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      Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review

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          Abstract

          Diffusion magnetic resonance imaging (dMRI) tractography is an advanced imaging technique that enables in vivo reconstruction of the brain’s white matter connections at macro scale. It provides an important tool for quantitative mapping of the brain’s structural connectivity using measures of connectivity or tissue microstructure. Over the last two decades, the study of brain connectivity using dMRI tractography has played a prominent role in the neuroimaging research landscape. In this paper, we provide a high-level overview of how tractography is used to enable quantitative analysis of the brain’s structural connectivity in health and disease. We focus on two types of quantitative analyses of tractography, including: 1) tract-specific analysis that refers to research that is typically hypothesis-driven and studies particular anatomical fiber tracts, and 2) connectome-based analysis that refers to research that is more data-driven and generally studies the structural connectivity of the entire brain. We first provide a review of methodology involved in three main processing steps that are common across most approaches for quantitative analysis of tractography, including methods for tractography correction, segmentation and quantification. For each step, we aim to describe methodological choices, their popularity, and potential pros and cons. We then review studies that have used quantitative tractography approaches to study the brain’s white matter, focusing on applications in neurodevelopment, aging, neurological disorders, mental disorders, and neurosurgery. We conclude that, while there have been considerable advancements in methodological technologies and breadth of applications, there nevertheless remains no consensus about the “best” methodology in quantitative analysis of tractography, and researchers should remain cautious when interpreting results in research and clinical applications.

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest.

            In this study, we have assessed the validity and reliability of an automated labeling system that we have developed for subdividing the human cerebral cortex on magnetic resonance images into gyral based regions of interest (ROIs). Using a dataset of 40 MRI scans we manually identified 34 cortical ROIs in each of the individual hemispheres. This information was then encoded in the form of an atlas that was utilized to automatically label ROIs. To examine the validity, as well as the intra- and inter-rater reliability of the automated system, we used both intraclass correlation coefficients (ICC), and a new method known as mean distance maps, to assess the degree of mismatch between the manual and the automated sets of ROIs. When compared with the manual ROIs, the automated ROIs were highly accurate, with an average ICC of 0.835 across all of the ROIs, and a mean distance error of less than 1 mm. Intra- and inter-rater comparisons yielded little to no difference between the sets of ROIs. These findings suggest that the automated method we have developed for subdividing the human cerebral cortex into standard gyral-based neuroanatomical regions is both anatomically valid and reliable. This method may be useful for both morphometric and functional studies of the cerebral cortex as well as for clinical investigations aimed at tracking the evolution of disease-induced changes over time, including clinical trials in which MRI-based measures are used to examine response to treatment.
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              Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain.

              An anatomical parcellation of the spatially normalized single-subject high-resolution T1 volume provided by the Montreal Neurological Institute (MNI) (D. L. Collins et al., 1998, Trans. Med. Imag. 17, 463-468) was performed. The MNI single-subject main sulci were first delineated and further used as landmarks for the 3D definition of 45 anatomical volumes of interest (AVOI) in each hemisphere. This procedure was performed using a dedicated software which allowed a 3D following of the sulci course on the edited brain. Regions of interest were then drawn manually with the same software every 2 mm on the axial slices of the high-resolution MNI single subject. The 90 AVOI were reconstructed and assigned a label. Using this parcellation method, three procedures to perform the automated anatomical labeling of functional studies are proposed: (1) labeling of an extremum defined by a set of coordinates, (2) percentage of voxels belonging to each of the AVOI intersected by a sphere centered by a set of coordinates, and (3) percentage of voxels belonging to each of the AVOI intersected by an activated cluster. An interface with the Statistical Parametric Mapping package (SPM, J. Ashburner and K. J. Friston, 1999, Hum. Brain Mapp. 7, 254-266) is provided as a freeware to researchers of the neuroimaging community. We believe that this tool is an improvement for the macroscopical labeling of activated area compared to labeling assessed using the Talairach atlas brain in which deformations are well known. However, this tool does not alleviate the need for more sophisticated labeling strategies based on anatomical or cytoarchitectonic probabilistic maps.

                Author and article information

                Journal
                9215515
                20498
                Neuroimage
                Neuroimage
                NeuroImage
                1053-8119
                1095-9572
                25 June 2022
                01 April 2022
                01 January 2022
                01 April 2023
                : 249
                : 118870
                Affiliations
                [a ]Brigham and Women’s Hospital, Harvard Medical School, Boston, USA
                [b ]Department of Computer Science, University of Verona, Verona, Italy
                [c ]State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
                [d ]Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
                [e ]IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
                [f ]Chinese Institute for Brain Research, Beijing, China
                [g ]Melbourne Neuropsychiatry Centre, University of Melbourne and Melbourne Health, Melbourne, Australia
                [h ]The University of Sydney, School of Biomedical Engineering, Sydney, Australia
                [i ]The Florey Institute of Neuroscience and Mental Health, Melbourne, Australia
                [j ]Florey Department of Neuroscience and Mental Health, The University of Melbourne, Melbourne, Australia
                [k ]Institute for Radiological Research, Chang Gung University, Taoyuan, Taiwan
                [l ]Department of Psychiatry, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
                Author notes
                [1]

                All middle authors have contributed equally to this work.

                [* ]Corresponding authors. fzhang@ 123456bwh.harvard.edu (F. Zhang), odonnell@ 123456bwh.harvard.edu (L.J. O’Donnell).
                Article
                NIHMS1816316
                10.1016/j.neuroimage.2021.118870
                9257891
                34979249
                8d0281d1-a017-4e72-aaf9-37d729d02937

                This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/)

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