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      Mindboggling morphometry of human brains

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          Abstract

          Mindboggle ( http://mindboggle.info) is an open source brain morphometry platform that takes in preprocessed T1-weighted MRI data and outputs volume, surface, and tabular data containing label, feature, and shape information for further analysis. In this article, we document the software and demonstrate its use in studies of shape variation in healthy and diseased humans. The number of different shape measures and the size of the populations make this the largest and most detailed shape analysis of human brains ever conducted. Brain image morphometry shows great potential for providing much-needed biological markers for diagnosing, tracking, and predicting progression of mental health disorders. Very few software algorithms provide more than measures of volume and cortical thickness, while more subtle shape measures may provide more sensitive and specific biomarkers. Mindboggle computes a variety of (primarily surface-based) shapes: area, volume, thickness, curvature, depth, Laplace-Beltrami spectra, Zernike moments, etc. We evaluate Mindboggle’s algorithms using the largest set of manually labeled, publicly available brain images in the world and compare them against state-of-the-art algorithms where they exist. All data, code, and results of these evaluations are publicly available.

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          Most cited references116

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          The WU-Minn Human Connectome Project: an overview.

          The Human Connectome Project consortium led by Washington University, University of Minnesota, and Oxford University is undertaking a systematic effort to map macroscopic human brain circuits and their relationship to behavior in a large population of healthy adults. This overview article focuses on progress made during the first half of the 5-year project in refining the methods for data acquisition and analysis. Preliminary analyses based on a finalized set of acquisition and preprocessing protocols demonstrate the exceptionally high quality of the data from each modality. The first quarterly release of imaging and behavioral data via the ConnectomeDB database demonstrates the commitment to making HCP datasets freely accessible. Altogether, the progress to date provides grounds for optimism that the HCP datasets and associated methods and software will become increasingly valuable resources for characterizing human brain connectivity and function, their relationship to behavior, and their heritability and genetic underpinnings. Copyright © 2013 Elsevier Inc. All rights reserved.
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            A multi-modal parcellation of human cerebral cortex

            Understanding the amazingly complex human cerebral cortex requires a map (or parcellation) of its major subdivisions, known as cortical areas. Making an accurate areal map has been a century-old objective in neuroscience. Using multi-modal magnetic resonance images from the Human Connectome Project (HCP) and an objective semi-automated neuroanatomical approach, we delineated 180 areas per hemisphere bounded by sharp changes in cortical architecture, function, connectivity, and/or topography in a precisely aligned group average of 210 healthy young adults. We characterized 97 new areas and 83 areas previously reported using post-mortem microscopy or other specialized study-specific approaches. To enable automated delineation and identification of these areas in new HCP subjects and in future studies, we trained a machine-learning classifier to recognize the multi-modal ‘fingerprint’ of each cortical area. This classifier detected the presence of 96.6% of the cortical areas in new subjects, replicated the group parcellation, and could correctly locate areas in individuals with atypical parcellations. The freely available parcellation and classifier will enable substantially improved neuroanatomical precision for studies of the structural and functional organization of human cerebral cortex and its variation across individuals and in development, aging, and disease.
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              Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system.

              The surface of the human cerebral cortex is a highly folded sheet with the majority of its surface area buried within folds. As such, it is a difficult domain for computational as well as visualization purposes. We have therefore designed a set of procedures for modifying the representation of the cortical surface to (i) inflate it so that activity buried inside sulci may be visualized, (ii) cut and flatten an entire hemisphere, and (iii) transform a hemisphere into a simple parameterizable surface such as a sphere for the purpose of establishing a surface-based coordinate system. Copyright 1999 Academic Press.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput. Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, CA USA )
                1553-734X
                1553-7358
                23 February 2017
                February 2017
                : 13
                : 2
                : e1005350
                Affiliations
                [1 ]Child Mind Institute, New York, New York, United States of America
                [2 ]McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
                [3 ]Department of Otolaryngology, Harvard Medical School, Boston, Massachusetts, United States of America
                [4 ]Department of Electrical and Computer Engineering, University of Akron, Akron, Ohio, United States of America
                [5 ]University of Louvain, Louvain, Belgium
                [6 ]Columbia University, New York, New York, United States of America
                [7 ]TankThink Labs, Boston, Massachusetts, United States of America
                [8 ]Harvard Medical School, Cambridge, Massachusetts, United States of America
                [9 ]Sage Bionetworks, Seattle, Washington, United States of America
                [10 ]University of California San Francisco, San Francisco, California, United States of America
                Hebrew University of Jerusalem, ISRAEL
                Author notes

                The authors have declared that no competing interests exist.

                • Conceptualization: AKl.

                • Data curation: AKl.

                • Formal analysis: AKl ECN.

                • Funding acquisition: AKl.

                • Investigation: AKl.

                • Methodology: AKl FSB JG YH.

                • Project administration: AKl.

                • Resources: AKl.

                • Software: AKl SSG FSB JG YH ES NL BR MR AKe.

                • Supervision: AKl.

                • Validation: AKl.

                • Visualization: AKl JG MR ECN AKe.

                • Writing – original draft: AKl.

                • Writing – review & editing: AKl SSG FSB JG YH ES NL MR.

                [¤a]

                Current Address: Axinesis, Wavre, Belgium

                [¤b]

                Current Address: Philips, Vantaa, Finland

                [¤c]

                Current Address: Blend Labs, Inc., San Francisco, California, United States of America

                [¤d]

                Current Address: 1010data, New York, New York, United States of America

                [¤e]

                Current Address: FØCAL, Cambridge, Massachusetts, United States of America

                Author information
                http://orcid.org/0000-0002-0707-2889
                http://orcid.org/0000-0002-5312-6729
                http://orcid.org/0000-0002-0698-0295
                http://orcid.org/0000-0003-3554-043X
                Article
                PCOMPBIOL-D-16-01285
                10.1371/journal.pcbi.1005350
                5322885
                28231282
                bfe5bfb0-7140-4bfb-849c-73a650f417be
                © 2017 Klein et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 7 August 2016
                : 8 January 2017
                Page count
                Figures: 14, Tables: 3, Pages: 40
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100000025, National Institute of Mental Health;
                Award ID: MH084029
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000025, National Institute of Mental Health;
                Award ID: MH074813
                Award Recipient :
                Funded by: National Institute of Mental Health (US)
                Award ID: 3U01MH092250-03S1
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000027, National Institute on Alcohol Abuse and Alcoholism;
                Award ID: NCANDA-USA Consortium BD2K
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000070, National Institute of Biomedical Imaging and Bioengineering;
                Award ID: 1R01EB020740-01A1
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000070, National Institute of Biomedical Imaging and Bioengineering;
                Award ID: 1P41EB019936-01A1
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000025, National Institute of Mental Health;
                Award ID: 3R01MH092380-04S2
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000025, National Institute of Mental Health;
                Award ID: 1U01MH108168-01
                Award Recipient :
                AKl received funding from the following grants: National Institute of Mental Health ( https://www.nimh.nih.gov/) R01 MH084029, U01 MH074813, and U01 supplement 3U01MH092250-03S1, and the National Institute on Alcohol Abuse and Alcoholism ( https://www.niaaa.nih.gov/) NCANDA-USA Consortium BD2K supplement. SSG was partially supported by NIH grant 1R01EB020740. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
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                Custom metadata
                All software, data, and documentation are freely available under Apache v2.0 and Creative Commons licenses. All input, output, example, and evaluation data as well as reports and figures are publicly accessible from the Mindboggle Open Science Framework website ( https://osf.io/ydyxu/). The Mindboggle-101 manually edited label data are available on the Mindboggle101 Open Science Framework website ( https://osf.io/nhtur/), the Mindboggle-101 Harvard Dataverse website ( https://dataverse.harvard.edu/dataverse/mindboggle101), and the Mindboggle data website ( http://www.mindboggle.info/data.html). The Mindboggle software is available through its GitHub repository ( https://github.com/nipy/mindboggle) and all documentation is available on the Mindboggle website ( http://mindboggle.info/).

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                Quantitative & Systems biology

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