Tractography | EPFL Graph Search

In neuroscience, tractography is a 3D modeling technique used to visually represent nerve tracts using data collected by diffusion MRI. It uses special techniques of magnetic resonance imaging (MRI) and computer-based diffusion MRI. The results are presented in two- and three-dimensional images called tractograms. In addition to the long tracts that connect the brain to the rest of the body, there are complicated neural circuits formed by short connections among different cortical and subcortical regions. The existence of these tracts and circuits has been revealed by histochemistry and biological techniques on post-mortem specimens. Nerve tracts are not identifiable by direct exam, CT, or MRI scans. This difficulty explains the paucity of their description in neuroanatomy atlases and the poor understanding of their functions. The most advanced tractography algorithm can produce 90% of the ground truth bundles, but it still contains a substantial amount of invalid results. Tractography is performed using data from diffusion MRI. The free water diffusion is termed "isotropic" diffusion. If the water diffuses in a medium with barriers, the diffusion will be uneven, which is termed anisotropic diffusion. In such a case, the relative mobility of the molecules from the origin has a shape different from a sphere. This shape is often modeled as an ellipsoid, and the technique is then called diffusion tensor imaging. Barriers can be many things: cell membranes, axons, myelin, etc.; but in white matter the principal barrier is the myelin sheath of axons. Bundles of axons provide a barrier to perpendicular diffusion and a path for parallel diffusion along the orientation of the fibers. Anisotropic diffusion is expected to be increased in areas of high mature axonal order. Conditions where the myelin or the structure of the axon are disrupted, such as trauma, tumors, and inflammation reduce anisotropy, as the barriers are affected by destruction or disorganization. Anisotropy is measured in several ways.

Hierarchical Microstructure Informed Tractography

Gabriel Girard, Alessandro Daducci, Maxime Descoteaux

Background: Tractography uses diffusion magnetic resonance imaging to noninvasively infer the macroscopic pathways of white matter fibers and it is the only available technique to probe in vivo the structural connectivity of the brain. However, despite this unique and compelling ability and its wide range of possible neurological applications, tractography is still limited, lacks anatomical precision, and suffers from a serious sensitivity/specificity trade-off. For this reason, in the past few years, tractography postprocessing techniques have emerged and proved effective for improving the quality of the reconstructions. Among them, the Convex Optimization Modeling for Microstructure Informed Tractography formulation allows incorporating the anatomical prior that fibers are naturally organized in fascicles, and has obtained exceptional results in increasing the accuracy of the estimated tractograms. Methods: We propose an extension to this idea and introduce a multilevel grouping of the streamlines to capture the white matter arrangement in fascicles and subfascicles. We tested our proposed formulation in synthetic and in vivo data. Results: Our experiments show that using multiple levels allows considering information about the white matter organization more adequately and helps to improve further the accuracy of the resulting tractograms. Conclusion: This new formulation represents a further important step toward a more accurate structural connectivity estimation.

MARY ANN LIEBERT, INC2021

On the cortical connectivity in the macaque brain: A comparison of diffusion tractography and histological tracing data

Jean-Philippe Thiran, Gabriel Girard, Maxime Descoteaux, Giorgio Innocenti, Tim Bjørn Dyrby

Diffusion-weighted magnetic resonance imaging (DW-MRI) tractography is a non-invasive tool to probe neural connections and the structure of the white matter. It has been applied successfully in studies of neurological disorders and normal connectivity. Recent work has revealed that tractography produces a high incidence of false-positive connections, often from “bottleneck” white matter configurations. The rich literature in histological connectivity analysis studies in the macaque monkey enables quantitative evaluation of the performance of tractography algorithms. In this study, we use the intricate connections of frontal, cingulate, and parietal areas, well established by the anatomical literature, to derive a symmetrical histological connectivity matrix composed of 59 cortical areas. We evaluate the performance of fifteen diffusion tractography algorithms, including global, deterministic, and probabilistic state-of-the-art methods for the connectivity predictions of 1711 distinct pairs of areas, among which 680 are reported connected by the literature. The diffusion connectivity analysis was performed on a different ex-vivo macaque brain, acquired using multi-shell DW-MRI protocol, at high spatial and angular resolutions. Across all tested algorithms, the true-positive and true-negative connections were dominant over false-positive and false-negative connections, respectively. Moreover, three-quarters of streamlines had endpoints location in agreement with histological data, on average. Furthermore, probabilistic streamline tractography algorithms show the best performances in predicting which areas are connected. Altogether, we propose a method for quantitative evaluation of tractography algorithms, which aims at improving the sensitivity and the specificity of diffusion-based connectivity analysis. Overall, those results confirm the usefulness of tractography in predicting connectivity, although errors are produced. Many of the errors result from bottleneck white matter configurations near the cortical grey matter and should be the target of future implementation of methods.

2020

On the intrahemispheric connectivity of the monkey: a diffusion tractography and tract tracing analysis

Gabriel Girard, Maxime Descoteaux, Giorgio Innocenti

In this work, we compare diffusion tractography with neuronal retrograde tract tracing of the frontal, cingulate and parietal areas of the monkey. In this work, we compare diffusion tractography with neuronal retrograde tract tracing of the frontal, cingulate and parietal areas of the monkey. We analyze the agreements between the tractography and the tracing for connected and not connected regions. We report an accuracy of 0.71across all pairs of regions, with twice the number of true positive than false positive connections. Some regions show accuracy higher than 0.80, while other regions show accuracy lower than 0.6. A further analysis of the location of false positive and false negative connections will help understand the limitations and improve diffusion tractography algorithms. We analyze the agreements between the tractography and the tracing for connected and not connected regions. We report an accuracy of 0.71 across all pairs of regions, with twice the number of true positive than false positive connections. Some regions show accuracy higher than 0.80, while other regions show accuracy lower than 0.6. Further analysis of the location of false positive and false negative connections will help understand the limitations and improve diffusion tractography algorithms.

International Society for Magnetic Resonance in Medicine2019

On the cortical connectivity in the macaque brain: A comparison of diffusion tractography and histological tracing data

Jean-Philippe Thiran, Gabriel Girard, Maxime Descoteaux, Giorgio Innocenti, Tim Bjørn Dyrby

2020