Improving the Tractography Pipeline: on Evaluation, Segmentation, and Visualization

Recent advances in tractography allow for connectomes to be constructed in vivo. These have applications for example in brain tumor surgery and understanding of brain development and diseases. The large size of the data produced by these methods lead to a variety problems, including how to evaluate tractography outputs, development of faster processing algorithms for tractography and clustering, and the development of advanced visualization methods for verification and exploration. This thesis presents several advances in these fields. First, an evaluation is presented for the robustness to noise of multiple commonly used tractography algorithms. It employs a Monte–Carlo simulation of measurement noise on a constructed ground truth dataset. As a result of this evaluation, evidence for obustness of global tractography is found, and algorithmic sources of uncertainty are identified. The second contribution is a fast clustering algorithm for tractography data based on k–means and vector fields for representing the flow of each cluster. It is demonstrated that this algorithm can handle large tractography datasets due to its linear time and memory complexity, and that it can effectively integrate interrupted fibers that would be rejected as outliers by other algorithms. Furthermore, a visualization for the exploration of structural connectomes is presented. It uses illustrative rendering techniques for efficient presentation of connecting fiber bundles in context in anatomical space. Visual hints are employed to improve the perception of spatial relations. Finally, a visualization method with application to exploration and verification of probabilistic tractography is presented, which improves on the previously presented Fiber Stippling technique. It is demonstrated that the method is able to show multiple overlapping tracts in context, and correctly present crossing fiber configurations

Data-Driven Sequence of Changes to Anatomical Brain Connectivity in Sporadic Alzheimer's Disease

Model-based investigations of transneuronal spreading mechanisms in neurodegenerative diseases relate the pattern of pathology severity to the brain’s connectivity matrix, which reveals information about how pathology propagates through the connectivity network. Such network models typically use networks based on functional or structural connectivity in young and healthy individuals, and only end-stage patterns of pathology, thereby ignoring/excluding the effects of normal aging and disease progression. Here, we examine the sequence of changes in the elderly brain’s anatomical connectivity over the course of a neurodegenerative disease. We do this in a data-driven manner that is not dependent upon clinical disease stage, by using event-based disease progression modeling. Using data from the Alzheimer’s Disease Neuroimaging Initiative dataset, we sequence the progressive decline of anatomical connectivity, as quantified by graph-theory metrics, in the Alzheimer’s disease brain. Ours is the first single model to contribute to understanding all three of the nature, the location, and the sequence of changes to anatomical connectivity in the human brain due to Alzheimer’s disease. Our experimental results reveal new insights into Alzheimer’s disease: that degeneration of anatomical connectivity in the brain may be a viable, even early, biomarker and should be considered when studying such neurodegenerative diseases

Building connectomes using diffusion MRI: why, how and but

Why has diffusion MRI become a principal modality for mapping connectomes in vivo? How do different image acquisition parameters, fiber tracking algorithms and other methodological choices affect connectome estimation? What are the main factors that dictate the success and failure of connectome reconstruction? These are some of the key questions that we aim to address in this review. We provide an overview of the key methods that can be used to estimate the nodes and edges of macroscale connectomes, and we discuss open problems and inherent limitations. We argue that diffusion MRI-based connectome mapping methods are still in their infancy and caution against blind application of deep white matter tractography due to the challenges inherent to connectome reconstruction. We review a number of studies that provide evidence of useful microstructural and network properties that can be extracted in various independent and biologically-relevant contexts. Finally, we highlight some of the key deficiencies of current macroscale connectome mapping methodologies and motivate future developments

Tractographie de la matière blanche par réseaux de neurones récurrents

La matière blanche du cerveau fait encore l'objet de nombreuses études. Grâce à l'IRM de diffusion, on peut étudier de façon non invasive la connectivité du cerveau avec une précision sans précédent. La reconstruction de la matière blanche --- la tractographie --- n'est pas parfaite cependant. En effet, la tractographie tend à reconstruire tous les chemins possibles au sein de la matière blanche; l'expertise des neuroanatomistes est donc requise pour distinguer les chemins qui sont possibles anatomiquement de ceux qui résultent d'une mauvaise reconstruction. Cette connaissance est difficile à exprimer et à codifier sous forme de règles logiques. L'intelligence artificielle a refait surface dans les années 1990 --- suite à une amélioration remarquable de la vitesse des processeurs --- en tant que solution viable à plusieurs problèmes qui étaient considérés comme fondamentalement > et quasi impossibles à résoudre pour une machine. Celle-ci représente un outil unique pour intégrer l'expertise des neuroanatomistes dans le processus de reconstruction de la matière blanche, sans avoir à fournir de règles explicitement. Un modèle peut ainsi apprendre la définition d'un chemin valide à partir d'exemples valides, pour ensuite reproduire ce qu'il a appris, sans répéter les erreurs classiques. Plus particulièrement, les réseaux de neurones récurrents sont une famille de modèles créés spécifiquement pour le traitement de séquences de données. Comme une fibre de matière blanche est représentée par une séquence de points, le lien se fait naturellement. Malgré leur potentiel énorme, l'application des réseaux récurrents à la tractographie fait face à plusieurs problèmes techniques. Cette thèse se veut très exploratoire, et détaille donc les débuts de l'utilisation des réseaux de neurones récurrents pour la tractographie par apprentissage, des problèmes qui sont apparus suite à la création d'une multitude d'algorithmes basés sur l'intelligence artificielle, ainsi que des solutions développées pour répondre à ces problèmes. Les résultats de cette thèse ont démontré le potentiel des réseaux de neurones récurrents pour la reconstruction de la matière blanche, en plus de contribuer à l’avancement du domaine grâce à la création d’une base de données publique pour la tractographie par apprentissage

Methods and models for brain connectivity assessment across levels of consciousness

The human brain is one of the most complex and fascinating systems in nature. In the last decades, two events have boosted the investigation of its functional and structural properties. Firstly, the emergence of novel noninvasive neuroimaging modalities, which helped improving the spatial and temporal resolution of the data collected from in vivo human brains. Secondly, the development of advanced mathematical tools in network science and graph theory, which has recently translated into modeling the human brain as a network, giving rise to the area of research so called Brain Connectivity or Connectomics. In brain network models, nodes correspond to gray-matter regions (based on functional or structural, atlas-based parcellations that constitute a partition), while links or edges correspond either to structural connections as modeled based on white matter fiber-tracts or to the functional coupling between brain regions by computing statistical dependencies between measured brain activity from different nodes. Indeed, the network approach for studying the brain has several advantages: 1) it eases the study of collective behaviors and interactions between regions; 2) allows to map and study quantitative properties of its anatomical pathways; 3) gives measures to quantify integration and segregation of information processes in the brain, and the flow (i.e. the interacting dynamics) between different cortical and sub-cortical regions. The main contribution of my PhD work was indeed to develop and implement new models and methods for brain connectivity assessment in the human brain, having as primary application the analysis of neuroimaging data coming from subjects at different levels of consciousness. I have here applied these methods to investigate changes in levels of consciousness, from normal wakefulness (healthy human brains) or drug-induced unconsciousness (i.e. anesthesia) to pathological (i.e. patients with disorders of consciousness)

Doctor of Philosophy

dissertationDiffusion magnetic resonance imaging (dMRI) has become a popular technique to detect brain white matter structure. However, imaging noise, imaging artifacts, and modeling techniques, etc., create many uncertainties, which may generate misleading information for further analysis or applications, such as surgical planning. Therefore, how to analyze, effectively visualize, and reduce these uncertainties become very important research questions. In this dissertation, we present both rank-k decomposition and direct decomposition approaches based on spherical deconvolution to decompose the fiber directions more accurately for high angular resolution diffusion imaging (HARDI) data, which will reduce the uncertainties of the fiber directions. By applying volume rendering techniques to an ensemble of 3D orientation distribution function (ODF) glyphs, which we call SIP functions of diffusion shapes, one can elucidate the complex heteroscedastic structural variation in these local diffusion shapes. Furthermore, we quantify the extent of this variation by measuring the fraction of the volume of these shapes, which is consistent across all noise levels, the certain volume ratio. To better understand the uncertainties in white matter fiber tracks, we propose three metrics to quantify the differences between the results of diffusion tensor magnetic resonance imaging (DT-MRI) fiber tracking algorithms: the area between corresponding fibers of each bundle, the Earth Mover's Distance (EMD) between two fiber bundle volumes, and the current distance between two fiber bundle volumes. Based on these metrics, we discuss an interactive fiber track comparison visualization toolkit we have developed to visualize these uncertainties more efficiently. Physical phantoms, with high repeatability and reproducibility, are also designed with the hope of validating the dMRI techniques. In summary, this dissertation provides a better understanding about uncertainties in diffusion magnetic resonance imaging: where and how much are the uncertainties? How do we reduce these uncertainties? How can we possibly validate our algorithms

Indirect Structural Connectivity As a Biomarker for Stroke Motor Recovery

In this dissertation project, we demonstrated that diffusion magnetic resonance imaging and measures of indirect structural brain connectivity are sensitive to changes in fiber integrity and connectivity to remote regions in the brain after stroke. Our results revealed new insights into the effects local lesions have on global connectivity—in particular, the cerebellum—and how these changes in connectivity and integrity relate to motor impairment. We tested this methodology on two stroke groups—subacute and chronic—and were able to show that indirect connectivity is sensitive to differences in connectivity during stroke recovery. Our work can inform clinical methods for rehabilitating motor function in stroke individuals. By introducing methodology that extends local damage to remotely connected motor related areas, we can measure Wallerian degeneration in addition to providing the framework to predict improvements in motor impairment score based on structural connectivity at the subacute stage.We used diffusion magnetic resonance imaging (dMRI), probabilistic tractography, and novel graph theory metrics to quantify structural connectivity and integrity after stroke. In the first aim, we improved on a measure of indirect structural connectivity in order to detect remote gray matter regions with reduced connectivity after stroke. In a region-level analysis, we found that indirect connectivity was more sensitive to remote changes in connectivity after stroke than measures of direct connectivity, in particular in cortical, subcortical, and cerebellar gray matter regions that play a central role in sensorimotor function. Adding this information to the integrity of the corticospinal tract (CST) improved our ability to predict motor impairment. In the second aim, we investigated the relationship between white matter integrity, connectivity, and motor impairment by developing a unified measure of white matter structure that extends local changes in white matter integrity along remotely connected fiber tracks. Our measure uniquely identified damaged fiber tracks outside the CST, correlated with motor impairment in the CST better than the FA, and also was able to relate white matter structure in the superior cerebellar peduncle to motor impairment. Our final aim used a novel connectome similarity metric and the measure of indirect structural connectivity in order to identify cross-sectional differences in white matter structure between subacute and chronic stroke. We found more reductions in indirect connectivity in the chronic stroke cerebellar fibers than the subacute group, Additionally, the indirect connectivity of the superior cerebellar peduncle at the subacute stage correlated with the improvement in motor impairment score for the paired participants. In conclusion, indirect connectivity is an important measure of global brain damage and motor impairment after stroke, and can be a useful metric to relate to brain function and stroke recovery