Image guidance in neurosurgical procedures, the "Visages" point of view.

This paper gives an overview of the evolution of clinical neuroinformatics in the domain of neurosurgery. It shows how image guided neurosurgery (IGNS) is evolving according to the integration of new imaging modalities before, during and after the surgical procedure and how this acts as the premise of the Operative Room of the future. These different issues, as addressed by the VisAGeS INRIA/INSERM U746 research team (http://www.irisa.fr/visages), are presented and discussed in order to exhibit the benefits of an integrated work between physicians (radiologists, neurologists and neurosurgeons) and computer scientists to give adequate answers toward a more effective use of images in IGNS

Local Analysis of Human Cortex in MRI Brain Volume

This paper describes a method for subcortical identification and labeling of 3D medical MRI images. Indeed, the ability to identify similarities between the most characteristic subcortical structures such as sulci and gyri is helpful for human brain mapping studies in general and medical diagnosis in particular. However, these structures vary greatly from one individual to another because they have different geometric properties. For this purpose, we have developed an efficient tool that allows a user to start with brain imaging, to segment the border gray/white matter, to simplify the obtained cortex surface, and to describe this shape locally in order to identify homogeneous features. In this paper, a segmentation procedure using geometric curvature properties that provide an efficient discrimination for local shape is implemented on the brain cortical surface. Experimental results demonstrate the effectiveness and the validity of our approach

Statistical Study on Cortical Sulci of Human Brains

Abstract. A method for building a statistical shape model of sulci of the human brain cortex is described. The model includes sulcal fundi that are defined on a spherical map of the cortex. The sulcal fundi are first extracted in a semi-automatic way using an extension of the fast march-ing method. They are then transformed to curves on the unit sphere via a conformal mapping method that maps each cortical point to a point on the unit sphere. The curves that represent sulcal fundi are parameterized with piecewise constant-speed parameterizations. Intermediate points on these curves correspond to sulcal landmarks, which are used to build a point distribution model on the unit sphere. Statistical information of local properties of the sulci, such as curvature and depth, are embedded in the model. Experimental results are presented to show how the models are built.

Estimation robuste 3D d'un champ de déformation pour le recalage inter-sujet d'images cérébrales

Nous proposons dans cet article une méthode 3D d'estimation du flot optique conduisant à un recalage monomodalité de volumes cérébraux. La formulation énergétique du problème est enrichie par l'utilisation d'estimateurs robustes. Enfin le schéma d'optimisation proposé est multirésolution et multigrille, afin d'accélérer la recherche et d'améliorer la qualité de l'estimation. Des résultats sur des données réelles sont présentés et discutés

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

Multimodal Investigation of Brain Network Systems: From Brain Structure and Function to Connectivity and Neuromodulation

The field of cognitive neuroscience has benefited greatly from multimodal investigations of the human brain, which integrate various tools and neuroimaging data to understand brain functions and guide treatments for brain disorders. In this dissertation, we present a series of studies that illustrate the use of multimodal approaches to investigate brain structure and function, brain connectivity, and neuromodulation effects. Firstly, we propose a novel landmark-guided region-based spatial normalization technique to accurately quantify brain morphology, which can improve the sensitivity and specificity of functional imaging studies. Subsequently, we shift the investigation to the characteristics of functional brain activity due to visual stimulations. Our findings reveal that the task-evoked positive blood-oxygen-level dependent (BOLD) response is accompanied by sustained negative BOLD responses in the visual cortex. These negative BOLD responses are likely generated through subcortical neuromodulatory systems with distributed ascending projections to the cortex. To further explore the cortico-subcortical relationship, we conduct a multimodal investigation that involves simultaneous data acquisition of pupillometry, electroencephalography (EEG), and functional magnetic resonance imaging (fMRI). This investigation aims to examine the connectivity of brain circuits involved in the cognitive processes of salient stimuli. Using pupillary response as a surrogate measure of activity in the locus coeruleus-norepinephrine system, we find that the pupillary response is associated with the reorganization of functional brain networks during salience processing. In addition, we propose a cortico-subcortical integrated network reorganization model with potential implications for understanding attentional processing and network switching. Lastly, we employ a multimodal investigation that involves concurrent transcranial magnetic stimulation (TMS), EEG, and fMRI to explore network perturbations and measurements of the propagation effects. In a preliminary exploration on brain-state dependency of TMS-induced effects, we find that the propagation of left dorsolateral prefrontal cortex TMS to regions in the lateral frontoparietal network might depend on the brain-state, as indexed by the EEG prefrontal alpha phase. Overall, the studies in this dissertation contribute to the understanding of the structural and functional characteristics of brain network systems, and may inform future investigations that use multimodal methodological approaches, such as pupillometry, brain connectivity, and neuromodulation tools. The work presented in this dissertation has potential implications for the development of efficient and personalized treatments for major depressive disorder, attention deficit hyperactivity disorder, and Alzheimer's disease

Structural brain patterns in Anorexia Nervosa: a multimodal MRI evaluation

Introduction. Cortical and white matter structural abnormalities in Anorexia Nervosa (AN) have been recently investigated, but no attempt has been made to explore the organizational patterns that govern the relationships between different brain areas and to characterize the neurobiology of the disorder in the different stages of its course. Aims of the present work are to characterize cortical and white matter network architecture by means of different structural indices and computational techniques, to observe the presence of any correlation between clinical variables and networks characteristics and to investigate the structural organizational patterns in the different stage of AN course. Methods and Materials. 38 patients with acute AN, 38 healthy controls and 20 patients in full remission from AN were included in this study. All participants underwent high-resolution MRI. An analysis of cortical structural co-variance was performed using cortical thickness and gyrification indices. The anatomical complexity of the cortex was explored by means of Fractal Dimension (FD). Connectomic tools were applied to DTI tractography data to investigate the white matter network architecture. Results. Patients with AN showed unbalanced integration and segregation properties in cortical thickness, gyrification and DTI networks both on global and regional levels. Patients with a poor outcome at a three years follow-up assessment showed higher segregation measures and lower small-worldness in the gyrification network. The FD analysis revealed a reduced cortical complexity in the AN group. Discussion. Alterations in structural covariance patterns in AN are likely to reflect the metabolic consequences of the disorder as well as deviations in normal developmental trajectories. Lower FD in AN indicates a reduction of cortical complexity in the acute stages of the disorder and evidenced that this structural index is sensitive to the effects of malnutrition