%

“…By mapping the diffusion of water through the brain's fibers, DTI can recover major fiber pathways in the brain, and patterns of anatomical connectivity, with broad applications in psychiatry, neurology, and brain mapping (Thomason and Thompson, 2011). DTI-based white matter abnormalities are widely reported in developmental and degenerative brain diseases including Alzheimer's disease and mild cognitive impairment (Fellgiebel et al, 2004;Naggara et al, 2006;Oishi et al, 2011), schizophrenia (White et al, 2008;Ellison-Wright and Bullmore, 2009;Patel et al, 2011), bipolar disorder (Sussmann et al, 2009;Heng et al, 2010), attention-deficit/hyperactivity disorder (Konrad and Eickhoff, 2010), and autism (Alexander et al, 2007;Ke et al, 2009). These studies show the utility of DTI in neuropsychiatric research.…”

Section: Introductionmentioning

confidence: 99%

“…We hypothesize that this would help predict brain integrity, as measured by DTI-derived FA, more powerfully than a single-locus genetic test. We focus on the corpus callosum, as it is the largest white matter structure in the brain, easy to examine at the brain's midline, highly heritable (Chiang et al, 2009;Brouwer et al, 2010;Kochunov et al, 2010), and well studied in neurology and psychiatry as the primary commissure connecting the two brain hemispheres (Foong et al, 2000;Alexander et al, 2007). We chose FA as the DTI measure of white matter structure, as it has been shown to have higher heritability than other DTI parameters, such as radial and axial diffusivity (Kochunov et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

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Predicting White Matter Integrity from Multiple Common Genetic Variants

Kohannim

Jahanshad

Braskie

et al. 2012

Neuropsychopharmacol

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Several common genetic variants have recently been discovered that appear to influence white matter microstructure, as measured by diffusion tensor imaging (DTI). Each genetic variant explains only a small proportion of the variance in brain microstructure, so we set out to explore their combined effect on the white matter integrity of the corpus callosum. We measured six common candidate singlenucleotide polymorphisms (SNPs) in the COMT, NTRK1, BDNF, ErbB4, CLU, and HFE genes, and investigated their individual and aggregate effects on white matter structure in 395 healthy adult twins and siblings (age: 20-30 years). All subjects were scanned with 4-tesla 94-direction high angular resolution diffusion imaging. When combined using mixed-effects linear regression, a joint model based on five of the candidate SNPs (COMT, NTRK1, ErbB4, CLU, and HFE) explained B6% of the variance in the average fractional anisotropy (FA) of the corpus callosum. This predictive model had detectable effects on FA at 82% of the corpus callosum voxels, including the genu, body, and splenium. Predicting the brain's fiber microstructure from genotypes may ultimately help in early risk assessment, and eventually, in personalized treatment for neuropsychiatric disorders in which brain integrity and connectivity are affected.

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“…Accordingly, a third limitation of the current study has to do with not directly testing the 'connectivity' hypothesis of autism. For example, MRI-based diffusion tensor imaging (DTI) and functional neuroimaging methods that directly test connectivity will be important next steps in unraveling aberrant pathways specific to autism [52].…”

Section: Discussionmentioning

confidence: 99%

Quantitative temporal lobe differences: Autism distinguished from controls using classification and regression tree analysis

Neeley

Bigler

Krasny

et al. 2007

Brain and Development

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SummaryThe temporal lobe is thought to be abnormal in autism, yet standard volumetric analyses are often unrevealing when age, sex, IQ and head size are controlled. Quantification of temporal lobe structures were obtained in male subjects with autism and controls, where subjects with head circumference (HC) defined macrocephaly were excluded, so that volume differences were not just related to the higher prevalence of macrocephaly in autism. Various statistical methods were applied to the analysis including a classification and regression tree (CART) method, a nonparametric technique that helps define patterns of relationships that may be meaningful in distinguishing temporal lobe differences between subjects with autism and age and IQ matched controls. Subjects with autism were also compared to a separate control group with reading disorder (RD), with the prediction that the temporal lobe morphometric analysis of the reading disorder controls would be more similar to that of the autism group. The CART method yielded a high specificity in classifying autism subjects from controls based on the relationship between the volume of the left fusiform gyrus (LFG) gray and white matter, the right temporal stem (RTS) and the right inferior temporal gyrus gray matter (RITG-GM). Reading disordered individuals were Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Author Manuscript more similar to subjects with autism. Simple size differences did not distinguish the groups. These findings demonstrate different relationships within temporal lobe structures that distinguish subjects with autism from controls. Results are discussed in terms of pathological connectivity within the temporal lobe as it relates to autism. HHS Public Access

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Diffusion tensor imaging of the corpus callosum in Autism

Cited by 531 publications

References 68 publications

Autism genetics: searching for specificity and convergence

Autism genetics: searching for specificity and convergence

Predicting White Matter Integrity from Multiple Common Genetic Variants

Quantitative temporal lobe differences: Autism distinguished from controls using classification and regression tree analysis

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