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Mindboggling morphometry of human brains

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Klein, Arno [Child Mind Institute, New York] Ghosh, Satrajit S. [Massachusetts Institute of Technology] Bao, Forrest S. [University of Akron] Giard, Joachim [UCL] Häme, Yrjö [Columbia University] Schneidman, Dina

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.

Document type Article de périodique (Journal article) – Article de recherche
Publication date 2017
Language Anglais
Journal information "PLOS Computational Biology" - Vol. 13, no.2, p. e1005350 (2017)
Publisher Public Library of Science (PLoS)
issn 1553-734X
e-issn 1553-7358
Publication status Publié
Affiliation UCL - SST/ICTM/ELEN - Pôle en ingénierie électrique
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Bibliographic reference Klein, Arno ; Ghosh, Satrajit S. ; Bao, Forrest S. ; Giard, Joachim ; Häme, Yrjö ; et. al. Mindboggling morphometry of human brains. In: PLOS Computational Biology, Vol. 13, no.2, p. e1005350 (2017)
Permanent URL http://hdl.handle.net/2078.1/202304