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      The Effects of FreeSurfer Version, Workstation Type, and Macintosh Operating System Version on Anatomical Volume and Cortical Thickness Measurements

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          Abstract

          FreeSurfer is a popular software package to measure cortical thickness and volume of neuroanatomical structures. However, little if any is known about measurement reliability across various data processing conditions. Using a set of 30 anatomical T1-weighted 3T MRI scans, we investigated the effects of data processing variables such as FreeSurfer version (v4.3.1, v4.5.0, and v5.0.0), workstation (Macintosh and Hewlett-Packard), and Macintosh operating system version (OSX 10.5 and OSX 10.6). Significant differences were revealed between FreeSurfer version v5.0.0 and the two earlier versions. These differences were on average 8.8±6.6% (range 1.3–64.0%) (volume) and 2.8±1.3% (1.1–7.7%) (cortical thickness). About a factor two smaller differences were detected between Macintosh and Hewlett-Packard workstations and between OSX 10.5 and OSX 10.6. The observed differences are similar in magnitude as effect sizes reported in accuracy evaluations and neurodegenerative studies.

          The main conclusion is that in the context of an ongoing study, users are discouraged to update to a new major release of either FreeSurfer or operating system or to switch to a different type of workstation without repeating the analysis; results thus give a quantitative support to successive recommendations stated by FreeSurfer developers over the years. Moreover, in view of the large and significant cross-version differences, it is concluded that formal assessment of the accuracy of FreeSurfer is desirable.

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          Most cited references17

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

          Yoav Benjamini, Yosef Hochberg (1995)
          Article 0 comments Cited 23674 times – based on 0 reviews     Review now
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            A hybrid approach to the skull stripping problem in MRI.

            F Ségonne, A M Dale, E. Busa … (2004)
            We present a novel skull-stripping algorithm based on a hybrid approach that combines watershed algorithms and deformable surface models. Our method takes advantage of the robustness of the former as well as the surface information available to the latter. The algorithm first localizes a single white matter voxel in a T1-weighted MRI image, and uses it to create a global minimum in the white matter before applying a watershed algorithm with a preflooding height. The watershed algorithm builds an initial estimate of the brain volume based on the three-dimensional connectivity of the white matter. This first step is robust, and performs well in the presence of intensity nonuniformities and noise, but may erode parts of the cortex that abut bright nonbrain structures such as the eye sockets, or may remove parts of the cerebellum. To correct these inaccuracies, a surface deformation process fits a smooth surface to the masked volume, allowing the incorporation of geometric constraints into the skull-stripping procedure. A statistical atlas, generated from a set of accurately segmented brains, is used to validate and potentially correct the segmentation, and the MRI intensity values are locally re-estimated at the boundary of the brain. Finally, a high-resolution surface deformation is performed that accurately matches the outer boundary of the brain, resulting in a robust and automated procedure. Studies by our group and others outperform other publicly available skull-stripping tools. Copyright 2004 Elsevier Inc.
            Article 0 comments Cited 647 times – based on 0 reviews     Review now
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              Automated manifold surgery: constructing geometrically accurate and topologically correct models of the human cerebral cortex.

              B Fischl, A. Liu, A.M. Dale (2001)
              Highly accurate surface models of the cerebral cortex are becoming increasingly important as tools in the investigation of the functional organization of the human brain. The construction of such models is difficult using current neuroimaging technology due to the high degree of cortical folding. Even single voxel misclassifications can result in erroneous connections being created between adjacent banks of a sulcus, resulting in a topologically inaccurate model. These topological defects cause the cortical model to no longer be homeomorphic to a sheet, preventing the accurate inflation, flattening, or spherical morphing of the reconstructed cortex. Surface deformation techniques can guarantee the topological correctness of a model, but are time-consuming and may result in geometrically inaccurate models. In order to address this need we have developed a technique for taking a model of the cortex, detecting and fixing the topological defects while leaving that majority of the model intact, resulting in a surface that is both geometrically accurate and topologically correct.
              Article 0 comments Cited 497 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                : Role: Editor
                Journal
                Journal ID (nlm-ta): PLoS One
                Journal ID (iso-abbrev): PLoS ONE
                Journal ID (publisher-id): plos
                Journal ID (pmc): plosone
                Title: PLoS ONE
                Publisher: Public Library of Science (San Francisco, USA )
                ISSN (Electronic): 1932-6203
                Publication date Collection: 2012
                Publication date (Electronic): 1 June 2012
                Volume: 7
                Issue: 6
                Electronic Location Identifier: e38234
                Affiliations
                [1 ]Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, Alzheimer Center Limburg, The Netherlands
                [2 ]European Graduate School of Neuroscience (EURON), Maastricht University, Maastricht, The Netherlands
                [3 ]Cognitive Neurology Section, Institute of Neuroscience and Medicine-3, Research Centre Jülich, Jülich, Germany
                [4 ]King's College London, King's Health Partners, Department of Psychosis Studies Institute of Psychiatry, London, United Kingdom
                Wake Forest School of Medicine, United States of America
                Author notes

                Conceived and designed the experiments: EHBMG PH HILJ JvO MM. Performed the experiments: EHBMG PH RM NR. Analyzed the data: EHBMG PH HILJ JvO MM. Contributed reagents/materials/analysis tools: EHBMG HILJ RM NR. Wrote the paper: EHBMG PH HJ RM NR JvO MM.

                Article
                Publisher ID: PONE-D-12-02390
                DOI: 10.1371/journal.pone.0038234
                PMC ID: 3365894
                PubMed ID: 22675527
                SO-VID: 9e33a750-1f56-4fa3-bd64-430e3d269c74
                Copyright © Gronenschild 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
                Date received : 12 January 2012
                Date accepted : 1 May 2012
                Page count
                Pages: 13
                Categories
                Subject: Research Article
                Subject: Biology
                Subject: Anatomy and Physiology
                Subject: Neurological System
                Subject: Neuroanatomy
                Subject: Computational Biology
                Subject: Computational Neuroscience
                Subject: Neuroscience
                Subject: Computational Neuroscience
                Subject: Neuroanatomy
                Subject: Neuroimaging
                Subject: Computer Science
                Subject: Algorithms
                Subject: Computer Applications
                Subject: Numerical Analysis
                Subject: Software Engineering
                Subject: Software Tools
                Subject: Mathematics
                Subject: Applied Mathematics
                Subject: Algorithms
                Subject: Medicine
                Subject: Anatomy and Physiology
                Subject: Neurological System
                Subject: Neuroanatomy
                Subject: Neurology
                Subject: Neurodegenerative Diseases
                Subject: Neuroimaging
                Subject: Radiology
                Subject: Diagnostic Radiology
                Subject: Magnetic Resonance Imaging
                Subject: Science Policy
                Subject: Research Assessment
                Subject: Research Validity

                ScienceOpen disciplines: Uncategorized
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                ScienceOpen disciplines: Uncategorized

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