Brain fiber architecture, genetics, and intelligence: a high angular resolution diffusion imaging (HARDI) study

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, resampling 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) 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.

Mapping genetic influences on brain fiber architecture with high angular resolution diffusion imaging (HARDI)

We report the first 3D maps of genetic effects on brain fiber complexity. We analyzed HARDI brain imaging data from 90 young adult twins using an information-theoretic measure, the Jensen-Shannon divergence (JSD), to gauge the regional complexity of the white matter fiber orientation distribution functions (ODF). HARDI data were fluidly registered using Karcher means and ODF square-roots for interpol ation; each subject's JSD map was computed from the spatial coherence of the ODFs in each voxel's neighborhood. We evaluated the genetic influences on generalized fiber anisotropy (GFA) and complexity (JSD) using structural equation models (SEM). At each voxel, genetic and environmental components of data variation were estimated, and their goodness of fit tested by permutation. Color-coded maps revealed that the optimal models varied for different brain regions. Fiber complexity was predominantly under genetic control, and was higher in more highly anisotropic regions. These methods show promise for discovering factors affecting fiber connectivity in the brain.

Memory strength effects in fMRI studies: A matter of confidence

To investigate potentially dissociable recognition memory responses in the hippocampus and perirhinal cortex, fMRI studies have often used confidence ratings as an index of memory strength. Confidence ratings, although correlated with memory strength, also reflect sources of variability, including task-irrelevant item effects and differences both within and across individuals in terms of applying decision criteria to separate weak from strong memories. We presented words one, two, or four times at study in each of two different conditions, focused and divided attention, and then conducted separate fMRI analyses of correct old responses on the basis of subjective confidence ratings or estimates from single- versus dual-process recognition memory models. Overall, the effect of focussing attention on spaced repetitions at study manifested as enhanced recognition memory performance. Confidence- versus model-based analyses revealed disparate patterns of hippocampal and perirhinal cortex activity at both study and test and both within and across hemispheres. The failure to observe equivalent patterns of activity indicates that fMRI signals associated with subjective confidence ratings reflect additional sources of variability. The results are consistent with predictions of single-process models of recognition memory.

Bivariate genome-wide association study of genetically correlated neuroimaging phenotypes from DTI and MRI through a seemingly unrelated regression model

Large multisite efforts (e.g., the ENIGMA Consortium), have shown that neuroimaging traits including tract integrity (from DTI fractional anisotropy, FA) and subcortical volumes (from T1-weighted scans) are highly heritable and promising phenotypes for discovering genetic variants associated with brain structure. However, genetic correlations (rg) among measures from these different modalities for mapping the human genome to the brain remain unknown. Discovering these correlations can help map genetic and neuroanatomical pathways implicated in development and inherited risk for disease. We use structural equation models and a twin design to find rg between pairs of phenotypes extracted from DTI and MRI scans. When controlling for intracranial volume, the caudate as well as related measures from the limbic system - hippocampal volume - showed high rg with the cingulum FA. Using an unrelated sample and a Seemingly Unrelated Regression model for bivariate analysis of this connection, we show that a multivariate GWAS approach may be more promising for genetic discovery than a univariate approach applied to each trait separately.

Diffusion imaging protocol effects on genetic associations

Large multi-site image-analysis studies have successfully discovered genetic variants that affect brain structure in tens of thousands of subjects scanned worldwide. Candidate genes have also associated with brain integrity, measured using fractional anisotropy in diffusion tensor images (DTI). To evaluate the heritability and robustness of DTI measures as a target for genetic analysis, we compared 417 twins and siblings scanned on the same day on the same high field scanner (4-Tesla) with two protocols: (1) 94-directions; 2mm-thick slices, (2) 27-directions; 5mm-thickness. Using mean FA in white matter ROIs and FA skeletons derived using FSL, we (1) examined differences in voxelwise means, variances, and correlations among the measures; and (2) assessed heritability with structural equation models, using the classical twin design. FA measures from the genu of the corpus callosum were highly heritable, regardless of protocol. Genome-wide analysis of the genu mean FA revealed differences across protocols in the top associations.

A genetic analysis of cortical thickness in 372 twins

Imaging genetics is a new field of neuroscience that blends methods from computational anatomy and quantitative genetics to identify genetic influences on brain structure and function. Here we analyzed brain MRI data from 372 young adult twins to identify cortical regions in which gray matter volume is influenced by genetic differences across subjects. Thickness maps, reconstructed from surface models of the cortical gray/white and gray/CSF interfaces, were smoothed with a 25 mm FWHM kernel and automatically parcellated into 34 regions of interest per hemisphere. In structural equation models fitted to volume values at each surface vertex, we computed components of variance due to additive genetic (A), shared (C) and unique (E) environmental factors, and tested their significance. Cortical regions in the vicinity of the perisylvian language cortex, and at the frontal and temporal poles, showed significant additive genetic variance, suggesting that volume measures from these regions may provide quantitative phenotypes to narrow the search for quantitative trait loci that influence brain structure.

Scalar connectivity measures from fast-marching tractography reveal heritability of white matter architecture

Recent advances in diffusion-weighted MRI (DWI) have enabled studies of complex white matter tissue architecture in vivo. To date, the underlying influence of genetic and environmental factors in determining central nervous system connectivity has not been widely studied. In this work, we introduce new scalar connectivity measures based on a computationally-efficient fast-marching algorithm for quantitative tractography. We then calculate connectivity maps for a DTI dataset from 92 healthy adult twins and decompose the genetic and environmental contributions to the variance in these metrics using structural equation models. By combining these techniques, we generate the first maps to directly examine genetic and environmental contributions to brain connectivity in humans. Our approach is capable of extracting statistically significant measures of genetic and environmental contributions to neural connectivity.

Modeling meat yield based on measurements of body traits in genetically improved giant freshwater prawn (GFP) Macrobrachium rosenbergii

A single-generation dataset consisting of 1,730 records from a selection program for high growth rate in giant freshwater prawn (GFP, Macrobrachium rosenbergii) was used to derive prediction equations for meat weight and meat yield. Models were based on body traits [body weight, total length and abdominal width (AW)] and carcass measurements (tail weight and exoskeleton-off weight). Lengths and width were adjusted for the systematic effects of selection line, male morphotypes and female reproductive status, and for the covariables of age at slaughter within sex and body weight. Body and meat weights adjusted for the same effects (except body weight) were used to calculate meat yield (expressed as percentage of tail weight/body weight and exoskeleton-off weight/body weight). The edible meat weight and yield in this GFP population ranged from 12 to 15 g and 37 to 45 %, respectively. The simple (Pearson) correlation coefficients between body traits (body weight, total length and AW) and meat weight were moderate to very high and positive (0.75–0.94), but the correlations between body traits and meat yield were negative (−0.47 to −0.74). There were strong linear positive relationships between measurements of body traits and meat weight, whereas relationships of body traits with meat yield were moderate and negative. Step-wise multiple regression analysis showed that the best model to predict meat weight included all body traits, with a coefficient of determination (R 2) of 0.99 and a correlation between observed and predicted values of meat weight of 0.99. The corresponding figures for meat yield were 0.91 and 0.95, respectively. Body weight or length was the best predictor of meat weight, explaining 91–94 % of observed variance when it was fitted alone in the model. By contrast, tail width explained a lower proportion (69–82 %) of total variance in the single trait models. It is concluded that in practical breeding programs, improvement of meat weight can be easily made through indirect selection for body trait combinations. The improvement of meat yield, albeit being more difficult, is possible by genetic means, with 91 % of the variation in the trait explained by the body and carcass traits examined in this study.

Surgical fusion of early onset severe scoliosis increases survival in Rett syndrome: A cohort study

Aim Scoliosis is a common co-morbidity in Rett syndrome and spinal fusion may be recommended if severe. We investigated the impact of spinal fusion on survival and risk of severe lower respiratory tract infection in Rett syndrome. Method Data were ascertained from hospital medical records, the Australian Rett Syndrome Database, a longitudinal and population-based registry, and from the Australian Institute of Health and Welfare National Death Index database. Cox regression and generalized estimating equation models were used to estimate the effects of spinal surgery on survival and severe respiratory infection respectively in 140 females who developed severe scoliosis (Cobb angle ≥45°) before adulthood. Results After adjusting for mutation type and age of scoliosis onset, the rate of death was lower in the surgery group (hazard ratio [HR] 0.30, 95% confidence interval [CI] 0.12–0.74; p=0.009) compared to those without surgery. Rate of death was particularly reduced for those with early onset scoliosis (HR 0.17, 95% CI 0.06–0.52; p=0.002). There was some evidence to suggest that spinal fusion was associated with a reduction in risk of severe respiratory infection among those with early onset scoliosis (risk ratio 0.41, 95% CI 0.16–1.03; p=0.06). Interpretation With appropriate cautions, spinal fusion confers an advantage to life expectancy in Rett syndrome.

Does vigorous physical activity provide additional benefits beyond those of moderate?

Background Although guidelines suggest that vigorous physical activity (PA) confers “extra” benefits compared with those from moderate-intensity activity alone, the magnitude of this additional benefit is unclear. The aim was to compare the reduction in risk of hypertension (HT) and depressive symptoms (DS) for 12 yr in middle-age women who reported (a) only moderate-intensity PA (MOPA) and (b) a combination of moderate and vigorous PA (MVPA), after controlling for overall volume of activity. Methods The study involved 11,285 participants in the Australian Longitudinal Study on Women’s Health, who completed surveys in 1998 (age = 46–52 yr), 2001, 2004, 2007, and 2010. Generalized estimating equation models (with 3-yr time lag) were used to examine the relationship between PA in seven categories from 0 to >2000 MET·min·wk−1 and occurrence of HT and DS for women who reported MOPA or MVPA. Results For HT, risk was slightly lower for MVPA than for MOPA across the entire range of PA levels, but this difference was only significant at the highest PA level (>2000; odds ratio [OR] = 0.80 MOPA and 0.56 MVPA). For DS, OR values were similar in both groups up to 500 MET·min·wk−1, then slightly lower for MVPA than for MOPA at higher PA levels. Again, this difference was only significant at the highest PA level (>2000; OR = 0.57 MOPA and 0.42 MVPA). OR values were slightly attenuated in adjusted models. Conclusions Doing both vigorous and moderate activity does not have significant additional benefits in terms of HT and DS, above those from moderate-intensity activity alone, except at very high levels of PA.

Prospective relationships between physical activity and optimism in young and mid-aged women

BACKGROUND: There is growing evidence that regular physical activity (PA) reduces the risk of poor mental health. Less research has focused on the relationship between PA and positive wellbeing. The study aims were to assess the prospective associations between PA and optimism, in both young and mid-aged women. METHODS: 9688 young women (born 1973-1978) completed self-report surveys in 2000 (age 22 to 27), 2003, 2006, and 2009; and 11,226 mid-aged women (born 1946-1951) completed surveys in 2001 (age 50-55) 2004, 2007, and 2010, as part of the Australian Longitudinal Study on Women's Health. Generalized estimating equation models (with 3-year time lag) were used to examine the relationship between PA and optimism in both cohorts. RESULTS: In both cohorts, women reporting higher levels of PA had greater odds of reporting higher optimism over the 9-year period, (young, OR = 5.04, 95% CI: 3.85-6.59; mid-age, OR = 5.77, 95% CI: 4.76-7.00) than women who reported no PA. Odds were attenuated in adjusted models, with depression accounting for a large amount of this attenuation (young, OR = 2.00, 95% CI: 1.57-2.55; mid-age, OR = 1.64 95% CI: 1.38-1.94). CONCLUSIONS: Physical activity can promote optimism in young and mid-aged women over time, even after accounting for the negative effects of other psychosocial indicators such as depression.

Heritability of brain network topology in 853 twins and siblings

Anatomical brain networks change throughout life and with diseases. Genetic analysis of these networks may help identify processes giving rise to heritable brain disorders, but we do not yet know which network measures are promising for genetic analyses. Many factors affect the downstream results, such as the tractography algorithm used to define structural connectivity. We tested nine different tractography algorithms and four normalization methods to compute brain networks for 853 young healthy adults (twins and their siblings). We fitted genetic structural equation models to all nine network measures, after a normalization step to increase network consistency across tractography algorithms. Probabilistic tractography algorithms with global optimization (such as Probtrackx and Hough) yielded higher heritability statistics than 'greedy' algorithms (such as FACT) which process small neighborhoods at each step. Some global network measures (probtrackx-derived GLOB and ST) showed significant genetic effects, making them attractive targets for genome-wide association studies.

Three Carrier Ambiguity Resolutions : Generalised problems, models and solutions

In this paper, the problems of three carrier phase ambiguity resolution (TCAR) and position estimation (PE) are generalized as real time GNSS data processing problems for a continuously observing network on large scale. In order to describe these problems, a general linear equation system is presented to uniform various geometry-free, geometry-based and geometry-constrained TCAR models, along with state transition questions between observation times. With this general formulation, generalized TCAR solutions are given to cover different real time GNSS data processing scenarios, and various simplified integer solutions, such as geometry-free rounding and geometry-based LAMBDA solutions with single and multiple-epoch measurements. In fact, various ambiguity resolution (AR) solutions differ in the floating ambiguity estimation and integer ambiguity search processes, but their theoretical equivalence remains under the same observational systems models and statistical assumptions. TCAR performance benefits as outlined from the data analyses in some recent literatures are reviewed, showing profound implications for the future GNSS development from both technology and application perspectives.

Models of collective cell spreading with variable cell aspect ratio : a motivation for degenerate diffusion models

Continuum diffusion models are often used to represent the collective motion of cell populations. Most previous studies have simply used linear diffusion to represent collective cell spreading, while others found that degenerate nonlinear diffusion provides a better match to experimental cell density profiles. In the cell modeling literature there is no guidance available with regard to which approach is more appropriate for representing the spreading of cell populations. Furthermore, there is no knowledge of particular experimental measurements that can be made to distinguish between situations where these two models are appropriate. Here we provide a link between individual-based and continuum models using a multi-scale approach in which we analyze the collective motion of a population of interacting agents in a generalized lattice-based exclusion process. For round agents that occupy a single lattice site, we find that the relevant continuum description of the system is a linear diffusion equation, whereas for elongated rod-shaped agents that occupy L adjacent lattice sites we find that the relevant continuum description is connected to the porous media equation (pme). The exponent in the nonlinear diffusivity function is related to the aspect ratio of the agents. Our work provides a physical connection between modeling collective cell spreading and the use of either the linear diffusion equation or the pme to represent cell density profiles. Results suggest that when using continuum models to represent cell population spreading, we should take care to account for variations in the cell aspect ratio because different aspect ratios lead to different continuum models.