Determination of three-dimensional muscle architectures: validation of the DTI-based fiber tractography method by manual digitization.

In the last decade, diffusion tensor imaging (DTI) has been used increasingly to investigate three-dimensional (3D) muscle architectures. So far there is no study that has proved the validity of this method to determine fascicle lengths and pennation angles within a whole muscle. To verify the DTI method, fascicle lengths of m. soleus as well as their pennation angles have been measured using two different methods. First, the 3D muscle architecture was analyzed in vivo applying the DTI method with subsequent deterministic fiber tractography. In a second step, the muscle architecture of the same muscle was analyzed using a standard manual digitization system (MicroScribe MLX). Comparing both methods, we found differences for the median pennation angles (P < 0.001) but not for the median fascicle lengths (P = 0.216). Despite the statistical results, we conclude that the DTI method is appropriate to determine the global fiber orientation. The difference in median pennation angles determined with both methods is only about 1.2° (median pennation angle of MicroScribe: 9.7°; DTI: 8.5°) and probably has no practical relevance for muscle simulation studies. Determining fascicle lengths requires additional restriction and further development of the DTI method.

Structure-function relationships in the context of reinforcement-related learning: a combined diffusion tensor imaging-functional magnetic resonance imaging study.

In the context of probabilistic learning, previous functional magnetic resonance imaging studies have shown decreasing uncertainty accompanying decreasing neuronal activation in task-relevant networks. Moreover, initial evidence points to a relationship between white matter structure and cognitive performance. Little is known, however, about the structural correlates underlying individual differences in activation and performance in the context of probabilistic learning. This combined functional magnetic resonance imaging-diffusion tensor imaging study aimed at investigating the individual ability to reduce processing resources with decreasing uncertainty in direct relation to individual characteristics in white matter brain structure. Results showed that more successful learners, as compared with less successful learners, exhibited stronger activation decreases with decreasing uncertainty. An increased mean and axial diffusivity in, among others, the inferior and superior longitudinal fasciculus, the posterior part of the cingulum bundle, and the corpus callosum were detectable in less successful learners compared with more successful learners. Most importantly, there was a negative correlation between uncertainty-related activation and diffusivity in a fronto-parieto-striatal network in less successful learners only, indicating a direct relation between diffusivity and the ability to reduce processing resources with decreasing uncertainty. These findings indicate that interindividual variations in white matter characteristics within the normal population might be linked to neuronal activation and critically influence individual learning performance.

DTI measurements of isotropic and anisotropic media.

The ability to measure different rates of diffusion along different directions is one of the features that distinguish DTI from other imaging methods. It allows to extract and visualize information on tissue microstructure and microdynamics. However, to correctly determine the full diffusion tensor, the so-called b-matrix has to be calculated by taking into account the non-negligible influences of image gradients and cross-terms between imaging and diffusion gradients. In this work validation of this b-matrix correction was investigated by determining self-diffusion coefficients of several isotropic media on a 1.5 T clinical whole-body scanner. To investigate these influences on the measurements of anisotropic media the same experiments were performed with a carrot.