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      Tractostorm: The what, why, and how of tractography dissection reproducibility

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          Abstract

          Abstract Investigative studies of white matter (WM) brain structures using diffusion MRI (dMRI) tractography frequently require manual WM bundle segmentation, often called “virtual dissection.” Human errors and personal decisions make these manual segmentations hard to reproduce, which have not yet been quantified by the dMRI community. It is our opinion that if the field of dMRI tractography wants to be taken seriously as a widespread clinical tool, it is imperative to harmonize WM bundle segmentations and develop protocols aimed to be used in clinical settings. The EADC‐ADNI Harmonized Hippocampal Protocol achieved such standardization through a series of steps that must be reproduced for every WM bundle. This article is an observation of the problematic. A specific bundle segmentation protocol was used in order to provide a real‐life example, but the contribution of this article is to discuss the need for reproducibility and standardized protocol, as for any measurement tool. This study required the participation of 11 experts and 13 nonexperts in neuroanatomy and “virtual dissection” across various laboratories and hospitals. Intra‐rater agreement (Dice score) was approximately 0.77, while inter‐rater was approximately 0.65. The protocol provided to participants was not necessarily optimal, but its design mimics, in essence, what will be required in future protocols. Reporting tractometry results such as average fractional anisotropy, volume or streamline count of a particular bundle without a sufficient reproducibility score could make the analysis and interpretations more difficult. Coordinated efforts by the diffusion MRI tractography community are needed to quantify and account for reproducibility of WM bundle extraction protocols in this era of open and collaborative science.

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          Most cited references61

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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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            Changes in connectivity profiles define functionally distinct regions in human medial frontal cortex.

            A fundamental issue in neuroscience is the relation between structure and function. However, gross landmarks do not correspond well to microstructural borders and cytoarchitecture cannot be visualized in a living brain used for functional studies. Here, we used diffusion-weighted and functional MRI to test structure-function relations directly. Distinct neocortical regions were defined as volumes having similar connectivity profiles and borders identified where connectivity changed. Without using prior information, we found an abrupt profile change where the border between supplementary motor area (SMA) and pre-SMA is expected. Consistent with this anatomical assignment, putative SMA and pre-SMA connected to motor and prefrontal regions, respectively. Excellent spatial correlations were found between volumes defined by using connectivity alone and volumes activated during tasks designed to involve SMA or pre-SMA selectively. This finding demonstrates a strong relationship between structure and function in medial frontal cortex and offers a strategy for testing such correspondences elsewhere in the brain.
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              Interrater agreement and interrater reliability: key concepts, approaches, and applications.

              Evaluations of interrater agreement and interrater reliability can be applied to a number of different contexts and are frequently encountered in social and administrative pharmacy research. The objectives of this study were to highlight key differences between interrater agreement and interrater reliability; describe the key concepts and approaches to evaluating interrater agreement and interrater reliability; and provide examples of their applications to research in the field of social and administrative pharmacy. This is a descriptive review of interrater agreement and interrater reliability indices. It outlines the practical applications and interpretation of these indices in social and administrative pharmacy research. Interrater agreement indices assess the extent to which the responses of 2 or more independent raters are concordant. Interrater reliability indices assess the extent to which raters consistently distinguish between different responses. A number of indices exist, and some common examples include Kappa, the Kendall coefficient of concordance, Bland-Altman plots, and the intraclass correlation coefficient. Guidance on the selection of an appropriate index is provided. In conclusion, selection of an appropriate index to evaluate interrater agreement or interrater reliability is dependent on a number of factors including the context in which the study is being undertaken, the type of variable under consideration, and the number of raters making assessments. Copyright © 2013 Elsevier Inc. All rights reserved.
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                Author and article information

                Journal
                Human Brain Mapping
                Hum Brain Mapp
                Wiley
                1065-9471
                1097-0193
                January 10 2020
                January 10 2020
                Affiliations
                [1 ]Sherbrooke Connectivity Imaging Laboratory (SCIL)Université de Sherbrooke Sherbrooke Canada
                [2 ]Neurosurgery Unit, Department of Neuroscience and NeurorehabilitationBambino Gesù Children's Hospital, IRCCS Rome Italy
                [3 ]Computer Science DepartmentUniversity of Verona Verona Italy
                [4 ]Athinoula A. Martinos Center for Biomedical ImagingMassachusetts General Hospital and Harvard Medical School Boston MA
                [5 ]Cardiff University Brain Research Imaging Centre (CUBRIC), School of PsychologyCardiff University Cardiff UK
                [6 ]Signal Processing Lab (LTS5)École Polytechnique Fédérale de Lausanne Lausanne Switzerland
                [7 ]Department of NeurologyUniversity of California San Francisco CA
                [8 ]Imeka Solutions Sherbrooke Canada
                [9 ]Départment de neurochirurgieHôpital Lariboisière Paris France
                [10 ]Department of Psychological and Brain SciencesIndiana University Bloomington IN
                [11 ]UMR 1253, iBrainUniversité de Tours, Inserm Tours France
                [12 ]Brain Development Imaging Laboratories, Department of PsychologySan Diego State University San Diego CA USA
                [13 ]Learning Research & Development Center (LRDC)University of Pittsburgh Pittsburgh PA USA
                [14 ]ISAE‐SUPAERO Toulouse France
                [15 ]Department of NeurosurgeryStanford University Standford CA
                [16 ]Division of Neurosurgery, Emergency Department, "S. Chiara" HospitalAzienda Provinciale per i Servizi Sanitari (APSS) Trento Italy
                [17 ]Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives ‐ UMR 5293, CNRSCEA University of Bordeaux Bordeaux France
                Article
                10.1002/hbm.24917
                83003ba8-57ac-4660-8163-84c5e6a55779
                © 2020

                http://creativecommons.org/licenses/by/4.0/

                http://doi.wiley.com/10.1002/tdm_license_1.1

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