Investigating brain connectivity heritability in a twin study using diffusion imaging data

Heritability of brain anatomical connectivity has been studied with diffusion-weighted imaging (DWI) mainly by modeling each voxel's diffusion pattern as a tensor (e.g., to compute fractional anisotropy), but this method cannot accurately represent the many crossing connections present in the brain. We hypothesized that different brain networks (i.e., their component fibers) might have different heritability and we investigated brain connectivity using High Angular Resolution Diffusion Imaging (HARDI) in a cohort of twins comprising 328 subjects that included 70 pairs of monozygotic and 91 pairs of dizygotic twins. Water diffusion was modeled in each voxel with a Fiber Orientation Distribution (FOD) function to study heritability for multiple fiber orientations in each voxel. Precision was estimated in a test-retest experiment on a sub-cohort of 39 subjects. This was taken into account when computing heritability of FOD peaks using an ACE model on the monozygotic and dizygotic twins. Our results confirmed the overall heritability of the major white matter tracts but also identified differences in heritability between connectivity networks. Inter-hemispheric connections tended to be more heritable than intra-hemispheric and cortico-spinal connections. The highly heritable tracts were found to connect particular cortical regions, such as medial frontal cortices, postcentral, paracentral gyri, and the right hippocampus

Effect of Gradient Vectors Scheme and Noise Correction on Fractional Anisotropy in Diffusion Tensor Imaging of the Peripheral Nervous System

Diffusion Tensor Imaging (DTI) is a method widely used in research and clinic, especially for imaging and connectivity analysis of the white brain matter. Despite the many possibilities offered by DTI, this method suffers from an inherently low signal-to-noise ratio (SNR), since both the long echo time and the diffusion gradients weaken the signal. The SNR is particularly low at high spatial resolution, e.g. in the DTI of nerves. A low SNR leads to systematic and statistical errors in parameters calculated from the DTI, e.g. fractional anisotropy (FA). A low SNR can be partially compensated by increasing the number of diffusion directions or using methods for a posteriori noise correction. The most robust method for anatomical structures with unknown orientation is to distribute the diffusion gradients evenly in space. However, if the preferred direction of the anatomical structure is known in advance, it may be advantageous to limit the diffusion gradients to a cone centered on the axis of the structure. The aim of this work was to develop a DTI method with high accuracy and reliability for application in peripheral nerves. Two methods to reduce image noise were investigated: (1) A newly developed scheme of diffusion gradient vectors (DGV), where the vectors are restricted to a cone with an aperture angle Theta around the axis of the nerve and (2) different methods for a posteriori noise correction. For this purpose, Monte Carlo simulations were performed based on realistic values for diffusivity, FA and noise obtained from clinical investigations and studies. Furthermore, the methods were tested in a specially designed phantom simulating diffusion in peripheral nerves (FA = 0.65). These investigations were performed on a 3 Tesla whole-body magnetic resonance (MR) scanner. To determine the accuracy and reliability of the DTI using the appropriate measurement or correction procedures, systematic deviations of FA from baseline and the statistical error of FA were measured. The newly developed DGV scheme with limited space coverage was compared with gradient schemes with uniform space coverage (Jones, Downhill Simplex Method (DSM), gradient scheme of the manufacturer) based on their condition number (CN). The study showed that with the newly developed DGV scheme FA can be measured with high accuracy when the angle Theta is at least 45° or 60°. The minimum Theta depends on the number of gradient directions and on FA. Basically, the higher the FA value and the greater the number of gradients, the better the accuracy of the DGV scheme. For N = 30, the DGV allowed an exact determination of FA for the entire FA range (0.4 - 0.8) investigated in this study, if Theta ≥45° was. It could be shown that when using the new DGV scheme, a slight inclination of the investigated structure (≤30°) does not affect the accuracy of FA. CN of the developed DGV-scheme was higher than CN of the Jones-scheme and the DSM-scheme for N = 6; for N≥10 CN of the new DSM-scheme was lower than that of the Jones-scheme. However, it is also not to be expected that a method that concentrates the gradient vectors on a limited segment of space is as insensitive to interference as schemes with uniform gradient distribution. Nevertheless, the CN of the new DGV method was in the same order of magnitude as that of the other methods. A comparison of the different a posteriori correction methods showed that the power image method is the most effective and robust method and compensates for both the systematic and statistical errors of FA. The efficiency of the power image method is independent of the number of diffusion gradients used. In addition, the method works reliably - regardless of the method used for the coil combination (square sum versus adaptive combination). In contrast, both correction factor methods used in this study were less efficient in terms of noise correction; furthermore, the correction efficiency depended on the coil combination method. In conclusion, a combination of the newly developed DGV scheme with the power image method for a posteriori correction allows DTI of peripheral nerves with high SNR, high accuracy and reliability of the calculated parameters (e.g. FA) without the need for additional acquisition time. So far, however, these newly developed and tested methods have not yet been applied in studies or clinical trials

Diffusion tensor model links to neurite orientation dispersion and density imaging at high b-value in cerebral cortical gray matter

Diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) are widely used models to infer microstructural features in the brain from diffusion-weighted MRI. Several studies have recently applied both models to increase sensitivity to biological changes, however, it remains uncertain how these measures are associated. Here we show that cortical distributions of DTI and NODDI are associated depending on the choice of b-value, a factor reflecting strength of diffusion weighting gradient. We analyzed a combination of high, intermediate and low b-value data of multi-shell diffusion-weighted MRI (dMRI) in healthy 456 subjects of the Human Connectome Project using NODDI, DTI and a mathematical conversion from DTI to NODDI. Cortical distributions of DTI and DTI-derived NODDI metrics were remarkably associated with those in NODDI, particularly when applied highly diffusion-weighted data (b-value = 3000 sec/mm2). This was supported by simulation analysis, which revealed that DTI-derived parameters with lower b-value datasets suffered from errors due to heterogeneity of cerebrospinal fluid fraction and partial volume. These findings suggest that high b-value DTI redundantly parallels with NODDI-based cortical neurite measures, but the conventional low b-value DTI is hard to reasonably characterize cortical microarchitecture

Fiber consistency measures on brain tracts from digital streamline, stochastic and global tractography

La tractografía es el proceso que se emplea para estimar la estructura de las fibras nerviosas del interior del cerebro in vivo a partir de datos de Resonancia Magnética (MR). Existen varios métodos de tractografía, que generalmente se dividen en locales y globales. Los primeros intentan reconstruir cada fibra por separado, mientras que los segundos intentan reconstruir todas las estructuras neuronales a la vez, buscando una configuración que mejor se ajusta a los datos proporcionados. Dichos métodos globales han demostrado ser más precisos y fiables que los métodos de tractografía local, para datos sintéticos. Sin embargo hasta la fecha no hay estudios que definan la relación entre los parámetros de adquisición de la MR y los resultados de tractografía estocástica o global con datos reales. Esta tésis de Master pretende mostrar la influencia de ciertos parámetros de adquisición como el factor de difusión de las secuencias de adquisición, el espaciado entre voxels o el número de gradientes en la variabilidad de las tractografías obtenidas.Teoría de la Señal, Comunicaciones e Ingeniería TelemáticaMáster en Investigación en Tecnologías de la Información y las Comunicacione