Toward global tractography.

Diffusion-based tractography is an ill-posed problem, because the step-by-step reconstruction of a fibre bundle trajectory cannot afford any serious mistake in the evaluation of the local fibre orientations. Such evaluation is difficult, however, because the myriad fibres passing through a single voxel follow different directions. Modelling tractography as a global inverse problem is a simple framework which addresses the ill-posed nature of the problem. The key idea is that the results of tractography in the neighbourhood of an ambiguous local diffusion profile can help to infer the local fibre directions. This paper provides an overview of past achievements of global tractography and proposes guidelines for a future research programme in the hope that the potential of the technique will increase the interest of the community.

Robust clustering of massive tractography datasets.

This paper presents a clustering method that detects the fiber bundles embedded in any MR-diffusion based tractography dataset. Our method can be seen as a compressing operation, capturing the most meaningful information enclosed in the fiber dataset. For the sake of efficiency, part of the analysis is based on clustering the white matter (WM) voxels rather than the fibers. The resulting regions of interest are used to define subset of fibers that are subdivided further into consistent bundles using a clustering of the fiber extremities. The dataset is reduced from more than one million fiber tracts to about two thousand fiber bundles. Validations are provided using simulated data and a physical phantom. We see our approach as a crucial preprocessing step before further analysis of huge fiber datasets. An important application will be the inference of detailed models of the subdivisions of white matter pathways and the mapping of the main U-fiber bundles.

Real-time MR diffusion tensor and Q-ball imaging using Kalman filtering.

Magnetic resonance diffusion imaging (dMRI) has become an established research tool for the investigation of tissue structure and orientation. In this paper, we present a method for real time processing of diffusion tensor and Q-ball imaging. The basic idea is to use Kalman filtering framework to fit either the linear tensor or Q-ball model. Because the Kalman filter is designed to be an incremental algorithm, it naturally enables updating the model estimate after the acquisition of any new diffusion-weighted volume. Processing diffusion models and maps during ongoing scans provides a new useful tool for clinicians, especially when it is not possible to predict how long a subject may remain still in the magnet.

Human subinsular asymmetry studied by diffusion tensor imaging and fiber tracking.

BACKGROUND AND PURPOSE: Our aim was to improve our understanding of the subinsular white matter microstructural asymmetries in healthy right-handed subjects. Structural brain asymmetries could be related to functional asymmetries such as hemisphere language dominance or handedness. Besides the known gray matter asymmetries, white matter asymmetries could also play a key role in the understanding of hemispheric specialization, notably that of language. MATERIALS AND METHODS: White matter asymmetries were studied by diffusion tensor imaging at 1.5T (41 diffusion-gradient directions; b-value set to 700 s/mm(2); matrix, 128(2); in-plane resolution, 1.875 x 1.875 mm; section thickness, 2.0 mm) and fiber tracking (BrainVISA software). The main white matter bundles passing through the subinsular area were segmented, and fractional anisotropy (FA) was measured along each of the segmented bundles. RESULTS: In line with published results, we found an asymmetry of the arcuate fasciculus and the subinsular white matter, namely left-greater-than-right FA in right-handed controls. Furthermore, by segmenting major tracts coursing through this region, we showed that the subinsular portions of the uncinate fasciculus (UF) and the inferior occipitofrontal fasciculus (IOF) contribute to this FA asymmetry. Those tracts have been reported to be likely implicated in the language network. CONCLUSION: Because the left hemisphere hosts language functions in most right-handers, the significant leftward asymmetry observed within the arcuate fasciculus, the subinsular part of the UF and IOF may be related to the hemispheric specialization for language.

Uncinate fasciculus fiber tracking in mesial temporal lobe epilepsy. Initial findings.

In temporal lobe epilepsy (TLE) due to hippocampal sclerosis (HS), ictal discharge spread to the frontal and insulo-perisylvian cortex is commonly observed. The implication of white matter pathways in this propagation has not been investigated. We compared diffusion tensor imaging (DTI) measurements along the uncinate fasciculus (UF), a major tract connecting the frontal and temporal lobes, in patients and controls. Ten right-handed patients referred for intractable TLE due to a right HS were investigated on a 1.5-T MR scanner including a DTI sequence. All patients had interictal fluorodeoxyglucose PET showing an ipsilateral temporal hypometabolism associated with insular and frontal or perisylvian hypometabolism. The controls consisted of ten right-handed healthy subjects. UF fiber tracking was performed, and its fractional anisotropy (FA) values were compared between patients and controls, separately for the right and left UF. The left-minus-right FA UF asymmetry index was computed to test for intergroup differences. Asymmetries were found in the control group with right-greater-than-left FA. This asymmetrical pattern was lost in the patient group. Right FA values were lower in patients with right HS versus controls. Although preliminary, these findings may be related to the preferential pathway of seizure spread from the mesial temporal lobe to frontal and insulo-perisylvian areas.

Assessment of the early organization and maturation of infants' cerebral white matter fiber bundles: a feasibility study using quantitative diffusion tensor imaging and tractography.

The human infant is particularly immature at birth and brain maturation, with the myelination of white matter fibers, is protracted until adulthood. Diffusion tensor imaging offers the possibility to describe non invasively the fascicles spatial organization at an early stage and to follow the cerebral maturation with quantitative parameters that might be correlated with behavioral development. Here, we assessed the feasibility to study the organization and maturation of major white matter bundles in eighteen 1- to 4-month-old healthy infants, using a specific acquisition protocol customized to the immature brain (with 15 orientations of the diffusion gradients and a 700 s mm(-2)b factor). We were able to track most of the main fascicles described at later ages despite the low anisotropy of the infant white matter, using the FACT algorithm. This mapping allows us to propose a new method of quantification based on reconstructed tracts, split between specific regions, which should be more sensitive to specific changes in a bundle than the conventional approach, based on regions-of-interest. We observed variations in fractional anisotropy and mean diffusivity over the considered developmental period in most bundles (corpus callosum, cerebellar peduncles, cortico-spinal tract, spino-thalamic tract, capsules, radiations, longitudinal and uncinate fascicles, cingulum). The results are in good agreement with the known stages of white matter maturation and myelination, and the proposed approach might provide important insights on brain development.

MR diffusion-based inference of a fiber bundle model from a population of subjects.

This paper proposes a method to infer a high level model of the white matter organization from a population of subjects using MR diffusion imaging. This method takes as input for each subject a set of trajectories stemming from any tracking algorithm. Then the inference results from two nested clustering stages. The first clustering converts each individual set of trajectories into a set of bundles supposed to represent large white matter pathways. The second clustering matches these bundles across subjects in order to provide a list of candidates for the bundle model. The method is applied on a population of eleven subjects and leads to the inference of 17 such candidates.

Fiber tracking in q-ball fields using regularized particle trajectories.

Most of the approaches dedicated to fiber tracking from diffusion-weighted MR data rely on a tensor model. However, the tensor model can only resolve a single fiber orientation within each imaging voxel. New emerging approaches have been proposed to obtain a better representation of the diffusion process occurring in fiber crossing. In this paper, we adapt a tracking algorithm to the q-ball representation, which results from a spherical Radon transform of high angular resolution data. This algorithm is based on a Monte-Carlo strategy, using regularized particle trajectories to sample the white matter geometry. The method is validated using a phantom of bundle crossing made up of haemodialysis fibers. The method is also applied to the detection of the auditory tract in three human subjects.

A framework to study the cortical folding patterns.

This paper describes a decade-long research program focused on the variability of the cortical folding patterns. The program has developed a framework of using artificial neuroanatomists that are trained to identify sulci from a database. The framework relies on a renormalization of the brain warping problem, which consists in matching the cortices at the scale of the folds. Another component of the program is the search for the alphabet of the folding patterns, namely, a list of indivisible elementary sulci. The search relies on the study of the cortical folding process using antenatal imaging and on backward simulations of morphogenesis aimed at revealing traces of the embryologic dimples in the mature cortical surface. The importance of sulcal-based morphometry is illustrated by a simple study of the correlates of handedness on asymmetry indices. The study shows for instance that the central sulcus is larger in the dominant hemisphere.

Object-based morphometry of the cerebral cortex.

Most of the approaches dedicated to automatic morphometry rely on a point-by-point strategy based on warping each brain toward a reference coordinate system. In this paper, we describe an alternative object-based strategy dedicated to the cortex. This strategy relies on an artificial neuroanatomist performing automatic recognition of the main cortical sulci and parcellation of the cortical surface into gyral patches. A set of shape descriptors, which can be compared across subjects, is then attached to the sulcus and gyrus related objects segmented by this process. The framework is used to perform a study of 142 brains of the International Consortium for Brain Mapping (ICBM) database. This study reveals some correlates of handedness on the size of the sulci located in motor areas, which was not detected previously using standard voxel based morphometry.

Coordinate-based versus structural approaches to brain image analysis.

A basic issue in neurosciences is to look for possible relationships between brain architecture and cognitive models. The lack of architectural information in magnetic resonance images, however, has led the neuroimaging community to develop brain mapping strategies based on various coordinate systems without accurate architectural content. Therefore, the relationships between architectural and functional brain organizations are difficult to study when analyzing neuroimaging experiments. This paper advocates that the design of new brain image analysis methods inspired by the structural strategies often used in computer vision may provide better ways to address these relationships. The key point underlying this new framework is the conversion of the raw images into structural representations before analysis. These representations are made up of data-driven elementary features like activated clusters, cortical folds or fiber bundles. Two classes of methods are introduced. Inference of structural models via matching across a set of individuals is described first. This inference problem is illustrated by the group analysis of functional statistical parametric maps (SPMs). Then, the matching of new individual data with a priori known structural models is described, using the recognition of the cortical sulci as a prototypical example.

A framework based on spin glass models for the inference of anatomical connectivity from diffusion-weighted MR data - a technical review.

A family of methods aiming at the reconstruction of a putative fascicle map from any diffusion-weighted dataset is proposed. This fascicle map is defined as a trade-off between local information on voxel microstructure provided by diffusion data and a priori information on the low curvature of plausible fascicles. The optimal fascicle map is the minimum energy configuration of a simulated spin glass in which each spin represents a fascicle piece. This spin glass is embedded into a simulated magnetic external field that tends to align the spins along the more probable fiber orientations according to diffusion models. A model of spin interactions related to the curvature of the underlying fascicles introduces a low bending potential constraint. Hence, the optimal configuration is a trade-off between these two kind of forces acting on the spins. Experimental results are presented for the simplest spin glass model made up of compass needles located in the center of each voxel of a tensor based acquisition.