In vivo diffusion tensor magnetic resonance imaging and fiber tracking of the mouse brain.

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.