Caroline Brun

Generalized Tensor-Based Morphometry of HIV/AIDS Using Multivariate Statistics on Deformation Tensors

This paper investigates the performance of a new multivariate method for tensor-based morphometry (TBM). Statistics on Riemannian manifolds are developed that exploit the full information in deformation tensor fields. In TBM, multiple brain images are warped to a common neuroanatomical template via 3-D nonlinear registration; the resulting deformation fields are analyzed statistically to identify group differences in anatomy. Rather than study the Jacobian determinant (volume expansion factor) of these deformations, as is common, we retain the full deformation tensors and apply a manifold version of Hotellings test to them, in a Log-Euclidean domain. In 2-D and 3-D magnetic resonance imaging (MRI) data from 26 HIV/AIDS patients and 14 matched healthy subjects, we compared multivariate tensor analysis versus univariate tests of simpler tensor-derived indices: the Jacobian determinant, the trace, geodesic anisotropy, and eigenvalues of the deformation tensor, and the angle of rotation of its eigenvectors. We detected consistent, but more extensive patterns of structural abnormalities, with multivariate tests on the full tensor manifold. Their improved power was established by analyzing cumulative-value plots using false discovery rate (FDR) methods, appropriately controlling for false positives. This increased detection sensitivity may empower drug trials and large-scale studies of disease that use tensor-based morphometry.

Acute and Chronic Changes in Diffusivity Measures after Sports Concussion

Despite negative neuroimaging findings in concussed athletes, studies indicate that the acceleration and deceleration of the brain after concussive impacts result in metabolic and electrophysiological alterations that may be attributable to changes in white matter resulting from biomechanical strain. In the present study we investigated the effects of sports concussion on white matter using three different diffusion tensor imaging (DTI) measures: fractional anisotropy (FA), mean diffusivity (MD), and axial diffusivity (AD). We compared a group of 10 non-concussed athletes with a group of 18 concussed athletes of the same age (mean age 22.5 years) and education (mean 16 years) using a voxel-based approach (VBA) in both the acute and chronic post-injury phases. All concussed athletes were scanned 1-6 days post-concussion and again 6 months later in a 3T Siemens Trio(™) MRI. Three 2×2 repeated-measures analyses of variance (ANOVAs) were conducted, one for each measure of DTI used in the current study. There was a main group effect of FA, which was increased in dorsal regions of both corticospinal tracts (CST) and in the corpus callosum in concussed athletes at both time points. There was a main group effect of AD in the right CST, where concussed athletes showed elevated values relative to controls at both time points. MD values were decreased in concussed athletes, in whom analyses revealed significant group differences in the CST and corpus callosum at both time points. Although the use of VBA does limit the analyses to large tracts, and it has clinical limitations with regard to individual analyses, our results nevertheless indicate that sports concussions do result in changes in diffusivity in the corpus callosum and CST that are not detected using conventional neuroimaging techniques.

3D characterization of brain atrophy in Alzheimer's disease and mild cognitive impairment using tensor-based morphometry

Tensor-based morphometry (TBM) creates three-dimensional maps of disease-related differences in brain structure, based on nonlinearly registering brain MRI scans to a common image template. Using two different TBM designs (averaging individual differences versus aligning group average templates), we compared the anatomical distribution of brain atrophy in 40 patients with Alzheimers disease (AD), 40 healthy elderly controls, and 40 individuals with amnestic mild cognitive impairment (aMCI), a condition conferring increased risk for AD. We created an unbiased geometrical average image template for each of the three groups, which were matched for sex and age (mean age: 76.1 years+/-7.7 SD). We warped each individual brain image (N=120) to the control group average template to create Jacobian maps, which show the local expansion or compression factor at each point in the image, reflecting individual volumetric differences. Statistical maps of group differences revealed widespread medial temporal and limbic atrophy in AD, with a lesser, more restricted distribution in MCI. Atrophy and CSF space expansion both correlated strongly with Mini-Mental State Exam (MMSE) scores and Clinical Dementia Rating (CDR). Using cumulative p-value plots, we investigated how detection sensitivity was influenced by the sample size, the choice of search region (whole brain, temporal lobe, hippocampus), the initial linear registration method (9- versus 12-parameter), and the type of TBM design. In the future, TBM may help to (1) identify factors that resist or accelerate the disease process, and (2) measure disease burden in treatment trials.

Multivariate statistics of the jacobian matrices in tensor based morphometry and their application to HIV/AIDS

Tensor-based morphometry (TBM) is widely used in computational anatomy as a means to understand shape variation between structural brain images. A 3D nonlinear registration technique is typically used to align all brain images to a common neuroanatomical template, and the deformation fields are analyzed statistically to identify group differences in anatomy. However, the differences are usually computed solely from the determinants of the Jacobian matrices that are associated with the deformation fields computed by the registration procedure. Thus, much of the information contained within those matrices gets thrown out in the process. Only the magnitude of the expansions or contractions is examined, while the anisotropy and directional components of the changes are ignored. Here we remedy this problem by computing multivariate shape change statistics using the strain matrices. As the latter do not form a vector space, means and covariances are computed on the manifold of positive-definite matrices to which they belong. We study the brain morphology of 26 HIV/AIDS patients and 14 matched healthy control subjects using our method. The images are registered using a high-dimensional 3D fluid registration algorithm, which optimizes the Jensen-Rényi divergence, an information-theoretic measure of image correspondence. The anisotropy of the deformation is then computed. We apply a manifold version of Hotellings T2 test to the strain matrices. Our results complement those found from the determinants of the Jacobians alone and provide greater power in detecting group differences in brain structure.

Tensor-Based Analysis of Genetic Influences on Brain Integrity Using DTI in 100 Twins

Information from the full diffusion tensor (DT) was used to compute voxel-wise genetic contributions to brain fiber microstructure. First, we designed a new multivariate intraclass correlation formula in the log-Euclidean framework. We then analyzed used the full multivariate structure of the tensor in a multivariate version of a voxel-wise maximum-likelihood structural equation model (SEM) that computes the variance contributions in the DTs from genetic (A), common environmental (C) and unique environmental (E) factors. Our algorithm was tested on DT images from 25 identical and 25 fraternal twin pairs. After linear and fluid registration to a mean template, we computed the intraclass correlation and Falconers heritability statistic for several scalar DT-derived measures and for the full multivariate tensors. Covariance matrices were found from the DTs, and inputted into SEM. Analyzing the full DT enhanced the detection of A and C effects. This approach should empower imaging genetics studies that use DTI.

A study of brain white matter plasticity in early blinds using tract-based spatial statistics and tract statistical analysis

Early blind individuals are known to exhibit structural brain reorganization. Particularly, early-onset blindness may trigger profound brain alterations that affect not only the visual system but also the remaining sensory systems. Diffusion tensor imaging (DTI) allows in-vivo visualization of brain white matter connectivity, and has been extensively used to study brain white matter structure. Among statistical approaches based on DTI, tract-based spatial statistics (TBSS) is widely used because of its ability to automatically perform whole brain white matter studies. Tract specific analysis (TSA) is a more recent method that localizes changes in specific white matter bundles. In the present study, we compare TBSS and TSA results of DTI scans from 12 early blind individuals and 13 age-matched sighted controls, with two aims: (a) to investigate white matter alterations associated with early visual deprivation; (b) to examine the relative sensitivity of TSA when compared with TBSS, for both deficit and hypertrophy of white matter microstructures. Both methods give consistent results for broad white matter regions of deficits. However, TBSS does not detect hypertrophy of white matter, whereas TSA shows a higher sensitivity in detecting subtle differences in white matter colocalized to the posterior parietal lobe.

The multivariate A/C/E model and the genetics of fiber architecture

We present a new algorithm to compute the voxel-wise genetic contribution to brain fiber microstructure using diffusion tensor imaging (DTI) in a dataset of 25 monozygotic (MZ) twins and 25 dizygotic (DZ) twin pairs (100 subjects total). First, the structural and DT scans were linearly co-registered. Structural MR scans were nonlinearly mapped via a 3D fluid transformation to a geometrically centered mean template, and the deformation fields were applied to the DTI volumes. After tensor re-orientation to realign them to the anatomy, we computed several scalar and multivariate DT-derived measures including the geodesic anisotropy (GA), the tensor eigenvalues and the full diffusion tensors. A covariance-weighted distance was measured between twins in the Log-Euclidean framework [2], and used as input to a maximum-likelihood based algorithm to compute the contributions from genetics (A), common environmental factors (C) and unique environmental ones (E) to fiber architecture. Quanititative genetic studies can take advantage of the full information in the diffusion tensor, using covariance weighted distances and statistics on the tensor manifold.

A new cryptic Dainty Frog from East Africa (Anura : Ranidae : Cacosternum ) : original article

Abstract We describe a new species of dainty frog in the genus Cacosternum (Ranidae) from East Africa. It is similar morphologically to Cacosternum boettgeri from the interior of southern Africa, and is distinguished on the basis of an advertisement call with double or treble pulsed notes. Skeletal and DNA sequence differences support the species status of this taxon. It is known from northern Tanzania and the highlands of Kenya.

Reducing structural variation to determine the genetics of white matter integrity across hemispheres - A DTI study of 100 twins

Studies of cerebral asymmetry can open doors to understanding the functional specialization of each brain hemisphere, and how this is altered in disease. Here we examined hemispheric asymmetries in fiber architecture using diffusion tensor imaging (DTI) in 100 subjects, using high-dimensional fluid warping to disentangle shape differences from measures sensitive to myelination. Confounding effects of purely structural asymmetries were reduced by using co-registered structural images to fluidly warp 3D maps of fiber characteristics (fractional and geodesic anisotropy) to a structurally symmetric minimal deformation template (MDT). We performed a quantitative genetic analysis on 100 subjects to determine whether the sources of the remaining signal asymmetries were primarily genetic or environmental. A twin design was used to identify the heritable features of fiber asymmetry in various regions of interest, to further assist in the discovery of genes influencing brain micro-architecture and brain lateralization. Genetic influences and left/right asymmetries were detected in the fiber architecture of the frontal lobes, with minor differences depending on the choice of registration template.

Brain Fiber Architecture in the Blind

Introduction: Blind people compensate for the loss of vision with enhanced abilities in other sensory and cognitive areas. These behavioral adaptations are often, at least in part, the result of cross-modal recruitment of visual cortices. Consequently, differences in white matter fiber architecture between blind and sighted individuals are of interest as they may reflect the brain’s capacity to adapt in response to chronic visual deprivation. Here we mapped the profile of differences in white matter fiber architecture between blind and sighted subjects using a new method that combines tensor-based morphometry and diffusion tensor imaging (DTI). Using a multivariate analysis of the full 6-dimensional tensor, group differences in fiber structure were more powerfully detected than with DTI-derived scalar statistics.