Mindboggling morphometry of human brains

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.

The ANTs Longitudinal Cortical Thickness Pipeline

“…However, none of these precision-type measurements, per se, indicate the utility of a pipelinespecific cortical thickness value as a potential biomarker. For example, Figure 8 in [26] confirms what was found in [67] which is that the range of ANTs cortical thickness values for a particular region exceeds those of FreeSurfer. However, for the same data, the demographic predictive capabilities of the former was superior to that of the latter.…”

“…For example, [66] used FreeSurfer cortical thickness measurements across image acquisition sessions to demonstrate improved reproducibility with the longitudinal stream over the cross-sectional stream. In [67] comparisons for ANTs, FreeSurfer, and the proposed method were made using the range of measurements and their correspondence to values published in the literature. However, none of these precision-type measurements, per se, indicate the utility of a pipelinespecific cortical thickness value as a potential biomarker.…”

High-amplitude co-fluctuations in cortical activity drive functional connectivity

“…The T1w-reference was then skull-stripped with a Nipype implementation of the antsBrainExtraction.sh workflow, using NKI as the target template. Brain surfaces were reconstructed using recon-all [38], and the brain mask estimated previously was refined with a custom variation of the method to reconcile ANTs-derived and FreeSurfer-derived segmentations of the cortical gray-matter using Mindboggle [39]. Spatial normalization to the ICBM 152 Nonlinear Asymmetrical template version 2009c [40] was performed through nonlinear registration with antsRegistration, using brain-extracted versions of both T1w volume and template.…”

Discrete pre-processing step effects in registration-based pipelines, a preliminary volumetric study on T1-weighted images

“…This resulted in templates derived from the statistical average of all input scans. Fourth, the lab-specific head template was segmented according to the Desikan-Killiany-Tourville protocol using the Joint Label Fusion toolkit and referencing the OASIS-TRT-20 dataset [22, 39–41]:…”