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Systematic changes to the apparent diffusion tensor of in vivo rat brain measured with an oscillating-gradient spin-echo sequence

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Leuze,  Christoph
Department Neurophysics, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Citation

Kershaw, J., Leuze, C., Aoki, I., Obata, T., Kanno, I., Ito, H., et al. (2013). Systematic changes to the apparent diffusion tensor of in vivo rat brain measured with an oscillating-gradient spin-echo sequence. NeuroImage, 70, 10-20. doi:10.1016/j.neuroimage.2012.12.036.


Cite as: http://hdl.handle.net/11858/00-001M-0000-000E-7DCC-2
Abstract
As the oscillating gradient spin-echo sequence has shown promise as a means to probe tissue microstructure, it was applied here to diffusion-tensor imaging of in vivo rat brain. The apparent diffusion tensor (ADT) was estimated for motion-probing gradient (MPG) frequencies in the range 33.3-133.3Hz, and regions-of-interest (ROIs) in the corpus callosum (CC), visual cortex (VC), cerebellar white matter (CBWM) and cerebellar grey matter (CBGM) were selected for detailed analysis. There were substantial, approximately linear changes to the ADT with increasing MPG frequency for all four ROIs. All ROIs showed clear increases in mean diffusivity. CBWM had a substantial decrease in fractional anisotropy, whereas the CC and VC had minor increases of the same parameter. All eigenvalues of the ADT tended to increase with frequency for the CBWM, CBGM and VC, but only the principal eigenvalue increased strongly for the CC. On the other hand, there was no evidence that the orientation of the principal eigenvector varied systematically with MPG frequency for any of the ROIs. The relationship between the behaviour of the eigenvalues and the behaviours of the mean diffusivity and fractional anisotropy is investigated in detail. Pixelwise linear fits to the MD from individual animals found elevated changes across the cerebellum. The data acquired for this work encompassed a range of effective diffusion-times from 7.5ms down to 1.875ms, and some ideas on how the results might be used to extract quantitative information about brain tissue microstructure are discussed.