Accurate Anisotropic Fast Marching for Diffusion-Based Geodesic Tractography

Using geodesics for inferring white matter fibre tracts from diffusion-weighted MR data is an attractive method for at least two reasons: (i) the method optimises a global criterion, and hence is less sensitive to local perturbations such as noise or partial volume effects, and (ii) the method is fast, allowing to infer on a large number of connexions in a reasonable computational time. Here, we propose an improved fast marching algorithm to infer on geodesic paths. Specifically, this procedure is designed to achieve accurate front propagation in an anisotropic elliptic medium, such as DTI data. We evaluate the numerical performance of this approach on simulated datasets, as well as its robustness to local perturbation induced by fiber crossing. On real data, we demonstrate the feasibility of extracting geodesics to connect an extended set of brain regions

Functional MRI-derived priors for solving the EEG/MEG inverse problem

Introduction Because of their excellent temporal accuracy (of the order of 1 ms), electroencephalography (EEG) and magnetoencephalography (MEG) provide the most relevant data for studying the temporal dynamics of brain activity. However, difficulties arise when trying to localize the electromagnetic sources of this activity from EEG/MEG scalp recordings. This mathematical inverse problem is indeed ill-posed and largely underdetermined. An efficient way of constraining the problem and thereby reducing the solution space is to perform a regularization. By taking some anatomical and/or functional a priori knowledge into account, the regularization process may yield a more consistent localization of the electromagnetic sources. Anatomical priors have already been used (e.g., [1]) but only few regularization methods (e.g., [2]) have introduced functional information so far. In this study, we propose a new multimodal approach for solving the EEG/MEG inverse problem. This method involves

Accurate anisotropic fast marching for diffusion-based geodesic tractography

Using geodesics for inferring white matter fibre tracts from diffusion-weighted MR data is an attractive method for at least two reasons: (i) the method optimises a global criterion, and hence is less sensitive to local perturbations such as noise or partial volume effects, and (ii) the method is fast, allowing to infer on a large number of connexions in a reasonable computational time. Here, we propose an improved fast marching algorithm to infer on geodesic paths. Specifically, this procedure is designedto achieve accurate front propagation in an anisotropic elliptic medium, such as DTI data. We evaluate the numerical performance of this approach on simulated datasets, as well as its robustness to local perturbation induced by fiber crossing. On real data, we demonstrate the feasibility of extracting geodesics to connect an extended set of brain regions

Individual differences in prefrontal cortical activation on the Tower of London planning task: implication for effortful processing

International audienceSolving challenging ('effortful') problems is known to involve the dorsal and dorsolateral prefrontal cortex in normal volunteers, although there is considerable individual variation. In this functional magnetic resonance imaging study, we show that healthy subjects with different levels of performance in the Tower of London planning task exhibit different patterns of brain activation. All subjects exhibited significant bilateral activation in the dorsolateral prefrontal cortex, the anterior and posterior cingulate areas and the parietal cortex. However, 'standard performers' (performance 70% correct) differed in the patterns of activation exhibited. Superior performers showed a significantly more spatially extended activation in the left dorsolateral prefrontal cortex than did standard performers, whereas the latter group tended to show increased activation of the anterior cingulate region

Identification of large-scale networks in the brain using fMRI.

Cognition is thought to result from interactions within large-scale networks of brain regions. Here, we propose a method to identify these large-scale networks using functional magnetic resonance imaging (fMRI). Regions belonging to such networks are defined as sets of strongly interacting regions, each of which showing a homogeneous temporal activity. Our method of large-scale network identification (LSNI) proceeds by first detecting functionally homogeneous regions. The networks of functional interconnections are then found by comparing the correlations among these regions against a model of the correlations in the noise. To test the LSNI method, we first evaluated its specificity and sensitivity on synthetic data sets. Then, the method was applied to four real data sets with a block-designed motor task. The LSNI method correctly recovered the regions whose temporal activity was locked to the stimulus. In addition, it detected two other main networks highly reproducible across subjects, whose activity was dominated by slow fluctuations (0-0.1 Hz). One was located in medial and dorsal regions, and mostly overlapped the "default" network of the brain at rest [Greicius, M.D., Krasnow, B., Reiss, A.L., Menon, V., 2003. Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proceedings of the National Academy of Sciences of the U.S.A. 100, 253-258]; the other was composed of lateral frontal and posterior parietal regions. The LSNI method we propose allows to detect in an exploratory and systematic way all the regions and large-scale networks activated in the working brain

Identification of a large-scale functional network in functional magnetic resonance imaging

In functional magnetic resonance imaging (fMRI), cerebral activity has been increasingly considered as the consequence of a network activation. Selecting the brain regions relevant for the network has thus become a key issue. We propose to define the so-called large-scale functional network involved in a particular task as a set of regions exhibiting strong intrinsic homogeneity, as well as at least one strong long-distance inter-regional interaction. We develop a method to identify such a network, and we validate it on a real dataset, in a context where the existence of a distributed network has already been demonstrated. Our results are compatible with previous studies. This new tool is thus promising for selecting regions when analyzing functional connectivity in fMRI. © 2004 IEEE

Simulation of anisotropic growth of low-grade gliomas using diffusion tensor imaging.

A recent computational model of brain tumor growth, developed to better describe how gliomas invade through the adjacent brain parenchyma, is based on two major elements: cell proliferation and isotropic cell diffusion. On the basis of this model, glioma growth has been simulated in a virtual brain, provided by a 3D segmented MRI atlas. However, it is commonly accepted that glial cells preferentially migrate along the direction of fiber tracts. Therefore, in this paper, the model has been improved by including anisotropic extension of gliomas. The method is based on a cell diffusion tensor derived from water diffusion tensor (as given by MRI diffusion tensor imaging). Results of simulations have been compared with two clinical examples demonstrating typical growth patterns of low-grade gliomas centered around the insula. The shape and the kinetic evolution are better simulated with anisotropic rather than isotropic diffusion. The best fit is obtained when the anisotropy of the cell diffusion tensor is increased to greater anisotropy than the observed water diffusion tensor. The shape of the tumor is also influenced by the initial location of the tumor. Anisotropic brain tumor growth simulations provide a means to determine the initial location of a low-grade glioma as well as its cell diffusion tensor, both of which might reflect the biological characteristics of invasion