Mindboggling morphometry of human brains

TLDR

Mindboggle is an open‑source brain morphometry platform that processes preprocessed T1‑weighted MRI data to produce volume, surface, and tabular shape metrics, addressing a field where few tools offer detailed shape measures that could serve as sensitive biomarkers for mental‑health disorders. The article documents Mindboggle and demonstrates its application to shape‑variation studies in healthy and diseased humans. Mindboggle derives surface‑based shape metrics such as area, volume, thickness, curvature, depth, Laplace‑Beltrami spectra, and Zernike moments, and its algorithms were evaluated on the largest publicly labeled brain image set and benchmarked against state‑of‑the‑art methods. This study represents the largest and most detailed human‑brain shape analysis to date, and all data, code, and evaluation results are publicly available.

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.