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Abstract
Diffusion MRI tractography allows in-vivo characterization of white matter architecture, including the localization and description of brain fibre bundles. However, some primary bundles are still only partially reconstructed, or not reconstructed at all. The acoustic radiation (AR) represents a primary sensory pathway that has been largely omitted in many tractography studies because its location and anatomical features make it challenging to reconstruct. In this study, we investigated the effects of acquisition and tractography parameters on the AR reconstruction using publicly available Human Connectome Project data. The aims of this study are: (i) using a subgroup of subjects and a reference AR for each subject, define an optimum set of parameters for AR reconstruction, and (ii) use the optimum parameters set on the full group to build a tractography-based atlas of the AR. Starting from the same data, the use of different acquisition and tractography parameters lead to very different AR reconstructions. Optimal results in terms of topographical accuracy and correspondence to the reference were obtained for probabilistic tractography, high b-values and default tractography parameters: these parameters were used to build an AR probabilistic tractography atlas. A significant left-hemispheric lateralization was found in the AR reconstruction of the 34 subjects.
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1 Massachusetts General Hospital and Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, USA (GRID:grid.32224.35) (ISNI:0000 0004 0386 9924); University of Trento, Center for Mind/Brain Sciences - CIMeC, Rovereto (TN), Italy (GRID:grid.11696.39) (ISNI:0000 0004 1937 0351)
2 Structural and Functional Connectivity Lab (SFC-LSB) Project, “S.Chiara” Hospital, Division of Neurosurgery, Trento APSS, Italy (GRID:grid.11696.39)
3 University of Trento, Center for Mind/Brain Sciences - CIMeC, Rovereto (TN), Italy (GRID:grid.11696.39) (ISNI:0000 0004 1937 0351); University of Trento, Department of Psychology and Cognitive Sciences, Trento, Italy (GRID:grid.11696.39) (ISNI:0000 0004 1937 0351)