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      3D interactive tractography-informed resting-state fMRI connectivity

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          Abstract

          In the past decade, the fusion between diffusion magnetic resonance imaging (dMRI) and functional magnetic resonance imaging (fMRI) has opened the way for exploring structure-function relationships in vivo. As it stands, the common approach usually consists of analysing fMRI and dMRI datasets separately or using one to inform the other, such as using fMRI activation sites to reconstruct dMRI streamlines that interconnect them. Moreover, given the large inter-individual variability of the healthy human brain, it is possible that valuable information is lost when a fixed set of dMRI/fMRI analysis parameters such as threshold values are assumed constant across subjects. By allowing one to modify such parameters while viewing the results in real-time, one can begin to fully explore the sensitivity of structure-function relations and how they differ across brain areas and individuals. This is especially important when interpreting how structure-function relationships are altered in patients with neurological disorders, such as the presence of a tumor. In this study, we present and validate a novel approach to achieve this: First, we present an interactive method to generate and visualize tractography-driven resting-state functional connectivity, which reduces the bias introduced by seed size, shape and position. Next, we demonstrate that structural and functional reconstruction parameters explain a significant portion of intra- and inter-subject variability. Finally, we demonstrate how our proposed approach can be used in a neurosurgical planning context. We believe this approach will promote the exploration of structure-function relationships in a subject-specific aspect and will open new opportunities for connectomics.

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          Most cited references46

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          Robust determination of the fibre orientation distribution in diffusion MRI: non-negativity constrained super-resolved spherical deconvolution.

          Diffusion-weighted (DW) MR images contain information about the orientation of brain white matter fibres that potentially can be used to study human brain connectivity in vivo using tractography techniques. Currently, the diffusion tensor model is widely used to extract fibre directions from DW-MRI data, but fails in regions containing multiple fibre orientations. The spherical deconvolution technique has recently been proposed to address this limitation. It provides an estimate of the fibre orientation distribution (FOD) by assuming the DW signal measured from any fibre bundle is adequately described by a single response function. However, the deconvolution is ill-conditioned and susceptible to noise contamination. This tends to introduce artefactual negative regions in the FOD, which are clearly physically impossible. In this study, the introduction of a constraint on such negative regions is proposed to improve the conditioning of the spherical deconvolution. This approach is shown to provide FOD estimates that are robust to noise whilst preserving angular resolution. The approach also permits the use of super-resolution, whereby more FOD parameters are estimated than were actually measured, improving the angular resolution of the results. The method provides much better defined fibre orientation estimates, and allows orientations to be resolved that are separated by smaller angles than previously possible. This should allow tractography algorithms to be designed that are able to track reliably through crossing fibre regions.
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            MRtrix: Diffusion tractography in crossing fiber regions

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              Anatomically-constrained tractography: improved diffusion MRI streamlines tractography through effective use of anatomical information.

              Diffusion MRI streamlines tractography suffers from a number of inherent limitations, one of which is the accurate determination of when streamlines should be terminated. Use of an accurate streamlines propagation mask from segmentation of an anatomical image confines the streamlines to the volume of the brain white matter, but does not take full advantage of all of the information available from such an image. We present a modular addition to streamlines tractography, which makes more effective use of the information available from anatomical image segmentation, and the known properties of the neuronal axons being reconstructed, to apply biologically realistic priors to the streamlines generated; we refer to this as "Anatomically-Constrained Tractography". Results indicate that some of the known false positives associated with tractography algorithms are prevented, such that the biological accuracy of the reconstructions should be improved, provided that state-of-the-art streamlines tractography methods are used. Copyright © 2012 Elsevier Inc. All rights reserved.
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                Author and article information

                Contributors
                Journal
                Front Neurosci
                Front Neurosci
                Front. Neurosci.
                Frontiers in Neuroscience
                Frontiers Media S.A.
                1662-4548
                1662-453X
                11 August 2015
                2015
                : 9
                : 275
                Affiliations
                [1] 1Centre de Recherche CHUS, University of Sherbrooke Sherbrooke, QC, Canada
                [2] 2Sherbrooke Connectivity Imaging Lab, Computer Science Department, Faculty of Science, University of Sherbrooke Sherbrooke, QC, Canada
                [3] 3Department of Nuclear Medicine and Radiobiology, Faculty of Medicine and Health Science, University of Sherbrooke Sherbrooke, QC, Canada
                [4] 4Division of Neurosurgery and Neuro-Oncology, Faculty of Medicine and Health Science, University of Sherbrooke Sherbrooke, QC, Canada
                [5] 5Department of Diagnostic Radiology, Faculty of Medicine and Health Science, University of Sherbrooke Sherbrooke, QC, Canada
                Author notes

                Edited by: Pedro Antonio Valdes-Sosa, Centro de Neurociencias de Cuba, Cuba

                Reviewed by: Hans J. Johnson, The University of Iowa, USA; Yasser Iturria Medina, Montreal Neurological Institute, Canada

                *Correspondence: Maxime Chamberland, Department of Nuclear Medicine and Radiobiology, Faculty of Medicine and Health Science, Université de Sherbrooke, 3001, 12e Avenue Nord, Sherbrooke, QC J1K 2R1, Canada maxime.chamberland@ 123456usherbrooke.ca

                This article was submitted to Brain Imaging Methods, a section of the journal Frontiers in Neuroscience

                †These authors have contributed equally to this work.

                Article
                10.3389/fnins.2015.00275
                4531323
                26321901
                d82fc012-1a0e-4e74-9177-8ebf337b1060
                Copyright © 2015 Chamberland, Bernier, Fortin, Whittingstall and Descoteaux.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 12 May 2015
                : 22 July 2015
                Page count
                Figures: 8, Tables: 6, Equations: 0, References: 74, Pages: 15, Words: 10095
                Categories
                Neuroscience
                Methods

                Neurosciences
                diffusion mri,resting-state fmri,tractography,structure-function,variability,visualization

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