Built on an analogy between the visual and auditory systems, the following dual stream model for language processing was suggested recently: a dorsal stream is involved in mapping sound to articulation, and a ventral stream in mapping sound to meaning. The goal of the study presented here was to test the neuroanatomical basis of this model. Combining functional magnetic resonance imaging (fMRI) with a novel diffusion tensor imaging (DTI)-based tractography method we were able to identify the most probable anatomical pathways connecting brain regions activated during two prototypical language tasks. Sublexical repetition of speech is subserved by a dorsal pathway, connecting the superior temporal lobe and premotor cortices in the frontal lobe via the arcuate and superior longitudinal fascicle. In contrast, higher-level language comprehension is mediated by a ventral pathway connecting the middle temporal lobe and the ventrolateral prefrontal cortex via the extreme capsule. Thus, according to our findings, the function of the dorsal route, traditionally considered to be the major language pathway, is mainly restricted to sensory-motor mapping of sound to articulation, whereas linguistic processing of sound to meaning requires temporofrontal interaction transmitted via the ventral route.DTI ͉ extreme capsule ͉ fMRI ͉ language networks ͉ arcuate fascicle ͉ extreme capsule

A multidiffusion-tensor model (MDT) is presented containing two anisotropic and one isotropic diffusion tensors. This approach has the ability to detect areas of fiber crossings and resolve the direction of crossing fibers. The mean diffusivity and the ratio of the tensor compartments were merged to one independent parameter by fitting MDT to the diffusion-weighted intensities of a two-point data acquisition scheme. By an F-test between the errors of the standard single diffusion tensor and the more complex MDT, fiber crossings were detected and the more accurate model was chosen voxel by voxel. The performance of crossing detection was compared with the spherical harmonics approach in simulations as well as in vivo. Similar results were found in both methods. The MDT model, however, did not only detect crossings but also yielded the single fiber directions. The FACT algorithm and a probabilistic connectivity algorithm were extended to support the MDT model. Key words: diffusion; DTI; fiber crossings; fiber tracking; multitensorAnisotropy of diffusion in the brain (1) is commonly analyzed by a diffusion tensor (DT) model (2). It is commonly assumed that the principal eigenvector, corresponding to the highest eigenvalue, is aligned along the fiber direction. The direction of this eigenvector has been successfully used for fiber tracking (3-6), which provides unique information about brain structure in vivo (7).Fiber tracking, however, is difficult in brain regions where several fiber bundles are crossing. In these cases, the directions of the principal eigenvectors do not correspond to the fiber directions anymore. The DT in these areas may even have an oblate disk-shape form (8) with an undefined direction of the principle vector. In such cases the common second-order diffusion tensor model is not valid anymore (8 -12). In a voxel with a high amount of isotropic tissue, such as CSF or gray matter, the direction of fibers can also be masked. This could cause errors in the reconstructed fiber trajectories and in connectivity estimation.With high angular resolution DWI measurements (HARDI), i.e., by diffusion encoding along 42-128 optimized directions instead of the six minimum diffusionencoding directions, more accurate and detailed information about diffusion in the brain can be obtained (13-15). Frank applied a spherical harmonics analysis (SH) (9) on HARDI data and was able to detect regions, where common DT models fail and several fiber directions in the same voxel exist. These voxels can be analyzed with a multitensor model (10,16,17) or with a generalized DT (18). Alternatively, Jansons and Alexander (19) introduced a persistent angular structure method (PAS MRI), which employs a maximum entropy method (20). A complex model is required to find multiple fiber directions from HARDI data. Alternatively, more detailed diffusion data measurements, e.g., by Q-space (21,22) or Q-ball (23) experiments, can provide information about fiber directions in crossing regions.The focus of this work was to investigate the p...

Reconstruction of neuronal fibers using diffusion-weighted (DW) MRI is an emerging method in biomedical research. Existing fiber-tracking algorithms are commonly based on the "walker principle." Fibers are reconstructed as trajectories of "walkers," which are guided according to local diffusion properties. In this study, a new method of fiber tracking is proposed that does not engage any "walking" algorithm. It resolves a number of inherent problems of the "walking" approach, in particular the reconstruction of crossing and spreading fibers.

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Until very recently, the study of neural architecture using fixed tissue has been a major scientific focus of neurologists and neuroanatomists. A non-invasive detailed insight into the brain's axonal connectivity in vivo has only become possible since the development of diffusion tensor magnetic resonance imaging (DT-MRI). This unique approach of analyzing axonal projections in the living brain was used in the present study to describe major white matter fiber tracts of the mouse brain and also to identify for the first time non-invasively the rich connectivity between the amygdala and different target regions. To overcome the difficulties associated with high spatially and temporally resolved DT-MRI measurements a 4-shot diffusion weighted spin echo (SE) echo planar imaging (EPI) protocol was adapted to mouse brain imaging at 9.4T. Diffusion tensor was calculated from data sets acquired by using 30 diffusion gradient directions while keeping the acquisition time at 91 min. Two fiber tracking algorithms were employed. A deterministic approach (fiber assignment by continuous tracking - FACT algorithm) allowed us to identify and generate the 3D representations of various neural pathways. A probabilistic approach was further used for the generation of probability maps of connectivity with which it was possible to investigate - in a statistical sense - all possible connecting pathways between selected seed points. We show here applications to determine the connection probability between regions belonging to the visual or limbic systems. This method does not require a priori knowledge about the projections' trajectories and is shown to be efficient even if the investigated pathway is long or three-dimensionally complex. Additionally, high resolution images of rotational invariant parameters of the diffusion tensor, such as fractional anisotropy, volume ratio or main eigenvalues allowed quantitative comparisons in-between regions of interest (ROIs) and showed significant differences between various white matter regions.

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