Adam Delora1, Aaron Gonzales1, Christopher S Medina1, Adam Mitchell1, Abdul Faheem Mohed1, Russell E Jacobs2, Elaine L Bearer3. 1. Department of Pathology, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, United States. 2. Beckman Institute, California Institute of Technology, Pasadena, CA 91125, United States. 3. Department of Pathology, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, United States; Division of Biology, California Institute of Technology, Pasadena, CA 91125, United States. Electronic address: ebearer@salud.unm.edu.
Abstract
BACKGROUND: Magnetic resonance imaging (MRI) is a well-developed technique in neuroscience. Limitations in applying MRI to rodent models of neuropsychiatric disorders include the large number of animals required to achieve statistical significance, and the paucity of automation tools for the critical early step in processing, brain extraction, which prepares brain images for alignment and voxel-wise statistics. NEW METHOD: This novel timesaving automation of template-based brain extraction ("skull-stripping") is capable of quickly and reliably extracting the brain from large numbers of whole head images in a single step. The method is simple to install and requires minimal user interaction. RESULTS: This method is equally applicable to different types of MR images. Results were evaluated with Dice and Jacquard similarity indices and compared in 3D surface projections with other stripping approaches. Statistical comparisons demonstrate that individual variation of brain volumes are preserved. COMPARISON WITH EXISTING METHODS: A downloadable software package not otherwise available for extraction of brains from whole head images is included here. This software tool increases speed, can be used with an atlas or a template from within the dataset, and produces masks that need little further refinement. CONCLUSIONS: Our new automation can be applied to any MR dataset, since the starting point is a template mask generated specifically for that dataset. The method reliably and rapidly extracts brain images from whole head images, rendering them useable for subsequent analytical processing. This software tool will accelerate the exploitation of mouse models for the investigation of human brain disorders by MRI.
BACKGROUND: Magnetic resonance imaging (MRI) is a well-developed technique in neuroscience. Limitations in applying MRI to rodent models of neuropsychiatric disorders include the large number of animals required to achieve statistical significance, and the paucity of automation tools for the critical early step in processing, brain extraction, which prepares brain images for alignment and voxel-wise statistics. NEW METHOD: This novel timesaving automation of template-based brain extraction ("skull-stripping") is capable of quickly and reliably extracting the brain from large numbers of whole head images in a single step. The method is simple to install and requires minimal user interaction. RESULTS: This method is equally applicable to different types of MR images. Results were evaluated with Dice and Jacquard similarity indices and compared in 3D surface projections with other stripping approaches. Statistical comparisons demonstrate that individual variation of brain volumes are preserved. COMPARISON WITH EXISTING METHODS: A downloadable software package not otherwise available for extraction of brains from whole head images is included here. This software tool increases speed, can be used with an atlas or a template from within the dataset, and produces masks that need little further refinement. CONCLUSIONS: Our new automation can be applied to any MR dataset, since the starting point is a template mask generated specifically for that dataset. The method reliably and rapidly extracts brain images from whole head images, rendering them useable for subsequent analytical processing. This software tool will accelerate the exploitation of mouse models for the investigation of humanbrain disorders by MRI.
Authors: Brian J Nieman; Nicholas A Bock; Jonathon Bishop; X Josette Chen; John G Sled; Janet Rossant; R Mark Henkelman Journal: NMR Biomed Date: 2005-11 Impact factor: 4.044
Authors: Mark W Woolrich; Saad Jbabdi; Brian Patenaude; Michael Chappell; Salima Makni; Timothy Behrens; Christian Beckmann; Mark Jenkinson; Stephen M Smith Journal: Neuroimage Date: 2008-11-13 Impact factor: 6.556
Authors: Mark Jenkinson; Christian F Beckmann; Timothy E J Behrens; Mark W Woolrich; Stephen M Smith Journal: Neuroimage Date: 2011-09-16 Impact factor: 6.556
Authors: Elaine L Bearer; Brett C Manifold-Wheeler; Christopher S Medina; Aaron G Gonzales; Frances L Chaves; Russell E Jacobs Journal: Neurobiol Aging Date: 2018-06-28 Impact factor: 4.673
Authors: Christopher S Medina; Octavian Biris; Tomas L Falzone; Xiaowei Zhang; Amber J Zimmerman; Elaine L Bearer Journal: Neuroimage Date: 2016-10-14 Impact factor: 6.556
Authors: Elaine L Bearer; Daniel Barto; Alden R H Reviere; Russell E Jacobs Journal: Proc Int Soc Magn Reson Med Sci Meet Exhib Int Soc Magn Reson Med Sci Meet Exhib Date: 2018-06
Authors: Elaine L Bearer; Daniel Barto; Russell E Jacobs Journal: Proc Int Soc Magn Reson Med Sci Meet Exhib Int Soc Magn Reson Med Sci Meet Exhib Date: 2019-05
Authors: Christopher S Medina; Taylor W Uselman; Daniel R Barto; Frances Cháves; Russell E Jacobs; Elaine L Bearer Journal: Front Cell Neurosci Date: 2019-12-03 Impact factor: 5.505