Characterization of the auditory system by fractional anisotropy (FA) and apparent diffusion coefficient (ADC) by nuclear magnetic resonance imaging

Magnetic Resonance Imaging (MRI) is a tool that allows visualizing the tissues of the human body in vivo without the need for invasive medical intervention, with the use of non-ionizing radiation. One of her techniques known as Diffusion Tensor Imaging (DTI) has opened the door to obtain a greater understanding of the human brain, not only from an anatomical point of view, but also from its functionality. This technique allows to visualize the Brownian movement of extracellular water molecules throughout brain tissue, with which valuable information can be obtained on the biological microstructure from certain diffusion parameters such as Fractional Anisotropy (FA) and the Apparent Diffusion Coefficient (ADC) using regions of interest (ROI) [Mori, S., & Zhang, J, 2006]. The central auditory system has been often ignored, this functional system is fundamental in our hearing, since it is there that the information received is processed and meanings are assigned to the perceived sounds [Nieuwenhuys, R., et al, 2007]. The diffusion-weighted images of 51 children were analyzed with an average age of (9.39 ± 2.68) years, 18 female and 33 males. A 1.5 Tesla Philips equipment was used, with a sequence of Fast Echo Planar Diffusion Weighted Imaging pulses covering the entire brain, Tr = 7711s, Echo train length 63, flip angle 90, acquisition matrix 124 x 124, 15 directions of gradients non-collinear, with fat suppression, 2 mm gap, 8-channel RF antenna of the skull with Sense technology, a post-processing was carried out for the correction of spurious currents with the FSL software (https://fsl.fmrib.ox.ac.uk/fsl). The MedInria software was used (https://med.inria.fr) to analyze the auditory functional tracts. When obtaining the diffusion parameters, a Pearson correlation coefficient was obtained for both parameters, resulting in a positive correlation between FA and a negative correlation for ADC [Brander, et al, 2010]. (Author)