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Title: The effects of SIFT on the reproducibility and biological accuracy of the structural connectome.
Austin Authors: Smith, Robert E;Tournier, Jacques-Donald;Calamante, Fernando;Connelly, Alan
Affiliation: The Florey Institute of Neuroscience and Mental Health, Heidelberg, Victoria, Australia
Florey Department of Neuroscience and Mental Health, University of Melbourne, Melbourne, Victoria, Australia
Department of Medicine, Northern Health, University of Melbourne, Melbourne, Victoria, Australia
Department of Medicine, Austin Health, The University of Melbourne, Heidelberg, Victoria, Australia
Issue Date: 12-Oct-2014
Publication information: Neuroimage 2014; 104: 253-65
Abstract: Diffusion MRI streamlines tractography is increasingly being used to characterise and assess the structural connectome of the human brain. However, issues pertaining to quantification of structural connectivity using streamlines reconstructions are well-established in the field, and therefore the validity of any conclusions that may be drawn from these analyses remains ambiguous. We recently proposed a post-processing method entitled "SIFT: Spherical-deconvolution Informed Filtering of Tractograms" as a mechanism for reducing the biases in quantitative measures of connectivity introduced by the streamlines reconstruction method. Here, we demonstrate the advantage of this approach in the context of connectomics in three steps. Firstly, we carefully consider the model imposed by the SIFT method, and the implications this has for connectivity quantification. Secondly, we investigate the effects of SIFT on the reproducibility of structural connectome construction. Thirdly, we compare quantitative measures extracted from structural connectomes derived from streamlines tractography, with and without the application of SIFT, to published estimates drawn from post-mortem brain dissection. The combination of these sources of evidence demonstrates the important role the SIFT methodology has for the robust quantification of structural connectivity of the brain using diffusion MRI.
Gov't Doc #: 25312774
DOI: 10.1016/j.neuroimage.2014.10.004
Journal: NeuroImage
Type: Journal Article
Subjects: Connectomics
Diffusion MRI
Structural connectome
Appears in Collections:Journal articles

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