#MEDINRIA VIDE SOFTWARE#

This finding was further replicated and validated via high–angular resolution diffusion imaging (HARDI Tuch et al.

Results indicated that a majority of axons were connected to the gyri in human, chimpanzee, and macaque monkey cortices. Therefore, we quantitatively characterized the relationship between axonal connection patterns and gyral and sulcal patterns in order to elucidate the intrinsic relationships between axonal wiring and cortical folding. 2010), we are able to quantify the folding patterns and fiber connection properties within the same structural substrate of a local cortical surface patch. By applying a joint analysis that considers DTI-derived fiber connection patterns within a neuroanatomic context of cortical folds ( Li et al. 2007, 2008) to reconstruct geometric representations of these primate cortices from the DTI data.

#MEDINRIA VIDE SOFTWARE#

In this paper, we acquired in vivo DTI data in order to quantitatively map the axons connected to the cerebral cortices in humans, chimpanzees, and macaque monkeys and applied in-house software tools ( Liu et al. Thanks to recent technical developments, diffusion tensor imaging (DTI) is now able to quantitatively map axonal fiber connections ( Mori 2006), and DTI-derived fiber tracts are well correlated with axon densities ( Mori et al. In this paper, based on quantitative analysis of macroscale neuroimaging data and microscale microscopic bioimaging data, we present a novel axonal pushing theory of cortical folding. However, due to a lack of joint and quantitative mapping between axonal wiring patterns and gyral and sulcal formations, the underlying mechanisms guiding these processes remain unclear. Therefore, at the conceptual level, it is reasonable to postulate that there is a close relationship between cortical folding and axonal wiring. Among many factors ( Barron 1950 Rakic 1988 Welker 1990 Sur and Rubenstein 2005) that play important roles in complex cortical folding, the axonal wiring process is believed to be a critical determinant ( O’Leary 1989 Van Essen 1997). Neuroscience research has demonstrated that the neural structures of gyri and sulci emerge from complex cortical folding processes during development ( Rakic 1988). In Proc.Cortical folding, diffusion tensor imaging, shape analysis Introduction Log-Euclidean Metrics for Fast and Simple Calculus on Diffusion Tensors.Ī Log-Euclidean Framework for Statistics on Diffeomorphisms. SIAM Journal on Matrix Analysis and Applications, Geometric Means in a Novel Vector Space Structure on Symmetric Positive-Definite Matrices. Is correctly reconstructed in the Log-Euclidean case, as opposed to the Euclidean case where the result isĬlinical DT-MRI Estimation, Smoothing and Fiber Tracking with Log-Euclidean Metrics. That Log-Euclidean means are more anisotropic their affine-invariant counterparts. Is not present in both Riemannian frameworks since determinants are monotonically interpolated. Swelling effect observed in the Euclidean case due to a parabolic interpolation of determinants. The coloring of ellipsoids is based on the direction ofĭominant eigenvectors, and was only added to enhance the contrast of tensor images. Top: linear interpolation on coefficients. In this framework, Riemannian computations can be converted into Euclidean ones once tensors have been transformed into their matrix logarithms.Īn anisotropic regularization can also be applied in the DTI Track module of MedINRIA, it is optional and may take time. This new approach is based on a novel vector space structure for tensors.

They also have excellent theoretical properties and yield similar results in practice, but with much simpler and faster computations. To remedy this limitation, a new family of Riemannian metrics called Log-Euclidean is used in MedInria. These metrics have excellent theoretical properties and provide powerful processing tools, but also lead in practice to complex and slow algorithms. Recently, affine-invariant Riemannian metrics have been proposed as a rigorous and general framework in which these defects are corrected. Usual Euclidean operations on matrices suffer from many defects on tensors, which have led to the use of many ad hoc methods. However, the processing of this type of data (i.e., symmetric positive-definite matrices) has proved difficult in recent years. The DTI Track module provides all necessary tools for in-deep DT-MRI analysis and fiber tracking.

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