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Working memory-related brain activation has been widely studied, and impaired activation patterns have been reported for several psychiatric disorders. We investigated whether variation in N-back working memory brain activation is genetically influenced in 60 pairs of twins, (29 monozygotic (MZ), 31 dizygotic (DZ); mean age 24.4 ± 1.7S.D.). Task-related brain response (BOLD percent signal difference of 2 minus 0-back) was measured in three regions of interest. Although statistical power was low due to the small sample size, for middle frontal gyrus, angular gyrus, and supramarginal gyrus, the MZ correlations were, in general, approximately twice those of the DZ pairs, with non-significant heritability estimates (14-30%) in the low-moderate range. Task performance was strongly influenced by genes (57-73%) and highly correlated with cognitive ability (0.44-0.55). This study, which will be expanded over the next 3 years, provides the first support that individual variation in working memory-related brain activation is to some extent influenced by genes.

Diffusion weighted magnetic resonance imaging is a powerful tool that can be employed to study white matter microstructure by examining the 3D displacement profile of water molecules in brain tissue. By applying diffusion-sensitized gradients along a minimum of six directions, second-order tensors (represented by three-by-three positive definite matrices) can be computed to model dominant diffusion processes. However, conventional DTI is not sufficient to resolve more complicated white matter configurations, e.g., crossing fiber tracts. Recently, a number of high-angular resolution schemes with more than six gradient directions have been employed to address this issue. In this article, we introduce the tensor distribution function (TDF), a probability function defined on the space of symmetric positive definite matrices. Using the calculus of variations, we solve the TDF that optimally describes the observed data. Here, fiber crossing is modeled as an ensemble of Gaussian diffusion processes with weights specified by the TDF. Once this optimal TDF is determined, the orientation distribution function (ODF) can easily be computed by analytic integration of the resulting displacement probability function. Moreover, a tensor orientation distribution function (TOD) may also be derived from the TDF, allowing for the estimation of principal fiber directions and their corre- In the past decade, diffusion magnetic resonance imaging (dMRI) has become a powerful tool for studying the structure of fibrous materials, including white matter connectivity and integrity in the living brain. By applying diffusion-sensitized gradients (1-3), dMRI characterizes the particle diffusivity profile in various tissues (see, e.g., 4-6 for early approaches).When the duration of the applied diffusion sensitization δ is much smaller than the time between the two pulses, the MR signal attenuation is related to the displacement probability function using a Fourier integral relationship with respect to a wave vector q (7,8).As water Brownian motion is constrained by the underlying tissue microstructure, measurements of the displacement probability function of water molecules thus provide great insight into this microstructure. In brain imaging, dMRI can be advantageous over conventional nondiffusion-weighted MRI as it can reveal the configuration and orientation of fiber tracts in white matter. This can allow virtual tractography to be conducted and brain connectivity inferred (9-14).In diffusion tensor MRI (DT-MRI) (15,16), the water displacement probability function is modeled using a zeromean 3D Gaussian distribution whose covariance matrix, a second-order symmetric positive definite tensor, thus represents the principal directions of diffusion or the orientation of local fiber tracts. Here, eigenvectors represent the three principal orthogonal directions of water diffusion at each point in the image, and the associated eigenvalues denote the relative mobility along these three directions. Thus, local fiber tract orientation is considered pa...

Despite the prominent use of the Suchey-Brooks (S-B) method of age estimation in forensic anthropological practice, it is subject to intrinsic limitations, with reports of differential interpopulation error rates between geographical locations. This study assessed the accuracy of the S-B method to a contemporary adult population in Queensland, Australia and provides robust age parameters calibrated for our population. Three-dimensional surface reconstructions were generated from computed tomography scans of the pubic symphysis of male and female Caucasian individuals aged 15-70 years (n = 195) in Amira and Rapidform. Error was analyzed on the basis of bias, inaccuracy and percentage correct classification for left and right symphyseal surfaces. Application of transition analysis and Chi-square statistics demonstrated 63.9 and 69.7% correct age classification associated with the left symphyseal surface of Australian males and females, respectively, using the S-B method. Using Bayesian statistics, probability density distributions for each S-B phase were calculated, providing refined age parameters for our population. Mean inaccuracies of 6.77 (±2.76) and 8.28 (±4.41) years were reported for the left surfaces of males and females, respectively; with positive biases for younger individuals (<55 years) and negative biases in older individuals. Significant sexual dimorphism in the application of the S-B method was observed; and asymmetry in phase classification of the pubic symphysis was a frequent phenomenon. These results recommend that the S-B method should be applied with caution in medico-legal death investigations of Queensland skeletal remains and warrant further investigation of reliable age estimation techniques.

We developed an analysis pipeline enabling population studies of HARDI data, and applied it to map genetic influences on fiber architecture in 90 twin subjects. We applied tensor-driven 3D fluid registration to HARDI, re-sampling the spherical fiber orientation distribution functions (ODFs) in appropriate Riemannian manifolds, after ODF regularization and sharpening. Fitting structural equation models (SEM) from quantitative genetics, we evaluated genetic influences on the Jensen-Shannon divergence (JSD), a novel measure of fiber spatial coherence, and on the generalized fiber anisotropy (GFA; [1]) a measure of fiber integrity. With random-effects regression, we mapped regions where diffusion profiles were highly correlated with subjects' intelligence quotient (IQ). Fiber complexity was predominantly under genetic control, and higher in more highly anisotropic regions; the proportion of genetic versus environmental control varied spatially. Our methods show promise for discovering genes affecting fiber connectivity in the brain.