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Mindboggling morphometry of human brains

Author

Listed:
  • Arno Klein
  • Satrajit S Ghosh
  • Forrest S Bao
  • Joachim Giard
  • Yrjö Häme
  • Eliezer Stavsky
  • Noah Lee
  • Brian Rossa
  • Martin Reuter
  • Elias Chaibub Neto
  • Anisha Keshavan

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.

Suggested Citation

  • Arno Klein & Satrajit S Ghosh & Forrest S Bao & Joachim Giard & Yrjö Häme & Eliezer Stavsky & Noah Lee & Brian Rossa & Martin Reuter & Elias Chaibub Neto & Anisha Keshavan, 2017. "Mindboggling morphometry of human brains," PLOS Computational Biology, Public Library of Science, vol. 13(2), pages 1-40, February.
    • Handle: RePEc:plo:pcbi00:1005350
    DOI: 10.1371/journal.pcbi.1005350
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    1. Matthew F. Glasser & Timothy S. Coalson & Emma C. Robinson & Carl D. Hacker & John Harwell & Essa Yacoub & Kamil Ugurbil & Jesper Andersson & Christian F. Beckmann & Mark Jenkinson & Stephen M. Smith , 2016. "A multi-modal parcellation of human cerebral cortex," Nature, Nature, vol. 536(7615), pages 171-178, August.
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    4. Nir Moneta & Mona M. Garvert & Hauke R. Heekeren & Nicolas W. Schuck, 2023. "Task state representations in vmPFC mediate relevant and irrelevant value signals and their behavioral influence," Nature Communications, Nature, vol. 14(1), pages 1-21, December.
    5. Farnaz Zamani Esfahlani & Joshua Faskowitz & Jonah Slack & Bratislav Mišić & Richard F. Betzel, 2022. "Local structure-function relationships in human brain networks across the lifespan," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    6. Angelika Maurer & Julian Klein & Jannik Claus & Neeraj Upadhyay & Leonie Henschel & Jason Anthony Martin & Lukas Scheef & Marcel Daamen & Theresa Schörkmaier & Rüdiger Stirnberg & Tony Stöcker & Alexa, 2022. "Effects of a 6-Month Aerobic Exercise Intervention on Mood and Amygdala Functional Plasticity in Young Untrained Subjects," IJERPH, MDPI, vol. 19(10), pages 1-19, May.
    7. Maria Salfer & Javier F Collado & Wolfgang Baumeister & Rubén Fernández-Busnadiego & Antonio Martínez-Sánchez, 2020. "Reliable estimation of membrane curvature for cryo-electron tomography," PLOS Computational Biology, Public Library of Science, vol. 16(8), pages 1-29, August.

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